HER2-enriched subtype and novel molecular subgroups drive aromatase inhibitor resistance and an increased risk of relapse in early ER+/HER2+ breast cancer

BACKGROUND: Oestrogen receptor positive/ human epidermal growth factor receptor positive (ER+/HER2+) breast cancers (BCs) are less responsive to endocrine therapy than ER+/HER2- tumours. Mechanisms underpinning the differential behaviour of ER+HER2+ tumours are poorly characterised. Our aim was to identify biomarkers of response to 2 weeks’ presurgical AI treatment in ER+/HER2+ BCs. METHODS: All available ER+/HER2+ BC baseline tumours (n=342) in the POETIC trial were gene expression profiled using BC360™ (NanoString) covering intrinsic subtypes and 46 key biological signatures. Early response to AI was assessed by changes in Ki67 expression and residual Ki67 at 2 weeks (Ki672wk). Time-To-Recurrence (TTR) was estimated using Kaplan-Meier methods and Cox models adjusted for standard clinicopathological variables. New molecular subgroups (MS) were identified using consensus clustering. FINDINGS: HER2-enriched (HER2-E) subtype BCs (44.7% of the total) showed poorer Ki67 response and higher Ki672wk (p<0.0001) than non-HER2-E BCs. High expression of ERBB2 expression, homologous recombination deficiency (HRD) and TP53 mutational score were associated with poor response and immune-related signatures with High Ki672wk. Five new MS that were associated with differential response to AI were identified. HER2-E had significantly poorer TTR compared to Luminal BCs (HR 2.55, 95% CI 1.14–5.69; p=0.0222). The new MS were independent predictors of TTR, adding significant value beyond intrinsic subtypes. INTERPRETATION: Our results show HER2-E as a standardised biomarker associated with poor response to AI and worse outcome in ER+/HER2+. HRD, TP53 mutational score and immune-tumour tolerance are predictive biomarkers for poor response to AI. Lastly, novel MS identify additional non-HER2-E tumours not responding to AI with an increased risk of relapse

LEARN: A multi-centre, cross-sectional evaluation of Urology teaching in UK medical schools

OBJECTIVE: To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. MATERIALS AND METHODS: LEARN was a national multicentre cross-sectional study. Year 2 to Year 5 medical students and FY1 doctors were invited to complete a survey between 3rd October and 20th December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS: 7,063/8,346 (84.6%) responses from all 39 UK medical schools were included; 1,127/7,063 (16.0%) were from Foundation Year (FY) 1 doctors, who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and clinical placements in the later years of medical school. 20.1% of FY1 doctors reported no undergraduate clinical attachment in urology. CONCLUSION: LEARN is the largest ever evaluation of undergraduate urology teaching. In the UK, teaching seemed satisfactory as evaluated by the BAUS undergraduate syllabus. However, many students report having no clinical attachments in Urology and some newly qualified doctors report never having inserted a catheter, which is a GMC mandated requirement. We recommend a greater emphasis on undergraduate clinical exposure to urology and stricter adherence to GMC mandated procedures

Abstract 187: Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures

Abstract PurposeTo use deep learning techniques to aid the automation of prostate cancer grading from histologic samples, thereby improving diagnostic accuracy and treatment selection. IntroductionAmong the most valuable tools in the evaluation of prostate cancer is the Gleason Score (GS) that is assigned by a pathologist to prostatic biopsies. This score represents the aggressiveness of the tumor and ranges from 3 to 5 (least to most aggressive) in a primary and secondary pattern. Here we use deep learning techniques to automate the classification of prostate samples by GS. By automating this process, we are able to reduce the human subjectivity and potential error of Gleason grading and are able to create a pipeline by which we can integrate gene expression to quickly determine cancer progression in individual patients. Methods733 prostate tissue slide images were downloaded along with clinical information on 500 individuals from the PRAD study contained in The Cancer Genome Atlas (TCGA). We used the Xception network architecture within the Keras software package and TensorFlow backend in Python. We used 120 images that were scored by a board-certified pathologist as training images (with minimal data augmentation) to create a refined deep convolutional neural network to automate Gleason grading to identify cancer regions of interest on test training slides. We then used this model to score new unannotated by a different pathologist blindly to gauge accuracy. Additionally, we have looked at gene expression by combining patients within a specific GS, and compared profiles within each group with gene expression software. From this we were able to select a set of unique markers to identify new patients by GS as well as identify the amount of genetic variance within a given dataset. Results We compared the model results of the Gleason grading from 10 images with the score that was reported in the TCGA and found that the system was able to correctly identify cancer regions at 93% and severity at 55%. We were able to identify 6,411 differentially expressed genes (DEG) between normal adjacent tissue and GS6, 186 DEGs between GS6 and GS7, 1,855 DEGs between GS7 and GS8, and 603 DEGs between GS8 and GS9. ConclusionsDeep learning can be applied to determine the histologic severity of tissue images. Given future modifications to improve the accuracy of our current model, we can be hopeful of producing an automated software workflow that will aid in identifying tumor areas, determining their severity, and influencing treatment decisions. Citation Format: Derek Van Booven, Victor Sandoval, Oleksander Kryvenko, Madhumita Parmar, Andres Briseño, Himanshu Arora. Automated deep-learning system for Gleason grading of prostate cancer using digital pathology and genomic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 187

