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

Differential predictive value of tissue-specific PD-L1 expression scores in adjuvant immunotherapy of melanoma

BACKGROUND

Adjuvant treatment of stage II-IV melanoma with PD-1-based immune checkpoint inhibitors (ICI) has improved relapse-free survival (RFS) and has therefore become a standard-of-care treatment option. Approximately 25%-30% of patients still recur within 1 year. Predictive biomarkers reflecting real-world data are desired. The predictive relevance of tumour tissue PD-L1 expression in the adjuvant setting remains inconclusive.

OBJECTIVES

This retrospective, observational study was conducted to evaluate the value of PD-L1 expression scores in different tumour tissue locations in predicting response towards adjuvant immunotherapeutic treatment.

METHODS

Tumour tissue taken prior to anti-PD-1 adjuvant ICI in 243 stage II-IV melanoma patients was collected at University Skin Cancer Center Hamburg. PD-L1 expression was evaluated on immune cells (ICS), tumour cells (TPS) and combined (CPS). Scores were determined by independent pathological physician quantification and correlated with therapy outcome at different cut-off (CO) levels (relapse-free survival, RFS) for different tumour tissue locations (primary tumour, metastases).

RESULTS

A total of 104 patients were eligible for analysis. Positivity of ICS, TPS and CPS showed no predictive RFS outcome association at different CO levels when analysed irrespective of tissue origin. In primary tumours, ICS at CO 1% showed a significantly improved RFS upon positivity (HR 0.22). In contrast, positivity to TPS (CO 1%) correlated significantly and independently with improved RFS when evaluated in metastatic tumour tissue specimens (HR 0.37).

CONCLUSIONS

PD-L1 tumour tissue expression may serve as a predictive biomarker for adjuvant ICI treatment response stratification in melanoma, but caution should be spent on the origin of tumour tissue analysed. The cell-type relevant for the predictive value of PD-L1 expression is tissue-specific with immune cells being important in primary tumours while tumour cells are key in metastases. The present results should be validated in a multicentre cohort.

Macrophage diversity in human cancers: New insight provided by single-cell resolution and spatial context

M1/M2 paradigm of macrophage plasticity has existed for decades. Now it becomes clear that this dichotomy doesn't adequately reflect the diversity of macrophage phenotypes in tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are a major population of innate immune cells in the TME that promotes tumor cell proliferation, angiogenesis and lymphangiogenesis, invasion and metastatic niche formation, as well as response to anti-tumor therapy. However, the fundamental restriction in therapeutic TAM targeting is the limited knowledge about the specific TAM states in distinct human cancer types. Here we summarized the results of the most recent studies that use advanced technologies (e.g. single-cell RNA sequencing and spatial transcriptomics) allowing to decipher novel functional subsets of TAMs in numerous human cancers. The transcriptomic profiles of these TAM subsets and their clinical significance were described. We emphasized the characteristics of specific TAM subpopulations - TREM2+, SPP1+, MARCO+, FOLR2+, SIGLEC1+, APOC1+, C1QC+, and others, which have been most extensively characterized in several cancers, and are associated with cancer prognosis. Spatial transcriptomics technologies defined specific spatial interactions between TAMs and other cell types, especially fibroblasts, in tumors. Spatial transcriptomics methods were also applied to identify markers of immunotherapy response, which are expressed by macrophages or in the macrophage-abundant regions. We highlighted the perspectives for novel techniques that utilize spatial and single cell resolution in investigating new ligand-receptor interactions for effective immunotherapy based on TAM-targeting.

