Client:

Collaborative R&D

Collaborators:

Kidwai Memorial Institute of Oncology and Indian Institute of Science (IISc)

Challenge:

In silico biomarker analysis. The specific project is characterizing the Indian population of breast cancer patients. All existing breast cancer biomarkers will be analyzed in the Idnian patient population. In addition, the project aims to discover prognostic markers that can classify triple negative breast cancer patients with respect to relapse, survival,  metastasis, and various therapeutic options.

Strand's Solution:

Strand has performed analysis on different publicly available breast cancer datasets. using gene ontology classification, gene set enrichment, PCA, clustering and various machine learning algorithms, we have identified putative markers that stratify breast cancer patients. These triple negative subclasses vary from each other in various aspects of tumro biology - invasion, angiogenesis, metastasis, stem cell differentiation, cell proliferation, immune responses and various biochemical pathways.

Putative gene signatures are being analyzed in the Indian population of breast cancer patients. Additional validation using RT-PCR is underway. 

Strand aims to identify putative biomarkers to impact clinical decisions in the treatment of triple negative breast cancer patients. We also aim to characterize the Indian population of breast cancer patients with respect to all available therapeutic options.