There are also recent efforts to stratify BRAF V600E colorectal tumors based on gene expression data. For example, Barras et al. 2017 compiled a dataset of 218 BRAF V600E mutated colorectal tumors and identified two subgroups and suggest that this heterogeneity may be the basis for poor clinical trial results.
I think that Cognoma could be a nice system to test this model. I think this could be a nice research based analysis for the group to undertake. Roughly, I would approach this analysis like this:
Build a model to predict BRAFV600E activating mutations
Would be nice to see a table of counts across different tumor types.
Hold out both COAD and READ tumors from this model
Assess training/testing performance
Apply model to COAD/READ tumors and investigate heterogeneity of predictions
Possibly apply model to other datasets listed in Barras et al.
My hypothesis is that cognoma will select BRAF V600E melanomas (SKCM) to appear similarly as a subset of BRAF V600E COAD/READ tumors that may respond to antibody treatment.
BRAF V600E mutations are common in several cancer types, including melanoma. This mutation is also present in about 8% of colorectal cancer patients (COAD and READ). However, there is emerging evidence that this subgroup is genomically heterogeneous, and Phase II Vemurafenib clinical trails have failed.
There are also recent efforts to stratify BRAF V600E colorectal tumors based on gene expression data. For example, Barras et al. 2017 compiled a dataset of 218 BRAF V600E mutated colorectal tumors and identified two subgroups and suggest that this heterogeneity may be the basis for poor clinical trial results.
I think that Cognoma could be a nice system to test this model. I think this could be a nice research based analysis for the group to undertake. Roughly, I would approach this analysis like this:
My hypothesis is that cognoma will select BRAF V600E melanomas (SKCM) to appear similarly as a subset of BRAF V600E COAD/READ tumors that may respond to antibody treatment.