Assessment of circulating copy number variant detection for cancer screening

<div><p>Current high-sensitivity cancer screening methods, largely utilizing correlative biomarkers, suffer from false positive rates that lead to unnecessary medical procedures and debatable public health benefit overall. Detection of circulating tumor DNA (ctDNA), a causal biomarker, has the potential to revolutionize cancer screening. Thus far, the majority of ctDNA studies have focused on detection of tumor-specific point mutations after cancer diagnosis for the purpose of post-treatment surveillance. However, ctDNA point mutation detection methods developed to date likely lack either the scope or analytical sensitivity necessary to be useful for cancer screening, due to the low (<1%) ctDNA fraction derived from early stage tumors. On the other hand, tumor-derived copy number variant (CNV) detection is hypothetically a superior means of ctDNA-based cancer screening for many tumor types, given that, relative to point mutations, each individual tumor CNV contributes a much larger number of ctDNA fragments to the overall pool of circulating free DNA (cfDNA). A small number of studies have demonstrated the potential of ctDNA CNV-based screening in select cancer types. Here we perform an in silico assessment of the potential for ctDNA CNV-based cancer screening across many common cancers, and suggest ctDNA CNV detection shows promise as a broad cancer screening methodology.</p></div

Theoretical limits of ctDNA CNV detection.

<p>A) Density plot for healthy donor cfDNA sequencing reads mapped to 10kb genomic bins. A negative binomial (red) and Poisson (blue) distribution was fit to the resultant data. B) The ctDNA CNV size limit of detection (in megabases) is plotted as a function of sequencing depth for single copy change at various ctDNA fractions. C) Same as panel B but for four copies gained.</p

Unsupervised cancer sample clustering with ctDNA detectable CNV events.

<p>Heat maps representing the results of unsupervised clustering of cancer samples using 100 Mb resolution (top panel) and 5 Mb resolution (bottom panel) of ctDNA CNV events. Deletions are depicted in blue and amplifications are depicted in red.</p

Performance of the KNN and random forest classification models for determination of cancer type.

<p>Performance of the KNN and random forest classification models for determination of cancer type.</p

Tumor classification performance.

<p>ROC curves at 5Mb (top panels) and 100Mb ctDNA CNV resolution (bottom panels) showing performance of cancer detection (left panels) and cancer type determination (right panels) for 11 major types of solid tumors—breast adenocarcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), colon and rectal carcinoma (COAD, READ), bladder urothelial carcinoma (BLCA), kidney renal clear cell carcinoma (KIRC), ovarian serous carcinoma (OV). A small overall increase in the AUC values when going from a 100 Mb resolution to 5 Mb resolution can be observed for both detection of cancer and determination of cancer type.</p

Performance of the classification models.

<p>The predictive performance of the KNN model (left panel) and the random forest model (right panel) is plotted. In general, random forest models outperform the KNN models. Positive predictive value (light gray) (PPV) remains stable across models and CNV size resolution. Accuracy (black) and true positive rate (dark gray) (TPR) remain stable at 5Mb and 100Mb resolutions for the KNN model but increase at 5Mb resolution for the random forest model.</p

Cancer type misclassification heatmap.

<p>The frequency of cross misclassifications is depicted in a heatmap for 5 Mb (panel A) and 100 Mb (panel B) ctDNA CNV resolution. Columns correspond to the known cancer type and rows correspond to the predicted cancer type. Misclassification frequency is depicted by the darkness of each cell, with darker color reflected a higher misclassification frequency. Correct classifications are set to 0. White = 0% misclassification. Dark blue = 100% misclassification.</p