Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multi-gene 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 tumours at high risk of metastasis would have a significant impact on management.Objective: To develop a robust and validated gene expression profile (GEP) 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 non-metastasising and 86 metastasising) were collected retrospectively from four centres. 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-centre study and larger prospective multicentre 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

Summary: 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 bigger picture: Methods that group genes into functional units to quantify pathway activities are critical in the analysis of biological systems. Although many components of biological pathways have been described in detail, these tend to be limited to well-studied genes. In contrast, the majority of possible components remain unexplored. Here, we present a machine-learning tool for constructing and predicting tissue-specific components of biological pathways from large biological datasets. Our algorithm, ACSNI, can tackle incomplete pathway descriptions and enhance current pathway analysis methods' performance. We anticipate that, by dissecting the complex signals in biological data in a flexible and context-specific manner, ACSNI can facilitate the full characterization of physiological systems of interest

Cumulative incidence and risk factors for cutaneous squamous-cell carcinoma metastases in organ transplant recipients:the SCOPE-ITSCC metastases study, a prospective multi-center 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, 37 developed metastases 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 &gt;2cm, 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 histological risk factors have been confirmed. High-stage, ulcerated cSCCs have the highest risk of metastasis.</p