The processes underlying ICI resistance are not fully

The gap

Evidence profile

Stated in the limitations and future work and discussion sections of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 5 representative gaps

• Cisplatin resistance in oral squamous cell carcinoma: mechanisms, reversal strategies, and emerging technologies (2026) · Frontiers in Physiology · doi

  this platinum agent Cisplatin remains a cornerstone in the treatment of OSCC, yet the clinical efficacy of is frequently compromised by the emergence of drug resistance. The present review synthesizes current evidence to demonstrate that cisplatin resistance is not attributable to a single cause, but rather constitutes a complex, adaptive system. This system arises from the interplay between tumor cell-intrinsic alterations and extrinsic microenvironmental factors (Cheng et al., 2021), encompassing multiple interconnected dimensions such as genetic and epigenetic reprogramming, metabolic remodeling, evasion of regulated cell death, acquisition of stem-like properties, and dysregulation of the immune landscape. To address this multifaceted challenge, the research paradigm is shifting from purely mechanistic dissection toward the development of multi-target and multimodal intervention strategies. These strategies include the design of specific inhibitors against key resistance nodes, the use of nanotechnology for targeted drug delivery (Morgovan et al., 2025), the combination of cisplatin with immunotherapy to remodel the immunosuppressive microenvironment (Shibata et al., 2025), the exploitation of tumor-specific metabolic vulnerabilities (Zou et al., 2025). Collectively, such approaches represent an integrated therapeutic network targeting resistance across molecular, cellular, and microenvironmental scales. Ultimately, overcoming cisplatin resistance requires a fundamental evolution in therapeutic approach, driven by emerging technologies. Patient-derived organoids offer a physiologically relevant platform for in vitro drug testing and personalized strategy validation (Um et al., 2025). Single-cell multi-omics technologies decode tumor heterogeneity at un preceden ted resolu tion, p in point ing r are resist ant subpopulations and the crosstalk of these populations with the microenvironment (Doerfler et al., 2025). Artificial intelligence and machine learning integrate vast multi-scale datasets to build predictive models and accelerate biomarker and drug discovery (Liu et al., 2025). The synergy of these technologies—where singlecell analysis provides high-resolution maps, organoids enable functional testing (Han et al., 2025), and AI facilitates data integration and optimization—is propelling the field from population-level observations toward precision interventions guided by cellular atlases and individualized models. In summary, advancing against cisplatin resistance in OSCC necessitates a cohesive framework that links systematic mechanistic understanding, innovative therapeutic strategies, and cutting-edge translational technologies.

  generalstated in limitationsevidence 5/5
  Keywords: resistance cisplatin cell technologies drug single tumor multi strategies therapeutic models oscc clinical review system
• Breast cancer chemotherapy in transition: predictive markers, resistance mechanisms, and new treatment approaches (2026) · Frontiers in Oncology · doi

  The advancement of chemotherapy in breast cancer is moving toward personalization rather than abandonment. Chemotherapy will likely remain essential for many patients, but its delivery is becoming more selective, adaptive, and biologically integrated. Future progress will depend on refining predictive markers, especially through multimodal approaches that combine genomic alterations, immune contexture, molecular subtyping, and dynamic biomarkers such as ctDNA. Better identification of true chemosensitive disease could minimize toxicity in low-benefit populations while enabling rational escalation in high-risk groups. At the same time, overcoming resistance will require moving beyond single-marker thinking. Resistance is rarely explained by one pathway alone; instead, it reflects evolving tumor ecosystems. Integrative profiling before, during, and after treatment may allow clinicians to identify emerging resistant clones and modify therapy accordingly. The neoadjuvant setting remains ideal for this research because it offers serial tissue access and direct assessment of response. Finally, future therapeutic strategies will likely blend chemotargeted agents, and posttherapy with immunotherapy, neoadjuvant residual disease–directed interventions.

  generalstated in future workevidence 5/5
  Keywords: chemotherapy moving likely future disease resistance emerging therapy neoadjuvant advancement breast cancer toward personalization rather
• The Role of PD-L1 in Treatment Decision-Making for Perioperative EGFR-Mutated Lung Cancer and its Genomic Analysis (2026) · Annals of Surgical Oncology · doi

  This study has several limitations. First, given the retrospective design and the relatively small sample size of the NeoGroup, some exploratory analyses (e.g., the association between TP53 co-mutations and pathological response) may be underpowered and should be interpreted cautiously. Second, in the AdjGroup, TP53 co-mutations were associated with PD-L1 expression and adverse clinical outcomes. Previous clinical evidence has shown that TP53 alterations are linked to inferior survival and therapeutic resistance in EGFR-M+ NSCLC treated with EGFR-TKIs.45 Beyond their prognostic role, TP53 dysfunction has been implicated in promoting chromosomal instability and genome doubling, which can drive intratumoral heterogeneity and diverse evolutionary trajectories under treatment pressure.46 Such genomic instability may contribute to increased tumor adaptability and resistance, possibly leading to poorer outcomes in patients harboring TP53 co-mutations in our cohort and external validation cohorts. Mechanistic studies suggest potential direct links between TP53 and PD-L1 regulation (e.g., a p53/miR-34/PD-L1 axis).47 However, because our targeted sequencing panels were not designed to comprehensively interrogate immuneregulatory signaling or transcriptomic immune programs, the molecular mechanisms linking TP53 alterations and PD-L1 expression in EGFR-M+ NSCLC remain incompletely defined and warrant further mechanistic and multiomics investigations. Third, the external validation cohorts differ from our institutional cohort in treatment background and patient selection. Therefore, these datasets were used to confirm the biological association between TP53 co-mutation and prognosis rather than to reproduce identical clinical outcomes. The consistent trend observed across independent EGFR-M+ cohorts supports the robustness of this finding, although some heterogeneity cannot be completely excluded. In addition, real-world treatment heterogeneity may influence outcome interpretation. While stratified analyses were performed to mitigate confounding, residual heterogeneity remains. Finally, the limited size of the NeoGroup meant that some uncommon EGFR alterations were included.

