Treatment with therapeutic anticoagulation is not associated with immunotherapy response in advanced cancer patients.

BACKGROUND: Recent preclinical data suggest that there may be therapeutic synergy between immune checkpoint blockade and inhibition of the coagulation cascade. Here, we investigate whether patients who received immune checkpoint inhibitors (ICI) and were on concomitant anticoagulation (AC) experienced better treatment outcomes than individuals not on AC.Affiliation: Kindly confirm if corresponding authors affiliation is identified correctly.The corresponding author's affiliation is correct. METHODS: We studied a cohort of 728 advanced cancer patients who received 948 lines of ICI at NYU (2010-2020). Patients were classified based on whether they did (n = 120) or did not (n = 828) receive therapeutic AC at any point during their treatment with ICI. We investigated the relationship between AC status and multiple clinical endpoints including best overall response (BOR), objective response rate (ORR), disease control rate (DCR), progression free survival (PFS), overall survival (OS), and the incidence of bleeding complications.Affiliations: Journal instruction requires a country for affiliations; however, this is missing in affiliations 1 to 5. Please verify if the provided country is correct and amend if necessary.The country is correct for all affiliations (1 - 5). RESULTS: Treatment with AC was not associated with significantly different BOR (P = 0.80), ORR (P =0.60), DCR (P =0.77), PFS (P = 0.59), or OS (P =0.64). Patients who received AC were significantly more likely to suffer a major or clinically relevant minor bleed (P = 0.05). CONCLUSION: AC does not appear to impact the activity or efficacy of ICI in advanced cancer patients. On the basis of our findings, we caution that there is insufficient evidence to support prospectively evaluating the combination of AC and immunotherapy.

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. These biomarkers provide invaluable information for diagnosis, prognosis and play an important role in prediction of treatment response. In this review, we summarize the most recent discoveries in each of these biomarker categories. We will discuss the challenges in their implementation and standardization and conclude with some perspectives in melanoma biomarker research.

Revisiting the association between skin toxicity and better response in advanced cancer patients treated with immune checkpoint inhibitors.

BACKGROUND: Immune checkpoint inhibition (ICI) improves survival outcomes for patients with several types of cancer including metastatic melanoma (MM), but serious immune-related adverse events requiring intervention with immunosuppressive medications occur in a subset of patients. Skin toxicity (ST) has been reported to be associated with better response to ICI. However, understudied factors, such as ST severity and potential survivor bias, may influence the strength of these observed associations. METHODS: To examine the potential confounding impact of such variables, we analyzed advanced cancer patients enrolled prospectively in a clinicopathological database with protocol-driven follow up and treated with ICI. We tested the associations between developing ST, stratified as no (n = 617), mild (n = 191), and severe (n = 63), and progression-free survival (PFS) and overall survival (OS) in univariable and multivariable analyses. We defined severe ST as a skin event that required treatment with systemic corticosteroids. To account for the possibility of longer survival associating with adverse events instead of the reverse, we treated ST as a time-dependent covariate in an adjusted model. RESULTS: Both mild and severe ST were significantly associated with improved PFS and OS (all P < 0.001). However, when adjusting for the time from treatment initiation to time of skin event, severe ST was not associated with PFS benefit both in univariable and multivariable analyses (P = 0.729 and P = 0.711, respectively). Receiving systemic steroids for ST did not lead to significant differences in PFS or OS compared to patients who did not receive systemic steroids. CONCLUSIONS: Our data reveal the influence of time to event and its severity as covariates in analyzing the relationship between ST and ICI outcomes. These differences in outcomes cannot be solely explained by the use of immunosuppressive medications, and thus highlight the importance of host- and disease-intrinsic factors in determining ICI response and toxicity. TRIAL REGISTRATION: The patient data used in this manuscript come from patients who were prospectively enrolled in two institutional review board-approved databases at NYU Langone Health (institutional review board #10362 and #S16-00122).

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. However, building a COVID-19 biorepository amidst the active pandemic is a complex and delicate task. To help facilitate rapid, robust, and regulated research on this novel virus, we report on the successful model implemented by New York University Langone Health (NYULH) within days of outbreak in the most challenging hot spot of infection globally. Using an amended institutional biobanking protocol, these efforts led to accrual of 11,120 patients presenting for SARS-CoV-2 testing, 4267 (38.4%) of whom tested positive for COVID-19. The recently reported genomic characterization of SARS-CoV-2 in the New York City Region, which is a crucial development in tracing sources of infection and asymptomatic spread of the novel virus, is the first outcome of this effort. While this growing resource actively supports studies of the New York outbreak in real time, a worldwide effort is necessary to build a collective arsenal of research tools to deal with the global crisis now, and to exploit the virus's biology for translational innovation that outlasts humanity's current dilemma.

