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PDF file, 1202KB, Supplementary Figure 1. Kaplan-Meier Survival Curves. Supplementary Figure 2. Kaplan-Meier Survival Curves. Supplementary Table 1. Univariate analysis of clinical covariates.

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.

Purpose: This study assesses the ability of multidrug resistance (MDR)–associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy.Experimental Design: We applied a customized TaqMan low density array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes.Results: Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (OS; P = 0.06), four covariates (age, stage, CA125 level, and surgical debulking) do (P = 0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in OS prediction (log-rank statistic P < 0.003). The predictive power of this 11-gene signature was confirmed by dividing high- and low-risk patient groups, as defined by their clinical covariates, into four specific risk groups on the basis of expression levels.Conclusion: This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer. Clin Cancer Res; 18(11); 3197–206. ©2012 AACR.

Supplementary Table 1 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 3 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 2 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Figures from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Emerging blood-based multi-cancer early detection (MCED) tests can detect a variety of cancer types across stages with a range of sensitivity, specificity, and ability to predict the origin of the cancer signal. However, little is known about the general US population's preferences for MCED tests.To quantify preferences for MCED tests among US adults aged 50-80 years using a discrete choice experiment (DCE).To quantify preferences for attributes of blood-based MCED tests, an online DCE was conducted with five attributes (true positives, false negatives, false positives, likelihood of the cancer type unknown, number of cancer types detected), among the US population aged 50-80 years recruited via online panels and social media. Data were analyzed using latent class multinomial logit models and relative attribute importance was obtained.Participants (N = 1700) were 54% female, mean age 63.3 years. Latent class modeling identified three classes with distinct preferences for MCED tests. The rank order of attribute importance based on relative attribute importance varied by latent class, but across all latent classes, participants preferred higher accuracy (fewer false negatives and false positives, more true positives) and screenings that detected more cancer types and had a lower likelihood of cancer type unknown. Overall, 72% of participants preferred to receive an MCED test in addition to currently recommended cancer screenings.While there is significant heterogeneity in cancer screening preferences, the majority of participants preferred MCED screening and the accuracy of these tests is important. While the majority of participants preferred adding an MCED test to complement current cancer screenings, the latent class analyses identified a small (16%) and specific subset of individuals who value attributes differently, with particular concern regarding false-negative and false-positive test results, who are significantly less likely to opt-in.

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.

Study Objective Assess performance of a multi-cancer detection (MCD) test. Design Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a prospective, longitudinal, case-control study. Samples from the second CCGA substudy were used to validate performance of an MCD test, which uses targeted methylation-based cell-free DNA sequencing and machine learning classifiers to detect cancer signal, including from clinically presenting gynecologic cancers, and predict cancer signal origin (CSO). Setting N/A. Patients or Participants MCD test sensitivity and CSO prediction accuracy were evaluated in a subgroup of participants with clinically presenting cancers (CPCs). Specificity was assessed in noncancer participants and a subgroup with significant nonmalignant conditions, including endometriosis. Interventions N/A. Measurements and Main Results Specificity was 99.5% (95% confidence interval [CI]: 98.2-99.9%; 396/398) for the noncancer group and 93.8% (71.7-99.7%; 15/16) for the significant nonmalignant conditions noncancer subgroup. Overall sensitivity was 66.4% (62.2-70.3%; 344/518) for CPC participants, 50.0% (15.0-85.0%; 2/4) for cervical, 70.6% (46.9-86.7%; 12/17) for ovarian, and 25.0% (13.8-41.1%; 9/36) for uterine cancers and varied by stage (Table). CSO prediction accuracy was broadly consistent with CSOs for CPC participants with cancers detected (excluding those with multiple, unknown primaries, or “other” cancer; 91.7% [88.3-94.3%; 300/327]). Conclusion Consistent with the overall CCGA study, this MCD test performed with high specificity and accuracy of CSO prediction in this subgroup analysis of CPCs, including gynecological cancers. Findings support potential use of this test for diagnostic workup of symptomatic disease.