SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Defining D-irAEs: consensus-based disease definitions for the diagnosis of dermatologic adverse events from immune checkpoint inhibitor therapy.

With an increasing number of patients eligible for immune checkpoint inhibitors, the incidence of immune-related adverse events (irAEs) is on the rise. Dermatologic immune-related adverse events (D-irAEs) are the most common and earliest to manifest, often with important downstream consequences for the patient. Current guidelines lack clarity in terms of diagnostic criteria for D-irAEs. The goal of this project is to better define D-irAE for the purposes of identification, diagnosis, and future study of this important group of diseases.The objectives of this project were to develop consensus guidance for an approach to D-irAEs including disease definitions and severity grading. Knowing that consensus among oncologists, dermatologists, and irAE subspecialists would be critical for usability, we formed a Dermatologic irAE Disease Definition Panel. The panel was composed of 34 experts, including oncologists, dermatologists, a rheumatologist, and an allergist/immunologist from 22 institutions across the USA and internationally. A modified Delphi consensus process was used, with two rounds of anonymous ratings by panelists and two virtual meetings to discuss areas of controversy. Panelists rated content for usability, appropriateness, and accuracy on 9-point scales in electronic surveys and provided free text comments. A working group aggregated survey responses and incorporated them into revised definitions. Consensus was based on numeric ratings using the RAND/UCLA Appropriateness Method with prespecified definitions.Following revisions based on panelist feedback, all items received consensus in the second round of ratings. Consensus definitions were achieved for 10 core D-irAE diagnoses: ICI-vitiligo, ICI-lichen planus, ICI-psoriasis, ICI-exanthem, ICI-bullous pemphigoid, ICI-Grover's, ICI-eczematous, ICI-eruptive atypical squamous proliferation, ICI-pruritus without rash, and ICI-erosive mucocutaneous. A standard evaluation for D-irAE was also found to reach consensus, with disease-specific exceptions detailed when necessary. Each disorder's description includes further details on disease subtypes, symptoms, supportive exam findings, and three levels of diagnostic certainty (definite, probable, and possible).These consensus-driven disease definitions standardize D-irAE classification in a useable framework for multiple disciplines and will be the foundation for future work. Given consensus on their accuracy and usability from a representative panel group, we anticipate that they can be used broadly across clinical and research settings.

Adjuvant Immunotherapy in Stage II Melanoma-Further Risk Stratification is Needed.

This Viewpoint reviews the evidence for immune checkpoint inhibitor use in the adjuvant setting, discusses the individual and societal risks, benefits, and costs associated with immune checkpoint inhibitors, and highlights the need for more targeted patient selection approaches.

Cancer Type and Histology Influence Cutaneous Immunotherapy Toxicities: A Multi-Institutional Cohort Study.

BACKGROUND: Cutaneous immune-related adverse events (cirAEs) are the most common toxicities to occur in the setting of immune checkpoint inhibitor (ICI) therapy. Identifying patients who are at increased risk of developing cirAEs may improve quality of life and outcomes. OBJECTIVE: To investigate the influence of cancer type and histology on the development of cirAEs in the setting of ICI therapy and survival outcomes. METHODS: This retrospective cohort study included patients between December 1, 2011, and October 30, 2020. They received ICIs from 2011-2020 with follow-up of outcomes through October 2021. We identified 3,668 ICI recipients who were seen at Mass General Brigham and Dana-Farber. Of these, 669 developed cirAEs. Records that were incomplete or categories of insufficient sample size were excluded from the study cohort. Multivariate Cox proportional hazards models were utilized to investigate the impact of cancer organ system and histology on cirAE development, after adjusting for demographics, Charlson Comorbidity Index, ICI type, cancer stage at ICI initiation, and year of ICI initiation. Time-varying Cox proportional hazards modeling was utilized to examine the impact of cirAE development on mortality. RESULTS: Compared to other non-epithelial cancers (neuroendocrine, leukemia, lymphoma, myeloma, sarcoma, and central nervous system malignancies), cutaneous squamous cell carcinoma (cSCC) (HR = 3.57, p < 0.001), melanoma (HR = 2.09, p < 0.001), head and neck adenocarcinoma (HR = 2.13, p = 0.009), genitourinary transitional cell carcinoma (HR = 2.15, p < 0.001), and genitourinary adenocarcinoma (HR = 1.53, p = 0.037) were at significantly higher risk of cirAEs in multivariate analyses. The increased risk of cirAEs translated into an adjusted survival benefit for melanoma (HR = 0.37, p < 0.001) and cSCC (HR = 0.51, p = 0.011). CONCLUSIONS: The highest rate of cirAEs and subsequent survival benefits were observed in cutaneous malignancies treated with ICIs. This study improves our understanding of patients who are at highest risk of developing cirAEs and would, therefore, benefit from appropriate counseling and closer monitoring by their oncologists and dermatologists throughout their ICI therapy. Limitations include its retrospective nature and cohort from one geography.

Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma.

BACKGROUND: The recent expansion of immunotherapy for stage IIB/IIC melanoma highlights a growing clinical need to identify patients at high risk of metastatic recurrence and, therefore, most likely to benefit from this therapeutic modality. OBJECTIVE: To develop time-to-event risk prediction models for melanoma metastatic recurrence. METHODS: Patients diagnosed with stage I/II primary cutaneous melanoma between 2000 and 2020 at Mass General Brigham and Dana-Farber Cancer Institute were included. Melanoma recurrence date and type were determined by chart review. Thirty clinicopathologic factors were extracted from electronic health records. Three types of time-to-event machine-learning models were evaluated internally and externally in the distant versus locoregional/nonrecurrence prediction. RESULTS: This study included 954 melanomas (155 distant, 163 locoregional, and 636 1:2 matched nonrecurrences). Distant recurrences were associated with worse survival compared to locoregional/nonrecurrences (HR: 6.21, P < .001) and to locoregional recurrences only (HR: 5.79, P < .001). The Gradient Boosting Survival model achieved the best performance (concordance index: 0.816; time-dependent AUC: 0.842; Brier score: 0.103) in the external validation. LIMITATIONS: Retrospective nature and cohort from one geography. CONCLUSIONS: These results suggest that time-to-event machine-learning models can reliably predict the metastatic recurrence from localized melanoma and help identify high-risk patients who are most likely to benefit from immunotherapy.

Seasonal Patterns in Tetracycline-Associated Hyperpigmentation Among Patients With Acne Vulgaris.

BACKGROUND: Oral tetracyclines (TCNs) are commonly prescribed for acne, but they have been shown to increase the risk of hyperpigmentation, particularly in the setting of sun exposure. OBJECTIVE: We evaluated seasonal trends in TCN-associated hyperpigmentation incidence in addition to Google search trends for hyperpigmentation-related terms. METHODS: We performed a retrospective review of acne patients seen at Massachusetts General Brigham and Women’s Hospital between 1992 and 2022. We calculated the incidence of new hyperpigmentation diagnoses for each drug cohort. We also analyzed search volume of hyperpigmentation-related terms extracted from Google Trends. RESULTS: Seasonal differences in new hyperpigmentation diagnoses were identified among acne patients prescribed doxycycline (P=0.016), with peak incidence in April. In the control group of patients who had never received a TCN, diagnoses peaked in May. There were no significant seasonal differences among patients prescribed minocycline (P=0.885). There was greater search volume for hyperpigmentation-related terms in spring and summer compared to fall and winter (P<0.001). Limitations of this study include its retrospective nature and reliance on prescription and diagnosis coding data. CONCLUSIONS: Our findings support the seasonal periodicity of acne-related hyperpigmentation, underscoring the importance of photoprotection counseling for patients with acne. Additionally, doxycycline may be associated with an earlier onset of hyperpigmentation, suggesting a potential benefit of considering minocycline or other alternatives to doxycycline. J Drugs Dermatol. 2023;22(11):e9-e11    doi:10.36849/JDD.7409e.

SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data.

Background: Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results: This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions: SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.

Use of 3-Dimensional Stereophotogrammetry to Detect Disease Progression in Craniofacial Morphea.

Importance: Objectively determining disease progression in craniofacial morphea (CM) is challenging, as clinical findings of disease activity are often lacking. Objective: To evaluate the utility of 3-dimensional (3D) stereophotogrammetry in detecting disease progression in CM over time. Design, Setting, and Participants: This prospective cohort study included 27 pediatric and adult patients with CM from 2 hospitals in Boston (Boston Children's Hospital and Brigham & Women's Hospital) consecutively enrolled from April 1, 2019, to March 1, 2023. Review of 3D stereophotogrammetry images and data analysis occurred from March 1 to April 1, 2023. Main Outcomes and Measures: Clinical and 3D stereophotogrammetry assessments were performed at 2- to 12-month intervals, depending on the clinical context. The 3D stereophotogrammetry images were then qualitatively rated as demonstrating no progression or definitive progression by an expert (board-certified plastic craniofacial surgeon) and nonexpert (board-certified dermatologist) in 3D stereophotogrammetry. In addition, κ coefficients were calculated for interrater reliability. Results: Of 27 patients with CM (19 female; median age, 14 [range, 5-40] years) and 3D stereophotogrammetry images obtained from a minimum of 2 time points (median, 4 [range, 2-10] images) spaced a median of 3 (range, 2-12) months apart, 10 experienced progression of their disease based on clinical assessments performed during the study period. In all cases in which clinical progression was favored, blinded qualitative assessment of 3D stereophotogrammetry images also favored progression with substantial interrater reliability (κ = 0.80 [95% CI, 0.61-0.99]). Furthermore, review of 3D stereophotogrammetry detected occult progression of asymmetry not noted on clinical examination in 3 additional patients. Conclusions and Relevance: In this prospective cohort study, blinded assessment of sequential 3D stereophotogrammetry images in patients with CM not only corroborated clinical assessment of disease progression but also detected occult progression of facial asymmetry not appreciable on clinical examination alone. Therefore, 3D stereophotogrammetry may serve as a useful adjunct to clinical examination of patients with CM over time. Future investigations are warranted to validate 3D stereophotogrammetry as an outcome measure in CM.

Somatic mutations reveal hyperactive Notch signaling and racial disparities in prurigo nodularis.

Prurigo nodularis (PN) is a chronic inflammatory skin disease that disproportionately affects African Americans and is characterized by pruritic skin nodules of unknown etiology. Little is known about genetic alterations in PN pathogenesis, especially relating to somatic events which are often implicated in inflammatory conditions. We thus performed whole-exome sequencing on 54 lesional and nonlesional skin biopsies from 17 PN patients and 10 atopic dermatitis (AD) patients for comparison. Somatic mutational analysis revealed that PN lesional skin harbors pervasive somatic mutations in fibrotic, neurotropic, and cancer-associated genes. Nonsynonymous mutations were most frequent in NOTCH1 and the Notch signaling pathway, a regulator of cellular proliferation and tissue fibrosis, and NOTCH1 mutations were absent in AD. Somatic copy-number analysis, combined with expression data, showed that recurrently deleted and downregulated genes in PN lesional skin are associated with axonal guidance and extension. Follow-up immunofluorescence validation demonstrated increased NOTCH1 expression in PN lesional skin fibroblasts and increased Notch signaling in PN lesional dermis. Finally, multi-center data revealed a significantly increased risk of NOTCH1-associated diseases in PN patients. In characterizing the somatic landscape of PN, we uncover novel insights into its pathophysiology and identify a role for dysregulated Notch signaling in PN.

Pre-Existing Inflammatory Disease Predicts Cutaneous Immunotherapy Toxicity Development: A Multi-Institutional Cohort Study.

