Cyclin A1 and P450 aromatase promote metastatic homing and growth of stem-like prostate cancer cells in the bone marrow

Bone metastasis is a leading cause of morbidity and mortality in prostate cancer (PCa). While cancer stem-like cells have been implicated as a cell of origin for PCa metastases, the pathways which enable metastatic development at distal sites remain largely unknown. In this study, we illuminate pathways relevant to bone metastasis in this disease. We observed that cyclin A1 (CCNA1) protein expression was relatively higher in PCa metastatic lesions in lymph node, lung, and bone/bone marrow. In both primary and metastatic tissues, cyclin A1 expression was also correlated with aromatase (CYP19A1), a key enzyme that directly regulates the local balance of androgens to estrogens. Cyclin A1 overexpression in the stem-like ALDHhigh subpopulation of PC3M cells, one model of PCa, enabled bone marrow integration and metastatic growth. Further, cells obtained from bone marrow metastatic lesions displayed self-renewal capability in colony forming assays. In the bone marrow, Cyclin A1 and aromatase enhanced local bone marrow-releasing factors, including androgen receptor, estrogen and matrix metalloproteinase MMP9 and promoted hte metastatic growth of PCa cells. Moreover, ALDHhigh tumor cells expressing elevated levels of aromatase stimulated tumor/host estrogen production and acquired a growth advantage in the presence of host bone marrow cells. Overall, these findings suggest that local production of steroids and MMPs in the bone marrow may provide a suitable microenvironment for ALDHhigh PCa cells to establish metastatic growths, offering new approaches to therapeutically target bone metastases

Assessing the accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastases in intermediate- and high-risk prostate cancer patients

Background: [18F]PSMA-1007 is a promising tracer for integrated positron emission tomography and computed tomography (PET/CT). Objective: Our aim was to assess the diagnostic accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastasis before robotic-assisted laparoscopy (RALP) with extended lymph node dissection (ePLND). Design, Setting and Participants: The study was a retrospective cohort in a tertiary referral center. Men with prostate cancer that underwent surgical treatment for intermediate- or high-risk prostate cancer between May 2019 and August 2021 were included. Interventions: [18F]PSMA-1007 PET/CT for initial staging followed by RALP and ePLND. Outcome measurements and statistical analyses: Sensitivity and specificity were calculated both for the entire cohort and for patients with lymph node metastasis ≥ 3 mm. Positive (PPV) and negative (NPV) predictive values were calculated. Results and limitations: Among 104 patients included in the analyses, 26 patients had lymph node metastasis based on pathology reporting and metastases were ≥ 3 mm in size in 13 of the cases (50%). In the entire cohort, the sensitivity and specificity of [18F]PSMA-1007 were 26.9% (95% confidence interval (CI); 11.6–47.8) and 96.2% (95% CI; 89.2–99.2), respectively. The sensitivity and specificity of [18F]PSMA-1007 to detect a lymph node metastasis ≥ 3 mm on PET/CT were 53.8% (95% CI; 25.1–80.8) and 96.7% (95% CI; 90.7–99.3), respectively. PPV was 70% and NPV 93.6%. Conclusions: In primary staging of intermediate- and high-risk prostate cancer, [18F]PSMA-1007 PET/CT is highly specific for prediction of lymph node metastases, but the sensitivity for detection of metastases smaller than 3 mm is limited. Based on our results, [18F]PSMA-1007 PET/CT cannot completely replace ePLND. Patient summary: This study investigated the use of an imaging method based on a prostate antigen-specific radiopharmaceutical tracer to detect lymph node prostate cancer metastasis. We found that it is unreliable to discover small metastasis

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer

PURPOSE: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. METHODS: = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2-) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan-Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. RESULTS: < 0.0001). CONCLUSIONS: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies

BACKGROUND: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation.OBJECTIVE: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies.DESIGN, SETTING, AND PARTICIPANTS: A total of 698 prostate biopsy sections from 174 patients were used for training. The training sections were annotated by two senior consultant pathologists. The final algorithm was tested on 37 biopsy sections from 21 patients, with digitised slide images from two different scanners.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Correlation, sensitivity, and specificity parameters were calculated.RESULTS AND LIMITATIONS: The algorithm shows high accuracy in detecting cancer areas (sensitivity: 100%, specificity: 68%). Compared with the pathologists, the algorithm also performed well in detecting cancer areas (intraclass correlation coefficient [ICC]: 0.99) and assigning the Gleason patterns correctly: Gleason patterns 3 and 4 (ICC: 0.96 and 0.94, respectively), and to a lesser extent, Gleason pattern 5 (ICC: 0.82). Similar results were obtained using two different scanners.CONCLUSIONS: Our AI-based algorithm can reliably detect prostate cancer and quantify the Gleason patterns in core needle biopsies, with similar accuracy as pathologists. The results are reproducible on images from different scanners with a proven low level of intraobserver variability. We believe that this AI tool could be regarded as an efficient and interactive tool for pathologists.PATIENT SUMMARY: We developed a sensitive artificial intelligence tool for prostate biopsies, which detects and grades cancer with similar accuracy to pathologists. This tool holds promise to improve the diagnosis of prostate cancer

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients.

