Preferentially Expressed Antigen in Melanoma Immunohistochemistry Labeling in Uveal Melanomas.

Introduction: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, and despite treatment of the primary tumor, approximately 15%-50% of patients will develop metastatic disease. Based on gene expression profiling (GEPs), UM can be categorized as Class 1A (low metastatic risk), Class 1B (intermediate metastatic risk), or Class 2 (high metastatic risk). PReferentially expressed Antigen in MElanoma (PRAME) status is an independent prognostic UM biomarker and a potential target for immunotherapy in metastatic UM. PRAME expression status can be detected in tumors using reverse-transcription polymerase chain reaction (RT-PCR). More recently, immunohistochemistry (IHC) has been developed to detect PRAME protein expression. Here, we employed both techniques to evaluate PRAME expression in 18 UM enucleations. Methods: Tumor material from the 18 UM patients who underwent enucleation was collected by fine-needle aspiration before or during enucleation and sent for GEP and PRAME analysis by RT-PCR. Histologic sections from these patients were stained with an anti-PRAME monoclonal antibody. We collected patient demographics and tumor characteristics and included this with our analysis of GEP class, PRAME status by RT-PCR, and PRAME status by IHC. PRAME IHC and RT-PCR results were compared. Results: Twelve males (12/18) and 6 females (6/18) with an average age of 60.6 years underwent enucleation for UM. TNM staging of the UM diagnosed Stage I in 2 patients (2/18), Stage II in 7 patients (7/18), Stage III in 8 patients (8/18), and Stage IV in 1 (1/18). GEP was Class 1A in 6 tumors (6/18), Class 1B in 6 tumors (6/18), and Class 2 in 6 tumors (6/18). PRAME IHC showed diffusely positive labeling of all UM cells in 2/18 enucleations; negative IHC labeling of UM cells in 9/18 enucleations; and IHC labeling of subsets of UM cells in 7/18 enucleations. Eleven of the 17 UMs tested for PRAME by both RT-PCR and IHC had consistent PRAME results. In the remaining 6/17 cases tested by both modalities, PRAME results were discordant between RT-PCR and IHC. Conclusions: We find that PRAME IHC distinguishes PRAME-positive and PRAME-negative UM tumor cells. Interestingly, IHC reveals focal PRAME expression in subsets of tumor cells consistent with tumor heterogeneity. PRAME RT-PCR and IHC provide concordant results in most of our cases. We suggest that discordance in PRAME results could arise from spatial or temporal variation in PRAME expression between tumor cells. Further studies are required to determine the prognostic implications of PRAME IHC in UM.

Long-lasting analgesia via targeted in situ repression of NaV1.7 in mice.

Current treatments for chronic pain rely largely on opioids despite their substantial side effects and risk of addiction. Genetic studies have identified in humans key targets pivotal to nociceptive processing. In particular, a hereditary loss-of-function mutation in NaV1.7, a sodium channel protein associated with signaling in nociceptive sensory afferents, leads to insensitivity to pain without other neurodevelopmental alterations. However, the high sequence and structural similarity between NaV subtypes has frustrated efforts to develop selective inhibitors. Here, we investigated targeted epigenetic repression of NaV1.7 in primary afferents via epigenome engineering approaches based on clustered regularly interspaced short palindromic repeats (CRISPR)-dCas9 and zinc finger proteins at the spinal level as a potential treatment for chronic pain. Toward this end, we first optimized the efficiency of NaV1.7 repression in vitro in Neuro2A cells and then, by the lumbar intrathecal route, delivered both epigenome engineering platforms via adeno-associated viruses (AAVs) to assess their effects in three mouse models of pain: carrageenan-induced inflammatory pain, paclitaxel-induced neuropathic pain, and BzATP-induced pain. Our results show effective repression of NaV1.7 in lumbar dorsal root ganglia, reduced thermal hyperalgesia in the inflammatory state, decreased tactile allodynia in the neuropathic state, and no changes in normal motor function in mice. We anticipate that this long-lasting analgesia via targeted in vivo epigenetic repression of NaV1.7 methodology we dub pain LATER, might have therapeutic potential in management of persistent pain states.

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers.

Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients.

PURPOSE: Currently known serum biomarkers do not predict clinical outcome in melanoma. S100-beta is widely established as a reliable prognostic indicator in patients with advanced metastatic disease but is of limited predictive value in tumor-free patients. This study was aimed to determine whether molecular profiling of the serum proteome could discriminate between early- and late-stage melanoma and predict disease progression. PATIENTS AND METHODS: Two hundred five serum samples from 101 early-stage (American Joint Committee on Cancer [AJCC] stage I) and 104 advanced stage (AJCC stage IV) melanoma patients were analyzed by matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (ToF; MALDI-ToF) mass spectrometry utilizing protein chip technology and artificial neural networks (ANN). Serum samples from 55 additional patients after complete dissection of regional lymph node metastases (AJCC stage III), with 28 of 55 patients relapsing within the first year of follow-up, were analyzed in an attempt to predict disease recurrence. Serum S100-beta was measured using a sandwich immunoluminometric assay. RESULTS: Analysis of 205 stage I/IV serum samples, utilizing a training set of 94 of 205 and a test set of 15 of 205 samples for 32 different ANN models, revealed correct stage assignment in 84 (88%) of 96 of a blind set of 96 of 205 serum samples. Forty-four (80%) of 55 stage III serum samples could be correctly assigned as progressors or nonprogressors using random sample cross-validation statistical methodologies. Twenty-three (82%) of 28 stage III progressors were correctly identified by MALDI-ToF combined with ANN, whereas only six (21%) of 28 could be detected by S100-beta. CONCLUSION: Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.