Selecting patients with HER2-low Breast Cancer: getting out of the tangle

The promising effect of antibody–drug conjugates on breast cancer with low expression of HER2 (HER2-low) raises many questions regarding the optimal selection of patients for this treatment. A key question is whether HER2 immunohistochemistry, an assay optimised to detect HER2 amplification, is reliable enough to assess HER2 protein levels to select patients with HER2-low breast cancer in daily pathology practices worldwide. Moreover, whether this assessment can be performed with sufficient reproducibility between pathologists in daily practices is debatable. Herein, we address the historical track record of the CAP-ASCO HER2 Guidelines, the reported limited reproducibility by pathologists of HER2 immunohistochemistry in the non-amplified cases, and the performance variation of different antibodies. Based on this summary, we propose solutions to improve the robustness to enable reliable identification of patients with HER2-low breast cancer

Expression of Kallikrein-related peptidase 6 in primary mucosal malignant melanoma of the head and neck

Mucosal melanomas of the head and neck (MMHN) are aggressive tumors with poor prognosis, different opposed to cutaneous melanoma. In this study, we characterized primary mucosal malignant melanoma for the expression of Kallikrein-related peptidase 6 (KLK6), a member of the KLK family with relevance to the malignant phenotype in various cancer types including cutaneous melanoma. Paraffin-embedded MMHN of 22 patients were stained immunohistochemically for KLK6 and results were correlated with clinical and pathological data. In 77.3% (17/22) of MMHN cases, positive KLK6 staining was found. Staining pattern for tumor cells showed a predominant cytoplasmic staining. However, in six cases we also observed a prominent nuclear staining. MMHN with a high KLK6 expression showed significantly better outcome concerning local recurrence-free survival (p = 0.013) and nuclear KLK6 staining was significantly associated with the survival status (p = 0.027). Overexpression of KLK6 was detected in more than 70% of MMHN and approximately 40% of tumors showed a strong expression pattern. Correlation between clinical outcome of MMHN patients and overexpression of KLK6 has not been addressed so far. Our data demonstrate for the first time increased levels of KLK6 in MMHN and strengthen the hypothesis that there might be a context-specific regulation and function of KLK6 in mucosal melanoma

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source

Protein-based oncopanel as addition to target sequencing in head and neck squamous cell carcinoma to individualize treatment decisions

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous group of cancers and patients have limited therapy options if primary treatment fails. Therefore, additional information about the biology of the tumor is essential. Here we performed a feasibility study of concurrently applying two precision diagnostic tools in a consecutive series of HNSCC patients. We analyzed tumor samples of 31 patients using a genomic (oncomine) and a proteomic, immunohistochemical approach (oncopanel) and compared the result, also in the focus on their overlapping therapeutical targets. We found no strong correlation between the two approaches and observed a higher proportion of marker expression for the immunohistochemical panel. However, both panels show in our HNSCC cohort distinct patterns with druggable targets. The data suggest that both approaches complement one another and can be applied side-by-side to identify the best targets for the development of individual treatment options for HNSCC patients

Targeted ultra-deep sequencing reveals recurrent and mutually exclusive mutations of cancer genes in blastic plasmacytoid dendritic cell neoplasm

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare haematopoietic malignancy characterized by dismal prognosis and overall poor therapeutic response. Since the biology of BPDCN is barely understood, our study aims to shed light on the genetic make-up of these highly malignant tumors. Using targeted high-coverage massive parallel sequencing, we investigated 50 common cancer genes in 33 BPDCN samples. We detected point mutations in NRAS (27.3% of cases), ATM (21.2%), MET, KRAS, IDH2, KIT (9.1% each), APC and RB1 (6.1% each), as well as in VHL, BRAF, MLH1, TP53 and RET (3% each). Moreover, NRAS, KRAS and ATM mutations were found to be mutually exclusive and we observed recurrent mutations in NRAS, IDH2, APC and ATM. CDKN2A deletions were detected in 27.3% of the cases followed by deletions of RB1 (9.1%), PTEN and TP53 (3% each). The mutual exclusive distribution of some mutations may point to different subgroups of BPDCN whose biological significance remains to be explored

The landscape of metastatic progression patterns across major human cancers

The majority of patients with solid malignancies die from metastatic burden. However, our current understanding of the mechanisms and resulting patterns of dissemination is limited. Here, we analyzed patterns of metastatic progression across 16 major cancer types in a cohort of 1008 patients with metastatic cancer autopsied between 2000 and 2013 to assess cancer specific progression patterns of disease and related risk predictions. The frequency and location of metastases were evaluated in and across 1) 16 major cancers, 2) smoking- and non-smoking-related cancers and 3) adeno- and squamous cell carcinoma. Associations between primary and secondary sites were analyzed by the fractional and the relative risk methods. We detected significantly different cancer specific patterns of metastatic progression with specific relative risk profiles for secondary site involvement. Histology and smoking etiology influenced these patterns. Backward analysis showed that metastatic patterns help to predict unknown primary sites. Solid malignancies maintain a unique and recurrent organ tropism to specific secondary sites which does not appear to be strongly influenced by advances in cancer medicine as shown by comparison with previous data sets. The delineated landscape of metastatic progression patterns is a comprehensive data resource to both clinical and basic scientists which aids fostering new hypotheses for cancer research and cancer therapies

J Womens Health (Larchmt)

BackgroundIdentifying and treating chronic diseases, their precursors, and other cardiovascular disease (CVD) risk factors during family planning visits may improve long-term health and reproductive outcomes among low-income women. A cross-sectional study design was used to describe the prevalence of chronic diseases (hypertension, high cholesterol, and diabetes), their precursors (pre-hypertension, borderline high cholesterol, and pre-diabetes), and related CVD risk factors (such as obesity, smoking, and physical inactivity) among low-income women of reproductive age.MethodsPrevalence of chronic diseases, their precursors, and related CVD risk factors were assessed for 462 out of 859 (53.8%) female family planning patients, ages 18\u201344 years, who attended a Title X clinic in eastern North Carolina during 2011 and 2012 and consented to participate. Data were obtained from clinical measurements, blood test results, and questionnaire. Differences in distribution of demographic and health care characteristics and CVD risk factors by presence of prehypertension and pre-diabetes were assessed by Pearson chi-square tests.ResultsThe prevalence of hypertension was 12%, high cholesterol 16%, and diabetes 3%. Nearly two-thirds of women with hypertension were newly diagnosed (62%) as were 75% of women with diabetes. The prevalence of pre-hypertension was 35%, pre-diabetes 31%, obesity 41%, smoking 32%, and physical inactivity 42%. The majority of participants (87%) had one or more chronic disease or related cardiovascular disease risk factor.ConclusionsCVD screening during family planning visits can identify significant numbers of women at risk for poor pregnancy outcomes and future chronic disease and can provide prevention opportunities if effective interventions are available and acceptable to this population.20132015-04-06T00:00:00ZCC999999/Intramural CDC HHS/United States5U48DP001944/DP/NCCDPHP CDC HHS/United States23531099PMC4386647673