Genomic profiling and comparative analysis of male versus female metastatic breast cancer across subtypes.

BACKGROUND: Male breast cancer (MaBC) has limited data on genomic alterations. We aimed to comprehensively describe and compare MaBC's genomics with female breast cancer's (FBC) across subtypes. METHODS: Using genomic data from Foundation Medicine, we categorized 253 MaBC into estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative (n = 210), ER-positive/HER2-positive (n = 22) and triple-negative (n = 20). One ER-negative/HER2-positive case was excluded due to n-of-1. The genomics of the final MaBC cohort (n = 252) were compared to a FBC cohort (n = 2708) stratified by molecular subtype, with adjusted p-values. In the overall MaBC and FBC cohorts, we compared mutational prevalence in cancer susceptibility genes (CSG) (ATM/BRCA1/BRCA2/CHEK2/PALB2). RESULTS: Comparing ER-positive/HER2-negative cases, MaBc had increased alterations in GATA3 (26.2% vs. 15.9%, p = 0.005), BRCA2 (13.8% vs. 5.3%, p < 0.001), MDM2 (13.3% vs. 6.14%, p = 0.004) and CDK4 (7.1% vs. 1.8%, p < 0.001); and decreased frequency of TP53 (11.0% vs. 42.6%, p < 0.001) and ESR1 mutations (5.7% vs. 14.6%, p < 0.001). Comparing ER-positive/HER2-positive cases, MaBC had increased short variants in ERBB2 (22.7% vs. 0.6%, p = 0.002), GATA3 (36.3% vs. 6.2%, p = 0.004), and MDM2 (36.3% vs. 4.9%, p = 0.002); decreased frequency of TP53 alterations was seen in MaBC versus FBC (9.1% vs. 61.7%, p < 0.001). Within triple-negative cases, MaBC had decreased alterations in TP53 compared to FBC (25.0% vs. 84.4%, p < 0.001). MaBC had higher frequency of CSG variants than FBC (22.6% vs. 14.6%, p < 0.05), with increased BRCA mutations in MaBC (14.6% vs. 9.1%, p < 0.05). CONCLUSIONS: Although MaBC and FBC share some common alterations, our study revealed several important differences relevant to tumor biology and implications for targeted therapies.

Impact of PROphet Test in Changing Physicians' Therapeutic Decision-Making for Checkpoint Immunotherapy in Non-Small-Cell Lung Cancer.

PURPOSE: Immune Checkpoint Inhibitor (ICI) regimens are approved for first-line treatment of metastatic nononcogene-driven NSCLC. Guidelines do not differentiate which patients with PD-L1 ≥ 50% should receive ICI monotherapy. The clinically validated PROphet NSCLC plasma proteomic-based test is designed to inform this therapeutic decision. METHODS: One hundred oncologists were presented with 3 "virtual" metastatic NSCLC cases with PD-L1 scores and asked to recommend an approved first-line regimen. They then watched an online educational webinar on the PROphetNSCLC test. Postwebinar, the same cases were represented with the addition of a PROphet result, and oncologists again recommended a first-line regimen. Responses were compared to assess the impact on first-line treatment selection. RESULTS: Treatment recommendation changed in 39.6% of PROphet-tested cases, with 93% of physicians changing at least 1 case. In the PD-L1 ≥ 50% group, 89% of physicians changed their recommendation, followed by 77%, in PD-L1 < 1%, and 36% in PD-L1 1% to 49%. ​In the PD-L1 ≥ 50%, PROphet POSITIVE group, the recommendation for ICI monotherapy increased from 60% to 89%. ​For the PD-L1 ≥ 50%, PROphet NEGATIVE group, the recommendation for monotherapy dropped from 60% to 9%. In the PD-L1 < 1%, PROphet NEGATIVE group, 35% of patients were spared toxicity from ICI compared to 11% in PROphet untested cases. CONCLUSION: Adding PROphet to PD-L1 expression impacted therapeutic decision making in first-line NSCLC. PROphet identifies those predicted to have an overall survival benefit from ICI monotherapy versus combination versus chemotherapy, improving the probability of efficacy and reducing toxicity for some patients.

Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non-Small Cell Lung Cancer.

PURPOSE: Current guidelines for the management of metastatic non-small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. PATIENTS AND METHODS: We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247) of patients undergoing PD-1/PD-L1 inhibitor-based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. RESULTS: Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor-based treatments, evidenced by high concordance between predicted and observed CB (R2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). CONCLUSION: Plasma proteome-based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor-based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.