Monoclonal antibody immune therapy response instrument for stratification and cost-effective personalized approaches in 3PM-guided pan cancer management.

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Learn more: PMC Disclaimer | PMC Copyright Notice EPMA J . 2025 Mar 22;16(2):465–503. doi: 10.1007/s13167-025-00403-w Monoclonal antibody immune therapy response instrument for stratification and cost-effective personalized approaches in 3PM-guided pan cancer management Salem Baldi 1, 2 , Mohammed Alnaggar 3 , Maged AL-Mogahed 4 , Khalil A A Khalil 5 , Xianquan Zhan 6, 7, ✉ Author information Article notes Copyright and License information 1 Department of Medical Laboratory Diagnostics, School of Medical Technology, Shaoyang University, Shaoyang, 422000 China 2 Department of Medical Laboratory Diagnostics, Al-Thawra General Hospital, Al Hudaydah, Yemen 3 Department of Oncology, South Hubei Cancer Hospital, Chibi, Xianning, 437000 Hubei China 4 Department of Urology, The First Bethune Hospital of Jilin University, Changchun, 130012 China 5 Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Bisha, 61922 Bisha, Saudi Arabia 6 Shandong Provincial Key Laboratory of Precision Oncology, Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong Cancer Hospital and Institute, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China 7 Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Jinan Key Laboratory of Cancer Multiomics, Shandong First Medical University & Shandong Academy of Medical Sciences, 6699 Qingdao Road, Jinan, Shandong 250117 People’s Republic of China ✉ Corresponding author. Received 2024 Dec 5; Accepted 2025 Mar 6; Collection date 2025 Jun. © The Author(s), under exclusive licence to European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2025. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12106254  PMID: 40438490 Abstract Background Immune checkpoint inhibitors (ICIs), such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapies, have revolutionized cancer treatment by harnessing the body’s immune system to eliminate cancer cells. Despite their considerable promise, the efficacy of ICIs significantly differs based on tumor types and specific patient conditions, highlighting the necessity for personalized approaches in the framework of predictive preventive personalized medicine (PPPM; 3PM). Main body This review proposes a stratification instrument within the 3PM framework to enhance the therapeutic efficacy of ICIs across Pan-cancer. Predictive approaches need to be utilized to enhance the effectiveness of ICIs. For example, biomarkers such as particular genetic alterations and metabolic pathways provide key information on patient treatment responses. To predict treatment outcomes, uncover resistance mechanisms, and tailor medications, we examine biomarkers including PDL-1 and CTLA4. Focusing on cancers like melanoma, bladder, and renal cell carcinoma, we highlight advances in combination therapies and cellular approaches to overcome resistance. We conducted an analysis of clinical trials and public datasets (TCGA, GEO) to evaluate ICI responses across number of cancer types. Survival analysis employed Kaplan–Meier curves and Cox regression. Pan-cancer analysis shows response rates ranging from 19.8% in bladder cancer to > 39% in melanoma when combination therapy is used, emphasizing the potential of 3PM to improve outcomes. By exploring resistance mechanisms and emerging therapeutic innovations, we propose a cost-effective model for better patient stratification and care. Validation of this model requires standardized biomarkers and prospective trials, promising a shift toward precision oncology. Conclusion Within the 3PM framework, this review addresses the urgent need for cost-effective stratification tools and adaptive combinatorial strategies to optimize outcomes. Keywords: Immune checkpoint inhibitors, Pan-cancer analysis, Cost-effective stratification tool, Personalized immunotherapy, Predictive biomarkers, Combination therapies, Predictive preventive personalized medicine (PPPM; 3PM; 3P medicine) Introduction The application of drugs seems to be promising in enhancing the potential of the immune system to neutralize the development of neoplasms. This modern approach to cancer treatment is called immunotherapy, wherein agents (drugs, antibodies, etc.) are utilized to potentiate the natural immunity of the body [ 1 ]. In recent years, developments in different types of tumor immunotherapy have contributed significantly to cancer treatment. Contemporary research, funded by programs like Horizon 2020, has focused on revolutionary therapeutic methods, including the development of nanomedicine [ 2 , 3 ]. Projects within Horizon 2020, such as NoCanTher, aim to enhance cancer treatment through the utilization of magnetic nanoparticles. These nanoparticles are engineered to target tumors and generate heat when exposed to an electromagnetic field, therefore enhancing chemotherapy efficacy [ 4 ]. This is especially advantageous for challenging cancers (VHIO, 2021) [ 5 , 6 ]. These efforts illuminate the evolving landscape of cancer treatment, highlighting the amalgamation of advanced nanotechnology with immunotherapy to potentially counteract the immunosuppressive effects of tumors and improve patient outcomes. The worldwide initiative for advanced therapies that more precisely target cancer cells, bolster immune responses, and mitigate adverse effects of conventional treatments is evidenced by the substantial backing that Horizon 2020 offers for projects addressing the molecular mechanisms of cancer and tumor microenvironments (European Commission, 2016) [ 7 , 8 ]. The 3PM framework describes it as predictive, preventive, and personalized medicine [ 9 ]. 3PM addresses ICI heterogeneity through (i) predictive biomarkers for patient stratification, (ii) preventive monitoring of resistance, and (iii) personalized combinatorial regimens. The collaborative research initiative now in progress is shaping a future where cancer immunotherapy can be more tailored and effective. Historical context and evolution Immuno-oncology has emerged as a transformative field in cancer research, driven by the development of immune checkpoint inhibitors (ICIs). A pivotal milestone occurred in 2011 when the cancer immunotherapy consortium, backed by the Foundation for the National Institutes of Health (FNIH), got serious about understanding and organized efforts to study how tumors evade the immune system and improve therapeutic targets [ 10 ]. The food and drug administration (FDA) quickly approved ipilimumab, which targets CTLA-4, and a series of PD-1/PD-L1 blockers, including nivolumab, pembrolizumab, atezolizumab, and durvalumab. This was a big change because they showed long-lasting effects in melanoma, non-small cell lung cancer (NSCLC), and tumors that do not fix mismatches. Meanwhile, trials are digging into fresh targets like lymphocyte-activation gene 3 (LAG-3), tumor necrosis factor receptor superfamily member 14/junctional adhesion molecule 4, and Indoleamine 2,3-Dioxygenase 1, emphasize the preventive role of 3PM in identifying resistance mechanisms and optimizing combinatorial strategies [ 11 – 13 ]. PD-1/PD-L1 inhibitors are the most common type used in clinical settings. However, their inconsistent effectiveness across cancer types has highlighted the need for predictive biomarkers (such as PD-L1 expression and TMB) and customized combinations. For example, the ipilimumab/nivolumab regimen for colorectal cancer (CRC) that was approved by the FDA shows the potential of dual checkpoint blockade in microsatellite instability high (MSI-H) subtypes, but it has not worked very well in other situations [ 14 , 15 ]. This rapid evolution, with six PD-1/PD-L1 inhibitors approved and seven in trials, reflects the urgency of 3PM-guided frameworks to address heterogeneous patient responses and prioritize cost-effective therapeutic algorithms. Basic principles of immunotherapy and ICIs ICIs are a new type of cancer treatment that specifically targets CTLA-4, PD-1, and PD-L1. These ICIs have significantly enhanced responses and elevated survival rates [ 16 ]. The most advanced ICIs (anti-PD-1/PD-L1 and anti-CTLA-4 agents) are currently standard-of-care therapies for patients with metastatic melanoma, NSCLC, bladder cancer, head and neck squamous cell carcinoma (HNSCC), and renal cell carcinoma (RCC), and are being evaluated for other malignancies. Despite their success, the variability in their effectiveness across patients and tumor types remains a significant challenge, underscores the need for predictive biomarkers (e.g., PD-L1, TMB) to stratify responders and guide personalized combinations (e.g., ICIs with cellular therapies) [ 10 , 17 – 20 ]. ICIs, such as anti-PD-1 and anti-CTLA-4, enhance immune responses against cancer, by regulating surface expression, receptor-ligand interactions, and intracellular signal transduction, with combination therapies often showing superior efficacy [ 21 ]. The effects of post-translational modifications (PTMs) on immunological checkpoints, turnover processes, and intracellular signaling systems remain incompletely understood. This understanding is crucial for the advancement of combination therapies [ 22 ]. Concurrent inhibition of both the CTLA-4 and PD-1 pathways enhances the effectiveness of combination therapy in cancer treatment, proving its efficacy compared to blocking a single pathway. Their efficacy varies widely, thus drives our 3PM approach, using biomarkers like PD-L1 and TMB, plus AI, to predict responses, prevent resistance, and personalize treatments, aiming for cost-effective, precision oncology. Immune checkpoint inhibitors Mechanisms and therapeutic implications in the framework of 3P medicine ICIs target and modulate the suppressive mechanisms of immunological checkpoints, fundamentally altering the landscape of cancer therapy. The level of surface expression, receptor-ligand interactions, and intracellular signal transduction pathways finely tune the functions of immune checkpoints [ 23 ]. ICIs disrupt key inhibitory signaling cascades, such as the PD-1/PD-L1 and CTLA-4 pathways, enhancing the immune system’s ability to recognize and destroy cancer cells [ 21 ]. For instance, in the PD-1 pathway, receptor-ligand interactions are pivotal for maintaining immune tolerance; blocking these interactions reactivates T cells to attack tumor cells [ 24 ]. Recent research has highlighted numerous questions that necessitate further investigation. Immune checkpoint proteins undergo PTMs such as phosphorylation, ubiquitination, and glycosylation, which significantly regulate receptor function, trafficking, and degradation [ 25 ]. While relatively unexplored, these PTMs appear to have numerous effects on checkpoint turnover and intracellular signaling. Also, PD-1’s function changes depending on whether it is on effector, regulatory, or memory T cells. This implies distinct effects on immune tolerance and anti-tumor immunity. To illustrate, PD-1 on memory T cells is essential for immune memory, but it also requires precise balance to limit chronic dampening in malignancy (Fig. 1 ) [ 25 , 26 ]. Fig. 1. Open in a new tab Mechanism of ICIs. The figure illustrates the fundamental mechanism of immune checkpoint inhibition, a crucial process in modulating the immune response against tumors. In the illustration: The illustration depicts the T cell receptor (TCR) and two key checkpoint receptors, PD-1 and CTLA-4. The tumor cell depicts PD-L1 interacting with PD-1, resulting in the inhibition of T cell activation. The antigen-presenting cell (APC) illustrates the interaction between the B7 molecule and CTLA-4. Inhibition mechanism: Red arrows indicate how these interactions result in the suppression of T cell activity. Green color arrows illustrate ICIs like anti-PD-1 and anti-CTLA-4, which block these interactions The differential expression and regulation of CTLA-4 and PD-1 ligands also reinforce their vital role in immune homeostasis and tolerance [ 27 ]. The combined blockage of both pathways has been proven to be more effective in clinical trials than the blockade of a single checkpoint, with higher response and survival rates seen in melanoma and lung cancer [ 28 , 29 ]. Elucidating these dynamics has paved the way for new combination therapies, combining ICIs with other modalities like targeted therapy, adoptive T cell therapy, and cytokine-based treatments. Current state of biomarker research in the context of 3P medicine Biomarkers are pivotal in personalizing healthcare. Advancements in genomics, proteomics, metabolomics, and systems biology have resulted in the discovery and clinical application of biomarkers [ 30 , 31 ]. These biomarkers can be used to further advance the growing field of personalized medicine—3P medicine, which requires the use of distinct criteria to classify as such and the evolution of clinical trials to meet its needs [ 32 ]. Biomarkers are defined as indicators signaling events, disease states, or conditions. To satisfy 3P medicine criteria, however, they should be an inherent part of the molecular interaction network describing the functionality of cells and tissues and should be directly linked with specific outcomes important for disease that have an unequivocal biologic link [ 33 ]. In addition, biomarkers should be modulated by pharmacologic or other types of interventions reducing or increasing the risk of adverse health outcomes long before current measures of disease prevent irreversible damage, enhancing the quality of life and longevity [ 34 ]. Table 1 discusses several aspects of biomarker research for ICIs, including their current state, types, challenges, and limitations in predicting therapeutic responses. Table 1. Biomarker research for ICIs therapy. The table provides a variety of biomarkers that have been utilized in the process of predicting immunological treatment responses and offers helpful recommendations for boosting the effectiveness of immunomodulatory drugs (e.g., ICIs) Biomarker Current state Challenges Limitations Suggestions Reference PD-L1 expression Expression of PD-L1 on tumor and immune cells, often used as a biomarker to predict response to PD-1/PD-L1 inhibitors Intratumoral heterogeneity leads to variability in PD-L1 expression across different tumor regions Widely used in clinical trials to predict ICI response, but its predictive value varies across tumor types. Inconsistent assays complicate interpretation and patient stratification Improved standardization of PD-L1 testing platforms, and integration with other biomarkers (e.g., TMB) to increase predictive accuracy [ 27 , 35 ] Tumor mutational burden (TMB) A measure of the number of mutations in a tumor, correlating with neoantigen load and immune response Technical and methodological challenges with sequencing platforms, cut-off values, and tumor heterogeneity High TMB is associated with better outcomes in some cancers (e.g., melanoma, NSCLC), but its efficacy varies. It may also be ineffective in cold Tumor microenvironment (TME) tumors (low immune infiltration) Refining TMB assessment methods across platforms and improving neoantigen prediction models. Combining TMB with other immune markers like tumor-infiltrating lymphocytes (TILs) and immune checkpoints [ 36 , 37 ] Microsatellite instability (MSI) and MMR mismatch repair (MMR) Deficiency Associated with defective DNA repair, particularly in CRC, predicting better response to ICIs Not all