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
5071 - Expression of estrogen receptor and programmed cell death-ligand 1 can be complementary prognostic factors in HPV-positive oropharyngeal squamous cell carcinoma
Presenter: Soohyeon Kwon
Session: Poster Display session 3
Resources:
Abstract
5306 - Real-world data of clinicopathologic characteristics of young oropharyngeal cancer patients.
Presenter: Maria Nieva
Session: Poster Display session 3
Resources:
Abstract
3407 - The clinical significance and biological mechanisms of miR-499a in high-tobacco exposed head and neck squamous cell carcinoma
Presenter: Shiqi Gong
Session: Poster Display session 3
Resources:
Abstract
3310 - Liquid biopsy for mutational profiling of locoregional recurrent and/or metastatic squamous cell carcinoma of the head and neck
Presenter: Rachel Galot
Session: Poster Display session 3
Resources:
Abstract
2362 - Blood-based testing of mutations in patients with Head and neck squamous cell carcinoma (HNSCC) using highly sensitive SafeSEQ technology
Presenter: Florentia Fostira
Session: Poster Display session 3
Resources:
Abstract
4533 - The head and neck Lung Immune Prognostic Index (HN-LIPI): a prognostic Score for Immune Checkpoint Inhibitors (ICI) in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck (R/M SCCHN) patients.
Presenter: Ruth Gabriela Herrera Gomez
Session: Poster Display session 3
Resources:
Abstract
5262 - Immune-related adverse events (irAEs) and outcome in recurrent/metastatic (R/M) Head and Neck Squamous Cell Carcinoma (HNSCC) patients (pts) treated by immune-checkpoints inhibitors (ICI)
Presenter: Neus Baste Rotllan
Session: Poster Display session 3
Resources:
Abstract
3725 - Intratumoral and peripheral exploratory biomarker analysis in patients with locoregional, recurrent head and neck squamous cell carcinoma (rHNSCC) treated with RM-1929 photoimmunotherapy
Presenter: Jack Bui
Session: Poster Display session 3
Resources:
Abstract
2533 - A nomogram based prognostic score to predict overall survival (OS) in recurrent-metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) patients (pts) treated with immune checkpoint inhibitors (ICI).
Presenter: Luay Mousa
Session: Poster Display session 3
Resources:
Abstract
2929 - Changes of the Commensal Microbiome during Treatment are Associated with Clinical Response in the Nasopharyngeal Carcinoma Patients
Presenter: Tingting Huang
Session: Poster Display session 3
Resources:
Abstract