Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

The Tumor Immune Landscape and Architecture of Tertiary Lymphoid Structures in Urothelial Cancer

Candidate immune biomarkers have been proposed for predicting response to immunotherapy in urothelial cancer (UC). Yet, these biomarkers are imperfect and lack predictive power. A comprehensive overview of the tumor immune contexture, including Tertiary Lymphoid structures (TLS), is needed to better understand the immunotherapy response in UC. We analyzed tumor sections by quantitative multiplex immunofluorescence to characterize immune cell subsets in various tumor compartments in tumors without pretreatment and tumors exposed to preoperative anti-PD1/CTLA-4 checkpoint inhibitors (NABUCCO trial). Pronounced immune cell presence was found in UC invasive margins compared to tumor and stroma regions. CD8⁺PD1⁺ T-cells were present in UC, particularly following immunotherapy. The cellular composition of TLS was assessed by multiplex immunofluorescence (CD3, CD8, FoxP3, CD68, CD20, PanCK, DAPI) to explore specific TLS clusters based on varying immune subset densities. Using a k-means clustering algorithm, we found five distinct cellular composition clusters. Tumors unresponsive to anti-PD-1/CTLA-4 immunotherapy showed enrichment of a FoxP3⁺ T-cell-low TLS cluster after treatment. Additionally, cluster 5 (macrophage low) TLS were significantly higher after pre-operative immunotherapy, compared to untreated tumors. We also compared the immune cell composition and maturation stages between superficial (submucosal) and deeper TLS, revealing that superficial TLS had more pronounced T-helper cells and enrichment of early TLS than TLS located in deeper tissue. Furthermore, superficial TLS displayed a lower fraction of secondary follicle like TLS than deeper TLS. Taken together, our results provide a detailed quantitative overview of the tumor immune landscape in UC, which can provide a basis for further studies.

Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning

Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.

Flexible co-data learning for high-dimensional prediction

Clinical research often focuses on complex traits in which many variables play a role in mechanisms driving, or curing, diseases. Clinical prediction is hard when data is high-dimensional, but additional information, like domain knowledge and previously published studies, may be helpful to improve predictions. Such complementary data, or co-data, provide information on the covariates, such as genomic location or P-values from external studies. We use multiple and various co-data to define possibly overlapping or hierarchically structured groups of covariates. These are then used to estimate adaptive multi-group ridge penalties for generalized linear and Cox models. Available group adaptive methods primarily target for settings with few groups, and therefore likely overfit for non-informative, correlated or many groups, and do not account for known structure on group level. To handle these issues, our method combines empirical Bayes estimation of the hyperparameters with an extra level of flexible shrinkage. This renders a uniquely flexible framework as any type of shrinkage can be used on the group level. We describe various types of co-data and propose suitable forms of hypershrinkage. The method is very versatile, as it allows for integration and weighting of multiple co-data sets, inclusion of unpenalized covariates and posterior variable selection. For three cancer genomics applications we demonstrate improvements compared to other models in terms of performance, variable selection stability and validation.

Contralateral parenchymal enhancement on MRI is associated with tumor proteasome pathway gene expression and overall survival of early ER+/HER2-breast cancer patients

Purpose

To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival.

Methods

Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355).

Results

CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate = .11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P = .143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P = .017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P = .020) for OS and 1.10 (95% CI = 1.02, 1.18; P = .012) for IDFS.

Conclusion

CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data.

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Contralateral parenchymal enhancement on MRI was associated with tumor proteasome gene expression in ER+/HER2-breast cancer.

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A high contralateral parenchymal enhancement was associated with a low proteasome gene expression in the breast cancer.

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Low proteasome tumor gene expression was associated with improved survival in an independent patient cohort.

