Abstract 3247: Genome-wide cell-free DNA mutation integration for sensitive cancer detection
Authors: Zviran Asaf , Hill Steven T. , Schulman Rafael , Shah Minita , Deochand Sunil , Ha Gavin , Reed Sarah , Rotem Denisse , Gydush Greg , Rhoades Justin , Huang Kevin , Liao Will , Maloney Dillon , Omans Nathan , Malbari Murtaza , Spinelli Cathy F. , Kazancioglu Selena , Robine Nicolas , Adalsteinsson Viktor , Houck-Loomis Brian , Altorki Nasser , Landau Dan A. 
Published: August 2018

Solid malignancies are often diagnosed at a late stage with dismal prognosis. Even after cancer is diagnosed, we lack sensitive tools to guide difficult therapeutic decisions such as adjuvant therapy. Sensitive cancer detection by blood biopsy can therefore transform care by enabling early detection and residual disease monitoring.

Cell free DNA mutation detection has shown significant promise in its ability to survey the somatic genome and enable detection of cancer mutations in the peripheral blood. However, the combination of low tumor fraction and limiting number of DNA fragments in a typical plasma sample, restrict the probability of detecting early stage cancer in cfDNA through current deep targeted sequencing methods.

Focusing on non-small cell lung cancer (NSCLC), we reasoned that we would need to supplant depth of sequencing with breadth of sequencing to overcome the fundamental limitation of low input of cfDNA. To do so, we apply whole genome sequencing (WGS) that allows us to base sensitive detection on the cumulative signal provided by 10,000-30,000 somatic mutations observed in a substantial proportion of NSCLC. We developed an analytic method that integrates genome-wide mutation signal to obtain a tumor fraction (TF) estimate, and thus allow sensitive detection of residual disease and quantitative dynamic monitoring of disease burden. Benchmarking on artificial plasma showed TF detection sensitivity as low as 1:100,000, two orders of magnitude more sensitive than currently available methods.

To test this method, we performed WGS on resected NSCLC and matched germline samples of 8 NSCLC patients, as well as on matched pre- and post-surgery cfDNA. Patient-specific somatic mutations were identified in the tumor/normal pairs and used for the estimation of TF in the matched plasma samples. We detect pre-surgery circulating tumor DNA (ctDNA) in all of the early-stage pre-operative samples and in ~40% post-operative patients, correlated with post-operative disease progression.

In early cancer detection, tumor DNA is not available, requiring de-novo mutation detection in cfDNA. To do so, we first trained a convolutional neuronal network to distinguish between cancer altered sequencing reads and reads affected by sequencing errors. This was followed by genome-wide pattern matching to a specific genomic signature that mark lung cancer mutations (Tobacco signature) indicating the presence of ctDNA in the patient plasma. Applying this method to the pre-operative early stage lung cancer samples and plasma samples from 5 patients with benign nodules (CT-detected) showed an accurate discrimination between malignant and benign nodules, suggesting a potential role in improving the positive predictive value of lung cancer screening in at-risk populations.

These results show that genome-wide mutation integration is a promising novel approach for ultra-sensitive early detection and residual disease monitoring.

Citation Format: Asaf Zviran, Steven T. Hill, Rafael Schulman, Minita Shah, Sunil Deochand, Gavin Ha, Sarah Reed, Denisse Rotem, Greg Gydush, Justin Rhoades, Kevin Huang, Will Liao, Dillon Maloney, Nathan Omans, Murtaza Malbari, Cathy F. Spinelli, Selena Kazancioglu, Nicolas Robine, Viktor Adalsteinsson, Brian Houck-Loomis, Nasser Altorki, Dan A. Landau. Genome-wide cell-free DNA mutation integration for sensitive cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3247.