Cumulative incidence and risk factors for cutaneous squamous cell carcinoma metastases in organ transplant recipients: The Skin Care in Organ Transplant Patients in Europe-International Transplant Skin Cancer Collaborative metastases study, a prospective multicenter study.

INTRODUCTION: Solid organ transplant recipients (SOTRs) are believed to have an increased risk of metastatic cutaneous squamous cell carcinoma (cSCC), but reliable data are lacking regarding the precise incidence and associated risk factors. METHODS: In a prospective cohort study, including 19 specialist dermatology outpatient clinics in 15 countries, patient and tumor characteristics were collected using standardized questionnaires when SOTRs presented with a new cSCC. After a minimum of 2 years of follow-up, relevant data for all SOTRs were collected. Cumulative incidence of metastases was calculated by the Aalen-Johansen estimator. Fine and Gray models were used to assess multiple risk factors for metastases. RESULTS: Of 514 SOTRs who presented with 623 primary cSCCs, metastases developed in 37 with a 2-year patient-based cumulative incidence of 6.2%. Risk factors for metastases included location in the head and neck area, local recurrence, size > 2 cm, clinical ulceration, poor differentiation grade, perineural invasion, and deep invasion. A high-stage tumor that is also ulcerated showed the highest risk of metastasis, with a 2-year cumulative incidence of 46.2% (31.9%-68.4%). CONCLUSIONS: SOTRs have a high risk of cSCC metastases and well-established clinical and histologic risk factors have been confirmed. High-stage, ulcerated cSCCs have the highest risk of metastasis.

Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multigene prognostic signature associated with metastasis.

BACKGROUND: Metastasis of cutaneous squamous cell carcinoma (cSCC) is uncommon. Current staging methods are reported to have sub-optimal performances in metastasis prediction. Accurate identification of patients with tumors at high risk of metastasis would have a significant impact on management. OBJECTIVE: To develop a robust and validated gene expression profile signature for predicting primary cSCC metastatic risk using an unbiased whole transcriptome discovery-driven approach. METHODS: Archival formalin-fixed paraffin-embedded primary cSCC with perilesional normal tissue from 237 immunocompetent patients (151 nonmetastasizing and 86 metastasizing) were collected retrospectively from four centers. TempO-seq was used to probe the whole transcriptome and machine learning algorithms were applied to derive predictive signatures, with a 3:1 split for training and testing datasets. RESULTS: A 20-gene prognostic model was developed and validated, with an accuracy of 86.0%, sensitivity of 85.7%, specificity of 86.1%, and positive predictive value of 78.3% in the testing set, providing more stable, accurate prediction than pathological staging systems. A linear predictor was also developed, significantly correlating with metastatic risk. LIMITATIONS: This was a retrospective 4-center study and larger prospective multicenter studies are now required. CONCLUSION: The 20-gene signature prediction is accurate, with the potential to be incorporated into clinical workflows for cSCC.

ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles.

Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).

The Genomic Landscape of Actinic Keratosis.

Actinic keratoses (AKs) are lesions of epidermal keratinocyte dysplasia and are precursors for invasive cutaneous squamous cell carcinoma (cSCC). Identifying the specific genomic alterations driving the progression from normal skin to skin with AK to skin with invasive cSCC is challenging because of the massive UVR-induced mutational burden characteristic at all stages of this progression. In this study, we report the largest AK whole-exome sequencing study to date and perform a mutational signature and candidate driver gene analysis on these lesions. We demonstrate in 37 AKs from both immunosuppressed and immunocompetent patients that there are significant similarities between AKs and cSCC in terms of mutational burden, copy number alterations, mutational signatures, and patterns of driver gene mutations. We identify 44 significantly mutated AK driver genes and confirm that these genes are similarly altered in cSCC. We identify azathioprine mutational signature in all AKs from patients exposed to the drug, providing further evidence for its role in keratinocyte carcinogenesis. cSCCs differ from AKs in having higher levels of intrasample heterogeneity. Alterations in signaling pathways also differ, with immune-related signaling and TGFß signaling significantly more mutated in cSCC. Integrating our findings with independent gene expression datasets confirms that dysregulated TGFß signaling may represent an important event in AK‒cSCC progression.