Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Loss of HIF1A From Pancreatic Cancer Cells Increases Expression of PPP1R1B and Degradation of p53 to Promote Invasion and Metastasis.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinomas (PDACs) are hypovascular, resulting in the up-regulation of hypoxia inducible factor 1 alpha (HIF1A), which promotes the survival of cells under low-oxygen conditions. We studied the roles of HIF1A in the development of pancreatic tumors in mice. METHODS: We performed studies with KrasLSL-G12D/+;Trp53LSL-R172H/+;Pdx1-Cre (KPC) mice, KPC mice with labeled pancreatic epithelial cells (EKPC), and EKPC mice with pancreas-specific depletion of HIF1A. Pancreatic and other tissues were collected and analyzed by histology and immunohistochemistry. Cancer cells were cultured from PDACs from mice and analyzed in cell migration and invasion assays and by immunoblots, real-time polymerase chain reaction, and liquid chromatography-mass spectrometry. We performed studies with the human pancreatic cancer cell lines PATU-8988T, BxPC-3, PANC-1, and MiaPACA-2, which have no or low metastatic activity, and PATU-8988S, AsPC-1, SUIT-2 and Capan-1, which have high metastatic activity. Expression of genes was knocked down in primary cancer cells and pancreatic cancer cell lines by using small hairpin RNAs; cells were injected intravenously into immune-competent and NOD/SCID mice, and lung metastases were quantified. We compared levels of messenger RNAs in pancreatic tumors and normal pancreas in The Cancer Genome Atlas. RESULTS: EKPC mice with pancreas-specific deletion of HIF1A developed more advanced pancreatic neoplasias and PDACs with more invasion and metastasis, and had significantly shorter survival times, than EKPC mice. Pancreatic cancer cells from these tumors had higher invasive and metastatic activity in culture than cells from tumors of EKPC mice. HIF1A-knockout pancreatic cancer cells had increased expression of protein phosphatase 1 regulatory inhibitor subunit 1B (PPP1R1B). There was an inverse correlation between levels of HIF1A and PPP1R1B in human PDAC tumors; higher expression of PPP1R1B correlated with shorter survival times of patients. Metastatic human pancreatic cancer cell lines had increased levels of PPP1R1B and lower levels of HIF1A compared with nonmetastatic cancer cell lines; knockdown of PPP1R1B significantly reduced the ability of pancreatic cancer cells to form lung metastases in mice. PPP1R1B promoted degradation of p53 by stabilizing phosphorylation of MDM2 at Ser166. CONCLUSIONS: HIF1A can act a tumor suppressor by preventing the expression of PPP1R1B and subsequent degradation of the p53 protein in pancreatic cancer cells. Loss of HIF1A from pancreatic cancer cells increases their invasive and metastatic activity.

A Genetically Engineered Primary Human Natural Killer Cell Platform for Cancer Immunotherapy.

Enhancing natural killer (NK) cell cytotoxicity by blocking inhibitory signaling could lead to improved NK-based cancer immunotherapy. Thus, we have developed a highly efficient method for editing the genome of human NK cells using CRISPR/Cas9 to knock out inhibitory signaling molecules. Our method efficiently edits up to 90% of primary peripheral blood NK cells. As a proof-of-principle we demonstrate highly efficient knockout of ADAM17 and PDCD1, genes that have a functional impact on NK cells, and demonstrate that these gene-edited NK cells have significantly improved activity, cytokine production, and cancer cell cytotoxicity. Furthermore, we were able to expand cells to clinically relevant numbers, without loss of activity. We also demonstrate that our CRISPR/Cas9 method can be used for efficient knockin of genes by delivering homologous recombination template DNA using recombinant adeno-associated virus serotype 6 (rAAV6). Our platform represents a feasible method for generating engineered primary NK cells as a universal therapeutic for cancer immunotherapy.

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges.

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment.

Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. In conclusion, our study shows that CRC microbiomes are correlated with tumor mutational profiles, pointing towards possible mechanisms of molecular interaction.

Meta-analytic framework for modeling genetic coexpression dynamics.

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third "coordinator" gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Flap endonuclease overexpression drives genome instability and DNA damage hypersensitivity in a PCNA-dependent manner.

Overexpression of the flap endonuclease FEN1 has been observed in a variety of cancer types and is a marker for poor prognosis. To better understand the cellular consequences of FEN1 overexpression we utilized a model of its Saccharomyces cerevisiae homolog, RAD27. In this system, we discovered that flap endonuclease overexpression impedes replication fork progression and leads to an accumulation of cells in mid-S phase. This was accompanied by increased phosphorylation of the checkpoint kinase Rad53 and histone H2A-S129. RAD27 overexpressing cells were hypersensitive to treatment with DNA damaging agents, and defective in ubiquitinating the replication clamp proliferating cell nuclear antigen (PCNA) at lysine 164. These effects were reversed when the interaction between overexpressed Rad27 and PCNA was ablated, suggesting that the observed phenotypes were linked to problems in DNA replication. RAD27 overexpressing cells also exhibited an unexpected dependence on the SUMO ligases SIZ1 and MMS21 for viability. Importantly, we found that overexpression of FEN1 in human cells also led to phosphorylation of CHK1, CHK2, RPA32 and histone H2AX, all markers of genome instability. Our data indicate that flap endonuclease overexpression is a driver of genome instability in yeast and human cells that impairs DNA replication in a manner dependent on its interaction with PCNA.

Single-cell sequencing in ovarian cancer: a new frontier in precision medicine.

PURPOSE OF REVIEW: This article discusses the advances, applications and challenges of using single-cell RNA sequencing data in guiding treatment decisions for ovarian cancer. RECENT FINDINGS: Genetic heterogeneity is a hallmark of ovarian cancer biology and underlies treatment resistance. Defining the different cell types present within a single ovarian cancer is difficult, but could ultimately lead to improvements in diagnosis and treatment. Next-generation sequencing technologies have rapidly increased our understanding of the molecular landscape of epithelial ovarian cancers, but the majority of these studies are conducted on bulk samples, resulting in data that represents an 'average' of all cells present. Single-cell sequencing provides a means to characterize heterogeneity with a tumor tissue in ovarian cancer patients and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and treatment response. SUMMARY: Single-cell sequencing provides a powerful tool in improving our understanding of tumor cell heterogeneity for the purpose of informing personalized cancer treatment.

Biological Insights into Chemotherapy Resistance in Ovarian Cancer.

The majority of patients with high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy; however, most will develop chemotherapy resistance. Gene signatures may change with the development of chemotherapy resistance in this population, which is important as it may lead to tailored therapies. The objective of this study was to compare tumor gene expression profiles in patients before and after treatment with neoadjuvant chemotherapy (NACT). Tumor samples were collected from six patients diagnosed with HGSOC before and after administration of NACT. RNA extraction and whole transcriptome sequencing was performed. Differential gene expression, hierarchical clustering, gene set enrichment analysis, and pathway analysis were examined in all of the samples. Tumor samples clustered based on exposure to chemotherapy as opposed to patient source. Pre-NACT samples were enriched for multiple pathways involving cell cycle growth. Post-NACT samples were enriched for drug transport and peroxisome pathways. Molecular subtypes based on the pre-NACT sample (differentiated, mesenchymal, proliferative and immunoreactive) changed in four patients after administration of NACT. Multiple changes in tumor gene expression profiles after exposure to NACT were identified from this pilot study and warrant further attention as they may indicate early changes in the development of chemotherapy resistance.