Abstract 2795: Integrating multiomics discovery approaches to identify biomarkers of therapeutic resistance in metastatic colorectal cancer through analyses of multiple sequential tumor and liquid biopsies; Qcroc01: NCT00984048
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is the 2nd leading cause of cancer related-death in Canada. Clinical responses of metastatic (m)CRC to first-line treatment range from 35 to 60%, but even responders inevitably develop therapeutic resistance. Studies aiming at understanding mechanisms of resistance have largely investigated primary tumors. However, selective pressures during therapy can lead to the expansion of resistant clones and tumor heterogeneity. This highlights the need to characterize the molecular changes of metastasis and plasma over time of treatment and response to decipher tumor evolution and therapeutic resistance mechanisms. In this multicenter study, 52 tissue samples from liver metastasis were collected at baseline (pre-biopsies) and at the time of resistance (post-biopsies) in responder and non-responder mCRC patients (n=44) undergoing the same standard first-line treatments. Multiple post-biopsies also have been harvested in 4 patients, to allow the assessment of tumor heterogeneity and as well as the evolution of the genomic complexity after treatment exposure. Analyses were carried out across multiple omic platforms to identify resistant signatures and characterize molecular changes during treatment. Biopsies were profiled using exome and transcriptome sequencing as well as high-density SNP array analysis to capture chromosomal anomalies, loss of heterozygosity (LOH) and copy number variations (CNV). Additionally, serial blood samples were collected for proteomic, ctDNA and cytokine analysis. Our preliminary analysis of transcriptomes performed on serial biopsies from a set of 11 patients identified genes consistently overexpressed at resistance. Cytogenetics analysis showed similar genomic profiles of matched pre- and post-biopsies and allowed the establishment of LOH and CNV catalogues of liver metastasis, while exome sequencing revealed cumulative somatic mutations over time of treatment, which suggests subclonal and acquired “driver” mutations of resistance. Plasma-derived ctDNA analysis was performed to investigate the mutational status during treatment and whether they correlate with their relative levels in biopsies. Immune gene expression analysis of a test set of 27 metastases revealed strong clustering of 7 metastases due to overexpression of transcripts related to active immune response, allowing to define novel subgroups of patients based on immune response status. Our study, using a multi-omic strategy and integration of independent molecular platforms to profile liver metastasis samples of responder and non-responder mCRC patients, constitutes an innovative approach to identify clinical biomarkers and molecular signature of resistance, which may enhance individualization of cancer medicine and customized therapy. Citation Format: Karen Gambaro, Maud Marques, Ryan Morin, Claudia Kleinman, Michael Witcher, Simon Turcotte, Benoit Samson, Bernard Lespérance, Yoo-Joung Ko, Richard Dalfen, Eve St-Hilaire, Lucas Sideris, Felix Couture, Sabine Tejpar, Ronald Burkes, Mohammed Harb, Francine Aubin, Thierry Alcindor, Errol Camlioglu, Adriana Aguilar, Mathilde Couetoux du Tertre1, Suzan McNamara, Adrian Gologan, Petr Kavan, Gerald Batist. Integrating multiomics discovery approaches to identify biomarkers of therapeutic resistance in metastatic colorectal cancer through analyses of multiple sequential tumor and liquid biopsies; Qcroc01: NCT00984048 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2795. doi:10.1158/1538-7445.AM2017-2795
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".