Genome-wide quantification of copy-number aberration impact on gene expression in ovarian high-grade serous carcinoma.

Copy-number alterations (CNAs) are a hallmark of cancer and can regulate cancer cell states via altered gene expression values. Herein, we have developed a copy-number impact (CNI) analysis method that quantifies the degree to which a gene expression value is impacted by CNAs and leveraged this analysis at the pathway level. Our results show that a high CNA is not necessarily reflected at the gene expression level, and our method is capable of detecting genes and pathways whose activity is strongly influenced by CNAs. Furthermore, the CNI analysis enables unbiased categorization of CNA categories, such as deletions and amplifications. We identified six CNI-driven pathways associated with poor treatment response in ovarian high-grade serous carcinoma (HGSC), which we found to be the most CNA-driven cancer across 14 cancer types. The key driver in most of these pathways was amplified wild-type KRAS, which we validated functionally using CRISPR modulation. Our results suggest that wild-type KRAS amplification is a driver of chemotherapy resistance in HGSC and may serve as a potential treatment target.

HRD related signature 3 predicts clinical outcome in advanced tubo-ovarian high-grade serous carcinoma.

OBJECTIVES: We evaluated usability of single base substitution signature 3 (Sig3) as a biomarker for homologous recombination deficiency (HRD) in tubo-ovarian high-grade serous carcinoma (HGSC). MATERIALS AND METHODS: This prospective observational trial includes 165 patients with advanced HGSC. Fresh tissue samples (n = 456) from multiple intra-abdominal areas at diagnosis and after neoadjuvant chemotherapy (NACT) were collected for whole-genome sequencing. Sig3 was assessed by fitting samples independently with COSMIC v3.2 reference signatures. An HR scar assay was applied for comparison. Progression-free survival (PFS) and overall survival (OS) were studied using Kaplan-Meier and Cox regression analysis. RESULTS: Sig3 has a bimodal distribution, eliminating the need for an arbitrary cutoff typical in HR scar tests. Sig3 could be assessed from samples with low (10%) cancer cell proportion and was consistent between multiple samples and stable during NACT. At diagnosis, 74 (45%) patients were HRD (Sig3+), while 91 (55%) were HR proficient (HRP, Sig3-). Sig3+ patients had longer PFS and OS than Sig3- patients (22 vs. 13 months and 51 vs. 34 months respectively, both p < 0.001). Sig3 successfully distinguished the poor prognostic HRP group among BRCAwt patients (PFS 19 months for Sig3+ and 13 months for Sig3- patients, p < 0.001). However, Sig3 at diagnosis did not predict chemoresponse anymore in the first relapse. The patient-level concordance between Sig3 and HR scar assay was 87%, and patients with HRD according to both tests had the longest median PFS. CONCLUSIONS: Sig3 is a prognostic marker in advanced HGSC and useful tool in patient stratification for HRD.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

POIBM: batch correction of heterogeneous RNA-seq datasets through latent sample matching.

MOTIVATION: RNA sequencing and other high-throughput technologies are essential in understanding complex diseases, such as cancers, but are susceptible to technical factors manifesting as patterns in the measurements. These batch patterns hinder the discovery of biologically relevant patterns. Unbiased batch effect correction in heterogeneous populations currently requires special experimental designs or phenotypic labels, which are not readily available for patient samples in existing datasets. RESULTS: We present POIBM, an RNA-seq batch correction method, which learns virtual reference samples directly from the data. We use a breast cancer cell line dataset to show that POIBM exceeds or matches the performance of previous methods, while being blind to the phenotypes. Further, we analyze The Cancer Genome Atlas RNA-seq data to show that batch effects plague many cancer types; POIBM effectively discovers the true replicates in stomach adenocarcinoma; and integrating the corrected data in endometrial carcinoma improves cancer subtyping. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/anthakki/poibm/ (archived at https://doi.org/10.5281/zenodo.6122436). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability.

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.

FUNGI: FUsioN Gene Integration toolset.

MOTIVATION: Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules. RESULTS: We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance. AVAILABILITYAND IMPLEMENTATION: FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Agile workflow for interactive analysis of mass cytometry data.

MOTIVATION: Single-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single-cell resolution. These large amounts of data, require dedicated, interactive tools for translating the data into knowledge. RESULTS: We present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automates the use of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood and cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples. AVAILABILITYAND IMPLEMENTATION: The method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cyto. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets.

MOTIVATION: Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. RESULTS: We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ALDH1A1-related stemness in high-grade serous ovarian cancer is a negative prognostic indicator but potentially targetable by EGFR/mTOR-PI3K/aurora kinase inhibitors.

