Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.

Seq-ing the SINEs of central nervous system tumors in cerebrospinal fluid.

It is often challenging to distinguish cancerous from non-cancerous lesions in the brain using conventional diagnostic approaches. We introduce an analytic technique called Real-CSF (repetitive element aneuploidy sequencing in CSF) to detect cancers of the central nervous system from evaluation of DNA in the cerebrospinal fluid (CSF). Short interspersed nuclear elements (SINEs) are PCR amplified with a single primer pair, and the PCR products are evaluated by next-generation sequencing. Real-CSF assesses genome-wide copy-number alterations as well as focal amplifications of selected oncogenes. Real-CSF was applied to 280 CSF samples and correctly identified 67% of 184 cancerous and 96% of 96 non-cancerous brain lesions. CSF analysis was considerably more sensitive than standard-of-care cytology and plasma cell-free DNA analysis in the same patients. Real-CSF therefore has the capacity to be used in combination with other clinical, radiologic, and laboratory-based data to inform the diagnosis and management of patients with suspected cancers of the brain.

Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules.

The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here, we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung, and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics.

Transcriptional Profiling of Saccharomyces cerevisiae Reveals the Impact of Variation of a Single Transcription Factor on Differential Gene Expression in 4NQO, Fermentable, and Nonfermentable Carbon Sources.

Cellular metabolism can change the potency of a chemical's tumorigenicity. 4-nitroquinoline-1-oxide (4NQO) is a tumorigenic drug widely used on animal models for cancer research. Polymorphisms of the transcription factor Yrr1 confer different levels of resistance to 4NQO in Saccharomyces cerevisiae To study how different Yrr1 alleles regulate gene expression leading to resistance, transcriptomes of three isogenic Scerevisiae strains carrying different Yrr1 alleles were profiled via RNA sequencing (RNA-Seq) and chromatin immunoprecipitation coupled with sequencing (ChIP-Seq) in the presence and absence of 4NQO. In response to 4NQO, all alleles of Yrr1 drove the expression of SNQ2 (a multidrug transporter), which was highest in the presence of 4NQO resistance-conferring alleles, and overexpression of SNQ2 alone was sufficient to overcome 4NQO-sensitive growth. Using shape metrics to refine the ChIP-Seq peaks, Yrr1 strongly associated with three loci including SNQ2 In addition to a known Yrr1 target SNG1, Yrr1 also bound upstream of RPL35B; however, overexpression of these genes did not confer 4NQO resistance. RNA-Seq data also implicated nucleotide synthesis pathways including the de novo purine pathway, and the ribonuclease reductase pathways were downregulated in response to 4NQO. Conversion of a 4NQO-sensitive allele to a 4NQO-resistant allele by a single point mutation mimicked the 4NQO-resistant allele in phenotype, and while the 4NQO resistant allele increased the expression of the ADE genes in the de novo purine biosynthetic pathway, the mutant Yrr1 increased expression of ADE genes even in the absence of 4NQO. These same ADE genes were only increased in the wild-type alleles in the presence of 4NQO, indicating that the point mutation activated Yrr1 to upregulate a pathway normally only activated in response to stress. The various Yrr1 alleles also influenced growth on different carbon sources by altering the function of the mitochondria. Hence, the complement to 4NQO resistance was poor growth on nonfermentable carbon sources, which in turn varied depending on the allele of Yrr1 expressed in the isogenic yeast. The oxidation state of the yeast affected the 4NQO toxicity by altering the reactive oxygen species (ROS) generated by cellular metabolism. The integration of RNA-Seq and ChIP-Seq elucidated how Yrr1 regulates global gene transcription in response to 4NQO and how various Yrr1 alleles confer differential resistance to 4NQO. This study provides guidance for further investigation into how Yrr1 regulates cellular responses to 4NQO, as well as transcriptomic resources for further analysis of transcription factor variation on carbon source utilization.