Klein, Eric A.

To examine the extent of the evaluation required to achieve diagnostic resolution and the test performance characteristics of a targeted methylation cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test, ~6200 participants ≥50 years with (cohort A) or without (cohort B) ≥1 of 3 additional specific cancer risk factors will be enrolled in PATHFINDER (NCT04241796), a prospective, longitudinal, interventional, multi-center study. Plasma cfDNA from blood samples will be analyzed to detect abnormally methylated DNA associated with cancer (i.e., cancer “signal”) and a cancer signal origin (i.e., tissue of origin). Participants with a “signal detected” will undergo further diagnostic evaluation per guiding physician discretion; those with a “signal not detected” will be advised to continue guideline-recommended screening. The primary objective will be to assess the number and types of subsequent diagnostic tests needed for diagnostic resolution. Based on microsimulations (using estimates of cancer incidence and dwell times) of the typical risk profiles of anticipated participants, the median (95% CI) number of participants with a “signal detected” result is expected to be 106 (87–128). Subsequent diagnostic evaluation is expected to detect 52 (39–67) cancers. The positive predictive value of the MCED test is expected to be 49% (39–58%). PATHFINDER will evaluate the integration of a cfDNA-based MCED test into existing clinical cancer diagnostic pathways. The study design of PATHFINDER is described here.

Abstract Introduction: A test that detects cancer signal across multiple cancer types and predicts signal (tissue) origin (SO) could aid in more efficient diagnostic workup and shorten time to cancer diagnosis in individuals with signs and symptoms.Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, longitudinal, multicenter, case-control study to develop and validate a multi-cancer detection test. The 2nd CCGA substudy utilized a targeted methylation-based cell-free DNA assay and machine learning algorithm and included assessment of test performance (sensitivity and SO prediction accuracy) in a subgroup of participants with clinically presenting cancers (CPCs) that were undiagnosed prior to blood draw. Specificity was assessed in the noncancer group and subgroups with confounding (nonmalignant) conditions (CCs; eg, cirrhosis) and noncancer participants enrolled in hematology clinics (HCs).Results: Specificity was 99.5% (95% confidence interval: 98.2-99.9%; 396/398), 93.8% (71.7-99.7%; 15/16), and 99.3% (96.0-100.0%; 136/137) for the noncancer group, CCs subgroup, and HCs subgroup, respectively. Overall sensitivity among those with CPCs was 66.4% (62.2-70.3%; 344/518). Sensitivity of cancer signal detection increased with increasing clinical stage (Table). SO prediction accuracy was 91.7% (88.3-94.3%; 300/327) among CPC participants with cancers detected, excluding those with multiple or unknown primaries. The test demonstrated prognostic value as detected cancer participants had worse survival probability than those not detected. Conclusions: This multi-cancer detection test detected cancer signals and predicted SO in individuals with CPCs with high specificity. These findings support further clinical development of this multi-cancer detection test that could accelerate the diagnostic resolution of symptomatic cancers. Table. Sensitivity by Clinical Stage Across Cancer Type in Clinically Presenting CancersClinical StagePositive Test/Total Cancer; Sensitivity (95% CI)All*344/518; 66.4% (62.2-70.3%)I33/122; 27.0% (20.0-35.5%)II60/102; 58.8% (49.1-67.9%)III103/121; 85.1% (77.7-90.4%)IV136/147; 92.5% (87.1-95.8%)Not expected to be staged9/21; 42.9% (24.5-63.5%)Non-informative2/4; 50.0% (15.0-85.0%)CI, confidence interval.*One participant who had a positive test result had multiple primaries with clinical stage I and not-expected-to-be-staged. Citation Format: Alan H. Bryce, Minetta C. Liu, Michael V. Seiden, David D. Thiel, Donald Richards, Carlos Becerra, Kathryn N. Kurtzman, Xiaoji Chen, Tony Wu, Quan Zhang, Jingjing Gao, Nan Zhang, Earl Hubbell, Arash Jamshidi, Eric T. Fung, Eric A. Klein. Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers [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 LB058.

