Tummala, Mohan K.

There are four solid tumors with common screening options in the average-risk population aged 21 to 75 years (breast, cervical, colorectal, and, based on personalized risk assessment, prostate), but many cancers lack recommended population screening and are often detected at advanced stages when mortality is high. Blood-based multi-cancer early detection tests have the potential to improve cancer mortality through additional population screening. Reported here is a post-hoc analysis from the third Circulating Cell-free Genome Atlas substudy to examine multi-cancer early detection test performance in solid tumors with and without population screening recommendations and in hematologic malignancies. Participants with cancer in the third Circulating Cell-free Genome Atlas substudy analysis were split into three subgroups: solid screened tumors (breast, cervical, colorectal, prostate), solid unscreened tumors, and hematologic malignancies. In this post hoc analysis, sensitivity is reported for each subgroup across all ages and those aged ⩾50 years overall, by cancer, and by clinical cancer stage. Aggregate sensitivity in the solid screened, solid unscreened, and hematologic malignancy subgroups was 34%, 66%, and 55% across all cancer stages, respectively; restricting to participants aged ⩾50 years showed similar aggregate sensitivity. Aggregate sensitivity was 27%, 53%, and 60% across stages I to III, respectively. Within the solid unscreened subgroup, aggregate sensitivity was >75% in 8/18 cancers (44%) and >50% in 13/18 (72%). This multi-cancer early detection test detected cancer signals at high (>75%) sensitivity for multiple cancers without existing population screening recommendations, suggesting its potential to complement recommended screening programs. Clinical trial identifier: NCT02889978.

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.

3072 Background: The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) previously demonstrated that a blood-based multi-cancer early detection (MCED) test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin. Cancer classes were defined within the CCGA study for sensitivity reporting. Separately, cancer types defined by the American Joint Committee on Cancer (AJCC) criteria, which outline unique staging requirements and reflect a distinct combination of anatomic site, histology and other biologic features, were assigned to each cancer participant using the same source data for primary site of origin and histologic type. Here, we report CCGA ‘cancer class’ designation and AJCC ‘cancer type’ assignment within the third and final CCGA3 validation substudy to better characterize the diversity of tumors across which a cancer signal could be detected with the MCED test that is nearing clinical availability. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall population N = 15,254). Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach, and test performance, including sensitivity, was assessed. For sensitivity reporting, CCGA cancer classes were assigned to cancer participants using a combination of the type of primary cancer reported by the site and tumor characteristics abstracted from the site pathology reports by GRAIL pathologists. Each cancer participant also was separately assigned an AJCC cancer type based on the same source data using AJCC staging manual (8th edition) classifications. Results: A total of 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). Sensitivity was reported for 24 cancer classes (sample sizes ranged from 10 to 524 participants), as well as an “other” cancer class (59 participants). According to AJCC classification, the MCED test was found to detect cancer signals across 50+ AJCC cancer types, including some types not present in the training set; some cancer types had limited representation. Conclusions: This MCED test that is nearing clinical availability and was evaluated in the third CCGA substudy detected cancer signals across 50+ AJCC cancer types. Reporting CCGA cancer classes and AJCC cancer types demonstrates the ability of the MCED test to detect cancer signals across a set of diverse cancer types representing a wide range of biologic characteristics, including cancer types that the classifier has not been trained on, and supports its use on a population-wide scale. Clinical trial information: NCT02889978.

3071 Background: Disparities in cancer screening and outcomes based on factors such as gender, socioeconomic status, and race/ethnicity are well documented.1 The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based multi-cancer early detection (MCED) test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict cancer signal origin. Findings stratified by race/ethnicity from the third and final CCGA validation sub-study are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified exploratory analysis from the third substudy, key objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, and sensitivity by clinical stage) among racial/ethnic groups. Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254). The groups stratified by race/ethnicity were White Non-Hispanic, Black Non-Hispanic, Other Non-Hispanic (including but not limited to Asian, Native Hawaiian, Pacific Islander, American Indian, Alaska Native), Hispanic (all races), and Other/unknown. The study was not powered to detect statistical differences between groups. Results: Cancer and non-cancer groups were predominantly White (2316/2823, 82.0% and 996/1254, 79.4%, respectively). Across racial/ethnic groups, specificity for cancer signal detection was 99.6% (White Non-Hispanic: 992/996, 95% confidence interval [99.0-99.8%]), 100.0% (Black Non-Hispanic: 85/85 [95.7-100.0%]), 100.0% (Other Non-Hispanic: 33/33 [89.6-100.0%]), 98.1% (Hispanic: 101/103 [93.2-99.5%]), and 100% (Other/unknown: 37/37 [90.6-100.0%]). Despite slight differences in cancer type and staging across racial/ethnic groups, overall sensitivity for cancer signal detection among groups ranged from 43.9% to 63.0% (White Non-Hispanic: 50.5%, 1169/2316 [48.4-52.5%], Black Non-Hispanic: 53.9%, 104/193 [46.8-60.8%], Other Non-Hispanic: 43.9%, 25/57 [31.8-56.7%], Hispanic: 63.0%, 121/192 [56.0-69.5%], and Other/unknown: 52.3%, 34/65 [40.4-64.0%]). For all racial/ethnic groups, sensitivity generally increased with clinical stage (with limited exceptions at Stage IV in some groups with small sample sizes). Conclusions: The MCED test demonstrated consistent specificity and sensitivity across racial/ethnic groups, though results are limited by sample size for some groups. These findings indicate broad applicability and support clinical implementation of this MCED test on a population scale. 1. Zavela et al. Brit J Cancer 2021. Clinical trial information: NCT02889978.

