oncology papers | Gist.Science

Transcriptomic Immune-related Signature Predictive of Chemoradiotherapy Response in Anal Squamous Cell Carcinoma

This study identifies a transcriptomic immune-related signature, characterized by CD8+ central memory T cell enrichment and tertiary lymphoid structures, that effectively predicts chemoradiotherapy response and survival outcomes in anal squamous cell carcinoma patients, while also revealing that high tumor mutational burden correlates with poorer prognosis despite its lack of association with treatment response.

Iseas, S., Golubicki, M., Lacunza, E. + 7 more2026-03-13🔬 oncology

Extracellular vesicles proteomics-based machine-learning model predicts immunotherapy response in NSCLC

This study developed a machine-learning model based on a four-protein panel (MUC1, MUC5B, MUC5AC, and ANPEP) derived from plasma extracellular vesicles that accurately predicts immunotherapy response and survival outcomes in non-small cell lung cancer patients.

Castillo, A., Boyero, L., Benedetti, J. C. + 6 more2026-03-13🔬 oncology

Joint Prediction of Adjuvant Therapy Response and Time-to-Response for Cancer Patients Using the Personalized-DrugRank Method

This paper introduces the Personalized-DrugRank method, which integrates patient-specific transcriptomic data with drug perturbation profiles to jointly predict cancer therapy response and time-to-response with high accuracy across multiple cancer types and regimens, thereby enhancing personalized clinical decision-making.

Romagnoli, F., Pellegrini, M.2026-03-13🔬 oncology

A systematic review and meta-analysis of glyphosate based herbicide exposure and risk of nonHodgkin's lymphoma

This systematic review and meta-analysis indicates that while modest associations exist for any glyphosate exposure, higher levels of exposure are consistently and significantly linked to an increased risk of non-Hodgkin's lymphoma, supporting a dose-response relationship.

Gagnier, J. J., C'Connor, J.2026-03-11🔬 oncology

nSIGHT™: A Data Discovery Platform for Visualization, Integration and Retrospective Analysis of Multimodal Clinical Research Data

nSIGHT™ is an intuitive, web-based platform designed to overcome clinical data interoperability barriers by enabling researchers to easily discover, visualize, integrate, and analyze de-identified multimodal cancer data for cohort construction, feasibility assessment, and retrospective hypothesis generation.

Zia, M. K., Plessinger, B., Eng, K. H. + 11 more2026-03-11🔬 oncology

Integrating AI-powered automated neurovascular bundle segmentation and radiomics for prostate cancer staging

This study presents an automated framework combining AI-driven neurovascular bundle segmentation and radiomics to accurately assess tumor proximity and predict key prostate cancer outcomes, including biochemical recurrence, perineural invasion, and extraprostatic extension.

Urbanos, G., Nogue-Infante, A., Ribas, G. + 9 more2026-03-11🔬 oncology

CT4CMS: Preoperative Computed Tomography-Based Consensus Molecular Subtyping Prediction in Colorectal Cancer Using Interpretable Deep Learning

This study presents CT4CMS, an interpretable deep learning framework that utilizes preoperative CT scans to noninvasively predict colorectal cancer consensus molecular subtypes, enabling molecular stratification and guiding personalized therapeutic decisions before surgery.

Zhang, X., Nie, X., Wu, T. + 14 more2026-03-10🔬 oncology

Beyond Binary MRD: Quantitative ctDNA Interpretation After Curative-Intent Surgery for Colorectal Cancer

This study demonstrates that post-operative minimal residual disease (MRD) detection in colorectal cancer is highly sensitive to analytical thresholds, revealing that a substantial proportion of residual disease signals reside below the conventional 100 ppm limit and that ultrasensitive ctDNA monitoring effectively identifies patients at risk of recurrence.

Kim, J., Ye, S., Kwak, J.-M. + 22 more2026-03-10🔬 oncology

Associations between spatial distribution of immune cell subsets and clinical outcomes in patients with advanced melanoma treated with immune checkpoint inhibitors: results from the PUMA challenge

The PUMA challenge established that automated detection of intra-tumoral lymphocytes on pre-treatment melanoma histopathology slides is the most consistent predictor of response and survival in patients treated with immune checkpoint inhibitors, whereas other immune cell subsets showed no independent association with clinical outcomes.

