Intratumor Heterogeneity Correlates With Reduced Immune Activity and Worse Survival in Melanoma Patients

Spatial transcriptomic analysis of tumour-immune cell interactions in melanoma arising from congenital melanocytic nevus

BACKGROUND

Studies on the interaction between tumour-infiltrating immune cells (TIICs) and tumour cells in melanoma arising from congenital melanocytic nevus (CMN) are lacking.

OBJECTIVE

The aim of this study was to determine the intratumoral immune landscape of TIICs and tumour cells during invasion and metastasis.

METHODS

Tissue specimens were obtained from patients with melanoma originating from CMN. Differential gene expression in melanoma cells and TIICs during invasion and metastasis was determined using spatial transcriptomics.

RESULTS

As invasion depth increased, the expression of LGALS3, known to induce tumour-driven immunosuppression, increased in melanoma cells. In T cells, the expression of genes that inhibit T-cell activation increased with increasing invasion depth. In macrophages, the expression of genes related to the anti-inflammatory M2 phenotype was upregulated with increasing invasion depth. Compared to primary tumour cells, melanoma cells in metastatic lesions showed upregulated expression of genes associated with cancer immune evasion, including AXL and EPHA2, which impede T-cell recruitment, and BST2, associated with M2 polarization. Furthermore, T cells showed increased expression of genes related to immunosuppression, and macrophages exhibited increased expression of genes associated with the M2 phenotype.

CONCLUSIONS

The interaction between melanomas arising from CMN and TIICs may be important for tumour progression and metastasis.

Evaluating the immunologically "cold" tumor microenvironment after treatment with immune checkpoint inhibitors utilizing PET imaging of CD4 + and CD8 + T cells in breast cancer mouse models

BACKGROUND

Immune-positron emission tomography (PET) imaging with tracers that target CD8 and granzyme B has shown promise in predicting the therapeutic response following immune checkpoint blockade (ICB) in immunologically "hot" tumors. However, immune dynamics in the low T-cell infiltrating "cold" tumor immune microenvironment during ICB remain poorly understood. This study uses molecular imaging to evaluate changes in CD4 + T cells and CD8 + T cells during ICB in breast cancer models and examines biomarkers of response.

METHODS

[89Zr]Zr-DFO-CD4 and [89Zr]Zr-DFO-CD8 radiotracers were used to quantify changes in intratumoral and splenic CD4 T cells and CD8 T cells in response to ICB treatment in 4T1 and MMTV-HER2 mouse models, which represent immunologically "cold" tumors. A correlation between PET quantification metrics and long-term anti-tumor response was observed. Further biological validation was obtained by autoradiography and immunofluorescence.

RESULTS

Following ICB treatment, an increase in the CD8-specific PET signal was observed within 6 days, and an increase in the CD4-specific PET signal was observed within 2 days in tumors that eventually responded to immunotherapy, while no significant differences in CD4 or CD8 were found at the baseline of treatment that differentiated responders from nonresponders. Furthermore, mice whose tumors responded to ICB had a lower CD8 PET signal in the spleen and a higher CD4 PET signal in the spleen compared to non-responders. Intratumoral spatial heterogeneity of the CD8 and CD4-specific PET signals was lower in responders compared to non-responders. Finally, PET imaging, autoradiography, and immunofluorescence signals were correlated when comparing in vivo imaging to ex vivo validations.

CONCLUSIONS

CD4- and CD8-specific immuno-PET imaging can be used to characterize the in vivo distribution of CD4 + and CD8 + T cells in response to immune checkpoint blockade. Imaging metrics that describe the overall levels and distribution of CD8 + T cells and CD4 + T cells can provide insight into immunological alterations, predict biomarkers of response to immunotherapy, and guide clinical decision-making in those tumors where the kinetics of the response differ.

