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Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. We used RμS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l'Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec-Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%-69%), and pT3a (22%-49%) was more frequent than pT3b (9%-12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. In this study, we developed classification models for the analysis of RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Radiotherapy has been optimized over the last decades not only through technological advances, but also through the translation of biological knowledge into clinical treatment schedules. Optimization of fractionation schedules and/or the introduction of simultaneous combined systemic treatment have significantly improved tumour cure rates in several cancer types. With modern techniques, we are currently able to measure factors of radiation resistance or radiation sensitivity in patient tumours; the definition of new biomarkers is expected to further enable personalized treatments. In this Review article, we overview important translation paths and summarize the quality requirements for preclinical and translational studies that will help to avoid bias in trial results. Translation of preclinical radiobiological research into clinical radiotherapy has largely improved treatment outcomes. Indicatively, based on the results of current biological research, fractionation schedules have been optimized, and combined treatments have been developed. In this Review, we discuss translation paths that have substantially improved radiotherapy and summarize what quality requirements within preclinical and translational studies will ensure the development of unbiased clinical trials.

Topoisomerase IIα (TOP2α) is essential for chromosomal condensation and segregation, as well as genomic integrity. Here we report that RNF168, an E3 ligase mutated in the human RIDDLE syndrome, interacts with TOP2α and mediates its ubiquitylation. RNF168 deficiency impairs decatenation activity of TOP2α and promotes mitotic abnormalities and defective chromosomal segregation. Our data also indicate that RNF168 deficiency, including in human breast cancer cell lines, confers resistance to the anti-cancer drug and TOP2 inhibitor etoposide. We also identify USP10 as a deubiquitylase that negatively regulates TOP2α ubiquitylation and restrains its chromatin association. These findings provide a mechanistic link between the RNF168/USP10 axis and TOP2α ubiquitylation and function, and suggest a role for RNF168 in the response to anti-cancer chemotherapeutics that target TOP2. The E3 ligase RNF168 is essential for the signalling of DNA double strand break and its mutations are associated with the RIDDLE syndrome. Here the authors identify TOP2a as substrate for RNF168 and USP10; providing a link between the RNF168/USP10 axis, TOP2a and the response to anti-cancer drugs that target TOP2.

Significance: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a ≥85  %   detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a ≥97.8  %   accuracy. Aim: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l’Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): +4  %   (+8  %  ), +7  %   (+9  %  ), +2  %   (6%), +9 (+9) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: +2.2  %   (+1.7  %  ), +4.5  %   (+3.6  %  ), +0  %   (+0  %  ), +2.3 (+0). While no global improvements were obtained for the normal versus cancer classification task [+0  %   (−2  %  ), +0  %   (−3  %  ), +2  %   (−2  %  ), +4 (+3)], the AUC was improved in both testing sets. Conclusions: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features.

Dose-escalated conformal radiotherapy is increasingly being used to radically treat prostate cancer with encouraging results and minimal long-term toxicity, yet little is known regarding the response of normal or malignant prostate cells to ionizing radiation (IR). To clarify the basis for cell killing during prostate cancer radiotherapy, we determined the IR-induced expression of several apoptotic- (bax, bcl-2, survivin and PARP) and G1-cell cycle checkpoint- (p53 and p21 WAF1/Cip1 ) related proteins, in both normal (PrEC-epithelial and PrSC-stromal) and malignant (LNCaP, DU-145 and PC-3; all epithelial) prostate cells. For these experiments, we chose doses ranging from 2 to 10 Gy, to be representative of the 1.8–2 Gy daily clinical fractions given during curative radiotherapy and the 8–10 Gy single doses given in palliative radiotherapy. We observed that IR-induced bax and p21 WAF1/Cip1 protein expression were attenuated selectively in normal stromal and epithelial cell cultures, yet maintained their p53-dependency in malignant cell lines. For each cell culture, we also determined total apoptotic and overall radiation cell kill using a short-term nuclear morphologic assay and a long-term clonogenic survival assay, respectively. Clonogenic survival, as measured by the surviving fraction at 2 Gy (SF2), ranged from 0.05 (PrEC) to 0.55 (DU-145), suggesting that malignant prostate cells are more radioresistant than normal prostate cells, for this series. IR-induced apoptotic cell kill was minimal (less than 6% cell after a dose of 10 Gy at times of 24–96 h) and was not dose-dependent. Furthermore, apoptotic kill was not correlated with either molecular apoptotic response or clonogenic cell kill. Using a flow cytometric proliferation assay with the PrSC (stromal) and DU-145 (epithelial) representative cultures, we observed that a senescent-like phenotype (SLP) emerges within a sub-population of cells post-irradiation that is non-clonogenic. Terminal growth arrest was dose-responsive at 96 h following irradiation and associated with long-term expression of both p21 WAF1/Cip1 and p16 INK4a genes. Future strategies for prostate radiotherapy prediction or novel treatments should additionally focus on terminal growth arrest as an important endpoint in prostate cancer therapy.

Topoisomerase IIα (TOP2α) is essential for chromosomal condensation and segregation, as well as genomic integrity. Here we report that RNF168, an E3 ligase mutated in the human RIDDLE syndrome, interacts with TOP2α and mediates its ubiquitylation. RNF168 deficiency impairs decatenation activity of TOP2α and promotes mitotic abnormalities and defective chromosomal segregation. Our data also indicate that RNF168 deficiency, including in human breast cancer cell lines, confers resistance to the anti-cancer drug and TOP2 inhibitor etoposide. We also identify USP10 as a deubiquitylase that negatively regulates TOP2α ubiquitylation and restrains its chromatin association. These findings provide a mechanistic link between the RNF168/USP10 axis and TOP2α ubiquitylation and function, and suggest a role for RNF168 in the response to anti-cancer chemotherapeutics that target TOP2.

Doc number: P19