Evelyne Louis

Detection of Lung Cancer through Metabolic Changes Measured in Blood Plasma

Introduction: Low‐dose computed tomography, the currently used tool for lung cancer screening, is characterized by a high rate of false‐positive results. Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. Therefore, this study aims to evaluate whether the metabolic phenotype of blood plasma allows detection of lung cancer. Methods: The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 233 patients with lung cancer and 226 controls. The validity of the model was examined by classifying an independent cohort of 98 patients with lung cancer and 89 controls. Results: The model makes it possible to correctly classify 78% of patients with lung cancer and 92% of controls, with an area under the curve of 0.88. Important moreover is the fact that the model is convincing, which is demonstrated by validation in the independent cohort with a sensitivity of 71%, a specificity of 81%, and an area under the curve of 0.84. Patients with lung cancer have increased glucose and decreased lactate and phospholipid levels. The limited number of patients in the subgroups and their heterogeneous nature do not (yet) enable differentiation between histological subtypes and tumor stages. Conclusions: Metabolic phenotyping of plasma allows detection of lung cancer, even in an early stage. Increased glucose and decreased lactate levels are pointing to an increased gluconeogenesis and are in accordance with recently published findings. Furthermore, decreased phospholipid levels confirm the enhanced membrane synthesis.

Metabolic phenotyping of human blood plasma: a powerful tool to discriminate between cancer types?

BACKGROUND Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. However, up to now it is not clear whether different cancer types are characterized by a specific metabolite profile. Therefore, this study aims to evaluate whether the plasma metabolic phenotype allows to discriminate between lung and breast cancer. PATIENTS AND METHODS The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 80 female breast cancer patients and 54 female lung cancer patients, all with an adenocarcinoma. The validity of the model was examined by permutation testing and by classifying an independent validation cohort of 60 female breast cancer patients and 81 male lung cancer patients, all with an adenocarcinoma. RESULTS The model allows to classify 99% of the breast cancer patients and 93% of the lung cancer patients correctly with an area under the curve (AUC) of 0.96 and can be validated in the independent cohort with a sensitivity of 89%, a specificity of 82% and an AUC of 0.94. Decreased levels of sphingomyelin and phosphatidylcholine (phospholipids with choline head group) and phospholipids with short, unsaturated fatty acid chains next to increased levels of phospholipids with long, saturated fatty acid chains seem to indicate that cell membranes of lung tumors are more rigid and less sensitive to lipid peroxidation. The other discriminating metabolites are pointing to a more pronounced response of the body to the Warburg effect for lung cancer. CONCLUSION Metabolic phenotyping of plasma allows to discriminate between lung and breast cancer, indicating that the metabolite profile reflects more than a general cancer marker. CLINICAL TRIAL REGISTRATION NUMBER NCT02362776.

1168PVALIDATION OF 1H-NMR-BASED METABOLOMICS AS A TOOL TO DETECT LUNG CANCER IN HUMAN BLOOD PLASMA

ABSTRACT Aim: Until today no effective method permits the early detection of lung cancer. Evidence has shown that disturbances in biochemical pathways which occur during the development of cancer provoke, changes in the metabolic phenotype. Recently, our research group has constructed a statistical classifier by means of multivariate orthogonal partial least squares-discriminant analysis (OPLS-DA). This classifier (constructed with 110 variables) allows to discriminate between 190 lung cancer patients (71% male, 29% female, age: 68 ± 10, BMI: 25.8 ± 4.7) and 182 controls (53% male, 47% female, age: 69 + 11, BMI: 28.1 ± 4.8) with a sensitivity of 76% and a specificity of 89%, with an AUC of 0.86. When only the 19 most discriminating variables (VIP value > 0.8) were selected to construct a classifier (i.e. glucose, lactate, myo-inositol, threonine, alanine, isoleucine and lipids signals) a sensitivity of 69%, a specificity of 83% and an AUC of 0.81 is achieved. The present study aims to examine the predictive accuracy of these statistical classifiers in an independent cohort of 50 lung cancer patients (60% male, 40% female, age: 67 ± 9, BMI: 25.6 ± 4.3) and 58 controls (64% male, 36% female, age: 63 ± 13, BMI: 26.9 ± 5.7). Methods: The metabolic phenotype of the plasma samples from this independent cohort is determined by 1H-NMR spectroscopy. Subsequently, the constructed classifiers are used to classify the independent samples. OPLS-DA is used as discriminant statistic. Results: By using the classifier constructed with all 110 variables, 72% of the lung cancer patients and 72% of the controls are correctly classified, with an AUC of 0.79. Moreover, when the classifier constructed with only the 19 most discriminating variables is used to classify the independent samples, a sensitivity of 82%, a specificity of 64% and an AUC of 0.79 is achieved. Conclusions: A statistical classifier constructed with only the most discriminating variables shows already a fair predictive accuracy, similar to this of the classifier build with all variables. Future experiments will investigate whether the constructed classifier can be used as a valid screening tool. Disclosure: All authors have declared no conflicts of interest.

