tbuckley/cabot-search huggingface dataset:
Analysis of tumor microenvironmental features of primary and synchronous liver metastases from patients with colorectal cancer using a deep learning algorithm.
Authors: Oluwadunni Eunice Emiloju, Bahar Saberzadeh Ardestani, Rory L Smoot, Rondell P. Graham, Rish Pai, Frank A. Sinicrope
Journal: Journal of Clinical Oncology (2025)

251 Background: The development of colorectal liver metastases (CRLM) is associated with poor prognosis, and recent data suggest that metastasis to the liver is associated with resistance to immunotherapy. We characterized the microenvironment of primary colorectal carcinomas (CRCs) relative to their synchronous CRLM using a validated segmentation algorithm that quantifies 15 distinct morphologic tumor features. Methods: Adult CRC patients with synchronous CRLM (N=57) at Mayo Clinic were identified from the electronic health record using Epic Slicer Dicer and from an internal database. Tumor H&amp;E sections were digitized and reviewed for quality control (RKP). QuantCRC (Aiforia, Inc) was applied to digitized images to extract 15 GI pathologist pre-defined morphological features. Tumor features were compared between primaries and CRLM using the Kruskal–Wallis test. The project was approved by the Mayo Clinic Institutional Review Board. Results: The study included 57 patients (median age 59 years [IQR 50, 73], 51% female) with CRC primaries and synchronous CRLM. Among primaries, 20 (35%) were right-sided and 37 (65%) were left-sided. QuantCRC identified 6 of 15 morphological features that differed significantly between primaries and their CRLM, including reduced stroma, more high-grade and necrosis, and a higher tumor: stromal ratio (TSR) in CRLM (Table). The increase in TIL density in CRLM vs the primary tumor was of borderline significance (p =0.053). Among patients with left-sided primary tumors, their CRLM had significantly higher TSR, percent high-grade, percent necrosis, and TIL density compared to the primary (all p values ≤ 0.02). Among right sided primaries, CRLM had a significantly reduced percent mature stroma (p=0.034) whereas percent necrosis was increased (p =0.01). Conclusions: Using deep learning, we identified tumor morphological features that differed significantly between primary CRC and their synchronous CRLM. This included higher TSR in CRLMs compared to primaries that is associated with epithelial-mesenchymal transition and has been shown to contribute to treatment resistance. Analysis of tumor morphological features with patient prognosis is ongoing. Deep learning-derived morphological features of primary CRC and CRLM. Morphological feature (Median) Primary tumor Liver metastasis P-value Tumor-Stroma Ratio 0.78 1.50 0.008 % High-grade 23.08 31.72 0.005 TIL Count 44.36 60.20 0.053 % Necrosis 6.35 19.32 &lt;0.001 %Signet Ring Cells 0.25 0.13 0.088 TB/PDC 0.89 1.13 0.484 % Stroma 54.89 40.98 &lt;0.001 % Immature Stroma 46.76 34.13 &lt;0.001 % Mature Stroma 6.27 5.08 0.030
Development and validation of a deep learning–based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: A retrospective multicenter cohort study.
Authors: Shenghan Lou, Fenqi Du, Huiying Li, Yanming Huang, Laishou Yang, Gen Li, Wenjie Song, Jingmin Xue, Hao Li, Genshen Mo, Hang Wang, Pai Wang, Zhuozhong Wang, Peng Han
Journal: Journal of Clinical Oncology (2025)

298 Background: The current TNM staging system fails to provide adequate information for prognosis and adjuvant chemotherapy benefits in colorectal cancer (CRC). Pathomics, an emerging field, shows promise in improving prognosis estimation and decision-making. In this study, we developed and validated a pathomics signature (PS CRC ) that directly analyzes hematoxylin and eosin–stained slides using deep learning to predict outcomes. Methods: A total of 883 whole slide images from two cohorts, Harbin Medical University Cancer Hospital and The Cancer Genome Atlas (TCGA), were retrospectively analyzed. An interpretable, multi-instance deep learning model was proposed to establish the PS CRC . Shapley additive explanations were employed to interpret the model's decisions, and gradient-weighted class activation mapping was applied to visualise the pathological phenotypes of the PS CRC . The transcriptomics data from TCGA cohort was used to explore the potential pathogenesis underlying the PS CRC . Results: The PS CRC was identified as an independent prognostic factor associated with both overall survival and disease-free survival. Incorporating the PS CRC into the TNM stage model resulted in a significant improvement in prognosis estimation, as evidenced by a notable increase in net reclassification improvement and integrated discrimination improvement. Moreover, among stage II and III CRC patients with low levels of PS CRC , satisfactory benefits from chemotherapy were observed. Notably, the main underlying features of PS CRC include tumor cell infiltration, adipocyte accumulation, fibrous tissue deposition, and stromal infiltration. Transcriptome analysis further support the relevance of PS CRC to tumor progression and immune suppression. Conclusions: Our finds highlight the significant potential of histopathology images-based deep learning in predicting prognosis and assess therapeutic response of CRC. The PS CRC could serve as an effective tool in clinical decision for CRC management, providing insights into the underlying pathogenic mechanisms. However, prospective studies are still necessary for further validation.