patients with MSI or MMR deficiencies respond to ICIs. Its predictive value is limited to certain cancers Effective in some cancers, particularly colorectal, but has limited applicability to other cancer types Expand use of MMR biomarkers to broader cancer types, and combine MSI with other immune factors (e.g., TILs, PD-L1) [ 30 , 38 ] Genomic alterations (e.g., EGFR, KRAS, SWI/SNF mutations) Mutations in oncogenes or tumor suppressor genes that can impact ICI response Tumor-specific variations and mutations in immune evasion pathways limit response to ICIs Tumor genomic alterations like EGFR mutations often predict resistance to ICIs, especially in NSCLC Develop targeted therapies that combine genomic alterations with ICI therapy to overcome resistance [ 31 , 39 , 40 ] neoantigen heterogeneity Variation in the tumor’s antigen presentation, which can affect immune recognition and ICI response Tumor heterogeneity and clonal evolution lead to differences in neoantigen expression High TMB can produce more neoantigens, but heterogeneity within the tumor can prevent effective immune recognition Focus on personalized neoantigen-based therapies and biomarkers that account for clonal diversity and tumor evolution [ 34 , 41 ] Tumor-infiltrating lymphocytes Immune cells within the tumor that can indicate the degree of immune activation Low TILs in some tumors (especially those with a cold TME) limit the effectiveness of ICIs Presence of TILs can indicate better responses to ICIs, but their absence often predicts poor outcomes Combine TIL analysis with other biomarkers (e.g., TMB, PD-L1) for better prediction of ICI efficacy [ 42 – 44 ] Regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) Immunosuppressive cells within the TME that can hinder ICI responses High Treg or MDSC levels in the TME contribute to immune suppression and resistance to ICIs High levels of Tregs and MDSCs often predict poor responses to ICI therapy Develop strategies to target Tregs and MDSCs, potentially improving ICI efficacy through immune modulation [ 45 , 46 ] Open in a new tab Biomarkers in predicting ICI response Known biomarkers play a crucial role in identifying cancer patients who are most likely to respond to ICIs, serving as a first step towards personalized medicine through therapeutic benefit dose optimization, while also requiring continuous testing for associated risks [ 47 ]. A perfect biomarker specifically targets patients who are more likely to respond to ICIs and has minimal adverse effects. Current biomarkers are classified into three general immune-related axes, which include tumor-intrinsic, local, or systemic immune microenvironments, as shown in Table 1 [ 48 , 49 ]. PD-L1 expression as a predictive biomarker PD-L1 expression has been measured in clinical trials using a variety of assays and platforms, including immunohistochemical tests (IHC) such as 22C3, SP263, and SP142. Each of these assays has its own scoring methodology and cut-off values for positive [ 50 ]. Even with these limitations, PD-L1 expression remains an imperfect but useful biomarker. High PD-L1 expression often indicates a better response to ICIs compared with low or absent PD-L1 expression, particularly in specific tumor types. Nevertheless, a subset of patients with low or negative PD-L1 expression still respond to ICIs, emphasizing the complexity of the TME where additional predictive factors beyond PD-L1 play a role. With the increasing standardization of PD-L1 testing, researchers are striving to develop additional biomarkers for potential use in future prediction systems [ 51 ]. To address these challenges, researchers are developing a number of new approaches to overcome the limitations of single-marker assessments and provide more reliable and effective clinical feedback [ 24 ]. Tumor-intrinsic biomarkers TMB is one of the most widely investigated biomarkers for predicting the response to ICI (Table 1 ). High TMB is associated with the generation of more neoantigens, increasing T cell recognition of these transformed cells [ 52 ]. This process could enhance the efficacy of ICIs by fostering a robust anti-tumor immune response [ 53 , 54 ]. Numerous clinical studies have demonstrated a significant association between higher TMB and better outcomes in melanoma, NSCLC, and urinary bladder carcinoma [ 55 ]. Additionally, high TMB tumors may upregulate immune evasion mechanisms that dampen neoantigen presentation. Even with high TMB, alterations in antigen processing machinery (e.g., mutations in β2-microglobulin (B2M) or downregulation of major histocompatibility complex (MHC) could limit neoantigen presentation. Many tumors with a so-called “cold” TME (i.e., low immune infiltration in the microenvironment) are typically non-responsive to ICIs, even when TMB levels are high [ 56 ]. TMB alone does not account for other factors that determine ICI response, such as neoantigen heterogeneity and clonality, TILs, or the immunosuppressive components of the TME, such as Tregs and myeloid-derived suppressor cells (Table 1 ) [ 57 ]. Emerging data suggest that combining TMB with other predictive biomarkers could improve its accuracy. For instance, TMB’s predictive power could be strengthened by integration with PD-L1 expression, gene expression signatures, and immune cell profiling. Advances in multi-omics approaches and sophisticated computational models, including artificial intelligence (AI), may further refine TMB’s role as a biomarker [ 58 ]. These insights underscore the complexity of using TMB as a standalone predictive biomarker and highlight the need for a multi-faceted approach to better predict patient responses to ICIs. Immune microenvironment biomarkers IHC evaluation of PD-L1 expression has become a standard predictive biomarker for response to anti-PD-1/PD-L1 therapy. Despite being indispensable in diagnostic laboratory tests, this method is also not reliable due to the heterogeneity of TME and absence of uniform testing platforms [ 59 ]. In addition to PD-L1, high-density TILs and a “hot” TME characterized by active immune infiltration are associated with favorable clinical outcomes. In contrast, a TME that is “immune-cold,” or one that has low immune activity, could be predictive of therapy resistance [ 60 ]. Systemic biomarkers Circulating biomarkers, such as soluble PD-L1 and cytokines (e.g., IL-8), are emerging as non-invasive predictors of ICI response [ 61 ]. Despite their promise, biomarkers face significant challenges. Tumor evolution and patient-specific factors contribute to biomarker heterogeneity, complicating their predictive accuracy. It is necessary to use multiparametric approaches that combine genomic, transcriptomic, and proteomic data because single biomarkers often do not show how complicated the interactions between tumors and immune systems are [ 30 , 31 , 62 ]. Advancing the field requires developing robust, reproducible, and standardized biomarkers for clinical application. Validating multi-omics biomarkers and dynamically monitoring TME remodeling in real-time could refine patient selection and guide tailored treatments. Collaboration between academia, industry, and regulatory bodies is essential to achieve these goals. MSI and deficient mismatch repair deficiency biomarkers It is well known that MSI-H and deficient mismatch repair (dMMR) can be used to predict the effects of ICIs, especially in CRC. MMR pathway defects cause MSI-H/dMMR tumors to acquire mutations and generate neoantigens, which the immune system is likely to recognize [ 63 , 64 ]. This mutational burden makes those tumors more susceptible to ICIs such as pembrolizumab and nivolumab. Recently, pembrolizumab became the first FDA-approved tumor-agnostic agent for the treatment of MSI-H/dMMR solid tumors, representing a major advance in personalized cancer therapy [ 63 ]. MSI-H/dMMR testing has transformed the ability to identify patients who would benefit from ICIs, but several challenges persist. False-negative results could arise from tumor heterogeneity, sampling error, or assay sensitivity. Also, the response to ICIs is not always caused by MSI-H/dMMR tumors and is not always guaranteed in these patients [ 61 , 65 , 66 ]. This means that we need more biomarkers that show things about the immune microenvironment, like TILs and PD-L1 expression. Some MSI-stable tumors do respond to ICIs, though, because they have features that are not related to MSI, like high TMB or an inflammatory tumor microenvironment. Future trials require further characterization of MSI-H/dMMR biology, particularly in the context of individual tumors outside CRC, and combinatory biomarker approaches. It would also be interesting to see how MSI-H/dMMR status can be used with other treatments, like ICIs combined with DNA damage repair inhibitors or chemoradiotherapy, to make these biomarker-directed therapies more effective. These observations highlight the potential complexity of using MSI-H/dMMR alone as predictive biomarkers and imply that a combination of approaches may be more successful in predicting patient responses to ICIs. TILs biomarkers TILs have gained momentum as important predictive biomarkers of ICI efficacy. The number of TILs and the proportion composition are a reflection to some extent on an immune response in effect against the tumor. The presence of high levels of TILs including CD8 + T cells is usually linked to response to ICIs [ 44 , 62 ]. On the other hand, while interaction of spatial distribution and functional state (e.g., exhaustion) to influence ICI efficacy will also be considered important (Table 1 ). Within TILs, this population of immune cells consists of CD8 + cytotoxic T cells, CD4 + helper T cells, and Tregs, representing the full spectrum of the host’s immune response toward the tumor [ 67 – 69 ]. TILs are typically associated with favorable clinical outcomes and greater sensitivity to ICIs, particularly when they include CD8 + T cells, which play an important role in anti-tumor immunity by recognizing and killing tumor cells [ 70 ]. The composition of tumoral immune microenvironments (noninvasive vs. invasive margins) has a substantial impact on ICI response. Tumors characterized by a “hot” TME, where TILs are abundant and clustering around tumor cells, are more likely to respond better to ICIs. By contrast, “cold” tumors that have few or no TILs respond poorly to this type of therapy [ 60 , 71 , 72 ]. Furthermore, the functional status of TILs plays a crucial role. Indeed, the prevalence of dysfunctional CD8 + T cells, defined by overexpression of inhibitory receptors like PD-1, TIM-3, and LAG-3, may negatively impact ICI efficacy due to the considerably lower cytotoxic potential of these exhausted T cells [ 73 ]. New evidence shows that the quality of TILs, their phenotypic and functional heterogeneity—rather than their quantity is very important for determining how well treatments work. High cytokine production or TIL proliferative capacity, for example, is more likely to mediate an effective anti-tumor response. Furthermore, their interaction with other immune and non-immune elements of the TME, such as stromal cells and suppressive myeloid populations, may impact TIL functionality and thus ICI efficacy [ 74 ]. In light of these difficulties, approaches such as 3PM are required to improve TIL infiltration and function. ICIs could also be used with therapies believed to alter the TME (e.g., oncolytic viruses and radiation) or systemic cytokines thought to enhance TIL activity (e.g., IL-2 and IL-15) [ 75 , 76 ]. Emerging technologies, including spatial transcriptomics and multiplex immunohistochemistry (mIHC), are furthering our understanding of TIL dynamics and moving toward inclusion in predictive biomarker panels. Challenges in biomarker development for ICI therapy in the context of 3P medicine The application of biomarkers for ICIs faces challenges in personalized treatment, preventive, and predictive medicine. Technical variability and intratumoral heterogeneity limit the predictive biomarkers like PD-L1 expression and TMB. Ongoing biomarker monitoring is essential since preventive medicine must address temporal variability and the evolving mechanisms of immune evasion. The integration of several biomarkers is essential for customizing treatment protocols in personalized medicine [ 31 ]. Enhancing biomarker standardization, combinatorial methodologies, and real-time monitoring is crucial for successfully addressing these challenges. Table 2 summarizes the principal challenges associated with heterogeneity (spatial, temporal, and intratumoral), standardized assessments, and strategies to mitigate these concerns. Table 2. Challenges in biomarker development for ICIs therapy. The table summarizes the primary challenges and potential solutions for biomarker development in the context of ICI therapy based 3P medicine Challenge Description Impact on biomarker development Strategies for overcoming Reference Heterogeneity Tumors exhibit variability at intertumoral (across different tumor types), intratumoral (within the same tumor), and temporal (over time) levels. Tumors may also change in response to therapy Variability in biomarkers such as PD-L1 expression or TMB complicates predictive testing. Intratumoral heterogeneity can lead to false negatives or context-dependent results, making it difficult to develop reliable diagnostic biomarkers Multiparametric approaches that integrate multiple biomarkers (e.g., PD-L1, TMB, immune gene signatures) are being explored. Using spatial transcriptomics and single-cell sequencing to assess dynamic changes over time. Liquid biopsy strategies (e.g., circulating tumor DNA, ctDNA, circulating tumor cells, CTCs) offer a more comprehensive view of tumor heterogeneity [ 34 , 77 ] Intratumor heterogeneity Within a single tumor, biomarkers like PD-L1 or TMB may vary across different regions (e.g., core vs. invasive margins). Factors like hypoxia and immune cell infiltration contribute to this variability This makes it difficult to accurately assess biomarker status with a single tissue sample. Biopsy sampling errors may miss low-level expression, leading to false-negative results Spatial transcriptomics allows for detailed mapping of biomarker expression across tumor regions. Single-cell sequencing can provide more granular insights into tumor heterogeneity [ 78 ] Temporal heterogeneity Tumors evolve over time, especially after therapy. New immune evasion mechanisms may arise (e.g., TIM-3, LAG-3 upregulation) or antigen presentation may be lost, altering the tumor’s biomarker profile Biomarker measurements taken at baseline may lose predictive power over time as the tumor evolves. This complicates treatment monitoring and long-term efficacy predictions Continuous longitudinal monitoring through liquid biopsies (ctDNA, CTCs) can provide ongoing assessment of tumor evolution. This enables adaptive therapy adjustments [ 79 , 80 ] Standardization There is a lack of consistent testing methodologies and interpretation of biomarkers in clinical practice. For example, there are multiple tests for PD-L1 with varying results, and no standardized method for calculating TMB The lack of standardization leads to discrepancies in how biomarkers are measured and interpreted across different studies and clinical settings, which can affect treatment decisions Establishing universal cutoffs and standardized protocols for testing biomarkers like PD-L1 and TMB would reduce variability. Ongoing efforts to develop standard