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.Keywords: KRAS; algorithm; colorectal cancer biomarkers; colorectal cancer metastasis; colorectal cancer progression; gene mutations

FDG-PET/CT for lymph node staging prior to radical cystectomy

Background: 18F-Fluorodeoxyglucose positron emission combined with computed tomography (FDG-PET/CT) has been proposed to improve preoperative staging in patients with bladder cancer subjected to radical cystectomy (RC). Objective: Our aim was to assess the accuracy of FDG-PET/CT for lymph node staging ascertained at the multidisciplinary tumour board compared to lymph node status in the surgical lymphadenectomy specimen obtained at RC, and to explore potential factors associated with false-positive FDG-PET/CT results. Design, setting and participants: Consecutive patients with bladder cancer undergoing RC with extended lymph node dissection between 2011 and 2019 without preoperative chemotherapy in a tertial referral cystectomy unit were included in the study. Outcome measurements and statistical analyses: Sensitivity, specificity, positive and negative predictive values and likelihood ratios were calculated. Potential factors investigated for association with false-positive FDG-PET/CT were; bacteriuria within four weeks prior to FDG-PET/CT, Bacillus Calmette–Guerin (BCG) treatment within 12 months prior to FDG-PET/CT and transurethral resection of bladder tumour (TURB) within four weeks prior to FDG-PET/CT. Results: Among 157 patients included for analysis, 44 (28%) were clinically node positive according to FDG-PET/CT. The sensitivity and specificity for detection of lymph node metastasis were 50% and 84%, respectively, and the corresponding positive predictive and negative predictive values were 61% and 76%. Positive and negative likelihood ratios were 3.0 and 0.6, respectively. No association was found between bacteriuria, previous BCG treatment or TURB within 28 days and false-positive FDG-PET/CT results. Conclusions: Preoperative FDG-PET/CT prior to RC had a clinically meaningful high specificity (84%) but lower sensitivity (50%) for detection of lymph node metastases compared to lymph node status in an extended pelvic lymphadenectomy template. We could not identify any factors associated with false-positive FDG-PET/CT outcomes

Systematic Augmentation in HSV Space for Semantic Segmentation of Prostate Biopsies

In recent years, the combination of the digitization of the field of pathology and increased computational power has led to a big increase in research of computer-aided diagnostics using systems based on artificial intelligence (AI). This includes detection and classification of prostate cancer, where several studies have shown great promise in automated prostate cancer grading using deep learning based AI systems. However, there is still work to be done to ensure that these algorithms are invariant to possible variations of the digitized microscopy images they are applied to. A standard method in deep learning to increase the variation of the training data is dataset augmentation. All of these studies apply some augmentation of their data, however, there is a lack of evaluation of different methods and their impact on this crucial part of the AI systems. In this study, we look into different color augmentation methods for the task of segmentation of prostate biopsies. Furthermore, we introduce a novel color augmentation method based on stereographic projection. Our results affirm the importance of studying different augmentation methods and indicate a gain in performance using our method

Domain-adversarial neural network for improved generalization performance of gleason grade classification

When training a deep learning model, the dataset used is of great importance to make sure that the model learns relevant features of the data and that it will be able to generalize to new data. However, it is typically difficult to produce a dataset without some bias toward any specific feature. Deep learning models used in histopathology have a tendency to overfit to the stain appearance of the training data - if the model is trained on data from one lab only, it will usually not be able to generalize to data from other labs. The standard technique to overcome this problem is to use color augmentation of the training data which, artificially, generates more variations for the network to learn. In this work we instead test the use of a so called domain-adversarial neural network, which is designed to prevent the model from being biased towards features that in reality are irrelevant such as the origin of an image. To test the technique, four datasets from different hospitals for Gleason grading of prostate cancer are used. We achieve state of the art results for these particular datasets, and furthermore for two of our three test datasets the approach outperforms the use of color augmentation

Complete metabolic response with [18F]fluorodeoxyglucose-positron emission tomography/computed tomography predicts survival following induction chemotherapy and radical cystectomy in clinically lymph node positive bladder cancer

Objective: To determine whether repeated [18F]fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET-CT) scans can predict increased cancer-specific survival (CSS) after induction chemotherapy followed by radical cystectomy (RC). Patients and Methods: Between 2007 and 2018, 86 patients with clinically lymph node (LN)-positive bladder cancer (T1–T4, N1–N3, M0–M1a) were included and underwent a repeated FDG-PET-CT during cisplatin-based induction chemotherapy. The 71 patients that had a response to chemotherapy underwent RC. Response to chemotherapy was evaluated in LNs through repeated FDG-PET-CT and stratified as partial response or complete response using three different methods: maximum standardised uptake value (SUVmax), adapted Deauville criteria, and total lesion glycolysis (TLG). Progression-free survival (PFS) and CSS were analysed for all three methods by Cox regression analysis. Results: After a median follow-up of 40 months, 15 of the 71 patients who underwent RC had died from bladder cancer. Using SUVmax and the adapted Deauville criteria, multivariable Cox regression analyses adjusting for age, clinical tumour stage and LN stage showed that complete response was associated with increased PFS (hazard ratio [HR] 3.42, 95% confidence interval [CI] 1.20–9.77) and CSS (HR 3.30, 95% CI 1.02–10.65). Using TLG, a complete response was also associated with increased PFS (HR 5.17, 95% CI 1.90–14.04) and CSS (HR 6.32, 95% CI 2.06–19.41). Conclusions: Complete metabolic response with FDG-PET-CT predicts survival after induction chemotherapy followed by RC in patients with LN-positive bladder cancer and comprises a novel tool in evaluating response to chemotherapy before surgery. This strategy has the potential to tailor treatment in individual patients by identifying significant response to chemotherapy, which motivates the administration of a full course of induction chemotherapy with a higher threshold for suspending treatment due to toxicity and side-effects