115 papers
Oncology is the dynamic field dedicated to understanding and treating cancer, a complex group of diseases where cells grow uncontrollably. This area of study spans everything from identifying genetic mutations that drive tumor formation to developing new therapies and improving patient care strategies. Because cancer research moves at a rapid pace, staying updated with the very latest findings is essential for both experts and the curious public.
At Gist.Science, we curate the most recent preprints in oncology directly from medRxiv, ensuring you have immediate access to cutting-edge research before it undergoes formal peer review. We process every new submission in this category, transforming dense scientific manuscripts into both plain-language overviews and detailed technical summaries. This dual approach makes critical discoveries accessible to everyone, regardless of their background in medicine or biology.
Below are the latest oncology papers added from medRxiv, complete with our simplified explanations and full technical breakdowns to help you navigate the latest breakthroughs in cancer science.
This study demonstrates that elective node-sparing upfront boost strategies in head-and-neck cancer radiotherapy significantly reduce peripheral blood lymphocyte DNA damage and radiation-induced lymphopenia while promoting favorable immune signatures, thereby supporting their use to enhance radiotherapy-immunotherapy synergy.
This longitudinal study of 45 oral cancer patients reveals that surgical site infections are associated with younger age, advanced tumor stage, and persistent post-operative dysbiosis dominated by *Prevotella* and *Porphyromonas*, while early post-surgical abundance of *Aggregatibacter* serves as a significant predictive biomarker for infection risk.
This study identifies a 13-gene signature derived from integrative single-cell and bulk transcriptomic analyses of melanoma that effectively predicts immunotherapy response across multiple cancer types, demonstrating particular clinical utility in stratifying patients with low tumor mutational burden.
This study demonstrates that the structural embedding of glioblastomas within the brain's white matter network, quantified by tract density and the number of connected cortical regions, governs both local peritumor and distant pathological neuronal hyperactivity while also correlating with reduced patient functional status.
The paper introduces Patient2Sentence (P2S), a large language model framework that compresses complex oncology electronic health records into concise, standardized sentences, achieving non-inferior clinical trial eligibility screening accuracy compared to full-record analysis while significantly reducing computational costs and enhancing interpretability.
This prospective study of 52 patients with unresectable locally advanced non-small cell lung cancer reveals that pretreatment cachexia is highly prevalent and significantly associated with specific inflammatory cytokines, nutritional deficits, and body composition changes, thereby identifying novel biomarkers and potential therapeutic targets.
This study provides the first external validation of the PHS601 polygenic hazard score in two independent Norwegian cohorts, demonstrating its superior ability to stratify prostate cancer risk and predict age-at-onset compared to rare pathogenic variant screening.
A microsimulation study of multicancer early detection screening in Canada suggests that while overdiagnosis of indolent tumors is likely to be modest overall, the risk increases significantly with age and false positive results could substantially increase the diagnostic workload for healthcare systems.
This study demonstrates that in pediatric patients with relapsed or refractory B-cell acute lymphoblastic leukemia treated with CD19 CAR-T therapy, pre-infusion bone marrow blast levels exceeding 20% and RAS mutations are independent predictors of inferior survival outcomes, with RAS mutations specifically correlating with CD19-negative relapses.
This paper introduces Virtual Spectral Decomposition (VSD), an explainable AI framework that utilizes a modality-agnostic, interpretable-by-design approach and dendritic tile selection to analyze tissue composition and immune phenotyping across pancreatic, lung, and breast cancers, offering clinically verifiable insights without the opacity of traditional black-box deep learning models.