Emerging dimensions of autophagy in melanoma

Macroautophagy/autophagy has previously been regarded as simply a way for cells to deal with nutrient emergency. But explosive work in the last 15 years has given increasingly new knowledge to our understanding of this process. Many of the functions of autophagy that are unveiled from recent studies, however, cannot be reconciled with this conventional view of cell survival but, instead, point to autophagy being integrally involved at a deeper level of cell biology, playing a critical role in maintaining homeostasis and promoting an integrated stress/immune response. The new appreciation of the role of autophagy in the evolutionary trajectory of cancer and cancer interaction with the immune system provides a mechanistic framework for understanding the clinical benefits of autophagy-based therapies. Here, we examine current knowledge of the mechanisms and functions of autophagy in highly plastic and aggressive melanoma as a model disease of human malignancy, while highlighting emerging dimensions indicating that autophagy is at play beyond its classical face.Abbreviation: AMBRA1: autophagy and beclin 1 regulator 1; AMPK: AMP-activated protein kinase; ATF4: activating transcription factor 4; ATG: autophagy related; BRAF: B-Raf proto-oncogene, serine/threonine kinase; CAFs: cancer-associated fibroblasts; CCL5: C-C motif chemokine ligand 5; CQ: chloroquine; CRISPR: clustered regularly interspaced short palindromic repeats; CTLA4: cytotoxic T-lymphocyte associated protein 4; CTL: cytotoxic T lymphocyte; DAMPs: danger/damage-associated molecular patterns; EGFR: epidermal growth factor receptor; EIF2A/eIF2α: eukaryotic translation initiation factor 2A; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 3; ER: endoplasmic reticulum; FITM2: fat storage inducing transmembrane protein 2; HCQ: hydroxychloroquine; ICB: immune checkpoint blockade; ICD: immunogenic cell death; LDH: lactate dehydrogenase; MAPK: mitogen-activated protein kinase; MTORC1: mechanistic target of rapamycin kinase complex 1; NDP52: nuclear dot protein 52; NFKB/NF-κ B: nuclear factor kappa B; NBR1: the neighbor of BRCA1; NK: natural killer; NRF1: nuclear respiratory factor 1; NSCLC: non-small-cell lung cancer; OPTN: optineurin; PDAC: pancreatic ductal adenocarcinoma; PDCD1/PD-1: programmed cell death 1; PPT1: palmitoyl-protein thioesterase 1; PTEN: phosphatase and tensin homolog; PTK2/FAK1: protein tyrosine kinase 2; RAS: rat sarcoma; SQSTM1/p62: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TAX1BP1: Tax1 binding protein 1; TFEB: transcription factor EB; TGFB/TGF-β: transforming growth factor beta; TMB: tumor mutational burden; TME: tumor microenvironment; TSC1: TSC complex subunit 1; TSC2: TSC complex subunit 2; ULK1: unc-51 like autophagy activating kinase 1; UVRAG: UV radiation resistance associated.

Androgen deprivation triggers a cytokine signaling switch to induce immune suppression and prostate cancer recurrence

Androgen deprivation therapy (ADT) is an effective but not curative treatment for advanced and recurrent prostate cancer (PC). We investigated the mechanisms controlling the response to androgen-deprivation by surgical castration in genetically-engineered mouse models (GEMM) of PC, using high frequency ultrasound imaging to rigorously measure tumor volume. Castration initially causes almost all tumors to shrink in volume, but many tumors subsequently recur within 5-10 weeks. Blockade of tumor necrosis factor (TNF) signaling a few days in advance of castration surgery, using a TNFR2 ligand trap, prevents regression in a PTEN-deficient GEMM. Following tumor regression, a basal stem cell-like population within the tumor increases along with TNF protein levels. Tumor cell lines in culture recapitulate these in vivo observations, suggesting that basal stem cells are the source of TNF. When TNF signaling blockade is administered immediately prior to castration, tumors regress but recurrence is prevented, implying that a late wave of TNF secretion within the tumor, which coincides with the expression of NFkB regulated genes, drives recurrence. The inhibition of signaling downstream of one NFkB-regulated protein, chemokine C-C motif ligand 2 (CCL2), prevents post-castration tumor recurrence, phenocopying post-castration (late) TNF signaling blockade. CCL2 was originally identified as a macrophage chemoattractant and indeed at late times after castration gene sets related to chemotaxis and migration are up-regulated. Importantly, enhanced CCL2 signaling during the tumor recurrence phase coincides with an increase in pro-tumorigenic macrophages and a decrease in CD8 T cells, suggesting that recurrence is driven at least in part by tumor immunosuppression. In summary, we demonstrate that a therapy-induced switch in TNF signaling, a consequence of the increased stem cell-like character of the residual tumor cells surviving ADT, induces an immunosuppressive tumor microenvironment and concomitant tumor recurrence.

