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Record W2244438265 · doi:10.1158/1538-7445.am2015-615

Abstract 615: Noninvasive diagnosis of actionable mutations by deep sequencing of circulating tumor DNA in multiple myeloma

2015· article· en· W2244438265 on OpenAlexaff
Rayan Kaedbey, Olena Kis, Arnavaz Danesh, Mark Dowar, Tiantian Li, Zhihua Li, Jessica Liu, Mark Mansour, Mahadeo A. Sukhai, Tong Zhang, Suzanne Kamel‐Reid, Trevor J. Pugh, Suzanne Trudel

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsKRASNeuroblastoma RAS viral oncogene homologLiquid biopsyCirculating tumor DNADNA sequencingDeep sequencingMedicineMultiple myelomaCancerWhole genome sequencingGenomeCancer researchGeneBiologyGeneticsInternal medicineColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background: Genome sequencing of multiple myeloma (MM) tumors has revealed recurrent mutations that serve as a fertile ground for targeted therapies. Indeed, activating mutations of KRAS, NRAS and BRAF have been reported in approximately 27%, 24% and 4% of MM cases. Based on this observation, we initiated a Phase II NCI-CTEP sponsored clinical trial of trametinib in patients with MM (PHL-9460). Detection of mutations currently require bone marrow aspirates that are invasive and can yield suboptimal samples. Targeted, ultra-deep sequencing of circulating tumor DNA (ctDNA) is a promising tool for accessing the tumor genome that has not been well studied in MM. We set out to determine the feasibility of detecting ctDNA in MM and of identifying actionable mutations and mutational load using liquid biopsy through ctDNA analysis. Methods: MM patients enrolled onto PHL-9460 or those with heavy tumor burden were identified and consented to have their peripheral blood drawn for analysis. Where possible, matched tumor DNA was also obtained. Cell-free DNA was extracted from 7-15 mL of plasma isolated within ¬1 hour of blood draw using the QIAamp Circulating Nucleic Acid Kit and tagged with barcoded sequencing adapters for subsequent pooling. All exons of KRAS, NRAS, BRAF, PIK3CA and EGFR genes were isolated using a custom hybrid capture panel (IDT xGen Lockdown) and sequenced on an Illumina HiSeq 2000. Reads were aligned to the human genome reference (hg19) using bwa and somatic mutations were detected using muTect. Results: We have collected 25 samples from 23 patients, 7 from patients on PHL-9460, 5 newly diagnosed, and 13 from relapsed patients having received 3.3 median prior lines of therapy (range 1-7). To date, 11 samples from 10 patients have been sequenced. The sample with the lowest DNA yield failed due to low library complexity (range 16.6-3872 ng, median yield: 197 ng). From the remaining 10 samples, the mean target coverage ranged from 31,500 to 32,500. Somatic mutations in KRAS, NRAS, or PIK3CA genes were present in 5 of 9 patients with mutant allele frequencies ranging from 1.1% to 32% (3 KRAS and 2 NRAS of which 2 cases also had a low frequency PIK3CA mutation). We did not uncover mutations in BRAF or EGFR. For patients with matched tumor DNA, mutations in ctDNA concurred with those found in tumor DNA sequencing (4 of 4 tumors with known genotypes). Two patients with NRAS mutations enrolled onto PHL-9460 have responded to trametinib (1 partial and 1 minor response) and remain on therapy. Conclusion: ctDNA analysis in this cohort has identified key mutations in MM. The rate of RAS or RAF mutations in ctDNA compared to matched tumors and correlation to response to trametinib is ongoing and will be presented. Preliminary data suggest that ctDNA may be a reliable method of detecting mutations in MM and an alternative to bone marrow biopsy. Citation Format: Rayan Kaedbey, Olena Kis, Arnavaz Danesh, Mark Dowar, Tiantian Li, Zhihua Li, Jessica Liu, Mark Mansour, Mahadeo Sukhai, Tong Zhang, Suzanne Kamel-Reid, Trevor J. Pugh, Suzanne Trudel. Noninvasive diagnosis of actionable mutations by deep sequencing of circulating tumor DNA in multiple myeloma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 615. doi:10.1158/1538-7445.AM2015-615

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.076
GPT teacher head0.357
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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