PHLPP1 regulates CFTR activity and lumen expansion through AMPK.

Complex organ development depends on single lumen formation and its expansion during tubulogenesis. This can be achieved by correct mitotic spindle orientation during cell division, combined with luminal fluid filling that generates hydrostatic pressure. Using a human 3D cell culture model, we have identified two regulators of these processes. We find that pleckstrin homology leucine-rich repeat protein phosphatase (PHLPP) 2 regulates mitotic spindle orientation, and thereby midbody positioning and maintenance of a single lumen. Silencing the sole PHLPP family phosphatase in Drosophila melanogaster, phlpp, resulted in defective spindle orientation in Drosophila neuroblasts. Importantly, cystic fibrosis transmembrane conductance regulator (CFTR) is the main channel regulating fluid transport in this system, stimulated by phosphorylation by protein kinase A and inhibited by the AMP-activated protein kinase AMPK. During lumen expansion, CFTR remains open through the action of PHLPP1, which stops activated AMPK from inhibiting ion transport through CFTR. In the absence of PHLPP1, the restraint on AMPK activity is lost and this tips the balance in the favour of channel closing, resulting in the lack of lumen expansion and accumulation of mucus.

Multi-omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma.

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival.

BACKGROUND: Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer. METHODS: DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT. RESULTS: DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049). CONCLUSION: Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor.

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

Rank-based Bayesian variable selection for genome-wide transcriptomic analyses.

Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Bayesian Mallows model for variable selection that allows for a full probabilistic analysis, leading to coherent quantification of uncertainties. Simulation studies demonstrate the versatility and robustness of the proposed method in a variety of scenarios, as well as its superiority with respect to several competitors when varying the data dimension or data generating process. We use the novel approach to analyze genome-wide RNAseq gene expression data from ovarian cancer patients: several genes that affect cancer development are correctly detected in a completely unsupervised fashion, showing the usefulness of the method in the context of signature discovery for cancer genomics. Moreover, the possibility to also perform uncertainty quantification plays a key role in the subsequent biological investigation.

Aberrant DNA methylation impacts gene expression and prognosis in breast cancer subtypes.

DNA methylation has a substantial impact on gene expression, affecting the prognosis of breast cancer (BC) patients dependent on molecular subtypes. In this study, we investigated the prognostic relevance of the expression of genes reported as aberrantly methylated, and the link between gene expression and DNA methylation in BC subtypes. The prognostic value of the expression of 144 aberrantly methylated genes was evaluated in ER+/HER2-, HER2+, and ER-/HER2- molecular BC subtypes, in a meta-analysis of two large transcriptomic cohorts of BC patients (n = 1,938 and n = 1,640). The correlation between gene expression and DNA methylation in distinct gene regions was also investigated in an independent dataset of 104 BCs. Survival and Pearson correlation analyses were computed for each gene separately. The expression of 48 genes was significantly associated with BC prognosis (p < 0.05), and 32 of these prognostic genes exhibited a direct expression-methylation correlation. The expression of several immune-related genes, including CD3D and HLA-A, was associated with both relapse-free survival (HR = 0.42, p = 3.5E-06; HR = 0.35, p = 1.7E-08) and overall survival (HR = 0.50, p = 5.5E-04; HR = 0.68, p = 4.5E-02) in ER-/HER2- BCs. On the overall, the distribution of both positive and negative expression-methylation correlation in distinct gene regions have different effects on gene expression and prognosis in BC subtypes. This large-scale meta-analysis allowed the identification of several genes consistently associated with prognosis, whose DNA methylation could represent a promising biomarker for prognostication and clinical stratification of patients with distinct BC subtypes.

Integrated analysis of high-resolution DNA methylation profiles, gene expression, germline genotypes and clinical end points in breast cancer patients.