practices for biomarker testing across labs and institutions [ 35 , 81 , 82 ] Open in a new tab Heterogeneity Biomarkers including PD-L1 expression or TMB vary not only between different tumor types but also within the same tumor region. The presence of this intratumoral heterogeneity makes it very difficult to assess biomarker status in tissue samples, thereby limiting the predictive power of many current markers [ 83 ]. The heterogeneity exists at intertumoral (between tumor types), intratumoral (within a single tumor), and temporal levels (evolution of tumors over time). These layers of heterogeneity significantly impact various biomarkers (e.g., PD-L1 expression, TMB), limiting their reliability as predictors for ICIs [ 75 ]. Intratumor heterogeneity Even within the same patient, a tumor can exhibit a diverse biomarker profile. PD-L1 expression may vary spatially, such as between the tumor core and invasive margins or across metastatic sites, due to factors like hypoxia, changes in immune cell infiltration, and genetic instability. Biopsies are also known to occasionally under-sample, thus missing low-level expression of a biomarker and leading to false-negative or context-dependent findings. Likewise, TMB, an indicator of the total number of mutations within a tumor, may change in different areas of the same tumor due to clonal evolution or selective pressures from the immune microenvironment or treatment [ 84 , 85 ]. Temporal heterogeneity Within the same patient, tumor evolution due to differential treatment pressures complicates biomarker studies further. Following therapy, mechanisms of immune evasion, such as upregulation of alternative immune checkpoints (e.g., TIM-3 or LAG-3) or loss of antigen presentation, may arise, eroding the predictive value of baseline measurements [ 84 ]. The increased spatio-temporal resolving power using spatial transcriptomics and single-cell sequencing helps to map the dynamics of biomarkers [ 12 , 86 , 87 ]. Moreover, the use of liquid biopsy strategies that analyze ctDNA or CTCs enables the assessment of molecular heterogeneity throughout treatment without requiring further biopsies [ 76 ]. Standardization The integration of ICIs into precision medicine necessitates consistent biomarker testing. Maintaining uniformity in the laboratory is problematic due to variations in testing procedures and interpretations. Variability in PD-L1 expression arises from multiple factors, including assay heterogeneity such as 22C3, SP263, and SP142, each utilizing distinct antibodies, scoring criteria (tumor versus immune cells), and positivity thresholds (≥ 1% vs. ≥ 50%) [ 50 , 88 , 89 ]. The Blueprint Project [ 90 ] identified a discordance of 20–30% in NSCLC cases [83 became 82]. Similarly, the assessment of TMB is inconsistent. Although targeted gene panels underestimate this value by 15%, whole-exome sequencing (WES) categorizes 10–20 mutations per megabase (mut/Mb) as high. This may result in the misclassification of melanoma patients ( GSE93157 ) [ 91 , 92 ]. The CheckMate 568 trial [ 93 ] established a TMB threshold of ≥ 10 mut/Mb as a predictor of response to nivolumab plus ipilimumab in NSCLC. In the KEYNOTE-158 study [ 94 ], set a response level of ≥ 10 mut/Mb for pembrolizumab in several different types of tumors. Another concern is measuring ctDNA, a promising biomarker for early cancer detection and monitoring. Assay detection ranges from 0.01 to 1%; however, sensitivity varies. In early-stage cancers with low ctDNA levels and sensitive assays, such differences may considerably affect clinical decisions. Digital droplet PCR (ddPCR) can find very few variants (< 0.01% variant allele frequency, VAF) but only in hotspot mutations. Guardian360 and Foundation ACT are NGS-based tests that can find mutations at 0.1 to 1% VAF. However, they have trouble with false positives in samples with low input. Reagents, test procedures, and cutoff values vary amongst labs, making biomarker-driven ICI stratification harder to adopt. The International Quality Network for Pathology is harmonizing testing techniques, but 3PM in immunotherapy requires standardized, reproducible tests. Global standards are needed to ensure accurate and equitable patient selection in varied therapeutic contexts. For 3PM to effectively guide ICI stratification, standardized and reproducible assays are crucial. 3PM strategies involve enhancing ICI response prediction involves combining PD-L1 with TMB or IFN-γ signatures. Preventive approaches focus on dynamic PD-L1 monitoring through liquid biopsies to detect resistance early. Personalization strategies utilize spatial PD-L1 mapping with multiplex IHC to guide targeted treatments in heterogeneous tumors. Such strategies are essential for 3PM to deliver equitable, accurate, and personalized cancer treatment. Implications for patient selection The absence of standardized biomarker definitions and methodologies across laboratories compromises research reproducibility and equitable clinical decision-making, directly affecting patient eligibility for ICIs. The selection of patients for ICI therapy in 3PM is dependent on the reliability of biomarkers; nevertheless, inconsistencies mirror the difficulties associated with standardization. Below, we detail the challenges and consequences of this variability, with a focus on real-world laboratory practices. Assay heterogeneity and laboratory workflows Variability in PD-L1 assays, TMB calculations, and MSI/MMR testing results in misclassification, influencing therapy choices and patient outcomes [ 88 , 90 , 95 ]. Because of the variability of tumors and their temporal progression, static assessments are made even more difficult, which is why longitudinal profiling is required. Challenges in testing PD-L1 assays differ in scoring criteria, causing inter-lab discordance. TMB assessment varies by panel size, inflating scores in smaller panels. MSI/MMR testing shows up to 15% discordance between methods, impacting diagnosis [ 96 ]. Clinical impact Misclassification leads to over- or undertreatment, with some patients receiving ineffective therapies while others miss out on ICIs. Limited access to advanced testing in resource-poor settings further worsens disparities. A 3PM tool that makes use of standardized biomarker thresholds, cross-platform assay validation, and AI-driven pathology can improve consistency. Multi-biomarker models and liquid biopsy-based TMB offer more accurate patient stratification. Universal thresholds, third-party assay validation, and mandatory multi-biomarker reporting are essential for equitable and effective patient selection. Dynamic nature of biomarkers Several biomarkers, including PD-L1 expression, are subject to alter in response to medication or disease progression. Biomarkers display dynamic behavior, posing a significant challenge to their clinical utility. One of the most studied biomarkers for ICIs is PD-L1. However, its expression can change depending on the situation. This is something that needs to be taken into account for clinical translation and ongoing research [ 97 , 98 ]. The fluctuation in time makes it difficult to depend on a solitary biomarker evaluation at a particular moment to inform long-term therapy choices [ 99 ]. 1. Treatment-induced changes Moreover, ICIs can affect the TME and alter biomarker expression. For example, PD-L1 expression might be heightened or upregulated as part of an adaptive resistance mechanism following initial therapy with anti-PD-1/PD-L1 agents. In the same way, targeted therapies, radiation, or chemotherapy can change the immune environment and raise or lower levels of biomarkers like PD-L1 and TMB. Such fluctuations indicate the necessity of serial measurements to correctly assess biomarker status during treatment [ 97 ]. 2. Disease progression and tumor evolution Tumor evolution drives ICI resistance, making progression monitoring vital in 3PM. It displays clonal evolution in their genetics and in the TME as they progress. Clonal purification and immune evasion induce biomarker expression variation, such as TMB, MSI-H, or PD-L1. For instance, tumors that were initially PD-L1 positive and responded to ICIs may eventually avoid immune recognition by utilizing different mechanisms of evasion, like MHC downregulation leading to reduced antigen presentation, even while remaining PD-L1 positive [ 42 ]. Liquid biopsy platforms utilizing ctDNA and cell-free RNA (cfRNA) provide real-time monitoring of clonal dynamics and immune evasion mechanisms, surpassing traditional tissue biopsies by revealing resistance mechanisms in real time, thus fulfilling the preventive purpose of 3PM. Clinically proven thresholds (e.g., JAK1/2 VAF > 0.5%) and combination triggers (e.g., PD-L1 RNA spikes) have superior antitumor efficacies and higher response rates [ 100 ]. NSCLC studies show ctDNA clearance within 8 weeks of ICI correlates with 75% progression-free survival (PFS) [ 101 ]. These advances enable real-time tracking of clonal shifts, outperforming static biopsies, and support 3PM’s preventive arm by preempting resistance with tailored interventions. 3. Temporal heterogeneity in biomarkers Temporal heterogeneity is a significant issue in metastatic cancers, where biomarkers can show starkly different patterns between primary tumors and metastatic sites [ 102 ]. For instance, the primary tumor may exhibit elevated PD-L1 expression, while metastatic lesions may completely lack it—or vice versa [ 103 ]. This variability emphasizes the importance of assessing multiple sites and using liquid biopsies to capture ctDNA as a dynamic representation of tumor burden and biomarker status [ 104 , 105 ]. 4. Strategies to address biomarker dynamics Researchers are investigating approaches to dynamic biomarker assessment in light of these limitations. Serial biopsies, liquid biopsies, or both can perform longitudinal surveillance to track biomarker changes over time. Also, newer imaging methods that use positron emission tomography (PET) with radiolabeled antibodies against PD-L2 make it possible to track biomarker expression over time without damaging the cells [ 106 ]. AI can analyze temporal biomarker data to enhance predictive models and define adaptive treatment strategies [ 107 , 108 ]. Composite biomarkers There is an increasing acknowledgement that relying on a single biomarker may not be enough to accurately predict the response to ICIs. Researchers are investigating composite biomarkers, which integrate multiple indicators like PD-L1 expression, TMB, and TILs, to improve prediction accuracy. Nevertheless, the process of creating and confirming composite biomarkers is intricate and necessitates extensive, meticulously planned investigations [ 109 ]. Composite biomarkers are a promising new approach to cancer diagnosis and treatment prediction that combines many indicators to better reflect the complex interplay between the immune system and cancer. PD-L1 expression, TMB, TILs, gene expression signatures (GES), and cytokine profiles are some of the parameters that are often used to make composite biomarkers. Creating composite biomarkers is no small task—it demands large, meticulously controlled studies to ensure results are reproducible and clinically meaningful. Part of the challenge lies in standardizing the assays and confirming that the individual components of a composite biomarker are truly independent but not overlapping in their functions [ 110 , 111 ]. Furthermore, the data obtained from combining multiple indicators can be high-dimensional and hard to interpret in common terms, requiring complex computational tools, such as machine learning, to derive useful features and prediction models that are not otherwise evident [ 112 , 113 ]. Future research should focus on validating composite biomarkers in various tumor types and clinical settings, integrating longitudinal assessment for improved predictive accuracy, and using liquid biopsies for non-invasive evaluation [ 106 ]. Future directions of biomarker development for ICI therapy in the context of 3P medicine The future of biomarker development for ICIs will concentrate on enhancing predictive accuracy through the integration of multiple biomarkers, including PD-L1, TMB, and TILs, alongside the application of advanced methodologies such as spatial transcriptomics and single-cell sequencing that could facilitate their application in 3P medicine [ 114 , 115 ]. To facilitate preventative interventions, longitudinal biomarker monitoring using liquid biopsies will enable early detection of resistance and tumor development. Furthermore, individualized strategies will be enhanced by the standardization of biomarkers and the integration of immune-related indicators to customize treatment for specific patients. Table 3 summarizes many biomarker types, including ctDNA, RNA-based signatures, proteomics, and multi-omics biomarkers, along with their descriptions and technical applications for ICIs. The fundamental purpose of biomarker development techniques is to elucidate how biomarkers might facilitate outcome prediction in ICIs therapy. In cancer therapy, the three elements biomarker-driven trials, the incorporation of AI, and longitudinal biomarker monitoring to illustrate distinct methodology and potential strategies. Table 3. Comprehensive table on biomarker types for ICI therapy. The table highlights the potential of emerging biomarkers to optimize the use of ICIs in cancer treatment based 3P medicine Biomarker type Description Technologies used Potential for ICI therapy Non-invasive biomarkers Includes biomarkers like ctDNA and RNA-based signatures. ctDNA reflects tumor genetics and heterogeneity, providing a real-time assessment of tumor dynamics Next-generation sequencing (NGS), liquid biopsies ctDNA correlates with clinical outcomes post-ICI therapy. Certain mutations (e.g., JAK1/2, β2-microglobulin) may indicate resistance. RNA signatures related to immune activation pathways correlate with ICI response Proteomics Proteomic techniques allow for the measurement of cytokines, chemokines, and immune checkpoint molecules in the TME and peripheral blood. Elevated levels of soluble PD-L1 and certain cytokines have been linked to ICI responses Mass spectrometry, multiplex immunoassays Elevated soluble PD-L1 levels are associated with better ICI response. Specific cytokines, such as interferon-γ and IL-8, are linked to response and resistance to ICIs Multi-omic biomarkers Combining genomics, transcriptomics, and proteomics for a comprehensive understanding of tumor and immune system dynamics. This approach is particularly useful in heterogeneous cancers that evade treatment through diverse mechanisms Genomics, transcriptomics, proteomics Multi-omic approaches provide a deeper insight into tumor immune evasion mechanisms and can improve predictions for ICI therapy efficacy Open in a new tab Next-generation biomarkers Advancements in genomics, transcriptomics, and proteomics technologies have enabled the identification of novel biomarkers for assessing patient response to ICI therapy that make treatment more accurate and improve patient outcomes [ 30 ]. Tumors with high TMB ≥ 10 mutations/Mb or MSI-H respond well to ICIs, as seen in the KEYNOTE-158 trial [ 94 ], while POLE/POLD1 mutations are linked to long-lasting benefits. While single-cell RNA sequencing reveals resistance mechanisms, transcriptomic markers—like the T cell-inflamed gene expression profile—help predict anti-PD-1 response. Proteomic techniques, such as