Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

Abstract Background Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. Methods We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. Results Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. Conclusions In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples

Genetic landscape and personalized tracking of tumor mutations in Vietnamese women with breast cancer

Breast cancer is the leading cause of cancer death in Vietnamese women, but its mutational landscape and actionable alterations for targeted therapies remain unknown. After treatment, a sensitive biomarker to complement conventional imaging to monitor patients is also lacking. In this prospective multi‐center study, 134 early‐stage breast cancer patients eligible for curative‐intent surgery were recruited. Genomic DNA from tumor tissues and paired white blood cells were sequenced to profile all tumor‐derived mutations in 95 cancer‐associated genes. Our bioinformatic algorithm was then utilized to identify top mutations for individual patients. Serial plasma samples were collected before surgery and at scheduled visits after surgery. Personalized assay tracking the selected mutations were performed to detect circulating tumor DNA (ctDNA) in the plasma. We found that the mutational landscape of the Vietnamese was largely similar to other Asian cohorts, showing higher TP53 mutation frequency than in Caucasians. Alterations in PIK3CA and PI3K signaling were dominant, particularly in our triple‐negative subgroup. Using top‐ranked mutations, we detected ctDNA in pre‐operative plasma in 24.6–43.5% of the hormone‐receptor‐positive groups and 76.9–80.8% of the hormone‐receptor‐negative groups. The detection rate was associated with breast cancer subtypes and clinicopathological features that increased the risk of relapse. Interim analysis after a 15‐month follow‐up revealed post‐operative detection of ctDNA in all three patients that had recurrence, with a lead time of 7–13 months ahead of clinical diagnosis. Our personalized assay is streamlined and affordable with promising clinical utility in residual cancer surveillance. We also generated the first somatic variant dataset for Vietnamese breast cancer women that could lay the foundation for precision cancer medicine in Vietnam