Background: Relationships between pre-existing inflammatory diseases (pIDs) and cutaneous immune-related adverse events (cirAEs) have not been well-studied. This study is to investigate associations between pIDs and cirAEs among immune-checkpoint inhibitor (ICI) recipients at the Mass General Brigham healthcare system. Methods: Electronic health records were reviewed to ascertain cirAE status. Patients' pID status was determined using International Classification of Diseases (ICD) codes. Cox proportional hazard, logistic regression, and linear regression models were performed. Results: Among 3607 ICI recipients, 1354 had pIDs, and 672 developed cirAEs. After covariate adjustments, patients with cutaneous pIDs (HR:1.56, p<0.001) or both cutaneous and non-cutaneous pIDs (HR:1.76, p<0.001) had increased cirAE risk in contrast to patients with non-cutaneous pIDs alone (HR:1.01, p=0.9). In adjusted ordinal logistic regression modeling, cutaneous pIDs (OR:1.55, p<0.0001) and the presence of both cutaneous pIDs and non-cutaneous pIDs (OR:1.71, p=0.002) were associated with increased cirAE severity. The time to cirAE onset was different between the cutaneous pID group and the non-cutaneous pID group (Mean: 98 vs. 146 days, p=0.021; Beta: -0.11, p=0.033). Conclusions: ICI recipients with cutaneous pIDs should have increased clinical monitoring due to their increased risk of cirAE development, severity, and earlier onset.

Cutaneous immune-related adverse events from immune checkpoint inhibitor therapy: Moving beyond "maculopapular rash".

Uncoupling toxicity from therapeutic effect lies at the foundation of the current state of the field of cutaneous immune-related adverse events to immune checkpoint inhibitor therapy. This will be achieved through understanding the drivers of toxicity, tumor response, and resistance via large, well-powered population-level studies, institutional cohort data, and cellular-level data. Increasing diagnostic specificity through the application of consensus disease definitions has the power to improve clinical care and each approach to research. Cutaneous immune-related adverse events are associated with increased survival, and their treatment must invoke the maintenance of a delicate balance between immunosuppression, anti-tumor effect of immune checkpoint inhibitor therapy, and quality of life. The multidisciplinary care of cancer patients with adverse events is critical to optimizing clinical and translational research outcomes and, as such, dermatologists are vital to moving the study of cutaneous adverse events forward.

Antimalarials are not Effective as Pre-Exposure Prophylaxis for COVID-19: A Retrospective Matched Control Study.

The early phase of the COVID-19 pandemic prompted a repurposing of antiviral and immunomodulatory drugs as investigational therapeutics, including hydroxychloroquine and chloroquine. While antimalarials have been well-refuted as a treatment for COVID-19, data on these drugs' role in preventing SARS-CoV-2 infection as pre-exposure prophylaxis is more limited. We investigated the efficacy of antimalarial drugs as pre-exposure SARS-CoV-2 prophylaxis in a US tertiary-care center. We identified all adult patients exposed to antimalarials with active prescriptions from July 1, 2019 to February 29, 2020 and exact-matched antimalarial-treated study patients with controls on age, sex, race, and Charleston Comorbidity Index. We used multivariable logistic regression to calculate the odds ratio (OR) of COVID-19 diagnosis by antimalarial exposure, adjusting for demographics, comorbidities, local infection rates, and specific conditions identified in early studies as risk factors for COVID-19. There were 3,074 patients with antimalarial prescriptions and 58,955 matched controls. Hydroxychloroquine represented 98.8% of antimalarial prescriptions. There were 51 (1.7%) infections among antimalarial-exposed and 973 (1.6%) among controls. No protective effect for SARS-CoV-2 infection was demonstrated among antimalarial-exposed patients in the multivariate model (OR=1.06, 95% CI 0.80-1.40, P=0.70). These findings corroborate prior work demonstrating that hydroxychloroquine and related antimalarials do not have a role in protection against SARS-CoV-2.Klebanov N, Pahalyants V, Said JT, et al. Antimalarials are not effective as pre-exposure prophylaxis for COVID-19: a retrospective matched control study. J Drugs Dermatol. 2023;22(8):840-843. doi:10.36849/JDD.6593.