Molecular diagnosis and typing of lung cancer variants

摘要

Compositions and methods useful in determining the major morphological types of lung cancer are provided. The methods include detecting expression of at least one gene or biomarker in a sample. The expression of the gene or biomarker is indicative of the lung tumor subtype. The compositions include subsets of genes that are monitored for gene expression. The gene expression is capable of distinguishing between normal lung parenchyma and the major morphological types of lung cancer. The gene expression and somatic mutation data are useful in developing a complete classification of lung cancer that is prognostic and predictive for therapeutic response. The methods are suited for analysis of paraffin-embedded tissues. Methods of the invention include means for monitoring gene or biomarker expression including PCR and antibody-based detection. The biomarkers of the invention are genes and/or proteins that are selectively expressed at a high or low level in certain tumor subtypes. Biomarker expression can be assessed at the protein or nucleic acid level.

主权项

1. A method for diagnosing a lung tissue sample from a human patient as an adenocarcinoma or a squamous cell carcinoma, said method comprising detecting expression levels of all of the normal, small cell lung cancer (SCLC), carcinoid, adenocarcinoma/squamous cell carcinoma (AC/SCC) classifier biomarkers of Table 1 at the nucleic acid level by performing a reverse transcriptase polymerase chain reaction (RT-PCR) with primers specific to the classifier biomarkers;
comparing the detected levels of expression of said classifier biomarkers of Table 1 to the expression of said classifier biomarkers in at least one sample training set(s), wherein one of the sample training set(s) comprise expression data of said classifier biomarkers of Table 1 from an adenocarcinoma sample and one of the sample training set(s) comprise expression data of said classifier biomarkers of Table 1 from a squamous cell carcinoma sample, and the comparing step comprises applying a statistical algorithm which comprises determining a correlation between the expression data obtained from the human lung tissue sample and the expression data from the adenocarcinoma and the squamous cell carcinoma training set(s); and diagnosing a lung tissue sample as an adenocarcinoma or squamous cell carcinoma based on the results of the statistical algorithm.