The Prognostic Potential of Human Prostate Cancer-Associated Macrophage Subtypes as Revealed by Single-Cell Transcriptomics

Macrophages in the tumor microenvironment are causally linked with prostate cancer development and progression, yet little is known about their composition in neoplastic human tissue. By performing single cell transcriptomic analysis of human prostate cancer resident macrophages, three distinct populations were identified in the diseased prostate. Unexpectedly, no differences were observed between macrophages isolated from the tumorous and nontumorous portions of the prostatectomy specimens. Markers associated with canonical M1 and M2 macrophage phenotypes were identifiable, however these were not the main factors defining unique subtypes. The genes selectively associated with each macrophage cluster were used to develop a gene signature which was highly associated with both recurrence-free and metastasis-free survival. These results highlight the relevance of tissue-specific macrophage subtypes in the tumor microenvironment for prostate cancer progression and demonstrates the utility of profiling single-cell transcriptomics in human tumor samples as a strategy to design gene classifiers for patient prognostication. IMPLICATIONS: The specific macrophage subtypes present in a diseased human prostate have prognostic value, suggesting that the relative proportions of these populations are related to patient outcome. Understanding the relative contributions of these subtypes will not only inform patient prognostication, but will enable personalized immunotherapeutic strategies to increase beneficial populations or reduce detrimental populations.

Limited evolution of the actionable metastatic cancer genome under therapeutic pressure

Genomic profiling is critical for the identification of treatment options for patients with metastatic cancer, but it remains unclear how frequently this procedure should be repeated during the course of the disease. To address this, we analyzed whole-genome sequencing (WGS) data of 250 biopsy pairs, longitudinally collected over the treatment course of 231 adult patients with a representative variety of metastatic solid malignancies. Within the biopsy interval (median, 6.4 months), patients received one or multiple lines of (mostly) standard-of-care (SOC) treatments, with all major treatment modalities being broadly represented. SOC biomarkers and biomarkers for clinical trial enrollment could be identified in 23% and 72% of biopsies, respectively. For SOC genomic biomarkers, we observed full concordance between the first and the second biopsy in 99% of pairs. Of the 219 biomarkers for clinical trial enrollment that were identified in the first biopsies, we recovered 94% in the follow-up biopsies. Furthermore, a second WGS analysis did not identify additional biomarkers for clinical trial enrollment in 91% of patients. More-frequent genomic evolution was observed when considering specific genes targeted by small-molecule inhibitors or hormonal therapies (21% and 22% of cases, respectively). Together, our data demonstrate that there is limited evolution of the actionable genome of treated metastases. A single WGS analysis of a metastatic biopsy is generally sufficient to identify SOC genomic biomarkers and to identify investigational treatment opportunities.

The Cancer SENESCopedia: A delineation of cancer cell senescence

Cellular senescence is characterized as a stable proliferation arrest that can be triggered by multiple stresses. Most knowledge about senescent cells is obtained from studies in primary cells. However, senescence features may be different in cancer cells, since the pathways that are involved in senescence induction are often deregulated in cancer. We report here a comprehensive analysis of the transcriptome and senolytic responses in a panel of 13 cancer cell lines rendered senescent by two distinct compounds. We show that in cancer cells, the response to senolytic agents and the composition of the senescence-associated secretory phenotype are more influenced by the cell of origin than by the senescence trigger. Using machine learning, we establish the SENCAN gene expression classifier for the detection of senescence in cancer cell samples. The expression profiles and senescence classifier are available as an interactive online Cancer SENESCopedia.

