Citations

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.

Personalized treatment of genetically stratified subgroups has the potential to improve outcomes in many malignant tumors. This study distills clinically meaningful prognostic/predictive genomic marker for cervical adenocarcinoma using signature genomic aberrations and single-point nonsynonymous mutation-specific droplet digital PCR (ddPCR). Mutations in PIK3CA E542K, E545K, or H1047R were detected in 41.7% of tumors. PIK3CA mutation detected in the patient’s circulating DNA collected before treatment or during follow-up was significantly associated with decreased progression-free survival or overall survival. PIK3CA mutation in the circulating DNA during follow-up after treatment predicted recurrence with 100% sensitivity and 64.29% specificity. It is the first indication of the predictive power of PIK3CA mutations in cervical adenocarcinoma. The work contributes to the development of liquid biopsies for follow up surveillance and a possibility of tailoring management of this particular women’s cancer.

Background: While NKX2-5 plays pivotal roles in human cardiac development and disease, no ChIP-Seq studies of NKX2-5 in human cardiac tissues currently exist, resulting in an incomplete understanding of its direct gene targets. Modern computational methods which identify binding sites using transcription factor motif and tissue-specific experimental data can help to fill this knowledge gap. Objective: To use computational methods to identify likely NKX2-5 binding sites and downstream gene targets using human heart-specific experimental data. Methods: Human cardiomyocyte DNAse hypersensitivity data (2 replicates) were downloaded from the Encyclopedia of DNA Elements (ENCODE) database. The position weight matrix (PWM) representing the transcription factor motif of NKX2-5 was obtained from JASPAR. We applied the Protein Interaction Quantification (PIQ) algorithm to detect NKX2-5 binding sites using the PWM and DNAse hypersensitivity data as inputs. RNA-Seq data from 108 human heart-specific samples (atrial appendages and left ventricles) were downloaded from the Genotype-Tissue Expression (GTEx) database. Protein-coding genes significantly expressed in the heart (RPKM <= 1 based on the GTEx RNA-Seq data) that were within 100kb of the predicted binding sites were then identified. Pathway analysis of these genes was performed using Ingenuity Pathway Analysis (IPA). Results: 1283 binding sites for NKX2-5 were discovered by PIQ. Of 12698 protein-coding, heart-expressed genes, 625 were within 100kb of these binding sites. The identified genes were highly enriched in physiologic categories like “Vasculogenesis” and “Development of Cardiovascular Tissue” (p = 2.36x10-9 and 2.31x10-8 respectively). Notable genes included: cardiac transcription factors (MEF2A, TBX20); growth factors (TGFB2, BMP2); muscle and ion channel function (ACTA2, BIN1), and; calcium signaling (CALM2, CAMK2D). Conclusion: By using computational analyses of transcription factor motif and human heart-specific experimental data, we have identified candidate downstream targets of NKX2-5. Future work will include validation studies in an external cohort and analysis of associations between these candidate genes and cardiac disease.

A usual interstitial pneumonia (UIP) imaging pattern can be seen in both idiopathic pulmonary fibrosis (IPF) and connective tissue disease-related interstitial lung disease (CTD-ILD). The purpose of this multicenter study was to assess whether quantitative imaging data differ between IPF and CTD-ILD in the setting of UIP. Patients evaluated at two medical centers with CTD-ILD or IPF and a UIP pattern on CT or pathology served as derivation and validation cohorts. Chest CT data were quantitatively analyzed including total volumes of honeycombing, reticulation, ground-glass opacity, normal lung, and vessel related structures (VRS). VRS was compared with forced vital capacity percent predicted (FVC%) and percent predicted diffusing capacity of the lungs for carbon monoxide (DLCO%). There were 296 subjects in total, with 40 CTD-ILD and 85 IPF subjects in the derivation cohort, and 62 CTD-ILD and 109 IPF subjects in the validation cohort. VRS was greater in IPF across the cohorts on univariate (p < 0.001) and multivariable (p < 0.001–0.047) analyses. VRS was inversely correlated with DLCO% in both cohorts on univariate (p < 0.001) and in the derivation cohort on multivariable analysis (p = 0.003) but not FVC%. Total volume of normal lung was associated with DLCO% (p < 0.001) and FVC% (p < 0.001–0.009) on multivariable analysis in both cohorts. VRS appears to have promise in differentiating CTD-ILD from IPF. The underlying pathophysiological relationship between VRS and ILD is complex and is likely not explained solely by lung fibrosis.

Introduction: There are many treatment options for patients who have osteonecrosis of the femoral head (ONFH) and management strategies vary widely both among and within individual countries. Although many researchers have attempted to elucidate the optimal strategies for managing this disease, the lack of large-scale randomized control trials and the lack of agreement on disease staging have curtailed the development of clear-cut guidelines. Materials and Methods: The Association Research Circulation Osseous (ARCO) group sought to address three questions for the management of patients who have ONFH: 1) What imaging studies are most sensitive and specific for the diagnostic evaluation of patients who have ONFH?; 2) What is the best treatment strategy for preventing disease progression in patients who have pre-collapse lesions?; and 3) What is the best treatment strategy for patients who have post-collapse disease? The Patient, Intervention, Comparison, and Outcome (PICO) format was used to formulate the search strategy for each research question. A systematic review will be performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. ARCO participants have been allocated to three groups, each representing one of the PICO questions. After qualitative and quantitative analysis of the data extracted from studies pertaining to each of the three research questions, a set of evidence-based clinical practice guidelines will be proposed for the management of patients who have ONFH. Discussion: It is not always clear which treatment method is optimal for the management of ONFH. Thus, many surgeons have developed and performed various procedures based on patient-specific factors. As there is no consensus on the optimal treatment for various stages of disease, it was clear that developing evidence-based clinical practice guidelines would provide more structure and uniformity to management of these patients. Therefore, the results of this systematic review will lead to the development guidelines that may improve patient-care strategies and result in better outcomes for patients who have ONFH.

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.

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.

Abstract Background This secondary image analysis of a randomized trial of proton radiotherapy (PT) versus photon intensity-modulated radiotherapy (IMRT) compares tumor progression based on clinical radiological assessment versus Response Assessment in Neuro-Oncology (RANO). Methods Eligible patients were enrolled in the randomized trial and had MR imaging at baseline and follow-up beyond 12 weeks from completion of radiotherapy. “Clinical progression” was based on a clinical radiology report of progression and/or change in treatment for progression. Results Of 90 enrolled patients, 66 were evaluable. Median clinical progression-free survival (PFS) was 10.8 (range: 9.4–14.7) months; 10.8 months IMRT versus 11.2 months PT (P = .14). Median RANO-PFS was 8.2 (range: 6.9, 12): 8.9 months IMRT versus 6.6 months PT (P = .24). RANO-PFS was significantly shorter than clinical PFS overall (P = .001) and for both the IMRT (P = .01) and PT (P = .04) groups. There were 31 (46.3%) discrepant cases of which 17 had RANO progression more than a month prior to clinical progression, and 14 had progression by RANO but not clinical criteria. Conclusions Based on this secondary analysis of a trial of PT versus IMRT for glioblastoma, while no difference in PFS was noted relative to treatment technique, RANO criteria identified progression more often and earlier than clinical assessment. This highlights the disconnect between measures of tumor response in clinical trials versus clinical practice. With growing efforts to utilize real-world data and personalized treatment with timely adaptation, there is a growing need to improve the consistency of determining tumor progression within clinical trials and clinical practice.