A 10-year retrospective cohort study of ruxolitinib and association with nonmelanoma skin cancer in patients with polycythemia vera and myelofibrosis.

BACKGROUND: Clinical trials report occurrence of nonmelanoma skin cancers (NMSCs) with ruxolitinib in patients with polycythemia vera (PV) or myelofibrosis (MF); however, the level of risk and effect of covariates are not known in the real-world setting. OBJECTIVE: To systematically assess the risk of developing NMSC after ruxolitinib exposure in patients with PV or MF. METHODS: A 10-year retrospective cohort of patients with PV or MF at Stanford Medical Center was identified and matched according to age, gender, race, Charlson Comorbidity Index, disease diagnosis, and follow-up time. The main outcome measure was hazard ratio (HR) for NMSC (comprised of basal cell carcinoma and squamous cell carcinoma [SCC]) after ruxolitinib exposure, adjusted for covariates. RESULTS: The study cohort consisted of 564 patients (188 exposed to ruxolitinib for at least 4 weeks, 376 unexposed). Ruxolitinib-exposed patients with PV or MF had an adjusted NMSC HR of 2.69 (95% CI, 1.03-7.02). In particular, ruxolitinib exposure was associated with SCC (HR, 3.24; 95% CI, 1.45-7.22), with non-Janus kinase 2-mutated patients showing even higher SCC risk (HR, 7.40; 95% CI, 2.54-21.63). LIMITATIONS: Retrospective design. CONCLUSIONS: Our real-world results indicate that SCC risk is increased in patients with PV or MF taking ruxolitinib and support consideration of skin cancer monitoring.

Cutaneous adverse events in 155 patients with metastatic melanoma consecutively treated with anti-CTLA4 and anti-PD1 combination immunotherapy: Incidence, management, and clinical benefit.

BACKGROUND: In response to the increased use of combination checkpoint inhibitors (CPIs) and the resulting increased cutaneous adverse events (CAEs), this study reviewed patients with melanoma treated with combination CPIs to characterize CAE features and their clinical impact, correlation to adverse events in other organs, and correlation to tumor response. METHODS: Patients from the authors' institutional database who received at least 1 dose of ipilimumab in combination with either nivolumab or pembrolizumab between January 1, 2012, and December 31, 2017, for stage IV or unresectable stage III melanoma were identified. The time to next treatment (TTNT) was calculated from the start of CPI therapy to the start of the next treatment or death, and the development of CAEs was tested in a time-dependent Cox regression to identify associations with TTNT. RESULTS: Eighty-one patients (52.3%) experienced a total of 92 CAEs, including eczematous dermatitis (25.0%), morbilliform eruption (22.8%), vitiligo (12.0%), and pruritus without rash (8.7%). The median times to the onset and resolution of CAEs were 21 days (range, 0-341 days) and 50 days (range, 1-352 days), respectively. Most CAEs resolved after patients entered the CPI maintenance phase and treatment with oral antihistamines with or without topical steroids. CPI discontinuation occurred in 4 patients (2.6%) because of CAEs, in 49 (31.6%) because of other immune-related adverse events, and in 20 (12.9%) because of melanoma progression or death. For patients definitively treated with CPIs (n = 134; 86.5%), TTNT was significantly longer with CAEs than without CAEs (hazard ratio, 0.567; 95% CI, 0.331-0.972; P = .039). CONCLUSIONS: CAEs were mostly reversible and rarely required therapy discontinuation. The development of CAEs was associated with a longer TTNT, and this suggested a possible clinical benefit.

Application of least absolute shrinkage and selection operator logistic regression for the histopathological comparison of chondrodermatitis nodularis helicis and hyperplastic actinic keratosis.

BACKGROUND: The distinction between chondrodermatitis nodularis helicis (CNH) and hyperplastic actinic keratosis (HAK) on the ear can pose a diagnostic challenge. We aimed to identify histopathological characteristics that could distinguish between CNH and HAK on routine sections using penalized least absolute shrinkage and selection operator (LASSO) logistic regression analysis. METHODS: Cases of CNH (n = 80) and HAK (n = 28) were analyzed for selected histopathological characteristics. Fisher's exact test and LASSO regression were performed. RESULTS: In univariate analyses, the following were significantly associated with CNH: ulceration, acanthosis, granular layer in the majority of the lesion, hypergranulosis at the periphery of the lesion, hyperkeratosis at the periphery of the lesion, hyperparakeratosis at the periphery of the lesion, fibrosis, increased blood vessels, vertically oriented blood vessels, and fibrin. A LASSO model excluding atypia found that fibrin, fibrosis, presence of granular layer, ulceration, and vertically oriented blood vessels were most predictive of CNH. Keratinized strap cells were not a significant predictor. CONCLUSION: We have identified features that may aid in differentiating these entities and demonstrated that a LASSO regression model can identify predictors that may improve diagnostic accuracy. Our results indicate that the highest diagnostic accuracy in this dilemma is dependent on obtaining biopsy specimens with visible dermis.

Veterans Affairs Insurance Disparities for Metastatic Lung Cancer in the Hawaiian Islands.

INTRODUCTION: The highest concentration of military personnel in the United States is located in Hawaii where occupational exposures, such as to asbestos in the Pacific Fleet shipyards, predispose them to thoracic malignancies. For this reason, Veterans Affairs (VA) insurance outcomes for lung cancer in Hawaii are of interest. METHODS: All cases of lung cancer in the Hawaii Tumor Registry from 2000 to 2015 were evaluated. The selection criterion included evidence of extensive-stage SCLC (ES-SCLC) or metastatic NSCLC. Overall survival was compared using the Kaplan-Meier log-rank method. Univariate analysis and multivariable analysis (MVA) were carried out to understand the variables associated with overall survival. RESULTS: There were 434 cases of ES-SCLC and 2139 cases of metastatic NSCLC identified. VA insurance (median survival [MS], 2 mo), Medicaid (MS, 4 mo), and Medicare (MS, 4 mo) had worse survival (log-rank p < 0.001) than private insurance (MS, 8 mo). In ES-SCLC, VA insurance (hazard ratio [HR], 2.74; 95% confidence interval [CI]: 1.50-5.01; p = 0.001) and Medicaid (HR, 1.46; 95% CI: 1.04-2.03; p = 0.027) had significantly worse survival compared with private insurance on MVA. VA insurance (HR, 1.84; 95% CI: 1.34-2.53; p < 0.001) and Medicaid (HR, 1.40; 95% CI: 1.20-1.63; p < 0.001) also had worse survival compared with private insurance in metastatic NSCLC on MVA. CONCLUSIONS: VA insurance coverage was associated with dismal survival for metastatic lung cancer that was effectively similar to hospice or supportive care, compelling further investigation to identify reasons for this disparity.

Mass Spectrometry Imaging Enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.