Molecular alterations to inform anatomical-based prognosis for recurrence prediction in HNSCC.

R Rajnish Vasant Nagarkar (HCG Manavata Cancer Centre, Nashik, India) M Mohsina Hussain (HCG Manavata Cancer Centre, Nashik, India) S Sirshendu Roy (HCG Manavata Cancer Centre, Nashik, India) M Mukesh Chaudhary (HCG Manavata Cancer Centre, Nashik, India) S Srinivas Raut (HCG Manavata Cancer Centre, Nashik, India) S Sucheta Gandhe (HCG Manavata Cancer Centre, Nashik, India) Y Yogesh Pawar (HCG Manavata Cancer Centre, Nashik, India) G Ganesh Divekar (1Cell.Ai, Mumbai, India) T Trinica George (1Cell.Ai, Mumbai, India) G Gowhar Shafi (1Cell.Ai, Mumbai, India) M Mohan Uttarwar (1Cell.Ai, Foster City, CA) A Aarthi Ramesh (1Cell.Ai, Pune, India) S Sandhya Iyer H Hrishita Kothavade (1Cell.Ai, Mumbai, India)

Abstract

e18040 Background: Head and neck squamous cell carcinoma (HNSCC) remains a biologically heterogeneous malignancy with poor survival outcomes, primarily due to high recurrence rates and limited predictive biomarkers. The TP53 gene, a critical genomic gatekeeper, is frequently mutated in aggressive oral cavity cancers. The choice of personalized treatment modalities can be enhanced significantly by identifying accurate and reliable methods to predict which HNSCC patients are most likely to recur and consequently improve survival of patients. This stratification of patients has been difficult to obtain due to the numerous anatomic sites, the unpredictable clinical behavior and heterogeneous molecular features of these tumors. In this study we compared the prevalence of molecular alterations of TP53 and its co-occurring genes with anatomical features like depth of invasion (DOI), lymphovascular invasion (LVI), perineural invasion (PNI), extranodal extension (ENE) to evaluate the most consistent indicator disease-recurrence in the HNSCC patients. Methods: A total of 22 patients with histologically confirmed HNSCC were retrospectively analyzed for genomic alterations and clinical/anatomical attributes such as TNM stage, DOI, LVI, PNI, ENE, Disease Free Survival (DFS), and Overall Survival (OS). Next generation sequencing in these patient samples was performed using OncoIndx panel. Correlations of TP53, its co-occurring genomic alterations and the clinical/anatomical attributes of patients with their recurrence pattern was investigated. Results: Our analysis identified a total of 63.63% of patients (n=14/22) to be affected by disease recurrence of whom 85.71% (12/14) harbored TP53 mutations. Further, with respect to individual anatomical features of these disease-recurred patients, 42.85% (n=6/14) showed DOI >=1.0 cm, 7.14% (n=1/14) were LVI positive, 28.57% (n=4/14) were PNI positive, and 21.42% (n=3/14) were ENE positive. Further, 71.42% (n=10/14) of the patients were found to be positive for all the anatomical features together (DOI+LVI+PNI+ENE). Conclusions: TP53-enriched Indian HNSCC cohort demonstrates that molecular determinants (TP53/CDKN2A/MYC/PIK3CA signatures) supersede anatomic staging in predicting recurrence and immunotherapy response. The combined positivity for all anatomical features (71.42%) still fell short of the recurrence-prediction percentage associated with TP53-mutation status (85.71%), highlighting TP53 mutation status as a stronger predictor of recurrence in this HNSCC cohort.

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (14)

R

Rajnish Vasant Nagarkar

HCG Manavata Cancer Centre, Nashik, India

M

Mohsina Hussain

HCG Manavata Cancer Centre, Nashik, India

S

Sirshendu Roy

HCG Manavata Cancer Centre, Nashik, India

M

Mukesh Chaudhary

HCG Manavata Cancer Centre, Nashik, India

S

Srinivas Raut

HCG Manavata Cancer Centre, Nashik, India

S

Sucheta Gandhe

HCG Manavata Cancer Centre, Nashik, India

Y

Yogesh Pawar

HCG Manavata Cancer Centre, Nashik, India

G

Ganesh Divekar

1Cell.Ai, Mumbai, India

T

Trinica George

1Cell.Ai, Mumbai, India

G

Gowhar Shafi

1Cell.Ai, Mumbai, India

M

Mohan Uttarwar

1Cell.Ai, Foster City, CA

A

Aarthi Ramesh

1Cell.Ai, Pune, India

S

Sandhya Iyer

H

Hrishita Kothavade

1Cell.Ai, Mumbai, India