AUTHOR=Desai Sagar Sanjiv , K Raksha Rao , Jain Anika , Bawa Pushpinder Singh , Dutta Priyatam , Atre Gaurav , Subhash Anand , Rao Vishal U. S. , J Suvratha , Srinivasan Subhashini , Choudhary Bibha 

TITLE=Multidimensional Mutational Profiling of the Indian HNSCC Sub-Population Provides IRAK1, a Novel Driver Gene and Potential Druggable Target

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.723162

DOI=10.3389/fonc.2021.723162

ISSN=2234-943X

ABSTRACT=Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumours, classified according to their anatomical site. It is the sixth most prevalent type of malignancy in the world and is of major concern in Southeast Asia, especially in India as it accounts for 40% of all malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 50 Head and Neck squamous cell carcinoma samples. We have identified and catalogued gene signatures differentiating buccal from alveolar and tongue apart from known mutations in the oncogenes and tumor suppressors. Known tumour suppressor proteins like TP53 and TP63 showed a mutation frequency of more than 50%. Also, we report novel mutations in the genes, AKT1, SPECC1 and LRP1B, which are linked with tumor progression and patient survival. A highly curated screening process for identifying survival signatures based on correlation of variants with functional impact from exome and expression using transcriptome data from the GEPIA database returned 36 survival related genes. An independent LASSO regression analysis was performed and 4 dead and 3 alive gene signatures were obtained, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91 respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.