Maddala, Tara

INTRODUCTION: Barrett esophagus (BE) is the strongest known risk factor for developing esophageal adenocarcinoma (EAC), the second-most lethal cancer in the United States. Esopredict is a novel validated methylation-based biomarker assay that provides precise quantification of neoplastic progression risk in BE patients. Inherit challenges, including tissue heterogeneity, sampling error, interobserver variability, and inconsistent adherence to surveillance biopsy guidelines, may affect the predictive value results of Esopredict obtained at different anatomic locations or different sampling time points. METHODS: To investigate the spatiotemporal performance of Esopredict across multiple spatiotemporal sampling points, we profiled 220 biopsies obtained from 58 BE patients, including 11 patients with overlapping spatial and temporal biopsies. We focused on spatial profiling (i.e., multiple biopsies obtained at several anatomic locations during a single endoscopy) and temporal profiling (i.e., biopsies obtained from multiple endoscopies performed at different time points). Each patient had an initial histologic diagnosis of nondysplastic Barrett esophagus, indefinite for dysplasia, or low-grade dysplasia. Final follow-up (endpoint) biopsies showed either high-grade dysplasia or EAC (progressors), or nondysplastic Barrett esophagus, indefinite for dysplasia, or low-grade dysplasia (nonprogressors). Biopsies were analyzed with Esopredict to compute a progression risk score, which quantified the likelihood of future progression to high-grade dysplasia or EAC within 5 years. RESULTS: In 52 spatially profiled patients, Esopredict demonstrated a sensitivity of 81% (17/21 progressor patients), based on the highest-scoring biopsy from each patient; sensitivity increased to 100% (12/12) when end point biopsies occurred within 5 years of the index (initial) biopsy. In 28 temporally profiled patients, sensitivity was 100% (8/8 patients), based on the biopsy performed at the time point closest to the end point biopsy. DISCUSSION: Esopredict showed high predictive performance in multiple spatiotemporal samples in BE patients. These data further support the use of Esopredict as a robust test to distinguish high-risk BE patients, who may benefit from endoscopic eradication therapy or increased surveillance frequency, from low-risk patients, who may be candidates for less frequent surveillance and noninterventional observation.

INTRODUCTION: Esophageal adenocarcinoma (EAC) is the second-most lethal cancer in the United States, with Barrett esophagus (BE) being the strongest risk factor. Assessing the future risk of neoplastic progression in patients with BE is difficult; however, high-grade dysplasia (HGD) and early EAC are treatable by endoscopic eradication therapy (EET), with survival rates of 90%. Thus, it would be beneficial to develop a molecular assay to identify high-risk patients, who merit more frequent endoscopic surveillance or EET, as well as low-risk patients, who can avoid EET and undergo less frequent surveillance. METHODS: Deidentified endoscopic biopsies were acquired from 240 patients with BE at 6 centers and confirmed as future progressors or nonprogressors. Tissues were analyzed by a set of methylation-specific biomarker assays. Test performance was assessed in an independent validation set using 4 stratification levels: low risks, low-moderate risks, high-moderate risks, and high risks. RESULTS: Relative to patients in the low-risk group, high-risk patients were 15.2 times more likely to progress within 5 years to HGD or EAC. For patients in the high-risk category, the average risk of progressing to HGD or EAC within 5 years was 21.5%, 4-fold the BE population prevalence within 5 years, whereas low-risk patients had a progression risk of only 1.85%. DISCUSSION: This clinical assay, Esopredict, stratifies future neoplastic progression risk to identify higher-risk patients with BE who can benefit from EET or more frequent surveillance and lower-risk patients who can benefit from reduced surveillance.

3049 Background: For multi-cancer detection using cfDNA, TOO determination is critical to enable safe and efficient diagnostic follow-up. Previous array-based studies captured < 2% of genomic CpGs. Here, we report genome-wide fragment-level methylation patterns across 811 cancer cell methylomes representing 21 tumor types (97% of SEER cancer incidence), and define effects of this methylation database on TOO prediction within a machine learning framework. Methods: Genomic DNA from 655 formalin-fixed paraffin-embedded (FFPE) tumor tissues and 156 isolated cells from tumors was subjected to a prototype 30x whole-genome bisulfite sequencing (WGBS) assay, as previously reported in the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978). Two independent TOO models, one with and one without the methylation database, were fitted on training samples; each was used to predict on the test set. A WGBS classifier was used to detect cancer at 98% specificity; reported TOO results reflect percent agreement between predicted and true TOO among those detected cancers (166 cases: 81 stage I-III, 69 stage IV, 16 non-informative). Results: Genome-wide methylation data generated from this database allowed fragment-level analysis and coverage of ~30 million CpGs across the genome (~60-fold greater than array-based approaches). Incorrect TOO assignments decreased by 35% (20% to 13%) after incorporating methylation database information into TOO classification. Improvement was observed across all cancer types and was consistent in early-stage cancers (stage I-III). Respective performances in breast cancer (n = 23) were 87% vs 96%; in lung cancer (n = 32) were 85% vs 88%; in hepatobiliary (n = 10) were 70% vs 90%; and in pancreatic cancer (n = 17) were 94% vs 100%. Results using an optimized approach informed by these results in a large cohort of CCGA participants will be reported. Conclusions: Incorporating data from a large methylation database improved TOO performance in multiple cancer types. This supports feasibility of this methylation-based approach as an early cancer detection test across cancer types. Clinical trial information: NCT02889978.

