Faculty Bibliography

PURPOSE/UNASSIGNED:Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology. MATERIALS AND METHODS/UNASSIGNED:Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set. RESULTS/UNASSIGNED:Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%. CONCLUSIONS/UNASSIGNED:Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

INTRODUCTION/BACKGROUND:Delay between targeted prostate biopsy (PB) and pathologic diagnosis can lead to a concern of inadequate sampling and repeated biopsy. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real-time, label-free, high-resolution microscopic images of unprocessed, unsectioned tissue. This technology holds potential to decrease the time for PB diagnosis from days to minutes. We evaluated the concordance of pathologist interpretation of PB SRH as compared with traditional hematoxylin and eosin (H&E) stained slides. METHODS:, to create SRH images. The cores were then processed as per normal pathologic protocols. Sixteen PB containing a mix of benign and malignant histology were used as an SRH training cohort for four genitourinary pathologists, who were then tested on a set of 32 PBs imaged by SRH and processed by traditional H&E. Sensitivity, specificity, accuracy, and concordance for prostate cancer (PCa) detection on SRH relative to H&E were assessed. RESULTS:The mean pathologist accuracy for the identification of any PCa on PB SRH was 95.7%. In identifying any PCa or ISUP grade group 2-5 PCa, a pathologist was independently able to achieve good and very good concordance (κ: 0.769 and 0.845, respectively; p < 0.001). After individual assessment was completed a pathology consensus conference was held for the interpretation of the PB SRH; after the consensus conference the pathologists' concordance in identifying any PCa was also very good (κ: 0.925, p < 0.001; sensitivity 95.6%; specificity 100%). CONCLUSION/CONCLUSIONS:SRH produces high-quality microscopic images that allow for accurate identification of PCa in real-time without need for sectioning or tissue processing. The pathologist performance improved through progressive training, showing that ultimately high accuracy can be obtained. Ongoing SRH evaluation in the diagnostic and treatment setting hold promise to reduce time to tissue diagnosis, while interpretation by convolutional neural network may further improve diagnostic characteristics and broaden use.

Neoadjuvant therapy (NAT) is increasingly used for pancreatic ductal adenocarcinoma (PDAC); yet most patients only achieve partial response. Pathological treatment response grading focuses on assessing residual tumor burden, often overlooking changes in tumor microenvironment (TME). To address this gap, we compared tumor cells and TME of 13 NAT-naïve and 23 post-NAT PDACs using integrated spatial pathomics and transcriptomics, with validation in an independent single-cell spatial dataset. NAT significantly reduced tumor burden (14.7%-6.2%, p = 0.004), but systemic comparison of 13 cytomorphometric features of tumor cells alone did not reliably distinguish between naïve and NAT cases. In contrast, NAT profoundly remodeled TME by increasing cancer-associated fibroblast (CAF) and CD8⁺ T cell densities, promoting CD8⁺ T cell-tumor cell proximity and fibrosis, reducing tumor-associated neutrophils, and redistributing tertiary lymphoid structures (TLSs). Spatial transcriptomics shows NAT induced apoptosis, DNA-damage response, and AGC-kinase (S_TK_X) signaling in tumor cells, and upregulated complement pathway, p53 signaling, and cellular senescence program in TME. Cross-platform single-cell spatial analysis revealed decreased regulatory T cells (Treg) and a shift from myofibroblastic (mCAF) to inflammatory CAF (iCAF). Importantly, post-NAT patients with more fibrosis had longer overall survival (p = 0.02), and higher B-cell density showed a favorable trend (p = 0.06). Together, these results suggest that beyond tumor debulking, NAT induces a coordinated TME remodeling characterized by fibroblast reprogramming, matrix fibrosis, and immune spatial reorganization. Incorporating assessment of NAT-induced stromal and immune changes into TRG may improve prognostication and guide more precise therapy in post-NAT PDAC.

