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S. Lim

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Review Open access Jul 2026

Cancer therapy resistance converges on immune evasion: a unified framework from immunotherapy to targeted, endocrine, and DNA repair pathways

Cancer immunotherapy, including immune checkpoint inhibitors, chimeric antigen receptor T-cell (CAR-T) therapy, and bispecific antibodies, has transformed the treatment of advanced malignancies, yet primary and acquired resistance remain major barriers to durable benefit. Across these therapies, resistance reflects the failure of one or more of three shared requirements for effective antitumor immunity: tumor immune visibility, lost through defective antigen presentation and interferon signaling; immune access to tumor nests, restricted by stromal and vascular barriers; and effector cell function, undermined by suppressive myeloid and regulatory populations, metabolic restriction, and loss of tertiary lymphoid structures. In this review, we argue that resistance to targeted therapies, endocrine therapies, and PARP inhibitors should not be treated as a problem separate from immunotherapy resistance. Rather, these resistant states often converge on the same three axes that govern immunotherapy resistance (immune visibility, access, and effector function), yielding a unified framework across therapeutic classes. Using canonical immunotherapy resistance as a reference model, we examine how osimertinib resistance, androgen receptor (AR)-pathway-resistant states, and BRCA reversion-mediated PARP inhibitor resistance may remodel these same immune-evasion axes. Targeted therapy resistance provides the most complete case, with epithelial-mesenchymal transition, PD-L1 upregulation, STING suppression, and immune exclusion extending across all three axes. AR pathway reactivation shows plausible links to immune remodeling through direct MHC-I repression and myeloid reprogramming, although the clinical evidence base remains less mature. BRCA reversion illustrates a mechanistically suggestive dual-valence state in which restored DNA repair may diminish innate immune sensing while also generating computationally predicted junctional neopeptides whose immunogenicity remains to be validated. By reframing resistance as a new immune state that converges on the same three immune-evasion axes rather than a terminal event, this review shifts attention from why a therapy failed to which immune-evasion axis now predominates and how it might be countered. This perspective supports mechanism-matched combination therapy, adaptive molecular monitoring, and cautious exploitation of vulnerabilities generated by the resistant state itself. As an organizing model rather than a claim of uniform causality, the framework now requires prospective validation.

Ha-Eun Jeong, Heesun Choi, W. Lim et al. · 0 citations
Open access Aug 2026

megaMine: a scalable, rule-based framework for mining gene-cancer-drug evidence from biomedical literature

The rapid expansion of the oncology literature has outpaced manual curation of clinically relevant gene-cancer-drug associations and oncogenic driver evidence. Existing automated approaches often lack transparency or are difficult to scale across heterogeneous data sources. To address this gap, we developed megaMine, a transparent, rule-based, and context-aware literature-mining framework that integrates therapeutic and driver evidence from PubMed, PubTator, and Europe PMC by combining entity recognition, hierarchical heuristics, and contextual labeling. In therapy mode, megaMine was applied to approximately 100,000 oncology articles published between 2015 and 2025, yielding more than 23,000 structured sentence-level evidence records, with standardized annotations for drug response, resistance, and study context. Internal evaluation of context labels showed the strong separability between efficacy and non-efficacy evidence using ridge logistic regression (AUROC = 0.915; AUPRC = 0.941). Benchmarking against NCI/OncoKB-supported drug-cancer associations showed that curated clinical associations had higher megaMine composite evidence scores than unlabeled comparison pairs [median (IQR): 25.6 (9.07-72.5) vs. 3.61 (1.69-8.69); Wilcoxon rank-sum test, P < 2.2 × 10−16]. In driver mode, megaMine retrieved mutation- and biomarker-related evidence from an ERBB-focused gastric cancer query, generating 750 evidence rows from 200 PMIDs. These results demonstrate that deterministic and interpretable approaches can support scalable evidence extraction for downstream applications such as knowledge graph construction and literature-based evidence synthesis.

Muhammad Junaid, K. Prazanowska, Ha-Eun Jeong et al. · 0 citations

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