RESUMEN
We report the case of a 14 year-old teenager who has SC hemoglobinosis and presented with a tumor syndrome with a retro-peritoneal mass, a supraclavicular lymph node and a mid-renal lesion. The microscopic examination revealed an undifferentiated tumor proliferation infiltrating the lymph node parenchyma. This tumor proliferation was INI1/SMARCB1-deficient, and expressed cytokeratins. Given the fact that the histopathological data showed an undifferentiated INI1-deficient carcinoma and that the patient has a kidney lesion and a sickle cell trait, the final diagnosis was lymph node metastasis of SMARCB1-deficient renal medullary carcinoma (OMS 2022).
RESUMEN
BACKGROUND: In breast cancer, as in all cancers, genetic and epigenetic deregulations can result in out-of-context expressions of a set of normally silent tissue-specific genes. The activation of some of these genes in various cancers empowers tumours cells with new properties and drives enhanced proliferation and metastatic activity, leading to a poor survival prognosis. RESULTS: In this work, we undertook an unprecedented systematic and unbiased analysis of out-of-context activations of a specific set of tissue-specific genes from testis, placenta and embryonic stem cells, not expressed in normal breast tissue as a source of novel prognostic biomarkers. To this end, we combined a strict machine learning framework of transcriptomic data analysis, and successfully created a new robust tool, validated in several independent datasets, which is able to identify patients with a high risk of relapse. This unbiased approach allowed us to identify a panel of five biomarkers, DNMT3B, EXO1, MCM10, CENPF and CENPE, that are robustly and significantly associated with disease-free survival prognosis in breast cancer. Based on these findings, we created a new Gene Expression Classifier (GEC) that stratifies patients. Additionally, thanks to the identified GEC, we were able to paint the specific molecular portraits of the particularly aggressive tumours, which show characteristics of male germ cells, with a particular metabolic gene signature, associated with an enrichment in pro-metastatic and pro-proliferation gene expression. CONCLUSIONS: The GEC classifier is able to reliably identify patients with a high risk of relapse at early stages of the disease. We especially recommend to use the GEC tool for patients with the luminal-A molecular subtype of breast cancer, generally considered of a favourable disease-free survival prognosis, to detect the fraction of patients undergoing a high risk of relapse.
Asunto(s)
Neoplasias de la Mama , Femenino , Embarazo , Humanos , Masculino , Neoplasias de la Mama/genética , Recurrencia Local de Neoplasia/genética , Genes cdc , Mama , Enfermedad Crónica , Células Madre EmbrionariasRESUMEN
We report herein the case of a 33-year-old woman who was referred for FDG PET/CT staging prior to pregnancy after a 4-year lost to follow-up for a breast invasive ductal carcinoma (pT2N1 SBRII). FDG PET/CT revealed right supraclavicular lymphadenopathy potentially caused by breast carcinoma recurrence. No additional site was involved. Supraclavicular ultrasonography showed typical "snowstorm" appearance. MRI revealed signs of breast implant intracapsular rupture and signal intensity of silicone within a supraclavicular node. Fine-needle aspiration and microbiopsy of adenopathy finally confirmed silicone granuloma and ruled out breast cancer recurrence.