Dataset Information

Recurrent MED12 exon 2 mutations in benign breast fibroepithelial lesions in adolescents and young adults.

ABSTRACT:

Aims

Most benign breast fibroepithelial lesions (FEL) in adults harbour recurrent somatic MED12 exon 2 mutations and rare TERT promoter hotspot mutations. We sought to determine the frequency of MED12 exon 2 and TERT promoter hotspot mutations in fibroadenomas (FA) and benign phyllodes tumours (BePT) in adolescents and young adults.

Methods

DNA from 21 consecutive FAs and eight consecutive BePTs in adolescents and young adults was subjected to Sanger sequencing of the exon 2 of MED12 and the TERT promoter hotspot locus.

Results

We identified MED12 exon 2 mutations in 62% and 88% of FAs and BePTs, respectively, and no TERT promoter hotspot mutations. The majority of the MED12 exon 2 mutations identified were in-frame deletions (60%).

Conclusions

As in adults, benign FELs in juvenile patients harbour recurrent MED12 exon 2 mutations.

SUBMITTER: Pareja F

PROVIDER: S-EPMC6580856 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

Recurrent <i>MED12</i> exon 2 mutations in benign breast fibroepithelial lesions in adolescents and young adults.

Pareja Fresia F Da Cruz Paula Arnaud A Murray Melissa P MP Hoang Timothy T Gularte-Mérida Rodrigo R Brown David D da Silva Edaise M EM Sebastiao Ana Paula Martins APM Giri Dilip D DD Weigelt Britta B Reis-Filho Jorge S JS Brogi Edi E

Journal of clinical pathology 20181122 3

<h4>Aims</h4>Most benign breast fibroepithelial lesions (FEL) in adults harbour recurrent somatic <i>MED12</i> exon 2 mutations and rare <i>TERT</i> promoter hotspot mutations. We sought to determine the frequency of <i>MED12</i> exon 2 and <i>TERT</i> promoter hotspot mutations in fibroadenomas (FA) and benign phyllodes tumours (BePT) in adolescents and young adults.<h4>Methods</h4>DNA from 21 consecutive FAs and eight consecutive BePTs in adolescents and young adults was subjected to Sanger se ...[more]

PMID: 30467240

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Project description:BackgroundFibroepithelial lesions (FELs) of the breast encompass a broad spectrum of lesions, ranging from commonly encountered fibroadenomas (FAs) to rare phyllodes tumors (PTs). Accurately diagnosing and grading these lesions is crucial for making management decisions, but it can be challenging due to their overlapping features and the subjective nature of histological assessment. Here, we evaluated the role of digital nuclear morphometric analysis in FEL diagnosis and prognosis.MethodsA digital nuclear morphometric analysis was conducted on 241 PTs and 59 FAs. Immunohistochemical staining for cytokeratin and Leukocyte common antigen (LCA) was used to exclude non-stromal components, and nuclear area, perimeters, calipers, circularity, and eccentricity in the stromal cells were quantified with QuPath software. The correlations of these features with FEL diagnosis and prognosis was assessed.ResultsAll nuclear features, including area, perimeter, circularity, maximum caliper, minimum caliper and eccentricity, showed significant differences between FAs and benign PTs (p ≤ 0.002). Only nuclear area, perimeter, minimum caliper and eccentricity correlated significantly with PT grading (p ≤ 0.022). For differentiation of FAs from benign PTs, the model integrating all differential nuclear features demonstrated a specificity of 90% and sensitivity of 70%. For PT grading, the nuclear morphometric score showed a specificity of 78% and sensitivity of 96% for distinguishing benign/borderline from malignant PTs. In addition, a relationship of nuclear circularity was found with PT recurrence. The Kaplan-meier analysis, using the best cutoff determined by ROC curve, showed shorter event free survival in benign PTs with high circularity (chi-square = 4.650, p = 0.031).ConclusionsOur data suggested the digital nuclear morphometric analysis could have potentials to objectively differentiate different FELs and predict PT outcome. These findings could provide the evidence-based data to support the development of deep-learning based algorithm on nuclear morphometrics in FEL diagnosis.