Analytical validation of a multi-cancer early detection test with cancer signal origin using a cell-free DNA-based targeted methylation assay.

The analytical validation is reported for a targeted methylation-based cell-free DNA multi-cancer early detection test designed to detect cancer and predict the cancer signal origin (tissue of origin). A machine-learning classifier was used to analyze the methylation patterns of >105 genomic targets covering >1 million methylation sites. Analytical sensitivity (limit of detection [95% probability]) was characterized with respect to tumor content by expected variant allele frequency and was determined to be 0.07%-0.17% across five tumor cases and 0.51% for the lymphoid neoplasm case. Test specificity was 99.3% (95% confidence interval, 98.6-99.7%). In the reproducibility and repeatability study, results were consistent in 31/34 (91.2%) pairs with cancer and 17/17 (100%) pairs without cancer; between runs, results were concordant for 129/133 (97.0%) cancer and 37/37 (100%) non-cancer sample pairs. Across 3- to 100-ng input levels of cell-free DNA, cancer was detected in 157/182 (86.3%) cancer samples but not in any of the 62 non-cancer samples. In input titration tests, cancer signal origin was correctly predicted in all tumor samples detected as cancer. No cross-contamination events were observed. No potential interferent (hemoglobin, bilirubin, triglycerides, genomic DNA) affected performance. The results of this analytical validation study support continued clinical development of a targeted methylation cell-free DNA multi-cancer early detection test.

Harmonizing Cell-Free DNA Collection and Processing Practices through Evidence-Based Guidance.

Circulating cell-free DNA (cfDNA) is rapidly transitioning from discovery research to an important tool in clinical decision making. However, the lack of harmonization of preanalytic practices across institutions may compromise the reproducibility of cfDNA-derived data and hamper advancements in cfDNA testing in the clinic. Differences in cellular genomic contamination, cfDNA yield, integrity, and fragment length have been attributed to different collection tube types and anticoagulants, processing delays and temperatures, tube agitation, centrifugation protocols and speeds, plasma storage duration and temperature, the number of freeze-thaw events, and cfDNA extraction and quantification methods, all of which can also ultimately impact subsequent downstream analysis. Thus, there is a pressing need for widely applicable standards tailored for cfDNA analysis that include all preanalytic steps from blood draw to analysis. The NCI's Biorepositories and Biospecimen Research Branch has developed cfDNA-specific guidelines that are based upon published evidence and have been vetted by a panel of internationally recognized experts in the field. The guidelines include optimal procedures as well as acceptable alternatives to facilitate the generation of evidence-based protocols by individual laboratories and institutions. The aim of the document, which is entitled "Biospecimen Evidence-based Best Practices for Cell-free DNA: Biospecimen Collection and Processing," is to improve the accuracy of cfDNA analysis in both basic research and the clinic by improving and harmonizing practices across institutions.

A machine learning approach for somatic mutation discovery.

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

Direct detection of early-stage cancers using circulating tumor DNA.

Early detection and intervention are likely to be the most effective means for reducing morbidity and mortality of human cancer. However, development of methods for noninvasive detection of early-stage tumors has remained a challenge. We have developed an approach called targeted error correction sequencing (TEC-Seq) that allows ultrasensitive direct evaluation of sequence changes in circulating cell-free DNA using massively parallel sequencing. We have used this approach to examine 58 cancer-related genes encompassing 81 kb. Analysis of plasma from 44 healthy individuals identified genomic changes related to clonal hematopoiesis in 16% of asymptomatic individuals but no alterations in driver genes related to solid cancers. Evaluation of 200 patients with colorectal, breast, lung, or ovarian cancer detected somatic mutations in the plasma of 71, 59, 59, and 68%, respectively, of patients with stage I or II disease. Analyses of mutations in the circulation revealed high concordance with alterations in the tumors of these patients. In patients with resectable colorectal cancers, higher amounts of preoperative circulating tumor DNA were associated with disease recurrence and decreased overall survival. These analyses provide a broadly applicable approach for noninvasive detection of early-stage tumors that may be useful for screening and management of patients with cancer.

