Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the “Normics” algorithm: Proteins are ranked based on their expression level–corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood

NKX2-1 (NK2 homeobox 1)

Review on NKX2-1 (NK2 homeobox 1), with data on DNA, on the protein encoded, and where the gene is implicated

Delta-like protein 3 expression in paired chemonaive and chemorelapsed small cell lung cancer samples

Rovalpituzumab tesirine (Rova-T), an antibody-drug conjugate directed against Delta-like protein 3 (DLL3), is under development for patients with small cell lung cancer (SCLC). DLL3 is expressed on the majority of SCLC samples. Because SCLC is rarely biopsied in the course of disease, data regarding DLL3 expression in relapses is not available. The aim of this study was to investigate the expression of DLL3 in chemorelapsed (but untreated with Rova-T) SCLC samples and compare the results with chemonaive counterparts. Two evaluation methods to assess DLL3 expression were explored. Additionally, we assessed if DLL3 expression of chemorelapsed and/or chemonaive samples has prognostic impact and if it correlates with other clinicopathological data. The study included 30 paired SCLC samples, which were stained with an anti DLL3 antibody. DLL3 expression was assessed using tumor proportion score (TPS) and H-score and was categorized as DLL3 low (TPS < 50%, H-score ≤ 150) and DLL3 high (TPS ≥ 50%, H-score > 150). Expression data were correlated with clinicopathological characteristics. Kaplan-Meier curves were used to illustrate overall survival (OS) depending on DLL3 expression in chemonaive and chemorelapsed samples, respectively, and depending on dynamics of expression during course of therapy. DLL3 was expressed in 86.6% chemonaive and 80% chemorelapsed SCLC samples without significant differences between the two groups. However, the extent of expression varied in a substantial proportion of pairs (36.6% with TPS, 43.3% with H-score), defined as a shift from low to high or high to low expression. TPS and H-score provided comparable results. There were no profound correlations with clinicopathological data. Survival analysis revealed a trend toward a more favorable OS in DLL low-expressing chemonaive SCLC (p = 0.57) and, in turn, in DLL3 high-expressing chemorelapsed SCLC (p = 0.42) as well as in SCLC demonstrating a shift from low to high expression (p = 0.56) without being statistically significant. This is the first study to investigate DLL3 expression in a large cohort of rare paired chemonaive-chemorelapsed SCLC specimens. Comparative analysis revealed that DLL3 expression was not stable during the course of therapy, suggesting therapy-based alterations. Unlike in chemonaive samples, a high DLL3 expression in chemorelapsed samples indicated a trend for a more favorable prognosis. Our results highlight the importance to investigate DLL3 in latest chemorelapsed SCLC tumor tissue

Sex determining region Y-box 2 (SOX2) amplification is an independent indicator of disease recurrence in sinonasal cancer.

The transcription factor SOX2 (3q26.3-q27) is an embryonic stem cell factor contributing to the induction of pluripotency in terminally differentiated somatic cells. Recently, amplification of the SOX2 gene locus has been described in squamous cell carcinoma (SCC) of different organ sites. Aim of this study was to investigate amplification and expression status of SOX2 in sinonasal carcinomas and to correlate the results with clinico-pathological data. A total of 119 primary tumor samples from the sinonasal region were assessed by fluorescence in-situ hybridization and immunohistochemistry for SOX2 gene amplification and protein expression, respectively. Of these, 59 were SSCs, 18 sinonasal undifferentiated carcinomas (SNUC), 10 carcinomas associated with an inverted papilloma (INVC), 19 adenocarcinomas (AD) and 13 adenoid cystic carcinomas (ACC). SOX2 amplifications were found in subsets of SCCs (37.5%), SNUCs (35.3%), INVCs (37.5%) and ADs (8.3%) but not in ACCs. SOX2 amplification resulted in increased protein expression. Patients with SOX2-amplified sinonasal carcinomas showed a significantly higher rate of tumor recurrences than SOX2 non-amplified tumors. This is the first study assessing SOX2 amplification and expression in a large cohort of sinonasal carcinomas. As opposed to AD and ACC, SOX2 amplifications were detected in more than 1/3 of all SCCs, SNUCs and INVCs. We therefore suggest that SNUCs are molecularly closely related to SCCs and INVCs and that these entities represent a subgroup of sinonasal carcinomas relying on SOX2 acquisition during oncogenesis. SOX2 amplification appears to identify sinonasal carcinomas that are more likely to relapse after primary therapy, suggesting that these patients might benefit from a more aggressive therapy regime

Human prostate sphere-forming cells represent a subset of basal epithelial cells capable of glandular regeneration in vivo.

BackgroundProstate stem/progenitor cells function in glandular development and maintenance. They may be targets for tumor initiation, so characterization of these cells may have therapeutic implications. Cells from dissociated tissues that form spheres in vitro often represent stem/progenitor cells. A subset of human prostate cells that form prostaspheres were evaluated for self-renewal and tissue regeneration capability in the present study.MethodsProstaspheres were generated from 59 prostatectomy specimens. Lineage marker expression and TMPRSS-ERG status was determined via immunohistochemistry and fluorescence in situ hybridization (FISH). Subpopulations of prostate epithelial cells were isolated by cell sorting and interrogated for sphere-forming activity. Tissue regeneration potential was assessed by combining sphere-forming cells with rat urogenital sinus mesenchyme (rUGSM) subcutaneously in immunocompromised mice.ResultsProstate tissue specimens were heterogeneous, containing both benign and malignant (Gleason 3-5) glands. TMPRSS-ERG fusion was found in approximately 70% of cancers examined. Prostaspheres developed from single cells at a variable rate (0.5-4%) and could be serially passaged. A basal phenotype (CD44+CD49f+CK5+p63+CK8-AR-PSA-) was observed among sphere-forming cells. Subpopulations of prostate cells expressing tumor-associated calcium signal transducer 2 (Trop2), CD44, and CD49f preferentially formed spheres. In vivo implantation of sphere-forming cells and rUGSM regenerated tubular structures containing discreet basal and luminal layers. The TMPRSS-ERG fusion was absent in prostaspheres derived from fusion-positive tumor tissue, suggesting a survival/growth advantage of benign prostate epithelial cells.ConclusionHuman prostate sphere-forming cells self-renew, have tissue regeneration capability, and represent a subpopulation of basal cells

