Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating cells in LUAD, aiming to improve immunotherapy response during LUAD therapy.

Methods

The ConsensusClusterPlus method was used for clustering immune molecular subtypes of LUAD. Immune cell infiltration and immunotherapeutic potential in each subtype was evaluated employing single-sample gene set enrichment analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS). Immune-related co-expression modules were classified by weighted gene co-expression network analysis (WGCNA) analysis. The sequencing data of immune-related genes were comprehensively analyzed by introducing a new computational framework and 10 machine learning algorithms (a total of 101 combinations) to determine the prognostic genes, which were further combined to develop an immune prognostic signature (IMMPS) using the stepCox and Ridge methods. The expression of the signature genes was validated by quantitative real-time PCR (qRT-PCR).

Results

Samples from The Cancer Genome Atlas dataset (TCGA-LUAD) were divided into two subtypes (immunosuppressive subgroup C1 and immune-activated subgroup C2); notably, the C2 subgroup was more likely to benefit from immunotherapy (p < 0.05). An IMMPS developed based on seven immune infiltrating cell-related genes (SEMA7A, EFHD2, CHST11, SLC24A4, MAL, JCHAIN, and SCARF1) could accurately predict the overall survival of LUAD in five LUAD cohorts, with an average C-index higher than 0.69. LUAD patients with a low IMMPS value had a higher immune cell infiltration (p < 0.05). In addition, the IMMPS exhibited better prediction performance in comparison to 154 published gene signatures, suggesting that the IMMPS was an independent prognostic risk factor for evaluating the overall survival of LUAD patients. Since BTNL9 was the most relevant immune checkpoint gene, in vitro experiment showed that the expression of the seven key genes (SEMA7A, EFHD2, CHST11, SLC24A4, MAL, JCHAIN, and SCARF1) in LUAD cell lines was consistent with that in normal lung epithelial cells after inhibiting BTNL9 expression (p < 0.05).

Conclusions

Our results contributed to a better understanding of immunological characteristics of LUAD. The IMMPS could serve as a promising tool for improving the clinical outcome of patients suffering from LUAD.

The clinical application of artificial intelligence in cancer precision treatment

BACKGROUND

Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.

OBSERVATIONS

By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field.

CONCLUSIONS

Recent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.

Noninvasive early identification of durable clinical benefit from immune checkpoint inhibition: a prospective multicenter study (NCT04566432)

Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for patients with non-small cell lung cancer (NSCLC). In spite of durable responses in some patients, many patients develop early disease progression during the ICI treatment. Thus, early identification of patients with no durable benefit would facilitate the clinical decision for these patients. In this prospective, multicenter study, 101 non-EGFR/ALK patients who received ICI treatment were enrolled after screening 328 stage III-IV NSCLC patients. At the date of cutoff, 83 patients were eligible for ICI efficacy evaluation, with 56 patients having progress-free survival (PFS) over 6 months, which was defined as durable clinical benefit (DCB). A multimodal model was established by integrating normalized bTMB, early dynamic of ctDNA and the first RECIST response. This model could robustly predict DCB with area under the curve (AUC) of 0.878, sensitivity of 79.2% at 86.4% specificity (accuracy = 80.0%). This model was further validated in the independent cohort of the DIREct-On study with AUC of 0.887, sensitivity of 94.7% at 85.3% specificity (accuracy = 90.3%). Patients with higher predict scores had substantially longer PFS than those with lower scores (training cohort: median PFS 13.6 vs 4.2 months, P < 0.001, HR = 0.24; validation cohort: median PFS 11.0 vs 2.2 months, P < 0.001, HR = 0.17). Taken together, these results demonstrate that integrating early changes of ctDNA, normalized bTMB, and the first RECIST response can provide accurate, noninvasive, and early prediction of durable benefits for NSCLC patients treated with ICIs. Further prospective studies are warranted to validate these findings and guide clinical decision-making for optimal immunotherapy in NSCLC patients.

A disulfidptosis-related glucose metabolism and immune response prognostic model revealing the immune microenvironment in lung adenocarcinoma

Background

Lung adenocarcinoma accounts for the majority of lung cancer cases and impact survival rate of patients severely. Immunotherapy is an effective treatment for lung adenocarcinoma but is restricted by many factors including immune checkpoint expression and the inhibitory immune microenvironment. This study aimed to explore the immune microenvironment in lung adenocarcinoma via disulfidptosis.

Methods

Public datasets of lung adenocarcinoma from the TCGA and GEO was adopted as the training and validation cohort. Based on the differences in the expression of disulfidptosis -related genes, a glucose metabolism and immune response prognostic model was constructed. The prognostic value and clinical relationship of the model were further explored. Immune-related analyses were performed according to CIBERSORT, ssGSEA, TIDE, IPS.

