Faculty Bibliography

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.

We present the case of a 31-year-old female with 1.5 cm pigmented nodule on the scalp. Histopathological examination revealed a proliferation of relatively bland spindle cells and pigmented dendritic cells with interspersed lymphoid follicles diffusely infiltrating the adipose tissue. The microscopic differential diagnosis included pigmented dermatofibrosarcoma protuberans (DFSP). The spindle cells showed S-100 and CD34 labeling but were negative for SOX-10. Immunohistochemical stain for pan-TRK was positive, while fluorescence in-situ hybridization for PDGFB gene rearrangement was negative. Targeted RNA sequencing revealed a LMNA-NTRK1(Exon2/Exon10) fusion. This molecular result coupled with the histopathologic findings and immunohistochemical profile supported the diagnosis of the recently characterized NTRK-rearranged spindle cell neoplasm termed "lipofibromatosis-like neural tumor (LPF-NT)". These neoplasms typically occur in superficial soft tissue and are characterized by a distinctive immunoprofile (CD34+, S-100+, SOX10-). Histopathological differential diagnosis for LPF-NT tumors includes lipofibromatosis, DFSP, low-grade malignant peripheral nerve sheath tumor and spindle cell/desmoplastic melanoma. The pigmented dendritic cells reminiscent of pigmented DFSP and lymphoid follicles noted in our case have not been previously reported in LPF-NT, thus expanding the morphologic spectrum of this entity. LMNA-NTRK1 fusion serves both as a diagnostic and therapeutic biomarker, as cases with advanced disease may be amenable to targeted therapy using tyrosine kinase inhibitors. This article is protected by copyright. All rights reserved.

OBJECTIVES/OBJECTIVE:We validate the use of a lateral flow immunoassay (LFI) intended for rapid screening and qualitative detection of anti-SARS-CoV-2 IgM and IgG in serum, plasma, and whole blood, and compare results with ELISA. We also seek to establish the value of LFI testing on blood obtained from a capillary blood sample. METHODS:Samples collected by venous blood draw and finger stick were obtained from patients with SARS-CoV-2 detected by RT-qPCR and control patients. Samples were tested with Biolidics 2019-nCoV IgG/IgM Detection Kit lateral flow immunoassay, and antibody calls were compared with ELISA. RESULTS:Biolidics LFI showed clinical sensitivity of 92% with venous blood at 7 days after PCR diagnosis of SARS-CoV-2. Test specificity was 92% for IgM and 100% for IgG. There was no significant difference in detecting IgM and IgG with Biolidics LFI and ELISA at D0 and D7 (p = 1.00), except for detection of IgM at D7 (p = 0.04). Capillary blood of SARS-CoV-2 patients showed 93% sensitivity for antibody detection. CONCLUSIONS:Clinical performance of Biolidics 2019-nCoV IgG/IgM Detection Kit is comparable to ELISA and was consistent across sample types. This provides an opportunity for decentralized rapid testing and may allow point-of-care and longitudinal self-testing for the presence of anti-SARS-CoV-2 antibodies.

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epi-demiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in Spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.