Abstract Introduction: A multi-cancer early detection (MCED) test as a complement to existing screening tests could increase the number of cancer cases detected in a population, potentially improving patient outcomes and survival as well as decreasing harmful and aggressive treatments. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based MCED test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict their signal origin. Here, the results of the third and final pre-specified CCGA validation sub-study for a refined MCED test in a large cohort in preparation for clinical use are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall population N=15,254). In this sub-study (n=5309), key primary objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, sensitivity by clinical stage) and signal origin prediction (accuracy). cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning algorithm. The classifier was trained to target a specificity of 99.4% and locked before analysis of the independent validation set. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n=2823; non-cancer: n=1254 with non-cancer status confirmed at year-one follow-up). MCED test results are reported for this confirmed status set. Results: Mean (SD) age in the cancer and non-cancer groups was 62.6 (11.76) and 56.2 (12.63) years, respectively. Specificity for cancer signal detection was 99.5% (1248/1254; 95% confidence interval: 99.0-99.8%). Overall sensitivity for cancer signal detection was 51.5% (1453/2823; 49.6-53.3%); sensitivity increased with stage (Stage I: 16.8% [14.5-19.5%], Stage II: 40.4% [36.8-44.1%], Stage III: 77.0% [73.4-80.3%], Stage IV: 90.1% [87.5-92.2%]). Stage I-III sensitivity was 67.6% (593/877; 64.4-70.6%) in a pre-specified set of 12 high-signal cancers accounting for ~63% of annual US cancer deaths [1] and was 40.7% (863/2118; 38.7-42.9%) in all cancers. Cancer signals were detected across >50 cancer types [2]. Overall accuracy of signal origin prediction in true positives was 88.7% (87.0-90.2%). Conclusions: In this pre-specified, large-scale, clinical validation sub-study of CCGA, the MCED test detected cancer signals across >50 cancer types, which is critical to maximize the number of cancer cases detected in a population. This MCED test performed with high specificity and high accuracy of signal origin prediction. These data lay the foundation for population-scale clinical implementation of this test. 1.US Mortality Data 1969-2016 (www.seer.cancer.gov); based on 2015-2016. 2.Amin et al. CA Cancer J Clin. 2017;67:93e99. Citation Format: Eric A. Klein, Donald Richards, Allen Cohn, Mohan Tummala, Rosanna Lapham, David Cosgrove, Gina Chung, Jessica Clement, Jingjing Gao, Nathan Hunkapiller, Arash Jamshidi, Kathryn Kurtzman, Michael V. Seiden, Charles Swanton, Minetta C. Liu. Clinical validation of a targeted methylation-based multi-cancer early detection test [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 LB013.

Abstract Purpose: We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data. Experimental Design: As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics. Results: Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001). Conclusions: Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

3070 Background: A multi-cancer early detection (MCED) test that uses targeted methylation-based cfDNA technology to detect cancer and predict cancer signal origin (CSO) has potential to efficiently identify malignancies for which effective screening modalities do not exist. A previous version of a blood-based MCED test demonstrated favorable classification and test characteristics. Samples from the ongoing PATHFINDER study were reanalyzed in a prespecified interim analysis to evaluate performance of a more recent version of the test with an updated classifier (eg, updated CSO localization, hematological signal threshold) that is planned for clinical implementation as a general multi-cancer screening tool. Methods: PATHFINDER (NCT04241796) is an interventional, prospective study in which results (cancer signal detected/not detected and predicted CSO) using a previous version of the MCED test are returned to investigators, and those with a signal detected undergo further diagnostic testing. In this prespecified interim analysis, samples from those enrolled as of October 6, 2020 were reanalyzed with the more recent version of the MCED test (these results were not returned to investigators). The positive predictive value (PPV) for cancer detection, overall CSO accuracy, and concordance between the two test versions were assessed. Results: A total of 4011/4047 (99%) participants (pts) were analyzable (mean [SD] age 63.9 [8.7] years, 62% female, 92% white, 24% with prior cancer history, 39% ever smoker [4% current], 6% with genetic cancer predisposition). Cancer signal was detected in 0.95% (38/4011). A total of 27/38 also had signal detected by the previous version of the MCED test, including 19 who reached diagnostic resolution (13 with cancer diagnosis and 6 without); 11/38 were discordant positives. Nine different cancer types were detected in the 13 pts (2 stage I, 3 stage II, 2 stage III, and 3 stage IV); 1 had no AJCC stage expected, 1 metastatic recurrence and 1 stage evaluation underway. A conservative minimal PPV assuming all discordant positives are false positives, was 43.3% (13/30, 95% CI 27.4-60.8%) based on 19 pts with diagnostic resolution and 11 discordant positives. High negative percent agreement (PA) 99.7% (99.5-99.8%) between the two test versions was observed. Positive PA of 43.5% (95% CI, 31.9-55.9%) was consistent with the more stringent threshold for hematologic signal in the recent MCED version, as most discrepant cases had hematologic CSO with the previous MCED test. Among 13 detected cancers, accuracy of the top CSO prediction was 92.3% (12/13, 95% CI 66.7-99.6%). Conclusions: In this prespecified interim analysis, the more recent version of the MCED test detected cancers with high PPV and high accuracy of CSO prediction, supporting readiness for use in clinical practice. Full enrollment cohort data will be available at the meeting. Clinical trial information: NCT04241796.

