Predict Immunotherapy Response Research Articles

Prognosis of patients with advanced bile tract carcinoma: assessment using the modified-Gustave Roussy Immune Score (mGRIm-s) as a clinico-immunological tool

BackgroundThe emergence of immune checkpoint inhibitors (ICIs) has enhanced survival outcomes for certain patients with advanced biliary tract carcinoma (BTC). Pinpointing those who would benefit most from immunotherapy remains elusive. We investigated the predictive value of the modified Gustave Roussy Immune Score (mGRIm-s) in BTC patients treated with ICIs.MethodsData from 110 patients at Chinese People's Liberation Army General Hospital, spanning September 2015 to April 2021, were analyzed. The median follow-up duration was 38.7 months as of December 2023. Risk factors included low albumin, high lactate dehydrogenase, and an elevated neutrophil–lymphocyte ratio. Patients were stratified into low (patients with no risk factors) and high (patients with at least one risk factor) mGRIm-s groups based on these factors.ResultsSurvival outcomes post-immunotherapy favored the low mGRIm-s group, with significantly improved progression-free survival (PFS) and overall survival (OS) (8.50 months vs. 3.70 months and 21.60 months vs. 8.00 months). COX regression confirmed an elevated risk in the high mGRIm-s group. Subgroup analysis highlighted a notable survival advantage for low mGRIm-s patients receiving first-line immunotherapy.ConclusionsThis study underscores mGRIm-s's potential in predicting immunotherapy response in BTC, paving the way for more targeted approaches.

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients’ prognosis and immunotherapy response based on apoptosis-related signature.Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model’s validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer.Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines.Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.

The identification of a N6-methyladenosin-modifed immune pattern to predict immunotherapy response and survival in urothelial carcinoma.

Dysregulation of the immune system and N6-methyladenosine (m6A) contribute to immune therapy resistance and cancer progression in urothelial carcinoma (UC). This study aims to identify immune-related molecules, that are m6A-modified, and that are associated with tumor progression, poor prognosis, and immunotherapy response. We identified prognostic immune genes (PIGs) using Cox analysis and random survival forest variable hunting algorithm (RSF-VH) on immune genes retrieved from the Immunology Database and Analysis Portal database (ImmPort). The RM2Target database and MeRIP-seq analysis, combined with a hypergeometric test, assessed m6A methylation in these PIGs. We analyzed the correlation between the immune pattern and prognosis, as well as their association with clinical factors in multiple datasets. Moreover, we explored the interplay between immune patterns, tumor immune cell infiltration, and m6A regulators. 28 PIGs were identified, of which the 10 most significant were termed methylated prognostic immune genes (MPIGs). These MPIGs were used to create an immune pattern score. Kaplan-Meier and Cox analyses indicated this pattern as an independent risk factor for UC. We observed significant associations between the immune pattern, tumor progression, and immune cell infiltration. Differential expression analysis showed correlations with m6A regulators expression. This immune pattern proved effective in predicting immunotherapy response in UC in real-world settings. The study identified a m6A-modified immune pattern in UC, offering prognostic and therapeutic response predictions. This emphasizes that immune genes may influence tumor immune status and progression through m6A modifications.

Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.

Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results. To screen glioblastoma patients who may benefit from immunotherapy. Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS. We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders. The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.

A novel Natural killer cell-related gene signature for improving the prediction of prognosis and immunotherapy response in bladder cancer.

Bladder cancer (BLCA) is a commonly diagnosed cancer worldwide that exhibits high rates of recurrence and metastasis. Immunotherapy is increasingly being recognised in the clinical management of bladder cancer. In addition, the prospect of developing Natural Killer (NK) cell-related immunotherapy is promising in BLCA. We established and verified a prognostic signature based on NK cell-related gene expression. We then calculated the NKscore of BLCA samples and correlated it with the clinical outcomes, molecular subtypes of BLCA, tumour microenvironment (TME), and predicted efficacy of immune checkpoint inhibitors (ICI) and chemotherapy drugs to thoroughly explore the implications of the NKscore. Finally, the role of the NK signature gene HECTD1 in BLCA was verified by Quantitative Real-time PCR, Cell Counting Kit-8 Assay (CCK-8), Transwell Assay and Colony Formation Experiment. We analysed NK cell-associated genes and identified six genes with significant prognostic relevance. A high NK score significantly represents a worse prognosis. NKscore was significantly correlated with seven types of classical molecular subtype classifications of BLCA. In addition, NKscore positively correlates with NK-related immune checkpoints, suggesting that emerging NK cell immune checkpoint inhibitors, such as monalizumab, may have potential therapeutic promise for patients with high NKscore. The results of the T cell inflamed score (TIS) and tumour immune dysfunction exclusion (TIDE) score confirmed the suitability of immunotherapy for patients with a high NK score. Likewise, patients with a high NK score may be more suitable for several significant chemotherapeutic drugs. Functional experiments showed that the knockdown of HECTD1 significantly attenuated the proliferation, migration, and invasion ability of tumour cells. to sum up, the capability of our signature to predict prognosis and immunotherapy response was robust. Hopefully, these results will provide new insights for BLCA research and patient immunotherapy.

