Publications by authors named "M Jimenez-Linan"

BACKGROUNDDespite an overall poor prognosis, about 15% of patients with advanced-stage tubo-ovarian high-grade serous carcinoma (HGSC) survive 10 or more years after standard treatment.METHODSWe evaluated the tumor microenvironment of this exceptional, understudied group using a large international cohort enriched for long-term survivors (LTS; 10+ years; n = 374) compared with mid-term (MTS; 5-7.99 years; n = 433) and short-term survivors (STS; 2-4.

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Standard of care genetic testing has undergone significant changes in recent years. The British Gynecological Cancer Society and the British Association of Gynecological Pathologists (BGCS/BAGP) has re-assembled a multidisciplinary expert consensus group to update the previous guidance with the latest standard of care for germline and tumor testing in patients with ovarian cancer. For the first time, the BGCS/BAGP guideline group has incorporated a patient advisor at the initial consensus group meeting.

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Purpose: The purpose of this study was to evaluate RB1 expression and survival across ovarian carcinoma histotypes and how co-occurrence of BRCA1 or BRCA2 (BRCA) alterations and RB1 loss influences survival in tubo-ovarian high-grade serous carcinoma (HGSC).

Experimental Design: RB1 protein expression was classified by immunohistochemistry in ovarian carcinomas of 7,436 patients from the Ovarian Tumor Tissue Analysis consortium. We examined RB1 expression and germline BRCA status in a subset of 1,134 HGSC, and related genotype to overall survival (OS), tumor-infiltrating CD8+ lymphocytes, and transcriptomic subtypes.

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Article Synopsis
  • Researchers studied a common genetic change that happens in a type of ovarian cancer called high-grade serous carcinoma (HGSC), looking at how it affects patient survival.
  • They found that losing the RB1 protein was linked to longer survival in patients with HGSC, but it was the opposite for a different type of ovarian cancer called endometrioid cancer.
  • Patients with both RB1 loss and certain inherited genetic changes had much better survival rates compared to those with just one of these problems or none at all.
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Article Synopsis
  • * Researchers developed a machine learning model that combines clinical, blood-based, and radiomic data from patients to predict changes in disease volume after NACT, achieving an 8% improvement in prediction accuracy when integrating radiomics.
  • * The study shows the importance of using radiomics in patient response models, offering a potential path for creating new clinical trial methods focused on biomarkers in HGSOC treatment.
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