283 Background: Cancers of the esophagus, stomach, pancreas, gallbladder, liver, bile duct, colon and rectum will account for 17% of incident cancer diagnoses and 26% of cancer-related deaths in the US in 2019. We developed a methylation-based cfDNA early multi-cancer detection test that also can predict the tissue of origin (TOO) of these and other cancers types; performance of this test for gastrointestinal (GI) tract cancers is reported here. Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, multi-center, observational, case-control study with longitudinal follow-up, enrolling individuals with cancer ( > 20 cancers, all stages, newly diagnosed) and without cancer. Plasma cfDNA was subjected to a cross-validated targeted methylation (TM) sequencing assay. Methylation fragments were combined across targeted genomic regions and assigned a probability of cancer and a predicted TOO. GI cancer classes were upper GI (esophagus/stomach, n = 67), pancreas/gallbladder/extrahepatic bile duct (n = 95), liver/intrahepatic bile duct (n = 29), and colon/rectum (n = 121). Results: Detection across all GI cancers was 82% (95% CI 77-86) at a > 99% pre-set specificity. Overall predicted TOO accuracy was 92% (88-95) among the samples for which TOO was predicted (6/255 had indeterminate predicted TOO). The table shows performance by GI cancer type. Conclusions: Simultaneous detection at high specificity ( > 99%) of multiple cancer types, including GI cancers across stages at high sensitivity (82%), was shown using TM analysis of cfDNA. Accurate (92%) localization of cancers to specific regions of the GI tract was also achieved. Detection of multiple GI cancers from a single noninvasive blood test could be a practical method for detecting GI and other cancers, and may facilitate diagnostic work-ups. Clinical trial information: NCT02889978. [Table: see text]