Breast cancer is a heterogeneous disease for which alterations in DNA methylation patterns have been shown to be of biological and clinical importance. Here we report on the integrated analysis of molecular alterations including the methylation status of 27 gene promoters analyzed by highly quantitative pyrosequencing, and the association to gene expression, germline genotype and clinical parameters including survival. Breast cancer specific deregulation of DNA methylation (both hyper- and hypomethylation) was found in twenty genes including ACVR1, OGG1, IL8 and TFF1. The methylation level in the promoter regions was significantly negatively correlated to gene expression for twelve genes (such as MST1R, ST6GAL1 and TFF1) indicating that a gain of aberrant methylation (hypermethylation) inhibits gene expression. Multiple associations between molecular and clinical parameters were identified, and multivariate statistical analysis demonstrated that methylation was more strongly associated to clinical parameters than gene expression for the investigated genes. The methylation level of BCAP31 and OGG1 showed significant association to survival, and these associations were validated in a larger patient cohort (The Cancer Genome Atlas). Our study provides evidence for the promise of DNA methylation alterations for clinical applications.

Differential DNA methylation analysis of breast cancer reveals the impact of immune signaling in radiation therapy.

Radiotherapy (RT) is a central treatment modality for breast cancer patients. The purpose of our study was to investigate the DNA methylation changes in tumors following RT, and to identify epigenetic markers predicting treatment outcome. Paired biopsies from patients with inoperable breast cancer were collected both before irradiation (n = 20) and after receiving 10-24 Gray (Gy) (n = 19). DNA methylation analysis was performed by using Illumina Infinium 27K arrays. Fourteen genes were selected for technical validation by pyrosequencing. Eighty-two differentially methylated genes were identified in irradiated (n = 11) versus nonirradiated (n = 19) samples (false discovery rate, FDR = 1.1%). Methylation levels in pathways belonging to the immune system were most altered after RT. Based on methylation levels before irradiation, a panel of five genes (H2AFY, CTSA, LTC4S, IL5RA and RB1) were significantly associated with clinical response (p = 0.041). Furthermore, the degree of methylation changes for 2,516 probes correlated with the given radiation dose. Within the 2,516 probes, an enrichment for pathways involved in cellular immune response, proliferation and apoptosis was identified (FDR < 5%). Here, we observed clear differences in methylation levels induced by radiation, some associated with response to treatment. Our study adds knowledge on the molecular mechanisms behind radiation response.

An integrated omics approach highlights how epigenetic events can explain and predict response to neoadjuvant chemotherapy and bevacizumab in breast cancer.

Treatment with the anti-angiogenic drug bevacizumab in addition to chemotherapy has shown efficacy for breast cancer in some clinical trials, but better biomarkers are needed to optimally select patients for treatment. Here, we present an omics approach where DNA methylation profiles are integrated with gene expression and results from proteomic data in breast cancer patients to predict response to therapy and pinpoint response-related epigenetic events. Fresh-frozen tumor biopsies taken before, during, and after treatment from human epidermal growth factor receptor 2 negative non-metastatic patients receiving neoadjuvant chemotherapy with or without bevacizumab were subjected to molecular profiling. Here, we report that DNA methylation at enhancer CpGs related to cell cycle regulation can predict response to chemotherapy and bevacizumab for the estrogen receptor positive subset of patients (AUC = 0.874), and we validated this observation in an independent patient cohort with a similar treatment regimen (AUC = 0.762). Combining the DNA methylation scores with the scores from a previously published protein signature resulted in a slight increase in the prediction performance (AUC = 0.784). We also show that tumors receiving the combination treatment underwent more extensive epigenetic alterations. Finally, we performed an integrative expression-methylation quantitative trait loci analysis on alterations in DNA methylation and gene expression levels, showing that the epigenetic alterations that occur during treatment are different between responders and non-responders and that these differences may be explained by the proliferation-epithelial-to-mesenchymal transition axis through the activity of grainyhead like transcription factor 2. Using tumor purity computed from copy number data, we developed a method for estimating cancer cell-specific methylation to confirm that the association to response reflects DNA methylation in cancer cells. Taken together, these results support the potential for clinical benefit of the addition of bevacizumab to chemotherapy when administered to the correct patients.

Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma.

Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in the onset of epithelial ovarian cancer (OvC). TL in either target or surrogate tissue (blood) is currently being investigated for use as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is currently an urgent need for an appropriate approach to chemotherapy response prediction. We performed a monochrome multiplex qPCR measurement of TL in peripheral blood leukocytes (PBL) and tumor tissues of 209 OvC patients. The methylation status and gene expression of the shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n = 46) had shorter telomeres in PBL compared to treatment-resistant patients (n = 93; P = 0.037). In the patients with a different therapy response, transcriptomic analysis showed alterations in the peroxisome proliferator-activated receptor (PPAR) signaling pathway (q = 0.001). Moreover, tumor TL shorter than the median corresponded to better overall survival (OS) (P = 0.006). TPP1 gene expression was positively associated with TL in tumor tissue (P = 0.026). TL measured in PBL could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' OS.

Quantitative DNA methylation analyses reveal stage dependent DNA methylation and association to clinico-pathological factors in breast tumors.

BACKGROUND: Aberrant DNA methylation of regulatory genes has frequently been found in human breast cancers and correlated to clinical outcome. In the present study we investigate stage specific changes in the DNA methylation patterns in order to identify valuable markers to understand how these changes affect breast cancer progression. METHODS: Quantitative DNA methylation analyses of 12 candidate genes ABCB1, BRCCA1, CDKN2A, ESR1, GSTP1, IGF2, MGMT, HMLH1, PPP2R2B, PTEN, RASSF1A and FOXC1 was performed by pyrosequencing a series of 238 breast cancer tissue samples from DCIS to invasive tumors stage I to IV. RESULTS: Significant differences in methylation levels between the DCIS and invasive stage II tumors were observed for six genes RASSF1A, CDKN2A, MGMT, ABCB1, GSTP1 and FOXC1. RASSF1A, ABCB1 and GSTP1 showed significantly higher methylation levels in late stage compared to the early stage breast carcinoma. Z-score analysis revealed significantly lower methylation levels in DCIS and stage I tumors compared with stage II, III and IV tumors. Methylation levels of PTEN, PPP2R2B, FOXC1, ABCB1 and BRCA1 were lower in tumors harboring TP53 mutations then in tumors with wild type TP53. Z-score analysis showed that TP53 mutated tumors had significantly lower overall methylation levels compared to tumors with wild type TP53. Methylation levels of RASSF1A, PPP2R2B, GSTP1 and FOXC1 were higher in ER positive vs. ER negative tumors and methylation levels of PTEN and CDKN2A were higher in HER2 positive vs. HER2 negative tumors. Z-score analysis also showed that HER2 positive tumors had significantly higher z-scores of methylation compared to the HER2 negative tumors. Univariate survival analysis identifies methylation status of PPP2R2B as significant predictor of overall survival and breast cancer specific survival. CONCLUSIONS: In the present study we report that the level of aberrant DNA methylation is higher in late stage compared with early stage of invasive breast cancers and DCIS for genes mentioned above.

Transcriptomic pan-cancer analysis using rank-based Bayesian inference.

The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

ABCB1 Amplicon Contains Cyclic AMP Response Element-Driven TRIP6 Gene in Taxane-Resistant MCF-7 Breast Cancer Sublines.

A limited number of studies are devoted to regulating TRIP6 expression in cancer. Hence, we aimed to unveil the regulation of TRIP6 expression in MCF-7 breast cancer cells (with high TRIP6 expression) and taxane-resistant MCF-7 sublines (manifesting even higher TRIP6 expression). We found that TRIP6 transcription is regulated primarily by the cyclic AMP response element (CRE) in hypomethylated proximal promoters in both taxane-sensitive and taxane-resistant MCF-7 cells. Furthermore, in taxane-resistant MCF-7 sublines, TRIP6 co-amplification with the neighboring ABCB1 gene, as witnessed by fluorescence in situ hybridization (FISH), led to TRIP6 overexpression. Ultimately, we found high TRIP6 mRNA levels in progesterone receptor-positive breast cancer and samples resected from premenopausal women.

MRI Assessment of Changes in Tumor Vascularization during Neoadjuvant Anti-Angiogenic Treatment in Locally Advanced Breast Cancer Patients.

