Clinical and Molecular Heterogeneity in Patients with Innate Resistance to Anti-PD-1 +/- Anti-CTLA-4 Immunotherapy in Metastatic Melanoma Reveals Distinct Therapeutic Targets

Deciphering and overcoming Anti-PD-1 resistance in Melanoma: A comprehensive review of Mechanisms, biomarker Developments, and therapeutic strategies

Worldwide, tens of thousands of people die from melanoma each year, making it the most frequently fatal form of cutaneous cancer. Immunotherapeutic advancements, particularly with anti-PD-1 medications, have significantly enhanced treatment outcomes over recent decades. With the broad application of anti-PD-1 therapies, insights into the mechanisms of resistance have evolved. Despite the development of combination treatments and early predictive biomarkers, a comprehensive synthesis of these advancements is absent in the current literature. This review underscores the prevailing knowledge of anti-PD-1 resistance mechanisms and underscores the critical role of robust predictive biomarkers in stratifying patients for targeted combinations of anti-PD-1 and other conventional or innovative therapeutic approaches. Additionally, we offer insights that may shape future melanoma treatment strategies.

Recent Advances in Cancer Immunotherapy with a Focus on FDA-Approved Vaccines and Neoantigen-Based Vaccines

Cancer immunotherapies refer to the concept of retraining the immune system to target malignant cells. Multiple immunotherapeutic options exist including immune modulating antibodies, immune stimulating cytokines, chimeric antigen receptor T cell therapy, and vaccines. Overall, this field has advanced rapidly as knowledge of the tumor microenvironment, immunological pathways, and biotechnology expands. Specifically, advancements in neoantigen identification, characterization, and formulation into a vaccine show promise. This review is focused on previously United States Food and Drug Administration-approved cancer therapeutic vaccines and neoantigen-based vaccine developments along with the associated relevant clinical trials.

Classification of the tumor immune microenvironment and associations with outcomes in patients with metastatic melanoma treated with immunotherapies

BACKGROUND

Tumor microenvironment (TME) characteristics are potential biomarkers of response to immune checkpoint inhibitors in metastatic melanoma. This study developed a method to perform unsupervised classification of TME of metastatic melanoma.

METHODS

We used multiplex immunohistochemical and quantitative pathology-derived assessment of immune cell compositions of intratumoral and peritumoral regions of metastatic melanoma baseline biopsies to classify TME in relation to response to anti-programmed cell death protein 1 (PD-1) monotherapy or in combination with anti-cytotoxic T-cell lymphocyte-4 (ipilimumab (IPI)+PD-1).

RESULTS

Spatial profiling of CD8+T cells, macrophages, and melanoma cells, as well as phenotypic PD-1 receptor ligand (PD-L1) and CD16 proportions, were used to identify and classify patients into one of three mutually exclusive TME classes: immune-scarce, immune-intermediate, and immune-rich tumors. Patients with immune-rich tumors were characterized by a lower proportion of melanoma cells and higher proportions of immune cells, including higher PD-L1 expression. These patients had higher response rates and longer progression-free survival (PFS) than those with immune-intermediate and immune-scarce tumors. At a median follow-up of 18 months (95% CI: 6.7 to 49 months), the 1-year PFS was 76% (95% CI: 64% to 90%) for patients with an immune-rich tumor, 56% (95% CI: 44% to 72%) for those with an immune-intermediate tumor, and 33% (95% CI: 23% to 47%) for patients with an immune-scarce tumor. A higher response rate was observed in patients with an immune-scarce or immune-intermediate tumor when treated with IPI+PD-1 compared with those treated with PD-1 alone.

CONCLUSIONS

Our study provides an automatic TME classification method that may predict the clinical efficacy of immunotherapy for patients with metastatic melanoma.

