Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy

BACKGROUND

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

METHODS

We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

RESULTS

We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

CONCLUSION

Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

ALDH1A3-acetaldehyde metabolism potentiates transcriptional heterogeneity in melanoma

Cancer cellular heterogeneity and therapy resistance arise substantially from metabolic and transcriptional adaptations, but how these are interconnected is poorly understood. Here, we show that, in melanoma, the cancer stem cell marker aldehyde dehydrogenase 1A3 (ALDH1A3) forms an enzymatic partnership with acetyl-coenzyme A (CoA) synthetase 2 (ACSS2) in the nucleus to couple high glucose metabolic flux with acetyl-histone H3 modification of neural crest (NC) lineage and glucose metabolism genes. Importantly, we show that acetaldehyde is a metabolite source for acetyl-histone H3 modification in an ALDH1A3-dependent manner, providing a physiologic function for this highly volatile and toxic metabolite. In a zebrafish melanoma residual disease model, an ALDH1-high subpopulation emerges following BRAF inhibitor treatment, and targeting these with an ALDH1 suicide inhibitor, nifuroxazide, delays or prevents BRAF inhibitor drug-resistant relapse. Our work reveals that the ALDH1A3-ACSS2 couple directly coordinates nuclear acetaldehyde-acetyl-CoA metabolism with specific chromatin-based gene regulation and represents a potential therapeutic vulnerability in melanoma.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

Systematic investigation of chemo-immunotherapy synergism to shift anti-PD-1 resistance in cancer

Chemo-immunotherapy combinations have been regarded as one of the most practical ways to improve immunotherapy response in cancer patients. In this study, we integrate the transcriptomics data from anti-PD-1-treated tumors and compound-treated cancer cell lines to systematically screen for chemo-immunotherapy synergisms in silico. Through analyzing anti-PD-1 induced expression changes in patient tumors, we develop a shift ability score to measure if a chemotherapy or a small molecule inhibitor treatment can shift anti-PD-1 resistance in tumor cells. By applying shift ability analysis to 41,321 compounds and 16,853 shRNA treated cancer cell lines transcriptomic data, we characterize the landscape of chemo-immunotherapy synergism and experimentally validated a mitochondrial RNA-dependent mechanism for drug-induced immune activation in tumor. Our study represents an effort to mechanistically characterize chemo-immunotherapy synergism and will facilitate future pre-clinical and clinical studies.

PD-1 defines a distinct, functional, tissue-adapted state in Vδ1+ T cells with implications for cancer immunotherapy

Checkpoint inhibition (CPI), particularly that targeting the inhibitory coreceptor programmed cell death protein 1 (PD-1), has transformed oncology. Although CPI can derepress cancer (neo)antigen-specific αβ T cells that ordinarily show PD-1-dependent exhaustion, it can also be efficacious against cancers evading αβ T cell recognition. In such settings, γδ T cells have been implicated, but the functional relevance of PD-1 expression by these cells is unclear. Here we demonstrate that intratumoral TRDV1 transcripts (encoding the TCRδ chain of Vδ1+ γδ T cells) predict anti-PD-1 CPI response in patients with melanoma, particularly those harboring below average neoantigens. Moreover, using a protocol yielding substantial numbers of tissue-derived Vδ1+ cells, we show that PD-1+Vδ1+ cells display a transcriptomic program similar to, but distinct from, the canonical exhaustion program of colocated PD-1+CD8+ αβ T cells. In particular, PD-1+Vδ1+ cells retained effector responses to TCR signaling that were inhibitable by PD-1 engagement and derepressed by CPI.

Tozasertib activates anti-tumor immunity through decreasing regulatory T cells in melanoma

Although immune checkpoint therapy has significantly improved the prognosis of patients with melanoma, urgent attention still needs to be paid to the low patient response rates and the challenges of precisely identifying patients before treatment. Therefore, it is crucial to investigate novel immunosuppressive mechanisms and targets in the tumor microenvironment in order to reverse tumor immune escape. In this study, we found that the cell cycle checkpoint Aurora kinase B (AURKB) suppressed the anti-tumor immune response, and its inhibitor, Tozasertib, effectively activated T lymphocyte cytokine release in vitro and anti-tumor immunity in vivo. Tozasertib significantly inhibited melanoma xenograft tumor growth by decreasing the number of inhibitory CD4+ Treg cells in the tumors, which, in turn, activated CD8+ T cells. Single-cell analysis revealed that AURKB suppressed anti-tumor immunity by increasing MIF-CD74/CXCR4 signaling between tumor cells and lymphocytes. Our study suggests that AURKB is a newly identified anti-tumor immunity suppressor, whose inhibitors may be developed as novel anti-tumor immunity drugs and may have synergistic anti-melanoma effects with immune checkpoint therapies.

