Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Recent advances in nanotechnology for programmed death ligand 1-targeted cancer theranostics

Programmed cell death ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) checkpoint inhibitor-based immunotherapy has provided a unique and potent weapon against cancer in clinical practice. The likelihood of achieving beneficial effects from PD-L1/PD-1 immune checkpoint blockade (ICB) therapy is clinically assessed by detecting PD-L1 expression through invasive tissue biopsies. However, PD-L1 expression is susceptible to tumor heterogeneity and dynamic response to ICB therapy. Moreover, currently, anti-PD-L1 immunotherapy still faces challenges of the low targeting efficiency of antibody drugs and the risk of immune-associated adverse events. To overcome these issues, advanced nanotechnology has been developed for the purpose of quantitative, non-invasive, and dynamic analyses of PD-L1, and to enhance the efficiency of ICB therapy. In this review, we first introduce the nanoprobe-assisted in vitro/in vivo modalities for the selective and sensitive analysis of PD-L1 during the diagnostic and therapeutic process. On the other hand, the feasibility of fabricating diverse functional nanocarriers as smart delivery systems for precisely targeted delivery of PD-L1 immune checkpoint inhibitors and combined therapies is highlighted. Finally, the current challenges are discussed and future perspectives for PD-L1-targeted cancer theranostics in preclinical research and clinical settings are proposed.

An advanced comprehensive muti-cell-type-specific model for predicting anti-PD-1 therapeutic effect in melanoma

Rationale: Immune checkpoint inhibitors targeting the programmed cell death (PD)-1/PD-L1 pathway have promise in patients with advanced melanoma. However, drug resistance usually results in limited patient benefits. Recent single-cell RNA sequencing studies have elucidated that MM patients display distinctive transcriptional features of tumor cells, immune cells and interstitial cells, including loss of antigen presentation function of tumor cells, exhaustion of CD8+T and extracellular matrix secreted by fibroblasts to prevents immune infiltration, which leads to a poor response to immune checkpoint inhibitors (ICIs). However, cell subgroups beneficial to anti-tumor immunity and the model developed by them remain to be further identified. Methods: In this clinical study of neoadjuvant therapy with anti-PD-1 in advanced melanoma, tumor tissues were collected before and after treatment for single-nucleus sequencing, and the results were verified using multicolor immunofluorescence staining and public datasets. Results: This study describes four cell subgroups which are closely associated with the effectiveness of anti-PD-1 treatment. It also describes a cell-cell communication network, in which the interaction of the four cell subgroups contributes to anti-tumor immunity. Furthermore, we discuss a newly developed predictive model based on these four subgroups that holds significant potential for assessing the efficacy of anti-PD-1 treatment. Conclusions: These findings elucidate the primary mechanism of anti-PD-1 resistance and offer guidance for clinical drug administration for melanoma.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

Global trends in tumor microenvironment-related research on tumor vaccine: a review and bibliometric analysis

Background

Tumor vaccines have become crucial in cancer immunotherapy, but, only a limited number of phase III clinical trials have demonstrated clinical efficacy. The crux of this issue is the inability of tumor vaccines to effectively harmonize the tumor microenvironment with its intricate interplay. One factor that can hinder the effectiveness of vaccines is the natural immunosuppressive element present in the tumor microenvironment. This element can lead to low rates of T-cell response specific to antigens and the development of acquired resistance. Conversely, anticancer vaccines alter the tumor microenvironment in conflicting manners, inducing both immune activation and immunological evasion. Hence, comprehending the correlation between tumor vaccines and the tumor microenvironment would establish a foundation for forthcoming tumor treatment.

Objective

Our review explores the realm of research pertaining to tumor vaccinations and the tumor microenvironment. Our objective is to investigate the correlation between tumor vaccines and the tumor microenvironment within this domain. We then focus our review on the dominant international paradigms in this research field and visually illustrates the historical progression and emergent patterns observed in the past.

