Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs.

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Physical status, symptoms and health-related quality of life during a severe exacerbation of COPD: Recovery and discriminative capacity for future events.

OBJECTIVE: Severe acute exacerbations of chronic obstructive pulmonary disease (AECOPD) can have a negative impact on functional capacity, symptoms and health-related quality of life (HRQOL). This study aimed to i) investigate the recovery of muscle strength, functional capacity, symptoms, and HRQOL in patients after a severe AECOPD; ii) compare with matched patients with stable COPD (SCOPD); and iii) assess whether these assessments at hospital discharge could discriminate patients' risk for future events. METHODS: This observational study assessed patients with AECOPD during hospital discharge (T1) and one month after discharge (T2). Patients with SCOPD were assessed once. Quadriceps force, handgrip strength, short physical performance battery (SPPB), 6-min walk distance (6 MWD), COPD assessment test (CAT), London chest activity of daily living (LCADL), modified medical research council, checklist individual strength-fatigue, patient health questionnaire, and physical activity (Actigraph) were measured. Exacerbation-related readmission and mortality within six months and 1-year were collected. RESULTS: Forty-four patients with AECOPD were matched with 44 patients with SCOPD. At T2, a significant improvement was found for the SPPB total score, 6 MWD, CAT score, and LCADL score. Compared to patients with SCOPD, a worse LCADL score was found at T2 in patients with AECOPD. Patients with AECOPD that were readmitted or died had a worse SPPB classification and five-repetition sit-to-stand test at T1. CONCLUSION: Patients after severe AECOPD improved in functional capacity and HRQOL one month after hospital discharge, but ADL performance was still worse compared to SCOPD. Patients who were readmitted or died had significantly worse scores on functional tests at hospital discharge.

Randomised, controlled, open-label pragmatic trial evaluating changes in functional exercise capacity after primary care PUlmonary REhabilitation in patients with long COVID: protocol of the PuRe-COVID trial in Belgium.

INTRODUCTION: Long COVID is a prevalent condition with many multisystemic symptoms, such as fatigue, dyspnoea, muscle weakness, anxiety, depression and sleep difficulties, impacting daily life and (social and physical) functioning. Pulmonary rehabilitation (PR) may improve physical status and symptoms of patients with long COVID, yet the evidence is limited. Therefore, this trial aims to study the effect of primary care PR on exercise capacity, symptoms, physical activity and sleep in patients with long COVID. METHODS AND ANALYSIS: PuRe-COVID is a prospective, pragmatic, open-label, randomised controlled trial. A sample of 134 adult patients with long COVID will be randomised to a 12 week PR programme in primary care, supervised by a physiotherapist or to a control group, following no PR. A 3 month and 6 month follow-up period is foreseen. The primary endpoint will be the change in exercise capacity measured by 6-minute walk distance (6MWD) at 12 weeks, hypothesising a more significant improvement in the PR group. Other parameters, such as pulmonary function tests (including maximal inspiratory pressure/maximal expiratory pressure), patient-reported outcomes (COPD Assessment Test, modified Medical Research Council Dyspnoea Scale, Checklist Individual Strength, post-COVID-19 Functional Status, Nijmegen questionnaire, Hospital Anxiety and Depression Scale, Work Productivity and Activity Impairment Questionnaire and EuroQol-5D-5L), physical activity measured by an activity tracker, hand grip strength and sleep efficiency, are secondary and exploratory outcomes.The recruitment started on 19 April 2022, and 52 patients were included as of 14 December 2022. ETHICS AND DISSEMINATION: Ethical approval was obtained in Belgium from the relevant institutional review boards on 21 February 2022 (Antwerp University Hospital, approval number 2022-3067) and on 1 April 2022 (Ziekenhuis Oost-Limburg in Genk, approval number Z-2022-01). Findings from this randomised controlled trial will be disseminated in peer-reviewed publications and presentations at international scientific meetings. TRIAL REGISTRATION NUMBER: NCT05244044.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer.

BACKGROUND: Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. PATIENTS AND METHODS: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. RESULTS: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86-100]) and a sensitivity of 92% (95% CI [81-98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77-97]) and an AUC value of 0.97 (95% CI [0.93-1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82-99]) and an AUC value of 0.97 (95% CI [0.93-1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. CONCLUSIONS: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor's presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Modulation of thromboinflammation in hospitalized COVID-19 patients with aprotinin, low molecular weight heparin, and anakinra: The DAWn-Antico study.

Background: Thromboinflammation plays a central role in severe COVID-19. The kallikrein pathway activates both inflammatory pathways and contact-mediated coagulation. We investigated if modulation of the thromboinflammatory response improves outcomes in hospitalized COVID-19 patients. Methods: In this multicenter open-label randomized clinical trial (EudraCT 2020-001739-28), patients hospitalized with COVID-19 were 1:2 randomized to receive standard of care (SOC) or SOC plus study intervention. The intervention consisted of aprotinin (2,000,000 IE IV four times daily) combined with low molecular weight heparin (LMWH; SC 50 IU/kg twice daily on the ward, 75 IU/kg twice daily in intensive care). Additionally, patients with predefined hyperinflammation received the interleukin-1 receptor antagonist anakinra (100 mg IV four times daily). The primary outcome was time to a sustained 2-point improvement on the 7-point World Health Organization ordinal scale for clinical status, or discharge. Findings: Between 24 June 2020 and 1 February 2021, 105 patients were randomized, and 102 patients were included in the full analysis set (intervention N = 67 vs. SOC N = 35). Twenty-five patients from the intervention group (37%) received anakinra. The intervention did not affect the primary outcome (HR 0.77 [CI 0.50-1.19], p = 0.24) or mortality (intervention n = 3 [4.6%] vs. SOC n = 2 [5.7%], HR 0.82 [CI 0.14-4.94], p = 0.83). There was one treatment-related adverse event in the intervention group (hematuria, 1.49%). There was one thrombotic event in the intervention group (1.49%) and one in the SOC group (2.86%), but no major bleeding. Conclusions: In hospitalized COVID-19 patients, modulation of thromboinflammation with high-dose aprotinin and LMWH with or without anakinra did not improve outcome in patients with moderate to severe COVID-19.

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review.

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3-62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (18F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.

HLA-I diversity and tumor mutational burden by comprehensive next-generation sequencing as predictive biomarkers for the treatment of non-small cell lung cancer with PD-(L)1 inhibitors.

OBJECTIVES: Immune checkpoint inhibitors (ICIs) improved outcomes in non-small cell lung cancer (NSCLC) patients. We report the predictive utility of human leukocyte antigen class I (HLA-I) diversity and tumor mutational burden (TMB) by comprehensive next-generation sequencing. METHODS: 126 patients were included. TMB high was defined as ≥ 10 nonsynonymous mutations/Mb. Patients exhibit high HLA-I diversity if at least one locus was in the upper 15th percentile for DNA alignment scores. RESULTS: No difference in response rate (RR; 44.4% versus 30.9%; p = 0.1741) or 6-month survival rate (SR; 75.6% versus 77.8%; p = 0.7765) was noted between HLA-I high diversity and low diversity patients. HLA-I high diversity patients did significantly more often exhibit durable clinical benefit (DCB), defined as response or stable disease lasting minimally 6 months (64.4% [29/45] versus 43.2% [35/81]; p = 0.0223). TMB high patients exhibited higher RR (49.1% versus 25.4%; p = 0.0084) and SR 6 months after start ICI (85.5% versus 70.4%; p = 0.0468) than TMB low patients. The proportion of patients with DCB, did not differ significantly between TMB high and low subgroups (60.0% [33/55] versus 42.3% [30/71]; p = 0.0755). Patients with combined dual high TMB and HLA-I diversity had higher RR (63.2% versus 22.2%; p = 0.0033), but SR at 6 months did not differ significantly (84.2% versus 64,4%; p = 0.1536). A significantly higher rate of patients experienced DCB in dual high compared to the dual low group (73.7% [14/19] versus 35.6% [16/45]; p = 0.0052). Triple positive patients (high TMB and HLA-I diversity and PD-L1 positive) had higher RR (63.6% versus 0.0%; p = 0.0047) and SR at 6 months (100% versus 66.7%; p = 0.0378) compared to triple-negative patients. CONCLUSION: HLA-I diversity was able to predict durable clinical benefit in ICI treated NSCLC patients, but failed to confirm as a predictor of response or survival. TMB confirmed as a predictive biomarker.

Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics.

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

Detection of Lung Cancer via Blood Plasma and 1H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor.

Metabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72).

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET.

BACKGROUND: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV-including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. METHODS: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. RESULTS: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). CONCLUSION: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

Physical Tests Are Poorly Related to Patient-Reported Outcome Measures during Severe Acute Exacerbations of COPD.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) have a negative impact on patients' health status, including physical function and patient-reported outcomes. We aimed to explore the associations between physical tests and patient-reported outcome measures (PROMs) in hospitalised patients for an AECOPD. Patients were assessed on the day of discharge. Quadriceps force, handgrip strength, short physical performance battery (SPPB), five-repetition sit-to-stand test (5STS), four-meter gait speed test (4MGS), balance test, six-minute walk test (6MWT), COPD Assessment Test (CAT), London Chest Activity of Daily Living scale (LCADL), modified Medical Research Council (mMRC) dyspnea scale, Checklist of Individual Strength (CIS)-fatigue subscale, and Patient Health Questionnaire (PHQ-9) were collected. Sixty-nine patients with an AECOPD were included (54% female; age 69 ± 9 years; FEV1 39.2 (28.6-49.1%) predicted). Six-minute walk distance was strongly correlated with mMRC (ρ: -0.64, p < 0.0001) and moderately correlated with LCADL total score, subscales self-care and household activities (ρ ranging from -0.40 to -0.58, p < 0.01). Moreover, 4MGS was moderately correlated with mMRC (ρ: -0.49, p < 0.0001). Other correlations were weak or non-significant. During a severe AECOPD, physical tests are generally poorly related to PROMs. Therefore, a comprehensive assessment combining both physical tests and PROMs needs to be conducted in these patients to understand their health status.

Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer.

BACKGROUND: Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset. MATERIALS AND METHODS: The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume. RESULTS: The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%). CONCLUSION: When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

Fever and an abnormal chest X-ray during the COVID-19 pandemic.

During the COVID-19 pandemic, a 56-year-old man presented at our emergency department with fever and shortness of breath; Diffuse pulmonary nodular vascular spread lesions were found. Detailed history taking showed a four-week history of fever and night sweats. The man had been under treatment for 2 years with Adalimumab, a tumor-necrosis-factor (TNF) inhibitor, for ulcerative colitis. Before start, screening by tuberculin skin test was negative. Cultures en PCR on BAL and urine were positive for mycobacterium tuberculosis also ocular findings were present. The diagnosis of military tuberculosis was made.

The Metabolic Landscape of Lung Cancer: New Insights in a Disturbed Glucose Metabolism.

Metabolism encompasses the biochemical processes that allow healthy cells to keep energy, redox balance and building blocks required for cell development, survival, and proliferation steady. Malignant cells are well-documented to reprogram their metabolism and energy production networks to support rapid proliferation and survival in harsh conditions via mutations in oncogenes and inactivation of tumor suppressor genes. Despite the histologic and genetic heterogeneity of tumors, a common set of metabolic pathways sustain the high proliferation rates observed in cancer cells. This review with a focus on lung cancer covers several fundamental principles of the disturbed glucose metabolism, such as the "Warburg" effect, the importance of the glycolysis and its branching pathways, the unanticipated gluconeogenesis and mitochondrial metabolism. Furthermore, we highlight our current understanding of the disturbed glucose metabolism and how this might result in the development of new treatments.

Correlations between the metabolic profile and 18F-FDG-Positron Emission Tomography-Computed Tomography parameters reveal the complexity of the metabolic reprogramming within lung cancer patients.

Several studies have demonstrated that the metabolite composition of plasma may indicate the presence of lung cancer. The metabolism of cancer is characterized by an enhanced glucose uptake and glycolysis which is exploited by 18F-FDG positron emission tomography (PET) in the work-up and management of cancer. This study aims to explore relationships between 1H-NMR spectroscopy derived plasma metabolite concentrations and the uptake of labeled glucose (18F-FDG) in lung cancer tissue. PET parameters of interest are standard maximal uptake values (SUVmax), total body metabolic active tumor volumes (MATVWTB) and total body total lesion glycolysis (TLGWTB) values. Patients with high values of these parameters have higher plasma concentrations of N-acetylated glycoproteins which suggest an upregulation of the hexosamines biosynthesis. High MATVWTB and TLGWTB values are associated with higher concentrations of glucose, glycerol, N-acetylated glycoproteins, threonine, aspartate and valine and lower levels of sphingomyelins and phosphatidylcholines appearing at the surface of lipoproteins. These higher concentrations of glucose and non-carbohydrate glucose precursors such as amino acids and glycerol suggests involvement of the gluconeogenesis pathway. The lower plasma concentration of those phospholipids points to a higher need for membrane synthesis. Our results indicate that the metabolic reprogramming in cancer is more complex than the initially described Warburg effect.

Glutamine Addiction and Therapeutic Strategies in Lung Cancer.

Lung cancer cells are well-documented to rewire their metabolism and energy production networks to support rapid survival and proliferation. This metabolic reorganization has been recognized as a hallmark of cancer. The increased uptake of glucose and the increased activity of the glycolytic pathway have been extensively described. However, over the past years, increasing evidence has shown that lung cancer cells also require glutamine to fulfill their metabolic needs. As a nitrogen source, glutamine contributes directly (or indirectly upon conversion to glutamate) to many anabolic processes in cancer, such as the biosynthesis of amino acids, nucleobases, and hexosamines. It plays also an important role in the redox homeostasis, and last but not least, upon conversion to α-ketoglutarate, glutamine is an energy and anaplerotic carbon source that replenishes tricarboxylic acid cycle intermediates. The latter is generally indicated as glutaminolysis. In this review, we explore the role of glutamine metabolism in lung cancer. Because lung cancer is the leading cause of cancer death with limited curative treatment options, we focus on the potential therapeutic approaches targeting the glutamine metabolism in cancer.

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.

Does nivolumab for progressed metastatic lung cancer fulfill its promises? An efficacy and safety analysis in 20 general hospitals.

OBJECTIVES: In patients with refractory or recurrent non-small-cell lung cancer (NSCLC) after first line chemotherapy, phase III trials showed superiority of nivolumab, an IgG4 programmed death-1 immune-checkpoint-inhibitor antibody, over docetaxel. We evaluated case mix, effectiveness and safety of nivolumab upon implementation in general practice. MATERIALS AND METHODS: In 20 general hospitals, all consecutive NSCLC patients treated with nivolumab within the medical need program (inclusion period 12 months) in Flanders - Belgium were evaluated. RESULTS: There were 267 patients, Eastern Cooperative Oncology Group (ECOG) score was 2 in 24% and 0-1 in 76%. In 48%, two or more systemic regimens were given before nivolumab. The median overall survival was 7.8 months (95% confidence interval (CI) 6.3-9.3). At one year, the overall survival rate was 36.5±0.34%. Median progression-free survival was 3.7 months (95% CI 2.9-4.5). An objective response was obtained in 23.2%. ECOG score 2 and presence of liver metastasis strongly correlated with worse survival (p<0.00001). Treatment related adverse events grade 3 or 4 were reported in 21%, colitis (4%) and pneumonitis (7%) were most frequent. CONCLUSION: Upon implementation of nivolumab therapy in general hospitals, the case mix was characterized by a more heavily pretreated population with a substantial fraction of patients with ECOG score 2. The median overall survival is slightly inferior to what was published in the randomized phase III trials. An ECOG score 2 and the presence of liver metastasis correlated strongly with a worse survival. We report a high prevalence of serious adverse events.