Clock-like Mutation Signature May Be Prognostic for Worse Survival Than Signatures of UV Damage in Cutaneous Melanoma

Novel treatment modalities comprising immune checkpoint inhibitors and targeted therapies have revolutionized treatment of metastatic melanoma. Still, some patients suffer from rapid progression and decease within months after a diagnosis of stage IV melanoma. We aimed to assess whether genomic alterations may predict survival after the development of stage IV disease, irrespective of received therapy. We analyzed tumor samples of 79 patients with stage IV melanoma using a custom next-generation gene-sequencing panel, MelArray, designed to detect alterations in 190 melanoma-relevant genes. We classified the patients: first, as short survivors (survival ≤6 months after stage IV disease, n = 22) and long survivors (survival >6 months, n = 57); second, by using a cut-off of one year; and third, by comparing the longest surviving 20 patients to the shortest surviving 20. Among analyzed genes, no individual gene alterations, or combinations of alterations, could be dichotomously associated with survival. However, the cohort's mutational profiles closely matched three known mutational signatures curated by the Catalog of Somatic Mutations in Cancer (COSMIC): UV signature COSMIC_7 (cosine-similarity 0.932), clock-like signature COSMIC_5 (cosine-similarity 0.829), and COSMIC_30 (cosine-similarity 0.726). Patients with UV signature had longer survival compared to patients with clock-like and COSMIC 30 (p < 0.0001). Subgroup dichotomization at 6 months showed that 75% of patients with UV signature survived longer than 6 months, and about 75% of patients with clock-like signature survived less than 6 months after development of stage IV disease. In our cohort, clock-like COSMIC_5 mutational signature predicted poor survival while a UV signature COSMIC_7 predicted longer survival. The prognostic value of mutational signatures should be evaluated in prospective studies.

Unravelling Tumour Microenvironment in Melanoma at Single-Cell Level and Challenges to Checkpoint Immunotherapy

Melanoma is known as one of the most immunogenic tumours and is often characterised by high mutation burden, neoantigen load and immune infiltrate. The application of immunotherapies has led to impressive improvements in the clinical outcomes of advanced stage melanoma patients. The standard of care immunotherapies leverage the host immunological influence on tumour cells, which entail complex interactions among the tumour, stroma, and immune cells at the tumour microenvironmental level. However, not all cancer patients can achieve a long-term durable response to immunotherapy, and a significant proportion of patients develops resistance and still die from their disease. Owing to the multi-faceted problems of tumour and microenvironmental heterogeneity, identifying the key factors underlying tumour progression and immunotherapy resistance poses a great challenge. In this review, we outline the main challenges to current cancer immunotherapy research posed by tumour heterogeneity and microenvironment complexities including genomic and transcriptomic variability, selective outgrowth of tumour subpopulations, spatial and temporal tumour heterogeneity and the dynamic state of host immunity and microenvironment orchestration. We also highlight the opportunities to dissect tumour heterogeneity using single-cell sequencing and spatial platforms. Integrative analyses of large-scale datasets will enable in-depth exploration of biological questions, which facilitates the clinical application of translational research.

Tumor infiltrating lymphocytes (TILs) as a predictive biomarker of response to checkpoint blockers in solid tumors: a systematic review

Immunotherapy is a standard of care in many solid tumors but many patients derive limited benefit from it. There is increasing interest toward tumor infiltrating lymphocytes (TILs) since their presence may be related with good outcomes from treatment with immune checkpoint blockers. We aimed at systematically reviewing existing evidence about the role of TILs as possible predictors of response to immunotherapy in solid tumors. We reviewed 1193 records published from January 2010 until December 2021. Associations between TILs and outcomes were observed mainly in melanoma and breast cancer. Overall survival and overall response rate for advanced disease and pathological complete response for early-phase tumors were the most commonly assessed endpoints. No definitive conclusion can be drawn on the predictive role of TILs. Additional studies, exploiting data from prospective, randomized clinical trials should further evaluate TILs also with the aim of identifying standard cut-off to differentiate between high and low TILs.

Bright-Field Multiplex Immunohistochemistry Assay for Tumor Microenvironment Evaluation in Melanoma Tissues

Simple Summary

Bright-field (BF) immunohistochemistry (IHC) remains the gold standard for histopathological evaluations. The development of new BF multiplex IHC could be very useful for the study and characterization of the tumor microenvironment (TME) in melanoma samples. We herein compared different BF IHC multiplex protocols for the study of TME in primary cutaneous melanoma tissues and offered the best optimized protocol for visualization and evaluation. These methodologies are studied to maximize the quality of staining considering the tissue characteristics under examination, maintaining a high level of standardization and reproducibility.

