A histology-expression predictor has been developed for the most common types of lung cancer. This predictor can confirm histologic diagnosis in routinely collected paraffin samples of patients’ tumors and can complement and corroborate the findings of pathologists.
Knowing what type of lung cancer a patient has is critical to determine which drug will work best and which therapies are safest in the era of personalized medicine. Key to making that judgment is acquiring an adequate tumor specimen for the pathologist to determine the tumor’s histology, which is a molecular description of a tumor based on the appearance of cells under a microscope. But not all specimens are perfect, and they are sometimes so complex that a definitive diagnosis presents a challenge.
The new histology predictor, which was developed by scientists at the Universities of North Carolina and Utah, addresses the most common types of lung cancer: adenocarcinoma, carcinoid, small cell carcinoma, and squamous cell carcinoma. Their findings were reported in the Journal of Molecular Diagnostics (2013; 15(4):485-497).
“As we learn more about the genetics of lung cancer, we can use that understanding to tailor therapies to the individual’s tumor. We desperately needed to extend the analysis of genes (aka RNA) to paraffin samples that are routinely generated in clinical care, rather than fresh-frozen tissue. That is the major accomplishment of the current study and one of the first large scale endeavors in lung cancer to show this is possible,” said corresponding author Neil Hayes, MD, MPH, of the University of North Carolina.
Hayes explained that the new predictor identifies the major histologic types of lung cancer in paraffin-embedded tissue specimens. This ability is immediately useful in confirming the histologic diagnosis in difficult tissue-biopsy specimens.
The scientists used 442 samples of formalin-fixed paraffin-embedded specimens from lung cancer patients at UNC and the University of Utah Health Sciences Center as they developed their predictor.
First author Matthew Wilkerson, PhD, also of UNC, explained that they sought to determine if gene expression could accurately predict histology. The researchers already knew of lung cancer genes that were differentially expressed in the different tumor types, so they measured them in tumor paraffin specimens. The next step was to develop a predictor in an independent set of tumor samples. The predictor was compared with the actual clinical diagnosis, and additional pathologists reviewed the samples. The predictor had accuracy that was at least as good as the pathologists, with the mean accuracy of the predictor being 84%. Compared with pathologist diagnoses, the predictor had accuracy and precision that was similar to that of the pathologists.