Combination immunotherapy including OncoVEXmGMCSF creates a favorable tumor immune micro-environment in transgenic BRAF murine melanoma.

Talimogene Laherparepvec (OncoVEXmGMCSF), an oncolytic virus, immune checkpoint inhibitor anti-programmed cell death protein 1 (anti-PD1), and BRAF inhibition (BRAFi), are all clinically approved for treatment of melanoma patients and are effective through diverse mechanisms of action. Individually, these therapies also have an effect on the tumor immune microenvironment (TIME). Evaluating the combination effect of these three therapies on the TIME can help determine when combination therapy is most appropriate for further study. In this study, we use a transgenic murine melanoma model (Tyr::CreER; BRAFCA/+; PTENflox/flox), to evaluate the TIME in response to combinations of BRAFi, anti-PD1, and OncoVEXmGMCSF. We find that mice treated with the triple combination BRAFi + anti-PD1 + OncoVEXmGMCSF have decreased tumor growth compared to BRAFi alone and prolonged survival compared to control. Flow cytometry shows an increase in percent CD8 + /CD3 + cytotoxic T Lymphocytes (CTLs) and a decrease in percent FOXP3 + /CD4 + T regulatory cells (Tregs) in tumors treated with OncoVEXmGMCSF compared to mice not treated with OncoVEXmGMCSF. Immunogenomic analysis at 30d post-treatment shows an increase in Th1 and interferon-related genes in mice receiving OncoVEXmGMCSF + BRAFi. In summary, treatment with combination BRAFi + anti-PD1 + OncoVEXmGMCSF is more effective than any single treatment in controlling tumor growth, and groups receiving OncoVEXmGMCSF had more tumoral infiltration of CTLs and less intratumoral Tregs in the TIME. This study provides rational basis to combine targeted agents, oncolytic viral therapy, and checkpoint inhibitors in the treatment of melanoma.

Correction to: The Role of Oncolytic Viruses in the Treatment of Melanoma.

A correction was made to a sentence in the original article to provide additional clarification in the "Other Oncolytic Viruses" section.

The Role of Oncolytic Viruses in the Treatment of Melanoma.

PURPOSE OF REVIEW: Oncolytic virotherapy is a new approach to the treatment of cancer and its success in the treatment of melanoma represents a breakthrough in cancer therapeutics. This paper provides a review of the current literature on the use of oncolytic viruses (OVs) in the treatment of melanoma. RECENT FINDINGS: Talimogene laherparepvec (T-VEC) is the first OV approved for the treatment of melanoma and presents new challenges as it enters the clinical setting. Several other OVs are at various stages of clinical and pre-clinical development for the treatment of melanoma. Reports from phase Ib-III clinical trials combining T-VEC with checkpoint blockade are encouraging and demonstrate potential added benefit of combination immunotherapy. OVs have recently emerged as a standard treatment option for patients with advanced melanoma. Several OVs and therapeutic combinations are in development. Immunooncolytic virotherapy combined with immune checkpoint inhibitors is promising for the treatment of advanced melanoma.

Rates of Internal Hiring of Ophthalmology Faculty from their Institution of Training at Top Academic Medical Centers: A Cross-Sectional Study.

Background Throughout graduate and postgraduate education, trainees need to gauge the impact of training location on future institutions of practice. Objective We assessed rates of internal hiring of ophthalmology faculty at academic institutions from their same institution of training. Methods This was a cross-sectional study. We included 1,246 clinical ophthalmology faculty at the 13 top-ranked institutions listed in the 2021 U.S. News and World Report. Primary, emeritus, adjunct, and affiliate faculty were included. Publicly available information was collected from institutional websites and other online sources. Statistical analyses were conducted using t -tests or Mann-Whitney tests, chi-squared or Fisher's exact tests, and multivariate logistic regression. The main outcome measured was internal hires, defined as faculty who had completed residency and/or fellowship training at their current institution. Results In total, 47.3% of faculty were internal hires who completed residency or fellowship at the same institution. Among externally trained faculty, 27.7% completed residency and 56.0% completed fellowship at another top 13 programs. Internal hires were more frequently fellowship-trained, had a greater number of publications, and practiced in smaller departments ( p < 0.001, p < 0.001, and p = 0.002, respectively). A greater proportion of internal hires held leadership positions ( p = 0.012). Faculty practicing in the Midwest or West and with more years since residency graduation were less likely (odds ratio [OR], 0.29, 95% confidence interval [CI], 0.18-0.48; OR, 0.49, 95% CI, 0.31-0.78; OR, 0.98, 95% CI, 0.97-0.99, respectively) to be internal hires. Faculty with non-R01 National Institutes of Health funding were more likely to be internal hires (OR, 1.82, 95% CI: 1.12-2.96). Conclusions Training institution is key to determining the institution of practice. These results may be beneficial for trainees to consider when selecting a training program.

Neoadjuvant chemoradiation alters the immune microenvironment in pancreatic ductal adenocarcinoma.

