Abstract 4325: A Phase II exploratory study to identify biomarkers prognostic of clinical response to regorafenib in patients with metastatic colorectal cancer who have failed first-line therapy
Bibliographic record
Abstract
Abstract Single-agent regorafenib is approved in Canada and in the US for patients with metastatic colorectal cancer (mCRC) who have failed previous lines of therapy1. Identification of prognostic biomarkers is crucial to ensure the best therapeutic strategies for mCRC patients. The goal of this clinical study (NCT01949194) was to explore putative molecular signatures of response and resistance to regorafenib in metastatic tumors and serial blood samples. We used a multi-omics approach to profile metastatic tumor tissues and serial blood samples from 47 mCRC patients who received single-agent regorafenib as second-line therapy after failing first-line therapy with an oxaliplatin or irinotecan-containing regimen with or without bevacizumab. Whole exome sequencing was performed to assess both the mutational and copy number (CN) landscapes of metastatic tissues from 29 patients and the transcriptome was interrogated using RNA sequencing. A 14-gene panel was used to profile serial plasma samples. Molecular aberrations were then correlated with lesion-specific treatment response using RECIST 1.1 and progression free survival (PFS) to investigate potential associations.The copy number aberration (CNA) landscape of metastatic tumors was assessed using Nexus Copy Number Software and 20 significant focal aberrations were identified using Genomic Identification of Significant Target in Cancer (GISTIC) test (q-bound<0.05). We interrogated all CNA regions across the genome using the log-rank statistic test and identified 38 CNA regions associated with PFS (permutated p-value <0.05). Interestingly, CN gains targeting three genes, GPR52, PTGS2 and MYC, classified within the drug resistance gene category in the drug-interaction database (DGIdb), were associated with a shorter PFS (Kaplan Meier analysis). The mutational landscape showed typical mutation rates for the top 5 driver genes: APC, KRAS, BRAF, PIK3CA and TP53. However, KRAS mutations in exons 12 and 13 appeared enriched in intrinsically resistant lesions compared to stable and acquired resistance lesions (83% versus 41%). At the transcriptome level, three main pathways were associated with intrinsic resistance (TGF-β signaling activation and MAPK activation) and shorter PFS (adherens junction). The profiling of plasma samples showed a higher proportion of samples with non-detectable ctDNA in patients exhibiting longer PFS (PFS ≥ 9 months, 70%) compared to patients with shorter PFS (< 9 months, 18%). This study allowed the identification of several candidate genes and regions at different molecular levels that warrant further validation in order to stratify patients who will likely respond, display intrinsic resistance or develop acquired resistance to regorafenib treatment. 1- Grothey A, Van Cutsem E, Sobrero A, Siena S, Falcone A, Ychou M, et al. Regorafenib monotherapy for previously treated metastatic colorectal cancer (CORRECT): an international, multicentre, randomized, placebo-controlled, phase 3 trial. Lancet. 2013 Jan 26;381(9863):303-12. Citation Format: Karen Gambaro, Maud Marques, Suzan McNamara, Mathilde Couetoux du Tertre, Cyrla Hoffert, Archana Srivastava, Sophie Mathieu, Anna schab, Thierry Alcindor, Adrian Langleben, Lucas Sideris, Mahmoud Adbelsalam, Mustapha Tehfe, Gerald Batist, Petr Kavan. A Phase II exploratory study to identify biomarkers prognostic of clinical response to regorafenib in patients with metastatic colorectal cancer who have failed first-line therapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4325.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".