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

Identification of a CpG-based signature coupled with gene expression as prognostic indicators for melanoma: a preliminary study

DNA methylation is an important part of the genomic biology, which recently allowed the identification of key biomarkers for a variety of cancers, including cutaneous melanoma. Despite the current knowledge in cutaneous melanoma, there is a clear need for new efficient biomarkers in clinical application of detection. We use The Cancer Genome Atlas data as a training set and a multi-stage screening strategy to identify prognostic characteristics of melanoma based on DNA methylation. Three DNA methylation CpG sites were identified to be related to the overall survival in the skin cutaneous melanoma cohort. This signature was validated in two independent datasets from Gene Expression Omnibus. The stratified analysis by clinical stage, age, gender, and grade retained the statistical significance. The methylation signature was significantly correlated with immune cells and anti-tumor immune response. Moreover, gene expression corresponding to the candidate CpG locus was also significantly correlated with the survival rate of the patient. About 49% of the prognostic effects of methylation are mediated by affecting the expression of the corresponding genes. The prognostic characteristics of DNA methylation combined with clinical information provide a better prediction value tool for melanoma patients than the clinical information alone. However, more experiments are required to validate these findings. Overall, this signature presents a prospect of novel and wide-ranging applications for appropriate clinical adjuvant trails.

Single-cell RNA sequencing in melanoma: what have we learned so far?

Over the past decade, there have been remarkable improvements in the treatment and survival rates of melanoma patients. Treatment resistance remains a persistent challenge, however, and is partly attributable to intratumoural heterogeneity. Melanoma cells can transition through a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The diverse stromal and immune contexture of the tumour microenvironment also contributes to intratumoural heterogeneity and disparities in treatment response in melanoma patients. Recent advances in single-cell sequencing technologies have enabled a more detailed understanding of melanoma heterogeneity and the underlying transcriptional programs that regulate melanoma cell diversity and behaviour. In this review, we examine the concept of intratumoural heterogeneity and the challenges it poses to achieving long-lasting treatment responses. We focus on the significance of next generation single-cell sequencing in advancing our understanding of melanoma diversity and the unique insights gained from single-cell studies.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

Mismatch repair deficiency is not sufficient to elicit tumor immunogenicity

DNA mismatch repair deficiency (MMRd) is associated with a high tumor mutational burden (TMB) and sensitivity to immune checkpoint blockade (ICB) therapy. Nevertheless, most MMRd tumors do not durably respond to ICB and critical questions remain about immunosurveillance and TMB in these tumors. In the present study, we developed autochthonous mouse models of MMRd lung and colon cancer. Surprisingly, these models did not display increased T cell infiltration or ICB response, which we showed to be the result of substantial intratumor heterogeneity of mutations. Furthermore, we found that immunosurveillance shapes the clonal architecture but not the overall burden of neoantigens, and T cell responses against subclonal neoantigens are blunted. Finally, we showed that clonal, but not subclonal, neoantigen burden predicts ICB response in clinical trials of MMRd gastric and colorectal cancer. These results provide important context for understanding immune evasion in cancers with a high TMB and have major implications for therapies aimed at increasing TMB.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells

Background

Melanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity.

Materials and methods

Cells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines.

Results

Following batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration.

Conclusion

The differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

Towards a consensus definition of immune exclusion in cancer

Background

The immune cell topography of solid tumors has been increasingly recognized as an important predictive factor for progression of disease and response to immunotherapy. The distribution pattern of immune cells in solid tumors is commonly classified into three categories - namely, "Immune inflamed", "Immune desert" and "Immune excluded" - which, to some degree, connect immune cell presence and positioning within the tumor microenvironment to anti-tumor activity.

Materials and methods

In this review, we look at the ways immune exclusion has been defined in published literature and identify opportunities to develop consistent, quantifiable definitions, which in turn, will allow better determination of the underlying mechanisms that span cancer types and, ultimately, aid in the development of treatments to target these mechanisms.

Results

The definitions of tumor immune phenotypes, especially immune exclusion, have largely been conceptual. The existing literature lacks in consistency when it comes to practically defining immune exclusion, and there is no consensus on a definition. Majority of the definitions use somewhat arbitrary cut-offs in an attempt to place each tumor into a distinct phenotypic category. Tumor heterogeneity is often not accounted for, which limits the practical application of a definition.