A phase 2 study of intravenous pembrolizumab and intratumorally injected autologous dendritic cells (DCs) in refractory colorectal cancer (CRC).
Authors: Sarbajit Mukherjee, Yu Fujiwara, Eoghan Connors, Per Basse, Connor Huck, Marija Vujcic, Joanna Stanson, Kathleen Kokolus, Pawel Kalinski
Journal: Journal of Clinical Oncology (2025)

TPS319 Background: Immune checkpoint inhibitors (ICIs) have shown limited benefit in CRC outside the microsatellite instability high (MSI-H) population. Deficient cytotoxic T lymphocyte (CTL) trafficking to the tumor microenvironment (TME) and excess pro-tumorigenic cytokines and immunosuppressive cells may contribute to primary ICI resistance in microsatellite stable (MSS) CRC. Selective enrichment of T cells with specific chemokine receptors may enhance antitumor immunity. Studies highlight the role of IL-12 in improving progression-free survival (PFS) and overall survival (OS) in patients on DC therapies. Our group developed ST-aDC1s, optimized for high production of high levels of IL-12 and CTL-attracting chemokines when injected intratumorally. In preclinical models, ST-aDC1s combined with anti-PD-1/PD-L1 therapy showed therapeutic synergy, suggesting ST-aDC1s as a promising alternative to traditional DC vaccines for inducing CTL entry into tumors. Methods: We designed an open-label, non-randomized, phase 2 study of intratumoral autologous ST-αDC1 combined with pembrolizumab in recurrent/metastatic unresectable MSS CRC. Eligible patients must have prior treatment with, or contraindication to fluoropyrimidine, irinotecan, oxaliplatin, anti-VEGF, and anti-EGFR therapy (if RAS wild type), be anti-PD-1/PD-L1 therapy naïve, have ECOG PS ≤1, and have at least two target lesions, one amendable to biopsy and injection. Leukapheresis will collect monocytes for ST-αDC1 generation. Patients will receive intratumoral ST-αDC1s (6 million) on days 1 and 8 with biopsies, and an optional dose on day 50 before a third pembrolizumab dose. Pembrolizumab 200 mg IV will start on day 8, given every 3 weeks for 4 doses during the study period. Pembrolizumab will continue as conventional care afterwards. CT scans will occur after 2 and 4 doses of pembrolizumab. A safety lead-in cohort will include six patients, with up to 17 patients enrolled. The primary endpoint is objective response rate (ORR) at 12 weeks per RECIST 1.1. Secondary endpoints are safety, PFS, OS, and ORR per iRECIST. Correlative analysis will assess baseline levels and post-treatment changes in infiltrating T cells, CD4/CD8 ratios, FoxP3+ Tregs, and Teff-attracting and Treg-favoring chemokines from biopsy specimens, and immune cell subsets and cytokine levels from peripheral blood. With an expected ORR under 5% for standard treatment, 17 patients are required to show at least a 25% ORR with 81.2% power and a one-sided type I error rate of 0.05. A Simon two-stage Optimal design will enroll 9 patients in stage 1, and if ≥1 response occurs, 8 more patients will be added in stage 2. If ≥3 responses are observed, the treatment will be deemed promising for further study. Clinical trial information: NCT05518032 .
Meta-analysis of phase II/III randomized controlled trials (RCTs) to determine the incidence of hypertension and proteinuria in patients with gastrointestinal (GI) cancers treated with fruquintinib.
