Santarelli, M. | Search bioRxiv

bioRxiv2026

Single-Cell Transcriptomic Signatures Enable Stratified Combination Therapy for Platinum-Resistant Ovarian Cancer

In high-grade serous carcinoma (HGSC), extensive intra-tumoral heterogeneity hinders complete eradication and remains a major obstacle to developing combination therapies capable of eliminating subpopulations resistant to standard-of-care treatment. Using single-cell RNA sequencing of 72 samples from 54 HGSC patients spanning treatment-naive, post-neoadjuvant chemotherapy and relapse stages, we established a carboplatin-anchored framework that identifies transcriptional signatures of intrinsic (pre-existing) and adaptive (therapy-induced) resistance in individual tumors and prioritizes mechanistically matched drugs to potentiate carboplatin efficacy. Candidate compounds were ranked by integrating orthogonal resources--viability (GDSC, PRISM) and perturbational transcriptomics (L1000, Perturb-seq)--to reduce context bias. Among 64 candidates, three carboplatin adjuvants enhanced long-term efficacy in patient-derived organoids (PDOs), and pevonedistat further significantly reduced tumor burden in orthotopic xenografts. This tiered validation pipeline--from short-term and long-term PDOs and in vivo orthoptic xenografts--establishes a translational framework linking single cell resistance programs to actionable, tumor-specific, carboplatin-anchored combinations for HGSC.

cancer biology↗

bioRxiv2019

Towards a gold standard for benchmarking gene set enrichment analysis

BackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.\n\nResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.\n\nConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.\n\nAvailabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

bioinformatics↗