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. Digital spatial profiling with multiplex immunohistochemistry and immunofluorescence were used to validate the signatures. The efficacy of combinational regimens driven by these signatures was tested through in vitro assays using low-passage cell lines. Pathogenic NF1 mutations were identified in 27% of cases. NF1-mutant melanoma expressed higher proliferative markers MK167 and CDC20 than NF1 wild-type (P = 0.008), which was independently validated both in The Cancer Genome Atlas dataset (P = 0.01, P = 0.03) and with immunohistochemistry (P = 0.013, P = 0.036), respectively. Digital spatial profiling analysis showed upregulation of LY6E within the tumor cells (false discovery rate < 0.01, log2 fold change > 1), confirmed with multiplex immunofluorescence showing colocalization of LY6E in melanoma cells. The combination of MAPK/extracellular signal‒regulated kinase kinase and CDC20 coinhibition induced both cytotoxic and cytostatic effects, decreasing CDC20 expression in multiple NF1-mutant cell lines. In conclusion, NF1-mutant melanoma is associated with a distinct genomic and transcriptomic profile. Our data support investigating CDC20 inhibition with MAPK pathway inhibitors as a targeted regimen in this melanoma subtype.

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 = 0.96) and then predicts for mutated BRAF (area under the curve = 0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, whole-slide images were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, showing that mutated BRAF nuclei were significantly larger and rounder than BRAF‒wild-type nuclei. Finally, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to an area under the curve of 0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, but machine learning‒based analysis of whole-slide images also has the potential to be integrated into higher-order models for understanding tumor biology.

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). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan-Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400). RESULTS: The multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400). CONCLUSIONS: Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

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. Patients had melanoma (n=268), lung cancer (n=128) or other primary malignancies (n=233). We tested the association between BMI changes prior to the start of treatment, baseline prognostic nutritional index (PNI), baseline BMI category and multiple clinical end points including best overall response (BOR), objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS) and overall survival (OS). RESULTS: Decreasing pretreatment BMI and low PNI were associated with worse BOR (p=0.04 and p=0.0004), ORR (p=0.01 and p=0.0005), DCR (p=0.01 and p<0.0001), PFS (p=0.02 and p=0.01) and OS (p<0.001 and p<0.001). Baseline BMI category was not significantly associated with any treatment outcomes. CONCLUSION: Standard of care measures of worsening nutritional status more accurately associate with immunotherapy outcomes than static measurements of BMI. Future studies should focus on determining whether optimizing pretreatment nutritional status, a modifiable variable, leads to improvement in immunotherapy response.

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. METHODS: We analyzed a cohort of 1315 prospectively enrolled patients. We assessed primary tumor and nodal classification of stage I-III patients using AJCC7 and AJCC8 to assign disease stages at diagnosis. We compared recurrence-free (RFS) and overall survival (OS) using Kaplan-Meier curves and log-rank tests. We then compared concordance indices of discriminatory prognostic ability and area under the curve of 5-year survival to predict RFS and OS. All statistical tests were two-sided. RESULTS: Stage IIC patients continued to have worse outcomes than stage IIIA patients, with a 5-year RFS of 26.5% (95% confidence interval [CI] = 12.8% to 55.1%) vs 56.0% (95% CI = 37.0% to 84.7%) by AJCC8 (P = .002). For stage I, removing mitotic index as a T classification factor decreased its prognostic value, although not statistically significantly (RFS concordance index [C-index] = 0.63, 95% CI = 0.56 to 0.69; to 0.56, 95% CI = 0.49 to 0.63, P = .07; OS C-index = 0.48, 95% CI = 0.38 to 0.58; to 0.48, 95% CI = 0.41 to 0.56, P = .90). For stage II, prognostication remained constant (RFS C-index = 0.65, 95% CI = 0.57 to 0.72; OS C-index = 0.61, 95% CI = 0.50 to 0.72), and for stage III, AJCC8 yielded statistically significantly enhanced prognostication for RFS (C-index = 0.65, 95% CI = 0.60 to 0.70; to 0.70, 95% CI = 0.66 to 0.75, P = .01). CONCLUSIONS: Compared with AJCC7, we demonstrate that AJCC8 enables more accurate prognosis for patients with stage III melanoma. Restaging a large cohort of patients can enhance the analysis of active clinical trials.

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. We analyzed this association stratified by first vs. second or greater-line of treatment and treatment type adjusting for age, gender, stage, lactate dehydrogenase, Eastern Cooperative Oncology Group performance status, number of metastatic sites, and body mass index classification changes. In our cohort, the patients who were overweight or obese did not have different progression-free survival than patients with normal body mass index. Stratifying this cohort by first vs. non-first line immunotherapy revealed a moderate but insignificant association between being overweight or obese and better progression-free survival in patients who received first line. Conversely, an association with worse progression-free survival was observed in patients who received non-first line immune checkpoint inhibitors. Specifically, overweight and obese patients receiving combination immunotherapy had a statistically significant survival benefit, whereas patients receiving the other treatment types showed heterogeneous trends. We caution the scientific community to consider several important points prior to drawing conclusions that could potentially influence patient care, including preclinical data associating obesity with aggressive tumor biology, the lack of congruence amongst several investigations, and the limited reproduced comprehensiveness of these studies.