Conclusions

We have identified two key issues in existing definitions of immune exclusion, establishing clinically relevant cut-offs within the spectrum of immune cell infiltration as well as tumor heterogeneity. We propose an approach to overcome these limitations, by reporting the degree of immune cell infiltration, tying cut-offs to clinically meaningful outcome measures, maximizing the number of regions of a tumor that are analyzed and reporting the degree of heterogeneity. This will allow for a consensus practical definition for operationalizing this categorization into clinical trial and signal-seeking endpoints.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer

BACKGROUND

Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

METHODS

To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

RESULTS

A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

CONCLUSIONS

This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

Heterogeneity in Melanoma

There is growing evidence that tumour heterogeneity has an imperative role in cancer development, evolution and resistance to therapy. Continuing advancements in biomedical research enable tumour heterogeneity to be observed and studied more critically. As one of the most heterogeneous human cancers, melanoma displays a high level of biological complexity during disease progression. However, much is still unknown regarding melanoma tumour heterogeneity, as well as the role it plays in disease progression and treatment response. This review aims to provide a concise summary of the importance of tumour heterogeneity in melanoma.

Mechanisms of immune activation and regulation: lessons from melanoma

Melanoma, a skin cancer that develops from pigment cells, has been studied intensively, particularly in terms of the immune response to tumours, and has been used as a model for the development of immunotherapy. This is due, in part, to the high mutational burden observed in melanomas, which increases both their immunogenicity and the infiltration of immune cells into the tumours, compared with other types of cancers. The immune response to melanomas involves a complex set of components and interactions. As the tumour evolves, it accumulates an increasing number of genetic and epigenetic alterations, some of which contribute to the immunogenicity of the tumour cells and the infiltration of immune cells. However, tumour evolution also enables the development of resistance mechanisms, which, in turn, lead to tumour immune escape. Understanding the interactions between melanoma tumour cells and the immune system, and the evolving changes within the melanoma tumour cells, the immune system and the microenvironment, is essential for the development of new cancer therapies. However, current research suggests that other extrinsic factors, such as the microbiome, may play a role in the immune response to melanomas. Here, we review the mechanisms underlying the immune response in the tumour and discuss recent advances as well as strategies for treatment development.

OrienX010, an oncolytic virus, in patients with unresectable stage IIIC-IV melanoma: a phase Ib study

BACKGROUND

Melanoma in people of Asian descent presents primarily in non-sun-exposed areas, such as acral and mucosal melanoma. Compared with the predominant sun-exposed area melanomas in Caucasians, acral and mucosal melanomas do not respond as well to immunotherapy and are associated with a worse prognosis. Hence, there is an urgent need for improved treatment for melanoma in Asians. This phase Ib trial evaluated the safety and efficacy of the modified herpes simplex virus-1 oncolytic virus OrienX010 in Chinese patients with unresectable stage IIIC-IV melanoma.

METHODS

Patients were treated in two different cohorts. In cohort 08 (n=12), patients received up to 5 mL of 8×107 pfu/mL OrienX010 intratumoral injections every 2 weeks until disease progression and responses were evaluated every 6 weeks. In cohort 09 (n=14), patients received up to 10 mL of 8×107 pfu/mL OrienX010 intratumoral injections and responses were evaluated every 8 weeks.

RESULTS

Between June 2014 and May 2017, 26 patients were enrolled, including 18 (69.2%) patients with acral melanoma. Fever and injection site reaction were the most frequent adverse events. Only one patient experienced a grade ≥3 adverse event and no dose-limiting toxicities were observed. The objective response rate was 19.2% and the disease control rate was 53.8%. The median duration of response was 6.0 months. Antitumor effects were observed in 54.6% of injected lesions and 48.8% of non-injected lesions, including one (16.7%) of six evaluable distant lung metastases. The median progression-free survival was 2.9 months and overall survival was 19.2 months. Compared with patients treated in cohort 08, patients treated in cohort 09 had an improved objective response rate (28.6% vs 8.3%) and a median progression-free survival of 3.0 months vs 2.8 months.

CONCLUSIONS

OrienX010 oncolytic virotherapy has a tolerable safety profile with antitumor effects in both injected and non-injected metastases and warrants further evaluation in patients with melanoma. Based on these results, the higher cohort 09 dose (up to 10 mL of 8×107 pfu/mL every 2 weeks) was selected as the recommended phase II dose for ongoing trials.