HOXB13 is a prostate-specific transcription factor best known for its role as an androgen receptor (AR) cofactor. Recent evidence suggests that HOXB13 plays critical AR-independent functions in repressing lipogenic programs and promoting prostate cancer (PCa) metastasis. However, the mechanisms linking HOXB13 loss to tumor metastasis remain unclear. Here, we show that p300 and CBP co-occupy lipogenic enhancers suppressed by HOXB13 and HDAC3 and are essential for enhancer activation and target gene expression following HOXB13 depletion. Loss of HOXB13 induces lipid-sensitive matrix metalloproteinases (MMPs), promoting increased cell motility. Importantly, pharmacological inhibition of p300 and CBP blocks HOXB13-loss-driven lipogenesis, reduces MMP expression, and decreases cell migration in vitro and tumor metastasis in vivo. Analysis of clinical samples revealed that HOXB13 expression is reduced in metastatic hormone-sensitive PCa compared with matched primary tumors, further supporting its role in tumor metastasis. These findings demonstrate that HOXB13 downregulation promotes PCa metastasis through p300- and CBP-dependent lipogenic and motility pathways, which may be targeted by p300 inhibition.

BACKGROUND:Dickkopf-related protein 1 (DKK1) is a Wingless-related integrate site (Wnt) signaling modulator that is upregulated in prostate cancers (PCa) with low androgen receptor expression. DKN-01, an IgG4 that neutralizes DKK1, delays PCa growth in pre-clinical DKK1-expressing models. These data provided the rationale for a clinical trial testing DKN-01 in patients with metastatic castration-resistant PCa (mCRPC). METHODS:(combination) for men with mCRPC who progressed on ≥1 AR signaling inhibitors. DKK1 status was determined by RNA in-situ expression. The primary endpoint of the phase 1 dose escalation cohorts was the determination of the recommended phase 2 dose (RP2D). The primary endpoint of the phase 2 expansion cohorts was objective response rate by iRECIST criteria in patients treated with the combination. RESULTS:18 pts were enrolled into the study-10 patients in the monotherapy cohorts and 8 patients in the combination cohorts. No DLTs were observed and DKN-01 600 mg was determined as the RP2D. A best overall response of stable disease occurred in two out of seven (29%) evaluable patients in the monotherapy cohort. In the combination cohort, five out of seven (71%) evaluable patients had a partial response (PR). A median rPFS of 5.7 months was observed in the combination cohort. In the combination cohort, the median tumoral DKK1 expression H-score was 0.75 and the rPFS observed was similar between patients with DKK1 H-score ≥1 versus H-score = 0. CONCLUSION/CONCLUSIONS:DKN-01 600 mg was well tolerated. DKK1 blockade has modest anti-tumor activity as a monotherapy for mCRPC. Anti-tumor activity was observed in the combination cohorts, but the response duration was limited. DKK1 expression in the majority of mCRPC is low and did not clearly correlate with anti-tumor activity of DKN-01 plus docetaxel.

Neoadjuvant therapy (NAT) is increasingly being used for pancreatic ductal adenocarcinoma (PDAC). This study investigates how NAT differentially impacts PDAC's carcinoma cells and the tumor microenvironment (TME). Spatial transcriptomics was used to compare gene expression profiles in carcinoma cells and the TME of 23 NAT-treated versus 13 NAT-naïve PDACs. Findings were validated by single-nucleus RNA sequencing (snRNA-seq) analysis. NAT induces apoptosis and inhibits proliferation of carcinoma cells and coordinately upregulates multiple complement genes (C1R, C1S, C3, C4B and C7) within the TME. Higher TME complement expression following NAT is associated with increased immunomodulatory and neurotrophic cancer-associated fibroblasts (CAFs); more CD4⁺ T cells; reduced immune exhaustion gene expression, and improved overall survival. snRNA-seq analysis demonstrates C3 complement is mainly upregulated in CAFs. These findings suggest that local complement dynamics could serve as a novel biomarker for prognosis, evaluating treatment response, and guiding therapeutic strategies in NAT-treated PDAC patients.

