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.

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action.

To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.

Snail-induced epithelial-to-mesenchymal transition of MCF-7 breast cancer cells: systems analysis of molecular changes and their effect on radiation and drug sensitivity.

BACKGROUND: Epithelial-to-mesenchymal transition (EMT) has been associated with the acquisition of metastatic potential and the resistance of cancer cells to therapeutic treatments. MCF-7 breast cancer cells engineered to constitutively express the zinc-finger transcriptional repressor gene Snail (MCF-7-Snail cells) have been previously shown to display morphological and molecular changes characteristic of EMT. We report here the results of a comprehensive systems level molecular analysis of changes in global patterns of gene expression and levels of glutathione and reactive oxygen species (ROS) in MCF-7-Snail cells and the consequence of these changes on the sensitivity of cells to radiation treatment and therapeutic drugs. METHODS: Snail-induced changes in global patterns of gene expression were identified by microarray profiling using the Affymetrix platform (U133 Plus 2.0). The resulting data were processed and analyzed by a variety of system level analytical methods. Levels of ROS and glutathione (GSH) were determined by fluorescent and luminescence assays, and nuclear levels of NF-κB protein were determined by an ELISA based method. The sensitivity of cells to ionizing radiation and anticancer drugs was determined using a resazurin-based cell cytotoxicity assay. RESULTS: Constitutive ectopic expression of Snail in epithelial-like, luminal A-type MCF-7 cells induced significant changes in the expression of >7600 genes including gene and miRNA regulators of EMT. Mesenchymal-like MCF-7-Snail cells acquired molecular profiles characteristic of triple-negative, claudin-low breast cancer cells, and displayed increased sensitivity to radiation treatment, and increased, decreased or no change in sensitivity to a variety of anticancer drugs. Elevated ROS levels in MCF-7-Snail cells were unexpectedly not positively correlated with NF-κB activity. CONCLUSIONS: Ectopic expression of Snail in MCF-7 cells resulted in morphological and molecular changes previously associated with EMT. The results underscore the complexity and cell-type dependent nature of the EMT process and indicate that EMT is not necessarily predictive of decreased resistance to radiation and drug-based therapies.

SNAIL-induced epithelial-to-mesenchymal transition produces concerted biophysical changes from altered cytoskeletal gene expression.

A growing body of evidence suggests that the developmental process of epithelial-to-mesenchymal transition (EMT) is co-opted by cancer cells to metastasize to distant sites. This transition is associated with morphologic elongation and loss of cell-cell adhesions, though little is known about how it alters cell biophysical properties critical for migration. Here, we use multiple-particle tracking (MPT) microrheology and traction force cytometry to probe how genetic induction of EMT in epithelial MCF7 breast cancer cells changes their intracellular stiffness and extracellular force exertion, respectively, relative to an empty vector control. This analysis demonstrated that EMT alone was sufficient to produce dramatic cytoskeletal softening coupled with increases in cell-exerted traction forces. Microarray analysis revealed that these changes corresponded with down-regulation of genes associated with actin cross-linking and up-regulation of genes associated with actomyosin contraction. Finally, we show that this loss of structural integrity to expedite migration could inhibit mesenchymal cell proliferation in a secondary tumor as it accumulates solid stress. This work demonstrates that not only does EMT enable escape from the primary tumor through loss of cell adhesions but it also induces a concerted series of biophysical changes enabling enhanced migration of cancer cells after detachment from the primary tumor.

Glycomic analysis of membrane glycoproteins with bisecting glycosylation from ovarian cancer tissues reveals novel structures and functions.

Biomarkers capable of detecting and targeting epithelial ovarian cancer cells for diagnostics and therapeutics would be extremely valuable. Ovarian cancer is the deadliest reproductive malignancy among women in the U.S., killing over 14 000 women each year. Both the lack of presenting symptoms and high mortality rates illustrate the need for earlier diagnosis and improved treatment of this disease. The glycosyltransferase enzyme GnT-III encoded by the Mgat3 gene is responsible for the addition of GlcNAc (N-acetylglucosamine) to form bisecting N-linked glycan structures. GnT-III mRNA expression is amplified in ovarian cancer tissues compared with normal ovarian tissue. We use a lectin capture strategy coupled to nano-ESI-RPLC-MS/MS to isolate and identify the membrane glycoproteins and unique glycan structures associated with GnT-III amplification in human ovarian cancer tissues. Our data illustrate that the majority of membrane glycoproteins with bisecting glycosylation are common to both serous and endometrioid histological subtypes of ovarian cancer, and several have been reported to participate in signaling pathways such as Notch, Wnt, and TGFß.

p66Shc longevity protein regulates the proliferation of human ovarian cancer cells.

