Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA

Abstract

Background: Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity.

Participants and methods: The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets. Plasma cfDNA underwent bisulfite sequencing targeting a panel of >100 000 informative methylation regions. A classifier was developed and validated for cancer detection and tissue of origin (TOO) localization.

Results: Performance was consistent in training and validation sets. In validation, specificity was 99.3% [95% confidence interval (CI): 98.3% to 99.8%; 0.7% false-positive rate (FPR)]. Stage I-III sensitivity was 67.3% (CI: 60.7% to 73.3%) in a pre-specified set of 12 cancer types (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach), which account for ∼63% of US cancer deaths annually, and was 43.9% (CI: 39.4% to 48.5%) in all cancer types. Detection increased with increasing stage: in the pre-specified cancer types sensitivity was 39% (CI: 27% to 52%) in stage I, 69% (CI: 56% to 80%) in stage II, 83% (CI: 75% to 90%) in stage III, and 92% (CI: 86% to 96%) in stage IV. In all cancer types sensitivity was 18% (CI: 13% to 25%) in stage I, 43% (CI: 35% to 51%) in stage II, 81% (CI: 73% to 87%) in stage III, and 93% (CI: 87% to 96%) in stage IV. TOO was predicted in 96% of samples with cancer-like signal; of those, the TOO localization was accurate in 93%.

Conclusions: cfDNA sequencing leveraging informative methylation patterns detected more than 50 cancer types across stages. Considering the potential value of early detection in deadly malignancies, further evaluation of this test is justified in prospective population-level studies.

Keywords: cancer; cell-free DNA; methylation; next-generation sequencing.

Figure 1.. The CCGA study for development and validation of a cfDNA-based assay for multi-cancer detection.

(A) CCGA study design. The CCGA study included three pre-specified sub-studies designed to discover, train, and validate an assay for multi-cancer detection and localization. The burgundy, shaded boxes highlight the second sub-study, which is the focus of this report. (B) Methylation biology discriminates cancer from non-cancer. One circulating cfDNA fragment is represented on the top left; individual CpGs are indicated as burgundy (methylated) or teal (unmethylated) circles. This assay interrogated fragment-level methylation patterns as indicated on the bottom left (‘Fragment-Level CpG Sites’). In non-cancer participants (top right), cfDNA is shed from cells across the body including WBCs and is present in plasma. These DNA fragments retain methylation marks from the originating cells as indicated in this example from a region on chromosome 10. Individual cfDNA fragment sequencing reads are indicated as horizontal lines of differing sizes and are aligned vertically. In non-cancer participants, these fragments are largely unmethylated as indicated by the almost uniformly teal fragments. In a participant with lung cancer (bottom right), the plasma contains a mix of methylated (burgundy) and unmethylated (teal) fragments as the circulating cfDNA is a mixture of tumor cfDNA and cfDNA from other cells in the body. Sequencing of the tumor tissue sample confirms that this region is almost entirely methylated as indicated. Note that tumor tissue is not a requirement for this assay but is illustrative. (C) Target selection. A large database of methylation patterns (‘data input types’) was constructed from WGBS analysis of cfDNA and tissue samples from the CCGA study as well as WGBS analysis of a set of commercially sourced tissue samples. Systematic examination of the fragment-level methylation signature (‘methylation information type’) from these samples allowed the identification of a large number of genomic regions (‘target selection’) containing informative biological signatures of cancer and TOO.

Figure 2.. Pre-classifier sample preparation and preprocessing overview.

Illustration of how cfDNA fragments from the blood are processed: cfDNA was extracted from plasma, subjected to bisulfite treatment, and regions of interest were pulled down, followed by sequencing and alignment. In this way the methylation state of fragments was obtained.

Figure 3.. Participant disposition.

A total of 4841 participants (2836 cancer, 2005 non-cancer) from the CCGA study and 2202 non-cancer participants from the STRIVE study were included in this pre-specified analysis. Of these, 3133 samples from CCGA were allocated to training (1742 cancer, 1391 non-cancer) and 1354 were allocated to validation (740 cancer, 614 non-cancer); 1587 samples from STRIVE were allocated to training and 615 to validation. STRIVE non-cancer samples were used to train the classifier and to ensure >99% specificity was achieved with >90% confidence (see Methods, supplementary information, available at Annals of Oncology online). Participant disposition is indicated. Overall, 3052 samples in training (1531 cancer, 1521 non-cancer) and 1264 samples in validation (654 cancer, 610 non-cancer) were analyzable and in the pre-specified primary analysis population. Samples reserved for pre-specified future analyses (as indicated) included, for example, samples lacking 1-year follow-up and samples from participants with carcinoma in situ (CIS) (see Methods).

Figure 4.. Targeted methylation cfDNA test performance.

(A) Specificity. Specificity was >99% in the training and validation sets. Importantly, this represents a consistent, single false-positive rate (FPR) across the >50 cancer types in this study. (B) Sensitivity. Sensitivity (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets. It excludes 45 samples in training and 21 samples in validation without stage information (e.g. leukemias). (C) Tissue of origin. Tissue of origin (TOO) accuracy (y-axis) is reported by clinical stage (x-axis) in the pre-specified cancer types (left panel) and in all cancer types (right panel) for training and validation. Numbers indicate samples in training|validation sets.

Figure 5.. Sensitivity in individual tumors by stage.

Sensitivity at 99.8% specificity (training) or 99.3% specificity (validation) with 95% confidence intervals is reported for individual cancer types with at least 50 samples. Clinical stage is indicated below the plots as is the number of samples in training and validation (separated by a vertical line).

Figure 6.. Tissue of origin accuracy by individual cancer type in the training and validation sets.

Confusion matrices representing the accuracy of tissue of origin (TOO) localization in the (A) training and (B) validation sets. Agreement between the actual (x-axis) and predicted (y-axis) TOO per sample using the targeted methylation classifier is depicted. Color corresponds to the proportion of predicted TOO calls. Included participants (training: n = 844, validation: n = 359) are those with cancer predicted as having cancer at 99.8% specificity (training) or 99.3% specificity (validation). The TOO calls were assigned in 95% (806/844) of cases in training and in 96% (344/359) of cases in validation; calls were correct in 92% (744/806) of cases in training and in 93% (321/344) of cases in validation.