TRIAL REGISTRATION NUMBER

CTR20140631 (cohort 08), CTR20150881 (cohort 09).

Management of heterogeneous tumor response patterns to immunotherapy in patients with metastatic melanoma

Immunotherapy has revolutionized the treatment of metastatic melanoma. Response to therapy can be complex to evaluate, as Response Evaluation Criteria in Solid Tumor (RECIST) does not capture heterogeneous responses. In this retrospective single-institution analysis, we describe the management, clinicopathological characteristics, RECIST and disease course of metastatic melanoma patients with a heterogeneous response to first-line anti-CLTA-4 and/or anti-PD-1 between September 2011 and September 2020. In 196 patients, 37 had a heterogeneous response to immunotherapy (19%). Distinct identified responses included a mixed response (MR) (15%), pseudoprogressive disease (PP) (3%), and a sarcoid-like reaction (2%). Patients with a MR and possibly no response to therapy (MR-NR) had a higher median lactic acid dehydrogenase (LDH) (P = 0.01), were more often male (P = 0.04), had more involved disease sites (P = 0.01), and had brain metastasis more frequently (P = 0.02). MR patients with later response to therapy (MR-R) and PP patients had a longer overall survival of 1.7 [95% confidence interval (CI), 1.1-2.7] and 1.6 years (95% CI, 1.3-2.0) versus MR-NR 1.2 (0.7-1.7) (P < 0.01). In this cohort study, we identified prognostic clinical characteristics that can contribute to clinical decision-making for patients with a MR. Additionally, patients with pseudoprogression had benefited from therapy continuation, suggesting the importance of not halting therapy early in case of suspected PP. The male sex, more involved disease sites, brain metastasis and had a higher median LDH were associated with a poor survival for patients with a MR, suggesting that these clinical variables could be used to predict whether a mixed responder will possibly respond to therapy.

Immune System Disorders, Cancer and Viral Infections: A New Treatment Opportunity for the Immune Checkpoint Inhibitors

The relationship between viral infections and cancer is well known and has been established for decades. Multiple tumours are generated from alterations secondary to viral infections 2 resulting from a dysregulation of the immune system in many cases. Certain causal relationships, such as that between the Epstein–Barr virus (EBV) in nasopharyngeal cancer or hepatitis C and B viruses in hepatocarcinoma, have been clearly established, and their implications for the prognosis and treatment of solid tumours are currently unknown. Multiple studies have evaluated the role that these infections may have in the treatment of solid tumours using immunotherapy. A possible relationship between viral infections and an increased response to immune checkpoint inhibitors (ICIs) has been established at a theoretical level in solid neoplasms, such as EBV-positive cavum cancer and human papillomavirus (HPV)-positive and oropharyngeal cancer. These could yield a greater response associated with the activation of the immune system secondary to viral infection, the consequence of which is an increase in survival in these patients. That is why the objective of this review is to assess the different studies or clinical trials carried out in patients with solid tumours secondary to viral infections and their relationship to the response to ICIs.

Magnetic nanoparticles in theranostics of malignant melanoma

Malignant melanoma is an aggressive tumor with a tendency to metastasize early and with an increasing incidence worldwide. Although in early stage, melanoma is well treatable by excision, the chances of cure and thus the survival rate decrease dramatically after metastatic spread. Conventional treatment options for advanced disease include surgical resection of metastases, chemotherapy, radiation, targeted therapy and immunotherapy. Today, targeted kinase inhibitors and immune checkpoint blockers have for the most part replaced less effective chemotherapies. Magnetic nanoparticles as novel agents for theranostic purposes have great potential in the treatment of metastatic melanoma. In the present review, we provide a brief overview of treatment options for malignant melanoma with different magnetic nanocarriers for theranostics. We also discuss current efforts of designing magnetic particles for combined, multimodal therapies (e.g., chemotherapy, immunotherapy) for malignant melanoma.

Melanoma Single-Cell Biology in Experimental and Clinical Settings

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.