161 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 study establishes a cross-species single-cell atlas revealing that mechanical injury-induced regeneration of pre-ciliated cells in the distal uterine tube creates a vulnerable window for ovarian cancer initiation when combined with TP53 and RB1 inactivation, highlighting a key difference between human and mouse models.
This study utilizes single-cell transcriptomic profiling across eight T cell lymphoma entities to elucidate their developmental origins and validate SYK inhibitors and TIM3 as promising therapeutic targets through integrated computational and experimental approaches.
This study reveals that mutant p53 (specifically the R273H variant) hijacks PARP via its C-terminal domain to facilitate replication stress adaptation and drive breast cancer metastasis, a mechanism that can be selectively targeted by the synergistic combination of the PARP inhibitor talazoparib and the alkylating agent temozolomide to suppress tumor growth and metastasis in triple-negative breast cancer models.
By integrating multi-omics data from 3,628 samples across 24 cancers using the parseMetab R package, this study reveals that pan-cancer metabolic reprogramming is characterized by the consistent upregulation of glycan biosynthesis and nucleotide metabolism, uncovering universal vulnerabilities that could be targeted for therapeutic intervention.
This study establishes a proteogenomic framework for soft tissue leiomyosarcoma by identifying three distinct molecular subtypes with unique signaling pathways, clinical outcomes, and therapeutic vulnerabilities, including a validated immunohistochemical classifier for clinical application.
This paper presents the updated MutationAssessor (r4) within cBioPortal, which leverages refined evolutionary conservation analysis and expanded protein sequence data to provide functional impact scores for approximately 4 million somatic mutations across over 320,000 human tumor samples, while validating its predictions against clinical databases and germline variant frequencies.
This study reveals that STING functions as an interferon-independent, cell-autonomous guardian of epithelial genome stability by regulating homologous recombination and ATM signaling to prevent chromosomal instability and tumor evolution, while its loss creates a selective vulnerability to CDK inhibition.
This study demonstrates that low-intensity alternating magnetic field therapy (Asha) improves survival and reduces metastasis in triple-negative breast cancer by inducing tumor cell ER stress, which triggers interferon-mediated immune reprogramming of the tumor microenvironment and sensitizes tumors to anti-PD1 checkpoint blockade.
This study reveals that the loss of SMAD4 drives chromosomal instability and tumourigenesis by reprogramming translation to downregulate the mitotic protein CDK11B, a mechanism validated in both experimental models and patient tumours.
This paper introduces a mechanistic nonlocal tumour-growth model featuring singular acceleration to explain explosive dynamics observed in empirical scaling laws, providing rigorous mathematical analysis of finite-time blow-up and stability while employing Bayesian inference to quantify model parameters and predict critical escalation thresholds from data.