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
2171 - CCND1 Amplification Contributes to Immunosuppression in Head and Neck Squamous Cell Carcinoma and the Association with a Poor Response to Immune Checkpoint Inhibitors
Presenter: Chloe Huang
Session: Poster Display session 3
Resources:
Abstract
2624 - Efficacy of PD-1/PD-L1 inhibitors in the treatment of non-small cell lung cancer patients with sensitive genes mutation
Presenter: Hui-Juan Cui
Session: Poster Display session 3
Resources:
Abstract
3494 - Neutrophil to Lymphocyte Ratio (NLR) kinetics as predictors of outcomes in metastatic renal cell carcinoma (mRCC) and non-small cell lung cancer (NSCLC) patients treated with nivolumab (N).
Presenter: Audrey Simonaggio
Session: Poster Display session 3
Resources:
Abstract
3964 - Predictive markers of checkpoint inhibitor activity in adult metastatic solid tumours
Presenter: Alexandra Pender
Session: Poster Display session 3
Resources:
Abstract
3041 - Blood-based TMB (bTMB) correlates with tissue-based TMB (tTMB) in a multi-cancer Phase I IO Cohort
Presenter: Daniel Araujo
Session: Poster Display session 3
Resources:
Abstract
3910 - Analysis of Molecular Profile Complexities for Immunotherapy Decision Support
Presenter: Robert Dóczi
Session: Poster Display session 3
Resources:
Abstract
4836 - The Role of Tumor Neoantigens in the Differential Response to Immunotherapy (IO) in EGFR and BRAF Mutated Lung Cancers - Quantity or Quality?
Presenter: Katrina Case
Session: Poster Display session 3
Resources:
Abstract
1929 - Impact of previous corticosteroid (CS) exposure on efficacy of Programmed Cell Death-(Ligand) 1 blockade in patients with advanced Non-Small-Cell Lung Cancer (NSCLC): a single Center retrospective analysis
Presenter: Fabrizio Nelli
Session: Poster Display session 3
Resources:
Abstract
2601 - Comparison 18F-FDG-PET/CT criteria for prediction of therapy response and clinical outcome in patients with metastatic melanoma treated with Ipilimumab and PD-1 inhibitors
Presenter: Sabrina Vari
Session: Poster Display session 3
Resources:
Abstract
3628 - Predictive model for survival in advanced non-small-cell lung cancer (NSCLC) treated with frontline pembrolizumab
Presenter: Xabier Mielgo Rubio
Session: Poster Display session 3
Resources:
Abstract