Cancers are primarily classified on the basis of where in the body the disease originates – also know as tissue type of origin. As a result, cancers are referred to as lung, breast, kidney, bladder, etc. However, according to a new study, one in ten cancer patients would be classified differently using a new classification system based on molecular subtypes instead of the current tissue-of-origin system. This reclassification could lead to different therapeutic options for those patients. An article discussing the new classification system was published in the August 7, 2014 edition of Cell.
“It’s only ten percent that were classified differently, but it matters a lot if you’re one of those patients,” said senior author Josh Stuart, PhD, a professor of biomolecular engineering at UC Santa Cruz.
A comprehensive analysis
Stuart helped organize the study as part of the Pan-Cancer Initiative of the Cancer Genome Atlas or TCGA project. A large team of researchers from multiple institutions performed a comprehensive analysis of molecular data from thousands of patients representing 12 different types of cancer. This was the most comprehensive and diverse collection of tumors ever analyzed by systematic genomic methods. Each tumor type was characterized using six different “platforms” or methods of molecular analysis–mostly genomic platforms such as DNA and RNA sequencing, plus a protein expression analysis.
The 10% reclassification rate in the current study is likely an underestimate due to the unequal representation of different tumors. …If our study had included as many bladder cancers as breast cancers, for example, we would have reclassified 30% [of all tumors].
Different molecular subtypes The research team used statistical analyses of the molecular data to divide the tumors into groups or “clusters,” first analyzing the data from each platform separately and then combining them in an integrated cross-platform analysis developed by co-first author Katherine Hoadley of the University of North Carolina. All six platforms as well as the integrated analysis converged on the same divisions of the cancers into 11 major subtypes. Five of those subtypes were nearly identical to their tissue-of-origin counterparts. But some tissue-of-origin categories split into several different molecular subtypes, and some subtypes encompass tumors with several different tissues of origin.
Bladder cancer was a particularly interesting group, because it split into seven different clusters, with most samples falling into one of three subtypes. One subtype was bladder cancer only, but some bladder cancers clustered with lung adenocarcinomas, and others with a subtype called ‘squamous-like’ that includes some lung cancers, some head-and-neck cancers, and some bladder cancers. “If you look at survival rates, the bladder cancers that clustered with other tumor types had a worse prognosis. So this is not just an academic exercise,” Stuart explained.
Reconfirming cancer subtypes
Other findings from the study reconfirmed cancer subtypes that were already recognized, such as the different subtypes of breast cancer based on well-characterized biomarkers. “The findings provide a more refined, quantitative picture of the differences between breast cancer subtypes,” Stuart noted. “These the results reinforce, for example, the idea that basal-like breast cancers are a unique tumor type. “Basal-like breast cancers are as different from luminal breast cancers as they are from lung cancers,” he explained.”
“The fact that all six platforms for molecular analysis identified the same set of subtypes, both individually and in multi-platform analyses, is an important result,” Stuart said. “Not only does it give the researchers confidence in the subtypes they identified, it also means that different kinds of data can be used to classify a tumor.”
“We can now say what the telltale signatures of the subtypes are, so you can classify a patient’s tumor just based on the gene expression data, or just based on mutation data, if that’s what you have,” Stuart explained. “Having a molecular map like this could help get a patient into the right clinical trial.”
Validating findings
Although follow-up studies are needed to validate the findings, this new analysis lays the groundwork for classifying tumors into molecularly defined subtypes. The new classification scheme could be used to enroll patients in clinical trials and could lead to different treatment options based on molecular subtypes.
According to Stuart, the percentage of tumors that are reclassified based on molecular signatures is likely to grow as more samples and tumor types are included in the analysis (the next major Pan-Cancer analysis will include 21 tumor types). Coauthor Christopher Benz, an oncologist at the Buck Institute for Research on Agingand UC San Francisco, noted: “The 10% reclassification rate in the current study is likely an underestimate due to the unequal representation of different tumors. If our study had included as many bladder cancers as breast cancers, for example, we would have reclassified 30%.”
The researchers reported that each molecular subtype may reflect tumors arising from distinct cell types. For example, the data showed a marked difference between cancers of epithelial and non-epithelial origins. “We think the subtypes reflect primarily the cell of origin. Another factor is the nature of the genomic lesion, and third is the microenvironment of the cell and how surrounding cells influence it,” Stuart said. “We are disentangling the signals from these different factors so we can gauge each one for its prognostic power.”
The study involved an enormous amount of molecular and clinical data, which was managed by data coordinator Kyle Ellrott, a software developer in Stuart’s lab at UC Santa Cruz. The data sets and results have been made available to other researchers through the Synapse web site. Stuart worked with the bioinformatics company Sage Bionetworks to create Synapse as a data repository for the Pan-Cancer Initiative.
“It’s a huge amount of information, and all the data is available as programmable data sets that other researchers can use to do further analysis,” Stuart said. “The scale of this project is hard to imagine. All of the data that the TCGA project has been churning out got funneled into this paper, and it’s giving us an unbiased look at what the data have to tell us about cancer.”
Better trial designs
The authors of the study believe that this study ? and future iterations of it ? will fuel better clinical trial designs whereby patients become eligible for novel therapeutics based on this type of genomic reclassification of tumors. “Although follow-up studies are needed to validate and refine this newly proposed cancer classification system, it will ultimately provide the biologic foundation for that era of personalized cancer treatment that patients and clinicians eagerly await,” Benz observed.
For more information:
Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MD, Niu B, et al. Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin. Cell. 2014 Aug 14;158(4):929-44. doi: 10.1016/j.cell.2014.06.049. Epub 2014 Aug 7. [Article][PubMed]
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