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer (HGSC). Cancer stem cells are the major contributors to relapse and treatment failure as they can survive conventional therapy. Our objectives were to characterise stemness features in primary patient-derived cell lines, correlate stemness markers with clinical outcome and test the response of our cells to both conventional and exploratory drugs. Tissue and ascites samples, treatment-naive and/or after neoadjuvant chemotherapy, were prospectively collected. Primary cancer cells, cultured under conditions favouring either adherent or spheroid growth, were tested for stemness markers; the same markers were analysed in tissue and correlated with chemotherapy response and survival. Drug sensitivity and resistance testing was performed with 306 oncology compounds. Spheroid growth condition HGSC cells showed increased stemness marker expression (including aldehyde dehydrogenase isoform I; ALDH1A1) as compared with adherent growth condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naive tumours into two clusters and identified a distinct subgroup of HGSC with enriched stemness features. Expression of ALDH1A1, but not most other stemness markers, was increased after neoadjuvant chemotherapy and its expression in treatment-naive tumours correlated with chemoresistance and reduced survival. In drug sensitivity and resistance testing, five compounds, including two PI3K-mTOR inhibitors, demonstrated significant activity in both cell culture conditions. Thirteen compounds, including EGFR, PI3K-mTOR and aurora kinase inhibitors, were more toxic to spheroid cells than adherent cells. Our results identify stemness markers in HGSC that are associated with a decreased response to conventional chemotherapy and reduced survival if expressed by treatment-naive tumours. EGFR, mTOR-PI3K and aurora kinase inhibitors are candidates for targeting this cell population. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ErbB2-driven breast cancer cell invasion depends on a complex signaling network activating myeloid zinc finger-1-dependent cathepsin B expression.

Aberrant ErbB2 receptor tyrosine kinase activation in breast cancer is strongly linked to an invasive disease. The molecular basis of ErbB2-driven invasion is largely unknown. We show that cysteine cathepsins B and L are elevated in ErbB2 positive primary human breast cancer and function as effectors of ErbB2-induced invasion in vitro. We identify Cdc42-binding protein kinase beta, extracellular regulated kinase 2, p21-activated protein kinase 4, and protein kinase C alpha as essential mediators of ErbB2-induced cysteine cathepsin expression and breast cancer cell invasiveness. The identified signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling network that is crucial for invasion in vitro, and defines a specific role and targets for the identified serine-threonine kinases.

Glycomic Profiling Highlights Increased Fucosylation in Pseudomyxoma Peritonei.

Pseudomyxoma peritonei (PMP) is a subtype of mucinous adenocarcinoma that most often originates from the appendix, and grows in the peritoneal cavity filling it with mucinous ascites. KRAS and GNAS mutations are frequently found in PMP, but other common driver mutations are infrequent. As altered glycosylation can promote carcinogenesis, we compared N-linked glycan profiles of PMP tissues to those of normal appendix. Glycan profiles of eight normal appendix samples and eight low-grade and eight high-grade PMP specimens were analyzed by mass spectrometry. Our results show differences in glycan profiles between PMP and the controls, especially in those of neutral glycans, and the most prominent alteration was increased fucosylation. We further demonstrate up-regulated mRNA expression of four fucosylation-related enzymes, the core fucosylation performing fucosyltransferase 8 and three GDP-fucose biosynthetic enzymes in PMP tissues when compared with the controls. Up-regulated protein expression of the latter three enzymes was further observed in PMP cells by immunohistochemistry. We also demonstrate that restoration of fucosylation either by salvage pathway or by introduction of an expression of intact GDP-mannose 4,6-dehydratase enhance expression of MUC2, which is the predominant mucin molecule secreted by the PMP cells, in an intestinal-derived adenocarcinoma cell line with defective fucosylation because of deletion in the GDP-mannose 4,6-dehydratase gene. Thus, altered glycosylation especially in the form of fucosylation is linked to the characteristic mucin production of PMP. Glycomic data are available via ProteomeXchange with identifier PXD010086.

Identifying differentially methylated sites in samples with varying tumor purity.

Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. Availability and implementation: The method is freely available at https://bitbucket.org/anthakki/dmml. Supplementary information: Supplementary data are available at Bioinformatics online.

Tissue-specific pioneer factors associate with androgen receptor cistromes and transcription programs.

