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Background: 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., Methods and Findings: 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., Conclusions: 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., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: F.D. holds a fellowship co-sponsored by Mitacs and ODS Medical. ODS Medical is a medical device company. A.B. reports grant from Astellas Pharma Canada, Merck and IMV, personal fees from Merck, outside the submitted work; In addition, A.B. has an issued patent on MAGE antigen binding proteins with GSK biologicals.

Background: Age-related immune remodelling is thought to be associated with resistance to immune checkpoint inhibitors (ICIs) in cancer. Patients older than 70 years, representing >50% of the population with non-small cell lung cancer (NSCLC) according to SEER database, are underrepresented in clinical trials exploring ICIs. The objective of this study was to determine if patients with NSCLC older than ≥70 years had inferior clinical outcomes with ICIs., Methods: We conducted a retrospective analysis of 381 patients treated with anti-PD-(L)1 ICI for advanced NSCLC at the Dijon Cancer Center (n = 177), University of Montreal Hospital (n = 106) and Quebec Heart and Lung Institute (n = 98). Age was considered as a categorical variable. Patients' baseline characteristics were compared using the Chi-squared test. Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test in a univariate analysis. Multivariate cox regression model was used to determine hazard ratios and 95% confidence intervals for progression-free survival (PFS) and overall survival (OS) between the groups, adjusting for other clinicopathologic features., Results: Among 381 patients included, 335 (88%) received ICI after platinum chemotherapy. The median age was 66 (range 37-89) and 33% were older than 70 years of age. Considering age as a categorical variable, differences in age were not associated with PFS or OS. Subgroup analysis and multivariate cox regression did not reveal significant interaction of age with outcomes. ECOG performance status was the only significant factor in the three cohorts., Conclusions: Unlike previously described in the era of chemotherapy, age was not associated with outcomes in NSCLC patients treated with ICI., Competing Interests: Declaration of Competing Interest All authors and co-authors declare no conflict of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Great advances in analytical technology coupled with accelerated new drug development and growing understanding of biological challenges, such as tumor heterogeneity, have required a change in the focus for biobanking. Most current banks contain samples of primary tumors, but linking molecular signatures to therapeutic questions requires serial biopsies in the setting of metastatic disease, next-generation of biobanking. Furthermore, an integration of multidimensional analysis of various molecular components, that is, RNA, DNA, methylome, microRNAome and post-translational modifications of the proteome, is necessary for a comprehensive view of a tumor's biology. While data using such biopsies are now regularly presented, the preanalytical variables in tissue procurement and processing in multicenter studies are seldom detailed and therefore are difficult to duplicate or standardize across sites and across studies. In the context of a biopsy-driven clinical trial, we generated a detailed protocol that includes morphological evaluation and isolation of high-quality nucleic acids from small needle core biopsies obtained from liver metastases. The protocol supports stable shipping of samples to a central laboratory, where biopsies are subsequently embedded in support media. Designated pathologists must evaluate all biopsies for tumor content and macrodissection can be performed if necessary to meet our criteria of >60% neoplastic cells and <20% necrosis for genomic isolation. We validated our protocol in 40 patients who participated in a biopsy-driven study of therapeutic resistance in metastatic colorectal cancer. To ensure that our protocol was compatible with multiplex discovery platforms and that no component of the processing interfered with downstream enzymatic reactions, we performed array comparative genomic hybridization, methylation profiling, microRNA profiling, splicing variant analysis and gene expression profiling using genomic material isolated from liver biopsy cores. Our standard operating procedures for next-generation biobanking can be applied widely in multiple settings, including multicentered and international biopsy-driven trials.