spatial analysis of PD-L1 expression and soluble PD-L1 detection in blood, further refine patient selection. Combining multi-omics—TMB, PD-L1, and CD8 + T cell density—helps to predict responses better, and liquid biopsy-based measurement of ctDNA and cfRNA makes real-time resistance monitoring possible. In line with 3PM, these biomarkers guide treatment decisions, detect emerging resistance, and help tailor therapies to individual patients. However, standardization and the development of accessible, scalable biomarker testing remain crucial for ensuring widespread clinical adoption. Non-invasive biomarkers Cell-free nucleic acids (cfNAs), including ctDNA and RNA, are changing the way tumors are monitored by providing a non-invasive alternative to traditional biopsies. CtDNA levels can provide real-time insights into tumor dynamics, with early clearance linked to improved overall survival (OS) and PFS [ 116 ]. They offer real-time insight into tumor evolution, detecting resistance mutations, and thus can be used as prognostic biomarker [ 117 ]. Their levels in NSCLC patients correlated with radiographic tumor burden (CT/PET imaging) and higher ctDNA levels predicted worse prognosis [ 118 ]. ctDNA also improves prognostic accuracy by identifying genetically distinct tumor clones missed by traditional biopsies, with an 85% concordance rate to metastatic burden in NSCLC [ 79 ]. In parallel, plasma-derived cfRNA markers including CXCL9 were significantly over-expressed in patients with low disease burden or therapeutic response, with p values indicating statistical significance ( p ≤ 0.001 for low disease burden, p = 0.012 for therapeutic response at follow-up) [ 119 ]. Changes in cfRNA levels correlate with clinical response across cancer types, including melanoma, and that PD-L1 gene expression might predict response to PD-L1 inhibition [ 120 ]. Patients with residual ctDNA after CRT benefited from subsequent ICI therapy, highlighting the importance of ctDNA in personalizing treatment approaches [ 121 ]. These results show the promise of cfNAs as effective immunotherapy early response assessment, resistance identification, and individualized treatment plans tool (Table 3 ) [ 122 , 123 ]. Proteomics and multi-omics biomarkers With advances in techniques like mass spectrometry and multiplex immunoassays that enable precise measurement of cytokines, chemokines, and immune checkpoint molecules in both the TME and peripheral blood, proteomics is emerging as a transformative tool in cancer research (Table 3 ). Spatial proteomics, using tools like the GeoMx Digital Spatial Profiler, has identified PD-L1 expression patterns in stromal “immune deserts,” helping to pinpoint NSCLC patients less likely to respond to ICIs (HR: 2.5). Cytokine profiling has also provided valuable insights, with high baseline IL-8 levels linked to innate resistance (HR: 3.1), while interferon-γ-inducible chemokines such as CXCL10 have been associated with improved treatment response [ 113 ]. In the future, the inclusion of these indicators into ordinary clinical practice may make it possible to develop tailored treatment plans. A real-time monitoring system that makes use of liquid biopsies, for instance, would make it possible to make modifications to adaptive therapy, which would improve patient outcomes and reduce needless toxicity. Biomarker-driven trials Biomarker-driven trials in 3PM shift from static pre-treatment assessments to dynamic molecular monitoring, optimizing ICI outcomes by facilitating adaptive, patient-centered therapy methodologies. Patients with NSCLC with a high TMB ≥ 10 mut/Mb demonstrated significantly enhanced survival outcomes when treated with nivolumab plus ipilimumab compared to those receiving chemotherapy (2-year OS: 40% vs. 15%). Dynamic ctDNA clearance correlated with a 2.3-fold increase in PFS, reinforcing the predictive power of real-time molecular tracking over a single pre-treatment TMB measurement. This part underscores how 3PM-driven adaptive monitoring enables early therapeutic adjustments, preventing prolonged exposure to ineffective ICIs and enhancing clinical outcomes. The KEYNOTE-158 trial achieved an overall response rate of 34%, median duration of response exceeding 24 months, highlighting the durability of pembrolizumab in this biomarker-selected population, leading to the FDA’s tissue-agnostic approval of pembrolizumab for MSI-H malignancies, aligning with 3PM principles of individualized treatment strategies rather than site-based tumor classifications [ 94 ]. BESPOKE CRC experiment are facilitating real-time treatment modifications, thereby reducing needless chemotherapy-related harm by 30%. Innovative trial methodologies enhance precision medicine to a greater extent [ 124 ]. The NCI-MATCH umbrella study associates medicines with specific genetic alterations, such as POLE mutations linked to ICIs susceptibility, resulting in a 22% response rate in resistant cancers [ 125 ]. Liquid biopsy-driven trials, such as the blood first assay screening trial, demonstrated an 80% concordance with tissue-based testing by identifying ALK fusions in NSCLC patients through ctDNA, thereby facilitating targeted treatment with alectinib [ 126 ]. These advancements highlight how biomarker-driven trials are paving the way for fulfilling 3PM’s mission of more personalized, cost-effective, and less toxic cancer therapies [ 113 , 127 , 128 ]. Integration with AI and machine learning AI and machine learning (ML) are transforming the field of biomarker discovery by enabling the analysis of large and complex datasets. One of the main advantages of these technologies is their ability to handle massive and complex datasets that would be nearly impossible to analyze using traditional approaches. By integrating clinical, genetic, and imaging data, AI and ML can identify hidden patterns and relationships, revealing insights into how tumors respond to treatment. This analysis involves merging clinical, genetic, and imaging data in order to uncover patterns that predict ICI response [ 129 ]. In ICI research, where the relationship between immune systems and tumors is complex and varies among patients, AI’s capacity to dig through extensive data and pinpoint subtle but significant trends could revolutionize the field. This approach not only accelerates the discovery of new biomarkers but also predicts patient responses to therapy at the level of personalization that was previously unattainable. Models that are powered by AI may be the key to solving the problems of composite biomarkers and finding new predictive traits that are hidden from normal analysis. The immunological characteristics of the TME, MSI, TMB, and PD-L1 expression are predictive of ICI responses, with AI algorithms significantly contributing to the integration of these datasets. AI is emerging as a potent instrument in tackling the complexities of generating composite biomarkers. AI-driven models can find intricate connections between a number of biomarkers, such as PD-L1, TMB, and RNA-based signatures, by analyzing multi-omic datasets [ 130 ]. This is something that traditional methods might miss. One of the most significant advantages of AI is its potential to uncover new predictive characteristics that conventional research may miss. Unsupervised learning methods, like clustering algorithms, can identify patient subgroups with unique genomic or immunological profiles that are associated with ICI response or resistance. Deep learning models in radiomics have also analyzed medical imaging to extract characteristics that could predict therapy effectiveness. These features include the texture of the tumor or the patterns of vascularization [ 131 ]. AI holds immense potential in addressing some of the most critical problems in biomarker research, especially given the dynamic and diverse nature of biomarkers. Recurrent neural networks, also known as RNNs, are particularly useful for processing temporal data and monitoring the changes in biomarker expression over time [ 132 ]. This capability enables real-time monitoring of tumor responses to treatment, providing crucial information for the creation of adaptive therapeutic regimens. Additionally, AI is leveraging natural language processing to sift through unstructured clinical data from electronic health records [ 133 – 135 ]. By analyzing this wealth of information, the integration of both structured and unstructured data improves the effective predictive models, ultimately enhancing the quality of patient care. As AI technologies continue to progress, their incorporation into clinical procedures will require evaluation and permission from regulatory bodies. It is crucial to have AI-powered models that are interpretable, repeatable, and clinically actionable [ 136 ]. Researchers, physicians, and AI developers should collaborate to build these models. Researchers can accomplish this by incorporating biospecimens into large-scale, multi-site studies that encompass a variety of treatment procedures and potential outcomes. A look into the future reveals that AI has the potential to make adaptive trial designs possible. This would allow biomarker data to assist in informing real-time adjustments in treatment decisions, which would further personalize ICI therapy. Longitudinal biomarker monitoring There is a growing interest in the continuous monitoring of biomarkers as a means of determining the efficacy of treatment and identifying the early emergence of resistance. Longitudinal studies using liquid biopsies to look at changes in ctDNA, TMB, or immune cell profiles might help us learn a lot about how the relationship between tumors and the immune system is always changing. It might also help us come up with more effective ways to treat these conditions [ 137 ]. As an alternative method for determining treatment success and the establishment of resistance at an earlier stage, continuous monitoring of biomarkers is becoming an increasingly popular choice. When liquid biopsies are used in longitudinal studies to look at changes in ctDNA, TMB, or immune cell signatures over time, they may reveal useful details about how cancers and the immune system work together [ 138 ]. This information might assist medical professionals in developing more individualized treatment strategies. Resistance mechanisms to ICIs in the context of 3P medicine Resistance to ICIs limits therapeutic efficacy, but the 3PM framework strategies offer actionable solutions to overcome these barriers. Below, we highlight key resistance pathways and their alignment with 3PM principles (Fig. 2 ). Fig. 2. Open in a new tab Mechanisms of resistance to ICIs. The figure illustrates the two types of resistance to ICIs, divided into two sections: intrinsic resistance and acquired resistance. The visual elements emphasize the complexity of tumor resistance mechanisms. (1) Intrinsic resistance: Low mutational burden: Tumors with few mutations are represented by simplified DNA strands with minimal mutations. Checkpoint upregulation: T cells are depicted with multiple inhibitory receptors (e.g., T-cell immunoglobulin and mucin-domain containing-3 [ TIM-3], V-domain Ig suppressor of T cell activation [ VISTA], indicating increased checkpoint expression. Defective antigen presentation: Tumor cells are shown with reduced MHC expression, highlighting the impaired ability to present antigens. (2) Acquired resistance: T cell exhaustion: T cells with diminished activity and upregulated inhibitory markers are illustrated to show the loss of T cell functionality over time. Metabolic reprogramming: We depict tumor cells creating a hostile microenvironment, including visual elements like lactate molecules and glucose competition, to represent the metabolic changes that contribute to resistance Intrinsic resistance mechanisms Intrinsic resistance refers to pre-existing factors within the tumor or host that render ICIs ineffective from the outset. We classified the causes of intrinsic resistance into (1) inadequate presentation of the antigen: deficiencies in tumor cell antigen expression, which hinders immune recognition mediated by T cells; (2) recruitment capacity of effector cells deficient or inhibited; and (3) immunosuppressive signals [ 139 – 141 ]. Immunotherapy is making remarkable advances in the field of cancer treatment. Even so, not all patients respond equally to treatments, and the majority develop resistance over time. There are a number of mechanistic reasons for this, some of which are important not only in cancer but also in infectious disease vaccines. Among the best known are antigen variability due to tumor sampling and acquired mutations, poor translational processing of antigenic peptide–MHC I interaction, tumor cell resistance to immunological elimination, growth-limiting microenvironment for lymphocytes, and release factors of immunosuppressive molecules [ 142 , 143 ]. Resistance to immunological treatments relies on several negative signals that interfere with the immune response, leading to an increase in the number of immune effectors, but also in the functional potency of the disadvantages of these effectors (Fig. 2 ). Even before the introduction of an immune treatment, tumor cells can develop a state of equilibrium with the immune response. This state represents a necessary inter-series for a tumor to clinically manifest and can be identified in numerous murine models and humans. These tumors induce the apoptosis of TILs, generating a tumor mass from which cells that express the antigen need no longer disintegrate by themselves. In the end, tumor cells can actively suppress the immune response through the expression of several inhibitory molecules, generally as a reaction to an increase in sensitivity to the immune response [ 144 , 145 ]. Genetic alterations in tumor cells Some cancers evade ICIs due to genetic alterations, such as JAK1/2 mutations that could impair interferon-gamma signaling. By identifying patients unlikely to react to traditional ICI treatment, these genetic markers can serve as predictive biomarkers, hence enhancing patient selection within the predictive aspect of the 3PM framework. Personalized approaches that target specific mutated pathways with combination therapies for these patients are being considered, aligning with personalized medicine that improves outcomes [ 145 – 147 ]. Tumor immunogenicity The ability of the host immune system to recognize abnormal antigens expressed by the tumor tissue as non-self and capable of eliciting an anti-tumor immune response is usually referred to as tumor immunogenicity. Tumor immunogenicity is a central axis in shaping and priming naïve and effector/suppressor T cell responses. It is often characterized by a low TMB or a lack of neoantigens—can bypass immune recognition, therefore lowering the efficacy of ICIs [ 2 ]. By utilizing predictive assessment of TMB or neoantigen load in advance, medical professionals can identify the patients most likely to respond to ICIs, therefore complementing the predictive strategy of 3PM. The tumor cell receptor (TCR) and B cell receptor (BCR) repertoires are key factors contributing to the overall tumor immunogenicity and tumor/immune cell co-evolution of cancer. Hence, analysis of full-length TCRs and BCRs in both the TME and peripheral blood of cancer patients is crucial for predicting response and resistance to immunotherapy, detailed analysis of resistance mechanisms, patient prognosis, and more favorable responses to combination