Leukocytic Infiltration of Intraductal Carcinoma of the Prostate: An Exploratory Study.

Intraductal carcinoma of the prostate (IDC-P) is an aggressive histological subtype of prostate cancer (PCa) detected in approximately 20% of radical prostatectomy (RP) specimens. As IDC-P has been associated with PCa-related death and poor responses to standard treatment, the purpose of this study was to explore the immune infiltrate of IDC-P. Hematoxylin- and eosin-stained slides from 96 patients with locally advanced PCa who underwent RP were reviewed to identify IDC-P. Immunohistochemical staining of CD3, CD8, CD45RO, FoxP3, CD68, CD163, CD209 and CD83 was performed. For each slide, the number of positive cells per mm2 in the benign tissues, tumor margins, cancer and IDC-P was calculated. Consequently, IDC-P was found in a total of 33 patients (34%). Overall, the immune infiltrate was similar in the IDC-P-positive and the IDC-P-negative patients. However, FoxP3+ regulatory T cells (p < 0.001), CD68+ and CD163+ macrophages (p < 0.001 for both) and CD209+ and CD83+ dendritic cells (p = 0.002 and p = 0.013, respectively) were less abundant in the IDC-P tissues compared to the adjacent PCa. Moreover, the patients were classified as having immunologically "cold" or "hot" IDC-P, according to the immune-cell densities averaged in the total IDC-P or in the immune hotspots. The CD68/CD163/CD209-immune hotspots predicted metastatic dissemination (p = 0.014) and PCa-related death (p = 0.009) in a Kaplan-Meier survival analysis. Further studies on larger cohorts are necessary to evaluate the clinical utility of assessing the immune infiltrate of IDC-P with regards to patient prognosis and the use of immunotherapy for lethal PCa.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 1: Raman spectroscopy fiber-optics system and in situ tissue characterization.

SIGNIFICANCE: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. AIM: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. APPROACH: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ (in vivo) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. RESULTS: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). CONCLUSIONS: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

Image-guided Raman spectroscopy navigation system to improve transperineal prostate cancer detection. Part 2: in-vivo tumor-targeting using a classification model combining spectral and MRI-radiomics features.

SIGNIFICANCE: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. AIM: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). APPROACH: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. RESULTS: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade group = 1, and 21 as grade group >1, according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% (sensitivity = 81 % and a specificity = 85 % ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ≥1 / grade group <1 (accuracy = 87 % ) or grade group >1 / grade group ≤1 (accuracy = 91 % ). CONCLUSIONS: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens.

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.

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

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.

Wide-field optical spectroscopy system integrating reflectance and spatial frequency domain imaging to measure attenuation-corrected intrinsic tissue fluorescence in radical prostatectomy specimens.

The development of a multimodal optical imaging system is presented that integrates endogenous fluorescence and diffuse reflectance spectroscopy with single-wavelength spatial frequency domain imaging (SFDI) and surface profilometry. The system images specimens at visible wavelengths with a spatial resolution of 70 µm, a field of view of 25 cm2 and a depth of field of ∼1.5 cm. The results of phantom experiments are presented demonstrating the system retrieves absorption and reduced scattering coefficient maps using SFDI with <6% reconstruction errors. A phase-shifting profilometry technique is implemented and the resulting 3-D surface used to compute a geometric correction ensuring optical properties reconstruction errors are maintained to <6% in curved media with height variations <20 mm. Combining SFDI-computed optical properties with data from diffuse reflectance spectra is shown to correct fluorescence using a model based on light transport in tissue theory. The system is used to image a human prostate, demonstrating its ability to distinguish prostatic tissue (anterior stroma, hyperplasia, peripheral zone) from extra-prostatic tissue (urethra, ejaculatory ducts, peri-prostatic tissue). These techniques could be integrated in robotic-assisted surgical systems to enhance information provided to surgeons and improve procedural accuracy by minimizing the risk of damage to extra-prostatic tissue during radical prostatectomy procedures and eventually detect residual cancer.

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions.

PURPOSE: Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy. METHODS: The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation. RESULTS: In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61 cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions. CONCLUSION: Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.

Hematoxylin and Eosin Counterstaining Protocol for Immunohistochemistry Interpretation and Diagnosis.

Hematoxylin and eosin (H&E) staining is a well-established technique in histopathology. However, immunohistochemistry (IHC) interpretation is done exclusively with hematoxylin counterstaining. Our goal was to investigate the potential of H&E as counterstaining (H&E-IHC) to allow for visualization of a marker while confirming the diagnosis on the same slide. The quality of immunostaining and the fast-technical performance were the main criteria to select the final protocol. We stained multiple diagnostic tissues with class I IHC tests with different subcellular localization markers (anti-CK7, CK20, synaptophysin, CD20, HMB45, and Ki-67) and with double-staining on prostate tissues with anti-high molecular weight keratins/p63 (DAB detection) and p504s (alkaline phosphatase detection). To validate the efficacy of the counterstaining, we stained tissue microarrays from the Canadian Immunohistochemistry Quality Control (cIQc) with class II IHC tests (ER, PR, HER2, and p53 markers). Interobserver and intraobserver concordance was assessed by κ statistics. Excellent agreement of H&E-IHC interpretation was observed in comparison with standard IHC from our laboratory (κ, 0.87 to 1.00), and with the cIQc reference values (κ, 0.81 to 1.00). Interobserver and intraobserver agreement was excellent (κ, 0.89 to 1.00 and 0.87 to 1.00, respectively). We therefore show for the first time the potential of using H&E counterstaining for IHC interpretation. We recommend the H&E-IHC protocol to enhance diagnostic precision for the clinical workflow and research studies.

