Faculty Bibliography

PURPOSE/OBJECTIVE: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/METHODS: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:= 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.

Background: Myocardial infarction with non-obstructive coronary arteries (MINOCA) occurs in 6-15% of MI and disproportionately affects women. Scientific statements recommend multi-modality imaging in MINOCA to define the underlying cause. We performed coronary optical coherence tomography (OCT) and cardiac magnetic resonance imaging (CMR) to assess mechanisms of MINOCA. Methods: In this prospective, multicenter, international, observational study, we enrolled women with a clinical diagnosis of MI. If invasive coronary angiography revealed <50% stenosis in all major arteries, multi-vessel OCT was performed, followed by CMR (cine imaging, late gadolinium enhancement, and T2-weighted imaging and/or T1 mapping). Angiography, OCT, and CMR were evaluated at blinded, independent core laboratories. Culprit lesions identified by OCT were classified as definite or possible. The CMR core laboratory identified ischemia-related and non-ischemic myocardial injury. Imaging results were combined to determine the mechanism of MINOCA, when possible. Results: Among 301 women enrolled at 16 sites, 170 were diagnosed with MINOCA, of whom 145 had adequate OCT image quality for analysis; 116 of these underwent CMR. A definite or possible culprit lesion was identified by OCT in 46.2% (67/145) of participants, most commonly plaque rupture, intra-plaque cavity or layered plaque. CMR was abnormal in 74.1% (86/116) of participants. An ischemic pattern of CMR abnormalities (infarction or myocardial edema in a coronary territory) was present in 53.4% of participants undergoing CMR (62/116). A non-ischemic pattern of CMR abnormalities (myocarditis, takotsubo syndrome or non-ischemic cardiomyopathy) was present in 20.7% (24/116). A cause of MINOCA was identified in 84.5% of the women with multi-modality imaging (98/116), higher than with OCT alone (p<0.001) or CMR alone (p=0.001). An ischemic etiology was identified in 63.8% of women with MINOCA (74/116), a non-ischemic etiology was identified in 20.7% (24/116), and no mechanism was identified in 15.5% (18/116). Conclusions: Multi-modality imaging with coronary OCT and CMR identified potential mechanisms in 84.5% of women with a diagnosis of MINOCA, three-quarters of which were ischemic and one-quarter of which were non-ischemic, alternate diagnoses to MI. Identification of the etiology of MINOCA is feasible and has the potential to guide medical therapy for secondary prevention. Clinical Trial Registration: URL: https://clinicaltrials.gov Unique Identifier: NCT02905357.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the adoption of TIL into clinical practice. Computational image analysis offers a solution to this obstacle, representing a methodological approach for reproducibly counting TIL. We sought to evaluate the ability of a TIL-quantifying machine learning algorithm to predict survival in primary melanoma. Digitized hematoxylin and eosin (H&E) slides from prospectively enrolled patients in the NYU melanoma database were scored for % TIL using machine learning and manually graded by pathologists using Clark's model. We evaluated the association of % TIL with recurrence-free survival (RFS) and overall survival (OS) using Cox proportional hazards modeling and concordance indices. Discordance between algorithmic and manual TIL quantification was assessed with McNemar's test and visually by an attending dermatopathologist. In total, 453 primary melanoma patients were scored using machine learning. Automated % TIL scoring significantly differentiated survival using an estimated cutoff of 16.6% TIL (log-rank P < 0.001 for RFS; P = 0.002 for OS). % TIL was associated with significantly longer RFS (adjusted HR = 0.92 [0.84-1.00] per 10% increase in % TIL) and OS (adjusted HR = 0.90 [0.83-0.99] per 10% increase in % TIL). In comparison, a subset of the cohort (n = 240) was graded for TIL by melanoma pathologists. However, TIL did not associate with RFS between groups (P > 0.05) when categorized as brisk, nonbrisk, or absent. A standardized and automated % TIL scoring algorithm can improve the prognostic impact of TIL. Incorporation of quantitative TIL scoring into the AJCC staging criteria should be considered.

OBJECTIVE:Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. MATERIALS AND METHODS/METHODS:This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009-2017) and from the Health and Retirement Survey (HRS, 1995-2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. RESULTS:The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. DISCUSSION AND CONCLUSION/CONCLUSIONS:EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

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:0.001 and p<0.001). Baseline BMI category was not significantly associated with any treatment outcomes. CONCLUSION/CONCLUSIONS: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.

Intron retention (IR) is the least well-understood alternative splicing type in animals, and its prevalence and function in physiological and pathological processes have long been underestimated. Cellular senescence contributes to individual aging and age-related diseases and can also serve as an important cancer prevention mechanism. Dynamic IR events have been observed in senescence models and aged tissues; however, whether and how IR impacts senescence remain unclear. Through analyzing polyA⁺ RNA-seq data from human replicative senescence models, we found IR was prevalent and dynamically regulated during senescence and IR changes negatively correlated with expression alteration of corresponding genes. We discovered that knocking down (KD) splicing factor U2AF1, which showed higher binding density to retained introns and decreased expression during senescence, led to senescence-associated phenotypes and global IR changes. Intriguingly, U2AF1-KD-induced IR changes also negatively correlated with gene expression. Furthermore, we demonstrated that U2AF1-mediated IR of specific gene (CPNE1 as an example) contributed to cellular senescence. Decreased expression of U2AF1, higher IR of CPNE1, and reduced expression of CPNE1 were also discovered in dermal fibroblasts with age. We discovered prevalent IR could fine-tune gene expression and contribute to senescence-associated phenotypes, largely extending the biological significance of IR.

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).