therapies [ 148 , 149 ]. Therefore, future studies might potentially focus on the role of tumor heterogeneity and dynamics of tumor cell surface biomolecules on B and T cell tumor infiltration, distribution, and function, and further investigate the B and T cell pro-immune response heterogeneity and fierce immune escape mechanisms, via the combination of TCR, BCR, and tumor cell surface antigenic analysis. Combining ICIs with cancer vaccines can improve immune recognition and thereby increase the visibility of the tumor to the immune system, so overcoming resistance in low-immunogenic tumors. Customizing treatment depending on an individual’s immunogenic profile promises a more individualized and successful therapeutic approach as well. Checkpoint molecule upregulation Checkpoint molecules include T cell immunoglobulin and mucin-domain containing-3 (TIM-3 ) , V-domain Ig suppressor of T cell activation (VISTA ), and LAG-3, as illustrated in Fig. 2 . Tumor biopsy analysis has shown that TIM-3/VISTA upregulation is associated with reduced response rates, making it a valuable predictive biomarker for resistance detection [ 150 ]. Studies have shown that PD-1 and TIM-3 worked additively in promoting alveolar macrophage tolerance to LPS and/or type 1 IFN [ 151 , 152 ]. A preventive approach using dual PD-1/LAG-3 inhibition shows a 50% improvement in PFS in NSCLC, therefore addressing this challenge. For personalized treatment, neoadjuvant anti-TIM-3 therapy is being explored in TIM-3-positive head and neck cancers, offering a tailored approach to overcoming immune evasion and enhancing ICI effectiveness [ 153 , 154 ]. Defective antigen presentation Tumors can bypass the immune system by altering human leukocyte antigen (HLA) class I expression or B2M alterations, resulting in reduced response to ICIs. Predictive biomarkers for antigen presentation deficits can identify individuals who may need therapies to restore or bypass them, such as combining ICIs with antigen presentation enhancers, offering tailored medicine in 3PM. Baseline MHC-I screening can prevent ICI-unresponsive patients, improving treatment selection. Personalized IFN-γ therapy can improve ICI efficacy and overcome resistance in HLA-deficient malignancies by restoring antigen presentation (Fig. 2 ) [ 155 , 156 ]. Acquired resistance mechanisms Acquired resistance to the immune checkpoint blockade of the PD-1, PDL-1, and CTLA-4 pathways is relatively well known, and so far, includes (1) compensatory immunosuppressive signals; (2) immune evasion by downregulating the expression of antigens, as well as altered antigen presentation and antigen processing to prevent CTL-mediated recognition; and (3) functional changes in the effector T cells. Below, we categorize resistance mechanisms into 3PM-aligned actionable insights: Predictive biomarkers for early resistance detection include VISTA, TIGIT, LAG-3, IDO, TIM-3, and adenosine signaling (Fig. 2 ). 3PM strategy involves serial liquid biopsies which can track plasma levels of soluble LAG-3, TIM-3, or adenosine metabolites to provide early warning of resistance. Moreover, loss of HLA genes is common in some types of cancer, such as malignant melanoma, making NGS a valuable tool for identifying HLA loss of heterozygosity (LOH) in resistant tumors. In up to 70% of patients in the later stages of treatment, inactivating mutations that affect antigen presentation occur [ 157 ]. The changes result in poor effector function and T cell exhaustion, marked by high TOX/PD-1 + CD8 + T cells or a low TCF7 phenotype, further predict resistance and can be detected using single-cell RNA sequencing [ 158 , 159 ]. Preventive strategies to delay resistance involve combinatorial checkpoint blockade strategies—such as pairing anti-PD-1 therapy with anti-TIGIT (tiragolumab) or anti-adenosine agents (ciforadenant)—can mitigate compensatory immune escape mechanisms. Some refractory cancers, such as pancreatic cancer, have issues with low numbers of clonal neoantigens, which might respond better when ICIs are paired with neoantigen vaccines or HDAC inhibitors to boost immunogenicity. In addition, T cells from patients with acquired resistance also have defects in glycolytic metabolism and intratumoral mitochondrial function, thus restoring T cell metabolic function through this can further improve response to ICIs [ 160 ]. Furthermore, ubiquitin proteolysis appears to be a factor that promotes primary resistance mechanisms and endometrial adenocarcinoma. The number of putative resistance mechanisms may stem from one or more heterogeneous predictors; whether CD8 + T cells and CD8 + T cell infiltration are higher, and key pathways, such as vascular endothelial growth factor, mTOR, and WNT pathway activation, and suppressive immune cell signatures, are also particularly associated with resistance [ 161 – 164 ]. Personalized interventions offer targeted approaches for resistant cancers, such as utilizing PORCN inhibitors like LGK974 to counteract WNT/β-catenin-driven T cell exclusion, or combining VEGF inhibitors (e.g., bevacizumab) with anti-PD-1 therapy to address angiogenesis-driven resistance[ 165 ]. T cell exhaustion When it comes to acquired resistance, one of the most common mechanisms is T cell exhaustion, which is characterized by decreased effector activity and increased expression of inhibitory receptors [ 166 , 167 ]. Clinical data show that exhausted T cells relate to lower response rates to ICIs and shorter PFS time when treated [ 168 , 169 ]. Adoptive T cell therapy is a useful way to invigorate exhausted T cells when blockades are not effective enough. Ablative therapies can partly decrease the frequency of exhausted T cells in patients treated. The co-expression of PD-1 and TIM-3 in T cells has emerged as a significant predictor of poor response to ICIs [ 170 , 171 ]. TME is an important induction factor of T cell exhaustion. The immunosuppressive microenvironment mediated by checkpoint molecules and cytokines could induce PD-1 expression and subsequent T cell exhaustion. In accordance with the 3PM’s preventive strategy, monitoring exhaustion markers such as PD-1 and TIM-3 levels can serve as an early indicator of the need for preventive measures. These interventions may include combining ICIs with medicines that restore T cell function. Some mechanisms can accelerate and amplify the progression of T cell exhaustion in tumor microenvironment [ 172 , 173 ]. Metabolism-related exhaustion of T cells also contributes to the ineffective resistance. For instance, the depletion of nutrients and hypoxia shifted T cells to oxidative phosphorylation, which is associated with the acceleration of T cell exhaustion, reducing the proliferation of T cells and decreasing the generation of cytokines. By personalizing treatment based on exhaustion profiles, it is possible to make more tailored alterations to the therapeutic process. Strategies like as IL-15 agonists or adoptive T cell therapy can help rejuvenate fatigued T cell clones, restoring their anti-tumor activity and boosting therapeutic success [ 8 ]. These strategies can be used for tailored intervention. TME modifications The TME is composed of immune and non-immune cells, including mesenchymal and bone marrow-derived stromal cells, fibroblasts, vascular and lymphatic endothelial cells, pericytes, and neuronal elements that interact in a highly structured and dynamic manner with tumor cells through the extracellular matrix and molecular signaling. It also contains a complex immune cellular network, including CD4 + /CD8 + T cells, CD19 + B cells, natural killer cells, regulatory T cells, tumor-associated macrophages, DCs, and myeloid-derived suppressor cells (Fig. 3 ). Fig. 3. Open in a new tab TME immune-suppressive landscape. The figure provides an overview of the TME, emphasizing its immune-suppressive landscape, which is a significant factor in resistance to immunotherapy. In the illustration: Central Tumor Mass: Shown surrounded by a variety of immune cells. Suppressive cells: Regulatory T cells (Tregs) are depicted releasing TGF-β and IL-10, contributing to immune suppression. MDSCs: Illustrated inhibiting T cell activation. M2 tumor-associated macrophages (M2-TAMs): Supporting tumor growth. Effector cells: CD8 + T cells are shown in an exhausted state, with high PD-1 expression, indicating reduced functionality. DCs: Attempting to present antigens to T cells. CD4 + Helper T cells (Th1 subtype): Participating in the immune response. Natural killer (NK) Cells: Engaging in tumor cell destruction Most resistance mechanisms to ICIs therapies occur in the microenvironment, where immune cells heavily crosstalk with cancer cells [ 174 ]. A tumor can create an environment that weakens the immune system by attracting Tregs, MDSCs, or immunosuppressive cytokines (like TGF-β or IL-10) [ 167 ]. Elevated peripheral MDSC counts exceeding 5% have been correlated with disease progression, making them a valuable predictive marker. As a preventive strategy, CCR2 inhibitors have been shown to block MDSC recruitment, particularly in pancreatic cancer, potentially limiting tumor-driven immune suppression. For personalized treatment, CSF1R inhibitors have demonstrated a 40% response rate in macrophage-rich tumors, offering a targeted approach to overcoming myeloid-driven resistance. Predictive analysis of TME composition can guide personalized therapies that target specific immunosuppressive cells or pathways, enhancing efficacy within 3PM’s personalized arm. Metabolic reprogramming Cancer cells have drastically remodeled their metabolism to support high rates of proliferation and cope with the poor vasculature, a signaling hallmark of cancer cells. In addition, some bioactive metabolites fuel the activity of immunosuppressive cells including myeloid-derived suppressor cells and tumor-associated macrophages, further contributing to an immunosuppressive tumor microenvironment. Also, the competition for important resources in the TME, such as glucose and amino acids, may weaken immune cells, which would make the immune system less effective [ 175 ]. Predictive metabolic profiling can identify patients at risk of metabolic resistance, and preventive strategies, such as metabolic inhibitors combined with ICIs, can be used for personalized treatment, aligning with 3PM’s preventive and personalized goals. Elevated lactate levels in the TME, as detected via FDG-PET imaging, have been identified as a predictor of immune resistance, with a hazard ratio (HR) of 2.3 [ 176 ]. The expression of the glucose transporter 3 (GLUT3), which has a high affinity for glucose, is significantly upregulated in resistant tumors [ 177 ]. High GLUT3 expression has been described in different cancer types that are associated with poor outcomes. Silencing GLUT3 in combination with ICB has been shown to prevent tumor relapse in highly treatment-resistant tumors. It can be hypothesized that the enhanced glycolytic activity in resistant tumors might be associated with higher glucose influx through the membrane of tumor cells in response to immunotherapy [ 176 , 178 ]. Additionally, personalized approaches such as ketogenic diets are being explored in clinical trials as a strategy to normalize glucose competition, potentially improving therapeutic outcomes. Emerging mechanisms of resistance The immune system plays a key role in preventing tumor formation, but tumors can develop various resistance mechanisms. ICIs block the inhibitory signals produced by the tumor and induce effective anti-tumor immunity. The microbiome also represents an extrinsic signaling regulator, leading to potential new treatment strategies. Here, we reframe these mechanisms into strategies aligned with 3PM principles. Microbiome influence The gut microbiota is now well recognized for their substantial role in the regulation of the immune system. Predictive biomarkers such gut microbiome profiling using stool metagenomics to identify patients with “resistant” microbiomes. Certain bacterial strains in the stomach may affect the effectiveness of ICIs. ICIs don’t work as well in people who have dysbiosis, which is an imbalance in the gut microbiota [ 179 ]. Dysbiosis, particularly low levels of Akkermansia, has been associated with anti-PD-1 therapy failure. It is well established that the microbiome not only influences the immune response, but also can be involved in the modulation of the response to ICIs. Predictive assessment of microbiome composition, such as abundance of Akkermansia muciniphila, can help identify patients who may benefit from microbiome-targeted interventions, like probiotics or fecal microbiota transplantation (FMT), as part of personalized therapy within 3PM [ 180 ]. A fresh study also linked the response to ICIs with the abundance of gut associated Akkermansia [ 180 , 181 ], but the exact mechanisms on how this operates are not yet fully understood, making clinical translation challenging. Altering the microbiome by dietary changes, probiotics, or FMT is a potential strategy in combating resistance [ 182 – 184 ]. Predictive strategy such as transplant responder-derived microbiota in high-risk patients to prevent resistance. For personalized interventions, probiotic protocols tailored to baseline microbiome profiles offer a targeted strategy to optimize immune responses and enhance therapeutic success. Epigenetic modifications Immunological escape and resistance to cancer treatments are driven by epigenetic changes, including histone modifications and DNA methylation. These processes might be deliberately focused inside the 3PM paradigm to improve immunotherapy results [ 185 ]. Predictive strategies make sure that liquid biopsies are used to find methylation of the PD-L1 promoter in ctDNA and to profile methylation of HLA and MHC using NGS to sort patients into groups for epigenetic combination therapies [ 186 , 187 ]. For personalized treatment, DNA methyltransferase (DNMT) inhibitors are being explored as a targeted approach in MLH1-hypermethylated CRC, potentially improving therapeutic responses. Indeed, these marks represent a common strategy used by tumor cells to set the aberrant expression of genes involved in other cancer immune evasion hallmarks, including a wide range of genes encoding immune checkpoints, different immunomodulatory cytokines, HLA/MHC-related molecules, and proteins shaping the cytophagy classical and non-classical pathways. Predictive epigenetic profiling can also guide the use of epigenetic therapies, like DNMT inhibitors, in combination with ICIs to reverse resistance, offering a personalized approach within 3PM. Oncogenic pathway activation Activated cell signaling pathways confer resistance to ICIs via enabling tumor cells to evade cytotoxic T cell activity. Key enzymes turning on oncogenic pathways such as RET, MEK, and Src, among other non-receptor tyrosine proteases, physically interact with histones and actively shape the chromatin landscape to globally regulate the transcriptional profile of not just cancer, but also multiple stromal cells present within the tumor microenvironment [ 188 , 189 ]. To mitigate this resistance, MEK inhibitors can suppress MAPK-driven immunosuppression, offering a preventive strategy to enhance immune responsiveness. Mutations cause the subsequent activation of the gene transcription profile of the cancer cells in which these proteins are deregulated, and these mutations intimately interact with the epigenetic machinery of these cells. Predictive genetic testing for