The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

PCAWG working groups focused on unified analyses of copynumber variation 6 , structural variants 7,8 , germline variants 5 , mutational signatures 9 and identification of driver genes 8 , among others 5. Here, we report the joint analysis of available matched transcriptome and genome profiling for 1,188 samples from 27 tumour types by the PCAWG Transcriptome Working Group 5 , providing the largest, to our knowledge, resource of RNA phenotypes and their underlying genetic changes in cancer so far (Extended Data Fig. 1, Methods, Supplementary Results, Supplementary Table 23). We demonstrate the importance of transcriptomics data in understanding how different dimensions of specific DNA alterations contribute to carcinogenesis and map out the landscape of cancer-related RNA alterations.

Translation initiation is orchestrated by the cap binding and 43S preinitiation complexes (PIC). Eukaryotic initiation factor 1A (EIF1A) is essential for recruitment of the ternary complex and for assembling the 43S PIC. Recurrent EIF1AX mutations in papillary thyroid cancers are mutually exclusive with other drivers, including RAS . EIF1AX mutations are enriched in advanced thyroid cancers, where they display a striking co-occurrence with RAS , which cooperates to induce tumorigenesis in mice and isogenic cell lines. The C-terminal EIF1AX-A113splice mutation is the most prevalent in advanced thyroid cancer. EIF1AX-A113splice variants stabilize the PIC and induce ATF4, a sensor of cellular stress, which is co-opted to suppress EIF2α phosphorylation, enabling a general increase in protein synthesis. RAS stabilizes c-MYC, an effect augmented by EIF1AX-A113splice. ATF4 and c-MYC induce expression of amino acid transporters and enhance sensitivity of mTOR to amino acid supply. These mutually reinforcing events generate therapeutic vulnerabilities to MEK, BRD4, and mTOR kinase inhibitors. SIGNIFICANCE:Mutations of EIF1AX, a component of the translation PIC, co-occur with RAS in advanced thyroid cancers and promote tumorigenesis. EIF1AX-A113splice drives an ATF4-induced dephosphorylation of EIF2α, resulting in increased protein synthesis. ATF4 also cooperates with c-MYC to sensitize mTOR to amino acid supply, thus generating vulnerability to mTOR kinase inhibitors.EIF1AX is an essential subunit of the translation PIC (10, 12). We performed coimmunoprecipitation (co-IP) experiments to probe for possible aberrant interactions of EIF1AX mutants with components of the TC and the PIC. IP of Research.on July 10, 2020.

MotivationDeep learning techniques have yielded tremendous progress in the field of computational biology over the last decade, however many of these techniques are opaque to the user. To provide interpretable results, methods have incorporated biological priors directly into the learning task; one such biological prior is pathway structure. While pathways represent most biological processes in the cell, the high level of correlation and hierarchical structure make it complicated to determine an appropriate computational representation.ResultsHere, we present pathway module Variational Autoencoder (pmVAE). Our method encodes pathway information by restricting the structure of our VAE to mirror gene-pathway memberships. Its architecture is composed of a set of subnetworks, which we refer to as pathway modules. The subnetworks learn interpretable latent representations by factorizing the latent space according to pathway gene sets. We directly address correlation between pathways by balancing a module-specific local loss and a global reconstruction loss. Furthermore, since many pathways are by nature hierarchical and therefore the product of multiple downstream signals, we model each pathway as a multidimensional vector. Due to their factorization over pathways, the representations allow for easy and interpretable analysis of multiple downstream effects, such as cell type and biological stimulus, within the contexts of each pathway. We compare pmVAE against two other state-of-the-art methods on two single-cell RNA-seq case-control data sets, demonstrating that our pathway representations are both more discriminative and consistent in detecting pathways targeted by a perturbation.Availability and implementationhttps://github.com/ratschlab/pmvae