A personalized probabilistic approach to ovarian cancer diagnostics.

OBJECTIVE: The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer. METHODS: Serum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal. RESULTS: Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer. CONCLUSIONS: An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.

The Clinical Significance of Genetic Variation in Ovarian Cancer.

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring.

Association of Genetic Ancestry and Molecular Signatures with Cancer Survival Disparities: A Pan-Cancer Analysis.

While overall cancer mortality has steadily decreased in recent decades, cancer health disparities among racial and ethnic population groups persist. Here we studied the relationship between cancer survival disparities (CSD), genetic ancestry (GA), and tumor molecular signatures across 33 cancers in a cohort of 9,818 patients. GA correlated with race and ethnicity but showed observable differences in effects on CSD, with significant associations identified in four cancer types: breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSCC), kidney renal clear cell carcinoma (KIRC), and skin cutaneous carcinoma (SKCM). Differential gene expression and methylation between ancestry groups associated cancer-related genes with CSD, of which, seven protein-coding genes [progestin and adipoQ receptor family member 6 (PAQR6), Lck-interacting transmembrane adaptor 1 (LIME1), Sin3A-associated protein 25 (SAP25), MAX dimerization protein 3 (MXD3), coiled-coil glutamate rich protein 2 (CCER2), refilin A (RFLNA), and cathepsin W (CTSW)] significantly interacted with GA and exacerbated observed survival disparities. These findings indicated that regulatory changes mediated by epigenetic mechanisms have a greater contribution to CSD than population-specific mutations. Overall, we uncovered various molecular mechanisms through which GA might impact CSD, revealing potential population-specific therapeutic targets for groups disproportionately burdened by cancer. SIGNIFICANCE: This large-cohort, multicancer study identifies four cancer types with cancer survival disparities and seven cancer-related genes that interact with genetic ancestry and contribute to disparities.

Tumor suppressor genes and allele-specific expression: mechanisms and significance.

Recent findings indicate that allele-specific expression (ASE) at specific cancer driver gene loci may be of importance in onset/progression of the disease. Of particular interest are loss-of-function (LOF) of tumor suppressor gene (TSGs) alleles. While LOF tumor suppressor mutations are typically considered to be recessive, if these mutant alleles can be significantly differentially expressed relative to wild-type alleles in heterozygotes, the clinical consequences could be significant. LOF TSG alleles are shown to be segregating at high frequencies in world-wide populations of normal/healthy individuals. Matched sets of normal and tumor tissues isolated from 233 cancer patients representing four diverse tumor types demonstrate functionally important changes in patterns of ASE in individuals heterozygous for LOF TSG alleles associated with cancer onset/progression. While a variety of molecular mechanisms were identified as potentially contributing to changes in ASE patterns in cancer, changes in DNA copy number and allele-specific alternative splicing possibly mediated by antisense RNA emerged as predominant factors. In conclusion, LOF TSGs are segregating in human populations at significant frequencies indicating that many otherwise healthy individuals are at elevated risk of developing cancer. Changes in ASE between normal and cancer tissues indicates that LOF TSG alleles may contribute to cancer onset/progression even when heterozygous with wild-type functional alleles.

An atlas of transposable element-derived alternative splicing in cancer.

Transposable element (TE)-derived sequences comprise more than half of the human genome, and their presence has been documented to alter gene expression in a number of different ways, including the generation of alternatively spliced transcript isoforms. Alternative splicing has been associated with tumorigenesis for a number of different cancers. The objective of this study was to broadly characterize the role of human TEs in generating alternatively spliced transcript isoforms in cancer. To do so, we screened for the presence of TE-derived sequences co-located with alternative splice sites that are differentially used in normal versus cancer tissues. We analysed a comprehensive set of alternative splice variants characterized for 614 matched normal-tumour tissue pairs across 13 cancer types, resulting in the discovery of 4820 TE-generated alternative splice events distributed among 723 cancer-associated genes. Short interspersed nuclear elements (Alu) and long interspersed nuclear elements (L1) were found to contribute the majority of TE-generated alternative splice sites in cancer genes. A number of cancer-associated genes, including MYH11, WHSC1 and CANT1, were shown to have overexpressed TE-derived isoforms across a range of cancer types. TE-derived isoforms were also linked to cancer-specific fusion transcripts, suggesting a novel mechanism for the generation of transcriptome diversity via trans-splicing mediated by dispersed TE repeats. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

The Phenotypic Consequences of Genetic Divergence between Admixed Latin American Populations: Antioquia and Chocó, Colombia.

Genome-wide association studies have uncovered thousands of genetic variants that are associated with a wide variety of human traits. Knowledge of how trait-associated variants are distributed within and between populations can provide insight into the genetic basis of group-specific phenotypic differences, particularly for health-related traits. We analyzed the genetic divergence levels for 1) individual trait-associated variants and 2) collections of variants that function together to encode polygenic traits, between two neighboring populations in Colombia that have distinct demographic profiles: Antioquia (Mestizo) and Chocó (Afro-Colombian). Genetic ancestry analysis showed 62% European, 32% Native American, and 6% African ancestry for Antioquia compared with 76% African, 10% European, and 14% Native American ancestry for Chocó, consistent with demography and previous results. Ancestry differences can confound cross-population comparison of polygenic risk scores (PRS); however, we did not find any systematic bias in PRS distributions for the two populations studied here, and population-specific differences in PRS were, for the most part, small and symmetrically distributed around zero. Both genetic differentiation at individual trait-associated single nucleotide polymorphisms and population-specific PRS differences between Antioquia and Chocó largely reflected anthropometric phenotypic differences that can be readily observed between the populations along with reported disease prevalence differences. Cases where population-specific differences in genetic risk did not align with observed trait (disease) prevalence point to the importance of environmental contributions to phenotypic variance, for both infectious and complex, common disease. The results reported here are distributed via a web-based platform for searching trait-associated variants and PRS divergence levels at http://map.chocogen.com (last accessed August 12, 2020).

Analyzing Mechanisms of Metastatic Cancer Cell Adhesive Phenotype Leveraging Preparative Adhesion Chromatography Microfluidic.

An integrated, parallel-plate microfluidic device is engineered to interrogate and fractionate cells based on their adhesivity to a substrate surface functionalized with adhesive ligand in a tightly controlled flow environment to elucidate associated cell-intrinsic pathways. Wall shear stress levels and endothelial presentation of E-selectin are modeled after the inflamed vasculature microenvironment in order to simulate in vitro conditions under which in vivo hematogenous metastasis occurs. Based on elution time from the flow channel, the collection of separate fractions of cells-noninteracting and interacting-at high yields and viabilities enables multiple postperfusion analyses, including flow cytometry, in vivo metastasis modeling, and transcriptomic analysis. This platform enables the interrogation of flow-regulated cell molecular profiles, such as (co)expression levels of natively expressed selectin ligands sLex , CD44, and carcinoembryonic antigen, and cancer stem cell marker CD24. This additionally reveals E-selectin adhesivity exhibited by metastatic human colon carcinoma cells to be a transient phenotype. Facile and rapid, this methodology for unbiased, label free sorting of large populations of cells based on their adhesion in flow represents a method of studying flow-regulated adhesion in vitro for the identification of molecular drug targets for development as antimetastatic cancer therapeutics.