these mutations can inform the use of targeted therapies in combination with ICIs, embodying personalized medicine within 3PM, enhancing treatment efficacy [ 190 ]. ICIs may work better when mixed with drugs that block the pathways of interest in cancer [ 191 ]. For personalized treatment, tumor-agnostic RET inhibitors are being investigated in clinical trials ( NCT04161391 ), providing a targeted approach for patients with RET-driven malignancies. Strategies to overcome resistance Researchers have actively studied the mechanisms underlying fleeting responses or resistance to ICIs. To address this challenge, we are exploring strategies within the framework of 3PM. This approach focuses on identifying which patients are most likely to respond to treatment, preventing resistance before it occurs, and tailoring therapies to each individual for optimal outcomes. Table 4 depicting several strategies that are being investigated to overcome resistance to ICIs, with the goal of enhancing their therapeutic efficacy. Table 4. Comparison of key strategies, ligands, intracellular signaling effects, and therapeutic inhibitors. The table provides an overview of how each strategy works at the molecular level, its impact on intracellular signaling, and the relevant therapeutic inhibitors that target these pathways Strategy Ligands Intracellular Signaling Effects Therapeutic inhibitors Combination therapies PD-1, CTLA-4, TCR, tumor antigens Enhances T cell activation, reverses immune suppression in the tumor microenvironment, boosts effector T cell responses Anti-PD-1, Anti-CTLA-4, Targeted agents, Chemotherapy, Radiation, ACT, Cancer vaccines Targeting exhausted T Cells PD-1, Tim-3, LAG-3, CTLA-4 Inhibition of inhibitory signals (e.g., PD-1, Tim-3) restores T cell function, reduces exhaustion, enhances cytokine production Anti-PD-1, Anti-Tim-3, Anti-LAG-3, Anti-CTLA-4 TME modulation PD-L1, PD-1, TGF-β, IL-10, VEGF Modulation of immune suppressive signals, enhancement of immune cell infiltration, increased immune activation in TME Anti-PD-1, Anti-PD-L1, Anti-TGF-β, Anti-VEGF, Cytokine-based therapies, Small molecules targeting immune suppressive cells Microbiome modulation Bacterial ligands, gut microbiota, TLRs Modulation of immune system through gut microbiome, enhancing systemic immune responses, potentially altering TME and immune tolerance Probiotics, Prebiotics, Fecal microbiota transplants, TLR agonists Epigenetic therapy Histone modifications, DNA methylation marks Epigenetic changes restore immune recognition, promote gene expression of immune-related pathways, increase tumor immunogenicity HDAC inhibitors, DNA methylation inhibitors, Bromodomain inhibitors Neuronal signaling and tumor interaction Neuronal signaling and tumor interaction Neuronal signaling and tumor interaction Neuronal signaling and tumor interaction Open in a new tab Combination therapies strategy A combination therapy strategy within the 3PM framework aims to optimize treatment efficacy and overcome resistance. Integrating immunoclonal antibodies with additional therapies, including targeted agents, chemotherapy, radiation, or alternative immunotherapies such as adaptive cell therapy (ACT) or cancer vaccines, may effectively address resistance to these antibodies [ 159 ]. Underlying immune resistance mechanisms include a lack of induced effector T cell response, insufficient pre-existing T cell immunity, lack of exhausted T cell infiltration, resistance to T cell-mediated destruction, an immunosuppressive tumor microenvironment, or a combination of these factors. Strategies to overcome resistance to ICIs include combination with adjuvant signals, modulation of the tumor microenvironment, targeting exhausted T cells, microbiome modulation, epigenetic therapy, and neuronal signaling and tumor interaction strategies (Fig. 4 ) [ 159 ]. Predictively, biomarker-driven selection of combinatorial regimens, such as anti-PD-1 plus anti-CTLA-4 therapies, is guided by factors like TMB, immune infiltrate profiles, and oncogenic signaling pathways to identify patients most likely to benefit [ 192 , 193 ]. However, only a subset of patients benefits from monotherapy treatment because many tumor types are inherently resistant or acquire resistance to immune checkpoint blockade. Recent investigations into understanding resistance to this therapy have led to the development of novel strategies like combination therapies with ICIs for better patient outcomes. Such strategies to overcome resistance include approaches like combination therapy, modulation of the tumor microenvironment, targeting exhausted T cells, microbiome modulation, epigenetic therapy, and modulation of neuronal signaling and tumor interaction strategies [ 194 , 195 ]. As a preventive approach, the prophylactic use of low-dose chemotherapy or radiotherapy alongside ICIs in high-risk populations can help prime the immune system and reduce adaptive resistance (Table 4 ) [ 194 , 195 ]. Personalization further refines this strategy by tailoring therapeutic combinations—such as ICIs with VEGF or PARP inhibitors—based on tumor subtype, genetic alterations (e.g., BRCA mutations), or immune microenvironment signatures, ensuring a more precise and effective treatment approach. Fig. 4. Open in a new tab Combination therapy strategies with ICIs. Description: This figure depicts various combination therapy strategies with ICIs using a Venn diagram or interconnecting circles, highlighting how these therapies synergistically enhance the immune response and target cancer cells. And they give combined effects with each other Modulation of the TME strategy Combining anti-angiogenic agents with ICIs exemplifies the approach to overcoming resistance and enhancing durable responses. Targeted and immunotherapy combination strategies, such as the combination of anti-angiogenesis agents and ICIs exemplifies the 3PM approach to overcoming resistance and enhancing durable responses. Medications that deplete Tregs or MDSCs, as well as drugs that suppress TGF-β signaling, are now under investigation. Increasing T cell activation and decreasing immunosuppression is the goal of these studies [ 196 , 197 ]. Outside the tumor cells, there are immunosuppressive cells and a humoral environment, making it difficult for T cells to reach the tumor. Personalized combination therapies in cancers with high αvβ3 integrin expression (e.g., glioblastoma) combine cilengitide (integrin antagonist) + anti-PD-1, which might enhance T cell entry. Angiogenesis promotes tumor growth, and the formation of new tumor blood vessels makes it difficult for T cells to enter the tumor. Predictive biomarkers for patient classification exhibiting elevated VEGF expression may derive advantages from combination anti-angiogenic and ICI regimens [ 198 , 199 ]. Preventive strategies ensure early use of anti-angiogenics (e.g., bevacizumab), which prevents VEGF-driven immunosuppression, normalizes vasculature, and enhances T cell infiltration before resistance emerges. Targeting exhausted T cells strategy T cell exhaustion is a mechanism of immune subversion that can lead to immunosuppression, which is sustained immune dysfunction in response to prolonged antigenic stimuli. Strategies to reverse this exhausted phenotype are now being exploited as a way to make these tumors responsive to cancer immunotherapies once again. A targeting exhausted T Cells strategy within the 3PM medicine framework focuses on restoring T cell functionality and enhancing immune response. Researchers are investigating techniques to rejuvenate depleted T lymphocytes, with the goal of restoring their functioning and improving their reactivity to ICIs. These methods use various forms of IL-2 or agonists of co-stimulatory molecules like 4-1BB and OX40 [ 200 ]. As a preventive approach, administering IL-2 or IL-15 cytokine therapy in early-stage cancers helps sustain T cell function and reduce the risk of exhaustion. Strategies that harness the infected DCs to restore and enhance the antitumor activity of exhausted T cells, thereby augmenting their responsivity to ICIs, are under investigation. This approach tries to capitalize on the recent success of DC-based immunotherapy with tumor mRNA-based cancer vaccines. Predictively, single-cell sequencing enables the identification of T-cell exhaustion markers such as TIM-3 and LAG-3, guiding the selection of patients who may benefit from co-stimulatory agonists like 4-1BB or OX40 agonists. On a personalized level, adoptive cell therapies such as CAR-T or TILs can be engineered with epigenetic modifiers like EZH2 inhibitors, allowing for the reversal of exhaustion in patient-specific T-cell clones and enhancing therapeutic efficacy. Microbiome modulation strategy The role of the gut microbiome in immune response, especially its functions in enhancing checkpoint inhibitor efficacy, has emerged as an exciting frontier for cancer immunotherapy. The emerging data from preclinical and clinical studies suggested an important link between microbiota composition and response to cancer immunotherapy [ 201 , 202 ]. The application of the 3PM approach in microbiome modulation plays a crucial role in optimizing ICI therapy [ 196 ]. A novel concept in modifying antitumoral immunotherapy has emerged, which supports the use of combinatorial treatment with checkpoint inhibitors and microbial-targeted therapy for efficient personalized cancer management [ 203 ]. Preventively, maintaining antibiotic stewardship in cancer patients is essential to preserving beneficial commensal bacteria, such as Bifidobacterium, which have been linked to improved ICI efficacy. Such bacteria have been used as biological carriers for delivering genes or drugs [ 203 ]. Personalized strategies involve tailoring microbial consortia or dietary interventions, such as high-fiber diets, to an individual’s microbiome composition, thereby promoting favorable immune responses like Th1/Th17 polarization. Epigenetic therapy A successful anti-tumor immune response fundamentally relies on selective gene expression programs within immune cells. The 3PM approach in epigenetic therapy offers innovative strategies for enhancing cancer treatment. Personalized approaches focus on tumor-specific targeting of chromatin remodelers, such as using EZH2 inhibitors in SMARCB1-deficient cancers, to reprogram the tumor microenvironment and convert “cold” tumors into “hot,” making them more responsive to immunotherapy [ 204 , 205 ]. In recent years, there has been great progress in developing small molecules that influence these mechanisms. They include methylation and acetylation inhibitors, together with histone deacetylase inhibitors and noncoding RNA molecule inhibitors. Additionally, it has already been demonstrated that a number of these molecules have completely different pharmacokinetic characteristics, genetic and metabolomic factors, and the risk spectrum of the major adverse reactions that can be observed in patients receiving them [ 206 ]. Consequently, early intervention with low-dose epigenetic modifiers, like azacitidine, in premalignant lesions can help prevent immune evasion and tumor progression. Neuronal signaling and tumor interaction strategies Neurotransmitters such as dopamine and norepinephrine act as both immune modulators and signaling molecules in the tumor microenvironment [ 207 , 208 ]. It has been demonstrated that the response of breast cancer to immune checkpoint blockade treatment is related to the peripheral norepinephrine levels, which regulate the immunosuppressive function of cancer-associated macrophages. The 3PM approach in addressing neuronal signaling and tumor interactions provides a novel avenue for improving immunotherapy outcomes. Predictively, measuring peripheral catecholamine levels or assessing sympathetic nerve density can help identify patients at risk of resistance to ICIs and inform the use of β-blockers, such as propranolol, to counteract tumor-promoting adrenergic signaling [ 209 ]. Preventively, stress-reduction strategies—including mindfulness practices and beta-blockers—can be implemented in high-anxiety patients to reduce norepinephrine-driven immunosuppression and support a more favorable immune response [ 210 ]. Because of the increased production of catecholamines in the tumor microenvironment, modulating neurotransmitter signaling may provide an effective combination strategy for cancer-related inflammation [ 207 , 211 , 212 ]. Personalized interventions involve tailoring treatments such as selective serotonin reuptake inhibitors or gamma-aminobutyric acid modulators for patients with comorbid depression to restore neurotransmitter balance and enhance ICI efficacy. Working hypothesis and expected impacts in the framework of 3P medicine 3P medicine is revolutionizing the research and implementation of medicines like ICIs in cancer immunotherapy. Through the examination of patterns across a wide range of datasets, this framework intends to provide an accurate prediction of how patients will react to ICIs. Additionally, it places an emphasis on avoiding resistance by recognizing possible problems at an early stage, such as newly developing mutations or changes in the microenvironment of the tumor, and treating these concerns before they have an influence on the effectiveness of therapy. Working hypothesis By systematically integrating advanced biomarker discovery and longitudinal monitoring into the 3PM framework, we hypothesize that (i) predictive tools based on composite biomarkers will effectively identify patients who are most likely to benefit from ICIs; (ii) early detection of resistance mechanisms will inform preventive strategies, enabling timely therapeutic adjustments such as switching treatments or introducing combination therapies; and (iii) personalized therapeutic regimens, tailored to the specific tumor and immune profiles of each patient, will maximize treatment efficacy while minimizing adverse effects. Expected impacts The key expected impacts of integrating advanced biomarker strategies and longitudinal monitoring into the 3PM framework in oncology and immunotherapy include enhanced predictive accuracy, better prevention of resistance, personalized therapeutic approaches, cost-efficiency in healthcare, integration of epigenetics and microbiome research, and global implementation of personalized protocols. By addressing the heterogeneity of tumors and immune responses, this model will significantly enhance the therapeutic landscape for ICIs and other emerging cancer therapies. Study design We aim to identify predictive biomarkers for ICI efficacy, resistance, and patient survival rate by analyzing clinical trial data and utilizing advanced bioinformatic tools. Additionally, the goal is to evaluate the effectiveness of combination therapies in addressing resistance mechanisms and enhancing clinical outcomes. Analysis of immune therapy responses in pan cancer: 3P medicine implications Pan-cancer analysis investigates the response to immune therapy in a collection of cancer types from the broadest perspective. Herein, we performed an integrative genomics analysis on the integration of different high-throughput sequencing data from the publicly available TCGA database using Home for researcher tool to investigate the response of multiple cancers to immunotherapy [ 213 ]. We obtained the gene expression datasets from gene expression omnibus (GEO), a free and publicly accessible resource. The gene series expression (GSE) databases include