Baseline tumour measurements predict survival in advanced non-small cell lung cancer.
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ABSTRACT:
SUBMITTER: Gerber DE
PROVIDER: S-EPMC3776984 | biostudies-other | 2013 Sep
REPOSITORIES: biostudies-other
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Project description:Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.
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Project description:The prognosis for advanced-stage non-small cell lung cancer (NSCLC) is usually poor. However, survival may be variable and difficult to predict. In the current study, we aimed to identify circulating metabolites as potential predictive biomarkers for overall survival of advanced-stage (III/IV) NSCLC patients treated with first-line platinum-based chemotherapy.Using two-stage study design, we performed global metabolomic profiling in blood of 220 advanced-stage NSCLC patients, including 110 with poor survival and 110 with good survival. Metabolomic profiling was conducted using Metabolon platform. The association of each metabolite with survival was assessed by Cox proportional hazard regression model with adjustment for covariates.We found levels of 4 metabolites, caffeine, paraxanthine, stachydrine, and methyl glucopyranoside (alpha+beta), differed significantly between NSCLC patients with poor and good survival in both discovery and validation phases (P<0.05). Interestingly, majority of the identified metabolites are involved in caffeine metabolism, and 2 metabolites are related to coffee intake. In fact, caffeine metabolism pathway was the only significant pathway identified which significantly differed between NSCLC patients with poor and good survival (P=1.48E-07) in the pathway analysis. We also found 4 metabolites whose levels were significantly associated with good survival in both discovery and validation phases. Strong cumulative effects on overall survival were observed for these 4 metabolites. In conclusion, we identified a panel of metabolites including metabolites in caffeine metabolism pathway that may predict survival outcome in advanced-stage NSCLC patients. The identified small metabolites may be useful biomarker candidates to help identify patients who may benefit from platinum-based chemotherapy.
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Project description:Nicotinic acetylcholine receptors (nAChRs) play a key role in carcinogenesis and progression of lung cancer; and polymorphisms in CHRNA5-A3 and CHRNB3-A6, two gene clusters encoding nAChR subunits, have been associated with lung cancer risk. In this study, we investigated whether variants in the two gene clusters were associated with prognosis of advanced non-small cell lung cancer (NSCLC). A total of 165 stage IIIB-IV NSCLC patients were enrolled in this study. Three polymorphisms (rs667282 and rs3743073 in CHRNA5-A3 and rs13280604 in CHRNB3-A6) were genotyped using the TaqMan method. Overall survival (OS) was estimated using the log-rank test and the Cox models. Our results showed that patients with CHRNA5-A3 rs667282 TT or TC genotypes had a significantly shorter OS than those carrying the CC genotype (Log-rank, P = 0.043). Furthermore, multivariate Cox regression analysis showed that rs667282 TT/TC genotypes are significantly associated with increased risk of overall deaths (adjusted hazard ratio, 1.7; 95% CI, 1.1-2.7). However, the similar results were not observed for other two polymorphisms. Furthermore, no evident association was found between these variants and clinicopathologic features of advanced NSCLC. Our present study suggested that rs667282 in CHRNA5-A3 may modify the prognosis of patients with advanced NSCLC.
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Project description:A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.
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Project description:BackgroundWe aimed to assess the prognostic role of neutrophilia, lymphocytopenia and the neutrophil-to-lymphocyte ratio (NLR), and to design models to define the prognosis of patients receiving first-line chemo- or targeted therapy for advanced non-small cell lung cancer (NSCLC).Materials and methodsWe retrospectively analysed 401 consecutive patients with advanced NSCLC treated with first line chemo- or targeted therapy. Patients were stratified into two groups with pre-treatment NLR ? 3.7 (Group A) vs. < 3.7 (Group B). The best NLR cut-off was identified by ROC curve analysis.ResultsAt baseline 264 patients had NLR?3.7 (Group A), whilst 137 had lower NLR (Group B). Median OS was 10.8 months and 19.4 months in the two groups (p < 0.001), while median PFS was 3.6 months and 5.6 months, respectively (p = 0.012). At multivariate analysis, ECOG-PS?2, stage IV cancer, non-adenocarcinoma histology, EGFR wild-type status and NLR were predictors of worse OS. Stage IV cancer, wild type EGFR status and NLR?3.7 were independent prognostic factors for worse PFS. Patients were stratified according to the presence of 0-1 prognostic factors (8%), 2-3 factors (73%) and 4-5 factors (19%) and median OS in these groups was 33.7 months, 14.6 months and 6.6 months, respectively (p < 0.001). Similarly, patients were stratified for PFS based on the presence of 0-1 prognostic factor (15%), 2 factors (41%) and 3 factors (44%). The median PFS was 8.3 months, 4.6 months and 3.3 months respectively (p < 0.001).ConclusionsPre-treatment NLR is an independent prognostic factor for patients with advanced NSCLC treated with first-line therapies.
