Comprehensive molecular analysis of driver mutations in non-small cell lung carcinomas and its correlation with PD-L1

Aditi Aggarwal1, Shivani Sharma1, Zoya Brar1

  • 1Department of Molecular Pathology, CORE Diagnostics, Gurugram, Haryana, India.

PubMed
Abstract

Insights

Next-generation sequencing identified common mutations in non-small cell lung carcinoma (NSCLC) in Indian patients. These molecular alterations correlate with patient demographics and PD-L1 expression, guiding personalized treatment strategies.

Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Non-small cell lung carcinoma (NSCLC) understanding has advanced, enabling personalized treatment through molecular alterations like EGFR, ALK, ROS1, BRAF, and MET mutations.
  • Next-generation sequencing (NGS) is crucial for identifying these targetable driver mutations, offering advantages over single-gene testing.

Purpose of the Study:

  • To profile mutations in five hot-spot genes (EGFR, ALK, ROS1, BRAF, MET) in Indian NSCLC patients using NGS.
  • To correlate these mutations with clinicopathologic characteristics and PD-L1 expression.

Main Methods:

  • Targeted DNA and RNA-based NGS panel profiling five hot-spot genes in 552 stage IV NSCLC patients.
  • Correlation analysis of mutations with clinicopathologic features and PD-L1 status in 252 tumors.

Main Results:

  • Sixty percent of tumors had mutations; EGFR (38.41%) and ALK (12.14%) were most common.
  • Specific mutations showed correlations with demographics (age, smoking status) and PD-L1 expression (e.g., EGFR exon 19 deletion in PD-L1 negative, exon 21 in PD-L1 positive).
  • Histopathologic subtypes were associated with specific mutations (e.g., acinar with EGFR, solid with ALK).

Conclusions:

  • This study presents one of the largest NSCLC cohorts for targeted mutational profiling and PD-L1 correlation in India.
  • Mutation prevalence varies by demographics and histology, providing insights for personalized NSCLC management.
  • Understanding these correlations aids in refining targeted therapy selection and predicting treatment response.

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