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

Recent advancements in the B7/CD28 immune checkpoint families: new biology and clinical therapeutic strategies

The B7/CD28 families of immune checkpoints play vital roles in negatively or positively regulating immune cells in homeostasis and various diseases. Recent basic and clinical studies have revealed novel biology of the B7/CD28 families and new therapeutics for cancer therapy. In this review, we discuss the newly discovered KIR3DL3/TMIGD2/HHLA2 pathways, PD-1/PD-L1 and B7-H3 as metabolic regulators, the glycobiology of PD-1/PD-L1, B7x (B7-H4) and B7-H3, and the recently characterized PD-L1/B7-1 cis-interaction. We also cover the tumor-intrinsic and -extrinsic resistance mechanisms to current anti-PD-1/PD-L1 and anti-CTLA-4 immunotherapies in clinical settings. Finally, we review new immunotherapies targeting B7-H3, B7x, PD-1/PD-L1, and CTLA-4 in current clinical trials.

Development and validation of a tobacco smoking-related index for predicting overall survival and immunotherapy response in bladder cancer

Bladder cancer is one of the top five most prevalent cancers in the United States and a major cause of cancer-related mortality worldwide. Meanwhile, tobacco smoking is a well-established modifiable risk factor for bladder cancer, with a population-attributable risk of approximately 50%. But the relationship between the prognosis of bladder cancer and tobacco smoking remains unclear. To further explore the potential relationship between tobacco smoking and bladder cancer prognosis, the bladder cancer dataset from The Cancer Genome Atlas Program was used to build a tobacco smoking-related signature known as the "smoker index" for prognosis prediction. Additionally, we validated the efficacy of the signature with some external datasets. Finally, we preliminarily verified the role of CGB5, the hub gene in the smoker index, through pan-cancer analysis and in vitro assays. The study digs into the underlying connection between tobacco smoking and the prognosis of bladder cancer from a multi-omics perspective.

signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data

Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.

Molecular Skin Cancer Diagnosis: Promise and Limitations

Skin cancer is a significant and increasing global health burden. Although the current diagnostic workflow is robust and able to provide clinically actionable results, it is subject to notable limitations. The training and expertise required for accurate diagnoses using conventional skin cancer diagnostics are significant, and patient access to this workflow can be limited by geographic location or unforeseen events, such as coronavirus disease 2019 (COVID-19). Molecular biomarkers have transformed diagnostics and treatment delivery in oncology. With rapid advancements in molecular biology techniques, understanding of the underlying molecular mechanism of cancer pathologies has deepened, yielding biomarkers that can be used to monitor the course of malignant diseases. Herein, commercially available, clinically validated, and emerging skin cancer molecular biomarkers are reviewed. The qualities of an ideal molecular biomarker are defined. The potential benefits and limitations of applying molecular biomarker testing over the course of skin cancer from susceptibility to treatment are explored, with a view to outlining a future model of molecular biomarker skin cancer diagnostics.

Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma

Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.

Comprehensive analysis of glycoprotein VI-mediated platelet activation signaling pathway for predicting pan-cancer survival and response to anti-PD-1 immunotherapy

Platelets play a vital role in cancer and immunity. However, few comprehensive studies have been conducted on the role of platelet-related signaling pathways in various cancers and their responses to immune checkpoint blockade (ICB) therapy. In the present study, we focused on the glycoprotein VI-mediated platelet activation (GMPA) signaling pathway and comprehensively evaluated its roles in 19 types of cancers listed in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Cox regression and meta-analyses showed that for all 19 types of cancers, patients with high GMPA scores tended to have a good prognosis. Furthermore, the GMPA signature score could serve as an independent prognostic factor for patients with skin cutaneous melanoma (SKCM). The GMPA signature was linked to tumor immunity in all 19 types of cancers, and was correlated with SKCM tumor histology. Compared to other signature scores, the GMPA signature scores for on-treatment samples were more robust predictors of the response to anti-PD-1 blockade in metastatic melanoma. Moreover, the GMPA signature scores were significantly negatively correlated with EMMPRIN (CD147) and positively correlated with CD40LG expression at the transcriptomic level in most cancer patient samples from the TCGA cohort and on-treatment samples from anti-PD1 therapy cohorts. The results of this study provide an important theoretical basis for the use of GMPA signatures, as well as GPVI-EMMPRIN and GPVI-CD40LG pathways, to predict the responses of cancer patients to various types of ICB therapy.

