Abstract 3006: Molecular characterization of baseline and serial multiple myeloma patients from the MMRF CoMMpass study
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) is a hematological malignancy of plasma cells accounting for ~2% of new cancer cases each year in the United States. Our understanding of MM pathogenesis has improved dramatically with the development of whole genome analysis technologies, however, to date no study has comprehensively analyzed a large cohort of MM patients. The Multiple Myeloma Research Foundation CoMMpass Study (NCT01454297) is a fully accrued observational clinical trial with 1143 newly diagnosed MM patients from sites in the United States, Canada, Spain, and Italy. Clinical parameters are collected at baseline and every three months through the eight-year observation period. Tumor samples are collected and characterized using whole genome, exome, and RNA sequencing at diagnosis and each progression event. This unique prospective study design differentiates CoMMpass from other large cancer genomics studies performed to date. This represents the first analysis of the CoMMpass interim analysis 12 dataset including 982 of whom are molecularly characterized at baseline. Median follow-up of the cohort exceeds 2 years, and while the median OS has still not been reached, median PFS of the cohort is 36 months. We identified a median of 153 non-immunoglobulin mutations, 29 structural events, and 133 copy number (CN) events per tumor at baseline. In order to identify a set of significantly mutated genes, we applied a consensus-based approach identifying 60 genes mutated in at least 1% of the baseline cohort. Consensus clustering of the CN and gene expression profiles identified 14 and 12 distinct MM subtypes, respectively. Integration of WGS and RNA sequencing data identified 1163 cross-validated fusion transcripts. An integrated analysis of all data sources identified a series of potential gain-of-function and loss-of-function genes, from which a pathway analysis highlighted alterations in the NF-kB, Ras, DNA repair, and cell-cycle pathways. This dataset includes serial data for 121 patients (171 specimens), 21 of whom had multiple progression events. Mutational analyses revealed that ~ 24% of mutations identified at progression were not identified at the previous timepoint. Although rarely mutated at baseline, RRBP1 was frequently mutated at relapse and may represent a novel driver of disease progression or treatment resistance in MM. In progression samples we observed a greater proportion of patients with NRAS mutations, attributable to four patients who acquire NRAS mutations and five patients with baseline KRAS mutations that shift to NRAS mutations at relapse. All patients who exhibit a KRAS to NRAS shift were bortezomib treated, suggesting this shift may represent a mechanism of resistance. This comprehensive study has identified distinct genetic subgroups with variable clinical outcome and demonstrates the value of prospective collections to identify mechanisms of progression and resistance. Citation Format: Sheri Skerget, Austin Christofferson, Sara Nasser, Jessica Aldrich, Daniel Penaherrera, Christophe Legendre, Martin Boateng, Lori Cuyugan, Jonathan Adkins, Erica Tassone, The MMRF CoMMpass Network, Jen Yesil, Daniel Auclair, Winnie Liang, Jonathan J. Keats. Molecular characterization of baseline and serial multiple myeloma patients from the MMRF CoMMpass study [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 3006.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".