p66Shc functions as a longevity protein in murine and exhibits oxidase activity in regulating diverse biological activities. In this study, we investigated the role of p66Shc protein in regulating ovarian cancer (OCa) cell proliferation. Among three cell lines examined, the slowest growing OVCAR-3 cells have the lowest level of p66Shc protein. Transient transfection with p66Shc cDNA expression vector in OVCAR-3 cells increases cell proliferation. Conversely, knock-down of p66Shc by shRNA in rapidly growing SKOV-3 cells results in decreased cell growth. In estrogen (E2)-treated CaOV-3 cells, elevated p66Shc protein level correlates with ROS level, ErbB-2 and ERK/MAPK activation, and cell proliferation. Further, the E2-stimulated proliferation of CaOV-3 cells was blocked by antioxidants and ErbB-2 inhibitor. Additionally, in E2-stimulated cells, the tartrate-sensitive, but not the tartrate-resistant, phosphatase activity decreases; concurrently, the tyrosine phosphorylation of ErbB-2 increases. Conversely, inhibition of phosphatase activity by L(+)-tartrate treatment increases p66Shc protein level, ErbB-2 tyrosine phosphorylation, ERK/MAPK activation, and cell growth. Further, inhibition of the ERK/MAPK pathway by PD98059 blocks E2-induced ERK/MAPK activation and cell proliferation in CaOV-3 cells. Moreover, immunohistochemical analyses showed that the p66Shc protein level was significantly higher in cancerous cells than in noncancerous cells in archival OCa tissues (n = 76; P = 0.00037). These data collectively indicate that p66Shc protein plays a critical role in up-regulating OCa progression.

Feasibility of detecting prostate cancer by ultraperformance liquid chromatography-mass spectrometry serum metabolomics.

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

Ectopic over-expression of miR-429 induces mesenchymal-to-epithelial transition (MET) and increased drug sensitivity in metastasizing ovarian cancer cells.

OBJECTIVE: We recently determined that the ectopic over-expression of miR-429 and other members of the miR-200 family of microRNAs in ovarian cancer (OC) mesenchymal-like cell lines induces mesenchymal-to-epithelial transition (MET) with a concomitant increase in sensitivity to platinum drugs. We sought to determine if metastasizing OC cells isolated from an OC patient could also be induced by miR-429 to undergo MET and become sensitized to established first-line platinum-based therapies. METHODS: We established and characterized a new primary cell line (OCI-984) from free-floating OC cells isolated from the ascites fluid of an advanced stage OC patient. miR-429 was ectopically over-expressed in these cells. RESULTS: The over-expression of miR-429 in OCI-984 cells induced morphological, functional and molecular changes consistent with MET and a concomitant significant increase in the sensitivity of the converted cells to cisplatin. CONCLUSIONS: Our findings indicate that the miR-200 family of microRNAs, and miR-429 in particular, play a critical role in the functioning of OC metastasizing cells and that targeted delivery of miR-429, and perhaps other miR-200 family members, in combination with platinum-based chemotherapies may be an effective strategy in reducing OC metastasis and tumor recurrence.

R-SAP: a multi-threading computational pipeline for the characterization of high-throughput RNA-sequencing data.

The rapid expansion in the quantity and quality of RNA-Seq data requires the development of sophisticated high-performance bioinformatics tools capable of rapidly transforming this data into meaningful information that is easily interpretable by biologists. Currently available analysis tools are often not easily installed by the general biologist and most of them lack inherent parallel processing capabilities widely recognized as an essential feature of next-generation bioinformatics tools. We present here a user-friendly and fully automated RNA-Seq analysis pipeline (R-SAP) with built-in multi-threading capability to analyze and quantitate high-throughput RNA-Seq datasets. R-SAP follows a hierarchical decision making procedure to accurately characterize various classes of transcripts and achieves a near linear decrease in data processing time as a result of increased multi-threading. In addition, RNA expression level estimates obtained using R-SAP display high concordance with levels measured by microarrays.

Molecular analysis of the inhibitory effect of N-acetyl-L-cysteine on the proliferation and invasiveness of pancreatic cancer cells.

Preliminary studies have suggested that the reactive oxygen species (ROS) scavenger N-acetyl-L-cysteine (NAC) may be effective in inhibiting the growth of pancreatic cancer cells. In-depth cellular and molecular analyses were carried out to determine NAC's mode of action in inhibiting the growth of a well-characterized pancreatic cancer cell line (AsPC-1). Standardized assays were used to monitor cellular growth, apoptosis, levels of ROS, cellular senescence, migration, and invasiveness. Cell stiffness was measured using atomic force microscopy. Gene expression was monitored by quantitative PCR. NAC significantly inhibits the growth and metastatic potential of AsPC-1 cells by inducing cell-cycle arrest in G1 and subsequent cellular senescence and decreased invasiveness. These anticancer properties are associated with an unexpected increase in the intracellular concentrations of ROS. NAC does not decrease the susceptibility of AsPC-1 cells to the anticancer drugs gemcitabine, mitomycin C, and doxorubicin. NAC-induced changes in gene expression are consistent with the onset of mesenchymal-to-epithelial transition. In conclusion, our findings indicate that NAC induces an integrated series of responses in AsPC-1 cells that make it a highly promising candidate for development as a pancreatic cancer therapeutic.