Breast adipocyte size associates with ipsilateral invasive breast cancer risk after ductal carcinoma in situ

Although ductal carcinoma in situ (DCIS) is a non-obligate precursor to ipsilateral invasive breast cancer (iIBC), most DCIS lesions remain indolent. Hence, overdiagnosis and overtreatment of DCIS is a major concern. There is an urgent need for prognostic markers that can distinguish harmless from potentially hazardous DCIS. We hypothesised that features of the breast adipose tissue may be associated with risk of subsequent iIBC. We performed a case-control study nested in a population-based DCIS cohort, consisting of 2658 women diagnosed with primary DCIS between 1989 and 2005, uniformly treated with breast conserving surgery (BCS) alone. We assessed breast adipose features with digital pathology (HALO®, Indica Labs) and related these to iIBC risk in 108 women that developed subsequent iIBC (cases) and 168 women who did not (controls) by conditional logistic regression, accounting for clinicopathological and immunohistochemistry variables. Large breast adipocyte size was significantly associated with iIBC risk (odds ratio (OR) 2.75, 95% confidence interval (95% CI) = 1.25-6.05). High cyclooxygenase (COX)-2 protein expression in the DCIS cells was also associated with subsequent iIBC (OR 3.70 (95% CI = 1.59-8.64). DCIS with both high COX-2 expression and large breast adipocytes was associated with a 12-fold higher risk (OR 12.0, 95% CI = 3.10-46.3, P < 0.001) for subsequent iIBC compared with women with smaller adipocyte size and low COX-2 expression. Large breast adipocytes combined with high COX-2 expression in DCIS is associated with a high risk of subsequent iIBC. Besides COX-2, adipocyte size has the potential to improve clinical management in patients diagnosed with primary DCIS.

Correction to: A role for the unfolded protein response stress sensor ERN1 in regulating the response to MEK inhibitors in KRAS mutant colon cancers

An amendment to this paper has been published and can be accessed via the original article.

Epigenetic profiling demarcates molecular subtypes of muscle-invasive bladder cancer

Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease that often recurs despite aggressive treatment with neoadjuvant chemotherapy and (radical) cystectomy. Basal and luminal molecular subtypes have been identified that are linked to clinical characteristics and have differential sensitivities to chemotherapy. While it has been suggested that epigenetic mechanisms play a role in defining these subtypes, a thorough understanding of the biological mechanisms is lacking. This report details the first genome-wide analysis of histone methylation patterns of human primary bladder tumours by chromatin immunoprecipitations and next-generation sequencing (ChIP-seq). We profiled multiple histone marks: H3K27me3, a marker for repressed genes, and H3K4me1 and H3K4me3, which are indicators of active enhancers and active promoters. Integrated analysis of ChIP-seq data and RNA sequencing revealed that H3K4 mono-methylation demarcates MIBC subtypes, while no association was found for the other two histone modifications in relation to basal and luminal subtypes. Additionally, we identified differentially methylated H3K4me1 peaks in basal and luminal tumour samples, suggesting that active enhancers play a role in defining subtypes. Our study is the first analysis of histone modifications in primary bladder cancer tissue and provides an important resource for the bladder cancer community.

Functional Categorization of BRCA1 Variants of Uncertain Clinical Significance in Homologous Recombination Repair Complementation Assays

PURPOSE

Because BRCA1 is a high-risk breast/ovarian cancer susceptibility gene, BRCA1 sequence variants of uncertain clinical significance (VUS) complicate genetic counseling. As most VUS are rare, reliable classification based on clinical and genetic data is often impossible. However, all pathogenic BRCA1 variants analyzed result in defective homologous recombination DNA repair (HRR). Thus, BRCA1 VUS may be categorized based on their functional impact on this pathway.

EXPERIMENTAL DESIGN

Two hundred thirty-eight BRCA1 VUS-comprising most BRCA1 VUS known in the Netherlands and Belgium-were tested for their ability to complement Brca1-deficient mouse embryonic stem cells in HRR, using cisplatin and olaparib sensitivity assays and a direct repeat GFP (DR-GFP) HRR assay. Assays were validated using 25 known benign and 25 known pathogenic BRCA1 variants. For assessment of pathogenicity by a multifactorial likelihood analysis method, we collected clinical and genetic data for functionally deleterious VUS and VUS occurring in three or more families.