Androgen receptor (AR) binds male sex steroids and mediates physiological androgen actions in target tissues. ChIP-seq analyses of AR-binding events in murine prostate, kidney and epididymis show that in vivo AR cistromes and their respective androgen-dependent transcription programs are highly tissue specific mediating distinct biological pathways. This high order of tissue specificity is achieved by the use of exclusive collaborating factors in the three androgen-responsive tissues. We find two novel collaborating factors for AR signaling in vivo--Hnf4α (hepatocyte nuclear factor 4α) in mouse kidney and AP-2α (activating enhancer binding protein 2α) in mouse epididymis--that define tissue-specific AR recruitment. In mouse prostate, FoxA1 serves for the same purpose. FoxA1, Hnf4α and AP-2α motifs are over-represented within unique AR-binding loci, and the cistromes of these factors show substantial overlap with AR-binding events distinct to each tissue type. These licensing or pioneering factors are constitutively bound to chromatin and guide AR to specific genomic loci upon hormone exposure. Collectively, liganded receptor and its DNA-response elements are required but not sufficient for establishment of tissue-specific transcription programs.

Oncogenic Herpesvirus Utilizes Stress-Induced Cell Cycle Checkpoints for Efficient Lytic Replication.

Kaposi's sarcoma herpesvirus (KSHV) causes Kaposi's sarcoma and certain lymphoproliferative malignancies. Latent infection is established in the majority of tumor cells, whereas lytic replication is reactivated in a small fraction of cells, which is important for both virus spread and disease progression. A siRNA screen for novel regulators of KSHV reactivation identified the E3 ubiquitin ligase MDM2 as a negative regulator of viral reactivation. Depletion of MDM2, a repressor of p53, favored efficient activation of the viral lytic transcription program and viral reactivation. During lytic replication cells activated a p53 response, accumulated DNA damage and arrested at G2-phase. Depletion of p21, a p53 target gene, restored cell cycle progression and thereby impaired the virus reactivation cascade delaying the onset of virus replication induced cytopathic effect. Herpesviruses are known to reactivate in response to different kinds of stress, and our study now highlights the molecular events in the stressed host cell that KSHV has evolved to utilize to ensure efficient viral lytic replication.

Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data.

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.

Transmembrane prostatic acid phosphatase (TMPAP) delays cells in G1 phase of the cell cycle.

BACKGROUND: Prostate adenocarcinoma is the most common form of prostate cancer. We have previously shown in a murine model that prostatic acid phosphatase (PAP) deficiency leads to increased cell proliferation and development of prostate adenocarcinoma. The association between PAP and prostate cancer has been reported. Indeed, high PAP enzymatic activity is detected in the serum of patients with metastatic disease while its expression is reduced in prostate cancer tissue. However, the molecular mechanisms behind the onset of the disease remains poorly understood. We previously identified a novel transmembrane prostatic acid phosphatase (TMPAP) isoform, which interacts with snapin. TMPAP is expressed on the plasma membrane, as well as endosomal/lysosomal and exosomal membrane vesicles by means of a tyrosine-based lysosomal targeting motif (Yxxϕ). METHODS: We used stable overexpression of the secreted isoform (SPAP) and TMPAP in LNCaP cells, live cell imaging, microarray and qRT-PCR analyses, and fluid phase uptake of HRP and transferrin. RESULTS: Our results indicate that the stable overexpression of TMPAP, but not SPAP in LNCaP cells reduces cell growth while increasing endo/exocytosis and cell size. Specifically, cells overexpressing TMPAP accumulate in the G1 phase of the cell cycle, and show altered gene expression profile. CONCLUSIONS: Our data suggests that TMPAP may function as a non-canonical tumor suppressor by delaying cell growth in G1 phase of the cell cycle.

Prognostic value of tumour budding in oesophageal cancer: a meta-analysis.

Recently, tumour budding (TB) has been suggested as a strong prognostic marker in oesophageal cancer. The aim of this systematic review is to test the prognostic value of TB in oesophageal cancer by a meta-analysis of previously published studies. We systematically reviewed the literature related to TB by using the bibliographic databases of Scopus, PubMed, and Web of Science. The search was limited to publications in the English language up to and including December 2014. There are 11 retrospective studies in which TB has been evaluated in oesophageal cancer. Two authors independently extracted the results from eligible studies. The meta-analysis of eligible studies revealed that TB is a significant prognosticator for overall survival in oesophageal cancer, with a risk ratio (RR) of 2.97 [95% confidence interval (CI) 1.81-4.85; P = 0.0023] in univariate analysis, and with an RR of 2.07 (95% CI 1.22-3.52; P = 0.017) in multivariate analysis. We conclude that a high TB score is a promising prognostic marker of poor survival in oesophageal cancer. Because of its simplicity, reproducibility and high predictive power, TB is strongly recommended to be included in the routine pathology report of oesophageal cancer.