The neu-protein and breast cancer

The neu-protein is overexpressed in about 20% of invasive duct cell carcinomas of the breast. The only reliable sign for neu-overexpression by immunohistochemistry is membrane staining. Its overexpression is correlated with decreased overall survival and disease free survival due to increased metastatic activity of neu-overexpressing tumour cells. This increased metastatic potential is a consequence of the motility enhancing activity of the neu-protein, which is exclusively expressed on pseudopodia, and to a lesser extent of its growth stimulating effect. From a clinical point of view, the assessment of neu-overexpression in breast cancer might become a useful tool in the future treatment of patients by chemotherapy, since patients whose tumour shows neu-overexpression benefit from higher doses of chemotherapy. The molecule plays a key role in the pathogenesis of Paget's disease of the breast. A chemotactic factor which is secreted by epidermal keratinocytes attracts the Paget cells to spread into the epidermis and acts via the neu-protein. In ductal carcinoma in situ, the combination of neu-overexpression and large cell type is highly correlated with extent of disease and therefore neu-overexpression might be a predictive marker for recurrence of disease after tumour resection

Generation of a monoclonal antibody directed against a human cell substrate adhesion molecule and the expression of the antigen in human tissues

Cell substrate adhesion is a prerequisite for invasion and the subsequent formation of metastases, Therefore, we designed monoclonal antibodies (MAbs) against epitopes on the extracellular cell membrane domain of SK-BR3 cells. One of the antibodies, called MAb 14C5, binds to an extracellular epitope of a plasma membrane antigen of SK-BR-3 and MCF-7 human breast cancer cells. This MAb 14C5 is able to inhibit cell substrate adhesion, not only on culture-treated plastic but also on host tissue, and therefore prevents invasion and metastases, We evaluated the tissue distribution of the 14C5 antigen by immunohistochemistry. The antigen is specifically overexpressed in 64% of invasive ductal adenocarcinomas of the breast (n=33), in all investigated cases of invasive squamous cell carcinoma (n=7) and in 40% of basocellular carcinomas of the skin (n=5). The 14C5 molecule is located on the cell membrane of the carcinoma cells, However, when the tumor is characterized by a highly invasive phenotype, 65% of the cases also show an extensive stromal expression on the fibroblasts between the tumor cells (n=71). This stromal expression is caused by the presence of the 14C5 antigen on the membrane of the adjacent fibroblasts, In normal tissues as well as in the stroma surrounding in situ carcinomas of the breast (n=15), no expression of the 14C5 antigen occurred, A 90-kDa protein was purified from lysates of human breast cancer cells using a 14C5 MAb Sepharose column and is considered as the antigen recognized by the MAb 14C5

Development of a natural language processing algorithm to detect chronic cough in electronic health records

BACKGROUND: Chronic cough (CC) is difficult to identify in electronic health records (EHRs) due to the lack of specific diagnostic codes. We developed a natural language processing (NLP) model to identify cough in free-text provider notes in EHRs from multiple health care providers with the objective of using the model in a rules-based CC algorithm to identify individuals with CC from EHRs and to describe the demographic and clinical characteristics of individuals with CC. METHODS: This was a retrospective observational study of enrollees in Optum’s Integrated Clinical + Claims Database. Participants were 18–85 years of age with medical and pharmacy health insurance coverage between January 2016 and March 2017. A labeled reference standard data set was constructed by manually annotating 1000 randomly selected provider notes from the EHRs of enrollees with ≥ 1 cough mention. An NLP model was developed to extract positive or negated cough contexts. NLP, cough diagnosis and medications identified cough encounters. Patients with ≥ 3 encounters spanning at least 56 days within 120 days were defined as having CC. RESULTS: The positive predictive value and sensitivity of the NLP algorithm were 0.96 and 0.68, respectively, for positive cough contexts, and 0.96 and 0.84, respectively, for negated cough contexts. Among the 4818 individuals identified as having CC, 37% were identified using NLP-identified cough mentions in provider notes alone, 16% by diagnosis codes and/or written medication orders, and 47% through a combination of provider notes and diagnosis codes/medications. Chronic cough patients were, on average, 61.0 years and 67.0% were female. The most prevalent comorbidities were respiratory infections (75%) and other lower respiratory disease (82%). CONCLUSIONS: Our EHR-based algorithm integrating NLP methodology with structured fields was able to identify a CC population. Machine learning based approaches can therefore aid in patient selection for future CC research studies

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study.

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.status: publishe

Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: the IVITA study.

High stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative breast cancer (TNBC) are associated with pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Histopathological assessment of sTILs in TNBC biopsies is characterized by substantial interobserver variability, but it is unknown whether this affects its association with pCR. Here, we aimed to investigate the degree of interobserver variability in an international study, and its impact on the relationship between sTILs and pCR. Forty pathologists assessed sTILs as a percentage in digitalized biopsy slides, originating from 41 TNBC patients who were treated with NAC followed by surgery. Pathological response was quantified by the MD Anderson Residual Cancer Burden (RCB) score. Intraclass correlation coefficients (ICCs) were calculated per pathologist duo and Bland-Altman plots were constructed. The relation between sTILs and pCR or RCB class was investigated. The ICCs ranged from -0.376 to 0.947 (mean: 0.659), indicating substantial interobserver variability. Nevertheless, high sTILs scores were significantly associated with pCR for 36 participants (90%), and with RCB class for eight participants (20%). Post hoc sTILs cutoffs at 20% and 40% resulted in variable associations with pCR. The sTILs in TNBC with RCB-II and RCB-III were intermediate to those of RCB-0 and RCB-I, with lowest sTILs observed in RCB-I. However, the limited number of RCB-I cases precludes any definite conclusions due to lack of power, and this observation therefore requires further investigation. In conclusion, sTILs are a robust marker for pCR at the group level. However, if sTILs are to be used to guide the NAC scheme for individual patients, the observed interobserver variability might substantially affect the chance of obtaining a pCR. Future studies should determine the 'ideal' sTILs threshold, and attempt to fine-tune the patient selection for sTILs-based de-escalation of NAC regimens. At present, there is insufficient evidence for robust and reproducible sTILs-guided therapeutic decisions

Epidemiology of native kidney disease in Flanders : results from the FCGG kidney biopsy registry

Background The Flemish Collaborative Glomerulonephritis Group (FCGG) registry is the first population-based native kidney biopsy registry in Flanders, Belgium. In this first analysis, we report on patient demographics, frequency distribution and incidence rate of biopsied kidney disease in adults in Flanders. Methods From January 2017 to December 2019, a total of 2054 adult first native kidney biopsies were included. A 'double diagnostic coding' strategy was used, in which every biopsy sample received a histopathological and final clinical diagnosis. Frequency distribution and incidence rate of both diagnoses were reported and compared with other European registries. Results The median age at biopsy was 61.1 years (interquartile range, 46.1-71.7); male patients were more prevalent (62.1%) and biopsy incidence rate was 129.3 per million persons per year. Immunoglobulin A nephropathy was the most frequently diagnosed kidney disease (355 biopsies, 17.3% of total) with a similar frequency as in previously published European registries. The frequency of tubulointerstitial nephritis (220 biopsies, 10.7%) and diabetic kidney disease (154 biopsies, 7.5%) was remarkably higher, which may be attributed to changes in disease incidence as well as biopsy practices. Discordances between histopathological and final clinical diagnoses were noted and indicate areas for improvement in diagnostic coding systems. Conclusions The FCGG registry, with its 'double diagnostic coding' strategy, provides useful population-based epidemiological data on a large Western European population and allows subgroup selection for future research