RESULTS

All three assays showed 100% sensitivity and specificity (95% confidence interval, 83%-100%). Out of 238 VUS, 45 showed functional defects, 26 of which were deleterious in all three assays. For 13 of these 26 variants, we could calculate the probability of pathogenicity using clinical and genetic data, resulting in the identification of 7 (likely) pathogenic variants.

CONCLUSIONS

We have functionally categorized 238 BRCA1 VUS using three different HRR-related assays. Classification based on clinical and genetic data alone for a subset of these variants confirmed the high sensitivity and specificity of our functional assays.

PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

Motivation

Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting.

Results

We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors.

Availability and implementation

PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE).

Supplementary information

Supplementary data are available at Bioinformatics online.

Radiogenomic Analysis of Breast Cancer by Linking MRI Phenotypes with Tumor Gene Expression

Background Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these associations was evaluated by using a sample permutation test and the false discovery rate. Results Gene expression and MRI data were obtained for 295 patients (mean age, 56 years ± 10.3 [standard deviation]). Larger and more irregular tumors showed increased expression of cell cycle and DNA damage checkpoint genes (false discovery rate <0.25; normalized enrichment statistic [NES], 2.15). Enhancement and sharpness of the tumor margin were associated with expression of ribosomal proteins (false discovery rate <0.25; NES, 1.95). Smoothness of enhancement, tumor size, and tumor shape were associated with expression of genes involved in the extracellular matrix (false discovery rate <0.25; NES, 2.25). Conclusion Breast cancer MRI phenotypes were related to their underlying molecular biology revealed by using RNA sequencing. The association between enhancements and sharpness of the tumor margin with the ribosome suggests that these MRI features may be imaging biomarkers for drugs targeting the ribosome. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Cho in this issue.

Spatial proximity of CD8 T cells to tumor cells as an independent biomarker for response to anti-PD-1 therapy

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Background: Only a subset of advanced melanoma patients respond to anti-PD-1 (aPD1) monotherapy. Upfront identification of (non-)responsiveness would help guide first-line treatment decisions, prevent overtreatment and unnecessary risk for toxicities. T cell density and expression of T cell related genes have been associated with response to aPD1, but are imperfect predictors. We investigated whether spatial proximity of CD8 T cells to tumor cells improves upon the predictive value of T cell density alone. Methods: Pretreatment tumor specimens from melanoma patients treated with aPD1 in the Netherlands Cancer Institute were stained for DAPI, SOX10/Melan-A, CD4, CD8, FOXP3 and PD-1 by multiplex immunofluorescence. Sections were imaged on Vectra and analyzed using HALO to optimize marker thresholds and demarcate tumor and stroma. T cell proximity to tumor cells was evaluated as difference in area under the curve between i) a spatial G-function quantifying T cell density around tumor cells in tumor areas and ii) analogous null distributions obtained by random permutation of cell labels. This assessment of co-clustering is independent of cell density and heterogeneity therein and does not reflect repulsion of T cells to stromal/marginal areas. Clinical characteristics, RECIST response and survival were collected from patient records. Associations between T cell density, T cell proximity to Sox10/Melan-A+ tumor cells, other clinical biomarkers (LDH, M stage and WHO performance status) and response were examined in a Bayesian hierarchical logistic regression. Results: Tumor specimens of 98 patients were included, of whom 45 were treated with aPD1 as first-line therapy and 33 had an objective response. CD8 T cell proximity to tumor cells was associated with response in an independent, comparatively strong, and tissue dependent manner (cutaneous tissue: 2.78 [2.45, 3.17], visceral: 2.30 [1.95, 2.72], lymphoid: 2.12 [1.88, 2.40], format: maximal posteriori odds ratio [89% equal-tailed credibility interval]), in a multivariate model correcting for CD8 T cell density (1.74 [1.62, 1.88]), LDH (1.93 [1.72, 2.16]), M stage (0.92 [0.87, 0.98]) and WHO performance status (0.79 [0.72, 0.88]). Our model achieved an area under the ROC curve of 77.7%, whereas an analogous model omitting the proximity variable achieved 73.1%. Conclusions: Our analyses show that spatial proximity of CD8 T cells to tumor cells functions as an independent biomarker for response to aPD1 and suggests that preexisting CD8 T cell tumor reactivity is reflected by this spatial proximity.