Targeting the immune microenvironment for ovarian cancer therapy

Ovarian cancer (OC) is an aggressive malignancy characterized by a complex immunosuppressive tumor microenvironment (TME). Immune checkpoint inhibitors have emerged as a breakthrough in cancer therapy by reactivating the antitumor immune response suppressed by tumor cells. However, in the case of OC, these inhibitors have failed to demonstrate significant improvements in patient outcomes, and existing biomarkers have not yet identified promising subgroups. Consequently, there remains a pressing need to understand the interplay between OC tumor cells and their surrounding microenvironment to develop effective immunotherapeutic approaches. This review aims to provide an overview of the OC TME and explore its potential as a therapeutic strategy. Tumor-infiltrating lymphocytes (TILs) are major actors in OC TME. Evidence has been accumulating regarding the spontaneous TILS response against OC antigens. Activated T-helpers secrete a wide range of inflammatory cytokines with a supportive action on cytotoxic T-cells. Simultaneously, mature B-cells are recruited and play a significant antitumor role through opsonization of target antigens and T-cell recruitment. Macrophages also form an important subset of innate immunity (M1-macrophages) while participating in the immune-stimulation context. Finally, OC has shown to engage a significant natural-killer-cells immune response, exerting direct cytotoxicity without prior sensitization. Despite this initial cytotoxicity, OC cells develop various strategies to induce an immune-tolerant state. To this end, multiple immunosuppressive molecules are secreted to impair cytotoxic cells, recruit regulatory cells, alter antigen presentation, and effectively evade immune response. Consequently, OC TME is predominantly infiltrated by immunosuppressive cells such as FOXP3+ regulatory T-cells, M2-polarized macrophages and myeloid-derived suppressor cells. Despite this strong immunosuppressive state, PD-1/PD-L1 inhibitors have failed to improve outcomes. Beyond PD-1/PD-L1, OC expresses multiple other immune checkpoints that contribute to immune evasion, and each representing potential immune targets. Novel immunotherapies are attempting to overcome the immunosuppressive state and induce specific immune responses using antibodies adoptive cell therapy or vaccines. Overall, the OC TME presents both opportunities and obstacles. Immunotherapeutic approaches continue to show promise, and next-generation inhibitors offer exciting opportunities. However, tailoring therapies to individual immune characteristics will be critical for the success of these treatments.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Spatial Transcriptomics Depict Ligand-Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors

Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted.

SIGNIFICANCE

Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.

Integrated tumor genomic and immune microenvironment analysis identifies predictive biomarkers associated with the efficacy of neoadjuvant therapy for triple-negative breast cancer

BACKGROUND

Although neoadjuvant chemotherapy (NAC) is currently the best therapy for triple-negative breast cancer (TNBC), resistance still occurs in a considerable proportion, thus it is crucial to understand resistance mechanisms and identify predictive biomarkers for patients selection.

METHODS

Biopsy samples were collected from 21 patients with TNBC who underwent NAC. Whole-exome sequencing (WES), targeted sequencing, and multiplex immunohistochemistry (mIHC) were carried out on the clinical samples and used to identify and validate potential biomarkers associated with response to NAC. In addition, data on 190 TNBC patients who had undergone chemotherapy were obtained from The Cancer Genome Atlas (TCGA) and analyzed to further validate our findings.

RESULTS

Both the tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were significantly higher in responders than in non-responders. Higher response rates and longer survival rates were observed in patients with higher TMB. Patients with higher ratios of CD8 to M2 macrophages had higher response rates and improved survival rates. Finally, the integrated analysis demonstrated that the combination of TMB and the ratio of CD8 T cells to M2 macrophages could further distinguish patients who benefitted from the treatment in both enrolled patients and public data.

CONCLUSIONS

The findings of this study indicated that the combination of TMB and the ratio of CD8 T cells to M2 macrophages may be a potential biomarker for improving the recognition of NAC responders, thereby providing a basis for developing precision NAC regimens.

Red Light Phototherapy Using Light-Emitting Diodes Inhibits Melanoma Proliferation and Alters Tumor Microenvironments

Background

Total annual cancer rates have decreased due to improved treatment and prevention. However, the incidence of melanoma is rising, and not all patients respond to immune and targeted approaches. Therefore, we sought to determine the efficacy of red light (RL) phototherapy in preclinical models of melanoma.

Methods

Melanoma cells (A375, B16F10, MNT-1) were irradiated with RL. Melanoma proliferation, apoptosis, oxidative stress, and p53 phosphorylation were measured in vitro. In C57BL/6 mice, phototherapy safety, B16F10 tumor growth, and immunocyte infiltration were assessed following RL.

Results

In vitro, 640 J/cm² RL decreased cellular proliferation without increasing apoptosis, while 1280 J/cm² increased apoptosis. RL increased intracellular reactive oxygen species generation and p53 phosphorylation. In animal models, 2560 J/cm² RL significantly prevented melanoma growth and increased the expression of CD103+ dendritic cells. 1280 and 1920 J/cm² RL decreased tumor volume, but not significantly. RL did not cause skin inflammation or erythema in normal skin.

Conclusion

RL represents a potentially safe and effective melanoma therapeutic. RL prevented tumor growth and increased the expression of immune markers, such as CD103, that are associated with favorable melanoma outcomes. Further research is needed to determine the optimal clinical treatment regimen for melanoma using RL.

Identification of Genes Potentially Associated with Melanoma Tumorigenesis Through Co-Expression Network Analysis

Background

Melanoma is one of the most malignant and aggressive skin tumors, and its incidence is increasing worldwide. However, few studies have investigated the process of tumorigenesis from normal skin to melanoma.