The Effect of Preservative and Temperature on the Analysis of Circulating Tumor DNA.

Purpose: Analysis of genomic alterations in cell-free DNA (cfDNA) is evolving as an approach to detect, monitor, and genotype malignancies. Methods to separate the liquid from the cellular fraction of whole blood for circulating tumor DNA (ctDNA) analyses have been largely unstudied, although these may be a critical consideration for assay performance.Experimental Design: To evaluate the influence of blood processing on cfDNA and ctDNA quality and yield, we compared the cfDNA levels in serum with those in plasma. Given the limitations of serum for ctDNA analyses, we evaluated the effects of two plasma processing approaches, K2EDTA and Cell-Free DNA BCT (BCT) tubes, on cfDNA and ctDNA recovery. A total of 45 samples from nine patients with cancer were collected in both tube types. Once collected, blood was processed into plasma immediately or kept at room temperature and processed into plasma at 1, 3, 5, or 7 days.Results: As early as 24 hours after collection, plasma isolated from blood collected in K2EDTA tubes contained an elevated level of cfDNA that increased over time compared with BCT tubes where no significant increase in cfDNA levels was observed. When samples from an additional six patients with cancer, collected in the same manner, were stored at 4°C in K2EDTA tubes over the course of 3 days, total cfDNA and ctDNA levels were comparable between samples collected in BCT tubes. At day 3, there was a trend toward a decrease in ctDNA levels in both tubes that was more pronounced when measuring the mutant allele fraction for cases stored at 4°C in K2EDTA tubes.Conclusions: In summary, methods of blood processing have a strong influence on cfDNA and ctDNA levels and should be a consideration when evaluating ctDNA in peripheral circulation. Clin Cancer Res; 23(10); 2471-7. ©2016 AACR.

The importance of integrated genomics to uncover clinically relevant cancer driver genes.

Tumor heterogeneity hinders the discovery of true cancer driver genes. The recently identified role for YY1-associated protein 1 (YY1AP1) as a critical oncoprotein that is activated in a particular subtype of hepatocellular carcinoma (HCC) was elucidated through integrative genomic profiling followed by functional studies. This strategy proved effective for finding a functional cancer driver with prognostic impact.

Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients.

Pancreatic adenocarcinoma has the worst mortality of any solid cancer. In this study, to evaluate the clinical implications of genomic alterations in this tumour type, we perform whole-exome analyses of 24 tumours, targeted genomic analyses of 77 tumours, and use non-invasive approaches to examine tumour-specific mutations in the circulation of these patients. These analyses reveal somatic mutations in chromatin-regulating genes MLL, MLL2, MLL3 and ARID1A in 20% of patients that are associated with improved survival. We observe alterations in genes with potential therapeutic utility in over a third of cases. Liquid biopsy analyses demonstrate that 43% of patients with localized disease have detectable circulating tumour DNA (ctDNA) at diagnosis. Detection of ctDNA after resection predicts clinical relapse and poor outcome, with recurrence by ctDNA detected 6.5 months earlier than with CT imaging. These observations provide genetic predictors of outcome in pancreatic cancer and have implications for new avenues of therapeutic intervention.

Personalized genomic analyses for cancer mutation discovery and interpretation.

Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to select therapies based on the identified mutations. A major question in these analyses is the extent to which these methods identify clinically actionable alterations and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated 815 tumor-normal paired samples from patients of 15 tumor types. We identified genomic alterations using next-generation sequencing of whole exomes or 111 targeted genes that were validated with sensitivities >95% and >99%, respectively, and specificities >99.99%. These analyses revealed an average of 140 and 4.3 somatic mutations per exome and targeted analysis, respectively. More than 75% of cases had somatic alterations in genes associated with known therapies or current clinical trials. Analyses of matched normal DNA identified germline alterations in cancer-predisposing genes in 3% of patients with apparently sporadic cancers. In contrast, a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest that matched tumor-normal sequencing analyses are essential for precise identification and interpretation of somatic and germline alterations and have important implications for the diagnostic and therapeutic management of cancer patients.