Davis, Michael J.

Abstract Patients with metastatic breast cancer (MBC) typically have short survival times and their successful treatment represents one of most challenging aspects of patient care. This poor prognostic behavior is in part due to molecular features including increased tumor cell clonal heterogeneity, multiple drug resistance mechanisms, and alterations of the tumor microenvironment. The AURORA US Metastasis Project was established with the goal to identify molecular features specifically associated with metastasis. We therefore collected and molecularly characterized specimens from 55 metastatic breast cancer (BC) patients representing 51 primary cancers and 102 metastases. The 153 unique tumors were assayed using RNAseq, tumor/germline DNA exomes and low pass whole genome sequencing, and global DNA methylation microarrays. We found intrinsic molecular subtype differences between primary tumors and their matched metastases to be rare in triple negative breast cancer (TNBC)/Basal-like subtype tumors. Conversely, tumor subtype changes were relatively frequent in estrogen receptor positive (ER+) cancers where ~30% of Luminal A cases switched to Luminal B or HER2-enriched (HER2E) subtypes. Clonal evolution studies identified changes in expression subtype coincident with DNA clonality shifts, especially involving HER2 amplification and/or the HER2E expression subtype. We further found evidence for ER-mediated downregulation of genes involved in cell-cell adhesion in metastases. Microenvironment differences varied according to tumor subtype where ER+/Luminal metastases had lower fibroblast and endothelial cell content, while TNBC/Basal-like metastases showed a dramatic decrease in B cells and T cells. In 17% of metastatic tumors, we identified DNA hypermethylation and/or focal DNA deletions near HLA-A that were associated with its’ significantly reduced expression, and with lower immune cell infiltrates. We also identified low immune cell features in brain and liver metastases when compared to other metastatic sites, even within the same patient. These findings have direct implications for the treatment of metastatic breast cancer patients with immune- and HER2-targeting therapies and suggest potential novel therapeutic avenues for the improvement of outcomes for some patients with MBC.

PurposeAt our institution, all proton patient plans undergo patient‐specific quality assurance (PSQA) prior to treatment delivery. For intensity‐modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency.MethodsWe used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D–3D γ‐index analysis comparing measurements in the plane of a two‐dimensional detector array with TPS predictions in a water phantom for each acquired measurement.ResultsDevice connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ‐index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan.ConclusionsThe automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.