Methods

From January 1, 1999 to February 7, 2023, 1420 articles on the interplay between tumor vaccines and the tumor microenvironment were published, according to The Clarivate Web of Science (WOS) database used in our review. A bibliometric review was designed for this collection and consisted of an evaluation. The evaluation encompassed various discernible attributes, including the year of publication, the journals in which the articles were published, the authors involved, the affiliated institutions, the geographical locations of the institutions, the references cited, and the keywords employed.

Results

Between the years 1999 and 2022, publications saw a significant increase, from 3 to 265 annually. With 72 papers published, Frontiers in Immunology had the most manuscripts published. The Cancer Research publication garnered the highest number of citations, amounting to 2874 citations. The United States exerts significant dominance in the subject, with the National Cancer Institute being recognized as a prominent institution in terms of both productivity and influence. Furthermore, Elizabeth M. Jaffee was recognized as the field's most prolific and influential author with 24 publications and 1,756 citations. The co-occurrence cluster analysis was conducted on the top 197 keywords, resulting in the identification of five distinct clusters. The most recent high-frequency keywords, namely immune therapy, dendritic cell, tumor microenvironment, cancer, and vaccine, signify the emerging frontiers in the interaction between tumor vaccines and the tumor microenvironment.

Conclusion

Our review uncovers insights into contemporary trends, global patterns of collaboration, fundamental knowledge, research areas of high interest, and emerging frontiers in the field of TME-targeted vaccines.

RPTOR mutation: a novel predictor of efficacious immunotherapy in melanoma

Identifying biomarkers to evaluate the therapeutic effect of immune checkpoint inhibitors (ICIs) is crucial. Regulatory Associated Protein of MTOR Complex 1 (RPTOR), one of the genes in the mTOR pathway, plays a role in regulating tumor progression. However, the connection between RPTOR mutation and the efficacy of ICIs in melanoma remains unclear. The data of ICIs-treated melanoma patients in discovery (n = 384) and validation (n = 320) cohorts were obtained from cBioPortal databases. The genomic data in the two cohorts was used to investigate the connection between RPTOR mutation and immunotherapy efficacy. The underlying mechanisms were explored based on data from the The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) cohort. Compared to melanoma patients with RPTOR wildtype (RPTOR-WT), RPTOR-mutation (RPTOR-Mut) patients achieved prolonged overall survival (OS) in both discovery cohort (median OS of 49.3 months vs. 21.7 months; HR = 0.41, 95% CI: 0.18-0.92; P = 0.026) and validation cohorts (not reached vs. 42.0 months; HR = 0.34, 95% CI: 0.11-1.06; P = 0.049). RPTOR-Mut melanoma patients exhibited a higher objective response rate (ORR) than RPTOR-WT patients in the discovery cohort (55.0% vs. 29.0%, P = 0.022). RPTOR-Mut patients exhibited higher TMB than RPTOR-WT patients in both discovery and validation cohorts (P < 0.001). RPTOR-Mut melanoma patients had an increased number of DNA damage response (DDR) mutations in TCGA-SKCM cohort. Immune cell infiltration analysis suggested that activated CD4 memory T cells were more enriched in RPTOR-Mut tumors. RPTOR-Mut melanoma patients had higher expression levels of immune-related genes than the RPTOR-WT patients. Our results suggest that RPTOR mutation could serve as a predictor of effective immunotherapy for melanoma.

CD39 identifies a specific CD8 + T cell population in lung adenocarcinoma-related metastatic pleural effusion

