Interrogating the impact of pregnancy on breast cancer biology using DNA copy number profiling

Poster Session 1: Poster Session 1 - Detection/Diagnosis :Circulating Tumor Cells.Abstract P1-05-17info:eu-repo/semantics/nonPublishe

Mutational and transcriptomic characterization of breast cancer (BC) arising in young patients (pts) and during pregnancy and their associations with long-term outcome.

Cancer Research (online) 2012, Vol 72, issue 24, Suppl 3: abstract P6-07-14info:eu-repo/semantics/nonPublishe

Biology of breast cancer during pregnancy using genomic profiling.

Breast cancer during pregnancy is rare and is associated with relatively poor prognosis. No information is available on its biological features at the genomic level. Using a dataset of 54 pregnant and 113 non-pregnant breast cancer patients, we evaluated the pattern of hot spot somatic mutations and did transcriptomic profiling using Sequenom and Affymetrix respectively. We performed gene set enrichment analysis to evaluate the pathways associated with diagnosis during pregnancy. We also evaluated the expression of selected cancer-related genes in pregnant and non-pregnant patients and correlated the results with changes occurring in the normal breast using a pregnant murine model. We finally investigated aberrations associated with disease-free survival (DFS). No significant differences in mutations were observed. Of the total number of patients, 18.6% of pregnant and 23% of non-pregnant patients had a PIK3CA mutation. Around 30% of tumors were basal, with no differences in the distribution of breast cancer molecular subtypes between pregnant and non-pregnant patients. Two pathways were enriched in tumors diagnosed during pregnancy: the G protein-coupled receptor pathway and the serotonin receptor pathway (FDR <0.0001). Tumors diagnosed during pregnancy had higher expression of PD1 (PDCD1; P=0.015), PDL1 (CD274; P=0.014), and gene sets related to SRC (P=0.004), IGF1 (P=0.032), and β-catenin (P=0.019). Their expression increased almost linearly throughout gestation when evaluated on the normal breast using a pregnant mouse model underscoring the potential effect of the breast microenvironment on tumor phenotype. No genes were associated with DFS in a multivariate model, which could be due to low statistical power. Diagnosis during pregnancy impacts the breast cancer transcriptome including potential cancer targets.Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

L1CAM in Early-Stage Type I Endometrial Cancer: Results of a Large Multicenter Evaluation

Contains fulltext : 124525.pdf (publisher's version ) (Closed access)BACKGROUND: Despite the excellent prognosis of Federation Internationale de Gynecologie et d'Obstetrique (FIGO) stage I, type I endometrial cancers, a substantial number of patients experience recurrence and die from this disease. We analyzed the value of immunohistochemical L1CAM determination to predict clinical outcome. METHODS: We conducted a retrospective multicenter cohort study to determine expression of L1CAM by immunohistochemistry in 1021 endometrial cancer specimens. The Kaplan-Meier method and Cox proportional hazard model were applied for survival and multivariable analyses. A machine-learning approach was used to validate variables for predicting recurrence and death. RESULTS: Of 1021 included cancers, 17.7% were rated L1CAM-positive. Of these L1CAM-positive cancers, 51.4% recurred during follow-up compared with 2.9% L1CAM-negative cancers. Patients bearing L1CAM-positive cancers had poorer disease-free and overall survival (two-sided Log-rank P < .001). Multivariable analyses revealed an increase in the likelihood of recurrence (hazard ratio [HR] = 16.33; 95% confidence interval [CI] = 10.55 to 25.28) and death (HR = 15.01; 95% CI = 9.28 to 24.26). In the L1CAM-negative cancers FIGO stage I subdivision, grading and risk assessment were irrelevant for predicting disease-free and overall survival. The prognostic relevance of these parameters was related strictly to L1CAM positivity. A classification and regression decision tree (CRT)identified L1CAM as the best variable for predicting recurrence (sensitivity = 0.74; specificity = 0.91) and death (sensitivity = 0.77; specificity = 0.89). CONCLUSIONS: To our knowledge, L1CAM has been shown to be the best-ever published prognostic factor in FIGO stage I, type I endometrial cancers and shows clear superiority over the standardly used multifactor risk score. L1CAM expression in type I cancers indicates the need for adjuvant treatment. This adhesion molecule might serve as a treatment target for the fully humanized anti-L1CAM antibody currently under development for clinical use