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Project description:BackgroundInflammation-nutritional markers of peripheral blood are easily assessed and can predict survival. The aim of this study was to investigate the association between inflammation-nutritional parameters and survival of anti-programmed death-1 (PD-1) therapy in non-small-cell lung cancer (NSCLC) patients.MethodsWe performed a retrospective study from March 2017 to April 2020 in advanced NSCLC patients treated with PD-1 inhibitors. Univariable and multivariable analyses were conducted to evaluate the relationship between peripheral blood parameters (absolute lymphocyte count [ALC], absolute neutrophil count [ANC], absolute monocyte count [AMC], absolute eosinocyte count [AEC], lactic dehydrogenase [LDH], plasma-albumin [ALB], neutrophil/lymphocyte ratio [NLR], and platelet/lymphocyte ratio [PLR]) measured before therapy initiation and prognosis.ResultsAmong 184 evaluable patients, 134 (72.8%) were male and the median age was 58 years (range 33-87) with 31 (16.8%) ≥70 years. An elevated ANC (≥7500/ul), NLR (≥5), and PLR (≥200) was significantly associated with worse objective response rate (ORR), progression-free survival (PFS), and overall survival (OS), while increased ALC (≥1000/ul) and ALB (≥3.5 g/dl) could significantly improve survival in terms of ORR, PFS, and OS. In multivariate analyses, higher AEC (≥150/ul) and AMC (≥650/ul) could significantly decrease the risk of death (hazard ratio [HR] 0.363, 95% confidence interval [CI] 0.141-0.931, p = 0.035; HR 0.370, 95% CI 0.203-0.675, p = 0.001). A higher NLR and PLR, and lower ALB were independent predictors of poor prognosis for OS (HR 1.964, 95% CI 1.027-3.755, p = 0.041; HR 4.255, 95% CI 2.364-7.658, p = 0.000; HR 1.962, 95% CI 1.213-3.174, p = 0.006, respectively).ConclusionOur research illustrated that pretreatment AEC, AMC, ALB, NLR, and PLR are independent predictors for survival in advanced NSCLC patients treated with PD-1 inhibitors.
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Project description:We used computed tomography (CT) to explore the prognostic value of cell-free (cf) DNA quantification and its predictive efficacy over time after chemotherapy in non-small-cell lung cancer (NSCLC) patients. In total, 177 NSCLC patients were enrolled in a prospective biomarker trial. Consecutive paired blood collection was performed to determine cfDNA concentrations at baseline CT and throughout serial follow-ups. The best cfDNA cut-off value to predict progression-free and overall survival was determined using X-tile analysis. Among 112 chemo-naive patients with stage IV adenocarcinoma, 43 were available for follow-up analysis. Cox regression multivariate analysis indicated that a high cfDNA concentration was an independent negative prognostic factor for progression-free survival (hazard ratio: 2.60; 95% confidence interval: 1.65-4.10; p = 0.008) and overall survival (hazard ratio: 2.63; 95% confidence interval: 1.66-4.17; p < 0.001). However, cfDNA concentration changes during treatment did not correlate with radiological CT responses at first follow-up or best response. No pattern was noted in the percent change in the cfDNA concentration from baseline or subsequently measured level to progression. The serum cfDNA concentration is thus associated with NSCLC patient prognosis, but does not appear to be a clinically valid marker for tumor responses.
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Project description:Background:The Hippo signalling pathway plays an important role in regulating organ size and cell proliferation. Down-regulation of large tumour suppressor (LATS) protein homologs LATS1 or LATS2 has been found in lung cancer. LATS1 and LATS2 are the core components of the Hippo signalling pathway. LATS1 and LATS2 share some conserved structural features and exhibit redundant biological functions. The aim of this study was to dissect the interaction between these two homologs. Methods:In lung adenocarcinoma (AD) cells, protein expression of LATS1 and LATS2 were determined by western blotting; cell viability and apoptosis were measured by MTT and annexin V staining after treatment with cisplatin; subcellular distributions of LATS proteins were determined by immunofluorescence microscopy; LATS2 expression was modulated by shRNA-mediated knockdown or ectopic expression in cancer cell lines. Results:Manipulation of the expression of these two LATS kinases influenced cisplatin response in advanced lung AD cell lines. High LATS2-to-LATS1 ratio in H2023 cells was associated with cisplatin resistance, while low LATS2-to-LATS1 ratio in CL1-0 and CL83 cells was associated with sensitivity to cisplatin. Manipulating the LATS2-to-LATS1 ratio by LATS2 over-expression in CL1-0 and CL83 rendered them resistant to cisplatin treatment, whereas LATS2 knockdown in H2023 alleviated the LATS2-to-LATS1 ratio and sensitized cancer cells to cisplatin exposure. Conclusions:Our data suggested that the ratio of expression of LATS kinases played a role in the modulation of cisplatin sensitivity in advanced lung AD, and targeting of LATS proteins as a novel therapeutic strategy for lung AD deserves further investigation.
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Project description:Mutated circulating cell-free DNA (cfDNA) has been suggested as a surrogate marker of tumour burden and aggressiveness of disease. We examined the association between the level of plasma mutant cfDNA and metabolic tumour burden (MTB) measured by 18F-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT). Furthermore, the presence of mutant cfDNA was correlated with patient survival.Forty-six advanced non-small cell lung cancer (NSCLC) patients were included. At the time of inclusion, blood sampling and a PET/CT scan were performed. cfDNA was isolated and next-generation sequencing (NGS) was performed (Ion AmpliSeq Colon and Lung Cancer panel v2). MTB was defined by a volumetric PET parameter.NGS succeeded in 41 patients. Mutations were detected in the blood of 24 patients. A significant correlation between the allele frequency of the most frequent mutation and MTB was found (P=0.001). Patients with detectable mutated cfDNA had a significantly shorter median overall survival compared with patients without (3.7 versus 10.6 months, P=0.019). This impact on survival was independent of the MTB.Level of mutated cfDNA tends to correlate with MTB in advanced-stage NSCLC patients. Patients with detectable mutant DNA in plasma had an inferior survival, indicating that this could be an important predictor of survival.
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