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Sinha and colleagues present PERCEPTION, a precision oncology computational pipeline that can predict the response and resistance of patients by analyzing single-cell transcriptomic data from their tumor samples.
Hsiehchen and colleagues assess the association between tumor mutational burden and survival in a real-world cohort of patients with microsatellite-stable cancers.
Salvadores and Supek derive three regional mutation density signatures from whole-genome profiles of >4,000 tumors that are associated with cell cycle gene expression and alter somatic mutation rates independently of tissue of origin.
Palmer and colleagues present a computational model of drug additivity that can predict clinical efficacy for the majority of combination therapy trials in advanced cancer that led to US Food and Drug Administration approvals between 1995–2020.
Zhang and colleagues analyze single-cell data from patients treated with immunotherapy in five cancer types and find that CXCL13-expressing subsets are implicated in response to treatment in the CD8+ and CD4+ T cell compartments.
Bao et. al. develop the algorithm Starfish, to identify six signatures of complex genomic rearrangements in human cancer genomics datasets, including a pattern called hourglass chromothripsis which is prominent in prostate cancer.
Zhang and colleagues perform systematic multiomics and functional integration of cell-surface proteins and develop a comprehensive catalog of cell-surface actionable targets across cancer, with a practical web platform to explore these data.
Ma et al. apply few-shot learning to train a neural network model on cell-line drug-response data, and they subsequently transfer it to distinct biological contexts including different tissues and patient-derived tumor cells and xenografts.
Degasperi et al. introduce a practical framework and Signal, an online tool, to analyze mutational signatures. They find evidence of tissue-specific variability in mutational signatures, which may impact tumor classification and clinical application.
Martínez-Jiménez et al. report how disruption of the ubiquitin–proteasome system affects cancer, estimating that >10% of driver mutations involve alterations in genes relevant in ubiquitin-mediated proteolysis, including E3 ligases and their targets.