Patients with pancreatic ductal adenocarcinoma (PDAC) have a grim prognosis despite complete surgical resection and intense systemic therapies. While immunotherapies have been beneficial with many different types of solid tumors, they have almost uniformly failed in the treatment of PDAC. Understanding how therapies affect the tumor immune microenvironment (TIME) can provide insights for the development of strategies to treat PDAC. We used quantitative multiplexed immunofluorescence (qmIF) quantitative spatial analysis (qSA), and immunogenomic (IG) analysis to analyze formalin-fixed paraffin embedded (FFPE) primary tumor specimens from 44 patients with PDAC including 18 treated with neoadjuvant chemoradiation (CRT) and 26 patients receiving no treatment (NT) and compared them with tissues from 40 treatment-naïve melanoma patients. We find that relative to NT tumors, CD3+ T cell infiltration was increased in CRT treated tumors (p = .0006), including increases in CD3+CD8+ cytotoxic T cells (CTLs, p = .0079), CD3+CD4+FOXP3- T helper cells (Th, p = .0010), and CD3+CD4+FOXP3+ regulatory T cells (Tregs, p = .0089) with no difference in CD68+ macrophages. IG analysis from micro-dissected tissues indicated overexpression of genes involved in antigen presentation, T cell activation, and inflammation in CRT treated tumors. Among treated patients, a higher ratio of Tregs to total T cells was associated with shorter survival time (p = .0121). Despite comparable levels of infiltrating T cells in CRT PDACs to melanoma, PDACs displayed distinct spatial profiles with less T cell clustering as defined by nearest neighbor analysis (p < .001). These findings demonstrate that, while CRT can achieve high T cell densities in PDAC compared to melanoma, phenotype and spatial organization of T cells may limit benefit of T cell infiltration in this immunotherapy-resistant tumor.

Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma.

Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards. ADTA was applied to a training cohort (n = 80) and a cutoff value was defined based on a Receiver Operating Curve. ADTA was then applied to a validation cohort (n = 145) and the previously determined cutoff value was used to stratify high and low risk patients, as demonstrated by Kaplan-Meier analysis (p ≤ 0.001). Multivariable Cox proportional hazards analysis was performed using ADTA, depth, and ulceration as co-variables and showed that ADTA contributed to DSS prediction (HR: 4.18, CI 1.51-11.58, p = 0.006). ADTA provides an effective and attainable assessment of TILs and should be further evaluated in larger studies for inclusion in staging algorithms.

FLT-PET At 6 Weeks Predicts Response Assessed by CT at 12 Weeks in Melanoma Patients Treated With Pembrolizumab.

PURPOSE: Investigate the ability of F-fluorothymidine (FLT) PET combined with CT at 6 weeks to predict treatment response at 12 weeks after treatment with pembrolizumab. METHODS: Five patients with unresectable stage IV melanoma were included in this single-institution pilot study. Patients underwent FLT-PET/CT (baseline and 6 weeks) and CT (baseline and 12 weeks). FLT-PET/CT response and CT response were assessed using PET Response Criteria in Solid Tumors and immune Response Evaluation Criteria in Solid Tumors, respectively. Patients were categorized as responders (complete response, partial response) and nonresponders (stable disease, progressive disease). Agreement between 6-week FLT-PET/CT and 12-week CT was calculated using Cohen kappa's agreement. Eight baseline FLT-PET/CT parameters were extracted: SUVmax, SUVpeak, SUVSD, SUVmean, proliferative tumor volume, total lesion proliferation, bone marrow-to-liver SUVmax ratio, and spleen-to-liver SUVmax ratio. Eight delta-parameters were extracted at 6 weeks by calculating variation in FLT uptake as percentage change from baseline. RESULTS: Agreement between 6-week FLT-PET/CT and 12-week CT was kappa = 0.615, P = 0.025. Three of 5 patients were categorized as responders on CT by immune Response Evaluation Criteria in Solid Tumors. At baseline, responders had a lower mean proliferative tumor volume and a higher bone marrow-to-liver SUVmax ratio. At 6 weeks, responders demonstrated a decrease in tumor volume and tumor proliferation. CONCLUSIONS: Our study illustrates the potential for FLT-PET/CT as an early predictor of response for patients with metastatic melanoma on anti-PD1 immunotherapy. Larger studies are indicated to confirm these findings.

Distinguishing melanophages from tumor in melanoma patients treated with talimogene laherparepvec.

Response to talimogene laherparepvec (T-Vec) is difficult to assess as pigmented macrophages that have ingested melanoma cells ('melanophages') persist after injection, mimicking melanoma. We used quantitative immunofluorescence (qIF) to (1) distinguish melanophages from melanoma in biopsies from two patients treated with T-Vec and (2) evaluate the tumor microenvironment pretreatment and posttreatment. Tissues were stained with 4',6-diamidino-2-phenylindole, cluster of differentiation (CD) 3, CD8, CD68, human leukocyte antigen-DR isotype (HLA-DR), and SRY-Box Transcription Factor 10 (SOX10), and multispectral images were analyzed. Post-T-Vec samples showed melanophages with cytoplasmic costaining of CD68, SOX10, and HLA-DR, without nuclear SOX10 expression. qIF revealed a dense immune infiltrate of CD3, CD8, and CD68 cells in post-T-Vec samples. Melanophages from tumors post-T-Vec stain the nuclear melanoma marker SOX10 in their cytoplasms as compared to melanoma cells that stain nuclear SOX10. This novel finding highlights the phagocytosis of melanoma cell components by macrophages after treatment with T-Vec. qIF may assist pathologists in determining whether lesions treated with immunotherapy contain residual viable melanoma.

