Development of a comprehensive cell-free DNA (cfDNA) assay for early detection of multiple tumor types: The Circulating Cell-free Genome Atlas (CCGA) study.

Klein, Eric A.; Hubbell, Earl; Maddala, Tara; Aravanis, Alex; Beausang, John F.; Filippova, Darya; Gross, Samuel; Jamshidi, Arash; Kurtzman, Kathryn N.; Shen, Ling; Valouev, Anton; Venn, Oliver; Zhang, Nan; Smith, David A.; Yeatman, Timothy J.; Tibshirani, Robert; Williams, Richard; Hartman, Anne‐Renee; Seiden, Michael V.; Liu, Minetta C.

doi:10.1200/jco.2018.36.15_suppl.12021

Cited by 55 publications

(62 citation statements)

References 6 publications

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“…A wide range of blood-based biomarker candidates, including DNA, RNA, protein, methylation, lipid, and metabolites, have been reported in the literature, with some showing notable potential for breast cancer detection [10]. Some key studies are the CancerSEEK [18] and GRAIL's Circulating Cell-Free Genome Atlas (CCGA) [19,20] projects that have employed circulating DNAs as cancer markers. Although these circulating DNA-based blood tests were able to identify multiple cancers, their sensitivities in discriminating breast cancer from healthy or non-cancer controls remained poor (i.e., 33% for CancerSEEK [18] and <60% for CCGA [20]).…”

Section: Discussionmentioning

confidence: 99%

“…Although these circulating DNA-based blood tests were able to identify multiple cancers, their sensitivities in discriminating breast cancer from healthy or non-cancer controls remained poor (i.e., 33% for CancerSEEK [18] and <60% for CCGA [20]). Besides, these tests were less sensitive in detecting breast cancer at early stages as compared to late stages [19,20]. Other potential biomarkers for differentiating breast cancers and benign breast lesions include protein biomarkers such as the combination of epithelial membrane antigen and cytokeratin-1 (EMA and CK1; AUC of 0.9010), and developmental endothelial locus-1 (Del-1; AUC of 0.9200) [21,22].…”

Section: Discussionmentioning

confidence: 99%

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A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

Loke

Munusamy

Koh

et al. 2019

Cancers

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Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

Loke

Munusamy

Koh

et al. 2019

Cancers

View full text Add to dashboard Cite

show abstract

“…First, because samples were obtained retrospectively, breaks in the chain of custody may have led to sample and labelling errors, which would impede the ability of an ML method to adequately learn. Additionally, the presence of CNVs in a small number of control samples (Supplemental Figure 2) has been previously observed in other cohorts and may be due to malignant or benign causes [5,14,37]; further follow up was not possible in this cohort. Another limitation of our study is that TF was estimated using copy-number inference from moderate-coverage whole-genome sequencing, which has a limit of detection of 3% for TF [28]; by contrast, targeted mutation detection would allow more sensitive characterization of TF.…”

Section: Discussionmentioning

confidence: 62%

“…These limitations are particularly important in early-stage cancer when the tumor is small and the shedding of DNA into the blood may be minimal. Indeed, many previous cfDNA studies have had stage distributions meaningfully different from those seen in screening populations [14][15][16].…”

Section: Introductionmentioning

confidence: 97%

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

Wan

Weinberg

Liu

et al. 2018

Preprint

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Background: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumorderived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.Methods: Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance. Results:In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions:A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies.Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

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“…However, these alterations come with low specificity for classification of pediatric solid tumors according to their histopathological diagnosis 35 . In addition, there is a high interest to develop a screening test based on cfDNA methylation in adult oncology 36 . Due to the rarity of cancer in children, we…”

Section: Discussionmentioning

confidence: 99%

Minimally invasive classification of pediatric solid tumors using reduced representation bisulfite sequencing of cell-free DNA: a proof-of-principle study

Paemel

Koker

Vandeputte

et al. 2019

Preprint

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In the clinical management of pediatric solid tumors, histological examination of tumor tissue obtained by a biopsy remains the gold standard to establish a conclusive pathological diagnosis. The DNA methylation pattern of a tumor is known to correlate with the histopathological diagnosis across cancer types and is showing promise in the diagnostic workup of tumor samples. This methylation pattern can be detected in the cell-free DNA.Here, we provide proof-of-concept of histopathologic classification of pediatric tumors using cell-free reduced representation bisulfite sequencing (cf-RRBS) from retrospectively collected plasma and cerebrospinal fluid samples. We determined the correct tumor type in 49 out of 60 (81.6%) samples starting from minute amounts (less than 10 ng) of cell-free DNA. We demonstrate that the majority of misclassifications were associated with sample quality and not with the extent of disease. Our approach has the potential to help tackle some of the remaining diagnostic challenges in pediatric oncology in a cost-effective and minimally invasive manner. Translational relevanceObtaining a correct diagnosis in pediatric oncology can be challenging in some tumor types, especially in renal tumors or central nervous system tumors. Furthermore, the diagnostic odyssey can result in anxiety and discomfort for these children. By applying a novel technique, reduced representation bisulfite sequencing on cell-free DNA (cf-RRBS), we show the feasibility of obtaining the histopathological diagnosis with a minimally invasive test on either plasma or cerebrospinal fluid. Furthermore, we were able to derive the copy number profile or tumor subtype from the same assay. Given that primary tumor material might be difficult to obtain, in particular in critically ill children or depending on the tumor location, and might be limited in terms of quantity or quality, our assay could become complementary to the classical tissue biopsy in difficult cases.

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Development of a comprehensive cell-free DNA (cfDNA) assay for early detection of multiple tumor types: The Circulating Cell-free Genome Atlas (CCGA) study.

Cited by 55 publications

References 6 publications

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

Minimally invasive classification of pediatric solid tumors using reduced representation bisulfite sequencing of cell-free DNA: a proof-of-principle study

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