30 research outputs found

    Villa Santa Isabel

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    Whole‐exome sequencing and RNA sequencing analyses of acinic cell carcinomas of the breast

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    AimsAcinic cell carcinoma of the breast (ACC) is a rare histologic form of triple‐negative breast cancer (TNBC). Despite its unique histology, targeted sequencing analysis has failed to identify recurrent genetic alterations other than those found in common forms of TNBC. Here, we subjected three breast ACCs to whole‐exome and RNA‐sequencing, seeking to define whether they would harbor a pathognomonic genetic alteration.Methods and ResultsTumor and normal DNA and RNA samples from three breast ACCs were subjected to whole‐exome sequencing. Somatic mutations, copy number alterations, mutational signatures and fusion genes were determined using state‐of‐the‐art bioinformatics methods. Our analyses revealed TP53 hotspot mutations associated with loss of heterozygosity of the wild‐type allele in two cases. Mutations affecting homologous recombination (HR) DNA repair‐related genes were found in two cases, and an MLH1 pathogenic germline variant was detected in one case. In addition, copy number analysis revealed the presence of a somatic BRCA1 homozygous deletion and focal amplification of 12q14.3‐12q21.1, encompassing MDM2, HMGA2, FRS2 and PTPRB. No oncogenic in‐frame fusion transcript was identified in the three breast ACCs analyzed.ConclusionsNo pathognomonic genetic alterations were detected in the ACCs analyzed. These tumors have somatic genetic alterations similar to those of common forms of TNBC and may display HR deficiency or microsatellite instability. These findings provide further insights as to why ACCs which are usually clinically indolent may evolve into or in parallel with high‐grade TNBC

    Stromal MED12 exon 2 mutations in complex fibroadenomas of the breast

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    © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.Aims: Here we explore the presence of mediator complex subunit 12 (MED12) exon 2 and telomerase reverse transcriptase (TERT) promoter hotspot mutations in complex fibroadenomas (CFAs) of the breast. Methods: The stromal components from 18 CFAs were subjected to Sanger sequencing of MED12 exon 2 and the TERT promoter hotspot loci. The epithelial and stromal components of two MED12 mutated CFAs were subjected to laser capture microdissection, and Sanger sequencing of MED12 exon 2, TERT promoter and PIK3CA exons 9 and 20, separately. Results: MED12 exon 2 mutations were identified in the stroma of 17% of CFAs. The analyses of epithelial and stromal components, microdissected separately, revealed that MED12 mutations were restricted to the stroma. No TERT promoter or PIK3CA mutations in exons 9 and 20 were detected in analysed CFAs. Conclusions: Like conventional fibroadenomas, MED12 exon 2 mutations appear to be restricted to the stromal component of CFAs, supporting the notion that CFAs are stromal neoplasms.This study was funded by the Breast Cancer Research Foundation. BW is funded by a Cycle for Survival grant, CS by a Fundação para a Ciência e Tecnologia grant (SFRH/BDE/110544/2015). FP is partially funded by a K12 CA184746 grant. The research reported in this paper was supported in part by a Cancer Centre Support Grant of the National Institutes of Health/National Cancer Institute (grant No P30CA008748).info:eu-repo/semantics/publishedVersio

    Predicting breast tumor proliferation from whole-slide images : the TUPAC16 challenge

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    Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a real-world scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task

    Prostate cancer-associated SPOP mutations confer resistance to BET inhibitors through stabilization of BRD4

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    The bromodomain and extra-terminal (BET) family of proteins, comprised of four members including BRD2, BRD3, BRD4 and the testis-specific isoform BRDT, largely function as transcriptional co-activators 1–3 and play critical roles in various cellular processes, including cell cycle, apoptosis, migration and invasion 4,5. As such, BET proteins enhance the oncogenic functions of major cancer drivers by either elevating their expression such as c-Myc in leukemia 6,7 or by promoting transcriptional activities of oncogenic factors such as AR and ERG in the prostate cancer setting 8. Pathologically, BET proteins are frequently overexpressed and clinically linked to various types of human cancers 5,9,10, therefore pursued as attractive therapeutic targets for selective inhibition in patients. To this end, a number of bromodomain inhibitors, including JQ1 and I-BET, have been developed 11,12 and shown promising outcomes in early clinical trials. Despite resistance to BET inhibitor has been documented in pre-clinical models 13–15 the molecular mechanisms underlying acquired resistance are largely unknown. Here, we report that Cullin 3SPOP earmarks BET proteins including BRD2, BRD3 and BRD4 for ubiquitination-mediated degradation. Pathologically, prostate cancer-associated SPOP mutants fail to interact with and promote the destruction of BET proteins, leading to their elevated abundance in SPOP-deficient prostate cancer. As a result, prostate cancer cells and prostate cancer patient-derived organoids harboring SPOP mutations are more resistant to BET inhibitor-induced cell growth arrest and apoptosis. Therefore, our results elucidate the tumor suppressor role of SPOP in prostate cancer by negatively controlling BET protein stability, and also provide a molecular mechanism for BET inhibitor resistance in prostate cancer patients bearing SPOP mutations

    Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

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    Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting

    El "retorno de lo reprimido": el papel de la sexualidad en la recepción del psicoanálisis en el círculo médico chileno, 1910-1940

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    Villa Santa Isabel

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