Authors: Ramaditya Srinivasmurthy, Hazem Aboaid, Kevin Nguyen, Daniel Thomas Jones, Rishi Kumar Nanda, Jason Ta, Kyaw Zin Thein
Journal: Journal of Clinical Oncology (2025)

119 Background: Fruquintinib is a selective inhibitor of vascular endothelial growth factor receptors which impedes angiogenesis associated with tumor growth. On November 8th, 2023, the US FDA approved fruquintinib for use as a third-line treatment for metastatic colorectal cancer (mCRC). With the introduction of novel cancer therapies, monitoring for adverse events is critical. This meta-analysis aims to assess the risk of hypertension (HTN) and proteinuria in patients with GI cancers treated with fruquintinib. Methods: We conducted a comprehensive search of MEDLINE, EMBASE, and COCHRANE databases from inception through August 12th, 2024, identifying Phase II/III RCTs that employed fruquintinib in GI cancers and reported HTN and proteinuria as adverse effects. The Mantel-Haenszel method was used to calculate pooled risk ratios (RR) with 95% confidence intervals (CI). Heterogeneity was assessed using Cochran’s Q-statistic, and a random effects model was applied. Results: Our analysis included 1872 patients, 1131 (60%) of whom received fruquintinib, from three Phase III RCTs (FRESCO, FRESCO-2, FRUTIGA) and one Phase II RCT. FRESCO, FRESCO-2, and the phase II trials compared fruquintinib to placebo in patients with mCRC. FRUTIGA trial involved patients with gastric or gastroesophageal junction adenocarcinoma, comparing fruquintinib + paclitaxel to placebo + paclitaxel. The incidence of any-grade HTN was higher in the fruquintinib group, 34.92% vs 7.55% (RR 3.96; 95% CI: 3.05-5.13; P&lt;0.00001); the rate of high-grade HTN was 13.96% vs 1.21% (RR 9.01; 95% CI: 4.67-17.40; P&lt;0.00001). In the mCRC subgroup, incidence of any-grade and high-grade HTN was 43.02% vs 10.45% (RR 3.97; 95% CI: 2.95-5.33; P&lt;0.00001), and 17.28% vs. 1.27% (RR 12.06; 95% CI: 5.19-28.01; P&lt;0.00001), respectively. Incidence of any-grade proteinuria was also found to be statistically significant and higher in the fruquintinib arm in both GI cancer cohort and mCRC subgroup: 27.3% vs 16.19% (RR 1.89; 95% CI: 1.30-2.74; P=0.0008), and 25.22% vs 11.73% (RR 2.29; 95% CI: 1.17-4.51; P=0.02). The rate of high-grade proteinuria was not statistically significant in both cohorts. In GI cancer group, 1.85% vs 0.53% (RR, 2.49; 95% CI: 0.86-7.16; P=0.09), and mCRC subgroup 2.17% vs 0.51% (RR, 2.87; 95% CI: 0.74-11.12; P=0.13). Conclusions: This meta-analysis revealed a higher incidence of any-grade and high-grade HTN with fruquintinib in GI cancer patients, with significant association in the mCRC subgroup. Fruquintinib was also noted to be associated with increased risk of any-grade proteinuria, however, incidence of high-grade proteinuria was not different in patients treated with fruquintinib compared to placebo. Prompt identification and early management of these adverse events are crucial.
Effect of changes in body composition and sociodemographic factors on colon cancer survival.
Authors: Ignacio Catala Vilaplana, Gillian V.H. Smith, Hannah K. Schulte, Howard J Lim, Gillian E Hanley, Kristin L. Campbell
Journal: Journal of Clinical Oncology (2025)

296 Background: Muscle mass, muscle quality, and adipose tissue volumes have been shown to affect colon cancer survival. Further research is required to understand how change in body composition during treatment impacts colon cancer outcomes, and how covariables such mental health and socioeconomic status affect this relationship. The aim of this project was to examine how changes in body composition during chemotherapy for colon cancer impact the duration of disease-free survival (DFS) and how sociodemographic covariables (age, sex, social isolation, depression, anxiety, income, community size) influence this relationship. Methods: Computed tomography (CT) scans from the time of diagnosis and the end of chemotherapy were obtained from individuals treated for stage III colon cancer with oxaliplatin at BC Cancer between 2012 and 2016. Whole body tissue volumes were estimated based on CT images at the level of the third lumbar vertebra, analysed using DAFS Express (Voronoi Health Analytics Inc., Vancouver BC). Muscle quantity was measured as skeletal muscle index (SMI), and muscle quality was measured as skeletal muscle density (SMD) and skeletal muscle gauge (SMG). Quantity of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and total adipose tissue (TAT) were also measured. Social isolation, anxiety, and depression symptoms were measured using the validated Psychosocial Screen for Cancer–Revised. Neighbourhood income and community population were estimated using postal codes. Cox proportional hazard models were calculated for the effect of body composition variables on DFS, and interactions with sociodemographic variables. Results: Significant reductions in SMI, SMD, SMG, VAT, IMAT and TAT were observed between diagnosis and post-chemotherapy (n=282). Improved survival was associated with higher SMD and lower IMAT at diagnosis and post-chemotherapy, lower VAT at diagnosis, and an increase in VAT or TAT during chemotherapy. Body composition had a more significant effect on survival in individuals who were older, female, or lived in smaller communities. Conclusions: Significant reductions in SMI, SMD, SMG, VAT, IMAT and TAT were observed between diagnosis and post-chemotherapy (n=282). Improved survival was associated with higher SMD and lower IMAT at diagnosis and post-chemotherapy, lower VAT at diagnosis, and an increase in VAT or TAT during chemotherapy. Body composition had a more significant effect on survival in individuals who were older, female, or lived in smaller communities.