Anti-VEGF (vascular endothelial growth factor) treatment improves response rates, but not progression-free or overall survival in advanced breast cancer. It has been suggested that subgroups of patients may benefit from this treatment; however, the effects of adding anti-VEGF treatment to a standard chemotherapy regimen in breast cancer patients are not well studied. Understanding the effects of the anti-vascular treatment on tumor vasculature may provide a selection of patients that can benefit. The aim of this study was to study the vascular effect of bevacizumab using clinical dynamic contrast-enhanced MRI (DCE-MRI). A total of 70 women were randomized to receive either chemotherapy alone or chemotherapy with bevacizumab for 25 weeks. DCE-MRI was performed at baseline and at 12 and 25 weeks, and in addition 25 of 70 patients agreed to participate in an early MRI after one week. Voxel-wise pharmacokinetic analysis was performed using semi-quantitative methods and the extended Tofts model. Vascular architecture was assessed by calculating the fractal dimension of the contrast-enhanced images. Changes during treatment were compared with baseline and between the treatment groups. There was no significant difference in tumor volume at any point; however, DCE-MRI parameters revealed differences in vascular function and vessel architecture. Adding bevacizumab to chemotherapy led to a pronounced reduction in vascular DCE-MRI parameters, indicating decreased vascularity. At 12 and 25 weeks, the difference between the treatment groups is severely reduced.

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers.

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesised that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression-methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer.

Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-ß, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

Quantification of Tumor Hypoxia through Unsupervised Modelling of Consumption and Supply Hypoxia MR Imaging in Breast Cancer.

The purpose of the present study is to investigate if consumption and supply hypoxia (CSH) MR-imaging can depict breast cancer hypoxia, using the CSH-method initially developed for prostate cancer. Furthermore, to develop a generalized pan-cancer application of the CSH-method that doesn't require a hypoxia reference standard for training the CSH-parameters. In a cohort of 69 breast cancer patients, we generated, based on the principles of intravoxel incoherent motion modelling, images reflecting cellular density (apparent diffusion coefficient; ADC) and vascular density (perfusion fraction; fp). Combinations of the information in these images were compared to a molecular hypoxia score made from gene expression data, aiming to identify a way to apply the CSH-methodology in breast cancer. Attempts to adapt previously proposed models for prostate cancer included direct transfers and model parameter rescaling. A novel approach, based on rescaling ADC and fp data to give more nuanced response in the relevant physiologic range, was also introduced. The new CSH-method was validated in a prostate cancer cohort with known hypoxia status. The proposed CSH-method gave estimates of hypoxia that was strongly correlated to the molecular hypoxia score in breast cancer, and hypoxia as measured in pathology slices stained with pimonidazole in prostate cancer. The generalized approach to CSH-imaging depicted hypoxia in both breast and prostate cancers and requires no model training. It is easy to implement using readily available technology and encourages further investigation of CSH-imaging in other cancer entities and in other settings, with the goal being to overcome hypoxia-induced resistance to treatment.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Complex molecular profile of DNA repair genes in epithelial ovarian carcinoma patients with different sensitivity to platinum-based therapy.

Epithelial ovarian carcinoma (EOC) is known for high mortality due to diagnosis at advanced stages and frequent therapy resistance. Previous findings suggested that the DNA repair system is involved in the therapeutic response of cancer patients and DNA repair genes are promising targets for novel therapies. This study aimed to address complex inter-relations among gene expression levels, methylation profiles, and somatic mutations in DNA repair genes and EOC prognosis and therapy resistance status. We found significant associations of DUT expression with the presence of peritoneal metastases in EOC patients. The high-grade serous EOC subtype was enriched with TP53 mutations compared to other subtypes. Furthermore, somatic mutations in XPC and PRKDC were significantly associated with worse overall survival of EOC patients, and higher FAAP20 expression in platinum-resistant than platinum-sensitive patients was observed. We found higher methylation of RAD50 in platinum-resistant than in platinum-sensitive patients. Somatic mutations in BRCA1 and RAD9A were significantly associated with higher RBBP8 methylation in platinum-sensitive compared to platinum-resistant EOC patients. In conclusion, we discovered associations of several candidate genes from the DNA repair pathway with the prognosis and platinum resistance status of EOC patients, which deserve further validation as potential predictive biomarkers.

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression.

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.