Venous Thromboembolism and Non-Small Cell Lung Cancer: A Pooled Analysis of National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) Trials.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3995-3995
Author(s):  
Lisa Hicks ◽  
Matthew Cheung ◽  
Baktiar Hasan ◽  
Keyue Ding ◽  
Lesley Seymour ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trials ◽  
Adjuvant Chemotherapy ◽  
Advanced Nsclc ◽  
Randomized Study ◽  
Pooled Analysis ◽  
Small Cell ◽  
Prior History ◽  
Small Cell Lung ◽  
History Of

Abstract Purpose: To determine the incidence of venous thromboembolism (VTE) in patients with early and advanced non-small cell lung cancer (NSCLC); to explore predictive factors for VTE occurrence during trials in these populations, and to investigate the effect of VTE on overall survival in patients with NSCLC. Patients and Methods: Data from three National Cancer Institute of Canada Clinical Trials Group trials were included in the analyses (n=1987). JBR.10 was a randomized study of post-resection adjuvant vinorelbine/cisplatin versus observation in stage IB/II NSCLC. BR.18 was a randomized study of paclitaxel/carboplatin ± the metalloproteinase inhibitor BMS-275291 in advanced NSCLC (1st line). BR.21 was a randomized study of erlotinib versus placebo in previously treated (2nd or 3rd line) NSCLC. The relationship between VTE, cancer treatment, concomitant medications, and baseline patient characteristics was explored with univariate and multivariate analysis. Cox regression analysis was performed to determine whether VTE was independently associated with survival. Each trial was analyzed separately; in addition a pooled analysis was performed with the advanced disease trials (BR.18 and BR.21). Results: The incidence of VTE was 0% in the observation arm of JBR.10 and ranged from 3% in the adjuvant chemotherapy arm of JBR.10 to 8% in BR.18. In JBR.10, VTE was independently associated with the administration of adjuvant chemotherapy (p=0.015), baseline obesity (p=.001), and low platelet count (p=.007). For patients with advanced NSCLC, VTE was significantly more common in patients receiving chemotherapy (BR.18) compared to patients receiving erlotinib or placebo (BR.21) (8% vs 2%, p< 0.0001). In BR.18, but not BR.21, VTE was associated with a prior history of VTE (p=0.001). When BR.18 and BR.21 were pooled, prior VTE remained significant. In BR.18 and BR.21, VTE was associated with shorter survival in multivariate analysis (HR=1.69, 95%CI 1.32–2.17, p<.0001); further analyses, including JBR.10, are planned to explore this finding. Conclusion: VTE is a frequent event in patients with advanced NSCLC and is associated with the administration of chemotherapy. Treatment with metalloproteinase or epidermal growth factor inhibitors does not appear to increase the risk of VTE. In early NSCLC, adjuvant chemotherapy, morbid obesity and a prior history of VTE are associated with increased risk. VTE is associated with shortened survival in patients with advanced NSCLC. Funding for this study was provided by the Canadian Cancer Society.

Pemetrexed Versus Pemetrexed and Carboplatin As Second-Line Chemotherapy in Advanced Non–Small-Cell Lung Cancer: Results of the GOIRC 02-2006 Randomized Phase II Study and Pooled Analysis With the NVALT7 Trial

2012 ◽  
Vol 30 (36) ◽  
pp. 4501-4507 ◽  
Author(s):  
Andrea Ardizzoni ◽  
Marcello Tiseo ◽  
Luca Boni ◽  
Andrew D. Vincent ◽  
Rodolfo Passalacqua ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Pooled Analysis ◽  
Small Cell ◽  
Second Line ◽  
Small Cell Lung ◽  
Platinum Based Chemotherapy ◽  
The Impact

Purpose To compare efficacy of pemetrexed versus pemetrexed plus carboplatin in pretreated patients with advanced non–small-cell lung cancer (NSCLC). Patients and Methods Patients with advanced NSCLC, in progression during or after first-line platinum-based chemotherapy, were randomly assigned to receive pemetrexed (arm A) or pemetrexed plus carboplatin (arm B). Primary end point was progression-free survival (PFS). A preplanned pooled analysis of the results of this study with those of the NVALT7 study was carried out to assess the impact of carboplatin added to pemetrexed in terms of overall survival (OS). Results From July 2007 to October 2009, 239 patients (arm A, n = 120; arm B, n = 119) were enrolled. Median PFS was 3.6 months for arm A versus 3.5 months for arm B (hazard ratio [HR], 1.05; 95% CI, 0.81 to 1.36; P = .706). No statistically significant differences in response rate, OS, or toxicity were observed. A total of 479 patients were included in the pooled analysis. OS was not improved by the addition of carboplatin to pemetrexed (HR, 90; 95% CI, 0.74 to 1.10; P = .316; P heterogeneity = .495). In the subgroup analyses, the addition of carboplatin to pemetrexed in patients with squamous tumors led to a statistically significant improvement in OS from 5.4 to 9 months (adjusted HR, 0.58; 95% CI, 0.37 to 0.91; P interaction test = .039). Conclusion Second-line treatment of advanced NSCLC with pemetrexed plus carboplatin does not improve survival outcomes as compared with single-agent pemetrexed. The benefit observed with carboplatin addition in squamous tumors may warrant further investigation.

scholarly journals Are clinical trial eligibility criteria an accurate reflection of a real-world population of advanced non-small-cell lung cancer patients?

