In recent years, genes encoding components of the RNA splicing machinery and regulatory factors have been characterized as a major class of mutated genes in myelodysplastic syndromes (MDS), with mutations occurring in a heterozygous gain‐of‐function pattern at recurrent amino acids. However, the effects on splicing patterns and consequent pathogenesis of malignancy remain poorly understood, in part due to challenges in isolating appropriate homogenous cellular populations carrying these mutations. We employed two complementary approaches to more effectively study splicing factors mutated in myeloid malignancies. We developed protocols to isolate bone marrow hematopoietic stem cells or peripheral blood progenitor cells from MDS patients known to carry splicing factor mutations. Our goal was to perform single‐cell whole transcriptome RNA sequencing and compare differences in splicing between mutated and non‐mutated cells from the same patient, as well as between patients. We also created genome‐edited cell lines, using the CRISPR/Cas system to alter single DNA base pairs in cell lines to reproduce the mutations found in patients. We successfully generated K562 cell lines with mutations in SRSF2 and U2AF1, two of the most frequently mutated splicing factors. Importantly, these cell lines demonstrate the viability of this novel technology in hematopoietic cells as well as provide effective tools to study the molecular consequences of splicing factor mutations. Together, these approaches will allow better characterization of the role of splicing factors in the pathogenesis of myeloid malignancies.

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.