3010 Background: PATHFINDER (NCT04241796) is an interventional, prospective study evaluating implementation of a blood-based multi-cancer early detection (MCED) test that uses targeted methylation-based cfDNA analysis to detect multiple cancer types and simultaneously predict cancer signal origin (CSO). We present a prespecified interim analysis of PATHFINDER evaluating an MCED test in a clinical setting. Methods: Participants (pts; ≥50y) were enrolled into 2 risk cohorts: non-elevated and elevated (smoking history, prior cancer [ > 3y post treatment], or genetic predisposition). MCED test results (cancer signal detected/not detected) were returned to investigators; pts with a signal detected also received a CSO prediction and underwent further diagnostic testing by their medical team. The primary objective was to assess the extent of diagnostic testing needed to achieve diagnostic resolution (eg, time to resolution, number/type of tests). Secondary endpoints included positive predictive value (PPV) and a measure of test satisfaction (following diagnostic resolution [signal detected] and post test [signal not detected]). Results: PATHFINDER consented 6796 pts before closing accrual on 12/4/20; as of October 6, 2020, 4086 consented, 4047 enrolled, and 4033 analyzable pts were included in the interim analysis (62.4% female, 92.1% white). Two study-related adverse events (anxiety of mild severity) were reported. Cancer signal was detected in 1.5% (62/4033) of pts; 40/62 reached diagnostic resolution to date. Kaplan-Meier estimate of median time to resolution was 78 (95% CI, 54-151) days. Among 40 pts that reached diagnostic resolution, ≥1 imaging test was performed in 93% (37/40); ≥1 invasive procedure was performed in 72% (13/18) versus 18% (4/22) of pts with diagnostic resolution of cancer versus no cancer, respectively. Based on results to date, PPV was 45% (95% CI, 30.7-60.2%; 18/40). Of 18 cancer diagnoses, 11 were solid tumors (3 stage IV, 6 stages I-III, 1 metastatic recurrence, 1 missing stage), and 7 were hematologic malignancies (1 stage IV, 4 stages I-III, 2 without AJCC stage). Accuracy of the top CSO prediction in true positives was 82.4% (95% CI, 59.0-93.8%; 14/17). Most pts were satisfied with the test (43.7% extremely satisfied, 30.7% very satisfied, 14.6% satisfied). Signal detection rate and test satisfaction were similar in the 2 risk cohorts; PPV tended to be higher in the elevated risk cohort, as expected. Conclusions: An interim analysis of this return of results study demonstrated promising MCED test results. Of 40 pts achieving diagnostic resolution, nearly half had a diagnostic workup confirming cancer; CSO was predicted with high accuracy for detected cancers. Taken together with the rarity of adverse events and high test satisfaction, these results support the feasibility of clinical implementation. Full enrollment cohort data will be available at the meeting. Clinical trial information: NCT04241796.

