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The pathologic analysis of lung cancer has become increasingly complex, calling for continued attention to improving the accuracy of the assessment and reducing the amount of tissue required.
Jimmy Ruiz, MD
Assistant Professor of Medicine,
Hematology and Oncology
Assistant Director of Cancer Health Equity
Wake Forest Baptist Medical Center —
Comprehensive Cancer Center
Non-small cell lung cancer (NSCLC) is no longer a single disease, but a constellation of cancer types pathologically classified by histology. Hence, a fundamental first step in diagnosis is the retrieval of adequate tissue for histological classification. Greater effort is required from pathologists to, whenever possible, avoid the diagnosis of carcinoma not otherwise specified (NOS).
With the majority of patients with NSCLC presenting with advanced disease, minimally invasive small volume biopsies are increasingly utilized for diagnosis. The fine-needle aspirate (FNA) is the most routinely utilized biopsy approach, and while sufficient for cancer diagnosis, the amount of cellular material from FNAs can be sparse. This is often cited as the limiting factor to the correct classification of NSCLC histology, a problem further compounded by the poor cellular differentiation characteristic of advanced-stage disease. Together, these limitations contribute to errors in NSCLC classification and interobserver disagreement among pathologists.
Despite the advancements in the identification of driver mutations and actionable targeted drugs like the ALK-inhibitor crizotinib and the EGFR-inhibitor erlotinib, chemotherapy remains standard care for the majority of patients with metastatic NSCLC. However, in recent years, NSCLC classification by histology has become essential for the optimal selection of treatment:
Thus, histological diagnosis in NSCLC has become a major determinant of drug efficacy and prevention of undue toxicity to patients.
Additional immunohistochemical (IHC) stains can aid in the classification of NSCLC. However, numerous tissue assays can significantly deplete the amount of tissue available for additional molecular testing. Testing for emerging predictive genomic biomarkers such as ROS1, BRAF, MET, and PIK3CA, in addition to standard ALK and EGFR testing, will only compound the issue of obtaining sufficient tissue from small volume biopsies.
Paramount to ensuring the retrieval of adequate tissue for comprehensive diagnosis of NSCLC is close collaboration and communication among all providers involved in the diagnosis, classification, and treatment of patients. At our institution, we have developed an algorithm that standardizes the retrieval of adequate tissue and the effective pathological processing of specimens for both histology and molecular characterization. Yet, there remain cases where the complete characterization of NSCLC is not possible due to the sparsity of viable tissue samples. This problem has been a central concern, motivating the development of diagnostic technologies that can accurately measure multiple biomarkers in minimal tissue specimens.
Several technologies are being developed to accurately differentiate between AC and SCC by analyzing protein, RNA, or DNA components of cancer tissues. By collaborating with RNA expression analysis experts, we have analyzed the gene expression profiles of hundreds of human lung tumors and identified a panel of eight genes that accurately discriminates NSCLC tumors by AC and SCC histology (Figure). We used a sensitive and reproducible gene expression profiling assay, Panomics QuantiGene Plex 2.0 expression analysis system (QGS). The QGS is a nucleic acid hybridization-based assay that allows direct quantification of mRNA transcripts from FFPE tissue sections. Our research team was able to validate the diagnostic accuracy of the eight-gene panel (termed the A/S signature) retrospectively by analyzing archival tissues derived from surgically resected NSCLC tumors.
This emerging technology may prove useful for discrimination of NSCLC tumor histologies that are otherwise difficult to define by conventional pathological means. There is clearly a need for a technology that can accomplish this goal, and many studies of alternative technologies are ongoing nationally.
Eight putative histotype-specific genes (on y axis) selected by microarray meta-analysis were profiled in 25 NSCLC tumors (FFPEs) using the QuantiGene RNA analysis system. By hierarchical cluster analysis, the gene expression patterns discern AC from SCC.
The pathologic analysis of lung cancer has become increasingly complex. The amount of tissue required for establishing not only a cancer diagnosis, but also a treatment plan, has also increased. While molecular genetic testing has increased in value, establishing the pathologic subtype continues to be of significant importance. This step is critical for selecting the most active chemotherapy agents and avoiding potentially fatal toxicities. Thus, we must continue to advance the emerging technologies under development for improving the accuracy of this assessment and reducing the amount of tissue required.