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
1908 - Androgen Receptor (AR) Aberrations in Patients (Pts) With Metastatic Castration-Sensitive Prostate Cancer (mCSPC) Treated With Apalutamide (APA) Plus Androgen Deprivation Therapy (ADT) in TITAN
Presenter: Kim Chi
Session: Poster Display session 3
Resources:
Abstract
4058 - 68Ga-PSMA guided bone biopsies for molecular diagnostics in metastatic castration resistant prostate cancer patients
Presenter: Anouk de Jong
Session: Poster Display session 3
Resources:
Abstract
2226 - Spatial-Temporal Change in Quantitative Total Bone Imaging (QTBI) and Circulating Tumor Cells (CTCs) in Metastatic Castration-Resistant Prostate Cancer (mCRPC) Treated With Enzalutamide (ENZA)
Presenter: Glenn Liu
Session: Poster Display session 3
Resources:
Abstract
5795 - Efficacy of Enzalutamide in Hormone-sensitive Metastatic Prostate Cancer: Clinical Utility of 18F-Choline PET and Whole Body MRI.
Presenter: Susanne Osanto
Session: Poster Display session 3
Resources:
Abstract
899 - Urine extracellular vesicle GATA2 mRNA alone and in a multigene test predicts initial prostate biopsy result
Presenter: Jungreem Woo
Session: Poster Display session 3
Resources:
Abstract
3094 - Circulating tumor cell (CTC) genomic landscape in neuroendocrine prostate cancer (NEPC) by single cell copy number analysis
Presenter: Vincenza Conteduca
Session: Poster Display session 3
Resources:
Abstract
2527 - Circulating Tumor Cells (CTC) count and Prostate-Specific Antigen (PSA) response measures in metastatic Castration-Resistant Prostate Cancer (mCRPC) patients (pts) treated with Docetaxel (Doc)
Presenter: Rebeca Lozano Mejorada
Session: Poster Display session 3
Resources:
Abstract
6106 - Assessing the clinical relevance of drug–drug interactions (DDI) with darolutamide (DARO)
Presenter: Christian Zurth
Session: Poster Display session 3
Resources:
Abstract
2237 - KEYNOTE-921: phase 3 study of pembrolizumab (pembro) plus docetaxel and prednisone for enzalutamide (enza)- or abiraterone (abi)-pretreated patients (pts) with metastatic castration-resistant prostate cancer (mCRPC).
Presenter: Daniel Petrylak
Session: Poster Display session 3
Resources:
Abstract
2241 - KEYNOTE-641: Phase 3 Study of Pembrolizumab (pembro) Plus Enzalutamide for Metastatic Castration-Resistant Prostate Cancer (mCRPC)
Presenter: Julie Graff
Session: Poster Display session 3
Resources:
Abstract