Discordant erectile function assessment between validated questionnaire scores and penile Doppler ultrasound in Peyronie's disease

Peyronie's disease results in curvature of the penis which may cause difficulty with penetrative intercourse. The diagnosis of Peyronie's disease is easily obtained through history and physical examination alone, but the severity of erectile dysfunction relies on patient history and use of validated questionnaires. However, erectile dysfunction questionnaires were not validated in the Peyronie's disease population and may not be a reliable assessment. Penile Doppler ultrasound is a noninvasive tool that assesses vascular function. We hypothesized that penile Doppler ultrasound will be discordant with International Index of Erectile Function (IIEF) results in men with Peyronie's disease and erectile dysfunction. In this cross-sectional study, we reviewed a prospectively collected database of men with Peyronie's disease. In total, 108 men had questionnaire and ultrasound data. Of them, 87 had erectile dysfunction based on IIEF-EF or IIEF-5 (SHIM). However, 48 (55%) of those men had normal vascular parameters. Interestingly, among a subgroup of 33 men with severe erectile dysfunction on IIEF-EF or IIEF-5, 20 (61%) had normal vascular parameters. Our study demonstrates significant discordance between questionnaires and penile Doppler ultrasound. Therefore, ultrasound may be a useful tool in the workup of men with Peyronie's disease and erectile dysfunction

Trends in outpatient versus inpatient urologic surgery at a university academic medical center.

To analyze trends in outpatient and inpatient urologic surgeries at a large university academic medical center and test the hypothesis that the proportion of outpatient surgeries has been increasing as compared to inpatient surgeries in urology. We analyzed a total of 33,054 claims for urologic surgeries at a large university academic medical center from 2010 to 2020, of which 23.2% met inpatient criteria (n = 7695), whereas 76.7% were outpatient (n = 25,359). Although outpatient claims increased yearly by an average of 24%, inpatient claims increased yearly by an average of only 1%. Over the same period, Medicare-specific outpatient claims mirrored these trends, and Medicare-specific inpatient claims decreased. Outcomes of inpatient surgeries are used as a metric for quality by the Centers for Medicare and Medicaid Services (CMS) as well as US News and World Report (USNWR) rankings. However, with increasing numbers of minimally invasive operations, a large proportion of urologic surgeries are performed on an outpatient basis. As this trend continues, it will be important for organizations like CMS and USNWR to incorporate methods of measuring quality that better reflect outpatient surgical outcomes for the urologic subspecialty. Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.</CopyrightInformation

Leptin Secreted in the Testicular Microenvironment Promotes the Endogenous Function of Leydig Cells Through Hedgehog Signaling Pathway

Objective: Testosterone deficiency (TD) is a common health concern, affecting around 1 in 5 men globally. However, the factors responsible for TD remain largely unknown. Leydig cells produce testosterone in the testes under the pulsatile control of luteinizing hormone (LH) from the pituitary gland. Leydig stem cells (LSC) have the potential to differentiate into adult Leydig cells, which can increase testosterone levels; however, the factors promoting differentiation are unknown. In the present study we evaluated the paracrine factors released from the testicular microenvironment (TME) (comprised of Sertoli and peritubular myoid cells) that modulate the differentiation of Leydig stem cells to adult Leydig cells. Additionally, we explored the underlying mechanism of action of these paracrine factors. Methods: Tissue samples were obtained from a total of 13 men with testicular failure, who underwent testis biopsies for sperm retrieval. Using an IRB approved protocol, about 10mg of testicular tissue from each sample were processed for LSC isolation, culturing, and characterization. Cytokine antibody array was performed to identify the paracrine factors released by Sertoli and peritubular myoid cells using unsorted and CD146 +ve sorted cells. The cells were treated with hedgehog signaling agonist and antagonist to validate the specificity of paracrine factors identified. Immunostaining was performed to evaluate changes at the protein level. Flow cytometry was performed to study the shift in the population of cells post leptin treatment. GraphPad Prism (GraphPad Software) was used for statistical analysis. Results: This study revealed that the TME plays an instrumental role in Leydig stem cell differentiation and testosterone production through regulation of the desert hedgehog (DHH) signaling pathway. TME-secreted leptin induces LSC differentiation and increases testosterone production. However, these effects are inversely concentration-dependent: positive at low leptin doses and negative at higher leptin doses. Mechanistically, leptin acts on LSCs upstream of DHH in a unidirectional fashion, as DHH gain or loss of function was shown to have no effects on Leptin levels. Conclusions: These findings identify leptin as a key paracrine factor released by cells within the TME that modulate LSC differentiation and testosterone production from adult Leydig cells, a finding that is key to developing new niche therapies for TD