Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.

BACKGROUND: Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay. OBJECTIVE: This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data. METHODS: Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors. RESULTS: In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P=.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P=.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P=.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P=.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F1-score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients. CONCLUSIONS: Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer.

INTRODUCTION: Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes. AREAS COVERED: In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings. EXPERT OPINION: The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

IMPROVER (Involving Men with Prostate Cancer in Engaged Research): Assessing Patient Experience With Testing, Diagnosis, And Surveillance.

BACKGROUND: Our understanding of patient experiences with prostate cancer testing for diagnosis and surveillance is limited. The aim of this study was to collaborate with patients and clinicians to understand their lived experience and unmet needs around the early detection, diagnosis and monitoring (active surveillance) of prostate cancer. METHODS: Two focus groups were held with patients (n = 20) and healthcare professionals (n = 16), to identify the main challenges in prostate cancer detection, diagnosis, and monitoring. This information formed the basis of an online questionnaire for broader dissemination. RESULTS: A total of 1138 analyzable responses were obtained from people tested for prostate cancer (69% tested positive) in Europe and the US. Only 29 healthcare professionals completed the survey. Almost one-third of people reported knowing very little/nothing about prostate cancer prior to testing. Prior disease awareness was significantly higher in those who tested negative (P < .0001). Most respondents (n = 857; 75%) felt informed about the steps involved in testing. Receiving written information was a key factor; 91% of those who felt uninformed were not given any written information. Overall, most people felt "satisfied" with the typical prostate cancer tests: PSA, DRE, mpMRI, and biopsy. However, dissatisfaction for prostate biopsy (12%) was almost double that of other tests (P < .0001). Most patients understood why each test was done, and felt that their results and next steps were clearly explained to them; though PSA scored lowest in all of these fields. Apart from PSA, test satisfaction was lower when used repeatedly for surveillance, compared to once-off detection/diagnosis. CONCLUSIONS: Greater public awareness and education around prostate cancer, as well as clear and accessible written information for patients at the beginning of their cancer journey is needed. Further research is needed into alternative, less invasive tests, particularly when used repeatedly in the surveillance population.

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. 'glszm Zone Entropy'), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical 'discovery' radiomics in the neuro-oncology space.

Health diplomacy in action: The cancer legacy of the Good Friday Agreement.

2023 marks the 25th anniversary of the Good Friday Agreement, which led peace in Northern Ireland. As well as its impact on peace and reconciliation, the Good Friday Agreement has also had a lasting positive impact on cancer research and cancer care across the island of Ireland. Pursuant to the Good Friday Agreement, a Memorandum of Understanding (MOU) was signed between the respective Departments of Health in Ireland, Northern Ireland and the US National Cancer Institute (NCI), giving rise to the Ireland - Northern Ireland - National Cancer Institute Cancer Consortium, an unparalleled tripartite agreement designed to nurture and develop linkages between cancer researchers, physicians and allied healthcare professionals across Ireland, Northern Ireland and the US, delivering world class research and better care for cancer patients on the island of Ireland and driving research and innovation in the US.

Cannabidiol Inhibits the Proliferation and Invasiveness of Prostate Cancer Cells.

Prostate cancer is the fifth leading cause of cancer death in men, responsible for over 375,000 deaths in 2020. Novel therapeutic strategies are needed to improve outcomes. Cannabinoids, chemical components of the cannabis plant, are a possible solution. Preclinical evidence demonstrates that cannabinoids can modulate several cancer hallmarks of many tumor types. However, the therapeutic potential of cannabinoids in prostate cancer has not yet been fully explored. The aim of this study was to investigate the antiproliferative and anti-invasive properties of cannabidiol (CBD) in prostate cancer cells in vitro. CBD inhibited cell viability and proliferation, accompanied by reduced expression of key cell cycle proteins, specifically cyclin D3 and cyclin-dependent kinases CDK2, CDK4, and CDK1, and inhibition of AKT phosphorylation. The effects of CBD on cell viability were not blocked by cannabinoid receptor antagonists, a transient receptor potential vanilloid 1 (TRPV1) channel blocker, or an agonist of the G-protein-coupled receptor GPR55, suggesting that CBD acts independently of these targets in prostate cancer cells. Furthermore, CBD reduced the invasiveness of highly metastatic PC-3 cells and increased protein expression of E-cadherin. The ability of CBD to inhibit prostate cancer cell proliferation and invasiveness suggests that CBD may have potential as a future chemotherapeutic agent.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

SRF inhibitors reduce prostate cancer cell proliferation through cell cycle arrest in an isogenic model of castrate-resistant prostate cancer.

