RT Journal Article
SR Electronic
T1 Integrated analysis of oral tongue squamous cell carcinoma identifies key variants and pathways linked to risk habits, HPV, clinical parameters and tumor recurrence
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 028845
DO 10.1101/028845
A1 Neeraja M Krishnan
A1 Saurabh Gupta
A1 Vinayak Palve
A1 Linu Varghese
A1 Swetansu Pattnaik
A1 Prachi Jain
A1 Costerwell Khyriem
A1 Arun K Hariharan
A1 Kunal Dhas
A1 Jayalakshmi Nair
A1 Manisha Pareek
A1 Venkatesh K Prasad
A1 Gangotri Siddappa
A1 Amritha Suresh
A1 Vikram D Kekatpure
A1 Moni Abraham Kuriakose
A1 Binay Panda
YR 2015
UL http://biorxiv.org/content/early/2015/10/11/028845.abstract
AB Oral tongue squamous cell carcinomas (OTSCC) are a homogenous group of tumors characterized by aggressive behavior, early spread to lymph nodes and a higher rate of regional failure. Additionally, the incidence of OTSCC among younger population (<50yrs) is on a rise; many of who lack the typical associated risk factors of alcohol and/or tobacco exposure. We present data on SNVs, indels, regions with LOH, and CNVs from fifty-paired oral tongue primary tumors and link the significant somatic variants with clinical parameters, epidemiological factors including HPV infection and tumor recurrence. Apart from the frequent somatic variants harbored in TP53, CASP8, RASA1, NOTCH and CDKN2A genes, significant amplifications and/or deletions were detected in chromosomes 6-9, and 11 in the tumors. Variants in CASP8 and CDKN2A were mutually exclusive. CDKN2A, P1K3CA, RASA1 and DMD variants were exclusively linked to smoking, chewing, HPV infection and tumor stage. We also performed whole-genome gene expression study that identified matrix metalloproteases to be highly expressed in tumors and linked pathways involving arachidonic acid and NF-κ-B to habits and distant metastasis, respectively. Functional knockdown studies in cell lines demonstrated the role of CASP8 in HPV-negative OTSCC cell line. Finally, we identified a 38-gene minimal signature that predicts tumor recurrence using an ensemble machine learning method. Taken together, this study links molecular signatures to various clinical and epidemiological factors in a homogeneous tumor population with a relatively high HPV prevalence.