204 papers
Cancer biology explores the complex ways cells grow out of control, investigating the genetic mutations and environmental factors that drive tumor formation. This field seeks to understand how healthy cells transform into malignant ones and how these rogue cells spread throughout the body. By decoding these fundamental mechanisms, researchers aim to develop more effective treatments that target the disease at its source while sparing healthy tissue.
At Gist.Science, we process every new preprint published in this category directly from bioRxiv to ensure you stay ahead of the curve. Our team provides both accessible plain-language overviews and detailed technical summaries for each study, bridging the gap between raw research data and practical understanding. Whether you are a specialist or a curious reader, our goal is to make these critical findings clear and actionable.
Below are the latest papers in cancer biology, offering fresh insights into the ongoing fight against this disease.
This paper introduces SPICE, a novel framework that deconvolves somatic copy-number alteration profiles into discrete evolutionary events to model focal selection, thereby identifying 460 cancer-relevant loci and expanding the understanding of mutational processes and selective forces in cancer genomes.
This study proposes a pathogen-driven model of colorectal carcinoma where Neuro-Immune Crypt-Associated (NICA) cells serve as a cellular origin, EBV-infected B-lineage cells induce HERV-H/F expression via paracrine signaling, and compromised antiviral surveillance allows these viral elements to persist, collectively redefining CRC as a disruption of the neuro-immune niche driven by chronic inflammatory pressure.
This study reveals that the MYC-ATF4-ASS1 axis in melanoma regulates intracellular arginine synthesis and shapes the tumor immune microenvironment, thereby determining tumor sensitivity to arginine-depleting therapies and enhancing CD8+ T cell-mediated antitumor immunity.
This study integrates experimental findings into a nonlinear dynamical model to demonstrate that p53 restoration suppresses retrotransposon-driven chromosomal instability by triggering a threshold-dependent collapse in L1 burst frequency via a let-7 feedback loop, thereby significantly reducing structural genome rearrangements in early tumorigenesis.
This study demonstrates that inhibiting mSWI/SNF chromatin remodeling complexes, specifically with the SMARCA4/2 inhibitor FHD-286, reverses epithelial-mesenchymal transition-mediated resistance and synergistically enhances the efficacy of KRAS inhibitors in KRAS-mutant non-small cell lung cancer.
This study identifies EZH1-dependent H3K27me1 as a key adaptive barrier limiting DNMT inhibitor efficacy in colorectal cancer and demonstrates that combining DNMT inhibitors with dual EZH1/2 inhibitors overcomes this resistance by eliminating H3K27 methylation, resolving induced bivalent chromatin states, and suppressing oncogenic transcription to enhance therapeutic response.
This study introduces TumorSim, an agent-based model analogous to GranSim, to compare the spatiotemporal immune dynamics of small cell lung cancer and tuberculosis granulomas, revealing shared two-phase formation processes and the dual, context-dependent role of CCL5 in tumor progression.
This study demonstrates that structurally unique benzoxaboroles selectively and stereoselectively bind to the cap-binding pocket of eukaryotic translation initiation factor 4E (eIF4E) through specific hydrogen bonding interactions, offering a promising new avenue for targeting this challenging drug site.
This study introduces Polymer-based Leukemic STem-cell Cultures (PLSTCs) to efficiently expand and trace Acute Myeloid Leukemia stem cells, revealing distinct clonal drivers of therapy resistance and identifying chondroitin-sulfate synthesis as a critical target for maintaining primitive stem cell states.
This study demonstrates that complex deep learning domain adaptation methods fail to outperform simple gradient boosting baselines in predicting single-cell anti-cancer drug sensitivity, revealing that performance gains primarily stem from target-informed hyperparameter tuning and sparse label supervision rather than adaptation strategies.