Abstract LB-231: Genomic profiling in serial metastatic colorectal tumors identifies copy number alterations and spatio temporal intra-patient heterogeneity profiles associated with clinical response. Q-CROC-01: NCT00984048
Bibliographic record
Abstract
Abstract Introduction: Colorectal cancer (CRC) is the third leading cause of cancer related deaths primarily due to its resistance to current treatments. Studies aiming at understanding mechanisms of resistance have largely investigated the genomic landscape of primary tumors at diagnosis. 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 over time of treatment and response to decipher tumor evolution and therapeutic resistance mechanisms. Methods: Metastatic liver tissue samples were collected at baseline (pre-biopsies) and at the time of resistance (post-biopsies) in responder and non-responder CRC patients undergoing the same first-line treatment. Paired pre/post biopsies were collected from 14 patients including 4 patients with multiple post-biopsies to assess temporal and spatio-temporal tumor heterogeneity following treatment exposure. Biopsies were profiled using exome and transcriptome sequencing as well as high-density Single-Nucleotide Polymorphism (SNP) array analysis to capture chromosomal anomalies, loss of heterozygosity and copy number (CN) variations. Results: Profiling of 45 samples with both high-density SNP array and exome sequencing revealed 97.4% similarity between both technologies in the identification of genes targeted by copy number changes. Using chemo-naïve biopsies, we identified 120 CN gains and 47 CN loss that were significantly associated with patient progression free survival. Integrative analysis with transcriptome data revealed that only 10% of the genomic CN gains and 17% of the CN loss correlated with their gene expression levels. Based on CN variants comparison between paired pre/post treatment samples, we found high temporal intra-patient heterogeneity over time of treatment. Interestingly, we observed a relationship between heterogeneity and tumor response; showing that acquired resistant tumors have the highest temporal variations. Conclusion: This study, using a multi-omic approach to profile serial liver metastatic samples in CRC patients, highlights the genomic changes in tumor composition after treatment exposure and constitutes an innovative approach to identify clinical biomarkers and molecular signatures of resistance. Citation Format: Mathilde Couetoux du Tertre, Maud Marques, Karen Gambaro, Michael Witcher, Benoit Samson, Bernard Lespérance, Yoo-Joung Ko, Richard Dalfen, Eve St-Hilaire, Lucas Sidéris, Félix Couture, Sabine Tejpar, Ronald Burkes, Mohammed Harb, Errol Camlioglu, Adrian Gologan, Vincent Pelsser, André Constantin, Suzan McNamara, Petr Kavan, Claudia Kleinman, Gerald Batist. Genomic profiling in serial metastatic colorectal tumors identifies copy number alterations and spatio temporal intra-patient heterogeneity profiles associated with clinical response. Q-CROC-01: NCT00984048 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-231.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".