Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

PURPOSE: The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. METHODS: Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS: To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION: Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

Cyclin A2/E1 activation defines a hepatocellular carcinoma subclass with a rearrangement signature of replication stress.

Cyclins A2 and E1 regulate the cell cycle by promoting S phase entry and progression. Here, we identify a hepatocellular carcinoma (HCC) subgroup exhibiting cyclin activation through various mechanisms including hepatitis B virus (HBV) and adeno-associated virus type 2 (AAV2) insertions, enhancer hijacking and recurrent CCNA2 fusions. Cyclin A2 or E1 alterations define a homogenous entity of aggressive HCC, mostly developed in non-cirrhotic patients, characterized by a transcriptional activation of E2F and ATR pathways and a high frequency of RB1 and PTEN inactivation. Cyclin-driven HCC display a unique signature of structural rearrangements with hundreds of tandem duplications and templated insertions frequently activating TERT promoter. These rearrangements, strongly enriched in early-replicated active chromatin regions, are consistent with a break-induced replication mechanism. Pan-cancer analysis reveals a similar signature in BRCA1-mutated breast and ovarian cancers. Together, this analysis reveals a new poor prognosis HCC entity and a rearrangement signature related to replication stress.

Palimpsest: an R package for studying mutational and structural variant signatures along clonal evolution in cancer.

Summary: Cancer genomes are altered by various mutational processes and, like palimpsests, bear the signatures of these different processes. The Palimpsest R package provides a complete workflow for the characterization and visualization of mutational signatures and their evolution along tumor development. The package covers a wide range of functions for extracting both base substitution and structural variant signatures, inferring the clonality of each alteration and analyzing the evolution of mutational processes between early clonal and late subclonal events. Palimpsest also estimates the probability of each mutation being due to each process to predict the mechanisms at the origin of driver events. Palimpsest is an easy-to-use toolset for reconstructing the natural history of a tumor using whole exome or whole genome sequencing data. Availability and implementation: Palimpsest is freely available at www.github.com/FunGEST/Palimpsest. Supplementary information: Supplementary data are available at Bioinformatics online.

Mutational signatures reveal the dynamic interplay of risk factors and cellular processes during liver tumorigenesis.

Genomic alterations driving tumorigenesis result from the interaction of environmental exposures and endogenous cellular processes. With a diversity of risk factors, liver cancer is an ideal model to study these interactions. Here, we analyze the whole genomes of 44 new and 264 published liver cancers and we identify 10 mutational and 6 structural rearrangement signatures showing distinct relationships with environmental exposures, replication, transcription, and driver genes. The liver cancer-specific signature 16, associated with alcohol, displays a unique feature of transcription-coupled damage and is the main source of CTNNB1 mutations. Flood of insertions/deletions (indels) are identified in very highly expressed hepato-specific genes, likely resulting from replication-transcription collisions. Reconstruction of sub-clonal architecture reveals mutational signature evolution during tumor development exemplified by the vanishing of aflatoxin B1 signature in African migrants. Finally, chromosome duplications occur late and may represent rate-limiting events in tumorigenesis. These findings shed new light on the natural history of liver cancers.

Mutational signature analysis identifies MUTYH deficiency in colorectal cancers and adrenocortical carcinomas.

Germline alterations in DNA repair genes are implicated in cancer predisposition and can result in characteristic mutational signatures. However, specific mutational signatures associated with base excision repair (BER) defects remain to be characterized. Here, by analysing a series of colorectal cancers (CRCs) using exome sequencing, we identified a particular spectrum of somatic mutations characterized by an enrichment of C > A transversions in NpCpA or NpCpT contexts in three tumours from a MUTYH-associated polyposis (MAP) patient and in two cases harbouring pathogenic germline MUTYH mutations. In two series of adrenocortical carcinomas (ACCs), we identified four tumours with a similar signature also presenting germline MUTYH mutations. Taken together, these findings demonstrate that MUTYH inactivation results in a particular mutational signature, which may serve as a useful marker of BER-related genomic instability in new cancer types. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets.

Genomic analyses promise to improve tumor characterization to optimize personalized treatment for patients with hepatocellular carcinoma (HCC). Exome sequencing analysis of 243 liver tumors identified mutational signatures associated with specific risk factors, mainly combined alcohol and tobacco consumption and exposure to aflatoxin B1. We identified 161 putative driver genes associated with 11 recurrently altered pathways. Associations of mutations defined 3 groups of genes related to risk factors and centered on CTNNB1 (alcohol), TP53 (hepatitis B virus, HBV) and AXIN1. Analyses according to tumor stage progression identified TERT promoter mutation as an early event, whereas FGF3, FGF4, FGF19 or CCND1 amplification and TP53 and CDKN2A alterations appeared at more advanced stages in aggressive tumors. In 28% of the tumors, we identified genetic alterations potentially targetable by US Food and Drug Administration (FDA)-approved drugs. In conclusion, we identified risk factor-specific mutational signatures and defined the extensive landscape of altered genes and pathways in HCC, which will be useful to design clinical trials for targeted therapy.