Results

We verified that the model could accurately predict the survival expectancy of lung adenocarcinoma patients. Patients with lung adenocarcinoma and a low-risk score had better survival outcomes according to the model. Moreover, the high-risk group tended to have an immunosuppressive effect, as reflected by the immune cell components, phenotypes and functions. We also found that the clinically relevant immune checkpoint CTLA-4 was significantly higher in low-risk group (P<0.05), indicating that the high-risk group may suffer worse tumor immunotherapy efficacy. Finally, we found that this model has accurate predictive value for the efficacy of immune checkpoint blockade in non-small cell lung cancer (P<0.05).

Conclusion

The prognostic model demonstrated the feasibility of predicting survival and immunotherapy efficacy via disulfidptosis-related genes and will facilitate the development of personalized anticancer therapy.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Pan-precancer and cancer DNA methylation profiles revealed significant tissue specificity of interrupted biological processes in tumorigenesis

DNA methylation (DNAme) alterations are known to initiate from the precancerous stage of tumorigenesis. Herein, we investigated the global and local patterns of DNAme perturbations in tumorigenesis by analysing the genome-wide DNAme profiles of the cervix, colorectum, stomach, prostate, and liver at precancerous and cancer stages. We observed global hypomethylation in tissues of both two stages, except for the cervix, whose global DNAme level in normal tissue was lower than that of the other four tumour types. For alterations shared by both stages, there were common hyper-methylation (sHyperMethyl) and hypo-methylation (sHypoMethyl) changes, of which the latter type was more frequently identified in all tissues. Biological pathways interrupted by sHyperMethyl and sHypoMethyl alterations demonstrated significant tissue specificity. DNAme bidirectional chaos indicated by the enrichment of both sHyperMethyl and sHypoMethyl changes in the same pathway was observed in most tissues and was a common phenomenon, particularly in liver lesions. Moreover, for the same enriched pathways, different tissues may be affected by distinct DNAme types. For the PI3K-Akt signalling pathway, sHyperMethyl enrichment was observed in the prostate dataset, but sHypoMethyl enrichment was observed in the colorectum and liver datasets. Nevertheless, they did not show an increased possibility in survival prediction of patients in comparison with other DNAme types. Additionally, our study demonstrated that gene-body DNAme changes of tumour suppressor genes and oncogenes may persist from precancerous lesions to the tumour. Overall, we demonstrate the tissue specificity and commonality of cross-stage alterations in DNA methylation profiles in multi-tissue tumorigenesis.

Single-cell transcriptome analysis reveals the clinical implications of myeloid-derived suppressor cells in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is the most common malignant tumor that arises in the epithelium of the head and neck regions. Myeloid-derived suppressor cells (MDSCs) are one of the tumor-infiltrating immune cell populations, which play a powerful role in inhibiting anti-tumor immune response. Herein, we employed a single-cell RNA sequencing (scRNA-seq) dataset to dissect the heterogeneity of myeloid cells. We found that SPP1 ⁺ tumor-associated macrophages (TAMs) and MDSCs were the most abundant myeloid cells in the microenvironment. By cell cluster deconvolution from bulk RNA-seq datasets of larger patient groups, we observed that highly-infiltrated MDSC was a poor prognostic marker for patients' overall survival (OS) probabilities. To better apply the MDSC OS prediction values, we identified a set of six MDSC-related genes (ALDOA, CD52, FTH1, RTN4, SLC2A3, and TNFAIP6) as the prognostic signature. In both training and test cohorts, MDSC-related prognostic signature showed a promising value for predicting patients' prognosis outcomes. Further parsing the ligand-receptor pairs of intercellular communications by CellChat, we found that MDSCs could frequently interact with cytotoxic CD8 ⁺ T cells, SPP1 ⁺ TAMs, and endothelial cells. These interactions likely contributed to the establishment of an immunosuppressive microenvironment and the promotion of tumor angiogenesis. Our findings suggest that targeting MDSCs may serve as an alternative and promising target for the immunotherapy of HNSC.

Proteogenomic characterization of ferroptosis regulators reveals therapeutic potential in glioblastoma

BACKGROUND

Ferroptosis is iron-dependent non-apoptotic cell death, that is characterized by the excessive accumulation of lipid peroxides. Ferroptosis-inducing therapy also shows promise in the treatment of cancers. However, ferroptosis-inducing therapy for glioblastoma multiforme (GBM) is still in the exploratory stage.

