Publications by authors named "S Di Cosimo"
Background: In HER2+ early breast cancer (EBC), we investigated tumor and immune changes during neoadjuvant treatment and their impact on residual disease (RD) biology and prognostic implications across 4 neoadjuvant studies of trastuzumab with or without lapatinib, and with or without chemotherapy: CALGB 40601, PAMELA, NeoALTTO and NSABP B-41.
Patients And Methods: We compared tumor and immune gene expression changes during neoadjuvant treatment and their association with with event-free survival (EFS) by uni- and multivariable Cox regression models in different cohorts and timepoints: 452 RD samples at baseline including 169 with a paired RD, and biomarker changes during neoadjuvant therapy, evaluating model performance via the c-index.
Results: Analysis of 169 paired tumor samples revealed a shift in intrinsic subtype proportions from HER2-Enriched at baseline (50.
In HER2-positive breast cancer, clinical outcome and sensitivity to HER2-targeted therapies are influenced by both tumor and microenvironment features. However, we are currently unable to depict the molecular heterogeneity of this disease with sufficient granularity. Here, by performing gene expression profiling in HER2-positive breast cancers from patients receiving adjuvant trastuzumab in the ALTTO clinical trial (NCT00490139), we identify and characterize five molecular subtypes associated with the risk of distant recurrence: immune-enriched, proliferative/metabolic-enriched, mesenchymal/stroma-enriched, luminal, and ERBB2-dependent.
View Article and Find Full Text PDFUp to 90 % of death from solid tumors are caused by metastases. By 2040, breast cancer (BC) is predicted to increase to over 3 million new cases. Additionally, with the personalization and intensification of BC follow-up, many patients will relapse with oligometastatic disease (OMD).
View Article and Find Full Text PDFArticle Synopsis
- The study addresses the growing concern of cardiovascular diseases (CVDs) among adolescent and young adult (AYA) survivors of breast cancer due to the cardiotoxic effects of cancer treatments.
- Researchers developed a Bayesian network model using data from over a thousand young female BC survivors to predict CVD risk, achieving strong classification performance and clear causal relationships.
- An application was created to provide individual risk assessments for patients, aimed at helping clinicians personalize follow-up care for AYA BC survivors at higher risk of developing CVDs.