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.

Mosaicism in Human Health and Disease.

Mosaicism refers to the occurrence of two or more genomes in an individual derived from a single zygote. Germline mosaicism is a mutation that is limited to the gonads and can be transmitted to offspring. Somatic mosaicism is a postzygotic mutation that occurs in the soma, and it may occur at any developmental stage or in adult tissues. Mosaic variation may be classified in six ways: (a) germline or somatic origin, (b) class of DNA mutation (ranging in scale from single base pairs to multiple chromosomes), (c) developmental context, (d) body location(s), (e) functional consequence (including deleterious, neutral, or advantageous), and (f) additional sources of mosaicism, including mitochondrial heteroplasmy, exogenous DNA sources such as vectors, and epigenetic changes such as imprinting and X-chromosome inactivation. Technological advances, including single-cell and other next-generation sequencing, have facilitated improved sensitivity and specificity to detect mosaicism in a variety of biological contexts.

A primary breast cancer with distinct foci of estrogen receptor-alpha positive and negative cells derived from the same clonal origin as revealed by whole exome sequencing.

BACKGROUND/PURPOSE: Tumor heterogeneity is a now well-recognized phenomenon that can affect the classification, prognosis and treatment of human cancers. Heterogeneity is often described in primary breast cancers based upon histologic subtypes, hormone- and HER2-receptor status, and immunolabeling for various markers, which can be seen within a single tumor as mixed cellular populations, or as separate discrete foci. EXPERIMENTAL DESIGN/METHODS: Here, we present a case report of a patient's primary breast cancer that had two separate but adjacent histologic components, one that was estrogen receptor (ER) positive, and the other ER negative. Each component was subjected to whole exome sequencing and compared for gene identity to determine clonal origin. RESULTS: Using prior bioinformatic tools, we demonstrated that both the ER positive and negative components shared many variants, including passenger and driver alterations. Copy number variations also supported the two components were derived from a single common clone. CONCLUSIONS: These analyses strongly suggest that the two ER components of this patient's breast cancer were derived from the same clonal origin. Our results have implications for the evolution of breast cancers with mixed histologies, and how they might be best managed for optimal therapy.

Mutational profiles of breast cancer metastases from a rapid autopsy series reveal multiple evolutionary trajectories.

Heterogeneity within and among tumors in a metastatic cancer patient is a well-established phenomenon that may confound treatment and accurate prognosis. Here, we used whole-exome sequencing to survey metastatic breast cancer tumors from 5 patients in a rapid autopsy program to construct the origin and genetic development of metastases. Metastases were obtained from 5 breast cancer patients using a rapid autopsy protocol and subjected to whole-exome sequencing. Metastases were evaluated for sharing of somatic mutations, correlation of copy number variation and loss of heterozygosity, and genetic similarity scores. Pathological features of the patients' disease were assessed by immunohistochemical analyses. Our data support a monoclonal origin of metastasis in 3 cases, but in 2 cases, metastases arose from at least 2 distinct subclones in the primary tumor. In the latter 2 cases, the primary tumor presented with mixed histologic and pathologic features, suggesting early divergent evolution within the primary tumor with maintenance of metastatic capability in multiple lineages. We used genetic and histopathological evidence to demonstrate that metastases can be derived from a single or multiple independent clones within a primary tumor. This underscores the complexity of breast cancer clonal evolution and has implications for how best to determine and implement therapies for early- and late-stage disease.

Whole-Exome Sequencing of Metaplastic Breast Carcinoma Indicates Monoclonality with Associated Ductal Carcinoma Component.

Purpose: Although most human cancers display a single histology, there are unusual cases where two or more distinct tissue types present within a primary tumor. One such example is metaplastic breast carcinoma, a rare but aggressive cancer with a heterogeneous histology, including squamous, chondroid, and spindle cells. Metaplastic carcinomas often contain an admixed conventional ductal invasive or in situ mammary carcinoma component, and are typically triple-negative for estrogen receptor, progesterone receptor, and HER-2 amplification/overexpression. An unanswered question is the origin of metaplastic breast cancers. While they may arise independently from their ductal components, their close juxtaposition favors a model that postulates a shared origin, either as two derivatives from the same primary cancer or one histology as an outgrowth of the other. Understanding the mechanism of development of these tumors may inform clinical decisions.Experimental Design: We performed exome sequencing for paired metaplastic and adjacent conventional invasive ductal carcinomas in 8 patients and created a pipeline to identify somatic variants and predict their functional impact, without having normal tissue. We then determined the genetic relationships between the histologically distinct compartments.Results: In each case, the tumor components have nearly identical landscapes of somatic mutation, implying that the differing histologies do not derive from genetic clonal divergence.Conclusions: A shared origin for tumors with differing histologies suggests that epigenetic or noncoding changes may mediate the metaplastic phenotype and that alternative therapeutic approaches, including epigenetic therapies, may be required for metaplastic breast cancers. Clin Cancer Res; 23(16); 4875-84. ©2017 AACR.