New cancer subtyping system can improve treatment options without disrupting clinical workflow.
Problem
Lung cancer is the leading cause of cancer death, with over 100,000 people dying per year.1 As most tumors are inoperable by the time the cancer is detected, patients are increasingly being placed on tailored treatment regimens. Prescribing targeted therapy hinges on the correct diagnosis of the subtype of lung cancer to ensure the most effective treatment plan and patient safety. Subtyping is commonly accomplished by diagnosis of a fine needle aspiration (FNA) biopsy, where a needle is inserted into the tumor to collect cells. These cells are deposited on a glass slide, stained, and then diagnosed by a pathologist. Though routinely used in the clinic, FNAs produce inconclusive results in 30% of cases. This is due to the similar visual appearance of cells among different subtypes and/or too little material, making diagnosis from pathology alone impossible. Diagnosis of FNAs can also take days and even weeks, delaying treatment and causing further distress and anxiety to patients. While some new diagnostic methods have emerged, they rely on complex protocols involving lengthy sample purification and isolation steps. There is a critical unmet need for an optimized and easy-to-use system that can rapidly subtype lung cancer to guide patient treatment strategies.
Solution
In collaboration with Erik Cressman and Ruth Katz at MD Anderson Cancer Center, Dr. Livia Eberlin’s group at The University of Texas at Austin has developed a method for rapid molecular analysis and subtyping of lung cancers from FNA biopsies. After the FNA biopsy is obtained, it is then analyzed by a mass spectrometry-based technique which measures hundreds of diagnostic molecules simultaneously. The researchers trained a predictive model using machine learning principles that can recognize the molecular “fingerprint” specific to each subtype and provide a diagnosis. Published in Clinical Chemistry, they showed that the diagnostic accuracy of this system is 87.5%, which is promising considering 30% of FNAs are undiagnosable by pathology.2 This user-friendly system analyzes FNA biopsies within a short timeframe and with no sample preparation, seamlessly fitting into the clinical workflow. Unlike other emerging techniques, this method does not harm the sample, allowing complementary assays to be performed to gain further insight into disease. This novel system may be valuable for improving and expediting subtyping of lung cancers, getting targeted treatment to patients faster.
References
1. American Cancer Society: Key Statistics for Lung Cancer, 2021. https://www.cancer.org/cancer/lung-cancer/about/key-statistics.html
2. Alena V. Bensussan, et al., Distinguishing Non-Small Cell Lung Cancer Subtypes in Fine Needle Aspiration Biopsies by Desorption Electrospray Ionization Mass Spectrometry Imaging, Clin. Chem., Volume 66, Issue 11, Nov 2020, pp.1424–1433, https://doi.org/10.1093/clinchem/hvaa207