Identifying Epistasis in Cancer Genomes: A Delicate Affair

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

Endonuclease FEN1 Coregulates ERα Activity and Provides a Novel Drug Interface in Tamoxifen-Resistant Breast Cancer

Estrogen receptor α (ERα) is a key transcriptional regulator in the majority of breast cancers. ERα-positive patients are frequently treated with tamoxifen, but resistance is common. In this study, we refined a previously identified 111-gene outcome prediction-classifier, revealing FEN1 as the strongest determining factor in ERα-positive patient prognostication. FEN1 levels were predictive of outcome in tamoxifen-treated patients, and FEN1 played a causal role in ERα-driven cell growth. FEN1 impacted the transcriptional activity of ERα by facilitating coactivator recruitment to the ERα transcriptional complex. FEN1 blockade induced proteasome-mediated degradation of activated ERα, resulting in loss of ERα-driven gene expression and eradicated tumor cell proliferation. Finally, a high-throughput 465,195 compound screen identified a novel FEN1 inhibitor, which effectively blocked ERα function and inhibited proliferation of tamoxifen-resistant cell lines as well as ex vivo-cultured ERα-positive breast tumors. Collectively, these results provide therapeutic proof of principle for FEN1 blockade in tamoxifen-resistant breast cancer. SIGNIFICANCE: These findings show that pharmacologic inhibition of FEN1, which is predictive of outcome in tamoxifen-treated patients, effectively blocks ERα function and inhibits proliferation of tamoxifen-resistant tumor cells.

Response of metastatic mouse invasive lobular carcinoma to mTOR inhibition is partly mediated by the adaptive immune system

Effective treatment of invasive lobular carcinoma (ILC) of the breast is hampered by late detection, invasive growth, distant metastasis, and poor response to chemotherapy. Phosphoinositide 3-kinase (PI3K) signaling, one of the major druggable oncogenic signaling networks, is frequently activated in ILC. We investigated treatment response and resistance to AZD8055, an inhibitor of mammalian target of rapamycin (mTOR), in the K14-cre;Cdh1^Flox/Flox;Trp53^Flox/Flox (KEP) mouse model of metastatic ILC. Inhibition of mTOR signaling blocked the growth of primary KEP tumors as well as the progression of metastatic disease. However, primary tumors and distant metastases eventually acquired resistance after long-term AZD8055 treatment, despite continued effective suppression of mTOR signaling in cancer cells. Interestingly, therapeutic responses were associated with increased expression of genes related to antigen presentation. Consistent with this observation, increased numbers of tumor-infiltrating major histocompatibility complex class II-positive (MHCII+) immune cells were observed in treatment-responsive KEP tumors. Acquisition of treatment resistance was associated with loss of MHCII+ cells and reduced expression of genes related to the adaptive immune system. The therapeutic efficacy of mTOR inhibition was reduced in Rag1^−/- mice lacking mature T and B lymphocytes, compared to immunocompetent mice. Furthermore, therapy responsiveness could be partially rescued by transplanting AZD8055-resistant KEP tumors into treatment-naïve immunocompetent hosts. Collectively, these data indicate that the PI3K signaling pathway is an attractive therapeutic target in invasive lobular carcinoma, and that part of the therapeutic effect of mTOR inhibition is mediated by the adaptive immune system.

