Abstract 1177: Genomic characteristics and clinical outcomes of early onset colorectal cancer (EOCRC): Findings from AACR Project GENIE Biopharma Collaborative registry
Bibliographic record
Abstract
Abstract Background: By 2030, the incidence of EOCRC is predicted to increase by more than 140%. A better understanding of the molecular features of EOCRC is needed. Methods: The AACR GENIE Biopharma Collaborative CRC 1.2-consortium dataset consists of clinical and genomic data from a cohort of CRC patients (pts) with next generation tumor sequencing (55-500 genes) from 2015 to 2017 at three academic centers. Imaging reports and medical oncology notes were retrospectively curated. EOCRC was defined as age at diagnosis < 50 years; while later onset CRC (LOCRC) as ≥ 50 years. Association with clinical variables was analyzed using Chi-Square or Fisher’s exact test. Associations with genomic alterations were analyzed using the Benjamini-Hochberg procedure for multiple testing; the associations were evaluated among the overall study population and by stage. Kaplan-Meier method was used to estimate the median overall survival (OS) and was adjusted to left truncation bias introduced by the inclusion criteria for this project. Results: A total of 1507 pts with CRC, 519 (34%) with EOCRC, were analyzed. EOCRC pts were more likely to be diagnosed with Stage IV than LOCRC (51% vs 44%, p<0.01). Left-sided primary tumors were more frequent in EOCRC (73% vs 57%, p<0.01). Among stage I-III, EOCRC was associated with longer OS compared to LOCRC (HR:1.43, 95%CI 1.07-1.91, p=0.01). Among stage IV CRC, no differences were observed in OS between EOCRC and LOCRC in a multivariate analysis including RAS/BRAF mutation (mt), MSI and liver, lung and peritoneal metastasis (HR: 1.01, 95% CI 0.8-1.28, p=0.92). The proportion of BRAF mt was higher in LOCRC (15 vs 7%, p<0.01), mainly in stage I-III (17% vs 7%, p=0.01). This association is significant after adjusting for tumor sidedness (OR: 1.77 95% CI 1.19-2.63, p<0.01). BRAF V600E mt is more frequent in LOCRC (11 vs 3%, p<0.01). APC and PI3KCA mt are more common in EOCRC, but only among right-sided tumors. There were not significant differences in copy number alterations or structural variants. APC, TP53, KRAS, BRAF and PI3KCA were analyzed according to stage. Among EOCRC, TP53 mt was more frequent in stage IV vs I-III (79 vs 70%, p=0.02). In LOCRC, both TP53 (76% vs 69%, p=0.01) and KRAS mt (47 vs 38%, p<0.01) were more frequent in stage IV vs I-III. Effect modification of the association between these genes and OS by age of onset was explored using an interaction test in stage IV and I-III. APC mt in stage I-III conferred better prognosis in EOCRC (HR 0.31 (0.18-0.51) for EOCRC; HR 0.72 (0.51-1.01) for LOCRC; interaction p<0.01). Conclusions: EOCRC does not show significant genomic differences from LOCRC except for lower frequency of BRAF mt. For right-sided tumors, APC and PI3KCA mt are more common in EOCRC. APC mt in stage I-III is associated with a better prognosis in EOCRC. Further characterization of the biology of EOCRC beyond genomic profiling is needed. Citation Format: Enrique Sanz Garcia, Eric Chen, Marios Giannakis, Gregory J. Riely, Jeremy L. Warner, Michele L. LeNoue-Newton, Jessica Weiss, Katrina Hueniken, Kenneth L. Kehl, Deborah Schrag, Andrea Cercek, Kimmie Ng, AACR GENIE BPC Core Team . Genomic characteristics and clinical outcomes of early onset colorectal cancer (EOCRC): Findings from AACR Project GENIE Biopharma Collaborative registry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1177.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".