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Non-Small Cell Lung Cancer Treatment (PDQ®): Treatment - Health Professional Information [NCI] - Cellular Classification of NSCLC

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Large cell carcinoma

In addition to the general category of large cell carcinoma, several uncommon variants are recognized in the WHO/IASLC classification, including the following:

  • LCNEC.
  • Basaloid carcinoma.
  • Lymphoepithelioma-like carcinoma.
  • Clear cell carcinoma.
  • Large cell carcinoma with rhabdoid phenotype.

Basaloid carcinoma is also recognized as a variant of squamous cell carcinoma, and rarely, adenocarcinomas may have a basaloid pattern; however, in tumors without either of these features, they are regarded as a variant of large cell carcinoma.

Neuroendocrine tumors

LCNEC is recognized as a histologically high-grade non-small cell carcinoma. It has a very poor prognosis similar to that of small cell lung cancer (SCLC). Atypical carcinoid is recognized as an intermediate-grade neuroendocrine tumor with a prognosis that falls between typical carcinoid and high-grade SCLC and LCNEC.

Neuroendocrine differentiation can be demonstrated by immunohistochemistry or electron microscopy in 10% to 20% of common NSCLCs that do not have any neuroendocrine morphology. These tumors are not formally recognized within the WHO/IASLC classification scheme because the clinical and therapeutic significance of neuroendocrine differentiation in NSCLC is not firmly established. These tumors are referred to collectively as NSCLC with neuroendocrine differentiation.

Carcinomas with pleomorphic, sarcomatoid, or sarcomatous elements

This is a group of rare tumors. Spindle cell carcinomas and giant cell carcinomas comprise only 0.4% of all lung malignancies, and carcinosarcomas comprise only 0.1% of all lung malignancies. In addition, this group of tumors reflects a continuum in histologic heterogeneity as well as epithelial and mesenchymal differentiation. On the basis of clinical and molecular data, biphasic pulmonary blastoma is regarded as part of the spectrum of carcinomas with pleomorphic, sarcomatoid, or sarcomatous elements.

Molecular Features

The identification of mutations in lung cancer has led to the development of molecularly targeted therapy to improve the survival of subsets of patients with metastatic disease.[2] In particular, subsets of adenocarcinoma now can be defined by specific mutations in genes encoding components of the epidermal growth factor receptor (EGFR) and downstream mitogen-activated protein kinases (MAPK) and phosphatidylinositol 3-kinases (PI3K) signaling pathways. These mutations may define mechanisms of drug sensitivity and primary or acquired resistance to kinase inhibitors. Other mutations of potential relevance to treatment decisions include:

  • Kirsten rat sarcoma viral oncogene (KRAS).
  • Anaplastic lymphoma kinase receptor (ALK).
  • Human epidermal growth factor receptor 2 (HER2).
  • V-raf murine sarcoma viral oncogene homolog B1 (BRAF).
  • PIK3 catalytic protein alpha (PI3KCA).
  • AKT1.
  • MAPK kinase 1 (MAP2K1 or MEK1).
  • MET, which encodes the hepatocyte growth factor receptor (HGFR).

These mutations are mutually exclusive, except for those in PIK3CA and EGFR mutations and ALK translocations.[3]

EGFR and ALK mutations predominate in adenocarcinomas that develop in nonsmokers, and KRAS and BRAF mutations are more common in smokers or former smokers. EGFR mutations strongly predict the improved response rate and progression-free survival of EGFR inhibitors. In a set of 2,142 lung adenocarcinoma specimens from patients treated at Memorial Sloan Kettering Cancer Center, EGFR exon 19 deletions and L858R were found in 15% of tumors from former smokers (181 of 1,218; 95% CI, 13–17), 6% from current smokers (20 of 344; 95% CI, 4–9), and 52% from never-smokers (302 of 580; 95% CI, 48–56; P < .001 for ever- vs. never-smokers).[4]

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WebMD Public Information from the National Cancer Institute

Last Updated: February 25, 2014
This information is not intended to replace the advice of a doctor. Healthwise disclaims any liability for the decisions you make based on this information.
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