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.

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Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labeling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average enabled the de novo construction of a functional protein correlation network, which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.

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Motivation
Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community. Results: In this article, we introduce the motivation and rationale of some parametric and nonparametric versions of PCA specifically geared for binary data. Using both realistic simulations of binary data as well as mutation, CNA and methylation data of the Genomic Determinants of Sensitivity in Cancer 1000 (GDSC1000), the methods were explored for their performance with respect to finding the correct number of components, overfit, finding back the correct low-dimensional structure, variable importance, etc. The results show that if a low-dimensional structure exists in the data, that most of the methods can find it. When assuming a probabilistic generating process is underlying the data, we recommend to use the parametric logistic PCA model, while when such an assumption is not valid and the data are considered as given, the nonparametric Gifi model is recommended.
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Background: Patients with BRCA1-like tumors correlate with improved response to DNA double-strand break-inducing therapy. A gene expression-based classifier was developed to distinguish between BRCA1-like and non-BRCA1-like tumors. We hypothesized that these tumors may also be more sensitive to PARP inhibitors than standard treatments. Methods: A diagnostic gene expression signature (BRCA1ness) was developed using a centroid model with 128 triple-negative breast cancer samples from the EU FP7 RATHER project. This BRCA1 ness signature was then tested in HER2-negative patients (n= 116) from the I-SPY 2 TRIAL who received an oral PARP inhibitor veliparib in combination with carboplatin (V-C), or standard chemotherapy alone. We assessed the association between BRCA1 ness and pathologic complete response in the V-C and control arms alone using Fisher ’s exact test, and the relative performance between arms (biomarker × treatment interaction, likelihood ratio p< 0.05) using a logistic model and adjusting for hormone receptor status (HR). Results: We developed a gene expression signature to identify BRCA1-like status. In the I-SPY 2 neoadjuvant setting the BRCA1ness signature associated signif icantly with response to V-C (p= 0.03), but not in the control arm (p = 0.45). We identified a significant interaction between BRCA1ness and V-C (p= 0.023) after correcting for HR. Conclusions: A genomic-based BRCA1-like signature was successfully translated to an expression-based signature (BRC1Aness). In the I-SPY 2 neoadjuvant setting, we determined that the BRCA1ness signature is capable of predicting benefit of V-C added to standard chemotherapy compared to standard chemotherapy alone. Trial registration: I-SPY 2 TRIAL beginning December 31, 2009: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer (I-SPY 2), NCT01042379.@en

Motivation: Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. Results: To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. @en

Although large-scale efforts for molecular profiling of cancer samples provide multiple data types for many samples, most approaches for finding candidate cancer genes rely on somatic mutations and DNA copy number only. We present a new method, OncoScape, which exploits five complementary data types across 11 cancer types to identify new candidate cancer genes. We find many rarely mutated genes that are strongly affected by other aberrations. We retrieve the majority of known cancer genes but also new candidates such as STK31 and MSRA with very high confidence. Several genes show a dual oncogene- and tumor suppressor-like behavior depending on the tumor type. Most notably, the well-known tumor suppressor RB1 shows strong oncogene-like signal in colon cancer. We applied OncoScape to cell lines representing ten cancer types, providing the most comprehensive comparison of aberrations in cell lines and tumor samples to date. This revealed that glioblastoma, breast and colon cancer show strong similarity between cell lines and tumors, while head and neck squamous cell carcinoma and bladder cancer, exhibit very little similarity between cell lines and tumors. To facilitate exploration of the cancer aberration landscape, we created a web portal enabling interactive analysis of OncoScape results (http://ccb.nki.nl/software/oncoscape).@en

Introduction
In triple negative breast cancers (TNBC) the initial response to chemotherapy is often favorable, but relapse and chemotherapy resistance frequently occur in advanced disease. Hence there is an urgent need for targeted treatments in this breast cancer subtype. In the current study we deep sequenced DNA of tumors prior to chemotherapy to search for predictors of response or resistance.
Methods
Next generation sequencing (NGS) was performed for 1,977 genes involved in tumorigenesis. DNA from 56 pre-treatment TNBC-biopsies was sequenced, as well as matched normal DNA. Following their tumor biopsy, patients started neoadjuvant chemotherapy with doxorubicin and cyclophosphamide. We studied associations between genetic alterations and three clinical variables: chemotherapy response, relapse-free survival and BRCA proficiency.
Results
The mutations observed were diverse and few recurrent mutations were detected. Most mutations were in TP53, TTN, and PIK3CA (55 %, 14 %, and 9 %, respectively). The mutation rates were similar between responders and non-responders (average mutation rate 9 vs 8 mutations). No recurrent mutations were associated with chemotherapy response or relapse. Interestingly, PIK3CA mutations were exclusively observed in patients proficient for BRCA1. Samples with a relapse had a higher copy number alteration rate, and amplifications of TTK and TP53BP2 were associated with a poor chemotherapy response.
Conclusions
In this homogenous cohort of TNBCs few recurrent mutations were found. However, PIK3CA mutations were associated with BRCA proficiency, which can have clinical consequences in the near future.
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