Castrate-resistant prostate cancer (CRPC) is challenging to treat, despite improvements with next-generation anti-androgens such as enzalutamide, due to acquired resistance. One of the mechanisms of such resistance includes aberrant activation of co-factors of the androgen receptor (AR), such as the serum response factor (SRF), which was associated with prostate cancer progression and resistance to enzalutamide. Here, we show that inhibition of SRF with three small molecules (CCG-1423, CCG-257081 and lestaurtinib), singly and in combination with enzalutamide, reduces cell viability in an isogenic model of CRPC. The effects of these inhibitors on the cell cycle, singly and in combination with enzalutamide, were assessed with western blotting, flow cytometry and ß-galactosidase staining. In the androgen deprivation-sensitive LNCaP parental cell line, a synergistic effect between enzalutamide and all three inhibitors was demonstrated, while the androgen deprivation-resistant LNCaP Abl cells showed synergy only with the lestaurtinib and enzalutamide combination, suggesting a different mechanism of action of the CCG series of compounds in the absence and presence of androgens. Through analysis of cell cycle checkpoint proteins, flow cytometry and ß-galactosidase staining, we showed that all three SRF inhibitors, singly and in combination with enzalutamide, induced cell cycle arrest and decreased S phase. While CCG-1423 had a more pronounced effect on the expression of cell cycle checkpoint proteins, CCG-257081 and lestaurtinib decreased proliferation also through induction of cellular senescence. In conclusion, we show that inhibition of an AR co-factors, namely SRF, provides a promising approach to overcoming resistance to AR inhibitors currently used in the clinic.

A potentially fatal cranial pathology in a specimen of Tarchia.

Skulls of the Mongolian ankylosaurids Shamosaurus, Tarchia, and Saichania were scanned for information about their internal anatomy. Computed tomography (CT) imaging of the Tarchia skull revealed substantial internal anatomical differences from known Campanian North American taxa, particularly in the morphology of the airway. In addition, unexpected anomalies were detected within the airway and sinuses. The anomalies include multiple bilaterally distributed, variably sized hyperdense (mineralized) concretions within the airway and sinuses, the largest of which, positioned in the right nasal cavity medial to the supraorbitals, has an asymmetric ovoid shape that tapers caudally and which is partially encased within a hemispherical trabeculated osseous proliferation (sinus exostosis). Immediately adjacent to the exostosis is a subcircular transosseous defect in the prefrontal region of the skull roof that is partially filled with trabeculated ossified material with similar architectural features as the larger exostosis. Irregularities along the internal and external surfaces of the cranial vault may be associated. The radiologic features of the hemicircumferential exostosis suggest a chronic reactive osteoproliferation, possibly in response to an ongoing inflammatory reaction to primary sinus infection or, in combination with the unilateral transosseous defect, traumatically introduced infection with potentially fatal consequences. This report underscores the value of CT scanning of fossil vertebrate specimens, which in this case revealed large internal lesions of the skull that, at the time the scan was performed, were otherwise indiscernible.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer.

BACKGROUND: Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease. OBJECTIVE: To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management. DESIGN, SETTING, AND PARTICIPANTS: Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models. RESULTS AND LIMITATIONS: The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study. CONCLUSIONS: The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP. PATIENT SUMMARY: We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

An unusual theropod specimen from the late Maastrichtian of New Jersey.

A set of fragmentary bones excavated from the Inversand Company Pit at Sewell, Gloucester County, NJ, contains portions of broken hollow femur bone that display unusual interior structure. Two hypotheses are considered; (1) the lumina represents the distinctive physical features of medullary bone as described by M. Schweitzer in a series of papers; or (2) the interior bone growth is a pathology. The specimen is attributed to the Theropoda on the basis of the possession of a pneumatic foramen in one of the bones. This specimen is from the upper part of the New Egypt Formation, just below the K/T boundary in the base of the overlying Hornerstown Formation. This stratigraphic horizon is the same as the type specimen of Dryptosaurus aquiluguis which was excavated from a marl pit nearby.

Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.

Breast cancer is the most common disease among women, with over 2.1 million new diagnoses each year worldwide. About 30% of patients initially presenting with early stage disease have a recurrence of cancer within 10 years. Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment. Artificial intelligence strategies that can predict the risk of recurrence of breast cancer could help breast cancer clinicians avoid ineffective overtreatment. Despite its significance, most breast cancer recurrence datasets are insufficiently large, not publicly available, or imbalanced, making these studies more difficult. This systematic review investigates the role of artificial intelligence in the prediction of breast cancer recurrence. We summarise common techniques, features, training and testing methodologies, metrics, and discuss current challenges relating to implementation in clinical practice. We systematically reviewed works published between 1 January 2011 and 1 November 2021 using the methodology of Kitchenham and Charter. We leveraged Springer, Google Scholar, PubMed, and IEEE search engines. This review found three areas that require further work. First, there is no agreement on artificial intelligence methodologies, feature predictors, or assessment metrics. Second, issues such as sampling strategies, missing data, and class imbalance problems are rarely addressed or discussed. Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment. We conclude that predicting breast cancer recurrence remains an open problem despite the use of artificial intelligence.