BACKGROUND:In-field or in-margin recurrence after partial gland cryosurgical ablation (PGCA) of prostate cancer (PCa) remains a limitation of the paradigm. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real time, label-free, high-resolution microscopic images of unprocessed, un-sectioned tissue which can be interpreted by humans or artificial intelligence (AI). We evaluated surgical team and AI interpretation of SRH for real-time pathologic feedback in the planning and treatment of PCa with PGCA. METHODS:About 12 participants underwent prostate mapping biopsies during PGCA of their PCa between January and June 2022. Prostate biopsies were immediately scanned in a SRH microscope at 20 microns depth using 2 Raman shifts to create SRH images which were interpreted by the surgical team intraoperatively to guide PGCA, and retrospectively assessed by AI. The cores were then processed, hematoxylin and eosin stained as per normal pathologic protocols and used for ground truth pathologic assessment. RESULTS:Surgical team interpretation of SRH intraoperatively revealed 98.1% accuracy, 100% sensitivity, 97.3% specificity for identification of PCa, while AI showed a 97.9% accuracy, 100% sensitivity and 97.5% specificity for identification of clinically significant PCa. 3 participants' PGCA treatments were modified after SRH visualized PCa adjacent to an expected MRI predicted tumor margin or at an untreated cryosurgical margin. CONCLUSION/CONCLUSIONS:SRH allows for accurate rapid identification of PCa in PB by a surgical team interpretation or AI. PCa tumor mapping and margin assessment during PGCA appears to be feasible and accurate. Further studies evaluating impact on clinical outcomes are warranted.

INTRODUCTION/UNASSIGNED:Balancing surgical margins and functional outcomes is crucial during radical prostatectomy for prostate cancer. Stimulated Raman Histology (SRH) is a novel, real-time imaging technique that provides histologic images of fresh, unprocessed, and unstained tissue within minutes, which can be interpreted by either humans or artificial intelligence. METHODS/UNASSIGNED:Twenty-two participants underwent robotic-assisted laparoscopic radical prostatectomy (RALP) with intraoperative SRH surgical bed assessment. Surgeons resected and imaged surgical bed tissue using SRH and adjusted treatment accordingly. An SRH convolutional neural network (CNN) was developed and tested on 10 consecutive participants. The accuracy, sensitivity, and specificity of the surgical team's interpretation were compared to final histopathological assessment. RESULTS/UNASSIGNED:A total of 121 SRH periprostatic surgical bed tissue (PSBT) assessments were conducted, an average of 5.5 per participant. The accuracy of the surgical team's SRH interpretation of resected PSBT samples was 98%, with 83% sensitivity, and 99% specificity. Intraoperative SRH assessment identified 43% of participants with a pathologic positive surgical margin intraoperatively. PSBT assessment using the CNN demonstrated no overlap in tumor probability prediction between benign and tumor infiltrated samples, mean 0.30% (IQR 0.10-0.43%) and 26% (IQR 18-34%, p<0.005), respectively. CONCLUSION/UNASSIGNED:SRH demonstrates potential as a valuable tool for real-time intraoperative assessment of surgical margins during RALP. This technique may improve nerve-sparing surgery and facilitate decision-making for further resection, reducing the risk of positive surgical margins and minimizing the risk of recurrence. Further studies with larger cohorts and longer follow-up periods are warranted to confirm the benefits of SRH in RALP.

BACKGROUND/UNASSIGNED:Neoadjuvant therapy (NAT) is increasingly being used for pancreatic ductal adenocarcinoma (PDAC) treatment. However, its specific effects on carcinoma cells and the tumor microenvironment (TME) are not fully understood. This study aims to investigate how NAT differentially impacts PDAC's carcinoma cells and TME. METHODS/UNASSIGNED:Spatial transcriptomics was used to compare gene expression profiles in carcinoma cells and the TME between 23 NAT-treated and 13 NAT-naïve PDAC patients, correlating with their clinicopathologic features. Analysis of an online single-nucleus RNA sequencing (snRNA-seq) dataset was performed for validation of the specific cell types responsible for NAT-induced gene expression alterations. RESULTS/UNASSIGNED:T cells, monocytes, and mast cells; and reduced immune exhaustion gene expression. snRNA-seq analysis demonstrates C3 complement was specifically upregulated in CAFs but not in other stroma cell types. CONCLUSIONS/UNASSIGNED:NAT can enhance complement production and signaling within the TME, which is associated with reduced immunosuppression in PDAC. These findings suggest that local complement dynamics could serve as a novel biomarker for prognosis, evaluating treatment response and resistance, and guiding therapeutic strategies in NAT-treated PDAC patients.