Abstract Background: Pracinostat, a potent oral Class I, II, IV histone deacetylase (HDAC) inhibitor, exhibited modest single-agent activity in relapsed AML. Inhibition of histone deacetylation and DNA hypermethylation induces re-expression of silenced genes in myeloid malignancies in a synergistic fashion. A pilot study of pracinostat with azacitidine (AZA) in myelodysplastic syndromes showed the combination to be active and well tolerated (Blood2012;120:3821). We conducted a Phase 2 study to evaluate pracinostat + AZA in patients ≥65 years with AML not eligible for induction chemotherapy. Minimum 1-year survival data were reported previously (Blood 2015;126:453). Here we report long-term survival and response analyses. Methods: Main eligibility: Age ≥65, untreated de novo or secondary AML, ≥20% bone marrow (BM) blasts, not candidate for intensive therapy due to co-morbidities and/or AML features, intermediate or high-risk cytogenetics, and no prior treatment with HDAC inhibitors or hypomethylating agents. Treatment consisted of pracinostat 60 mg orally 3 days/week on alternate days for 3 weeks and AZA 75 mg/m2 subcutaneously or intravenously daily for 7 days, with cycles repeated every 28 days until disease progression, lack of response or poor tolerability. The primary endpoint was a composite complete response rate (cCR) of CR + CRi+ MLFS by IWG criteria. Response was assessed at the end of Cycles 2, 4, 6 and then every 3 cycles or when clinically indicated. Secondary endpoints included overall response rate (cCR + PR), duration of response and overall survival (OS). Study registered under NCT01912274. Results: Between December 2013 and December 2014, 50 patients were enrolled at 15 U.S. sites. Baseline characteristics: Median age 75 years (range 66-84 years); 33 de novo and 17 secondary AML; 27 intermediate-risk and 21 high-risk cytogenetics and 2 not known; median BM blasts 40% (range 20%-89%), ECOG performance status 0-1 in 84% and 2 in 16%. The median number of treatment cycles was 6.5 (range 1-24+). A CR was achieved in 21 patients (42%), CRi in 2 (4%), and MLFS in 3 (6%) for a cCR rate of 52%. Median time to BM blasts <5% was 57 days (range 25-243 days) and exceeded 168 days (6 cycles) in 3 patients (12%). Median duration of cCR is 13.2 months (95% CI 10.9-21.5 months), with 6 of 26 patients (23%) continuing on therapy for a periods ranging from 20.5 to 29.5 months. With a median follow-up of 21 months, the median OS is 19.1 months (95% CI, 10.0-not reached). One-year and 2-year OS are 62% and 45%. Non-protocol therapies (NPT) were administered in 23 patients (46%). There was no effect on OS after censoring survival duration at the start of NPT. Response and OS by population subsets are summarized in Table 1. Non hematologic adverse events Grade ≥3 reported in >5% of patients: Fatigue 34%; anorexia 10%, cellulitis, pneumonia, asthenia and sepsis in 8%; urinary tract infection, nausea, back pain, hypoxia, hyponatremia, and syncope in 6%. Grade ≥ 3 hematologic toxicities in> 5% of patients: thrombocytopenia 46%; febrile neutropenia 44%; neutropenia 36%; anemia 30%; pancytopenia 6%. The 30-day and 60-day mortality rates were 2% and 10%. Conclusions: Pracinostat + AZA led to a high rate of responses in elderly patients with AML. Responses were durable and observed irrespective of age, cytogenetics risk, ECOG performance status, and de novo or secondary AML. Marrow remission was typically achieved early, but prolonged exposure was required in some patients to maximize response. The results compare favorably with historical single-agent AZA data in a similar AML population. A phase 3 study of AZA +/- pracinostat in untreated patients with AML unfit for standard induction chemotherapy is starting. Table 1. Response and Survival According to Baseline Characteristics Table 1. Response and Survival According to Baseline Characteristics Disclosures Atallah: Incyte: Consultancy; CTI biopharma: Consultancy; Novartis: Consultancy; BMS: Consultancy; Pfizer: Other: Grant review; Takeda: Research Funding; Ariad: Honoraria. Arellano:Cephalon: Research Funding. Odenike:Geron: Research Funding; Incyte: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Suneisis: Honoraria, Membership on an entity's Board of Directors or advisory committees; CTI/Baxter: Honoraria, Membership on an entity's Board of Directors or advisory committees; Spectrum: Honoraria, Membership on an entity's Board of Directors or advisory committees; Algeta: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ghalie:MEI Pharma: Employment.

Abstract Pracinostat, a potent oral pan-histone deacetylase inhibitor with modest single-agent activity in acute myeloid leukemia (AML), has shown synergistic antitumor activity when combined with azacitidine. This single-group, multicenter phase 2 study assessed the safety and efficacy of pracinostat combined with azacitidine in patients who were at least 65 years old with newly diagnosed AML and who were ineligible for standard induction chemotherapy. Patients received pracinostat 60 mg/d, 3 d/wk, for 3 consecutive weeks, plus azacitidine 75 mg/m2 daily for 7 days in a 28-day cycle. Primary endpoints were complete remission (CR), CR with incomplete count recovery (CRi), and morphologic leukemia-free state (MLFS) rates of the combination. Secondary endpoints included safety, progression-free survival (PFS), and overall survival (OS) of the regimen. Fifty patients (33 de novo, 12 secondary, and 5 therapy-related AML) were enrolled. Twenty-six patients (52%) achieved the primary endpoint of CR (42%), CRi (4%), and MLFS (6%). Median OS and PFS were 19.1 months (95% confidence interval [CI], 10-26.5 months) and 12.6 months (95% CI, 10-17.7 months), respectively, with a 1-year OS rate of 62%. Forty-three patients (86%) experienced at least 1 grade 3 or worse treatment-emergent adverse event with the combination, with infections (52%), thrombocytopenia (46%), and febrile neutropenia (44%) reported as the most common toxicities. The 30- and 60-day all-cause mortality rates were 2% and 10%, respectively. DNA sequencing revealed somatic mutations at baseline, and clearance rates correlated with response to treatment. Pracinostat plus azacitidine is a well-tolerated and active regimen in the frontline treatment of older patients with AML unfit for intensive therapy. A larger controlled trial is ongoing. This trial was registered at www.clinicaltrials.gov as #NCT01912274.