Mohs Micrographic Surgery in the Surgical Treatment Paradigm of Melanoma In Situ and Invasive Melanoma: A Clinical Review of Treatment Efficacy and Ongoing Controversies.

Mohs Micrographic Surgery (MMS) for treatment of melanoma offers several advantages over wide local excision (WLE), including complete histologic margin evaluation, same-day resection and closure, and sparing of healthy tissue in critical anatomic sites. Recently, a large volume of clinical data demonstrating efficacy in MMS treatment of melanoma was published, leading to emerging patient safety considerations of incurred treatment costs, risk of tumor upstaging, and failure of care coordination for sentinel lymph node biopsy (SLNB). MMS offers a safe, effective, and value-based treatment for both melanoma in situ (MIS) and invasive melanoma (IM), particularly with immunohistochemistry use on frozen sections. Compared to WLE, MMS treatment demonstrates similar or improved outcomes for local tumor recurrence, melanoma-specific survival, and overall survival at long-term follow-up. Tumor upstaging risk is low, and if present, alteration to clinical management is minimal. Discussion of SLNB for eligible head and neck IM cases should be done prior to MMS. Though challenging, successful multidisciplinary coordination of SLNB with MMS has been demonstrated. Herein, we provide a detailed clinical review of evidence for MMS treatment of cutaneous melanoma and offer recommendations to address current controversies surrounding the evolving paradigm of surgical management for both MIS and IM.

Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: A simulated, retrospective study.

Background: Artificial intelligence (AI) enabled tools have been proposed as 1 solution to improve health care delivery. However, research on downstream effects of AI integration into the clinical workflow is lacking. Objective: We aim to analyze how integration of an automated basal cell carcinoma detection and tumor mapping algorithm in a Mohs micrographic surgery unit impacts the work efficiency of clinical and laboratory staff. Methods: Slide, staff, and histotechnician waiting times were analyzed over a 20-day period in a Mohs micrographic surgery unit. A simulated AI workflow was created and the time differences between the real and simulated workflows were compared. Results: Simulated nonautonomous algorithm integration led to savings of 35.6% of slide waiting time, 18.4% of staff waiting time, and 18.6% of histotechnician waiting time per day. Algorithm integration on days with increased reconstruction complexity resulted in the greatest time savings. Limitations: One Mohs micrographic surgery unit was analyzed and simulated AI integration was performed retrospectively. Conclusions: AI integration results in reduced staff waiting times, enabling increased productivity and a streamlined clinical workflow. Schedules containing surgical cases with either increased repair complexity or numerous tumor removal stages stand to benefit most. However, significant logistical challenges must be addressed before broad adoption into clinical practice is realistic.

Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways, and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Challenges to current methods include limited focus on dermal elastosis variations and reliance on self-reported measures, which can introduce subjectivity and inconsistency. Spatial transcriptomics offers an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene in photoaging and preventing cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and interpatient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal cell and squamous cell keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Current challenges, including limited focus on dermal elastosis variations and reliance on self-reported measures, can introduce subjectivity and inconsistency. Spatial transcriptomics offer an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene on photoaging and prevent cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and inter-patient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal and squamous keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: a retrospective assessment.