a variety of cancer samples [ 214 ]. Our research employed both the survival R package and the survminer R package [ 213 ]. The pan-cancer analysis of immune therapy responses, including ICIs and cellular therapies, reveals significant variability in efficacy across cancer types, highlighting the need for 3P medicine to optimize outcomes. Immune check point therapies Bladder cancer The IMvigor210 study presents a broader overview of anti-PD-L1 efficacy (Durvalumab or Atezolizumab), with a combined partial and complete response rate of 19.8% [ 215 ]. Studies show response rates to anti-PD-L1 therapy (atezolizumab) around 20%, with better survival for responders, with 50% of patients experiencing disease progression (Table 5 ). The log-rank p value is significant ( p < 0.0001) (Fig. 5 A). Table 5. key findings and overall effectiveness of different ICs cancer therapies. The table summarizes the response and resistance rate in different types of cancer among patients treated with monoclonal antibodies Cancer Clinical trial study Immunotherapy PD SD PR CR NE Bladder IMvigor210 (348) Anti-PD-L1 167 (48%) 63 (81.1%) 43 (12.4) 25 (7.2%) 0% GSE176307 (90) Pembrolizumab (Anti-PD-1) N = 46 37 (80.4%) 2 (4.3%) 5 (11%) 2 (43%) 0% Atezolizumab(anti-PD-L1) N = 35 24 (68.6%) 2 (5.7%) 4 (11.4%) 5 (14.2%) 0% Brain PRJNA482620 (34) Anti-PD-1 0% 17 (50%) 17 (50%) 0% 0% Lung NSCLC_GSE135222 (27) Anti-PD-1 19 (70.3%) 0% 8 (29.6%) 0% 0% nonLUSC_GSE93157 (22) Anti-PD-1 9 (40.9%) 7 (31.8%) 5 (22.7%) 1 (4.5%) 0% Renal Braun_2020 (311) Anti-PD-1 (181) 69(38.1) 64(35.3) 13(7.1%) 26 (14.3) 30 (16.6%) Everolimus (130) 37 (28.4%) 67(51.5%) 13(10%) 5 (3.8%) 21 (16.1%) Melanoma GSE78220 (28) Anti-PD-1 13 (46.4%) 0% 0% 15 (53.6%) 0% GSE91061 (109) Anti-PD-1 44 (40.3) 34 (31.1) 14 (12.8%) 6 (5.5%) 11 (10%) GSE93157 (25) Anti-PD-1 14 (56%) 2 (8%) 8 (16%) 1 (4%) 0% GSE100797 (25) ACT 5 (20%) 10 (40%) 5 (20%) 5 (20%) 0% GSE106128 (47) DCs 0% 14 (29.9%) 0% 21(44.7%) 12 (25.5%) Nathanson_2017 (24) Anti-CTLA-4 0% 16 (66.7%) 0% 8(33.3%) 0% PRJEB23709 (91) Anti-PD-L1(50) 20 (40%) 7 (14%) 19 (38%) 4 (8%) 0% Anti-CTLA-4 and anti-PD-L1(41) 9 (22%) 6 (14.6%) 13 (31.7%) 13 (31.7%) 0% Phs000452 (153) Anti-PD-1 69 (45%) 21(13.7%) 42(27.5%) 18 (11.8%) 3 (2%) Open in a new tab Fig. 5. Open in a new tab Survival analysis of bladder cancer patients treated with ICIs. A Kaplan–Meier survival curves depicting the outcomes of bladder cancer patients treated with Anti-PDL-1 ( p < 0.0001) (IMvigor210 study). B Kaplan–Meier survival curves showing the results of individuals treated with Atezolizumab for bladder cancer ( GSE176307 ). C Results of the Kaplan–Meier survival analysis of patients treated with pembrolizumab for bladder cancer ( GSE176307 ). D The Kaplan–Meier survival curve ( GSE176307 ) illustrates the comparative outcomes of bladder cancer patients who received treatment with Atezolizumab and Pembrolizumab, respectively. PD denotes a progressing disease group; SD symbolizes a stable disease group; PR indicates a partial response group; CR represents a complete remission group; NE signifies a none-evaluated group. G1 represents group 1 treated with Atezolizumab; G2 represents group 2 treated with Pembrolizumab Similarly, in the GSE176307 cohort, atezolizumab-treated patients demonstrated a 25.6% response rate, with long-term survival benefits concentrated in responders ( p = 0.0048; Fig. 5 B, 5D). In contrast, pembrolizumab (anti-PD-1) showed a lower response rate (15.3%) and non-significant survival stratification ( p = 0.118; Fig. 5 C) [ 216 ]. Patients treated with atezolizumab (CR and PR groups) performed well in long-term survival curves (Fig. 5 D), which reflected the longer effectiveness of this immune drug. Notably, small cohorts treated with durvalumab ( n = 2) or nivolumab/avelumab ( n = 6) showed 0% responses, underscoring inter-drug heterogeneity (Table 5 ) [ 216 ]. Predictive biomarkers like PD-L1 expression can identify likely responders, fitting 3PM’s predictive arm. Preventive strategies, such as early combination therapies, can address the 50% progression rate, while personalized regimens based on tumor profiles enhance efficacy. Brain cancer Anti-PD-1 therapy elicited a 50% response rate (17/34 patients), with responders showing significantly prolonged median survival (3.9 vs. 1.6 years; p = 0.0471, HR = 2.932) (Fig. 6 A). Fig. 6. Open in a new tab Response and survival analysis in brain, lung, and renal cancers treated with ICIs. A Kaplan–Meier curves for brain cancer patients show a significant survival benefit for responders to anti-PD-1 therapy ( p = 0.0471). B nonLUSC_GSE93157 cancer patients exhibit varied responses to anti-PD-1 therapy, with a notable difference in survival between responders and non-responders ( p < 0.0001). C NSCLC_GSE135222 cancer patients exhibit varied responses to anti-PD-1 therapy, with a notable difference in survival between responders and progressive disease group ( p = 0.00113). D , E Kaplan–Meier curves for renal cancer patients (Braun_2020) show a significant survival benefit for responders to anti-PD-1 and Everolimus therapy, respectively. F Kaplan–Meier curves for renal cancer patients treated with Everolimus therapy show superior survival outcomes (green line) compared to those receiving anti-PD-1 (blue line), highlighting the potential of ICIs in achieving complete remission. PD denotes a progressing disease group; SD symbolizes a stable disease group; PR indicates a partial response group; CR represents a complete remission group; R indicates response group; NR non-response group; NE signifies a none-evaluated group; G1 represents group 1 treated with anti-PD-1; G2 represents group 2 treated with Everolimus Non-responders exhibited rapid progression, underscoring the need for biomarker-guided stratification (PRJNA482620) [ 217 ]. Predictive assessment of tumor characteristics can guide patient selection, supporting 3PM’s predictive strategy. Personalized adjustments for non-responders could improve outcomes. Lung cancer We also analyzed the effectiveness of anti-PD-1, including immunotherapy drugs, in treating different subtypes of lung cancer and response rates. Anti-PD-1 efficacy varied by subtype: 70% exhibited progression in NSCLC_GSE135222 vs. 38.4% in LUSC_GSE93157 [ 218 ]. Complete responses were rare (4.5% in non-LUSC), but responders showed significant survival benefits ( p < 0.0001; Fig. 6 B) [ 219 ]. Furthermore, death outcomes varied markedly between persons with NSCLC_GSE135222 who responded to anti-PD-1 treatment and those who progressed. The progressed group showed a worse survival outcome ( p = 0.00113; Fig. 6 C). In the study GSE126044 , there was low responsiveness, with only 25% achieving partial response and the majority, about 75%, experiencing progression or stable disease [ 220 ]. Predictive biomarkers like TMB can stratify patients, aligning with 3PM’s predictive arm. Preventive combination therapies can reduce progression, and personalized approaches for non-responders fit 3PM personalization. Renal cancer We also present the response rates of renal cancer patients to two types of therapies: anti-PD-1 and Everolimus, an mTOR-targeted therapy (Braun et al. cohort ( n = 311)) [ 221 ]. Anti-PD-1 achieved a 14.3% complete response rate vs. 3.8% with Everolimus, alongside prolonged median survival in responders (5.3 vs. 3.5 years; p < 0.0001; Fig. 6 D–F). However, 38.1% of anti-PD-1-treated patients experienced progressive disease (PD), compared to 28.6% with Everolimus (Table 6 ). Table 6. Key findings and overall effectiveness of different cancer therapies. The table summarizes the response and resistance rate in different types of cancer among patients treated with immune therapy Cancer type Therapy Outcome Conclusion Bladder Nivolumab vs. durvalumab and avelumab 100% progression with 0% response rate All showed no complete neither partial response Lung cancer Anti-PD-1vs. Nivolumab 23%, 25% progression, only 4.5% achieved complete response Nivolumab therapy showed a higher rate of response compared to Anti-PD-1 therapy Renal cancer Anti-PD-1 36.4% partial or complete response Anti-PD-1 therapy showed a higher rate of response with better survival outcomes Melanoma Anti-PD-1vs. Anti-CTLA-4 MAG-A3 Up to 53.7% response (anti-PD-1); 25% anti CTLA, combination therapy (anti-PD-1 + CTLA-4) showed 0% progression. Melanoma-associated antigen 3 (MAG-A3) showed 33.8% response rate Combination therapies enhanced survival, and MAG-A3 immune therapy showed significant survival benefit Esophagus cancer Neoadjuvant chemorad + Anti-PD-L1 (Atezolizumab) 29.9% response rate Combined neoadjuvant chemoradiotherapy with Atezolizumab demonstrated some response, though a large number of patients showed no significant improvement Head and neck cancer Anti-PD-1 40% response rate (partial or complete remission) Small sample size limits conclusions, but some patients showed positive responses to anti-PD-1 therapy Open in a new tab The median survival of non-responders to anti-PD-1 was 1.1 years; Everolimus non-responders survived 0.7 years (Fig. 6 E). Figure 6 D demonstrates that the log-rank test for OS yields a p value below 0.0001, indicating a statistically significant difference in survival rates among the groups. A smaller cohort ( GSE67501 , n = 11) reinforced anti-PD-1’s potential, with 18.1% partial responses despite high PD rates (63.6%). Notably, anti-PD-1 conferred a marginal survival advantage over Everolimus in responders (3.8 vs. 3.5 years; Fig. 6 F), underscoring its role as a first-line option for durable remission [ 222 ]. Predictive markers can guide therapy choice, supporting 3PM prediction. Personalized regimens based on response profiles enhance efficacy. Melanoma We compared immunotherapy therapies for melanoma patients. The therapies focus on illness progression and response rates using anti-PD-1, anti-CTLA-4, MAGE-A3, and ACT. Anti-PD-1 therapy demonstrated heterogeneous efficacy across melanoma cohorts, with response rates ranging from 39.3% (PHS000452) [ 41 , 223 ] to 53.6% ( GSE78220 ) [ 224 ]. Patients achieving complete responses exhibited exceptional survival benefits ( p < 0.0001; Fig. 7 A), while progressive disease correlated with dismal outcomes (median survival: 0.7–0.8 years; HR = 21.601, p = 0.00361; Fig. 7 B). Fig. 7. Open in a new tab Comparative survival outcomes in melanoma patients treated with various immunotherapies. The Kaplan–Meier survival curves ( A – D ) illustrate the survival probabilities of melanoma patients treated with anti-PD-1 of PHS000452, GSE78220 , GSE91061 , and GSE93157 , respectively. The Kaplan–Meier survival curves ( E ) illustrate the survival probabilities of melanoma patients treated with anti-CTLA-4 (Nathanson_2017). The Kaplan–Meier survival curves ( F , G ) illustrate the survival probabilities of melanoma patients treated with anti-PD-1 alone and combination therapies anti-PD-1 plus anti-CTLA-4, respectively ( PRJEB23709). The Kaplan–Meier survival curves (H&I) show the chance of survival for melanoma patients who are only receiving anti-PD-1 therapy compared to those who are receiving both anti-PD-1 and anti-CTLA-4 therapy together (PRJEB23709). PD denotes a progressing disease group; SD symbolizes a stable disease group; PR indicates a partial response group; CR represents a complete remission group; NE signifies a none-evaluated group; R represent a response group; NR denotes none-response group; G1 represents group 1 treated with anti-PD-1; G2 represents group 2 treated with anti-PD-1 and anti-CTLA-4 The GSE91061 study found that 40.3% of melanoma patients on anti-PD-1 therapy had 18.3% a partial or complete response, with significantly worse survival for those with progression ( p < 0.0001; Fig. 7 C) [ 225 ]. In contrast, GSE93157 [ 219 ] reported a higher partial response rate (46%), with survival analysis ( p < 0.00486; Fig. 7 D) suggesting better outcomes for the combined progressive and stable disease group, highlighting the variability in treatment efficacy. Anti-CTLA-4 monotherapy (Nathanson et al., 2017) achieved 33.3% complete response but lacked survival stratification power ( p = 0.0044; Fig. 7 E), underscoring anti-PD-1’s dominance as a first-line option [ 226 ]. A different recent study, PRJEB23709, profiled the effects of anti-PD-1 alone and combined with anti-CTLA4 in melanoma [ 227 ]. Result showed that combination therapy (anti-PD-1 + anti-CTLA-4) doubled complete response rates to 31% and reduced progression incidence to 22%. The Kaplan–Meier survival curves are plotted in Fig. 7 F to illustrate a study treatment groups and the efficacy of anti-PD-1 therapy alone, while the efficacy of immunotherapy targeting PD-1 and CTLA-4 is analyzed in Fig. 7 G. The significance in OS ( p = 0.0004) and the lack of significance ( p < 0.096) depicted in Figs. 7 F and G illustrate the comparisons among the groups with progressing disease, stable disease, partial response, and complete response. Figure 7 H shows that there were apparent differences in OS rates among the group 1 treated with anti-PD-1 alone (G1-PD, G1-SD) and group 2 treated with combined anti-PD-1 and anti-CTLA-4 groups (G2-PD, and G2-SD). The G1-PD group treated with anti-PD-1 had the worst survival, while the G2-SD group treated with both anti-PD-1 and anti-CTLA-4 had the best survival. The p value of 0.0065 shows that these differences are not random and actually existed in the population from which samples were taken. The p value was 0.837 for OS comparison in F i g. 7 I, showing no significant difference between the two groups: G1-R treated with anti-PD-1 and G2-R with combined therapies. Both groups have a survival probability of approximately 0.9 over time. The efficacy of immunotherapy targeting PD-1 and CTLA-4 in patients with melanoma was also analyzed using GSE115821 data, showing a modest response rate to anti-PD-1 (1/27), higher response to anti-CTLA-4 (2/8), and no response in the combined therapy group (0/2) [ 228 ]. On the other hand, GSE145996 study demonstrated encouraging response rates, with 50% of participants (2 patients) receiving a partial response treated with anti-PD-1 therapy [ 229 ]. These studies lack prognosis data for OS analysis, limiting its ability to predict long-term outcomes. PD-L1 and TMB are two predictive biomarkers that align with 3PM’s predictive framework and can assist in identifying candidates for combination therapies. Integrating preventive measures with tailored therapy based on tumor type facilitates 3PM customization and aids in postponing resistance. Esophagus cancer In a separate study ( GSE165252 ), 62.3% of patients with esophageal cancer who received combined neoadjuvant chemoradiotherapy and atezolizumab did not respond to treatment, while 29.9% of patients did respond [ 230 ]. The study did not have survival data available, so OS could not be analyzed. Although the study allows for exploration and hypothesis generation in other areas, the absence of survival data is a significant limitation, particularly regarding how generalizable the conclusions are about long-term survival outcomes related to the condition or treatment. The non-prognostic findings from clinical trials for targeted, immunological, and cellular treatment investigations are summarized in Table 6 . Predictive strategies could focus on identifying responsive subsets, while personalized approaches may be necessary for non-responders, aligning with 3PM. Head and neck cancer In a cohort of head and neck cancer patients treated with anti-PD-1 therapy ( GSE93157 ), one out of five patients showed progressive disease, two stabilized in response to treatment without showing metastatic progression, another achieved partial regression, while the last attained complete remission [ 219 ]. This indicates that 40% of the patients had some level of positive response (partial or complete) to the treatment, as shown in Table 6 . Small sample size limits survival analysis. Therefore, conclusions regarding patient survival cannot be drawn from these samples. Cellular therapy ACT is a form of cell-based immunotherapy, where the patient’s own immune cells are either reprogrammed or boosted to fight cancer more effectively. Adoptive cell therapy (ACT) and dendritic cell (DC) therapy demonstrated divergent efficacy in melanoma. ACT is distinct from monoclonal antibodies, which are lab-created molecules that target specific cancer-related proteins or immune checkpoints. Analysis of ACT therapy for melanoma results in 40% stable disease, 40% response, and 20% showed progressive diseases [ 231 ], with responders showing a median survival of 2 years vs. 0.7 years in non-responders ( p = 0.0068; Fig. 8 A). Fig. 8. Open in a new tab Survival analysis of melanoma patients treated with ACT and DC therapy. A Kaplan–Meier curves comparing the survival outcomes of melanoma patients treated with ACT, and B Kaplan–Meier curves comparing the survival outcomes of melanoma patients treated with DC therapies. PD denotes a progressing disease group; SD symbolizes a stable disease group; PR indicates a partial response group; CR represents a complete remission group; NE signifies a none-evaluated group; R represents a response group; NR denotes a none-response group Based on the GSE106128 dataset investigating the therapeutic value of DC in melanoma patients [ 232 ], DC therapy induced 74.6% disease control (partial response + SD), with responders achieving a 5.3-year median survival compared to 0.7 years in non-responders ( p < 0.0001; Fig. 8 B). Comparing the responders for each treatment, the DC-treated therapy exhibits more dramatic positive improvements in survival than the ACT immunotherapy, with responders surviving 2.6-fold longer (5.3 vs. 2 years; p < 0.0001), underscoring its potential for durable remission. Predictive profiling fits 3PM’s predictive arm and helps to identify candidates for cellular therapy. Personalized use for ICI non-responders will improve results, thereby supporting 3PM personalizing. More recently, a study in patients with melanoma evaluating melanoma-associated antigen 3 (MAGE-A3) immunotherapy by Montoya et al. ( GSE35640 ) was also promising [ 233 ]. Importantly, there were no cases of progressive disease in the study population ( n = 65), with 52.3% presenting a stable disease, and in 33.8%, there was an observed partial response, while as much as 13.8% of the cases were non-evaluable (Table 6 ). Survival analysis was not possible due to a lack of important prognosis information in this study. By integrating these findings into 3P medicine, we can enhance the prediction of treatment responses, prevent resistance through combination strategies, and provide personalized care, ultimately improving outcomes for patients receiving immune therapy. Study strength and limitations The study presents a comprehensive and forward-looking approach to improving the effectiveness of ICIs in cancer treatment, highlighting the potential of personalized medicine strategies in the framework of PPPM. Focusing on predictive indicators like genetic changes and metabolic pathways as means to improve the treatment effectiveness of ICIs is one of the study’s main strengths. The study identifies patient subgroups likely to respond or experience resistance or adverse effects; hence, it supports personalized treatment strategies that may enhance patient outcomes. The discussion on how these paradigms can be applied to treat malignant melanoma, an aggressive skin cancer, highlights significant progress made with novel immunotherapies such as DC therapy and adoptive cell transfer. This represents an exciting and rapidly growing field for personalized immunotherapy. Furthermore, focusing on the mechanisms of ICI resistance provides valuable insights for developing rational combination therapies. This includes integrating immune-modulatory agents and next-generation cell-based approaches to overcome these barriers. This proactive approach to improving ICI efficacy via combination therapies and individualized treatment is a major strength of the study. While the study shows strengths, it does have limitations. One notable limitation is the focus on the potential of ICI therapies in melanoma, bladder, and renal cancers, while recognizing that the results are less consistent in other malignancies. This underscores the importance of personalized approaches but also highlights the challenges ICIs face in delivering consistent results across a diverse range of cancers. The variability in ICI efficacy, particularly in cancers beyond melanoma, poses a major challenge to the universal application of these therapies. In addition, the focus on predictive biomarkers and the PPPM framework is promising. However, further investigation and clinical validation are necessary to translate these personalized strategies into practical applications. There is an urgent need for combination therapies involving immune-modulatory agents and cell-based approaches. However, the ideal combinations and long-term outcomes are still undefined. As a result, the effectiveness of these treatments remains uncertain, and there is a need for further clinical evidence to establish their role in cancer therapy. Future perspectives on immunotherapy and combination strategies Immunotherapies, such as ICIs, have demonstrated substantial efficacy in the treatment of a variety of malignancies; however, the degree to which they are successful varies depending on the kind of cancer being treated. To achieve complete remission across a wide variety of cancers, the future of immunotherapy in cancer treatment heavily depends on understanding the complex and diverse reactions to the currently available drugs. In order to improve treatment options, it is necessary for future research to investigate the underlying processes that are responsible for these various reactions. Examination of the immunological environment of tumors, comprehension of the mechanisms by which some cancers avoid detection by the immune system, and the identification of new biomarkers for predicting the effectiveness of ICIs are some of the procedures that fall under this category. The connections between genetic variables, such as TMB and MSI-enhanced responses to ICIs, must be clarified and investigated across a wider range of individuals and cancer types. This is a critical step in the fight against cancer. Monoclonal antibodies and emerging cellular therapies Monoclonal antibodies have achieved important advancements in the treatment of cancer. When compared to conventional chemotherapy, this method provides cancer patients with treatment alternatives that are both more selective and less harmful. However, these medicines often require additional methods to overcome resistance mechanisms. A novel and intriguing approach is immunotherapy, which involves the use of allogeneic gamma-delta (Vγ9Vδ2) T cells. This particular subgroup of T cells recognizes stress-induced ligands released on cancer cells, enabling them to identify cancer cells that ordinary T cells might miss. In contrast to conventional antibody treatments, which might not be able to properly target all types of cancers, Vγ9Vδ2 T cells possess the ability to immediately detect and eliminate tumor cells, which signifies a significant advantage [ 234 , 235 ]. When V 3 9Vµ2 T cells are used with immune cytokines, which are known to boost the immune response by blocking signals that stop T cells from working, they can have a hugely positive effect. This combination may be especially helpful for treating cancers that don’t respond well to ICIs alone, like those with low TMB or low immunogenicity. This is because it has a lot of different properties that work well together. Combination therapies and immunogenicity enhancement In order to investigate novel combination therapy, such as the combination of PD-1/PD-L1 inhibitors with HDAC inhibitors, further research is required. Through the modification of the tumor epigenome, HDAC inhibitors have the potential to increase the immunogenicity of tumors. This, in turn, leads to an increase in antigen presentation and the promotion of immune cell infiltration, which ultimately makes tumors more vulnerable to immune assault. These medications have the potential to sensitize tumors to ICI therapy, thereby enhancing the efficacy of these therapies. On the other hand, HDAC inhibitors have the ability to affect the TME by lowering the immunosuppressive character of the TME, which in turn increases the activation of immune cells. It is especially encouraging that this combo strategy is being used to treat malignancies that are fundamentally resistant to ICIs due to immune evasion mechanisms. These mechanisms include inadequate antigen presentation or suppression by tumor-associated macrophages. The promise of combination therapy is supported by clinical trials that are still in the early stages and have shown encouraging outcomes. Nevertheless, further study is required to determine the combinations, dose regimens, and patient selection criteria that offer the greatest potential for success. Additionally, the safety profiles of these combinations need to be thoroughly evaluated because the combination of numerous immunomodulatory drugs may raise the risk of harm. As an illustration, it is worth noting that although Vγ9Vδ2 T cells have remarkable efficacy in targeting malignancies, they also have the potential to induce cytokine release syndrome or other immune-related side effects, which must include careful management in clinical practice. Conclusion and expert recommendation in the framework of 3P medicine The future of immunotherapy lies in personalized, adaptive strategies that are grounded in the principles of 3P medicine, offering the potential for more effective, tailored therapies that account for individual patient needs, cancer heterogeneity, and long-term outcomes. (i) Predictive approaches: Predictive approaches in the 3P medicine framework involve identifying biomarkers to identify individuals most likely to benefit from these complex regimens is essential for personalizing therapy. Additionally, while establishing these combination methods, it is necessary to take into consideration cost-effectiveness and accessibility. This is because the incorporation of numerous therapies may significantly increase the financial burden of treatment. (ii) Targeted prevention: Targeted prevention focuses on identifying patients who are at high risk of developing cancer or experiencing resistance to treatments, allowing for early intervention to prevent progression or recurrence. This includes identifying patients at risk of immune-related adverse effects or those likely to develop resistance to ICIs. Additionally, early detection of resistance mechanisms, through regular monitoring of immune markers or tumor characteristics, can allow clinicians to preemptively adjust treatment plans. Combining predictive biomarkers with targeted prevention strategies could substantially improve patient outcomes by proactively managing risks and minimizing treatment failure. (iii) Personalization of medical services: Personalized and adaptable immunotherapy is the way of the future for cancer treatment. We are able to get stronger and more long-lasting responses by combining several immune-modulating techniques, such as ICIs, cellular treatments, and HDAC inhibitors. Personalized treatment regimens may also include adjustments over time, as the tumor evolves and potentially develops resistance to therapy. Furthermore, the personalization of medical services also extends to patient care and support, ensuring that individuals have access to services that address their physical, emotional, and psychological needs throughout their cancer journey. In order to improve outcomes and achieve complete remission across a larger population of patients, it will be necessary to conduct extensive research into the causes of resistance, the development of novel biomarkers, and the optimization of combination therapy. Expert recommendations focus on refining predictive biomarkers, exploring novel combination therapies, and continuing to advance personalized cancer care through an integrated approach that addresses the diverse mechanisms of resistance and enhances the reliability of treatment. In conclusion, we anticipated that future immunotherapy research should primarily focus on the following topics: refining predictive biomarkers (RPB): Researchers must focus on developing more precise and validated biomarkers that are capable of predicting treatment responses across different tumor types. This includes expanding the use of multi-omic data (genomics, transcriptomics, proteomics) to gain a more comprehensive understanding of individual tumors; integrating early detection and prevention strategies (IEDPS): Targeted prevention should be implemented through ongoing monitoring for early signs of resistance or recurrence. This could involve utilizing liquid biopsies, which allow for less invasive, continuous assessment of tumor dynamics, enabling clinicians to intervene early before disease progression; enhancing personalized treatment (EPT): personalized oncology requires that treatment regimens be tailored to the patient’s unique molecular and immune profile. This involves the use of personalized combination therapies, which could include ICIs, targeted therapies, or cell-based treatments, depending on the individual’s cancer type and genetic makeup. Additionally, the integration of patient-specific factors, such as immune system health and previous treatments, is essential for optimizing therapeutic strategies. Through these breakthroughs, we will get closer to developing cancer therapies that are more effective and long-lasting, as well as gaining greater knowledge of how to give the best care to each individual patient based on their specific disease and immune profile. Acknowledgements Not applicable Author contributions S.B. conceived the ideas, designed the study, and wrote the original draft. M.A-N., M.A-M., and K.A.A.K validated the results. X.Z. contributed expertise in 3PM, designed and critically revised the manuscript, reviewed, edited the paper, and provided the financial support. All authors have read and agreed to the published version of the manuscript. Funding The authors acknowledged the financial supports from the Shandong Provincial Taishan Scholar Engineering Project Special Funds (NO.tstp20221143 to X.Z.), Shandong Provincial Natural Science Foundation (ZR2021MH156 to X.Z.), the Shandong First Medical University Talent Introduction Funds (to X.Z.), the China National High-end Foreign Expert Talent Program (H20240743; Foreign Expert: X.Z.), and the Shandong First Medical University Scientific Research Cultivation Project in Emerging Strategic Fields (202403). Data availability No datasets were generated or analysed during the current study. Declarations Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. 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