  generalstated in limitationsevidence 5/5
  Keywords: egfr heterogeneity mutations clinical alterations treatment cohorts size neogroup analyses association expression outcomes resistance nsclc
• Patient-derived three-dimensional lung tumor models to evaluate response to therapy (2026) · npj Precision Oncology · doi

  While the processes underlying ICI resistance are not fully understood, some mechanisms influencing primary resistance, including tumor intrinsic factors (lack of tumor immunogenicity, loss of tumor antigen or HLA expression and aberrant signaling) and extrinsic factors (presence of immune suppressive cell populations, T cell exhaustion and upregulation of alternative immune checkpoints, and altered metabolism) have been described41,47-52 53-55. Distinct from prior reports, Several prior studies have identified key human TME components, such as tumor-infiltrating biomarkers of resistance and clinical outcome are lacking55.

  generalstated in discussionevidence 5/5
  Keywords: tumor resistance factors immune cell prior processes underlying fully understood mechanisms influencing primary including intrinsic
• Cancer drug response and resistance: molecular mechanisms and combating strategies (2026) · Signal Transduction and Targeted Therapy · doi

  Therapeutic resistance remains one of the major obstacles in cancer treatment, limiting the long-term efficacy of standard and targeted therapies.1 As summarized in this review, resistance is not solely dictated by tumor-intrinsic genetic alterations but is shaped by a dynamic interplay among clonal evolution, signaling rewiring, epigenetic remodeling, altered DNA damage repair, metabolic adaptation, dysregulated cell death, TCP, microenvironmental remodeling, and host–microbe–tumor interactions. Tumor hetero- geneity is a fundamental hallmark of cancer and a central driver of this process.9 The diversity of the genetic, transcriptomic, epigenetic, and phenotypic levels gives rise to intratumoral heterogeneity, which underlies the wide variability in treatment responses. Acquired resistance often originates from preexisting heterogeneity within the tumor and is further shaped by continuous diversification during therapy, enabling certain subclones to survive therapeutic pressure and eventually emerge as dominate resistant phenotypes.461 Primary early-stage tumors and advanced metastatic lesions also exhibit marked differences in tumor heterogeneity, drug resistance, and TME composi- tion.399,462 A pancancer whole-genome sequencing (WGS) study revealed that metastatic tumors frequently evolve from dominant subclones in the primary lesion, displaying higher clonality and lower intratumoral heterogeneity.463 These observations under- score the need to understand resistance not as a single molecular event but as an evolving, systems-level property of cancer. and microenvironmental Beyond genetic diversity, accumulating evidence indicates that therapeutic resistance is increasingly driven by non-genetic adaptive processes. Phenotypic plasticity, including EMT, lineage switching, CSC reprogramming, and DTP states, enables tumor cells to survive therapeutic pressure in the absence of stable genetic alterations.308,309 These transient adaptive states may subsequently serve as reservoirs from which genetically resistant clones emerge. Importantly, such plasticity is tightly regulated by epigenetic remodeling, metabolic adaptation, stress-response programs, cues, highlighting the dynamic and reversible nature of resistance.308,316 To address this evolutionary challenge, ecology- and evolution-informed treat- ment paradigms have been proposed. For example, adaptive therapy dynamically adjusts drug dosing to maintain a population of drug-sensitive cells, thereby constraining the competitive expansion of resistant clones.461 These model-driven, personalized dosing strategies aim to match the evolving tumor landscape in real time. Future therapeutic strategies should therefore move beyond targeting individual mutations and instead focus on disrupting the adaptive networks that sustain tumor evolution, cellular state transitions, and minimal residual disease. Another major challenge lies in the increasing recognition that resistance is not solely a tumor cell–intrinsic phenomenon. CAFs, TAMs, MDSCs, exhausted T cells, extracellular matrix remodeling, and microbial communities collectively shape a permissive ecosystem that protects tumor cells from therapeutic eradica- tion.135,136 The TME promotes resistance by restricting drug penetration, suppressing antitumor immunity, providing survival cytokines, maintaining CSC niches, and reinforcing immune- excluded or immune-cold states.123,135 Similarly, the intratumoral and gut microbiome can modulate therapeutic outcomes by altering drug metabolism, reshaping host immunity, producing and activating oncogenic immunomodulatory metabolites,

  generalstated in future workevidence 5/5
  Keywords: tumor resistance therapeutic genetic drug remodeling heterogeneity adaptive cells cancer evolution epigenetic intratumoral resistant states

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