Abstract 2300: AI enabled whole exome&amp; transcriptome liquid biopsy addressing MCED, MRD, and therapy selection on a single platform
Authors: Jim Abraham, Valeriy Domenyuk, Maria Perdigones Borderias, Takayuki Yoshino, Elisabeth I. Heath, Emil Lou, Stephen Liu, John Marshall, Wafik S. El-Deiry, Anthony Shields, Martin Dietrich, Yoshiaki Nakamura, Takao Fujisawa, David D. Halbert, Dominic Sacchetti, Seth Stahl, Adam Stark, Sergey Klimov, Sourabh Antani, Chadi Nabhan, Jeffrey Swensen, George Poste, Matt Oberley, Milan Radovich, George W. Sledge, David Spetzler
Journal: Cancer Research (2024)

Abstract Effective clinical management of cancer patients requires an accurate and early diagnosis, highly sensitive monitoring of minimal residual disease (MRD), and precise therapy selection. There are multiple tests available that attempt to address each of these needs independently with varying degrees of clinical utility. Caris Assure is a proprietary circulating nucleic acid sequencing platform that couples whole exome and transcriptome (WES/WTS) sequencing on white blood cells and plasma with advanced machine learning techniques to satisfy all three testing needs on one platform. This test detects SNVs, INDELs, structural variants, copy number, gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), fragment length, and aneuploidy of both somatic (tumor and clonal hematopoiesis) and germline origin. Caris’ extensive database of over 350,000 tissue WES/WTS from solid malignancies was used to train deep learning neural networks to identify the molecular underpinnings of cancer. These networks were then deployed on WES/WTS data from plasma and buffy coat in pursuit of signals that can inform early detection, MRD and therapy selection. Validation studies were performed to characterize the analytic and clinical performance of Assure on over 3000 patient blood samples. These samples include ~1000 non-cancer patients (controls), ~1700 newly diagnosed patients where blood was collected at surgery (early detection), ~500 early-stage patients during adjuvant therapy at multiple time points (MRD), and ~200 locally advanced/metastatic patients where matched tissue testing was also performed (therapy selection). For early detection, stratification of blood samples from patients with stage I-IV cancer versus those with no reported cancer resulted in an AUC &amp;gt; 0.99 and included over 30 types of solid tumors. Notably, at 99.5% specificity, the sensitivities for stages I-IV (n= 119, 54, 50, 27) were 73%, 80%, 76%, and 89%. In the MRD setting for high-risk patients, the disease-free survival of patients whose cancers were predicted to recur was significantly shorter (39.6 mo) than those predicted not to recur (93.4 mo) (HR: 5.18, 95%CI: 2.94-9.09, p&amp;lt;.00001). This performance was observed across multiple lineages of cancer including but not limited to breast, colon, lung, and bladder. Lastly, for therapy selection, detection of driver mutations where blood was collected within 30 days of matched tissue demonstrated high concordance with a PPA of 93.8% and PPV of 96.8%. CHIP correction proved to be essential as ~35% percent of patients had CHIP mutations, including KRAS, BRAF, ATM, BRCA1/2, findings that could lead to improper therapy selection. Herein, we demonstrate for the first time a single liquid biopsy assay that addresses the entire continuum of care in clinical oncology with optimal diagnostic, prognostic, and predictive utility for patients and physicians. Citation Format: Jim Abraham, Valeriy Domenyuk, Maria Perdigones Borderias, Takayuki Yoshino, Elisabeth I. Heath, Emil Lou, Stephen Liu, John Marshall, Wafik S. El-Deiry, Anthony Shields, Martin Dietrich, Yoshiaki Nakamura, Takao Fujisawa, David D. Halbert, Dominic Sacchetti, Seth Stahl, Adam Stark, Sergey Klimov, Sourabh Antani, Chadi Nabhan, Jeffrey Swensen, George Poste, Matt Oberley, Milan Radovich, George W. Sledge, David Spetzler. AI enabled whole exome&amp; transcriptome liquid biopsy addressing MCED, MRD, and therapy selection on a single platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2300.