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei : A quantitative analysis

When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision support. In this paper, we optimize and quantitatively compare two automated machine learning methods for thyroid gland tumor classification on two datasets to assist pathologists in decision-making regarding these methods and their parameters. The first method is a feature-based classification originating from common image processing and consists of cell nucleus segmentation, feature extraction, and subsequent thyroid gland tumor classification utilizing different classifiers. The second method is a deep learning-based classification which directly classifies the input images with a convolutional neural network without the need for cell nucleus segmentation. On the Tharun and Thompson dataset, the feature-based classification achieves an accuracy of 89.7% (Cohen’s Kappa 0.79), compared to the deep learning-based classification of 89.1% (Cohen’s Kappa 0.78). On the Nikiforov dataset, the feature-based classification achieves an accuracy of 83.5% (Cohen’s Kappa 0.46) compared to the deep learning-based classification 77.4% (Cohen’s Kappa 0.35). Thus, both automated thyroid tumor classification methods can reach the classification level of an expert pathologist. To our knowledge, this is the first study comparing feature-based and deep learning-based classification regarding their ability to classify samples with and without papillary thyroid carcinoma-like nuclei on two large-scale datasets

Comprehensive biomarker analysis of long-term response to trastuzumab in patients with HER2-positive advanced gastric or gastroesophageal adenocarcinoma

Background A subgroup of patients with HER2-positive metastatic gastric and gastroesophageal junction cancers shows long-term response under trastuzumab maintenance monotherapy. Obviously, HER2 status alone is not able to identify these patients. We performed this study to identify potential new prognostic biomarkers for this long-term responding patient group. Patients and methods Tumor samples of 19 patients with HER2-positive metastatic gastric and gastroesophageal junction cancer who underwent trastuzumab treatment were retrospectively collected from multiple centers. Patients were divided into long-term responding (n=7) or short-term responding group (n=12) according to progression-free survival (PFS≥12 months vs. PFS<12 months). Next generation sequencing and microarray-based gene expression analysis were performed along with HER2 and PD-L1 immunohistochemistry. Results Long-term responding patients had significantly higher PD-L1 combined positive scores (CPS) and CPS correlated with longer progression-free survival. PD-L1 positivity (CPS≥1) was further associated with an increased CD4+ memory T-cell score. The ERBB2 copy number as well as the tumor mutational burden could not discriminate between short-term and long-term responding patients. Genetic alterations and co-amplifications in HER2 pathway associated genes such as EGFR, which were connected to trastuzumab resistance, were present in 10% of the patients and equally distributed between the groups. Conclusion The study highlights the clinical relevance of PD-L1 testing also in the context of trastuzumab treatment and offers a biological rational by demonstrating elevated CD4+ memory T-cells scores in the PD-L1-positive group

Prognostic Value of the New Prostate Cancer International Society of Urological Pathology Grade Groups

Gleason grading is the best independent predictor for prostate cancer (PCa) progression. Recently, a new PCa grading system has been introduced by the International Society of Urological Pathology (ISUP) and is recommended by the World Health Organization (WHO). Following studies observed more accurate and simplified grade stratification of the new system. Aim of this study was to compare the prognostic value of the new grade groups compared to the former Gleason Grading and to determine whether re-definition of Gleason Pattern 4 might reduce upgrading from prostate biopsy to radical prostatectomy (RP) specimen. A cohort of men undergoing RP from 2002 to 2015 at the Hospital of Goeppingen (Goeppingen, Germany) was used for this study. In total, 339 pre-operative prostatic biopsies and corresponding RP specimens, as well as additional 203 RP specimens were re-reviewed for Grade Groups according to the ISUP. Biochemical recurrence-free survival (BFS) after surgery was used as endpoint to analyze prognostic significance. Other clinicopathological data included TNM-stage and pre-operative PSA level. Kaplan–Meier analysis revealed risk stratification of patients based on both former Gleason Grading and ISUP Grade Groups, and was statistically significant using the log-rank test (p &lt; 0.001). Both grading systems significantly correlated with TNM-stage and pre-operative PSA level (p &lt; 0.001). Higher tumor grade in RP specimen compared to corresponding pre-operative biopsy was observed in 44 and 34.5% of cases considering former Gleason Grading and ISUP Grade Groups, respectively. Both, former Gleason Grading and ISUP Grade Groups predict survival when applied on tumors in prostatic biopsies as well as RP specimens. This is the first validation study on a large representative German community-based cohort to compare the former Gleason Grading with the recently introduced ISUP Grade Groups. Our data indicate that the ISUP Grade Groups do not improve predictive value of PCa grading and might be less sensitive in deciphering tumors with 3 + 4 and 4 + 3 pattern on RP specimen. However, the Grade Group system results less frequently in an upgrading from biopsy to the corresponding RP specimens, indicating a lower risk to miss potentially aggressive tumors not represented on biopsies