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

Abstract Purpose: The role of immune-oncologic mechanisms of racial disparities in prostate cancer remains understudied. Limited research exists to evaluate the molecular underpinnings of immune differences in African American men (AAM) and European American men (EAM) prostate tumor microenvironment (TME). Experimental Design: A total of 1,173 radiation-naïve radical prostatectomy samples with whole transcriptome data from the Decipher GRID registry were used. Transcriptomic expressions of 1,260 immune-specific genes were selected to assess immune-oncologic differences between AAM and EAM prostate tumors. Race-specific differential expression of genes was assessed using a rank test, and intergene correlational matrix and gene set enrichment was used for pathway analysis. Results: AAM prostate tumors have significant enrichment of major immune-oncologic pathways, including proinflammatory cytokines, IFNα, IFNγ, TNFα signaling, ILs, and epithelial–mesenchymal transition. AAM TME has higher total immune content score (ICSHIGH) compared with 0 (37.8% vs. 21.9%, P = 0.003). AAM tumors also have lower DNA damage repair and are genomically radiosensitive as compared with EAM. IFITM3 (IFN-inducible transmembrane protein 3) was one of the major proinflammatory genes overexpressed in AAM that predicted increased risk of biochemical recurrence selectively for AAM in both discovery [HRAAM = 2.30; 95% confidence interval (CI), 1.21–4.34; P = 0.01] and validation (HRAAM = 2.42; 95% CI, 1.52–3.86; P = 0.0001) but not in EAM. Conclusions: Prostate tumors of AAM manifest a unique immune repertoire and have significant enrichment of proinflammatory immune pathways that are associated with poorer outcomes. Observed immune-oncologic differences can aid in a genomically adaptive approach to treating prostate cancer in AAM.

Abstract Background: Early detection decreases patient mortality for many cancers, but prevailing paradigms focus on distinct screening modalities for each cancer type. A minimally invasive test that detects multiple tumor types could substantially reduce cancer mortality and improve screening efficiency. In preliminary studies, we showed that a targeted methylation cell-free DNA (cfDNA) test can detect cancer and determine the tissue of origin (TOO) of abnormally methylated cfDNA with high sensitivity and specificity; however, blood was collected at the time of diagnosis and results were not disclosed to inform care. The current study includes disclosure of results to the study investigator to understand the care pathways prompted by a “signal detected” test result, and the benefits, harms, and burdens associated with a multi-cancer detection test. Methods: PATHFINDER (NCT04241796) is a prospective, longitudinal, multi-center clinical study. Approximately 6,200 participants with varying levels of cancer risk will be enrolled at 5-10 clinical sites in the United States. Participants must be ≥50 years of age. If participants have at least 1 of the following–a smoking history of ≥100 cigarettes, a genetic cancer predisposition, or a history of invasive or hematologic malignancy with definitive treatment completed >3 years prior to enrollment–they are eligible for the Elevated Risk Cohort (n = 4340); otherwise, they are eligible for the Non-Elevated Risk Cohort (n = 1860). Individuals being evaluated for cancer or who have a history of invasive or hematologic malignancy within 3 years of enrollment will be excluded. Blood drawn at study sites will be analyzed at GRAIL, Inc. (Menlo Park, CA). Participants and study investigators will be informed of the test results. Participants with “signal not detected” results will be advised to continue routine clinical care, including age- and risk-based cancer screening. Those with “signal detected” test results will be informed of the predicted origin and undergo diagnostic evaluation. The primary study objective is to determine the extent of diagnostic testing required to achieve diagnostic resolution following a “signal detected” test result, defined as the date when the study investigator determines to end diagnostic evaluation triggered by a “signal detected” test result. Health resource utilization, the number and types of tests and time required to reach this point, is the primary endpoint. Secondary objectives include evaluation of test performance (specificity, positive predictive value, and TOO accuracy), and assessment of participants' perceptions about the test and changes in anxiety and health-related quality of life. Exploratory aims include assessment of the number and types of invasive cancer diagnoses among participants with “signal not detected” test results and changes in attitude toward adherence to guideline-recommended screening. Citation Format: Lincoln Nadauld, Charles H. McDonnell III, Minetta C. Liu, Eric Klein, Tomasz M. Beer, Deborah Schrag, Andrew G. Hudnut, Richard Whittington, Bruce Taylor, Joanna Tierney, Catherine Marinac, Jafi Lipson, Margarita Lopatin, Karen C. Chung, Eric Fung, Anne-Renee Hartman, Geoffrey R. Oxnard. The PATHFINDER Study: Assessment of the implementation of an investigational multi-cancer early detection test into clinical practice [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr CT291.