Role of thyroid transcription factor-1 and P63 immunocytochemistry in cytologic typing of non-small cell lung carcinomas

Immunocytochemical Evaluation of TTF-1, Napsin-A, and p-63 for Subtyping of Non-Small Cell Lung Carcinoma and Clinicopathological Correlation

Background

Carcinoma of lung is the most common cause of cancer-associated mortality worldwide. About 70% of lung cancer cases are unresectable and present in advanced stages. So, cytology and small core needle biopsy specimen are available for diagnostic as well as prognostication workup. Subtyping of non-small cell lung cancer (NSCLC) is essential for the treatment and further workup study. For this, immunocytochemistry (ICC) plays a crucial role that helps in early diagnosis. Subtyping of NSCLC from cytology samples using ICC markers like TTF-1, Napsin-A, and p63 and their clinicopathological correlation are the aims of the study.

Materials and Methods

This ambispective study was conducted in the pathology department of a tertiary care hospital of eastern India for a 2-year period from 2018 to 2020. In our study, 46 cytologically diagnosed cases of NSCLC were included. Subtyping was done by cytomorphology and correlated with ICC expression, histopathology, and clinicopathological parameters.

Results

In our study, adenocarcinoma (ADC) was the most common (32.61%) cancer. Most cases of ADC showed positive expression of TTF-1 and Napsin-A, and p63 was positive in most cases of squamous cell carcinoma (SCC). Concordance with cytomorphology and ICC was 87.50% and 81.81% with ADC and SCC, respectively. Cyto-ICC-histo concordance was observed in 85.51% of ADC and 66.66% of SCC cases. Sensitivity was 100%, 93.1%, and 100% for TTF-1, Napsin-A, and p63, respectively. Specificity of both TTF-1 and Napsin-A was 88.2% and for p63 was 93.8%.

Conclusion

In small biopsy along with cytology samples, ICC is cost-effective and plays an important role in early diagnosis along with management of NSCLC.

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non-small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.