Abstract CCGA [NCT02889978] is the largest study of cfDNA-based early cancer detection; the first CCGA learnings from multiple cfDNA assays are reported here. This prospective, multi-center, observational study has enrolled 10,012 of 15,000 demographically-balanced participants at 141 sites. Blood was collected from participants with newly diagnosed therapy-naive cancer (C, case) and participants without a diagnosis of cancer (noncancer [NC], control) as defined at enrollment. This preplanned substudy included 878 cases, 580 controls, and 169 assay controls (n=1627) across 20 tumor types and all clinical stages. All samples were analyzed by: 1) Paired cfDNA and white blood cell (WBC)-targeted sequencing (60,000X, 507 gene panel); a joint caller removed WBC-derived somatic variants and residual technical noise; 2) Paired cfDNA and WBC whole-genome sequencing (WGS; 35X); a novel machine learning algorithm generated cancer-related signal scores; joint analysis identified shared events; and 3) cfDNA whole-genome bisulfite sequencing (WGBS; 34X); normalized scores were generated using abnormally methylated fragments. In the targeted assay, non-tumor WBC-matched cfDNA somatic variants (SNVs/indels) accounted for 76% of all variants in NC and 65% in C. Consistent with somatic mosaicism (i.e., clonal hematopoiesis), WBC-matched variants increased with age; several were non-canonical loss-of-function mutations not previously reported. After WBC variant removal, canonical driver somatic variants were highly specific to C (e.g., in EGFR and PIK3CA, 0 NC had variants vs 11 and 30, respectively, of C). Similarly, of 8 NC with somatic copy number alterations (SCNAs) detected with WGS, 4 were derived from WBCs. WGBS data revealed informative hyper- and hypo-fragment level CpGs (1:2 ratio); a subset was used to calculate methylation scores. A consistent “cancer-like” signal was observed in &lt;1% of NC participants across all assays (representing potential undiagnosed cancers). An increasing trend was observed in NC vs stages I-III vs stage IV (nonsyn. SNVs/indels per Mb [Mean±SD] NC: 1.01±0.86, stages I-III: 2.43±3.98; stage IV: 6.45±6.79; WGS score NC: 0.00±0.08, I-III: 0.27±0.98; IV: 1.95± 2.33; methylation score NC: 0±0.50; I-III: 1.02±1.77; IV: 3.94±1.70). These data demonstrate the feasibility of achieving &gt;99% specificity for invasive cancer, and support the promise of cfDNA assay for early cancer detection. Additional data will be presented on detected plasma:tissue variant concordance and on multi-assay modeling. Citation Format: Alexander A. Aravanis, Geoffrey R. Oxnard, Tara Maddala, Earl Hubbell, Oliver Venn, Arash Jamshidi, Ling Shen, Hamed Amini, John A. Beausang, Craig Betts, Daniel Civello, Konstantin Davydov, Saniya Fazullina, Darya Filippova, Sante Gnerre, Samuel Gross, Chenlu Hou, Roger Jiang, Byoungsok Jung, Kathryn Kurtzman, Collin Melton, Shivani Nautiyal, Jonathan Newman, Joshua Newman, Cosmos Nicolaou, Richard Rava, Onur Sakarya, Ravi Vijaya Satya, Seyedmehdi Shojaee, Kristan Steffen, Anton Valouev, Hui Xu, Jeanne Yue, Nan Zhang, Jose Baselga, Rosanna Lapham, Daron G. Davis, David Smith, Donald Richards, Michael V. Seiden, Charles Swanton, Timothy J. Yeatman, Robert Tibshirani, Christina Curtis, Sylvia K. Plevritis, Richard Williams, Eric Klein, Anne-Renee Hartman, Minetta C. Liu. Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) [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 LB-343.

