Development and testing of a lymphoma clinical trial-specific frailty index: a secondary analysis of the NCIC-CTG LY.12 clinical trial.

The prevalence of frailty in clinical trials of lymphoma is unknown. We conducted a secondary analysis of the phase III LY.12 trial in which patients with relapsed aggressive non-Hodgkin lymphoma were randomized to different salvage regimens before autologous stem cell transplant. The primary objective was to construct a lymphoma clinical trials-specific frailty index (LyFI) using previously described methods. The secondary objective was to describe the association of frailty withover all and event-free survival (OS, EFS). The LyFI was constructed using 619 patients, and11% (N = 70) were classified as frail. Frailty was associated with EFS (HR 1.94, 95%CI 1.53-2.46) and OS (HR 2.01, 95%CI 1.57-2.58) in univariable analysis, but was only significant as a continuous (not binary) variable in multivariable analysis controlling for prognostic score, suggesting limitations of a FI in this trial population. Future work could validate the FI using clinical assessments and/or apply it to an older trial population.

Adjunctive Statistical Standardization of Adjuvant Estrogen Receptor and Progesterone Receptor in Canadian Cancer Trials Group MA.27 Postmenopausal Breast Cancer Trial of Exemestane Versus Anastrozole.

PURPOSE: ASCO/College of American Pathologists guidelines recommend reporting estrogen receptor (ER) and progesterone receptor (PgR) as positive with (1%-100%) staining. Statistically standardized quantitated positivity could indicate differential associations of positivity with breast cancer outcomes. METHODS: MA.27 (ClinicalTrials.gov identifier: NCT00066573) was a phase III adjuvant trial of exemestane versus anastrozole in postmenopausal women with early-stage breast cancer. Immunochemistry ER and PgR HSCORE and % positivity (%+) were centrally assessed by machine image quantitation and statistically standardized to mean 0 and standard deviation (SD) 1 after Box-Cox variance stabilization transformations of square for ER; for PgR, (1) natural logarithm (0.1 added to 0 HSCOREs and 0%+) and (2) square root. Our primary end point was MA.27 distant disease-free survival (DDFS) at a median 4.1-year follow-up, and secondary end point was event-free survival (EFS). Univariate survival with cut points at SDs about a mean of 0 (≤-1; (-1, 0]; (0, 1]; >1) was described with Kaplan-Meier plots and examined with Wilcoxon (Peto-Prentice) test statistic. Adjusted Cox multivariable regressions had two-sided Wald tests and nominal significance P < .05. RESULTS: Of 7,576 women accrued, 3,048 women's tumors had machine-quantitated image analysis results: 2,900 (95%) for ER, 2,726 (89%) for PgR, and 2,582 (85% of 3,048) with both ER and PgR. Higher statistically standardized ER and PgR HSCORE and %+ were associated with better univariate DDFS and EFS (P < .001). In multivariable assessments, ER HSCORE and %+ were not significantly associated (P = .52-.88) with DDFS in models with PgR, whereas higher PgR HSCORE and %+ were significantly associated with better DDFS (P = .001) in models with ER. CONCLUSION: Adjunctive statistical standardization differentiated quantitated levels of ER and PgR. Patients with higher ER- and PgR-standardized units had superior DDFS compared with those with HSCOREs and %+ ≤-1.

deepAFT: A nonlinear accelerated failure time model with artificial neural network.

The Cox regression model or accelerated failure time regression models are often used for describing the relationship between survival outcomes and potential explanatory variables. These models assume the studied covariates are connected to the survival time or its distribution or their transformations through a function of a linear regression form. In this article, we propose nonparametric, nonlinear algorithms (deepAFT methods) based on deep artificial neural networks to model survival outcome data in the broad distribution family of accelerated failure time models. The proposed methods predict survival outcomes directly and tackle the problem of censoring via an imputation algorithm as well as re-weighting and transformation techniques based on the inverse probabilities of censoring. Through extensive simulation studies, we confirm that the proposed deepAFT methods achieve accurate predictions. They outperform the existing regression models in prediction accuracy, while being flexible and robust in modeling covariate effects of various nonlinear forms. Their prediction performance is comparable to other established deep learning methods such as deepSurv and random survival forest methods. Even though the direct output is the expected survival time, the proposed AFT methods also provide predictions for distributional functions such as the cumulative hazard and survival functions without additional learning efforts. For situations where the popular Cox regression model may not be appropriate, the deepAFT methods provide useful and effective alternatives, as shown in simulations, and demonstrated in applications to a lymphoma clinical trial study.

Canadian cancer trials group LY.17: A randomized phase II study evaluating novel salvage therapy pre-autologous stem cell transplant in relapsed/refractory diffuse large B-cell lymphoma-outcome of rituximab-dose-intensive cyclophosphamide, etoposide, cisplatin (R-DICEP) versus R-GDP.

The Canadian Cancer Trials Group (CCTG) LY.17 is an ongoing multi-arm randomized phase II trial evaluating novel salvage therapies compared with R-GDP (rituximab, gemcitabine, dexamethasone and cisplatin) in autologous stem cell transplantation (ASCT)-eligible patients with relapsed/refractory diffuse large B-cell lymphoma (RR-DLBCL). This component of the LY.17 trial evaluated a dose-intensive chemotherapy approach using a single cycle of inpatient R-DICEP (rituximab, dose-intensive cyclophosphamide, etoposide and cisplatin) to achieve both lymphoma response and stem cell mobilization, shortening time to ASCT. This report is the result of the protocol-specified second interim analysis of the 67 patients who were randomized to either 1 cycle of R-DICEP or to 3 cycles of R-GDP. The overall response rate (ORR) was 65.6% for R-DICEP and 48.6% for R-GDP. The ASCT rate was 71.9% versus 54.3%, and 1-year progression-free survival rate was 42% versus 32%, respectively, for R-DICEP versus R-GDP. Although the improvement in ORR for R-DICEP versus R-GDP exceeded the pre-specified 10% threshold to proceed to full accrual of 64 patients/arm, higher rates of grade 3-5 toxicities, and the need for hospitalization led to the decision to stop this arm of the study. CCTG LY.17 will continue to evaluate different salvage regimens that incorporate novel agents.