Abstract 567: cMonitor: Comprehensive and sensitive monitoring of diverse cancer treatment outcomes by exome-wide mutation analysis in plasma cfDNA
Authors: Li Shuo , Zeng Weihua , Ni Xiaohui , Stackpole Mary L. , Zhou Yonggang , Noor Zorawar , Yuan Zuyang , Garon Edward B. , Dubinett Steven M. , Li Wenyuan , Zhou Xianghong 
Published: July 2021

Abstract
Monitoring cancer patients for the early detection of minimal residual disease (MRD), cancer recurrence, and cancer progression is essential for assessing treatment response and predicting early relapse during/after treatment. Plasma cell-free DNA (cfDNA) provides unique opportunities for cancer monitoring given its non-invasive and comprehensive sampling of heterogeneous tumor clones. However, the low tumor content in cfDNA poses a major challenge for detecting tumor signals. Previous methods mostly rely on deep sequencing of small panels to capture the tumor signal. These methods usually require the labor-intensive design of customized gene panel, yet fail to identify the evolving tumor, which is essential for detecting second primary diseases or emerging subclones. To address these limitations, here we present OncoMonitor, a cancer monitoring method that comprehensively analyzes tumor mutations in cfDNA whole-exome sequencing data. Taking advantage of the availability of mutation information across the whole exome, our method (1) integrates all clonal tumor mutations identified from pre-treatment cfDNA samples (or tumor samples) to compensate for low tumor fraction in cfDNA, (2) suppresses sequencing errors at read level with an accurate random forest classifier to model the observed features from cfDNA fragments and further enhance the tumor signal, (3) builds sample-specific background noise distributions to predict recurrence and MRD to avoid interference from inter-individual variations and inter-experimental biases, and (4) detects tumor changes, especially second primary diseases, by identifying newly emerging tumor mutations de novo. Combining these techniques, for the first time, we show that OncoMonitor can sensitively and specifically detect both cancer recurrence/MRD and secondary primary cancers from plasma samples with low tumor fraction. Using simulated cfDNA sequence data with artificial mutation spike-ins, our method can detect recurrence at 0.025% tumor fraction with &gt; 95% sensitivity and 95% specificity, and the second primary disease at 0.1% tumor fraction with around 75% sensitivity and 100% specificity. In a cohort of 9 non-small-cell lung cancer patients, we show that OncoMonitor can provide comprehensive tumor changes for treatment response prediction and capture emerging tumor clones; this cannot be achieved by previous methods that are based only on mutations in the pre-treatment surgery samples. In summary, with broad genomic sequencing coverage and comprehensive mutation analysis, our method can identify patients suffering from MRD or cancer recurrence/progression, provide a thorough view of their tumor status, and enable early intervention and personalized treatment.
Citation Format: Shuo Li, Weihua Zeng, Xiaohui Ni, Mary L. Stackpole, Yonggang Zhou, Zorawar Noor, Zuyang Yuan, Edward B. Garon, Steven M. Dubinett, Wenyuan Li, Xianghong Zhou. cMonitor: Comprehensive and sensitive monitoring of diverse cancer treatment outcomes by exome-wide mutation analysis in plasma cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 567.