Genomic and Transcriptomic Analyses of NF1-Mutant Melanoma Identify Potential Targeted Approach for Treatment.

There is currently no targeted therapy to treat NF1-mutant melanomas. In this study, we compared the genomic and transcriptomic signatures of NF1-mutant and NF1 wild-type melanoma to reveal potential treatment targets for this subset of patients. Genomic alterations were verified using qPCR, and differentially expressed genes were independently validated using The Cancer Genome Atlas data and immunohistochemistry.

Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.

Baseline prognostic nutritional index and changes in pretreatment body mass index associate with immunotherapy response in patients with advanced cancer.

Background: Recent research suggests that baseline body mass index (BMI) is associated with response to immunotherapy. In this study, we test the hypothesis that worsening nutritional status prior to the start of immunotherapy, rather than baseline BMI, negatively impacts immunotherapy response.

Methods: We studied 629 patients with advanced cancer who received immune checkpoint blockade at New York University.

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Purpose: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma.

Experimental Design: We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN).

The urgency of utilizing COVID-19 biospecimens for research in the heart of the global pandemic.

The outbreak of the novel coronavirus disease 2019 (COVID-19) and consequent social distancing practices have disrupted essential clinical research functions worldwide. Ironically, this coincides with an immediate need for research to comprehend the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the pathology of COVID-19. As the global crisis has already led to over 15,000 deaths out of 175,000 confirmed cases in New York City and Nassau County, NY alone, it is increasingly urgent to collect patient biospecimens linked to active clinical follow up.

Melanoma Prognosis: Accuracy of the American Joint Committee on Cancer Staging Manual Eighth Edition.

Background: The American Joint Committee on Cancer (AJCC) maintains that the eighth edition of its Staging Manual (AJCC8) has improved accuracy compared with the seventh (AJCC7). However, there are concerns that implementation may disrupt analysis of active clinical trials for stage III patients. We used an independent cohort of melanoma patients to test the extent to which AJCC8 has improved prognostic accuracy compared with AJCC7.

The complex relationship between body mass index and response to immune checkpoint inhibition in metastatic melanoma patients.

Despite major improvements in combatting metastatic melanoma since the advent of immunotherapy, the overall survival for patients with advanced disease remains low. Recently, there is a growing number of reports supporting an "obesity paradox," in which patients who are overweight or mildly obese may exhibit a survival benefit in patients who received immune checkpoint inhibitors. We studied the relationship between body mass index and progression-free survival and overall survival in a cohort of 423 metastatic melanoma patients receiving immunotherapy, enrolled and prospectively followed up in the NYU Interdisciplinary Melanoma Cooperative Group database.

The "-OMICS" facet of melanoma: Heterogeneity of genomic, proteomic and metabolomic biomarkers.

In the recent decade, cutting edge molecular and proteomic analysis platforms revolutionized biomarkers discovery in cancers. Melanoma is the prototype with over 51,100 biomarkers discovered and investigated thus far. These biomarkers include tissue based tumor cell and tumor microenvironment biomarkers and circulating biomarkers including tumor DNA (cf-DNA), mir-RNA, proteins and metabolites.