Treatment and Monitoring Variability in US Metastatic Breast Cancer Care

Analytical validation of a novel comprehensive genomic profiling informed circulating tumor DNA monitoring assay for solid tumors

Emerging technologies focused on the detection and quantification of circulating tumor DNA (ctDNA) in blood show extensive potential for managing patient treatment decisions, informing risk of recurrence, and predicting response to therapy. Currently available tissue-informed approaches are often limited by the need for additional sequencing of normal tissue or peripheral mononuclear cells to identify non-tumor-derived alterations while tissue-naïve approaches are often limited in sensitivity. Here we present the analytical validation for a novel ctDNA monitoring assay, FoundationOne®Tracker. The assay utilizes somatic alterations from comprehensive genomic profiling (CGP) of tumor tissue. A novel algorithm identifies monitorable alterations with a high probability of being somatic and computationally filters non-tumor-derived alterations such as germline or clonal hematopoiesis variants without the need for sequencing of additional samples. Monitorable alterations identified from tissue CGP are then quantified in blood using a multiplex polymerase chain reaction assay based on the validated SignateraTM assay. The analytical specificity of the plasma workflow is shown to be 99.6% at the sample level. Analytical sensitivity is shown to be >97.3% at ≥5 mean tumor molecules per mL of plasma (MTM/mL) when tested with the most conservative configuration using only two monitorable alterations. The assay also demonstrates high analytical accuracy when compared to liquid biopsy-based CGP as well as high qualitative (measured 100% PPA) and quantitative precision (<11.2% coefficient of variation).

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

Comparison of endocrine therapy and chemotherapy as different systemic treatment modes for metastatic luminal HER2-negative breast cancer patients —A retrospective study

Purpose

The purpose of this study was to evaluate endocrine therapy and chemotherapy for first-line, maintenance, and second-line treatment of hormone receptor-positive HER-2-negative metastatic breast cancer (HR+HER-2-MBC) and the relationship between different treatment options and survival.

Patients and methods

The patients included in this study were all diagnosed with metastatic breast cancer (MBC) at Shandong Cancer Hospital from January 2013 to June 2017. Of the 951 patients with MBC, 307 patients with HR+HER-2-MBC were included in the analysis. The progression-free survival (PFS) and overall survival (OS) of the various treatment modes were evaluated using Kaplan–Meier analysis and the log-rank test. Because of the imbalance in data, we used the synthetic minority oversampling technique (SMOTE) algorithm to oversample the data to increase the balanced amount of data.

Results

This retrospective study included 307 patients with HR+HER-2-MBC; 246 patients (80.13%) and 61 patients (19.87%) were treated with first-line chemotherapy and first-line endocrine therapy, respectively. First-line endocrine therapy was better than first-line chemotherapy in terms of PFS and OS. After adjusting for known prognostic factors, patients receiving first-line chemotherapy had poorer PFS and OS outcomes than patients receiving first-line endocrine therapy. In terms of maintenance treatment, the endocrine therapy-endocrine therapy maintenance mode achieved the best prognosis, followed by the chemotherapy-endocrine therapy maintenance mode and chemotherapy-chemotherapy maintenance mode, and the no-maintenance mode has resulted in the worst prognosis. In terms of first-line/second-line treatment, the endocrine therapy/endocrine therapy mode achieved the best prognosis, while the chemotherapy/chemotherapy mode resulted in the worst prognosis. The chemotherapy/endocrine therapy mode achieved a better prognosis than the endocrine therapy/chemotherapy mode. There were no significant differences in the KI-67 index (&lt;15%/15-30%/≥30%) among the patients receiving first-line treatment modes, maintenance treatment modes, and first-line/second-line treatment modes. There was no statistical evidence in this study to support that the KI-67 index affected survival. However, in the first-line/second-line model, after SMOTE, we could see that KI-67 ≥ 30% had a poor prognosis.

Conclusions

Different treatment modes for HR+HER-2-MBC were analyzed. Endocrine therapy achieved better PFS and OS outcomes than chemotherapy. Endocrine therapy should be the first choice for first-line, maintenance, and second-line treatment of HR+HER-2-MBC.

Building Infrastructure to Exploit Evidence from Patient Preference Information (PPI) Studies: A Conceptual Blueprint

Patients are the most important actors in clinical research. Therefore, patient preference information (PPI) could support the decision-making process, being indisputable for research value, quality, and integrity. However, there is a lack of clear guidance or consensus on the search for preference studies. In this blueprint, an openly available and regularly updated patient preference management system for an integrated database (PPMSDB) that contains the minimal set of data sufficient to provide detailed information for each study (the so-called evidence tables in systematic reviews) and a high-level overview of the findings of a review (summary tables) is described. These tables could help determine which studies, if any, are eligible for quantitative synthesis. Finally, a web platform would provide a graphical and user-friendly interface. On the other hand, a set of APIs (application programming interfaces) would also be developed and provided. The PPMSDB, aims to collect preference measures, characteristics, and meta-data, and allow researchers to obtain a quick overview of a research field, use the latest evidence, and identify research gaps. In conjunction with proper statistical analysis of quantitative preference measures, these aspects can facilitate formal evidence-based decisions and adequate consideration when conducting a structured decision-making process. Our objective is to outline the conceptual infrastructure necessary to build and maintain a successful network that can monitor the currentness and validity of evidence.