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 integrates single-cell transcriptomics, Mendelian randomization, and colocalization analyses to identify immune-cell-specific causal genes and actionable drug targets, such as IGF1R and FAAH, for precision immunotherapy in prostate cancer.
This study demonstrates that an attention-enhanced U-Net model achieves robust and generalizable performance in detecting rare circulating tumor-associated cells across diverse clinical cohorts, supporting its utility for reliable multi-cancer early detection and diagnostic triaging.
This paper proposes a graph-constrained latent modeling framework that aligns histopathology-derived morphological features with a fixed gene coexpression network to predict pancreatic cancer molecular subtypes using only routine tissue slides, achieving high accuracy (85% AUC) and enabling virtual transcriptomics without requiring actual gene sequencing.
This study demonstrates that a concise, 3-4 minute educational video significantly and sustainably improves patient-specific knowledge regarding tumor genomic testing across diverse cancer types and both community and academic clinical settings.
This study demonstrates that axillary de-escalation is a feasible strategy for ypN1 breast cancer patients with a single residual positive node after neoadjuvant therapy, but not for those with multiple residual nodes, highlighting the critical prognostic heterogeneity within this disease category.
This study applies the Causal Analysis of Survival Trajectories (CAST) framework to 776 lower-grade glioma patients, revealing that chemotherapy provides consistent, sustained survival benefits with significant time-varying gains, while radiotherapy effects are more modest and sensitive to confounding, with age identified as the primary driver of treatment heterogeneity.
This systematic review and network meta-analysis protocol aims to evaluate and rank the comparative efficacy of second-line or later systemic treatments for unresectable, advanced, or recurrent soft tissue sarcomas in patients previously treated with anthracyclines, addressing the current lack of consistent clinical guidelines due to insufficient head-to-head evidence.
This qualitative study of reproductive-aged women with gynecological cancers in Ghana reveals that cancer-related infertility constitutes a profound multidimensional burden threatening womanhood and social standing, necessitating the integration of fertility counseling, psychosocial support, and economic protection into local cancer care.
This study demonstrates that combining Arylsulfatase B (ARSB), which promotes melanoma cell apoptosis via COP1 upregulation, with Pembrolizumab, which targets cytotoxic lymphocytes, yields synergistic effects that reduce tumor invasiveness and improve survival outcomes in metastatic B16F10 melanoma.
This study introduces and validates PROGRESS, a simplified machine-learning-derived risk stratification score for castration-resistant prostate cancer that utilizes three routinely available laboratory variables (PSA, ALP, and AST) to effectively distinguish patient risk subgroups and guide individualized clinical decision-making across diverse metastatic and non-metastatic settings.