We present a genome-wide method to map DNA double-strand breaks (DSBs) at nucleotide resolution by direct in situ breaks labeling, enrichment on streptavidin, and next-generation sequencing (BLESS). We comprehensively validated and tested BLESS using different human and mouse cells, DSBs-inducing agents, and sequencing platforms. BLESS was able to detect telomere ends, Sce endonuclease-induced DSBs, and complex genome-wide DSBs landscapes. As a proof of principle, we characterized the genomic landscape of sensitivity to replication stress in human cells, and identified over two thousand non-uniformly distributed aphidicolin-sensitive regions (ASRs) overrepresented in genes and enriched in satellite repeats. ASRs were also enriched in regions rearranged in human cancers, with many cancer-associated genes exhibiting high sensitivity to replication stress. Our method is suitable for genome-wide mapping of DSBs in various cells and experimental conditions with a specificity and resolution unachievable by current techniques.

Background Detection, isolation and enumeration of circulating tumor cells (CTCs) from cancer patients has become an important modality in clinical management of patients with breast cancer. Although CellSearch, an EpCAM based method that is used to isolate epithelial CTCs has gained immense importance, its inability to detect mesenchymal CTCs from breast cancer patients raises concerns for its utility as a clinical management tool. Methods To address this gap in technology, we recently discovered the utility of cell-surface vimentin (CSV) as a marker for detecting mesenchymal CTC from sarcoma tumors. Here in this study, we tested the sensitivity and specificity of detecting CTC from blood collected at a random time during therapy from each of the 58 patients with metastatic breast cancer utilizing 84-1 (mAb against CSV to detect epithelial mesenchymal transitioned CTC) and CellSearch methods. Also we tested the possibility of improving the sensitivity and specificity of detection using additional parameters including nuclear EpCAM localization and epithelial mesenchymal ratios. Results CTC counts using CSV were significant in differentiating treatment responding (stable) and treatment non-responding (progression) populations in comparison to the CellSearch method. The results also indicated that a summation of CTCs detected from both methods with a threshold of 8 CTCs/7.5mL increased the specificity of CTC detection substantially in comparison with other tested combinations as determined by ROC curves. Conclusions Collectively, utilizing a summation of CellSearch and CSV methods provide new insights into using CTC enumeration to assess therapeutic response and thus provides a new approach to personalized medicine in breast cancer patients.

Recent advances in the field of circulating tumor cells (CTC) have shown promise in this liquid biopsy-based prognosis of patient outcome. However, not all of the circulating cells are tumor cells, as evidenced by a lack of tumor-specific markers. The current FDA standard for capturing CTCs (CellSearch) relies on an epithelial marker and cells captured via CellSearch cannot be considered to have undergone EMT. Therefore, it is difficult to ascertain the presence and relevance of any mesenchymal or EMT-like CTCs. To address this gap in technology, we recently discovered the utility of cell-surface vimentin (CSV) as a marker for detecting mesenchymal CTCs from sarcoma, breast, and colon cancer. Here we studied peripheral blood samples of 48 prostate cancer (PCA) patients including hormone sensitive and castration resistant sub-groups. Blood samples were analyzed for three different properties including our own CSV-based CTC enumeration (using 84-1 mAb against CSV), CellSearch-based epithelial CTC counts, and serum prostate-specific antigen (PSA) quantification. Our data demonstrated that in comparison with CellSearch, the CSV-based method had greater sensitivity and specificity. Further, we observed significantly greater numbers of CTCs in castration resistant patients as measured by our CSV method but not CellSearch. Our data suggests CSV-guided CTC enumeration may hold prognostic value and should be further validated as a possible measurement of PCA progression towards the deadly, androgen-independent form.

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer’s, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.