Importance: Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumor removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. Objective: To develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. Design: A retrospective cohort study was conducted using frozen cSCC section slides and adjacent tissues. Setting: This study was conducted in a tertiary care academic center. Participants: Patients undergoing Mohs micrographic surgery for cSCC between January and March 2020. Exposures: Frozen section slides were scanned and annotated, delineating benign tissue structures, inflammation, and tumor to develop an AI algorithm for real-time margin analysis. Patients were stratified by tumor differentiation status. Epithelial tissues including epidermis and hair follicles were annotated for moderate-well to well differentiated cSCC tumors. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC at 50-micron resolution. Main Outcomes and Measures: The performance of the AI algorithm in identifying cSCC at 50-micron resolution was reported using the area under the receiver operating characteristic curve. Accuracy was also reported by tumor differentiation status and by delineation of cSCC from epidermis. Model performance using histomorphological features alone was compared to architectural features (i.e., tissue context) for well-differentiated tumors. Results: The AI algorithm demonstrated proof of concept for identifying cSCC with high accuracy. Accuracy differed by differentiation status, driven by challenges in separating cSCC from epidermis using histomorphological features alone for well-differentiated tumors. Consideration of broader tissue context through architectural features improved the ability to delineate tumor from epidermis. Conclusions and Relevance: Incorporating AI into the surgical workflow may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumors/neoplasms. Further algorithmic improvement is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumors, and to map tumors to their original anatomical position/orientation. Future studies should assess the efficiency improvements and cost benefits and address other confounding pathologies such as inflammation and nuclei. Funding sources: JL is supported by NIH grants R24GM141194, P20GM104416 and P20GM130454. Support for this work was also provided by the Prouty Dartmouth Cancer Center development funds. Key Points: Question: How can the efficiency and accuracy of real-time intraoperative margin analysis for the removal of cutaneous squamous cell carcinoma (cSCC) be improved, and how can tumor differentiation be incorporated into this approach?Findings: A proof-of-concept deep learning algorithm was trained, validated, and tested on frozen section whole slide images (WSI) for a retrospective cohort of cSCC cases, demonstrating high accuracy in identifying cSCC and related pathologies. Histomorphology alone was found to be insufficient to delineate tumor from epidermis in histologic identification of well-differentiated cSCC. Incorporation of surrounding tissue architecture and shape improved the ability to delineate tumor from normal tissue.Meaning: Integrating artificial intelligence into surgical procedures has the potential to enhance the thoroughness and efficiency of intraoperative margin analysis for cSCC removal. However, accurately accounting for the epidermal tissue based on the tumor's differentiation status requires specialized algorithms that consider the surrounding tissue context. To meaningfully integrate AI algorithms into clinical practice, further algorithmic refinement is needed, as well as the mapping of tumors to their original surgical site, and evaluation of the cost and efficacy of these approaches to address existing bottlenecks.

HDAC1/2 Control Proliferation and Survival in Adult Epidermis and Pre‒Basal Cell Carcinoma through p16 and p53.

HDAC inhibitors show therapeutic promise for skin malignancies; however, the roles of specific HDACs in adult epidermal homeostasis and in disease are poorly understood. We find that homozygous epidermal codeletion of Hdac1 and Hdac2 in adult mouse epidermis causes reduced basal cell proliferation, apoptosis, inappropriate differentiation, and eventual loss of Hdac1/2-null keratinocytes. Hdac1/2-deficient epidermis displays elevated acetylated p53 and increased expression of the senescence gene p16. Loss of p53 partially restores basal proliferation, whereas p16 deletion promotes long-term survival of Hdac1/2-null keratinocytes. In activated GLI2-driven pre-basal cell carcinoma, Hdac1/2 deletion dramatically reduces proliferation and increases apoptosis, and knockout of either p53 or p16 partially rescues both proliferation and basal cell viability. Topical application of the HDAC inhibitor romidepsin to the normal epidermis or to GLI2ΔN-driven lesions produces similar defects to those caused by genetic Hdac1/2 deletion, and these are partially rescued by loss of p16. These data reveal essential roles for HDAC1/2 in maintaining proliferation and survival of adult epidermal and basal cell carcinoma progenitors and suggest that the efficacy of therapeutic HDAC1/2 inhibition will depend in part on the mutational status of p53 and p16.

Quantitative Analysis of Frozen Section Histology in Mohs Micrographic Surgery.