Mapping the Immune Landscape in Metastatic Melanoma Reveals Localized Cell-Cell Interactions That Predict Immunotherapy Response.

While immune checkpoint-based immunotherapy (ICI) shows promising clinical results in patients with cancer, only a subset of patients responds favorably. Response to ICI is dictated by complex networks of cellular interactions between malignant and nonmalignant cells. Although insights into the mechanisms that modulate the pivotal antitumoral activity of cytotoxic T cells (Tcy) have recently been gained, much of what has been learned is based on single-cell analyses of dissociated tumor samples, resulting in a lack of critical information about the spatial distribution of relevant cell types. Here, we used multiplexed IHC to spatially characterize the immune landscape of metastatic melanoma from responders and nonresponders to ICI. Such high-dimensional pathology maps showed that Tcy gradually evolve toward an exhausted phenotype as they approach and infiltrate the tumor. Moreover, a key cellular interaction network functionally linked Tcy and PD-L1+ macrophages. Mapping the respective spatial distributions of these two cell populations predicted response to anti-PD-1 immunotherapy with high confidence. These results suggest that baseline measurements of the spatial context should be integrated in the design of predictive biomarkers to identify patients likely to benefit from ICI. SIGNIFICANCE: This study shows that spatial characterization can address the challenge of finding efficient biomarkers, revealing that localization of macrophages and T cells in melanoma predicts patient response to ICI. See related commentary by Smalley and Smalley, p. 3198.

Implementing subtype-specific pre-clinical models of breast cancer to study pre-treatment aspirin effects.

BACKGORUND: Prior data suggest pre-diagnostic aspirin use impacts breast tumour biology and patient outcome. Here, we employed faithful surgical resection models of HER2+ and triple-negative breast cancer (TNBC), to study outcome and response mechanisms across breast cancer subtypes. METHOD: NOD/SCID mice were implanted with HER2+ MDA-MB-231/LN/2-4/H2N, trastuzumab-resistant HER2+ HCC1954 or a TNBC patient-derived xenograft (PDX). A daily low-dose aspirin regimen commenced until primary tumours reached ~250 mm3 and subsequently resected. MDA-MB-231/LN/2-4/H2N mice were monitored for metastasis utilising imaging. To interrogate the survival benefit of pre-treatment aspirin, 3 weeks post-resection, HCC1954/TNBC animals received standard-of-care (SOC) chemotherapy for 6 weeks. Primary tumour response to aspirin was interrogated using immunohistochemistry. RESULTS: Aspirin delayed time to metastasis in MDA-MB-231/LN/2-4/H2N xenografts and decreased growth of HER2+ /TNBC primary tumours. Lymphangiogenic factors and lymph vessels number were decreased in HER2+ tumours. However, no survival benefit was seen in aspirin pre-treated animals (HCC1954/TNBC) that further received adjuvant SOC, compared with animals treated with SOC alone. In an effort to study mechanisms responsible for the observed reduction in lymphangiogenesis in HER2+ BC we utilised an in vitro co-culture system of HCC1954 tumour cells and mesenchymal stromal cells (MSC). Aspirin abrogated the secretion of VEGF-C in MSCs and also decreased the lymph/angiogenic potential of the MSCs and HCC1954 by tubule formation assay. Furthermore, aspirin decreased the secretion of uPA in HCC1954 cells potentially diminishing its metastatic capability. CONCLUSION: Our data employing clinically relevant models demonstrate that aspirin alters breast tumour biology. However, aspirin may not represent a robust chemo-preventative agent in the HER2+ or TNBC setting.

Analytical Validation of a Novel 6-Gene Signature for Prediction of Distant Recurrence in Estrogen Receptor-Positive, HER2-Negative, Early-Stage Breast Cancer.

BACKGROUND: OncoMasTR is a recently developed multigene prognostic test for early-stage breast cancer. The test has been developed in a kit-based format for decentralized deployment in molecular pathology laboratories. The analytical performance characteristics of the OncoMasTR test are described in this study. METHODS: Expression levels of 6 genes were measured by 1-step reverse transcription-quantitative PCR on RNA samples prepared from formalin-fixed, paraffin-embedded (FFPE) breast tumor specimens. Assay precision, reproducibility, input range, and interference were determined using FFPE-derived RNA samples representative of low and high prognostic risk scores. A pooled RNA sample derived from 6 FFPE breast tumor specimens was used to establish the linear range, limit of detection, and amplification efficiency of the individual gene expression assays. RESULTS: The overall precision of the OncoMasTR test was high with an SD of 0.16, which represents less than 2% of the 10-unit risk score range. Test results were reproducible across 4 testing sites, with correlation coefficients of 0.94 to 0.96 for the continuous risk score and concordance of 86% to 96% in low-/high-risk sample classification. Consistent risk scores were obtained across a > 100-fold RNA input range. Individual gene expression assays were linear up to quantification cycle values of 36.0 to 36.9, with amplification efficiencies of 80% to 102%. Test results were not influenced by agents used during RNA isolation, by low levels of copurified genomic DNA, or by moderate levels of copurified adjacent nontumor tissue. CONCLUSION: The OncoMasTR prognostic test displays robust analytical performance that is suitable for deployment by local pathology laboratories for decentralized use.

