The results show similar OS for patients diagnosed with BCP compared with nonpregnant patients. This information is important when patients are counseled and supports the option to start treatment with continuation of pregnancy.

PURPOSE Young women with germline BRCA mutations have unique reproductive challenges. Pregnancy after breast cancer does not increase the risk of recurrence; however, very limited data are available in patients with BRCA mutations. This study investigated the impact of pregnancy on breast cancer outcomes in patients with germline BRCA mutations. PATIENTS AND METHODS This is an international, multicenter, hospital-based, retrospective cohort study. Eligible patients were diagnosed between January 2000 and December 2012 with invasive early breast cancer at age ≤ 40 years and harbored deleterious germline BRCA mutations. Primary end points were pregnancy rate, and disease-free survival (DFS) between patients with and without a pregnancy after breast cancer. Pregnancy outcomes and overall survival (OS) were secondary end points. Survival analyses were adjusted for guarantee-time bias controlling for known prognostic factors. RESULTS Of 1,252 patients with germline BRCA mutations ( BRCA1, 811 patients; BRCA2, 430 patients; BRCA1/2, 11 patients) included, 195 had at least 1 pregnancy after breast cancer (pregnancy rate at 10 years, 19%; 95% CI, 17% to 22%). Induced abortions and miscarriages occurred in 16 (8.2%) and 20 (10.3%) patients, respectively. Among the 150 patients who gave birth (76.9%; 170 babies), pregnancy complications and congenital anomalies occurred in 13 (11.6%) and 2 (1.8%) cases, respectively. Median follow-up from breast cancer diagnosis was 8.3 years. No differences in DFS (adjusted hazard ratio [HR], 0.87; 95% CI, 0.61 to 1.23; P = .41) or OS (adjusted HR, 0.88; 95% CI, 0.50 to 1.56; P = .66) were observed between the pregnancy and nonpregnancy cohorts. CONCLUSION Pregnancy after breast cancer in patients with germline BRCA mutations is safe without apparent worsening of maternal prognosis and is associated with favorable fetal outcomes. These results provide reassurance to patients with BRCA-mutated breast cancer interested in future fertility.

Background High-grade serous tubo-ovarian cancer (HGSTOC) is characterised by extensive inter- and intratumour heterogeneity, resulting in persistent therapeutic resistance and poor disease outcome. Molecular subtype classification based on bulk RNA sequencing facilitates a more accurate characterisation of this heterogeneity, but the lack of strong prognostic or predictive correlations with these subtypes currently hinders their clinical implementation. Stromal admixture profoundly affects the prognostic impact of the molecular subtypes, but the contribution of stromal cells to each subtype has poorly been characterised. Increasing the transcriptomic resolution of the molecular subtypes based on single-cell RNA sequencing (scRNA-seq) may provide insights in the prognostic and predictive relevance of these subtypes. Methods We performed scRNA-seq of 18,403 cells unbiasedly collected from 7 treatment-naive HGSTOC tumours. For each phenotypic cluster of tumour or stromal cells, we identified specific transcriptomic markers. We explored which phenotypic clusters correlated with overall survival based on expression of these transcriptomic markers in microarray data of 1467 tumours. By evaluating molecular subtype signatures in single cells, we assessed to what extent a phenotypic cluster of tumour or stromal cells contributes to each molecular subtype. Results We identified 11 cancer and 32 stromal cell phenotypes in HGSTOC tumours. Of these, the relative frequency of myofibroblasts, TGF-β-driven cancer-associated fibroblasts, mesothelial cells and lymphatic endothelial cells predicted poor outcome, while plasma cells correlated with more favourable outcome. Moreover, we identified a clear cell-like transcriptomic signature in cancer cells, which correlated with worse overall survival in HGSTOC patients. Stromal cell phenotypes differed substantially between molecular subtypes. For instance, the mesenchymal, immunoreactive and differentiated signatures were characterised by specific fibroblast, immune cell and myofibroblast/mesothelial cell phenotypes, respectively. Cell phenotypes correlating with poor outcome were enriched in molecular subtypes associated with poor outcome. Conclusions We used scRNA-seq to identify stromal cell phenotypes predicting overall survival in HGSTOC patients. These stromal features explain the association of the molecular subtypes with outcome but also the latter’s weakness of clinical implementation. Stratifying patients based on marker genes specific for these phenotypes represents a promising approach to predict prognosis or response to therapy.

No abstract

No abstract