The Impact of Treatment Preferences in Second-Line Chemotherapy on the Prognosis of HER2-Negative Metastatic Breast Cancer

Multistate Pharmacometric Model to Define the Impact of Second-Line Immunotherapies on the Survival Outcome of the IMpower131 Study

Overall survival is defined as the time since randomization into the clinical trial to event of death or censor (end of trial or follow-up), and is considered to be the most reliable cancer end point. However, the introduction of second-line treatment after disease progression could influence survival and be considered a confounding factor. The aim of the current study was to set up a multistate model framework, using data from the IMpower131 study, to investigate the influence of second-line immunotherapies on overall survival analysis. The model adequately described the transitions between different states in patients with advanced squamous non-small cell lung cancer treated with or without atezolizumab plus nab-paclitaxel and carboplatin, and characterized the survival data. High PD-L1 expression at baseline was associated with a decreased hazard of progression, while the presence of liver metastasis at baseline was indicative of a high risk of disease progression after initial response. The hazard of death after progression was lower for participants who had longer treatment response, i.e., longer time to progression. The simulations based on the final multistate model showed that the addition of atezolizumab to the nab-paclitaxel and carboplatin regimen had significant improvement in the patients' survival (hazard ratio = 0.75, 95% prediction interval: 0.61-0.90 favoring the atezolizumab + nab-paclitaxel and carboplatin arm). The developed modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, and investigate the benefit of primary therapy in survival while accounting for the switch to alternative treatment in the case of disease progression.

A nine-lncRNA signature predicts distant relapse-free survival of HER2-negative breast cancer patients receiving taxane and anthracycline-based neoadjuvant chemotherapy

Multi-gene prognostic signatures of long non-coding RNAs (lncRNAs) provide new insights into mechanisms of HER2-negative breast cancer development and progression, and predict distant relapse-free survival (DRFS) of patients receiving taxane and anthracycline-based neoadjuvant chemotherapy. The aim of this study was to develop such a multi-lncRNAs signature. Optimal multiple candidate signature lncRNAs associated with DRFS were firstly identified by a univariate Cox proportional hazard regression survival analysis and a robust likelihood-based survival analysis of the GEO dataset GSE25055. A nine-lncRNA prognostic risk score model Risk Score = 0.0289 × EXP_LOC100507388 - 0.0814 × EXP_LINC00094 - 0.2422 × EXP_SMG7-AS1 - 0.2433 × EXP_PP14571 + 0.4690 × EXP_ASAP1-IT1 - 0.2483 × EXP_LOC103344931 - 0.2464 × EXP_FAM182A + 0.3349 × EXP_HCG26 - 0.0216 × EXP_LINC00963 was built according to the coefficients of multivariate survival analysis of the association between the candidate lncRNAs and survival. EXP_lncRNA was the standardized log2-transformed expression level of the gene. According to this model, higher scores predicted lower survival probability. The area under Receiver operating characteristic (ROC) curve (AUC) was 0.777 to 0.823 from 1- to 7- year survival rate. The model and its individual lncRNAs differentiated survival probability between the higher scores (expression) and the lower scores (expression). The nine-lncRNA signature had the robust prognostic power compared with ER, PR, tumor size (T), lymph node invasion (N), TNM stage, pathologic response, chemosensitivity prediction and PAM50 signature. These results were consistent with those based on the GEO dataset GSE25065. The predictive nomograms integrating both the nine-lncRNA signature classifier and clinical-pathological risk factors were robust in predicting 1-, 3- and 5- year survival probabilities. These results supported that the nine-lncRNA signature was a robust and effective model in predicting DRFS of patients with HER2-negative breast cancer following taxane and anthracycline-based neoadjuvant chemotherapy.

Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two-stage stochastic dynamic treatment regime approach

Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention-to-treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per-protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. To answer these clinical questions, we propose a sensitivity analysis method for subsequent treatments using the inverse probability of treatment weighting method for stochastic dynamic treatment regimes (DTRs). We formulate oncology clinical trials with subsequent treatments as two-stage designs in which initial treatments are randomized, but subsequent treatments are observational. In this formulation, we use stochastic DTRs to simulate specific proportions of each subsequent treatment and compare an initial experimental treatment with an initial control treatment under various proportions of each subsequent treatment. We applied our proposed method to a motivating randomized noninferiority trial for metastatic breast cancer. Simulation results are also reported to show the usefulness of the proposed method.