Genomic aberrations relate early and advanced stage ovarian cancer

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

Apparent Diffusion Coefficient Histogram Analysis for Assessing Tumor Staging and Detection of Lymph Node Metastasis in Epithelial Ovarian Cancer: Correlation with p53 and Ki-67 Expression

PURPOSE

To investigate the potential of apparent diffusion coefficient (ADC) histogram parameters in epithelial ovarian cancer (EOC) for distinguishing different tumor stages and determining lymph node status and correlations between ADC values and p53 and Ki-67 expression.

PROCEDURES

Forty-nine EOC patients underwent preoperative magnetic resonance imaging. Staging and lymph node status were determined postoperatively. ADC values were measured using histogram analysis and compared between groups. Relationships between ADCs and Ki-67 and p53 expression were explored.

RESULTS

DC parameters differed significantly between stage I vs II, I vs III, and I vs IV. The parameters were significantly lower in the lymph node-positive group than in the lymph node-negative group, were significantly negatively correlated with Ki-67 labeling index, and were all significantly lower in the mutation-type p53 group than in the wild-type p53 group.

CONCLUSIONS

ADC histogram analysis can help discriminate stage I from advanced-stage EOC and predict lymph node metastasis. ADC parameters were correlated with Ki-67 labeling index; the parameters may help indicate p53 expression.

Application and optimization of minimally invasive cell-free DNA techniques in oncogenomics

The conventional method of measuring biomarkers in malignant tissue samples has already given subversive growth in cancer diagnosis, prognosis, and therapy selection. However, the regression and heterogeneity associated with tumor tissue biopsy have urged for the development of an alternative approach. Considering the limitations, cell-free DNA has emerged as a surrogate alternative, facilitating preoperative chemoradiotherapy (p < 0.0001) treatment response in rectal cancer and detection of biomarker in lung cancer. This potential of cell-free DNA in several other cancers has yet to be explored based on clinical relevance by optimizing the preanalytical factors. This review has highlighted the crucial parameters from blood collection to cell-free DNA analysis that has a significant impact on the accuracy and reliability of clinical data. The quantity of cell-free DNA is also a limiting factor. Therefore, a proper preanalytical factor for blood collection, its stability, centrifugation speed, and plasma storage condition are to be optimized for developing cancer-specific biomarkers useful for clinical purpose. Liquid biopsy-based origin of cell-free DNA has revolutionized the area of cancer research. Lack of preanalytical and analytical procedures may be considered for identification of novel biomarkers through next-generation sequencing of tumor-originated cell-free DNA in contradiction to tissue biopsy for cancer-specific biomarkers.

Effects of Endocrine-Disrupting Chemicals and Epigenetic Modifications in Ovarian Cancer: A Review

Ovarian cancer (OC) is a relatively fatal female reproductive malignancy. Since the underlying causes are uncertain, it brings us to believe that both genetic and external factors contribute toward development of this lethal disorder. Exposure to endocrine-disrupting chemicals (EDCs) in the form of occupational usage of pesticides, fungicides, herbicides, plasticizers, cosmetics, and so on is potentially carcinogenic and their ability to cause epigenetic modifications has led us to hypothesize that they may play a catalytic role in OC progression. In response to synthetic chemicals, animal models have demonstrated disturbances in the development of ovaries and steroid hormonal levels but in humans, more research is required. The present review is an attempt to address the impact of EDCs on the hormonal system and gene methylation levels that may lead to malfunctioning of the ovaries which may consequently develop in the form of cancer. It can be concluded that endocrine disruptors do have a potential carcinogenicity and their high proportions in human body may cause epigenetic modifications, prompting ovarian surface epithelium to grow in an abnormal manner.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.