NEW YORK (GenomeWeb) – Researchers from Baylor College of Medicine and elsewhere have characterized nine major molecular subtypes of kidney cancer.
Baylor's Chad Creighton and his team analyzed the DNA methylation status and copy-number levels as well as the RNA and protein expression levels of nearly 900 renal cell carcinomas, as they reported Friday in Cell Reports. While analysis of these tumor samples broadly reflected their histological subtypes, it also placed these samples within nine molecularly determined subtypes. Some of the recurrent changes that distinguish the various subtypes, like to the immune checkpoint pathway, also suggest potential treatment approaches for those types, the researchers noted.
"Different types of cancer can show different pathways being dysregulated," Creighton said in a statement, "and for some of the pathways we have therapies we can use to target them."
He and his colleagues drew on a set of 894 primary renal cell carcinomas (RCC) that had been collected by three different projects that are part of The Cancer Genome Atlas. These samples included clear cell, chromophobe, and papillary RCC, as well as others whose histological designation wasn't entirely clear.
In an unsupervised analysis based on mRNA expression, DNA methylation, DNA copy number, microRNA expression, and protein expression data, the samples clustered into groups that largely replicated their histological type.
Many of the molecular differences between the clear cell, chromophobe, and papillary RCC, the researchers found, could be traced to their cells of origin. For instance, clear cell RCC samples had expression profiles that were most similar to those of cells from the glomerulus and proximal nephron, while chromophobe RCC samples were most similar to distal nephron cells.
But clustering by DNA methylation revealed additional renal cell carcinoma subtypes, including one with widespread hypermethylation linked to poor prognosis. Analysis by mRNA expression similarly uncovered additional subtypes.
Using a cluster of cluster analysis that combined the subgroup calls made by the various molecular platforms, the researchers homed in on nine genomic subtypes. Those subtypes included three that were mostly clear cell RCC cases — which they dubbed CC-e.1, CC-e.2, and CC-e.3 — four subtypes of predominantly papillary RCC cases — P-e.1a, P-e.1b, P-e.2, and P.CIMP-e — one subtype of largely chromophobe cases — Ch-e — and one mixed subtype.
These subtypes, Creighton and his colleagues noted, were associated with differing degrees of patient survival. Of the clear cell-enriched cases, CC-e.2 was linked with better survival and CC-e.3 with worse survival. Similarly, of the papillary-enriched cases, P-e.1a was associated with better survival and P.CIMP-e with worse.
Whole-exome and whole-genome sequencing further revealed recurrent mutations and rearrangements across the subtypes. Significantly mutated genes included VHL, TP53, chromatin modifier genes like PBRM1, SMARCB1, PI3K/ AKT/mTOR pathway genes, and MET, among others. The samples additionally housed an average 25 rearrangements, some of which involved TFE3, a nutrient-responsive transcription factor.
Some chromatin modifier gene mutations and other molecular alterations were present across a number of subtypes, the researchers found, and proteomic analysis indicated that both the PI3K/AKT and mTOR pathways were elevated in the clear cell-enriched and papillary-enriched subtypes, as compared to the Ch-e subtype.
The clear cell-associated subtype, the researchers reported, had high expression of genes that could be targets for immunotherapy, like PDCD1, CD247, and TNFRSF4.
These molecular differences between subtypes may present potential treatment targets, Creighton and his colleagues said.
"Not all patients have this [immune checkpoint] pathway activated, but molecular analysis would allow us to identify those patients that represent the best candidates for receiving therapies that target that pathway specifically," Creighton said. "If we have this information, then we may have an idea of what would work better for the patient. The molecular information can potentially help guide better decisions for treating each patient."