Intra-tumoral CD40 antibody with irreversible electroporation (IRE) in locally advanced pancreas cancer.
Authors: Rebekah Ruth White, Zachary Berman, Zev A. Wainberg, Aaron Miller, Karen Messer, Sumeet Vijay Ambarkhane, Stephen Philip Schoenberger
Journal: Journal of Clinical Oncology (2025)

TPS787 Background: Irreversible electroporation (IRE) is a form of non-thermal tumor ablation that is currently in clinical use for selected patients with locally advanced pancreatic cancer (LAPC). Preclinical and clinical studies have suggested that IRE generates an in situ vaccination effect by releasing tumor neoantigens in the setting of inflammation. Our published data from syngeneic, orthotopic mouse models demonstrate that combination of IRE with local CD40 agonism induces systemic anti-tumor immune effects, including neoantigen-specific T-cell responses and inhibition of liver metastases (1). Mitazalimab, a second-generation CD40 agonistic IgG1 mAb, has demonstrated promising clinical antitumor activity when delivered systemically in combination with modified FOLFIRINOX in metastatic pancreatic cancer (2). Methods: This clinical trial (NCT06205849) is a phase I dose-escalation study of an agonistic CD40 antibody (mitazalimab) injected intratumorally at the time of surgical IRE in patients with LAPC who have not demonstrated distant disease progression after a minimum of 4 months of optimal standard of care (SOC) chemotherapy (modified FOLFIRINOX). The doses investigated are 25, 75 (starting dose), and 200 µg/kg, in an interval 3+3 design, with a maximum of 18 subjects. The primary endpoints are the rates of dose limiting toxicities (DLTs) and treatment-emergent adverse events (AEs). The secondary endpoints are progression-free survival (PFS) and overall survival (OS). At a liberal alpha of 25%, appropriate for this proof-of-concept early phase study, we have 80% power to detect an improvement in PFS from 12 months (based on prior studies of IRE alone) to 18 months, with an expected 16 evaluable subjects and an anticipated 11 observed events, using a one-sided log-rank test. Candidate neoantigens will be identified by profiling nucleic acids derived from tumor biopsies obtained intraoperatively prior to IRE using our Identification-Prioritization-Validation (IPV) bioinformatic pipeline (3). Blood samples will be collected pre-operatively and 12-weeks post-operatively to evaluate for evidence of neoantigen-specific T-cell reactivity and other neoantigen-independent analyses of immune response. We have enrolled two patients since the study opened in September 2024. 1. J. Shankara Narayanan et al., JITC 2023. 2. J. Van Laethem et al., Lancet Oncol 2024. 3. A. Miller et al., Sci Transl Med. 2024. Clinical trial information: NCT06205849 .
NRG-GI008: Colon adjuvant chemotherapy based on evaluation of residual disease (CIRCULATE-North America).