Ethnic disparities in mortality and group-specific risk factors in the UK Biobank.

Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.

Correlating mechanical and gene expression data on the single cell level to investigate metastatic phenotypes.

Stiffness has been observed to decrease for many cancer cell types as their metastatic potential increases. Although cell mechanics and metastatic potential are related, the underlying molecular factors associated with these phenotypes remain unknown. Therefore, we have developed a workflow to measure the mechanical properties and gene expression of single cells that is used to generate large linked-datasets. The process combines atomic force microscopy to measure the mechanics of individual cells with multiplexed RT-qPCR gene expression analysis on the same single cells. Surprisingly, the genes that most strongly correlated with mechanical properties were not cytoskeletal, but rather were markers of extracellular matrix remodeling, epithelial-to-mesenchymal transition, cell adhesion, and cancer stemness. In addition, dimensionality reduction analysis showed that cell clustering was improved by combining mechanical and gene expression data types. The single cell genomechanics method demonstrates how single cell studies can identify molecular drivers that could affect the biophysical processes underpinning metastasis.

Isolation and characterization of stem-like cells from a human ovarian cancer cell line.

Increasing evidence supports the existence of a subpopulation of cancer cells capable of self-renewal and differentiation into diverse cell lineages. These cancer stem-like or cancer-initiating cells (CICs) also demonstrate resistance to chemo- and radiotherapy and may function as a primary source of cancer recurrence. We report here on the isolation and in vitro propagation of multicellular ovarian cancer spheroids from a well-established ovarian cancer cell line (OVCAR-3). The spheroid-derived cells (SDCs) display self-renewal potential, the ability to produce differentiated progeny, and increased expression of genes previously associated with CICs. SDCs also demonstrate higher invasiveness, migration potential, and enhanced resistance to standard anticancer agents relative to parental OVCAR-3 cells. Furthermore, SDCs display up-regulation of genes associated with epithelial-to-mesenchymal transition (EMT), anticancer drug resistance and/or decreased susceptibility to apoptosis, as well as, down-regulation of genes typically associated with the epithelial cell phenotype and pro-apoptotic genes. Pathway and biological process enrichment analyses indicate significant differences between the SDCs and precursor OVCAR-3 cells in TGF-beta-dependent induction of EMT, regulation of lipid metabolism, NOTCH and Hedgehog signaling. Collectively, our results indicate that these SDCs will be a useful model for the study of ovarian CICs and for the development of novel CIC-targeted therapies.

The serum vaspin levels are reduced in Japanese chronic hemodialysis patients.

BACKGROUND: Visceral adipose tissue-derived serine proteinase inhibitor (vaspin) is an adipokine identified in genetically obese rats that correlates with insulin resistance and obesity in humans. Recently, we found that 7% of the Japanese population with the minor allele sequence (A) of rs77060950 exhibit higher levels of serum vaspin. We therefore evaluated the serum vaspin levels in Japanese chronic hemodialysis patients. METHODS: Healthy Japanese control volunteers (control; n = 95, 49.9 ± 6.91 years) and Japanese patients undergoing hemodialysis therapy (HD; n = 138, 51.4 ± 10.5 years) were enrolled in this study, and serum samples were subjected to the human vaspin RIA system. RESULTS: The measurement of the serum vaspin levels demonstrated that a fraction of control subjects (n = 5) and HD patients (n = 11) exhibited much higher levels (> 10 ng/ml; Vaspin High group), while the rest of the population exhibited lower levels (< 3 ng/ml; Vaspin Low group). By comparing the patients in the Vaspin Low group, the serum vaspin levels were found to be significantly higher in the control subjects (0.87 ± 0.24 ng/ml) than in the HD patients (0.32 ± 0.15 ng/ml) (p < 0.0001). In the stepwise regression analyses, the serum creatinine and triglyceride levels were found to be independently and significantly associated with the vaspin concentrations in all subjects. CONCLUSIONS: The creatinine levels are negatively correlated with the serum vaspin levels and were significantly reduced in the Japanese HD patients in the Vaspin Low group.

A computational approach to generate highly conserved gene co-expression networks with RNA-seq data.

We describe a consensus approach for network construction based on fully conserved gene-gene interactions from randomly downsampled data subsets for an unbiased differential analysis of gene co-expression networks. The pipeline allows users to identify network nodes lost, conserved, and acquired in cancer as well as interpret the functional significance of these network changes. For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database. For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021).

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.