Purpose: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. Experimental Design: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). Results: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan–Meier (KM) analysis (P < 0.0001). Conclusions: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

The rates of melanoma and non‐melanoma skin cancer (NMSC) have been increasing over the last twenty years in the United States, and this has been attributed to increased ultraviolet radiation exposure (UVR). Given these rising rates, preventative measures have become increasingly important to reduce the incidence and promote early detection of these cancers. Skin cancer prevention remains a challenging task to accomplish mainly due to the lack of reliable and sensitive methods to provide objective risk information that can educate and motivate individuals to avoid sunburn. Currently, minimal erythema dose (MED) is used as a marker of UVR. However, it is not an ideal marker because significant cancer‐related molecular damage can occur after UVR exposure that cannot be detected by MED. Thus, over the recent years there has been significant interest in development of biomarkers indicative of exposure to UVR to improve early detection of cutaneous malignancies. Here, we will discuss emerging biomarkers for melanoma and NMSC that can help with risk stratification and targeted prevention and treatment.

Background Patients with cancer who are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are more likely to develop severe illness and die compared with those without cancer. The impact of immune checkpoint inhibition (ICI) on the severity of COVID-19 illness is unknown. The aim of this study was to investigate whether ICI confers an additional risk for severe COVID-19 in patients with cancer. Methods We analyzed data from 110 patients with laboratory-confirmed SARS-CoV-2 while on treatment with ICI without chemotherapy in 19 hospitals in North America, Europe and Australia. The primary objective was to describe the clinical course and to identify factors associated with hospital and intensive care (ICU) admission and mortality. Findings Thirty-five (32%) patients were admitted to hospital and 18 (16%) died. All patients who died had advanced cancer, and only four were admitted to ICU. COVID-19 was the primary cause of death in 8 (7%) patients. Factors independently associated with an increased risk for hospital admission were ECOG ≥2 (OR 39.25, 95% CI 4.17 to 369.2, p=0.0013), treatment with combination ICI (OR 5.68, 95% CI 1.58 to 20.36, p=0.0273) and presence of COVID-19 symptoms (OR 5.30, 95% CI 1.57 to 17.89, p=0.0073). Seventy-six (73%) patients interrupted ICI due to SARS-CoV-2 infection, 43 (57%) of whom had resumed at data cut-off. Interpretation COVID-19–related mortality in the ICI-treated population does not appear to be higher than previously published mortality rates for patients with cancer. Inpatient mortality of patients with cancer treated with ICI was high in comparison with previously reported rates for hospitalized patients with cancer and was due to COVID-19 in almost half of the cases. We identified factors associated with adverse outcomes in ICI-treated patients with COVID-19.

The signal transducer and activator of transcription 6 (STAT6) is a critical up-stream mediator of interleukin-13 (IL-13) and IL-4 signaling and is constitutively activated in malignant lymphocytes from Sezary syndrome (SS) and mycosis fungoides (MF), the most common subtypes of cutaneous T-cell lymphomas. By combining genome-wide expression profiling with pharmacological STAT6 inhibition, we have identified the genes regulated by STAT6 in MF/SS tumors. We found that STAT6 regulates several common pathways in MF/SS malignant lymphocytes that are associated with control of cell-cycle progression and genomic stability as well as production of Th2 cytokines. Using ex vivo skin explants from cutaneous MF tumors as well as Sezary cells derived from the blood of SS patients, we demonstrated that inhibition of STAT6 activation downregulates cytokine production and induces cell-cycle arrest in MF/SS malignant lymphocytes, inhibiting their proliferation but not their survival. Furthermore, we show that STAT6 promotes the protumoral M2-like phenotype of tumor-associated macrophages in the tumor microenvironment of advanced stage MF by upregulating the expression of genes associated with immunosuppression, chemotaxis, and tumor matrix remodeling. Thus, we show STAT6 to be a major factor in the pathogenesis and progression of MF/SS, promoting proliferation and invasion of the malignant lymphocytes while inducing a progressive depression of the antitumor immune response. Together, our results provide new insights into disease pathogenesis and offer new prospective targets for therapeutic intervention.

Objective: To investigate the progression of neural and motor features of Parkinson's disease in a longitudinal study, after washout of medication and bilateral subthalamic nucleus deep brain stimulation (STN DBS).Methods: Participants with clinically established Parkinson's disease underwent bilateral implantation of DBS leads (18 participants, 13 male) within the STN using standard functional frameless stereotactic technique and multi‐pass microelectrode recording. Both DBS leads were connected to an implanted investigative sensing neurostimulator (Activa™ PC + S, Medtronic, PLC). Resting state STN local field potentials (LFPs) were recorded and motor disability, (the Movement Disorder Society‐Unified Parkinson's Disease Rating Scale – motor subscale, MDS‐UPDRS III) was assessed off therapy at initial programming, and after 6 months, 1 year, and yearly out to 5 years of treatment. The primary endpoint was measured at 3 years. At each visit, medication had been held for over 12/24 h and DBS was turned off for at least 60 min, by which time LFP spectra reached a steady state.Results: After 3 years of chronic DBS, there were no increases in STN beta band dynamics (p = 0.98) but there were increases in alpha band dynamics (p = 0.0027, 25 STNs). Similar results were observed in a smaller cohort out to 5 years. There was no increase in the MDS‐UPDRS III score.Interpretation: These findings provide evidence that the beta oscillopathy does not substantially progress following combined STN DBS plus medication in moderate to advanced Parkinson's disease.