Methods

Several bioinformatics analyses, including GEO databases, Oncomine database, TCGA database, STRING, MCODE and cytoHubba plug-ins, GEPIA, TIMER and TRRUST and DGIdb, were performed to disclose the hub genes and immunology implicated in primary melanoma tumorigenesis. Finally, immunohistochemistry (IHC) and quantitative real-time PCR (qPCR) were used to validate the results of bioinformatics analysis in vitro.

Results

A total of 295 overlapping DEGs (ODEGs) (157 upregulated and 138 downregulated) and 9 hub genes were identified between primary melanoma and normal skin tissues. Functional analysis of these 9 hub genes indicated that the genes were primarily enriched in cell chemotaxis, the chemokine-mediated signaling pathway, the extracellular region, the extracellular space, chemokine activity and CXCR3 chemokine receptor binding. KEGG pathway enrichment showed that these genes were primarily involved in the chemokine signaling pathway, cytokine–cytokine receptor interaction, the toll-like receptor signaling pathway, the cytosolic DNA-sensing pathway and the TNF signaling pathway. Upregulated CCL5, CCL4, CXCL9 and CXCL10 demonstrated good overall survival (OS), and most of them have a higher expression in stage 0 and 1 of melanoma. Moreover, immune infiltration analysis showed that the above hub genes showed a strong positive correlation between their expression and infiltration of the six immune cell subsets. Transcription factor regulation network suggested that RELA and NFKB1 are the transcription factors of CCL4, CCL5, CXCL10 and CXCL2, while IRF7, IRF3 and IRF1 are the transcription factors of CCL5 and CXCL10. Drug–gene interaction analysis identified 46 drug–gene interactions. In vitro data demonstrated that the level of CCL4, CCL5, CXCL9 and CXCL10 is higher in melanoma than that in normal skin tissues, either at tissue or cell lines level.

Conclusion

In summary, we identified 4 key chemokine members related to tumorigenesis and progression in primary melanoma, and these results may help to elucidate melanoma tumorigenesis and facilitate its treatment.

Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors

Quantifying and comparing complex spatial biological datasets is crucial for medical applications and remains an active area of research. As datasets become more heterogeneous and complicated, so must the methods that are used to understand them. Multiparameter topology is built upon the assumption that the shape of data depends on multiple parameters, such as scale, outliers, or other parameters (e.g., cell density and oxygen levels in the case of tumors). A key difficulty encountered in multiparameter persistent homology (MPH) is interpreting and comparing data. The present work uses statistical MPH landscapes to overcome this difficulty and quantifies differences in synthetic data of immune cell infiltration as well as clinical tumor histology data of T cells, macrophages, and hypoxia.

Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers, artifacts, and mislabeled points—such as those from tissues, remains a challenge. The mathematical field that extracts information from the shape of data, topological data analysis (TDA), has expanded its capability for analyzing real-world datasets in recent years by extending theory, statistics, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes, a statistical tool with theoretical underpinnings. MPH landscapes, computed for (noisy) data from agent-based model simulations of immune cells infiltrating into a spheroid, are shown to surpass existing spatial statistics and one-parameter persistent homology. We then apply MPH landscapes to study immune cell location in digital histology images from head and neck cancer. We quantify intratumoral immune cells and find that infiltrating regulatory T cells have more prominent voids in their spatial patterns than macrophages. Finally, we consider how TDA can integrate and interrogate data of different types and scales, e.g., immune cell locations and regions with differing levels of oxygenation. This work highlights the power of MPH landscapes for quantifying, characterizing, and comparing features within the tumor microenvironment in synthetic and real datasets.

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune‐related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune‐related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune‐related biomarkers for metastatic melanoma.

Modern Aspects of Immunotherapy with Checkpoint Inhibitors in Melanoma

Although melanoma is one of the most immunogenic tumors, it has an ability to evade anti-tumor immune responses by exploiting tolerance mechanisms, including negative immune checkpoint molecules. The most extensively studied checkpoints represent cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein 1 (PD-1). Immune checkpoint inhibitors (ICI), which were broadly applied for melanoma treatment in the past decade, can unleash anti-tumor immune responses and result in melanoma regression. Patients responding to the ICI treatment showed long-lasting remission or disease control status. However, a large group of patients failed to respond to this therapy, indicating the development of resistance mechanisms. Among them are intrinsic tumor properties, the dysfunction of effector cells, and the generation of immunosuppressive tumor microenvironment (TME). This review discusses achievements of ICI treatment in melanoma, reasons for its failure, and promising approaches for overcoming the resistance. These methods include combinations of different ICI with each other, strategies for neutralizing the immunosuppressive TME and combining ICI with other anti-cancer therapies such as radiation, oncolytic viral, or targeted therapy. New therapeutic approaches targeting other immune checkpoint molecules are also discussed.