Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation-based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data.

Aneuploidy is a hallmark of cancer with tissue-specific prevalence patterns that suggest it plays a driving role in cancer initiation and progression. However, the contribution of aneuploidy to tumorigenesis depends on both cellular and genomic contexts. Whole-genome duplication (WGD) is a common macroevolutionary event that occurs in more than 30% of human tumors early in tumorigenesis. Although tumors that have undergone WGD are reported to be more permissive to aneuploidy, it remains unknown whether WGD also affects aneuploidy prevalence patterns. Here we analyzed clinical tumor samples from 5,586 WGD− tumors and 3,435 WGD+ tumors across 22 tumor types and found distinct patterns of aneuploidy in WGD− and WGD+ tumors. WGD+ tumors were characterized by more promiscuous aneuploidy patterns, in line with increased aneuploidy tolerance. Moreover, the genetic interactions between chromosome arms differed between WGD− and WGD+ tumors, giving rise to distinct cooccurrence and mutual exclusivity aneuploidy patterns. The proportion of whole-chromosome aneuploidy compared with arm-level aneuploidy was significantly higher in WGD+ tumors, indicating distinct dominant mechanisms for aneuploidy formation. Human cancer cell lines successfully reproduced these WGD/aneuploidy interactions, confirming the relevance of studying this phenomenon in culture. Finally, induction of WGD and assessment of aneuploidy in isogenic WGD−/WGD+ human colon cancer cell lines under standard or selective conditions validated key findings from the clinical tumor analysis, supporting a causal link between WGD and altered aneuploidy landscapes. We conclude that WGD shapes the aneuploidy landscape of human tumors and propose that this interaction contributes to tumor evolution. Significance: These findings suggest that the interactions between whole-genome duplication and aneuploidy are important for tumor evolution, highlighting the need to consider genome status in the analysis and modeling of cancer aneuploidy.

Network-based module discovery (NBMD) methods are central to analysis of omics data. Such algorithms receive a gene network and nodes' activity scores as input and report sub-networks (modules) that are putatively biologically active. Although such methods exist for almost two decades, only a handful of studies attempted to compare the biological signals captured by different methods. Here, we systematically evaluated six popular NBMD methods on gene expression (GE) and GWAS data . Notably, we observed that GO terms enriched in modules detected by these methods on the real data were often also enriched after randomly permuting the input data. To tackle this bias, we designed a method that evaluates the empirical significance of GO terms reported as enriched in modules. We used the method to fashion five novel performance criteria for evlautating NBMD methods. Last, we developed a novel NBMD algorithm called DOMINO. In extensive testing on GE and GWAS data it outperformed the other six algorithms. Software is available at https://github.com/Shamir-Lab/.

Aneuploidy – a hallmark of cancer – has tissue-specific recurrence patterns suggesting it plays a driving role in cancer initiation and progression. However, the contribution of aneuploidy to tumorigenesis depends on the cellular and genomic context in which it arises. Whole-genome duplication (WGD) is a common macro-evolutionary event that occurs in >25% of human tumors during the early stages of tumorigenesis. Although tumors that have undergone WGD are reported to be more permissive to aneuploidy than tumors that have not, it remains unknown whether WGD affects aneuploidy recurrence patterns in human cancers. Here we analyzed clinical tumor samples from 449 WGD- tumors and 157 WGD+ tumors across 22 tumor types. We found distinct recurrence patterns of aneuploidy in WGD- and WGD+ tumors. The relative prevalence of recurrent aneuploidies decreased in WGD+ tumors, in line with increased aneuploidy tolerance. Moreover, the genetic interactions between chromosome arms differed between WGD- and WGD+ tumors, giving rise to distinct co-occurrence and mutual exclusivity aneuploidy patterns. The proportion of whole-chromosome aneuploidy vs. arm-level aneuploidy was significantly higher in WGD+ tumors, indicating distinct dominant mechanisms for aneuploidy formation in WGD- and WGD+ tumors. Human cancer cell lines successfully reproduced these WGD/aneuploidy interactions, confirming the relevance of studying this phenomenon in culture. Lastly, we induced WGD in human colon cancer cell lines, and followed aneuploidy formation in the isogenic WGD+/WGD-cells under standard or selective conditions. These experiments validated key findings from the clinical tumor analysis, and revealed a causal link between WGD and altered aneuploidy landscapes. We conclude that WGD shapes the aneuploidy landscape of human tumors, and propose that the interaction between WGD and aneuploidy is a major contributor to tumor evolution.

Motivation Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal (“active modules”). Such modules can point out key molecular processes in the analyzed biological conditions. Results We recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. Availability The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.