A Step-by-Step Guide to Using BioNetFit.

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

Different Pathological Complete Response Rates According to PAM50 Subtype in HER2+ Breast Cancer Patients Treated With Neoadjuvant Pertuzumab/Trastuzumab vs. Trastuzumab Plus Standard Chemotherapy: An Analysis of Real-World Data.

Background: Double blockade with pertuzumab and trastuzumab combined with chemotherapy is the standard neoadjuvant treatment for HER2-positive early breast cancer. Data derived from clinical trials indicates that the response rates differ among intrinsic subtypes of breast cancer. The aim of this study is to determine if these results are valid in real-world patients. Methods: A total of 259 patients treated in eight Spanish hospitals were included and divided into two cohorts: Cohort A (132 patients) received trastuzumab plus standard neoadjuvant chemotherapy (NAC), and Cohort B received pertuzumab and trastuzumab plus NAC (122 patients). Pathological complete response (pCR) was defined as the complete disappearance of invasive tumor cells. Assignment of the intrinsic subtype was realized using the research-based PAM50 signature. Results: There were more HER2-enriched tumors in Cohort A (70 vs. 56%) and more basal-like tumors in Cohort B (12 vs. 2%), with similar luminal cases in both cohorts (luminal A 12 vs. 14%; luminal B 14 vs. 18%). The overall pCR rate was 39% in Cohort A and 61% in Cohort B. Better pCR rates with pertuzumab plus trastuzumab than with trastuzumab alone were also observed in all intrinsic subtypes (luminal PAM50 41 vs. 11.4% and HER2-enriched subtype 73.5 vs. 50%) but not in basal-like tumors (53.3 vs. 50%). In multivariate analysis the only significant variables related to pCR in both luminal PAM50 and HER2-enriched subtypes were treatment with pertuzumab plus trastuzumab (Cohort B) and histological grade 3. Conclusions: With data obtained from patients treated in clinical practice, it has been possible to verify that the addition of pertuzumab to trastuzumab and neoadjuvant chemotherapy substantially increases the rate of pCR, especially in the HER2-enriched subtype but also in luminal subtypes, with no apparent benefit in basal-like tumors.