May 16 2014
The Cancer Genome Atlas’ 3rd annual scientific symposium – a report
Earlier this month, I had the opportunity to attend the 3rd annual TCGA symposium at NIH, Bethesda. The TCGA symposium is an open scientific meeting that invites all scientists, who use or wish to use TCGA data, to share and discuss their novel research findings using this data. Although a frequent user of TCGA data, this was my first visit to the symposium and I was excited to see so many other researchers using these datasets to create new knowledge in cancer research. Here I have highlighted a few talks from the symposium.
Dr. Christopher C. Benz and team studied mutations across 12 different cancer types and found P1K3CA occurring in 8 types of cancer. Their analysis showed that breast and kidney cancers favor kinase domain mutations to enhance PI3K catalytic activity and drive cell proliferation, while lung and hand-and-neck squamous cancers favor helical domain mutations to preferentially enhance their malignant cell motility. It was interesting to see how different pathways are affected based on the domain of mutation, and such insights could help understand these mechanisms better.
Samir B. Amin and team profiled long intergenic non-coding RNA (lincRNA) interactions in cancer. The results of profiling show that cancer samples could be stratified/clustered according to cancer type and or cancer stage based on lincRNA expression data.
Another interesting talk was by Dr. Rehan Akbani whose team profiled proteomics data across multiple cancer types using reverse phase protein arrays (RPPA) to analyze more than 3000 patients from 11 TCGA diseases using 181 antibodies that target a panel of known cancer related proteins. Their findings identify several novel and potentially actionable single-tumor and cross-tumor targets and pathways. Their analyses also show that tumor samples demonstrate a much more complex regulation of protein expression than cell lines, most likely due to microenvironment i.e. stroma-tumor interactions and or immune cells – tumor interactions.
Gastric cancer (GC) is the third leading cause of death worldwide, after lung and liver cancers, respectively. Most clinical trials currently recruit patients with stomach cancer and find that all patients do not respond the same way to treatment, implying an underlying heterogeneity in the tumors. Adam Bass’s group at Dana Farber Cancer Institute did a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas. Using a cluster of clusters and iCluster methods, they have separated GC into four subtypes:
- Tumors positive for Epstein-Barr virus – displaying recurrent PIK3CA mutations and extreme DNA hypermethylation.
- Microsatellite unstable tumors – showing elevated mutation rates, including mutations of genes encoding targetable oncogenic signaling proteins.
- Genomically stable tumors – enriched for the diffuse histologic variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins.
- Tumors with chromosomal instability – showing marked aneuploidy and focal amplification of receptor tyrosine kinases.
They also found that tumor characteristics vary based on the tumor site in the stomach – tumors found in the middle of the stomach have more EBV positive and have strong methylation differences. Here’s hoping that understanding these tumor subtypes in GC will help develop treatments specific to each subtype and eventually improve gastric cancer survival in the future.
Even though the TCGA data analysis is synonymous with integrative analyses on multi-omics data, it was interesting to see in-depth analyses of single data types – including associations with viral DNA and yeast models; in-depth analysis of splicing, mRNA splicing mutations and copy number aberrations respectively. The TCGA data collection has not only compiled multi-omics data for various cancer types, but also imaging and pathology images for many samples that could be used for validation of results from ‘omics’ analyses.
Like a kid in a candy show, I was most surprised and excited to see a number of online portals and freely available software and tools showcased in the posters that take advantage of the TCGA big data collection. Some of them are highlighted below.
- CRAVAT 3.0 – predicts the functional effect of variant on their translated protein, predicts whether the submitted variants are cancer drivers or not.
- MAGI – For mutation annotation and gene interpretation
- SpliceSeq – Allows users to interactively explore splicing variation across TCGA tumor types
- TCGA Compass – Allows users to explore clinical data, methylation, miRNA and mRNA seq data from TCGA
- Cancer digital slide archive and VIPER – resources for TCGA pathology data
- TCPA – resource for cancer functional proteomics data
Downloadable tools from Github/R:
- THetA – Program for tumor Heterogeneity Analysis
- ABRA – Tool for improved indel detection
- Hotnet2 algorithm – Identifies significantly mutated sub-networks in a PPI network
- Switch plus – An R package in the making that uses segment copy number data on various cancer types to show differences in human and mouse models
It is energizing to see the collective efforts being taken to make this data collection more readable and parsable. I’m sure the biomedical informatics community will be more than pleased to know that it is becoming easier to explore and find what one is looking for within the TCGA data collection.
Comments by Krithika Bhuvaneshwar with contributions by Dr. Yuriy Gusev