Pan-Cancer Atlas Provides Argument for Molecular Classification

Jason Harris
Published: Wednesday, May 23, 2018
 Fabrice Andre, MD, PhD
Fabrice Andre, MD, PhD
Following up on 2014 study of the relationships between genetic mutations and multiple cancer types, The Cancer Genome Atlas (TCGA) Research Network has completed an integrative molecular analysis of roughly 10,000 tumor specimens and identified cancer subtypes that reflect shared molecular characteristics across traditional anatomic boundaries.1

“The study findings confirm that genomic alterations are shared across tumor types and suggest that cancer should be classified by these alterations rather than by tissue of origin,” said Fabrice André, MD, PhD, a professor in the Department of Medical Oncology at the Institut Gustave Roussy in Villejuif, France, and chair of the European Society for Medical Oncology (ESMO) Translational Research and Personalized Medicine Working Group. At the same time, “sometimes giving a drug matched to the mutation does not benefit patients,” he said,2 calling for further effort to interpret the TCGA findings, which are compiled in the Pan-Cancer Atlas.

The study revealed the dominant role of cell-oforigin patterns, wherein tumors originate from a single aberrant cell. However, investigators found similarities across histologically or anatomically related cancer types. Co–lead author Katherine A. Hoadley, PhD, told OncologyLive® that melanoma, for example, looked molecularly similar whether it was a tumor in the skin or the eye. “We saw molecular patterns that spanned different tumor types, particularly tumors of squamous histology,” said Hoadley, who is an assistant professor in the Cancer Genetics Program at the University of North Carolina Lineberger Comprehensive Cancer Center. “It didn’t matter if the tumors came from lung, head and neck, cervix, [or] bladder [cancers]. If they had squamous histology, we found all those tumors showing similar molecular phenotype— similar mutations, copy number patterns, expression of genes and proteins.”

The follow-up study was designed to answer questions left unanswered by the earlier analysis: whether including more tumor types would uncover more cross-tissue commonalities, demonstrate convergence or divergence of molecular subtypes, or significantly increase the number of patients with cancer whose treatment might be improved by the integrative method of classifying tumor types across molecular subtypes used in the TCGA analysis.

In the TCGA study, published in Cell,1 Hoadley et al identified 28 distinct molecular subtypes arising from the 33 tumor types analyzed across platforms or categories of gene and protein expression in which anomalies and mutations can occur—aneuploidy, messenger RNA (mRNA), microRNA (miRNA), DNA methylation, and reverse phase protein array (RPPA). Investigators used an iCluster clustering algorithm to help identify commonalities in molecular features of cancer, as opposed to classification based solely on tissue origin (Figure).

Not only did tumors from different cancer types fall into multiple platform-specific groups, but also samples within cancer types were dispersed across groups. For example, gastrointestinal tumors were clustered in the mRNA, miRNA, and RPPA platforms but also represented by several distinct DNA methylation clusters. Squamous histology cancers were classified by the mRNA, miRNA, and RPPA data but also further divided by aneuploidy and DNA methylation data.

Within pan-gynecologic cancers, ovarian serous cystadenocarcinoma, uterine corpus endometrial carcinoma, and estrogen receptor (ER)–positive liver hepatocellular carcinoma shared similarities at the protein level, based on RPPA data. However, mRNA, miRNA, and DNA methylation data were grouped by organ sites. “Also of note, 13% of BRCA formed a subtype distinct from the majority of other BRCA, influenced by the mRNA and DNA methylation platforms,” the authors wrote.

For 16 tumor types, more than 80% of samples were grouped in the same cluster. A single tumor type was dominant in 8 clusters (acute myeloid leukemia, brain lower-grade glioma [mutant isocitrate dehydrogenase 1], ovarian, uterine corpus endometrial carcinoma, thyroid carcinoma, prostate adenocarcinoma, liver hepatocellular carcinoma, and lung adenocarcinoma).

Other clusters contained tumors from similar or related cells or tissues, such as pan-kidney, melanoma of the skin and eye, and central nervous system/endocrine. Six tumor types (bladder urothelial carcinoma, uterine carcinosarcoma, head and neck squamous cell carcinoma, esophageal carcinoma, stomach adenocarcinoma, and cholangiocarcinoma) had a more diverse iCluster membership. In those cancers, fewer than 50% of tumors were represented in a given iCluster.

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