Biomarkers Predictive of Survival and Response to Immune Checkpoint Inhibitors in Melanoma.

Immunotherapy has revolutionized the treatment of melanoma. Targeting of the immune checkpoints cytotoxic T-lymphocyte-associated protein 4 and programmed cell death protein 1 has led to improved survival in a subset of patients. Unfortunately, the use of immune checkpoint inhibitors is associated with significant side effects and many patients do not respond to treatment. Thus, there is an urgent need both for prognostic biomarkers to estimate risk and for predictive biomarkers to determine which patients are likely to respond to therapy. In this review, prognostic and predictive biomarkers that are an active area of research are outlined. Of note, certain transcriptomic signatures are already used in the clinic, albeit not routinely, to prognosticate patients. In the predictive setting, programmed cell death protein ligand 1 expression has been shown to correlate with benefit but is not precise enough to be used as an exclusionary biomarker. Future investigation will need to focus on biomarkers that are easily reproducible, cost effective, and accurate. The use of readily available clinical material, such as serum or hematoxylin and eosin-stained images, may offer one such path forward.

Linking Transcriptomic and Imaging Data Defines Features of a Favorable Tumor Immune Microenvironment and Identifies a Combination Biomarker for Primary Melanoma.

Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8+ cytotoxic T lymphocytes (CTL) to CD68+ macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR+ macrophages, but not HLA-DR- macrophages. The HLA-DR- subset coexpressed CD163+CSF1R+ at higher levels than CD68+HLA-DR+ macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. SIGNIFICANCE: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

PURPOSE: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction. EXPERIMENTAL DESIGN: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT). A receiver operating characteristic (ROC) curve was generated on the basis of vote aggregation of individual image sequences, an optimized cutoff was selected, and the computational model was tested on a third independent population of 51 patients from Geisinger Health Systems (GHS). RESULTS: Area under the curve (AUC) in the YSM patients was 0.905 (P < 0.0001). AUC in the GHS patients was 0.880 (P < 0.0001). Using the cutoff selected in the YSM cohort, the computational model predicted DSS in the GHS cohort based on Kaplan-Meier (KM) analysis (P < 0.0001). CONCLUSIONS: The novel method presented is applicable to digital images, obviating the need for sample shipment and manipulation and representing a practical advance over current genetic and IHC-based methods.

Prognostic and Predictive Immunohistochemistry-Based Biomarkers in Cancer and Immunotherapy.

Immunotherapy has drastically improved the prognosis of many patients with cancer, but it can also lead to severe immune-related adverse events. Biomarkers, which are molecular markers that indicate a patient's disease outcome or a patient's response to treatment, are therefore crucial to helping clinicians weigh the potential benefits of immunotherapy against its potential toxicities. Immunohistochemistry (IHC) has thus far been a powerful technique for discovery and use of biomarkers such as CD8+ tumor-infiltrating lymphocytes. However, IHC has limited reproducibility. Thus, if more IHC-based biomarkers are to reach the clinic, refinement of the technique using multiplexing or automation is key.

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, N = 201, N = 76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.

Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II-III Melanoma.

PURPOSE: Biomarkers are needed to stratify patients with stage II-III melanoma for clinical trials of adjuvant therapy because, while immunotherapy is protective, it also confers the risk of severe toxicity. We previously defined and validated a 53-immune gene melanoma immune profile (MIP) predictive both of distant metastatic recurrence and of disease-specific survival (DSS). Here, we test MIP on a third independent population. EXPERIMENTAL DESIGN: A retrospective cohort of 78 patients with stage II-III primary melanoma was analyzed using the NanoString assay to measure expression of 53 target genes, and MIP score was calculated. Statistical analysis correlating MIP with DSS, overall survival, distant metastatic recurrence, and distant metastasis-free interval was performed using ROC curves, Kaplan-Meier curves, and standard univariable and multivariable Cox proportional hazards models. RESULTS: MIP significantly distinguished patients with distant metastatic recurrence from those without distant metastatic recurrence using ROC curve analysis (AUC = 0.695; P = 0.008). We defined high- and low-risk groups based on the cutoff defined by this ROC curve and find that MIP correlates with both DSS and overall survival by ROC curve analysis (AUC = 0.719; P = 0.004 and AUC = 0.698; P = 0.004, respectively). Univariable Cox regression reveals that a high-risk MIP score correlates with DSS (P = 0.015; HR = 3.2). CONCLUSIONS: MIP identifies patients with low risk of death from melanoma and may constitute a clinical tool to stratify patients with stage II-III melanoma for enrollment in clinical trials.