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Reply to "Better Prognostic Models May Result in Improved Patient Selection for Adjuvant Therapies After Complete Resection of Solitary Fibrous Tumors of the Pleura".

5 e46 Journal of Thoracic Oncology ® • Volume 10, Number 6, June 2015 To the Editor: Like Burotto et al. mentioned, a biomarker that can identify which patients with early-stage nonsmall-cell lung cancer are most likely to benefit from adjuvant therapy is eagerly awaited. Unfortunately, spatial intratumor heterogeneity limits the use of genetic biomarkers as the evaluation of a single region of the tumor can lead to incomplete and misleading information on mutation status. Therefore, intrapatient tumor heterogeneity poses a real challenge to the development of personalized cancer treatment. Sampling of a limited region of a tumor biopsy or a single block of a surgical resection specimen may not adequately capture the heterogeneity within a patient’s cancer. Many predictive and prognostic biomarkers that are routinely measured in clinical setting may be discordant within the primary tumor. Only the most aggressive subclones, sometimes only minimally present in the tumor, may survive the selection pressure of adjuvant treatment or are fit for metastasis. The assessment of intratumor heterogeneity within different time points is the key for success for an individualized cancer treatment, however, multiregion next generation sequencing is difficult to achieve in clinical setting. Therefore, it would be more appealing to use a noninvasive biomarker that is easily repeatable over time, and that gives information about the global tumor and its relation with the immediate environment. Major genetic changes lead to metabolic alterations in cancer cells. Metabolomics has the potential to provide this information because it measures and quantify the end products of cellular metabolism, namely, the metabolites. Metabolomics analysis is performed by several analytical methods. The two main tools in metabolomics for measuring the altered cancer metabolism, so-called metabolic profiling, are mass spectroscopy and nuclear magnetic resonance spectroscopy. Together with multivariate statistical methods, a parallel measurement of hundreds of metabolites in biological samples, such as blood, urine, exhaled breath condensate, and tumor tissue, can be performed. The two techniques are complementary, nuclear magnetic resonance spectroscopy being highly reproducible, inexpensive, and quantitative. Mass spectroscopy has the advantage of a high sensitivity and dynamic range, so that it can be used for the measurement of thousands of metabolites in a single measurement. Metabolomics is a relative new research domain in oncology, especially in lung cancer. Nevertheless, several research groups were already able to establish a clear distinct metabolic profile between lung cancer patients and healthy subjects. Our own research group validated this technique and could clearly distinguish healthy subjects from lung cancer patients with a sensitivity of 76% and a specificity of 89%. Moreover, recent publications in different cancer types are very promising about the use of metabolic profiling as a predictor of metastasis and drug toxicity, as a biomarker for the surveillance of disease relapse, and as a prognostic tool. So next to the field of genomics, transcriptomics, and proteomics, the upcoming importance of metabolomics cannot be neglected. Kirsten Stinkens, MD Faculty of Medicine and Life Sciences Hasselt University Hasselt, Belgium Department of Respiratory Medicine Ziekenhuis Oost-Limburg Genk, Belgium