pH evaluation of storage fluids and ancient DNA extraction from wet specimens in pathology museums

Pathology museums host ancient samples obtained during autopsies and generally used for educational purposes in the past. Such collections consist of dry and wet specimens showing diseases that no longer exist or with their natural course unmodified by modern therapies.1,2 In wet specimens, the preservation of macroscopic features due to the storage fluid has a great historical and paleopathological interest. Unfortunately, both original fixatives and storage fluids strongly influence tissue antigens and nucleic acids preservation.3 [...

Molecular Subtypes of Extra-pulmonary Neuroendocrine Carcinomas Identified by the Expression of Neuroendocrine Lineage-Specific Transcription Factors

Extra-pulmonary neuroendocrine carcinomas (EPNEC) represent a group of rare and heterogenous neoplasms with adverse clinical outcome. Their molecular profile is largely unexplored. Our aim was to investigate if the major transcriptional drivers recently described in high-grade pulmonary neuroendocrine carcinomas characterize distinct molecular and clinical subgroups of EPNEC. Gene expression of ASCL1, NEUROD1, DLL3, NOTCH1, INSM1, MYCL1, POU2F3, and YAP1 was investigated in a series of 54 EPNEC (including 10 cases with mixed components analyzed separately) and in a group of 48 pulmonary large cell neuroendocrine carcinomas (P-LCNEC). Unsupervised hierarchical cluster analysis classified the whole series into four major clusters. P-LCNEC were classified into two major clusters, the first ASCL1/DLL3/INSM1-high and the second (including four EPNEC) ASCL1/DLL3-low but INSM1-high. The remaining EPNEC cases were sub-classified into two other clusters. The first showed INSM1-high and alternative ASCL1/DLL3 or NEUROD1 high expression. The second was characterized mainly by MYCL1 and YAP1 overexpression. In the ten cases with mixed histology, ASCL1, DLL3, INSM1, and NEUROD1 genes were significantly upregulated in the neuroendocrine component. Higher gene-expression levels of NOTCH1 and INSM1 were associated with lower pT stage and negative nodal status. Low INSM1 gene expression was associated with shorter overall survival in the entire case series (p = 0.0017) and with a trend towards significance in EPNEC, only (p = 0.06). In conclusion, our results show that EPNEC possess distinct neuroendocrine-lineage-specific transcriptional profiles; moreover, low INSM1 gene expression represents a novel potential unfavorable prognostic marker in high-grade NECs including those in extra-pulmonary location

TERT Promoter Mutations are Associated with Visceral Spreading in Melanoma of the Trunk

Survival predictions are currently determined on the basis of NRAS/BRAF mutations, even though TERT promoter mutations have been recently associated with a poor prognosis in stage I-II melanomas. Usually, it is not recommended to perform a mutational test on primary melanoma, as the results do not always reflect the mutational status of metastases. In particular, trunk melanomas have been reported to have an unfavourable prognosis. A series of 105 advanced melanoma patients were analysed by TERT promoter Sanger sequencing. Univariate/multivariate binary logistic regression models were performed using progression to a visceral site as the dependent variable and patient/tumour characteristics as covariates. Performance of the model was assessed in an external independent primary melanoma patients' dataset. Male gender (odds ratio (OR), 344; 95% CI, 1.12⁻10.6; p = 0.031), AJCC (American Joint Committee on Cancer) classification (OR, 022; 95% CI, 0.07⁻0.67; p = 0.008), SLNB (Sentinel Lymph Node Biopsy) status (OR, 3.05; 95% CI, 1.06⁻8.78; p = 0.039) and TERT-mutated trunk lesions (OR, 3.78; 95% CI, 1.35⁻10.6; p = 0.011) were significantly associated with the risk of developing a visceral spreading as first site of progression using multivariate logistic regression analysis. These results were confirmed in the external validation control group. Therefore, in trunk primary melanomas, due to their high risk of progression to visceral sites, we encourage somatic TERT mutation analysis at diagnosis to identify those patients who would potentially benefit from a more intensive follow-up protocol and a prompt initiation of therapy

The impact of malignant nipple discharge cytology (NDc) in surgical management of breast cancer patients

BACKGROUND: The role of nipple discharge cytology (NDc) in the surgical management of breast cancer patients is unclear. We aimed: (i) to evaluate the effect of malignant NDc on the surgical approach to the nipple-areola complex, and (ii) to verify the association between malignant NDc and nipple malignancy. METHODS: We retrospectively analyzed a case series of 139 patients with NDc who underwent breast surgery. The clinical and histological findings, types of surgery with emphasis on nipple-areola complex amputation, immunohistochemical phenotypes of the carcinomas and measurements of the tumor-nipple distance were recorded. Additionally, in patients who showed HER2-positive lesions on definitive surgery, we evaluated the HER2 immunocytochemistry of the NDc smears. RESULTS: Thirty-two malignant and 107 benign/borderline NDc diagnoses were identified. All 32 malignant-NDc cases were histologically confirmed as malignant. Thirty borderline/benign-NDc cases were histologically diagnosed as malignant (sensitivity 58%). The majority of the patients with malignant NDc were treated with nipple-areola complex amputations in both the mastectomy and conservative surgery groups (P<0.001, chi251.77). Nipple involvement was strongly associated with HER2-positive ductal carcinoma in-situ (P<0.001, chi211.98). HER2 immunocytochemistry on the NDc revealed a 100% correlation with the immunocytochemistry performed on the surgical tissues. CONCLUSIONS: Malignant NDc influenced surgical management. The association of malignant NDc with nipple involvement is highly related to ductal carcinoma in-situ with HER2 overexpression. In case of HER2 positive NDc, nipple-areola complex involvement is more likely than in HER2 negative cases

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms

SIMPLE SUMMARY: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome, particularly for the intermediate domains of adenocarcinomas and large-cell neuroendocrine carcinomas. Moreover, subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. The aim of this study was to design and evaluate an objective and reproducible approach to the grading of lung NENs, potentially extendable to other NENs, by exploring a completely new perspective of interpreting the well-recognised proliferation marker Ki-67. We designed an automated pipeline to harvest quantitative information from the spatial distribution of Ki-67-positive cells, analysing its heterogeneity in the entire extent of tumour tissue—which currently represents the main weakness of Ki-67—and employed machine learning techniques to predict prognosis based on this information. Demonstrating the efficacy of the proposed framework would hint at a possible path for the future of grading and classification of NENs. ABSTRACT: Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs