Validation of a clinical prognostic model in Chinese patients with metastatic and advanced pretreated non-small cell lung cancer treated with gefitinib

Prognostic model of survival outcomes in non-small cell lung cancer patients initiated on afatinib: pooled analysis of clinical trial data

Objective

Several predictors of survival have been identified in EGFR-positive non-small cell lung cancer (NSCLC) patients treated with first generation EGFR inhibitors. Prognostic models of survival outcomes with afatinib have not been evaluated.

Methods

A prognostic tool for overall survival (OS)/ progression free survival (PFS) based on pre-treatment clinicopathological factors was developed for EGFR-positive advanced NSCLC patients treated with first-line afatinib using penalised regression of individual-participant data from LUX-Lung 3 and 6 (n = 468). Favourable, intermediate and poor risk groups were identified and externally validated using LUX-Lung 1 (n = 390) and LUX-Lung 2 (n = 129) trials that initiated afatinib following previous chemotherapy or EGFR inhibitor treatment.

Results

Discriminative performance was good in the development and validation cohorts. For patients treated with first-line afatinib, the median OS for the favourable, intermediate and poor risk groups were > 47.7, 29.3 and 16.4 months, respectively, and the median PFS were 17.3, 13.2 and 8.3 months, respectively. The improvement in median OS with afatinib use compared to chemotherapy was > 12.4 months for the favourable risk group, whereas no OS benefit was apparent for the poor risk group. The improvement in median PFS with afatinib use compared to chemotherapy was 10.2 months for the favourable risk group and 3.2 months for the poor risk group.

Conclusions

A prognostic tool was developed and validated to identify favourable, intermediate and poor risk groups for OS/PFS in EGFR-positive advanced NSCLC patients treated with afatinib. The prognostic groups can inform the likely absolute OS/PFS benefit expected from afatinib compared to chemotherapy in first-line treatment.

Decision support systems for incurable non-small cell lung cancer: a systematic review

BACKGROUND

Individually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients.

METHODS

A systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness.

RESULTS

The systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor.

CONCLUSIONS

Many DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness.

Refining Prognosis in Lung Cancer: A Report on the Quality and Relevance of Clinical Prognostic Tools

INTRODUCTION

Accurate, individualized prognostication for lung cancer patients requires the integration of standard patient and pathologic factors, biological, genetic, and other molecular characteristics of the tumor. Clinical prognostic tools aim to aggregate information on an individual patient to predict disease outcomes such as overall survival, but little is known about their clinical utility and accuracy in lung cancer.

METHODS

A systematic search of the scientific literature for clinical prognostic tools in lung cancer published from January 1, 1996 to January 27, 2015 was performed. In addition, web-based resources were searched. A priori criteria determined by the Molecular Modellers Working Group of the American Joint Committee on Cancer were used to investigate the quality and usefulness of tools. Criteria included clinical presentation, model development approaches, validation strategies, and performance metrics.

RESULTS

Thirty-two prognostic tools were identified. Patients with metastases were the most frequently considered population in non-small-cell lung cancer. All tools for small-cell lung cancer covered that entire patient population. Included prognostic factors varied considerably across tools. Internal validity was not formally evaluated for most tools and only 11 were evaluated for external validity. Two key considerations were highlighted for tool development: identification of an explicit purpose related to a relevant clinical population and clear decision points and prioritized inclusion of established prognostic factors over emerging factors.

CONCLUSIONS

Prognostic tools will contribute more meaningfully to the practice of personalized medicine if better study design and analysis approaches are used in their development and validation.