Faculty Bibliography

Neural infiltration in primary melanoma is a histopathologic feature that has been associated with desmoplastic histopathologic subtype and local recurrence in the literature. We tested the hypothesis that improved detection and characterization of neural infiltration into peritumoral or intratumoral location and perineural or intraneural involvement could have a prognostic relevance. We studied 128 primary melanoma cases prospectively accrued and followed at New York University using immunohistochemical detection with antihuman neurofilament protein and routine histology with hematoxylin and eosin. Neural infiltration, defined as the presence of tumor cells involving or immediately surrounding nerve foci, was identified and characterized using both detection methods. Neural infiltration rate of detection was enhanced by immunohistochemistry for neurofilament in matched-pair design (47% by immunohistochemistry versus 25% by routine histology). Immunohistochemical detection of neural infiltration was significantly associated with ulceration (P = .021), desmoplastic and acral lentiginous histologic subtype (P = .008), and head/neck/hands/feet tumor location (P = .037). Routinely detected neural infiltration was significantly associated with local recurrence (P = .010). Immunohistochemistry detected more intratumoral neural infiltration cases compared with routine histology (30% versus 3%, respectively). Peritumoral and intratumoral nerve location had no impact on clinical outcomes. Using a multivariate model controlling for stage, neither routinely detected neural infiltration nor enhanced immunohistochemical characterization of neural infiltration was significantly associated with disease-free or overall survival. Our data demonstrate that routinely detected neural infiltration is associated with local recurrence in all histologic subtypes but that improved detection and characterization of neural infiltration with immunohistochemistry in primary melanoma does not add to prognostic relevance.

Previous reports have demonstrated a role for hedgehog signaling in melanoma progression, prompting us to explore the therapeutic benefit of targeting this pathway in melanoma. We profiled a panel of human melanoma cell lines and control melanocytes for altered expression of hedgehog pathway members and determined the consequences of both genetic and pharmacological inhibition of the hedgehog pathway activator Smoothened (SMO) in melanoma, both in vitro and in vivo. We also examined the relationship between altered expression of hedgehog pathway mediators and survival in a well-characterized cohort of metastatic melanoma patients with prospectively collected follow up information. Studies revealed that over 40% of the melanoma cell lines examined harbored significantly elevated levels of the hedgehog pathway mediators SMO, GLI2, and PTCH1 compared to melanocytes (p < 0.05). SMO inhibition using siRNA and the small molecule inhibitor, NVP-LDE-225, suppressed melanoma growth in vitro, particularly in those cell lines with moderate SMO and GLI2 expression. NVP-LDE-225 also induced apoptosis in vitro and inhibited melanoma growth in a xenograft model. Gene expression data also revealed evidence of compensatory up-regulation of two other developmental pathways, Notch and WNT, in response to hedgehog pathway inhibition. Pharmacological and genetic SMO inhibition also downregulated genes involved in human embryonic stem cell pluripotency. Finally, increased SMO expression and decreased expression of the hedgehog pathway repressor GLI3 correlated with shorter post recurrence survival in metastatic melanoma patients. Our data demonstrate that hedgehog pathway inhibition might be a promising targeted therapy in appropriately selected metastatic melanoma patients.

To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6JxA/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans.

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.