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Results for “chemo-resistance”

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PubMed2026

Beyond the "cold" barrier: Redefining the clinical paradigm of immune checkpoint inhibitor therapy in ovarian cancer.

Ovarian cancer remains an immunologically "cold" tumor, with early all-comer immune checkpoint inhibitor (ICI) trials largely negative despite underlying immunogenicity. This review takes a clinician-centric, stage-specific view linking regimen choice, treatment line, and tumor-immune context to observed outcomes. In the neoadjuvant and first-line settings, unselected ICI combinations with chemotherapy and anti-angiogenic agents failed to improve progression-free survival, whereas adding a poly (ADP-ribose) polymerase (PARP) inhibitor to ICI maintenance yielded modest gains in biomarker-enriched cohorts. In recurrent disease, single-agent ICIs produced objective response rates of 8-15%, and most randomized combinations were negative. The phase III KEYNOTE-B96 trial in platinum-resistant disease demonstrated a progression-free survival benefit in the intention-to-treat population and an overall survival benefit in tumors with programmed death ligand 1 (PD-L1) combined positive score ≥ 1 when pembrolizumab was paired with weekly paclitaxel with or without bevacizumab, underscoring the value of an immunomodulatory chemotherapy backbone in earlier lines. Ovarian clear cell carcinoma emerges as an immunotherapy-sensitive, chemo-resistant subtype that warrants dedicated stratification. We explain why single-analyte biomarkers-PD-L1, tumor mutational burden, homologous recombination deficiency/BRCA1/2-have not reliably enriched benefit and outline a multidimensional approach integrating genomic scars (e.g., mutational signature 3), immune functional state (Immunoscore, CD8⁺ tumor-infiltrating lymphocyte density and CD8⁺: regulatory T-cell ratio), and spatial architecture (inflamed, excluded, desert phenotypes). This framework aims to move beyond the all-comer era toward context-informed precision immunotherapy in ovarian cancer.

Humans↗
PubMed2025

Identification of potential cell surface targets in patient-derived cultures toward photoimmunotherapy of high-grade serous ovarian cancer.

Tumor-targeted, activatable photoimmunotherapy (taPIT) has shown promise in preclinical models to selectively eliminate drug-resistant micrometastases that evade standard treatments. Moreover, taPIT has the potential to resensitize chemo-resistant tumor cells to chemotherapy, making it a complementary modality for treating recurrent high-grade serous ovarian cancer (HGSOC). However, the established implementation of taPIT relies on the overexpression of EGFR in tumor cells, which is not universally observed in HGSOCs. Motivated by the need to expand taPIT applications beyond EGFR, we conducted mRNA-sequencing and proteomics to identify alternative cell surface targets for taPIT in patient-derived HGSOC cell cultures with weak EGFR expression and lacking expression of other cell surface proteins commonly reported in the literature as overexpressed in ovarian cancers, such as FOLR1 and EpCAM. Our findings highlight TFRC and LRP1 as promising alternative targets. Notably, TFRC was overexpressed in 100% (N = 5) of the patient-derived HGSOC models tested, whereas only 60% of models had high EpCAM expression, suggesting that future larger cohort studies should include TFRC. While this study focuses on target identification, future work will expand the approaches developed here to larger HGSOC biopsy repositories and will also develop and evaluate antibody-photosensitizer conjugates targeting these proteins for taPIT applications.

Humans↗
PubMed2025

Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.

Gene signatures predictive of chemotherapeutic response have the potential to extend the reach of precision medicine by allowing oncologists to optimize treatment for individuals. Most published predictive signatures are only capable of predicting response for individual drugs, but most chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive signatures to rank the relative predicted sensitivity of different drugs for individual tumors. Using this approach, providers could efficiently screen against many therapeutics to optimize chemotherapy at any time, whether it be for a treatment-naive tumor or a chemo-resistant tumor requiring a new treatment strategy. To demonstrate the utility of the chemogram, we used predictive signatures (extracted from a previously established method) in our framework to rank predicted sensitivity among drugs within cell lines. We then compared the rank order of predicted and observed response against each drug. Across most cancer types, chemogram-generated predictions were more accurate than predictions made by randomly generated gene signatures, signatures extracted from differential expression alone, and was comparable to another established method of drug response prediction. Our framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response, but also correctly assign rankings of drug sensitivity on an individual basis. Additionally, scaling the chemogram to include more drugs does not compromise accuracy.

