76 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 combining plasma citrulline levels with faecal elastase-1 testing significantly improves the diagnostic accuracy for pancreatic ductal adenocarcinoma in symptomatic patients, achieving a combined AUC of 0.96 compared to 0.67 for faecal elastase-1 alone.
This study demonstrates that elevated TRAF6 expression serves as an independent prognostic biomarker for poor survival and radioresistance in cervical cancer, while its knockdown suppresses malignant phenotypes and enhances radiosensitivity, suggesting TRAF6 as a promising therapeutic target.
This study demonstrates that aqueous humor liquid biopsy enables comprehensive multi-omics profiling and highly accurate machine-learning-based molecular stratification of retinoblastoma, establishing a safe, non-invasive platform for risk assessment and precision diagnostics in eye-preserving treatment.
This study utilizes spatial multi-omics to reveal that non-beneficial hepatocellular carcinoma patients treated with Y90 radioembolization and nivolumab exhibit an intrinsically immune-deficient tumor microenvironment characterized by LAG-3-associated CD8+ T cell exhaustion and immunosuppressive macrophages, leading to the identification of a 72-gene signature for early prediction of progressive disease.
This Phase I trial demonstrated that the Exportin 1 inhibitor Selinexor can be safely combined with standard chemoradiation for newly diagnosed glioblastoma at a maximum tolerated dose of 60 mg twice weekly, showing preliminary efficacy signals and a potential radiosensitizing effect evidenced by delayed pseudoprogression.
This paper presents a six-stage computational framework that leverages published aggregate trial data to generate patient-level risk stratification and long-term toxicity predictions for rare cancers like Ewing sarcoma, overcoming the lack of individual patient datasets required for traditional machine learning while achieving high accuracy and significantly improved prognostic resolution.
This study utilizes an in silico clinical trial model to demonstrate that patient heterogeneity significantly impacts the efficacy of MV-BiTE therapy, suggesting that non-responders to standard protocols may benefit from more frequent, lower-dose administrations.
This study presents a robust cell-free RNA (cfRNA) liquid biopsy platform that utilizes RUVg-based normalization and an XGBoost classifier to achieve high sensitivity (73.7%) in detecting early-stage colorectal cancer, effectively overcoming the limitations of traditional cfDNA-based screening methods.
This retrospective study of 2,592 metastatic non-small-cell lung cancer patients reveals that while monoimmunotherapy has reduced initial treatment attrition, frailty and comorbidities still significantly limit therapy initiation and efficacy, suggesting that adopting specific product label criteria could better identify candidates for novel platinum-free first-line options.
This study presents the development and independent validation of the uLCI, an interpretable machine-learning model based on four urinary metabolites and clinical variables that effectively detects early-stage lung cancer with high accuracy and prognostic value, offering a promising non-invasive alternative to current screening limitations.