2018 ◽  
Vol 25 (4) ◽  
Author(s):  
K. Al-Baimani ◽  
H. Jonker ◽  
T. Zhang ◽  
G.D. Goss ◽  
S.A. Laurie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Small Cell ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Ecog Ps

Background Advanced non-small-cell lung cancer (nsclc) represents a major health issue globally. Systemic treatment decisions are informed by clinical trials, which, over years, have improved the survival of patients with advanced nsclc. The applicability of clinical trial results to the broad lung cancer population is unclear because strict eligibility criteria in trials generally select for optimal patients.Methods We performed a retrospective chart review of all consecutive patients with advanced nsclc seen in outpatient consultation at our academic institution between September 2009 and September 2012, collecting data about patient demographics and cancer characteristics, treatment, and survival from hospital and pharmacy records. Two sets of arbitrary trial eligibility criteria were applied to the cohort. Scenario A stipulated Eastern Cooperative Oncology Group performance status (ecog ps) 0–1, no brain metastasis, creatinine less than 120 μmol/L, and no second malignancy. Less-strict scenario B stipulated ecog ps 0–2 and creatinine less than 120 μmol/L. We then used the two scenarios to analyze treatment and survival of patients by trial eligibility status.Results The 528 included patients had a median age of 67 years, with 55% being men and 58% having adenocarcinoma. Of those 528 patients, 291 received at least 1 line of palliative systemic therapy. Using the scenario A eligibility criteria, 73% were trial-ineligible. However, 46% of “ineligible” patients actually received therapy and experienced survival similar to that of the “eligible” treated patients (10.2 months vs. 11.6 months, p = 0.10). Using the scenario B criteria, only 35% were ineligible, but again, the survival of treated patients was similar in the ineligible and eligible groups (10.1 months vs. 10.9 months, p = 0.57).Conclusions Current trial eligibility criteria are often strict and limit the enrolment of patients in clinical trials. Our results suggest that, depending on the chosen drug, its toxicities and tolerability, eligibility criteria could be carefully reviewed and relaxed.

Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non–Small-Cell Lung Cancer

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Kien Wei Siah ◽  
Sean Khozin ◽  
Chi Heem Wong ◽  
Andrew W. Lo
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Clinical Trials ◽  
Operating Characteristic ◽  
Advanced Nsclc ◽  
Characteristic Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Out Of Sample ◽  
Operating Characteristic Curve

PURPOSE The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with advanced non–small-cell lung cancer (NSCLC)—objective response (OR), progression-free survival (PFS), and overall survival (OS)—using routinely collected patient and disease variables. METHODS We aggregated patient-level data from 17 randomized clinical trials recently submitted to the US Food and Drug Administration evaluating molecularly targeted therapy and immunotherapy in patients with advanced NSCLC. To our knowledge, this is one of the largest studies of NSCLC to consider biomarker and inhibitor therapy as candidate predictive variables. We developed a stochastic tumor growth model to predict tumor response and explored the performance of a range of machine-learning algorithms and survival models. Models were evaluated on out-of-sample data using the standard area under the receiver operating characteristic curve and concordance index (C-index) performance metrics. RESULTS Our models achieved promising out-of-sample predictive performances of 0.79 area under the receiver operating characteristic curve (95% CI, 0.77 to 0.81), 0.67 C-index (95% CI, 0.66 to 0.69), and 0.73 C-index (95% CI, 0.72 to 0.74) for OR, PFS, and OS, respectively. The calibration plots for PFS and OS suggested good agreement between actual and predicted survival probabilities. In addition, the Kaplan-Meier survival curves showed that the difference in survival between the low- and high-risk groups was significant (log-rank test P < .001) for both PFS and OS. CONCLUSION Biomarker status was the strongest predictor of OR, PFS, and OS in patients with advanced NSCLC treated with immune checkpoint inhibitors and targeted therapies. However, single biomarkers have limited predictive value, especially for programmed death-ligand 1 immunotherapy. To advance beyond the results achieved in this study, more comprehensive data on composite multiomic signatures is required.

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