Abstract 5820
Background
DNA sequencing to identify variants is becoming increasingly valuable in clinical settings; including matching patients to approved targeted therapies, immunotherapies, and/or clinical trials. However, accurate calling of genetic variants from sequencing still remains challenging. With little corroboration between the different tools available, patients are at risk of being treated with therapies that are unsuitable for their cancer.
Methods
Here we present a novel machine learning based method for the accurate identification of somatic variants in cancer patient tumour samples, with a neural network architecture from encoded raw sequencing read information of tumour/normal sample pairings into an image, enabling it to classify whether a variant is germline, somatic, or sequencing error. The model was trained and tested on in-silico spike-in data using bam-surgeon, and then validated on a multi-cancer and multi-center dataset and benchmarked against industry standard variant callers.
Results
The approach, called somaticNET, outperforms existing industry standard tools in sensitivity and specificity, achieving an AUROC of ∼1.00 on the bam-surgeon dataset and an AUROC of ∼0.99 on the multi-cancer multicenter dataset. The model also works faster than other variant callers, in minutes compared to hours.
Conclusions
Using the power of machine learning for accurate somatic variant calling can improve patient matching to approved therapies and clinical trials, thus ensuring patients are given the right therapy at the right time to treat their cancer.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Cambridge Cancer Genomics.
Disclosure
G. Dubourg-Felonneau: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. D. Rebergen: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. C. Parsons: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. H. Thompson: Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. J.W. Cassidy: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment, Officer / Board of Directors: Cambridge Cancer Genomics. N. Patel: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics. H.W. Clifford: Leadership role, Shareholder / Stockholder / Stock options, Full / Part-time employment: Cambridge Cancer Genomics.
Resources from the same session
4868 - Evaluation of markers associated with efficacy of abiraterone acetate plus prednisone (AAP) in patients (pts) with castration-sensitive prostate cancer (mCSPC) from the LATITUDE study
Presenter: Kim Chi
Session: Poster Display session 3
Resources:
Abstract
4837 - LRP2, a potential new biomarker for Chinese younger aged intrahepatic cholangiocarcinoma patients
Presenter: Xiaoliang Shi
Session: Poster Display session 3
Resources:
Abstract
1286 - Reanalysis of the efficacy of molecular targeted agents (MTAs) given in the randomized trial SHIVA01 according to the ESMO ESCAT scale of actionability
Presenter: Aurelie Moreira
Session: Poster Display session 3
Resources:
Abstract
2736 - Comparison of Platforms for Determining Tumor Mutational Burden (TMB) From Blood Samples in Patients With Non-Small Cell Lung Cancer (NSCLC)
Presenter: Jonathan Baden
Session: Poster Display session 3
Resources:
Abstract
5045 - Comprehensive Pan-Cancer analysis of somatic mutations in drug transporters to reveal acquired and intrinsic drug resistance in 3149 metastatic cancer patients
Presenter: Sander Bins
Session: Poster Display session 3
Resources:
Abstract
4577 - Pan-Cancer Genomic Landscape of the Cyclin D1/FGF3,4,19 (11q13) Amplicon Including Associations with HPV Status, and ESR1 and AR Alterations
Presenter: Jennifer Johnson
Session: Poster Display session 3
Resources:
Abstract
5366 - Co-occurrence of NTRK fusions with other genomic biomarkers in cancer patients
Presenter: Xiaolong Jiao
Session: Poster Display session 3
Resources:
Abstract
4084 - Prospective comparative study of next-generation sequencing on fine needle aspirations versus core needle biopsies in cancer patients included in SHIVA02 trial
Presenter: Julien Masliah-Planchon
Session: Poster Display session 3
Resources:
Abstract
6017 - First national External Quality Assessement for the interpretation of somatic variants: assessment of 25 variants in colorectal, lung, ovarian cancers and melanoma in France
Presenter: Etienne Rouleau
Session: Poster Display session 3
Resources:
Abstract
2283 - Prospective testing of circulating tumor DNA in metastatic breast cancer facilitates clinical trial enrollment and precision oncology
Presenter: Andjelija Bujak
Session: Poster Display session 3
Resources:
Abstract