Humans↗
PubMed2026

Deconvolution of evolutionary architecture unmasks a high-risk, subclonal-rich subtype in treatment-naive small cell lung cancer.

BACKGROUND: Intratumoral heterogeneity (ITH) drives therapeutic resistance in small cell lung cancer (SCLC). However, conventional single-sample analysis has limited horizontal, cross-patient comparisons, leaving the overarching evolutionary architecture in treatment-naive tumors poorly understood. This study aims to deconvolve these architectures to identify clinically relevant evolutionary subtypes. METHODS: We analyzed whole-exome sequencing data from 41 treatment-naive SCLC patients. To overcome the cross-patient comparability bottleneck, we developed a novel probabilistic framework using a refined Gaussian Mixture Model (GMM). This standardized subclonal structures into four hierarchical strata, enabling the identification of evolutionary subtypes via unsupervised clustering. To address the scarcity of SCLC public data, prognostic concordance was robustly explored in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) based on shared smoking etiology, with lung adenocarcinoma (LUAD) serving as a negative control. RESULTS: The cohort robustly segregated into "Clonal-dominant" (Group 1, n=28) and "Subclonal-rich" (Group 2, n=13) subtypes. Group 1 evolution was primarily driven by tobacco signatures (SBS4). Conversely, Group 2 exhibited late-stage acquisition of a DNA mismatch repair deficiency (MMRd) signature (SBS15), fueling trace subclonal diversification. Clinically, Group 2 demonstrated a significantly lower objective response rate (ORR) to platinum-based regimens (25.0% vs. 81.3%, P=0.02). Furthermore, the Subclonal-rich architecture independently predicted inferior overall survival (OS) [adjusted hazard ratio (adj. HR) =2.93, P=0.02], driven predominantly by limited-stage disease. Cross-cancer analysis validated this histology-dependent, high-heterogeneity adverse pattern in early-stage LUSC but not in LUAD. CONCLUSIONS: This hypothesis-generating study demonstrates that a "Subclonal-rich" architecture, driven by acquired MMRd, identifies high-risk, chemo-resistant SCLC. Our GMM approach suggests that pre-existing heterogeneity may serve as a potential, histology-dependent prognostic marker that warrants prospective validation for tailoring future therapeutic regimens.

Gaussian Mixture Model (GMM)↗
PubMed2026

Whole-Exome Sequencing Identifies Candidate Genomic Features Associated with Response to Platinum-Based Chemotherapy and Ixabepilone-Based Treatment in Ovarian Cancer.

Carboplatin/paclitaxel (CP) chemotherapy is the cornerstone of therapy for advanced stage ovarian cancer (OC). However, despite initial sensitivity, this regimen cannot avoid the emergence of resistance. Ixabepilone &#xb1; bevacizumab (IB) is a combination recently added to NCCN guidelines for the treatment of platinum-resistant OC. It would be desirable to identify biomarkers able to differentiate patients who are resistant to CP and IB, and biomarkers that identify which patients may benefit from IB treatment. We analyzed whole-exome-sequencing (WES) data from 49 OC patients exposed to CP, including 28 platinum-sensitive vs. 21 platinum-resistant, and 31 additional platinum-resistant patients, including 16 responders (i.e., CR/PR) vs. 15 non-responders (SD/PD) to ixabepilone &#xb1; bevacizumab. Comprehensive genetic analyses were performed to identify alterations correlated with resistance to CP and IB. WES analysis of CP responders vs. non-responders revealed differences in HRD-signatures (p < 0.05), OS (p < 0.005) and gain/loss-of-function in multiple genes associated with tumor growth/progression including but not limited to ACVR2A, INHBA, MAP3K7, ATG5, SGK1, FYN, RSPO3, NOD1 and LRRK2. WES analysis of platinum-resistant IB-treated patients revealed additional nominally significant genes and deranged pathways including gains in the DROSHA and SDHA genes in responders vs. non-responders (p < 0.05). Patients harboring HRD-signatures showed significantly higher sensitivity to CP and prolonged survival compared to HRD-negative patients. Alterations in genes associated with tumor growth/progression correlated with resistance to CP regimen and may represent novel "druggable" candidate biomarkers for the targeted treatment of CP/IB-resistant patients. Further validation in independent cohorts and preclinical experiments in CP/IB-resistant models are warranted to establish the clinical utility of these findings.

Humans↗