Authors: Christopher Hanyoung Lieu, Guan Yu, Scott Kopetz, Shannon L. Puhalla, Peter C. Lucas, Ibrahim Halil Sahin, Dustin A. Deming, Philip Agop Philip, Theodore S. Hong, Yesenia Rojas-Khalil, Jonathan M. Loree, Norman Wolmark, Greg Yothers, Thomas J. George, Arvind Dasari
Journal: Journal of Clinical Oncology (2025)

TPS310 Background: Currently, there are no biomarkers validated prospectively in randomized studies for resected colon cancer (CC) to determine need for adjuvant chemotherapy (AC). However, circulating tumor DNA (ctDNA) represents a highly specific and sensitive approach (especially with serial monitoring) for identifying minimal/molecular residual disease (MRD) post-surgery in CC patients (pts), and may outperform traditional clinical and pathological features in prognosticating risk for recurrence. CC pts who do not have detectable ctDNA (ctDNA-) are at a much lower risk of recurrence and may be spared the toxicities associated with AC. Furthermore, for CC pts with detectable ctDNA (ctDNA+) who are at a very high risk of recurrence, the optimal AC regimen has not been established. We hypothesize that for pts whose CC has been resected, ctDNA status may be used to risk-stratify for making decisions about AC. Methods: In this prospective phase II/III trial, up to 1,912 pts with resected stage IIB, IIC, and III CC will be enrolled. Based on the post-operative ctDNA status using personalized and tumor-informed assay (Signatera, bespoke assay), those who are ctDNA- (Cohort A) will be randomized to immediate AC with fluoropyrimidine (FP)+oxaliplatin (Ox) for 3-6 mos per established guidelines v serial ctDNA monitoring. Patients who are ctDNA+ post-operatively, or with serial monitoring (Cohort B), will be randomized to FP+Ox v more intensive AC with addition of irinotecan (I) for 6 mos. One cycle of chemotherapy is allowed while awaiting ctDNA testing results for cohort assignment. The primary endpoints for Cohort A are time to ctDNA+ status (phase II) and disease-free survival (DFS) (phase III) in the immediate v delayed AC arms. The primary endpoint for Cohort B is DFS in the FP+Ox v FP+Ox+I arms for both phase II and phase III portions of the trial. Secondary endpoints include prevalence of detectable ctDNA post-operatively, time-to-event outcomes (overall survival and time to recurrence) by ctDNA status, and the assessment of compliance to adjuvant therapy. Biospecimens including archival tumor tissue, as well as post-operative plus serial matched/normal blood samples, will be collected for exploratory correlative research. Active enrollment across the NCTN started in June 2022 with CCTG sites joining in August 2023. Current accrual (as of 9-20-2024): 500/1,912. Clinical trial information: NCT05174169 .
Man versus machine: Comparative analysis of ChatGPT’s colon cancer guidance and expert opinion.
Authors: Sujata Ojha, William Steele Sessions, Boone Goodgame
Journal: Journal of Clinical Oncology (2025)

299 Background: AI Chatbots, such as ChatGPT, are increasingly being utilized across various aspects of society, including healthcare. Despite this growing usage, their role within the field of oncology remains underdeveloped. The objective of this study is to evaluate ChatGPT’s ability to accurately respond to patient inquiries regarding colon cancer by comparing its responses to assessments from expert clinical oncologists. Methods: Ten comprehensive questions were compiled by reviewing commonly asked questions from reputable sources, including the American Society of Colon &amp; Rectal Surgeons, Mount Sinai, the National Cancer Institute, Mayo Clinic, and the American Cancer Society. The questions were categorized into two categories based on their content: General Oncology Characteristics (covering symptoms, screening, and prevention) and for Diagnosis &amp; Treatment. These questions were then entered into ChatGPT, with prompts designed to simulate patient inquiries. The AI-generated responses were subsequently evaluated by oncology experts using a 5-point Likert scale to assess their accuracy and relevance, with scores reflecting the experts' level of agreement with the answers. Results: On a five-point Likert scale, with 1 representing "strongly disagree" and 5 representing "strongly agree," the mean score was 4.72. ANOVA analysis was performed and there was no statistically significant difference in the mean score across all raters (p = 0.221). However, ratings between the two categories were statistically significant (p=0.034). Conclusions: This study demonstrates that ChatGPT can provide accurate and relevant responses to patient inquiries about colon cancer, as assessed by medical oncology experts. With an average rating of 4.72 on a 5-point Likert scale, ChatGPT’s responses closely align with expert opinion, particularly for general characteristics category which included symptoms, prevention, and screening. However, responses related to diagnosis and treatment showed a statistically significant difference between expert opinions and AI, indicating that the experts agreed less with this component of AI's responses. These findings highlight the potential of AI chatbots in supplementing patient education in oncology, though further research is necessary to explore its limitations and expand its clinical utilities.