Abstract

The tumor microenvironment (TME) plays a crucial role in melanoma development, progression and response to treatment. As many of the most relevant TME cell phenotypes are defined by the simultaneous detection of more than two markers, the bright-field (BF) multiplex immunohistochemistry (IHC) technique has been introduced for the quantitative assessment and evaluation of the relative spatial distances between immune cells and melanoma cells. In the current study, we aimed to validate BF multiplex IHC techniques in the Ventana Discovery Ultra Immunostainer to be applied to the evaluation of the TME in variably pigmented melanoma tissues. The BF multiplex IHC staining was performed using different combinations of six immune-cell markers—CD3, CD4, CD8, CD20, CD68 and CD163—and the melanoma cell marker SOX10. Our results show that the BF double IHC Yellow/Purple protocol guarantees the maximum contrast in all the cell populations tested and the combination SOX10 (Green), CD8 (Yellow) and CD163 (Purple) of the BF triple IHC protocol ensures the best contrast and discrimination between the three stained cell populations. Furthermore, the labeled cells were clearly distinct and easily identifiable using the image analysis software. Our standardized BF IHC multiplex protocols can be used to better assess the immune contexts of melanoma patients with potential applications to drive therapeutic decisions within clinical trials.

Efficacy and safety of anti-PD1 monotherapy or in combination with ipilimumab after BRAF/MEK inhibitors in patients with BRAF mutant metastatic melanoma

BACKGROUND

Patients with V600BRAF mutant metastatic melanoma have higher rates of progression-free survival (PFS) and overall survival (OS) with first-line anti-PD1 (PD1]+anti-CTLA-4 (IPI) versus PD1. Whether this is also true after BRAF/MEKi therapy is unknown. We aimed to determine the efficacy and safety of PD1 versus IPI +PD1 after BRAF/MEK inhibitors (BRAF/MEKi).

METHODS

Patients with V600BRAF mutant metastatic melanoma treated with BRAF/MEKi who had subsequent PD1 versus IPI+PD1 at eight centers were included. The endpoints were objective response rate (ORR), PFS, OS and safety in each group.

RESULTS

Of 200 patients with V600E (75%) or non-V600E (25%) mutant metastatic melanoma treated with BRAF/MEKi (median time of treatment 7.6 months; treatment cessation due to progressive disease in 77%), 115 (57.5%) had subsequent PD1 and 85 (42.5%) had IPI+PD1. Differences in patient characteristics between PD1 and IPI+PD1 groups included, age (med. 63 vs 54 years), time between BRAF/MEKi and PD1±IPI (16 vs 4 days), Eastern Cooperative Oncology Group Performance Status (ECOG PS) of ≥1 (62% vs 44%), AJCC M1C/M1D stage (72% vs 94%) and progressing brain metastases at the start of PD1±IPI (34% vs 57%). Median follow-up from PD1±IPI start was 37.8 months (95% CI, 33.9 to 52.9). ORR was 36%; 34% with PD1 vs 39% with IPI+PD1 (p=0.5713). Median PFS was 3.4 months; 3.4 with PD1 vs 3.6 months with IPI+PD1 (p=0.6951). Median OS was 15.4 months; 14.4 for PD1 vs 20.5 months with IPI+PD1 (p=0.2603). The rate of grade 3 or 4 toxicities was higher with IPI+PD1 (31%) vs PD1 (7%). ORR, PFS and OS were numerically higher with IPI+PD1 vs PD1 across most subgroups except for females, those with <10 days between BRAF/MEKi and PD1±IPI, and those with stage III/M1A/M1B melanoma. The combination of ECOG PS=0 and absence of liver metastases identified patients with >3 years OS (area under the curve, AUC=0.74), while ECOG PS ≥1, progressing brain metastases and presence of bone metastases predicted primary progression (AUC=0.67).

CONCLUSIONS

IPI+PD1 and PD1 after BRAF/MEKi have similar outcomes despite worse baseline prognostic features in the IPI+PD1 group, however, IPI+PD1 is more toxic. A combination of clinical factors can identify long-term survivors, but less accurately those with primary resistance to immunotherapy after targeted therapy.