Malignant pleural effusion (MPE), which is a complex microenvironment that contains numerous immune and tumour signals, is common in lung cancer. Gene alterations, such as driver gene mutations, are believed to affect the components of tumour immunity in the microenvironment (TIME) of non-small-cell lung cancer. In this study, we have shown that pleural CD39 + CD8 + T cells are selectively elevated in lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFRwt) compared to those with newly diagnosed mutant EGFR (EGFRmu). Furthermore, these CD39 + CD8 + T cells are more prevalent in MPE with acquired resistance to EGFR-tyrosine kinase inhibitors (AR-EGFR-TKIs). Our analysis reveals that pleural CD39 + CD8 + T cells exhibit an exhausted phenotype while still retaining cytolytic function. Additionally, they have a higher T cell receptor (TCR) repertoire clonality compared to CD39-CD8 + T cells, which is a unique characteristic of LUAD-related MPE. Further investigation has shown that TCR-Vβ clonality tends to be more enhanced in pleural CD39 + CD8 + T cells from MPE with AR-EGFR-TKIs. In summary, we have identified a subset of CD8 + T cells expressing CD39 in MPE, which may potentially be tumour-reactive CD8 + T cells. This study provides new insights into the dynamic immune composition of the EGFRmu tumour microenvironment.

Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells

BACKGROUND

Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma.

METHODS

We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients.

RESULTS

This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8+ T cells infiltration.

CONCLUSIONS

In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.

Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends

Immune-checkpoint inhibitors (ICBs), in addition to targeting CTLA-4, PD-1, and PD-L1, novel targeting LAG-3 drugs have also been approved in clinical application. With the widespread use of the drug, we must deeply analyze the dilemma of the agents and seek a breakthrough in the treatment prospect. Over the past decades, these agents have demonstrated dramatic efficacy, especially in patients with melanoma and non-small cell lung cancer (NSCLC). Nonetheless, in the field of a broad concept of solid tumours, non-specific indications, inseparable immune response and side effects, unconfirmed progressive disease, and complex regulatory networks of immune resistance are four barriers that limit its widespread application. Fortunately, the successful clinical trials of novel ICB agents and combination therapies, the advent of the era of oncolytic virus gene editing, and the breakthrough of the technical barriers of mRNA vaccines and nano-delivery systems have made remarkable breakthroughs currently. In this review, we enumerate the mechanisms of each immune checkpoint targets, associations between ICB with tumour mutation burden, key immune regulatory or resistance signalling pathways, the specific clinical evidence of the efficacy of classical targets and new targets among different tumour types and put forward dialectical thoughts on drug safety. Finally, we discuss the importance of accurate triage of ICB based on recent advances in predictive biomarkers and diagnostic testing techniques.

Biomarkers and factors in small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

OBJECTIVE

The aim of this meta-analysis was to summarize the available results of immunotherapy predictors for small cell lung cancer (SCLC) and to provide evidence-based information for their potential predictive value of efficacy.

METHODS

We searched PubMed, EMBASE, Web of Science, The Cochrane Library, and ClinicalTrials (from January 1, 1975 to November 1, 2021). The hazard ratios (HR) and its 95% confidence intervals (CIs) and tumor response rate of the included studies were extracted.

RESULTS

Eleven studies were eventually included and the pooled results showed that programmed cell death ligand 1 (PD-L1) positive: objective response rate (ORR) (relative risk [RR] = 1.39, 95% CI [0.48, 4.03], p = 0.54), with high heterogeneity (p = 0.05, I²  = 56%); disease control rate [DCR] (RR = 1.31, 95% CI [0.04, 38.57], p = 0.88), with high heterogeneity (p = 0.04, I²  = 75%); overall survival (OS) (HR = 0.89, 95% CI [0.74, 1.07], p = 0.22); and progression-free survival (PFS) (HR = 0.83, 95% CI [0.59, 1.16], p = 0.27), with high heterogeneity (p = 0.005, I²  = 73.1%). TMB-High (TMB-H): OS (HR = 0.86, 95% CI [0.74, 1.00], p = 0.05); PFS (HR = 0.71, 95% CI [0.6, 0.85], p < 0.001). Lactate dehydrogenase (LDH) >upper limit of normal (ULN): OS (HR = 0.95, 95% CI [0.81, 1.11], p = 0.511). Asian patients: OS (HR = 0.87, 95% CI [0.72, 1.04], p = 0.135); White/Non-Asian patients: OS (HR = 0.83, 95% CI [0.76, 0.90], p < 0.001). Liver metastasis patients: OS (HR = 0.93, 95% CI [0.83, 1.05], p = 0.229); PFS (HR = 0.84, 95% CI [0.67, 1.06], p = 0.141). Central nervous system (CNS) metastasis patients: OS (HR = 0.91, 95% CI [0.71, 1.17], p = 0.474); PFS (HR = 1.03, 95% CI [0.66, 1.60], p = 0.903).

