Abstract PR012: Transcriptomic and pathway analyses patterns in early-onset and late-onset microsatellite stable colorectal cancer: Results from the ORIEN Network
Bibliographic record
Abstract
Abstract Introduction: Although ∼20% of early-onset colorectal cancer (EOCRC, age at diagnosis <50y) cases are due to germline mutations, the etiology of the majority of EOCRC cases remains poorly understood. EOCRC differs molecularly from late-onset colorectal cancer (LOCRC, age at diagnosis > 70y), with EOCRC tumors more frequently exhibiting high-grade histology, immune-related signatures, and microsatellite instability, while LOCRC is marked by DNA damage and oxidative stress pathways. Although consensus molecular subtypes (CMS) offer a framework for classification, they do not fully explain the rising EOCRC incidence, highlighting the need for integrative multiomic approaches to uncover underlying genetic, epigenetic, and environmental drivers for EOCRC. Methods: We leveraged genomic, transcriptomic, and clinical data from 1,135 sporadic microsatellite stable colorectal cancer (CRC) patients enrolled in the Total Cancer Care protocol and included in the Oncology Research Information Exchange Network (ORIEN) Avatar program across seven U.S. cancer centers, using standardized protocols for biospecimen collection, sequencing, and data harmonization. Whole exome sequencing (WES) and transcriptomic profiling (RNA-Seq) was conducted using standardized pipelines, followed by normalization and filtering. We characterized biological differences across EOCRC and LOCRC using differential expression, molecular subtyping, immune deconvolution, survival analysis, and integrated pathway analyses combining RNA-seq and WES data. Results: Our cohort included 27.8% EOCRC, 53.5% average-onset (50-69y) and 18.9% LOCRC cases. EOCRC patients were more likely to present with rectal tumors (24% vs. 14%), advanced stage (75% vs. 57%), and receive treatments at a higher proportion (radiotherapy: 35% vs. 23%; adjuvant therapy: 51% vs. 37%) compared to LOCRC. No significant differences were observed in common CRC mutations or tumor mutational burden. EOCRC cases were significantly enriched for the mesenchymal CMS4 subtype and depleted in CMS2 and CMS3 (p < 9.46 × 10-6), though 5-year survival did not differ by CMS (p-value=0.23). Notably, we observed that EOCRC cases with CMS2 or CMS4 were more likely to be overweight (BMI >= 25kg/m2) as compared to LOCRC (OR > 2). Transcriptomic analysis identified 328 differentially expressed genes (306 up-regulated and 22 down-regulated in EOCRC); GSEA analysis showed enrichment of Hedgehog and calcium signaling pathways (FDR < 0.1) in EOCRC. Conclusions: This comparison of EOCRC and LOCRC cases demonstrates clear differences in CMS subtypes, reveals specific associations with environmental factors, and suggests that calcium channel signaling and hedgehog signaling may play a crucial role in the development and progression of EOCRC compared to LOCRC. Citation Format: Sheetal Hardikar, Griffin Caryotakis, David A. Nix, Aaron Atkinson, Jamie Teer, Vaia Florou, Andreana Holowatyj, Michelle L. Churchman, David M. McKean, Phaedra Agius, Bodour Salhia, Ning Jin, Daniel Spakowicz, Micha Cavnar, Emily Baiyee. Toegel, Tiago Biachi de Castria, Patrick M. Boland, Ahmad Tarhini, Bryan P. Schneider, Matthew Reilley, Deepak Vadehra, Michele M. Gage, Howard Colman, Courtney Scaife, Jessica N. Cohan, Biljana Gigic, Adetunji Toriola, Christopher I. Li, Jane Figueiredo, Dorotha Byrd, David Shibata, Cornelia M. Ulrich, Aik Choo Tan, Erin M. Siegel. Transcriptomic and pathway analyses patterns in early-onset and late-onset microsatellite stable colorectal cancer: Results from the ORIEN Network [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr PR012.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".