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
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».