CONCLUSION

The available research results do not support the recommendation of PD-L1 positive and TMB-H as predictors for the application of immune checkpoint inhibitors (ICIs) in SCLC patients. LDH, baseline liver metastasis and CNS metastasis may be used as markers/influencing factors for predicting the efficacy of ICIs in SCLC patients. Non-Asian SCLC patients had better efficacy with ICIs in our results.

The Genomic Landscape of Melanoma and Its Therapeutic Implications

Melanoma is one of the most aggressive malignancies of the skin. The genetic composition of melanoma is complex and varies among different subtypes. With the aid of recent technologies such as next generation sequencing and single-cell sequencing, our understanding of the genomic landscape of melanoma and its tumor microenvironment has become increasingly clear. These advances may provide explanation to the heterogenic treatment outcomes of melanoma patients under current therapeutic guidelines and provide further insights to the development of potential new therapeutic targets. Here, we provide a comprehensive review on the genetics related to melanoma tumorigenesis, metastasis, and prognosis. We also review the genetics affecting the melanoma tumor microenvironment and its relation to tumor progression and treatment.

Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients

Background

Immune cells are crucial components of the tumor microenvironment (TME) and regulate cancer cell development. Nevertheless, the clinical implications of immune cell infiltration-related mRNAs for bladder cancer (BCa) are still unclear.

Methods

A 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms was employed to develop a consensus immune cell infiltration-related signature (IRS). The predictive performance of IRS in terms of prognosis and immunotherapy was comprehensively evaluated.

Results

The IRS demonstrated high accuracy and stable performance in prognosis prediction across multiple datasets including TCGA-BLCA, eight independent GEO datasets, our in-house cohort (PUMCH_Uro), and thirteen immune checkpoint inhibitors (ICIs) cohorts. Additionally, IRS was superior to traditional clinicopathological features (e.g., stage and grade) and 94 published signatures. Furthermore, IRS was an independent risk factor for overall survival in TCGA-BLCA and several GEO datasets, and for recurrence-free survival in PUMCH_Uro. In the PUMCH_Uro cohort, patients in the high-IRS group were characterized by upregulated CD8A and PD-L1 and TME of inflamed and immunosuppressive phenotypes. As predicted, these patients should benefit from ICI therapy and chemotherapy. Furthermore, in the ICI cohorts, the high-IRS group was related to a favorable prognosis and responders have dramatically higher IRS compared to non-responders.

Conclusions

Generally, these indicators suggested the promising application of IRS in urological practices for the early identification of high-risk patients and potential candidates for ICI application to prolong the survival of individual BCa patients.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

3D tumor explant as a novel platform to investigate therapeutic pathways and predictive biomarkers in cancer patients

Immunotherapy with immune checkpoint inhibitors can induce durable clinical responses in different human malignancies but the number of responding patients remains globally modest. The limited therapeutic efficacy of ICI depends on multiple factors, among which the immune suppressive features of the tumor microenvironment play a key role. For this reason, experimental models that enable dissection of the immune-hostile tumor milieu components are required to unravel how to overcome resistance and obtain full-fledged anti-tumor immunity. Recent evidence supports the usefulness of 3D ex vivo systems in retaining features of tumor microenvironment to elucidate molecular and immunologic mechanisms of response and resistance to immune checkpoint blockade. In this perspective article we discuss the recent advances in patient-derived 3D tumor models and their potential in support of treatment decision making in clinical setting. We will also share our experience with dynamic bioreactor tumor explant culture of samples from melanoma and sarcoma patients as a reliable and promising platform to unravel immune responses to immune checkpoint inhibitors.