BACKGROUND: Mohs micrographic surgery (MMS) relies on complete frozen tissue sections for the unique combination of high cure rates and tissue preservation. OBJECTIVE: To quantify and characterize the tissue sectioning process in MMS. MATERIALS AND METHODS: Two hundred eighty-four tissue blocks were analyzed. The histotechnician measured the depth cut into the tissue block for every section placed on the slide. The surgeon identified complete sections. RESULTS: First-stage complete sections were achieved at 285 µm. The ear and hair-bearing cheek required increased depth of sectioning compared with other sites. Small pieces of tissue achieved complete sections at a decreased depth compared with medium or large pieces of tissue. The methodology used in this study was able to identify statistically significant differences between histotechnicians. CONCLUSION: More than 2,000 measurements on 284 tissue blocks provided quantitative data of the tissue sectioning process in MMS, confirming that MMS allows evaluation of both peripheral and deep margins within hundreds of microns of the margin. Results from this study indicate a methodology that is easily implemented providing interpretable data that can be used to assess and improve tissue sectioning ensuring MMS remains the gold standard for removal of challenging cutaneous tumors.

Effect of Quadrivalent Human Papillomavirus Vaccination on Oral Squamous Cell Papillomas.

IMPORTANCE: Cutaneous verruca vulgaris lesions (warts) and oral squamous cell papillomas are common lesions caused by human papillomavirus (HPV). Multiple reports have described cases of wart resolution following quadrivalent HPV vaccination. We report the case of a patient with chronic oral papillomas with resolution after quadrivalent HPV vaccination and perform a review of the literature. OBSERVATIONS: An immunocompetent man in his 60s presented with chronic verrucous papules on the lips, tongue, and buccal mucosa refractory to multiple excisions. Biopsy showed squamous cell papilloma, and DNA sequencing revealed HPV-32. He received the quadrivalent HPV vaccine resulting in clearance of all lesions after 3 months. We found 8 reported cases of disseminated, recurrent warts with resolution after quadrivalent HPV vaccination. Improvement was seen within 4 weeks of vaccination, and resolution after 3 to 8 months. CONCLUSIONS AND RELEVANCE: We report the case of recurrent oral papillomas caused by HPV-32 with complete resolution after quadrivalent HPV vaccination and reviewed reports of resolution of recalcitrant and disseminated warts after vaccination. Production of cross-protective immunoglobulins and cytotoxic T cells is a possible mechanism. There remains a critical need for randomized clinical trials to assess efficacy of quadrivalent HPV vaccination for treatment of oral squamous papillomas and cutaneous verruca vulgaris.

Interconversion between intestinal stem cell populations in distinct niches.

Intestinal epithelial stem cell identity and location have been the subject of substantial research. Cells in the +4 niche are slow-cycling and label-retaining, whereas a different stem cell niche located at the crypt base is occupied by crypt base columnar (CBC) cells. CBCs are distinct from +4 cells, and the relationship between them is unknown, though both give rise to all intestinal epithelial lineages. We demonstrate that Hopx, an atypical homeobox protein, is a specific marker of +4 cells. Hopx-expressing cells give rise to CBCs and all mature intestinal epithelial lineages. Conversely, CBCs can give rise to +4 Hopx-positive cells. These findings demonstrate a bidirectional lineage relationship between active and quiescent stem cells in their niches.

Hdac1 and Hdac2 act redundantly to control p63 and p53 functions in epidermal progenitor cells.

Epidermal and hair follicle development from surface ectodermal progenitor cells requires coordinated changes in gene expression. Histone deacetylases alter gene expression programs through modification of chromatin and transcription factors. We find that deletion of ectodermal Hdac1 and Hdac2 results in dramatic failure of hair follicle specification and epidermal proliferation and stratification, phenocopying loss of the key ectodermal transcription factor p63. Although expression of p63 and its positively regulated basal cell targets is maintained in Hdac1/2-deficient ectoderm, targets of p63-mediated repression, including p21, 14-3-3σ, and p16/INK4a, are ectopically expressed, and HDACs bind and are active at their promoter regions in normal undifferentiated keratinocytes. Mutant embryos display increased levels of acetylated p53, which opposes p63 functions, and p53 is required for HDAC inhibitor-mediated p21 expression in keratinocytes. Our data identify critical requirements for HDAC1/2 in epidermal development and indicate that HDAC1/2 directly mediate repressive functions of p63 and suppress p53 activity.