Petrenko, E. | Search bioRxiv

bioRxiv2024

Cellular Dynamics Upon Immune Checkpoint Inhibition

Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assembled a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancers to map treatment-associated remodeling of the tumor immune microenvironment (TIME). Using a hierarchical reference-guided deep-phenotyping framework, we defined 77 immune and stromal subtypes and resolved four conserved TIME subtypes. Approximately 40% of tumors shifted between states during therapy, and the transition was more predictive of outcome than the baseline state. Across 1,988 bulk transcriptomic tumors, favorable transitions toward inflamed or B-cell-enriched subtype tracked with improved response and survival, while persistence in or shifts towards myeloid dominance indicated resistance. We derived a transition score that predicted outcomes for baseline tumors across independent cohorts. These findings establish immunotype transitions as a central determinant of ICI, offering new avenues for response prediction and rational immunotherapy design. HighlightsO_LIA pan-cancer meta-analysis maps treatment-associated remodeling of the tumor immune microenvironment. C_LIO_LIFour conserved TIME states emerge across cancer subtypes. C_LIO_LITIME transition patterns during treatment are associated with clinical outcome. C_LIO_LIBaseline immune programs derived transition scores predict treatment response and survival. C_LI

cancer biology↗

bioRxiv2024

SLAYER: Synthetic Lethality Analysis for Enhanced Targeted Therapy Implicates AhR inhibitor as a Target in RB1-Mutant Bladder Tumors

Synthetic lethality represents a promising therapeutic approach in precision oncology, yet systematic identification of clinically relevant synthetic lethal interactions remains challenging. Here we present SLAYER (Synthetic Lethality AnalYsis for Enhanced taRgeted therapy), a computational framework that integrates cancer genomic data and genome-wide CRISPR knockout screens to identify potential synthetic lethal interactions. SLAYER employs parallel analytical approaches examining both direct mutation-dependency associations and pathway-mediated relationships across 808 cancer cell lines. Our integrative method identified 4,332 statistically significant interactions, which were refined to 142 high-confidence candidates through stringent filtering for effect size, druggability, and clinical prevalence. Systematic validation against protein interaction databases revealed a 15-fold enrichment of known associations among SLAYER predictions compared to random gene pairs. Through pathway-level analysis, we identified inhibition of the aryl hydrocarbon receptor (AhR) as potentially synthetically lethal with RB1 mutations in bladder cancer. Experimental studies demonstrated selective sensitivity to AhR inhibition in RB1-mutant versus wild-type bladder cancer cells, which probably operates through indirect pathway-mediated mechanisms rather than direct genetic interaction. In summary, by integrating mutation profiles, gene dependencies, and pathway relationships, our approach provides a resource for investigating genetic vulnerabilities across cancer types.

bioinformatics↗