Mesoscopic characterization of prostate cancer using Raman spectroscopy: potential for diagnostics and therapeutics.

OBJECTIVE: To test if Raman spectroscopy (RS) is an appropriate tool for the diagnosis and possibly grading of prostate cancer (PCa). PATIENTS AND METHODS: Between 20 and 50 Raman spectra were acquired from 32 fresh and non-processed post-prostatectomy specimens using a macroscopic handheld RS probe. Each measured area was characterized and categorized according to histopathological criteria: tissue type (extraprostatic or prostatic); tissue malignancy (benign or malignant); cancer grade (Grade Groups [GGs] 1-5); and tissue glandular level. The data were analysed using machine-learning classification with neural network. RESULTS: The RS technique was able to distinguish prostate from extraprostatic tissue with a sensitivity of 82% and a specificity of 83% and benign from malignant tissue with a sensitivity of 87% and a specificity of 86%. In an exploratory fashion, RS differentiated benign from GG1 in 726/801 spectra (91%; sensitivity 80%, specificity 91%), from GG2 in 588/805 spectra (73%; sensitivity 76%, specificity 73%), from GG3 in 670/797 spectra (84%; sensitivity 86%, specificity 84%), from GG4 in 711/802 spectra (88%; sensitivity 77%, specificity 89%) and from GG5 in 729/818 spectra (89%; sensitivity 90%, specificity 89%). CONCLUSION: Current diagnostic approaches of PCa using needle biopsies have suboptimal cancer detection rates and a significant risk of infection. Standard non-targeted random sampling results in false-negative biopsies in 15-30% of patients, which affects clinical management. RS, a non-destructive tissue interrogation technique providing vibrational molecular information, resolved the highly complex architecture of the prostate and detect cancer with high accuracy using a fibre optic probe to interrogate radical prostatectomy (RP) specimens from 32 patients (947 spectra). This proof-of-principle paves the way for the development of in vivo tumour targeting spectroscopy tools for informed biopsy collection to address the clinical need for accurate PCa diagnosis and possibly to improve surgical resection during RP as a complement to histopathological analysis.

Multiple intestinal atresia with combined immune deficiency related to TTC7A defect is a multiorgan pathology: study of a French-Canadian-based cohort.

Hereditary multiple intestinal atresia (HMIA) is a rare cause of intestinal obstruction in humans associated with a profound combined immune deficiency. Deleterious mutations of the tetratricopeptide repeat domain-7A (TTC7A) gene lead to HMIA, although the mechanism(s) causing the disease in TTC7A deficiency has (have) not yet been clearly identified. To evaluate the consequences of TTC7A deficiency, we studied the morphology of several organs from HMIA patients at different developmental stages, as well as the expression of the TTC7A protein. We performed histological and immunohistochemical analyses on biopsies and autopsies of 6 patients and 1 fetus with HMIA. Moreover, we characterized for the first time the expression of the TTC7A protein by immunostaining it in several organs from control (including fetal samples), infants, and 1 fetus with HMIA. Besides the gastrointestinal tract, HMIA disease was associated with morphological alterations in multiple organs: thymus, lung, spleen, and liver. Moreover, we demonstrated that normal TTC7A protein was expressed in the cytoplasm of epithelial cells of the intestine, thymus, and pancreas. Surprisingly, altered TTC7A protein was highly expressed in tissues from patients, mainly in the epithelial cells. We have established that HMIA associated with a TTC7A defect is characterized by multiorgan impairments. Overall, this report suggests that TTC7A protein is critical for the proper development, preservation, and/or function of thymic and gastrointestinal epithelium.

Ligand-dependent corepressor contributes to transcriptional repression by C2H2 zinc-finger transcription factor ZBRK1 through association with KRAB-associated protein-1.

We identified a novel interaction between ligand-dependent corepressor (LCoR) and the corepressor KRAB-associated protein-1 (KAP-1). The two form a complex with C2H2 zinc-finger transcription factor ZBRK1 on an intronic binding site in the growth arrest and DNA-damage-inducible α (GADD45A) gene and a novel site in the fibroblast growth factor 2 (FGF2) gene. Chromatin at both sites is enriched for histone methyltransferase SETDB1 and histone 3 lysine 9 trimethylation, a repressive epigenetic mark. Depletion of ZBRK1, KAP-1 or LCoR led to elevated GADD45A and FGF2 expression in malignant and non-malignant breast epithelial cells, and caused apoptotic death. Loss of viability could be rescued by simultaneous knockdowns of FGF2 and transcriptional coregulators or by blocking FGF2 function. FGF2 was not concurrently expressed with any of the transcriptional coregulators in breast malignancies, suggesting an inverse correlation between their expression patterns. We propose that ZBRK1, KAP-1 and LCoR form a transcriptional complex that silences gene expression, in particular FGF2, which maintains breast cell viability. Given the broad expression patterns of both LCoR and KAP-1 during development and in the adult, this complex may have several regulatory functions that extend beyond cell survival, mediated by interactions with ZBRK1 or other C2H2 zinc-finger proteins.