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Whereas the lack of biomarkers in penile cancer (PeCa) impedes the development of efficacious treatment protocols, preliminary evidence suggests that c-MET and associated signaling elements may be dysregulated in this disorder. In the following study, we investigated whether c-MET and associated key molecular elements may have prognostic and therapeutic utility in PeCa. Formalin-fixed, paraffin-embedded tumor tissue from therapy-naïve patients with invasive PeCa was used for tissue microarray (TMA) analysis. Immunohistochemical staining was performed to determine the expression of the proteins c-MET, PPARg, β-catenin, snail, survivin, and n-MYC. In total, 94 PeCa patients with available tumor tissue were included. The median age was 64.9 years. High-grade tumors were present in 23.4%, and high-risk HPV was detected in 25.5%. The median follow-up was 32.5 months. High expression of snail was associated with HPV-positive tumors. Expression of β-catenin was inversely associated with grading. In both univariate COX regression analysis and the log-rank test, an increased expression of PPARg and c-MET was predictive of inferior disease-specific survival (DSS). Moreover, in multivariate analysis, a higher expression of c-MET was independently associated with worse DSS. Blocking c-MET with cabozantinib and tivantinib induced a significant decrease in viability in the primary PeCa cell line UKF-PeC3 isolated from the tumor tissue as well as in cisplatin- and osimertinib-resistant sublines. Strikingly, a higher sensitivity to tivantinib could be detected in the latter, pointing to the promising option of utilizing this agent in the second-line treatment setting.
Background: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efcient diagnostic algorithms.
Methods: Retrospectively, 106 prostate tissue samples from 48 patients (mean age,
66 ± 6.6 years) were included in the study. Patients sufered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open–source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.
Results: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and
ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of 0.93 ± 0.04, 0.91 ± 0.06, and 0.92 ± 0.05, respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02–0.06 for ERG and PIN-4.
Conclusions: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classifcation. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classifcation methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.
A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative ‘Connect to Decode’ (C2D) to generate the first and largest manually curated interactome of Mtb termed ‘interactome pathway’ (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.