Integrated genomic and transcriptomic profiling reveals distinct molecular features of colorectal cancer in an Indian cohort
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Updated: Oct 30, 2026

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
Published on: April 29, 2014
Anil Vashapaka1, Ravikanth Vishnubhotla1, Rao Gv2
1Asian Healthcare Foundation, Plot No 2/3/4/5, Survey No 136/1, Mindspace Road, Gachibowli, Hyderabad, Telangana, 500032, India.
Abstract:
Colorectal cancer (CRC) exhibits substantial molecular heterogeneity, requiring integrative approaches to define clinically meaningful tumour subtypes. Although large international consortia have extensively profiled CRC, global datasets underrepresent many ethnicities. Understanding how local tumors align with/diverge from globally characterized cohorts is essential to contextualize disease biology and refine precision oncology strategies. This study was designed as a comparative, hypothesis-generating analysis benchmarked against a Global Genomic Landscape (GGL) of 2,023 CRC tumors. Whole exome and transcriptome sequencing was performed on pathologically confirmed tumors (N = 20) and normal tissues (N = 5) using the Illumina HiSeqX. Bioinformatic pipelines identified somatic variants and dysregulated genes, which were compared with GGL data. We identified 814 dysregulated genes and ∼16,440 somatic variants. While the single base substitution (SBS) profile was similar to GGL, one double base substitution signature (DBS17) and two insertion-deletion signatures (ID7, ID19) were unique to our cohort. Six driver genes (ACVR2A/BAX/PRDM2/RGS12/RNF43/TCF7L2) with recurrent variants were observed in our cohort. Only three genes with emerging roles in CRC (MET/SOX9/TGIF1) overlapped across dysregulation, somatic variation, and GGL datasets. Indel hotspot variants in EIF2B3 (p.Ala151GlnfsTer57) and UBR5 (p.Glu2121LysfsTer28) were exclusive in non-survivors. The prevalence of canonical Western driver mutations (APC, TP53, KRAS, PIK3CA, SMAD4) was markedly lower in our cohort. Few drug-targetable genes overlapped with GGL; however, several approved or high-confidence targets were identified. Compared with the global genomic landscape, our findings suggest potential population-associated differences in driver architecture and therapeutic opportunities that warrant validation in larger independent Indian cohorts.