METHODS

We identified the differentially expressed ferroptosis regulators using Mann-Whitney U test in the proteome data from Clinical Proteomic Tumor Analysis Consortium (CPTAC). We next analyzed the effect of mutation on protein abundance. A multivariate Cox model was constructed to identify the prognostic signature.

RESULTS

In this study, we systemically portrayed the proteogenomic landscape of ferroptosis regulators in GBM. We observed that some mutation-specific ferroptosis regulators, such as down-regulated ACSL4 in EGFR-mutated patients and up-regulated FADS2 in IDH1-mutated patients, were linked to the inhibited ferroptosis activity in GBM. To interrogate the valuable treatment targets, we performed the survival analysis and identified five ferroptosis regulators (ACSL3, HSPB1, ELAVL1, IL33, and GPX4) as the prognostic biomarkers. We also validated their efficiency in external validation cohorts. Notably, we found overexpressed protein and phosphorylation abundances of HSPB1 were poor prognosis markers for overall survival of GBM to inhibit ferroptosis activity. Alternatively, HSPB1 showed a significant association with macrophage infiltration levels. Macrophage-secreted SPP1 could be a potential activator for HSPB1 in glioma cells. Finally, we recognized that ipatasertib, a novel pan-Akt inhibitor, could be a potential drug for suppressing HSPB1 phosphorylation, inducing ferroptosis of glioma cells.

CONCLUSION

In summary, our study characterized the proteogenomic landscape of ferroptosis regulators and identified that HSPB1 could be a candidate target for ferroptosis-inducing therapy strategy for GBM.

Radiotherapy induced immunogenic cell death by remodeling tumor immune microenvironment

Emerging evidence indicates that the induction of radiotherapy(RT) on the immunogenic cell death (ICD) is not only dependent on its direct cytotoxic effect, changes in the tumor immune microenvironment also play an important role in it. Tumor immune microenvironment (TIME) refers to the immune microenvironment that tumor cells exist, including tumor cells, inflammatory cells, immune cells, various signaling molecules and extracellular matrix. TIME has a barrier effect on the anti-tumor function of immune cells, which can inhibit all stages of anti-tumor immune response. The remodeling of TIME caused by RT may affect the degree of immunogenicity, and make it change from immunosuppressive phenotype to immunostimulatory phenotype. It is of great significance to reveal the causes of immune escape of tumor cells, especially for the treatment of drug-resistant tumor. In this review, we focus on the effect of RT on the TIME, the mechanism of RT in reversing the TIME to suppress intrinsic immunity, and the sensitization effect of the remodeling of TIME caused by RT on the effectiveness of immunotherapy.

A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

Intercellular Communication Reveals Therapeutic Potential of Epithelial-Mesenchymal Transition in Triple-Negative Breast Cancer

(1) Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high intra-tumoral heterogeneity. The epithelial-mesenchymal transition (EMT) is one of the inducers of cancer metastasis and migration. However, the description of the EMT process in TNBC using single-cell RNA sequencing (scRNA-seq) remains unclear. (2) Methods: In this study, we analyzed 8938 cellular gene expression profiles from five TNBC patients. We first scored each malignant cell based on functional pathways to determine its EMT characteristics. Then, a pseudo-time trajectory analysis was employed to characterize the cell trajectories. Furthermore, CellChat was used to identify the cellular communications. (3) Results: We identified 888 epithelium-like and 846 mesenchyme-like malignant cells, respectively. A further pseudo-time trajectory analysis indicated the transition trends from epithelium-like to mesenchyme-like in malignant cells. To characterize the potential regulators of the EMT process, we identified 10 dysregulated transcription factors (TFs) between epithelium-like and mesenchyme-like malignant cells, in which overexpressed forkhead box protein A1 (FOXA1) was recognized as a poor prognosis marker of TNBC. Furthermore, we dissected the cell-cell communications via ligand-receptor (L-R) interactions. We observed that tumor-associated macrophages (TAMs) may support the invasion of malignant epithelial cells, based on CXCL-CXCR2 signaling. The tumor necrosis factor (TNF) signaling pathway secreted by TAMs was identified as an outgoing communication pattern, mediating the communications between monocytes/TAMs and malignant epithelial cells. Alternatively, the TNF-related ligand-receptor (L-R) pairs showed promising clinical implications. Some immunotherapy and anti-neoplastic drugs could interact with the L-R pairs as a potential strategy for the treatment of TNBC. In summary, this study enhances the understanding of the EMT process in the TNBC microenvironment, and dissections of EMT-related cell communications also provided us with potential treatment targets.