Functional Gene Expression Signatures from On-Treatment Tumor Specimens Predict Anti-PD1 Blockade Response in Metastatic Melanoma

Functional gene expression signatures (FGES) from pretreatment biopsy samples have been used to predict the responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies. However, there are no predictive FGE signatures from patients receiving treatment. Here, using the Elastic Net Regression (ENLR) algorithm, we analyzed transcriptomic and matching clinical data from a dataset of patients with metastatic melanoma treated with ICB therapies and produced an FGE signature for pretreatment (FGES-PRE) and on-treatment (FGES-ON). Both the FGES-PRE and FGES-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.44-0.81 and 0.82-0.83, respectively. Then, we combined all test samples and obtained AUCs of 0.71 and 0.82 for the FGES-PRE and FGES-ON signatures, respectively. The FGES-ON signatures had a higher predictive value for prognosis than the FGES-PRE signatures. The FGES-PRE and FGES-ON signatures were divided into high- and low-risk scores using the signature score mean value. Patients with a high FGE signature score had better survival outcomes than those with low scores. Overall, we determined that the FGES-ON signature is an effective biomarker for metastatic melanoma patients receiving ICB therapy. This work would provide an important theoretical basis for applying FGE signatures derived from on-treatment tumor samples to predict patients' therapeutic response to ICB therapies.

Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Reveals a Tissue-Resident Macrophage-Related Signature for Predicting Immunotherapy Response in Breast Cancer Patients

Immune checkpoint therapy (ICT) is among the widely used treatments for breast cancer (BC), but most patients do not respond to ICT and the availability of the predictive biomarkers is limited. Emerging evidence indicates that tissue-resident macrophages (RTMs) inhibit BC progression, suggesting that their presence may predict immunotherapy response. A single-cell RNA-sequencing analysis of BC samples was performed to identify five RTM clusters with a mixed phenotype of M1-M2 macrophages. The comprehensive results showed that a high score of each RTM cluster was associated with a high infiltration of CD8+ T cells, M1 macrophages, and dendritic cells, and improved overall survival. In addition, a low score of each RTM cluster was associated with a high infiltration of M0 macrophages, naïve B cells and Tregs, and poor overall survival. Gene signatures from each RTM cluster were significantly enriched in responders compared with nonresponders. Each RTM cluster expression was significantly higher in responders than in nonresponders. The analyses of bulk RNA-seq datasets of BC samples led to identification and validation of a gene expression signature, named RTM.Sig, which contained the related genes of RTM clusters for predicting response to immunotherapy. This study highlights RTM.Sig could provide a valuable tool for clinical decisions in administering ICT.

Cutting edges and therapeutic opportunities on tumor-associated macrophages in lung cancer

Lung cancer is a disease with remarkable heterogeneity. A deep understanding of the tumor microenvironment (TME) offers potential therapeutic strategies against this malignant disease. More and more attention has been paid to the roles of macrophages in the TME. This article briefly summarizes the origin of macrophages, the mutual regulation between anti-tumoral immunity and pro-tumoral statuses derived from macrophage polarization, and the therapeutic opportunities targeting alternately activated macrophages (AAM)-type macrophage polarization. Among them, cellular components including T cells, as well as acellular components represented by IL-4 and IL-13 are key regulators driving the polarization of AAM macrophages. Novel treatments targeting macrophage-associated mechanisms are mainly divided into small molecule inhibitors, monoclonal antibodies, and other therapies to re-acclimate AMM macrophages. Finally, we paid special attention to an immunosuppressive subgroup of macrophages with T cell immunoglobulin and mucin domain-3 (TIM-3) expression. Based on cellular interactions with cancer cells, TIM3+ macrophages facilitate the proliferation and progression of cancer cells, yet this process exposes targets blocking the ligand-receptor recognition. To sum up, this is a systematic review on the mechanism of tumor-associated macrophages (TAM) polarization, therapeutic strategies and the biological functions of Tim-3 positive macrophages that aims to provide new insights into the pathogenesis and treatment of lung cancer.

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma

Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.

Protein Quality Control in Glioblastoma: A Review of the Current Literature with New Perspectives on Therapeutic Targets

Protein quality control allows eukaryotes to maintain proteostasis under the stress of constantly changing conditions. In this review, we discuss the current literature on PQC, highlighting flaws that must exist for malignancy to occur. At the nidus of PQC, the expression of BAG1-6 reflects the cell environment; each isoform directs proteins toward different, parallel branches of the quality control cascade. The sum of these branches creates a net shift toward either homeostasis or apoptosis. With an established role in ALP, Bag3 is necessary for cell survival in stress conditions including those of the cancerous niche (i.e., hypoxia, hypermutation). Evidence suggests that excessive Bag3–HSP70 activity not only sustains, but also propagates cancers. Its role is anti-apoptotic—which allows malignant cells to persist—and intercellular—with the production of infectious ‘oncosomes’ enabling cancer expansion and recurrence. While Bag3 has been identified as a key prognostic indicator in several cancer types, its investigation is limited regarding glioblastoma. The cochaperone HSP70 has been strongly linked with GBM, while ALP inhibitors have been shown to improve GBM susceptibility to chemotherapeutics. Given the highly resilient, frequently recurrent nature of GBM, the targeting of Bag3 is a necessary consideration for the successful and definitive treatment of GBM.