Abstract 1901: Untargeted metabolomic profiling identifies diagnostic and prognostic biomarkers of lung cancer.

Abstract

Abstract Lung cancer remains the most common cause of cancer deaths world-wide. Despite the intensive research over many years, the prognosis of this deadly disease is still very poor, with fewer than 15% of the patients surviving 5 years after primary diagnosis. While there are several methodologies described and proposed for early detection of lung cancer (spiral CT, circulating pro-inflammatory cytokines IL6, IL8 and CRP), the specificity and robustness remains to be achieved. What we readily know is that cancer cells have a distinguishable metabolic fingerprint compared to normal cells. Metabolomics holds promise to be able to detect and capture subtle shifts in multiple metabolic paths and cellular modifiers that will enable identification of critical components of cancer risk and tumor behavior. We conducted a first of its kind effort using mass spectrometry-based untargeted metabolic profiling of urine samples obtained from 469 lung cancer patients and 536 healthy population controls. We identified four robust biomarkers, high levels of which are associated with lung cancer diagnosis and poorer survival. After the adjustment for potential confounding factors, all four biomarkers were significantly associated with lung cancer diagnosis (FDR-adjusted p-values <0.05, ORs ranging from 1.9 to 5.1), whereas one of four was associated with diagnosis in early I and II stages (OR =3.3, p-value =0.002). Furthermore, all four biomarkers are associated with prognosis (HRs ranging from 1.49 to 1.97, after adjustment for potential confounders, p-values <0.02), whereas two were associated with survival in stages I and II (HRs of 1.83 and 9.33, p-values 0.03 and 0.0006 respectively). A combination of the four biomarkers resulted in stronger associations, suggesting that they may be independent of one another. Significantly higher levels of these biomarkers were confirmed in an independent sample set from the same cohort, confirming our findings and eliminating storage time as a potential confounder. A targeted quantitation was carried out in a representative subset of 198 samples, further validating previous findings from the untargeted screen. Furthermore, intraclass correlation analysis revealed high repeatability of two independent measurements over a year apart (ICCs between 0.82 and 0.99). Lastly, the metabolome of 62 tumor and 62 adjacent normal tissues was profiled (stage I adeno- and squamous cell- carcinomas), linking two urinary biomarkers directly to the tumor metabolism (FCs of 1.7 and 19.0; p-values 0.03 and <0.00001, respectively). In addition to their potential to further identify those high risk groups who would most benefit from an invasive screen, thereby minimizing the false positive rate, these markers may also illuminate novel lung carcinogenesis pathways, as well as potential therapeutic targets. Mechanistic studies elucidating effected pathways are ongoing. Citation Format: Majda Haznadar, Ewy Mathe, Andrew D. Patterson, Soumen K. Manna, Kristopher W. Krausz, Elise D. Bowman, Jeffrey R. Idle, Dickran G. Kazandjian, Frank J. Gonzalez, Curtis C. Harris. Untargeted metabolomic profiling identifies diagnostic and prognostic biomarkers of lung cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1901. doi:10.1158/1538-7445.AM2013-1901