Plant-derived cannabinoids as anticancer agents.

Substantial preclinical evidence demonstrates the antiproliferative, cytotoxic, and antimetastatic properties of plant-derived cannabinoids (phytocannabinoids) such as cannabidiol and tetrahydrocannabinol. The cumulative body of research into the intracellular mechanisms and phenotypic effects of these compounds supports a logical, judicious progression to large-scale phase II/III clinical trials in certain cancer types to truly assess the efficacy of phytocannabinoids as anticancer agents.

Emerging role for the Serum Response Factor (SRF) as a potential therapeutic target in cancer.

INTRODUCTION: The Serum Response Factor (SRF) is a transcription factor involved in three hallmarks of cancer: the promotion of cell proliferation, cell death resistance and invasion and metastasis induction. Many studies have demonstrated a leading role in the development and progression of multiple cancer types, thus highlighting the potential of SRF as a prognostic biomarker and therapeutic target, especially for cancers with poor prognosis. AREAS COVERED: This review examines the role of SRF in several cancers in promoting cellular processes associated with cancer development and progression. SRF co-factors and signaling pathways are discussed as possible targets to inhibit SRF in a tissue and cancer-specific way. Small-molecule inhibitors of SRF, such as the CCGs series of compounds and lestaurtinib, which could be used as cancer therapeutics, are also discussed. EXPERT OPINION: Targeting of SRF and its co-factors represents a promising therapeutic approach. Further understanding of the molecular mechanisms behind the action of SRF could provide a pipeline of novel molecular targets and therapeutic combinations for cancer. Basket clinical trials and the use of SRF immunohistochemistry as companion diagnostics will help testing of these new targets in patients.

Pre-clinical and clinical studies on the role of RBM3 in muscle-invasive bladder cancer: longitudinal expression, transcriptome-level effects and modulation of chemosensitivity.

BACKGROUND: The response to neoadjuvant cisplatin-based chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is impaired in up to 50% of patients due to chemoresistance, with no predictive biomarkers in clinical use. The proto-oncogene RNA-binding motif protein 3 (RBM3) has emerged as a putative modulator of chemotherapy response in several solid tumours but has a hitherto unrecognized role in MIBC. METHODS: RBM3 protein expression level in tumour cells was assessed via immunohistochemistry in paired transurethral resection of the bladder (TURB) specimens, cystectomy specimens and lymph node metastases from a consecutive cohort of 145 patients, 65 of whom were treated with NAC. Kaplan-Meier and Cox regression analyses were applied to estimate the impact of RBM3 expression on time to recurrence (TTR), cancer-specific survival (CSS), and overall survival (OS) in strata according to NAC treatment. The effect of siRNA-mediated silencing of RBM3 on chemosensitivity was examined in RT4 and T24 human bladder carcinoma cells in vitro. Cellular functions of RBM3 were assessed using RNA-sequencing and gene ontology analysis, followed by investigation of cell cycle distribution using flow cytometry. RESULTS: RBM3 protein expression was significantly higher in TURB compared to cystectomy specimens but showed consistency between primary tumours and lymph node metastases. Patients with high-tumour specific RBM3 expression treated with NAC had a significantly reduced risk of recurrence and a prolonged CSS and OS compared to NAC-untreated patients. In high-grade T24 carcinoma cells, which expressed higher RBM3 mRNA levels compared to RT4 cells, RBM3 silencing conferred a decreased sensitivity to cisplatin and gemcitabine. Transcriptomic analysis revealed potential involvement of RBM3 in facilitating cell cycle progression, in particular G1/S-phase transition, and initiation of DNA replication. Furthermore, siRBM3-transfected T24 cells displayed an accumulation of cells residing in the G1-phase as well as altered levels of recognised regulators of G1-phase progression, including Cyclin D1/CDK4 and CDK2. CONCLUSIONS: The presented data highlight the potential value of RBM3 as a predictive biomarker of chemotherapy response in MIBC, which could, if prospectively validated, improve treatment stratification of patients with this aggressive disease.