LBA8501 Background: Plasma cfDNA genomic analysis is used widely for the care of advanced lung cancer, but its suitability for early stage lung cancer detection is not well established. CCGA (NCT02889978) is a prospective, multi-center, observational study launched for the development of a noninvasive assay for cancer detection. Methods: Blood was prospectively collected (N = 1627) from 749 controls (no cancer diagnosis) and 878 participants (pts) with newly-diagnosed untreated cancer in this preplanned substudy, including 127 pts with lung cancer. Three prototype sequencing assays were performed: paired cfDNA and white blood cell (WBC) targeted sequencing (507 genes, 60,000X) for single nucleotide variants/indels; paired cfDNA and WBC whole genome sequencing (WGS) for copy number variation (30X); and cfDNA whole genome bisulfite sequencing (WGBS) for methylation (30X). For each assay, a classification model using 10-fold cross-validation was developed for all pts with cancer, then evaluated in the pts with lung cancer; sensitivity was estimated at 95% specificity. Results: We evaluated pts with lung cancer (127) and a subset of controls (580) with similar ages (mean±SD yrs: 67±9, 60±13), 85% and 43% were ever-smokers, and 46% and 22% were men, respectively. Of 3055 nonsynonymous mutations detected across 122 evaluable pts with lung cancer, > 50% were detected in WBC consistent with clonal hematopoiesis (CH). Accounting for CH, sensitivity in 63 stage I-IIIA pts evaluable across all 3 assays was 48% (35-61, targeted), 54% (41-67, WGS), and 56% (43-68, WGBS); in 54 stage IIIB-IV pts it was 85% (73-93, targeted), 91% (80-97, WGS), and 93% (82-98, WGBS) . Similar sensitivities were observed across histological subtypes (adenocarcinoma, squamous cell, small cell). Comparison to tumor WGS and multi-assay classification will be reported. Conclusions: Early stage lung cancers are detectable in cfDNA using a genome-wide sequencing approach. For lung cancer detection using targeted assays, CH must be accounted for to minimize false positives. Assay optimization is ongoing to allow further clinical development in the intended use population. Clinical trial information: NCT02889978.

Abstract Background: Mammography (digital 2D or digital 3D/tomosynthesis) is the cornerstone of current screening strategies for breast cancer, but new approaches are needed to further reduce the proportion of cancers diagnosed at advanced stages and more effectively identify those women in need of additional testing and biopsies. Circulating cell-free nucleic acids (cfNAs) shed from tumors, isolated from peripheral blood, and analyzed with ultra-deep and broad sequencing of cancer-associated genes, have great potential for early cancer detection. The ultimate goal is to develop blood cfNA cancer screening tests for use in conjunction with established risk factors and/or radiographic features for improved cancer detection. Development of these tests requires large, well-annotated cohorts of asymptomatic participants with adequate volumes of prediagnostic blood. The STRIVE Study cohort was recently established to train and validate cfNA-based tests for early detection of breast cancer. Eligibility criteria and trial design: The STRIVE Study is a new prospective, multi-ethnic mammography cohort that will recruit 120,000 subjects from 15+ US breast cancer screening centers (including Mayo Clinic and Sutter Health sites). Eligibility criteria require only that a participant has a scheduled routine screening mammogram at a participating center and has not received a biopsy prior to the research blood draw. Participants are recruited within 28 days of screening mammography (digital or tomosynthesis), consent electronically, provide blood samples, and complete an on-line risk factor questionnaire. Participants will be followed for all cancer diagnoses, cancer recurrences, and death for at least 5 years. Pertinent medical record information, imaging findings (including breast density), and follow-up data will be transferred electronically to a central database throughout the study period. Additional blood samples will be collected from participants with abnormal mammogram results, or who are diagnosed with cancer, to document and better understand the evolution of cfNA signals. Recruitment began in February 2017. Primary Aims: To train and validate a cfNA blood-based test to identify breast cancer overall in a cohort of women undergoing screening mammography. Statistical Methods: The study will be divided into a training phase (1/3 of participants) and an independent clinical validation phase (remaining 2/3 of participants). In the training phase, statistical machine learning techniques will be used to develop algorithms incorporating cfNA signals, clinical characteristics, or radiological features. In the validation phase, the prespecified locked algorithm developed from the training phase will be clinically validated in an independent group of women. Contact information for people with a specific interest in the trial: Additional details regarding the STRIVE Study are available on the ClinicalTrials.gov website (NCT03085888). For site-specific questions, please call 844-366-9738 for the Mayo Clinic and 1-855-578-7483 for Sutter Health. Citation Format: Liu MC, Cummings S, Vachon CM, Kerlikowske K, Couch FJ, Morris EA, Olson JE, Polley EC, Conners AL, Ellis RL, Patel B, Maimone IV S, Zhang N, Hamilton S, Clarke CA, Allen BA, Maddala T, Hartman A-R. Development of cell-free nucleic acid-based tests for early detection of breast cancer: The STRIVE study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr OT3-02-01.