Prognostic value of total lesion glycolysis and metabolic active tumor volume in non-small cell lung cancer

INTRODUCTION To predict the outcome of patients with non-small cell lung cancer (NSCLC) the currently used prognostic system (TNM) is not accurate enough. The prognostic significance of the SUVmax measured by PET remains controversial. This study aims to evaluate the prognostic value in overall survival and progression free survival of SUVmax, the total lesion glycolysis (TLG) and the mean metabolic active volume (MATV) in NSCLC. METHODS We retrospectively reviewed 105 patients (72 males, 33 females) with a new diagnosis of NSCLC (TNM stage I: 27.6%, II: 10.5%, III: 40.9% and IV: 21.0%) who underwent scanning with a PET/CT. For VOI definition a semi-automatic delineation tool was used. On PET images SUVmax, SUVmean and MATV of the primary tumor and the whole tumor burden were measured. TLG and MATV were measured by using a threshold of 50% of SUVmax. RESULTS OS and PFS are found to be higher in patients with low-SUVTmax and low-TLGT values. OS and PFS were significantly higher for low-SUVWTBmax, low-MATVWTB and low-TLGWTB values of the whole-tumor burden. Multivariate analysis of the whole-tumor burden revealed that the most important prognostic factors for OS are high MATVWTB and TLGWTB values, increasing stage and male gender. TLGWTB and stage are also independent prognosticators in PFS. CONCLUSION Only whole-body TLG is of prognostic value in NSCLC for both OS and PFS. Stratification of patients by TLGWTB might complement outcome prediction but the TNM stage remains the most important determinant of prognosis. MICROABSTRACT In order to predict the outcome of patients with non-small cell lung cancer (NSCLC) the currently used prognostic system (TNM) is not accurate enough. The prognostic significance of the standard uptake value (SUV) measured by PET remains controversial. This study aims to evaluate the prognostic value in overall survival (OS) and progression free survival (PFS) of the standard uptake value (SUV), the total lesion glycolysis (TLG) and the mean metabolic active volume (MATV) in NSCLC. The study reveals that TLG of the whole-tumor burden is an independent prognostic factor for OS and PFS in patients with NSCLC.

1631PPROGNOSTIC VALUE OF TOTAL LESION GLYCOLYSIS AND METABOLIC TUMOR VOLUME IN NON-SMALL CELL LUNG CANCER

ABSTRACT Aim: To predict the outcome of patients with NSCLC the currently used prognostic system (TNM) is not accurate enough.The prognostic significance of SUV measured by PET remains controversial. This retrospective study aims to evaluate the prognostic value in overall survival (OS) and progression free survival (PFS) of the standard uptake value(SUV), total lesion glycolysis(TLG) and the metabolic volume (MTV) in primary NSCLC. Methods: This study investigates 86 patients (58 male, 28 female) with a new diagnosis of NSCLC (TNM stage I : 24.4%, II:9.3%,III:38.4% and IV:27.9%) who underwent PET/CT.For VOI definition a semi-automatic delineation tool was used. On PET images SUVmax,SUVmean and MTV of the primary tumors were measured. PET parameters were corrected for lean body mass and glycemia.TLG was defined as SUVmean*MTV.TLG50 was calculated by using a treshold of 50% of the SUVmax.SUV index was calculated as the ratio of tumor SUVmax to liver SUVmean to improve reproducibility and to lower the influence of patient and scanner related factors. Results: Median follow up was 17 months.Pre-therapy median MTV,TLG50 and SUVmax were 10.37 +- 47.01 ml, 52.49 +- 239.33ml and 7.67+-5.55 respectively.OS and PFS were significantly higher in patients with values below the median values of MTV, TLG50 and SUVmax.Univariate analysis revealed gender(HR 0.3 95%CI 0.14-0.65 for OS and HR 0.33 95%CI 0.17-0.64 for PFS) and stage (HR 2.22 95%CI 1.58-3.11 for OS and HR 2.0 95%CI 1.5-2.68 for PFS) as additional prognostic factors. Multivariate analysis revealed that TLG50 (HR 2.15 95%CI 1.41-4.04), stage (HR 2.16 95%CI 1.52-3.06) and gender (HR 0.32 95%CI 0.15-0.70) were independent prognostic factors for OS.The same parameters were significant for PFS :TLG50 (HR 2.08 95%CI 1.15-3.73), stage (HR 2.1 95%CI 1.54-2.86) and gender (HR 0.28 95%CI 0.14-0.55).MTV and SUV index were not found to be significant in our multivariate model. Conclusions: Our study indicates that TLG50 of the primary tumor is an independent prognostic factor in patients with NSCLC for both OS and PFS. Further stratification of patients with the same TNM stage by TLG50 may improve outcome. Prospective studies and validation are needed for determination of the optimal cutoff value before transferring results into clinical practice. Disclosure: All authors have declared no conflicts of interest.