Androgen modulation of XBP1 is functionally driving part of the AR transcriptional program

Prostate cancer development and progression is largely dependent on androgen receptor (AR) signaling. AR is a hormone-dependent transcription factor, which binds to thousands of sites throughout the human genome to regulate expression of directly responsive genes, including pro-survival genes that enable tumor cells to cope with increased cellular stress. ERN1 and XBP1 - two key players of the unfolded protein response (UPR) - are among such stress-associated genes. Here, we show that XBP1 levels in primary prostate cancer are associated with biochemical recurrence in five independent cohorts. Patients who received AR-targeted therapies had significantly lower XBP1 expression, whereas expression of the active form of XBP1 (XBP1s) was elevated. In vitro results show that AR-induced ERN1 expression led to increased XBP1s mRNA and protein levels. Furthermore, ChIP-seq analysis revealed that XBP1s binds enhancers upon stress stimuli regulating genes involved in UPR processes, eIF2 signaling and protein ubiquitination. We further demonstrate genomic overlap of AR- and XBP1s-binding sites, suggesting genomic conversion of the two signaling cascades. Transcriptomic effects of XBP1 were further studied by knockdown experiments, which lead to decreased expression of androgen-responsive genes and UPR genes. These results suggest a two-step mechanism of gene regulation, which involves androgen-induced expression of ERN1, thereby enhancing XBP1 splicing and transcriptional activity. This signaling cascade may prepare the cells for the increased protein folding, mRNA decay and translation that accompanies AR-regulated tumor cell proliferation.

A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.

RUNX2/CBFB modulates the response to MEK inhibitors through activation of receptor tyrosine kinases in KRAS-mutant colorectal cancer

Intrinsic and acquired resistances are major hurdles preventing the effective use of MEK inhibitors for treatment of colorectal cancer (CRC). Some 35-45% of colorectal cancers are KRAS-mutant and their treatment remains challenging as these cancers are refractory to MEK inhibitor treatment, because of feedback activation of receptor tyrosine kinases (RTKs). We reported previously that loss of ERN1 sensitizes a subset of KRAS-mutant colon cancer cells to MEK inhibition. Here we show that the loss of RUNX2 or its cofactor CBFB can confer MEK inhibitor resistance in CRC cells. Mechanistically, we find that cells with genetically ablated RUNX2 or CBFB activate multiple RTKs, which coincides with high SHP2 phosphatase activity, a phosphatase that relays signals from the cell membrane to downstream pathways governing growth and proliferation. Moreover, we show that high activity of SHP2 is causal to loss of RUNX2-induced MEK inhibitor resistance, as a small molecule SHP2 inhibitor reinstates sensitivity to MEK inhibitor in RUNX2 knockout cells. Our results reveal an unexpected role for loss of RUNX2/CBFB in regulating RTK activity in colon cancer, resulting in reduced sensitivity to MEK inhibitors.

Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo

Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.

Modelling signalling networks from perturbation data

Motivation

Intracellular signalling is realized by complex signalling networks, which are almost impossible to understand without network models, especially if feedbacks are involved. Modular Response Analysis (MRA) is a convenient modelling method to study signalling networks in various contexts.

Results

We developed the software package STASNet (STeady-STate Analysis of Signalling Networks) that provides an augmented and extended version of MRA suited to model signalling networks from incomplete perturbation schemes and multi-perturbation data. Using data from the Dialogue on Reverse Engineering Assessment and Methods challenge, we show that predictions from STASNet models are among the top-performing methods. We applied the method to study the effect of SHP2, a protein that has been implicated in resistance to targeted therapy in colon cancer, using a novel dataset from the colon cancer cell line Widr and a SHP2-depleted derivative. We find that SHP2 is required for mitogen-activated protein kinase signalling, whereas AKT signalling only partially depends on SHP2.

Availability and implementation

An R-package is available at https://github.com/molsysbio/STASNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

iTOP: inferring the topology of omics data

Motivation

In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.

Results

We present iTOP, a methodology to infer a topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics.

Availability and implementation

An implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material.

Supplementary information

Supplementary data are available at Bioinformatics online.