Schuiveling, M., Liu, H., Eek, D. + 34 more2026-03-10🔬 oncology

The miR-362-3p/BCLAF1 axis regulates cisplatin sensitivity and metastatic progression in triple-negative breast cancer

This study identifies miR-362-3p as a specific predictor of cisplatin sensitivity in triple-negative breast cancer, demonstrating that it enhances platinum response by directly repressing BCLAF1, a key regulator of the DNA damage response.

Liu, Z., Wu, C., Uyemura, M. + 23 more2026-03-10🔬 oncology

Conversational Artificial Intelligence-Enabled Molecular Characterization of Sezary Syndrome Reveals Distinct Pathway-Level Alterations Compared with Non-Sezary Cutaneous T-Cell Lymphoma

This study utilizes a conversational artificial intelligence platform to analyze genomic data from Sezary syndrome and non-Sezary cutaneous T-cell lymphoma, revealing that while both share similar tumor mutation burdens, Sezary syndrome is distinctively characterized by qualitative differences in pathway-level alterations, particularly involving epigenetic regulation, immune escape, and transcriptional control.

Diaz, F. C., Waldrup, B., Carranza, F. G. + 2 more2026-03-10🔬 oncology

Single-cell transcriptome-wide Mendelian randomization and colocalization analyses reveal immune-cell-specific mechanisms and actionable drug targets in prostate cancer

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.

Hong, Y., Wang, Y., Wang, Y. + 2 more2026-03-10🔬 oncology

Attention-Enhanced U-Net Segmentation for Reliable Detection of Circulating Tumor-Associated Cells.

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.

Cristofanilli, M., Limaye, S., Rohatgi, N. + 15 more2026-03-09🔬 oncology

Sex-stratified Integrated Analysis of US lung Cancer Mortality, 1994-2020

This ecological time series study reveals that while US lung cancer mortality declined significantly from 1994 to 2020, the drivers of this reduction differ by sex, with air pollution and healthcare spending being more influential for men and smoking and socioeconomic inequality being more critical for women, necessitating tailored strategies to sustain future progress.

Islam, M. R., Sayin, S. I., Islam, H. + 6 more2026-03-06🔬 oncology

Gene to Morphology Alignment via Graph Constrained Latent Modeling for Molecular Subtype Prediction from Histopathology in Pancreatic Cancer

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.

Leyva, A., Akbar, A., Niazi, K.2026-03-06🔬 oncology

OncoRAG: Graph-Based Retrieval Enabling Clinical Phenotyping from Oncology Notes Using Local Mid-Size Language Models

OncoRAG is a locally deployable, graph-based retrieval pipeline that utilizes a mid-size language model to accurately extract clinical features from multilingual oncology notes without fine-tuning, significantly reducing manual effort while maintaining performance comparable to human curation for downstream survival analysis.

Salome, P., Knoll, M., Walz, D. + 9 more2026-03-06🔬 oncology

Time of Day as an Unmeasured Confounder in Oncology Trials

This paper argues that time-of-day acts as a significant unmeasured confounder in oncology trials, as evidenced by a recent lung cancer study showing drastically different survival outcomes based on treatment timing, and advocates for systematically measuring or randomizing treatment time to optimize therapeutic efficacy.

Somer, J., Benor, G., Alpert, A. + 2 more2026-03-06🔬 oncology

Application of a Concise Video to Improve Patient Understanding of Tumor Genomic Testing in Community and Academic Practice Settings

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.

Veney, D. J., Wei, L., Miller, J. R. + 10 more2026-03-06🔬 oncology

Conversational Artificial Intelligence Agents-Enabled Dissection of RTK-RAS and MAPK Pathway Dependencies in Gemcitabine-Treated Pancreatic Ductal Adenocarcinoma (PDAC)

This study utilizes conversational AI agents to analyze 184 pancreatic ductal adenocarcinoma tumors, revealing distinct age- and treatment-specific RTK-RAS and MAPK pathway dependencies beyond KRAS mutations that inform precision oncology strategies for gemcitabine-treated patients.

Diaz, F. C., Waldrup, B., Carranza, F. G. + 2 more2026-03-05🔬 oncology

When Survival Improves But Quality of Life Does Not: A Model-Based Meta-Analysis of Immune Checkpoint Inhibitors

This model-based meta-analysis demonstrates that while conventional single-timepoint analyses often fail to show quality-of-life benefits for immune checkpoint inhibitors, modeling longitudinal trajectories reveals significantly faster QoL improvement rates that are strongly associated with overall survival, suggesting that such advanced methods better capture meaningful patient benefits alongside survival gains.

Sun, Y., Chang, S., Tang, K. + 4 more2026-03-05🔬 oncology

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