Clinical Models to Define Response and Survival With Anti-PD-1 Antibodies Alone or Combined With Ipilimumab in Metastatic Melanoma

PURPOSE

Currently, there are no robust biomarkers that predict immunotherapy outcomes in metastatic melanoma. We sought to build multivariable predictive models for response and survival to anti-programmed cell death protein 1 (anti-PD-1) monotherapy or in combination with anticytotoxic T-cell lymphocyte-4 (ipilimumab [IPI]; anti-PD-1 ± IPI) by including routine clinical data available at the point of treatment initiation.

METHODS

One thousand six hundred forty-four patients with metastatic melanoma treated with anti-PD-1 ± IPI at 16 centers from Australia, the United States, and Europe were included. Demographics, disease characteristics, and baseline blood parameters were analyzed. The end points of this study were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The final predictive models for ORR, PFS, and OS were determined through penalized regression methodology (least absolute shrinkage and selection operator method) to select the most significant predictors for all three outcomes (discovery cohort, N = 633). Each model was validated internally and externally in two independent cohorts (validation-1 [N = 419] and validation-2 [N = 592]) and nomograms were created.

RESULTS

The final model for predicting ORR (area under the curve [AUC] = 0.71) in immunotherapy-treated patients included the following clinical parameters: Eastern Cooperative Oncology Group Performance Status, presence/absence of liver and lung metastases, serum lactate dehydrogenase, blood neutrophil-lymphocyte ratio, therapy (monotherapy/combination), and line of treatment. The final predictive models for PFS (AUC = 0.68) and OS (AUC = 0.77) included the same variables as those in the ORR model (except for presence/absence of lung metastases), and included presence/absence of brain metastases and blood hemoglobin. Nomogram calculators were developed from the clinical models to predict outcomes for patients with metastatic melanoma treated with anti-PD-1 ± IPI.

CONCLUSION

Newly developed combinations of routinely collected baseline clinical factors predict the response and survival outcomes of patients with metastatic melanoma treated with immunotherapy and may serve as valuable tools for clinical decision making.

Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance

We concurrently examine the whole genome, transcriptome, methylome, and immune cell infiltrates in baseline tumors from 77 patients with advanced cutaneous melanoma treated with anti-PD-1 with or without anti-CTLA-4. We show that high tumor mutation burden (TMB), neoantigen load, expression of IFNγ-related genes, programmed death ligand expression, low PSMB8 methylation (therefore high expression), and T cells in the tumor microenvironment are associated with response to immunotherapy. No specific mutation correlates with therapy response. A multivariable model combining the TMB and IFNγ-related gene expression robustly predicts response (89% sensitivity, 53% specificity, area under the curve [AUC], 0.84); tumors with high TMB and a high IFNγ signature show the best response to immunotherapy. This model validates in an independent cohort (80% sensitivity, 59% specificity, AUC, 0.79). Except for a JAK3 loss-of-function mutation, for patients who did not respond as predicted there is no obvious biological mechanism that clearly explained their outlier status, consistent with intratumor and intertumor heterogeneity in response to immunotherapy.

The Research Progress of PD-1/PD-L1 Inhibitors Enhancing Radiotherapy Efficacy

Approximately 60%–70% of patients with malignant tumours require radiotherapy. The clinical application of immune checkpoint inhibitors (ICIs), such as anti-PD-1/PD-L1, has revolutionized cancer treatment and greatly improved the outcome of a variety of cancers by boosting host immunity.However, radiotherapy is a double-edged sword for PD-1/PD-L immunotherapy. Research on how to improve radiotherapy efficacy using PD-1/PD-L1 inhibitor is gaining momentum. Various studies have reported the survival benefits of the combined application of radiotherapy and PD-1/PD-L1 inhibitor. To fully exerts the immune activation effect of radiotherapy, while avoiding the immunosuppressive effect of radiotherapy as much as possible, the dose selection, segmentation mode, treatment timing and the number of treatment sites of radiotherapy play a role. Therefore, we aim to review the effect of radiotherapy combined with anti-PD-1/PD-L1 on the immune system and its optimization.