[18F]FMISO-PET imaging reveals the role of hypoxia severity in checkpoint blockade response

ContextHypoxia within the tumor microenvironment is a critical factor influencing the efficacy of immunotherapy, including immune checkpoint inhibition. Insufficient oxygen supply, characteristic of hypoxia, has been recognized as a central determinant in the progression of various cancers. The reemergence of evofosfamide, a hypoxia-activated prodrug, as a potential treatment strategy has sparked interest in addressing the role of hypoxia in immunotherapy response. This investigation sought to understand the kinetics and heterogeneity of tumor hypoxia and their implications in affecting responses to immunotherapeutic interventions with and without evofosfamide. PurposeThis study aimed to investigate the influence of hypoxia on immune checkpoint inhibition, evofosfamide monotherapy, and their combination on colorectal cancer (CRC). Employing positron emission tomography (PET) imaging, we developed novel analytical methods to quantify and characterize tumor hypoxia severity and distribution. ProceduresMurine CRC models were longitudinally imaged with [18F]-fluoromisonidazole (FMISO)-PET to quantify tumor hypoxia during checkpoint blockade (anti-CTLA-4 + and anti-PD1 +/− evofosfamide). Metrics including maximum tumor [18F]FMISO uptake (FMISOmax) and mean tumor [18F]FMISO uptake (FMISOmean) were quantified and compared with normal muscle tissue (average muscle FMISO uptake (mAvg) and muscle standard deviation (mSD)). Histogram distributions were used to evaluate heterogeneity of tumor hypoxia. FindingsSevere hypoxia significantly impeded immunotherapy effectiveness consistent with an immunosuppressive microenvironment. Hypoxia-specific PET imaging revealed a striking degree of spatial heterogeneity in tumor hypoxia, with some regions exhibiting significantly more severe hypoxia than others. The study identified FMISOmax as a robust predictor of immunotherapy response, emphasizing the impact of localized severe hypoxia on tumor volume control during therapy. Interestingly, evofosfamide did not directly reduce hypoxia but markedly improved the response to immunotherapy, uncovering an alternative mechanism for its efficacy. ConclusionsThese results enhance our comprehension of the interplay between hypoxia and immune checkpoint inhibition within the tumor microenvironment, offering crucial insights for the development of personalized cancer treatment strategies. Non-invasive hypoxia quantification through molecular imaging evaluating hypoxia severity may be an effective tool in guiding treatment planning, predicting therapy response, and ultimately improving patient outcomes across diverse cancer types and tumor microenvironments. It sets the stage for the translation of these findings into clinical practice, facilitating the optimization of immunotherapy regimens by addressing tumor hypoxia and thereby enhancing the efficacy of cancer treatments.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD). We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments. Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells. Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

Crosstalk of disulfidptosis-related subtypes identifying a prognostic signature to improve prognosis and immunotherapy responses of clear cell renal cell carcinoma patients

BackgroundDisulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC.MethodsThe expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort.ResultsWe identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort.ConclusionThe DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication

BackgroundImmunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.MethodsPublic single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.ResultsThe assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.ConclusionsWe unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The “BayesPrism” and “Seurat” R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing

BackgroundHepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.MethodsThe GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.ResultsOur findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.ConclusionOur research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.

Tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer

Tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer

5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer.

Immune checkpoint inhibitors (ICIs) drastically improve therapeutic outcomes for lung cancer, but accurately predicting individual patient responses to ICIs remains a challenge. We performed the genome-wide profiling of 5-hydroxymethylcytosine (5hmC) in 85 plasma cell-free DNA (cfDNA) samples from lung cancer patients and developed a 5hmC signature that was significantly associated with progression-free survival (PFS). We built a 5hmC predictive model to quantify the 5hmC level and validated the model in the validation, test, and control sets. Low weighted predictive scores (wp-scores) were significantly associated with a longer PFS compared to high wp-scores in the validation [median 7.6 versus 1.8 months; p = 0.0012; hazard ratio (HR) 0.12; 95% confidence interval (CI), 0.03-0.54] and test (median 14.9 versus 3.3 months; p = 0.00074; HR 0.10; 95% CI, 0.02-0.50) sets. Objective response rates in patients with a low or high wp-score were 75.0% (95% CI, 42.8-94.5%) versus 0.0% (95% CI, 0.0-60.2%) in the validation set (p = 0.019) and 80.0% (95% CI, 44.4-97.5%) versus 0.0% (95% CI, 0.0-36.9%) in the test set (p = 0.0011). The wp-scores were also significantly associated with PFS in patients receiving single-agent ICI treatment (p < 0.05). In addition, the 5hmC predictive signature demonstrated superior predictive capability to tumor programmed death-ligand 1 and specificity to ICI treatment response prediction. Moreover, we identified novel 5hmC-associated genes and signaling pathways integral to ICI treatment response in lung cancer. This study provides proof-of-concept evidence that the cfDNA 5hmC signature is a robust biomarker for predicting ICI treatment response in lung cancer.

RNA M6A modification shaping cutaneous melanoma tumor microenvironment and predicting immunotherapy response.

Recent years have seen rising mortality rates linked to cutaneous melanoma (SKCM), despite advances in immunotherapy. Understanding RNA N6-methyladenosine (M6A) significance in SKCM is crucial for prognosis, tumor microenvironment (TME), immune cell presence, and immunotherapy efficacy. We analyzed 23 M6A regulators using SKCM samples from TCGA and GEO databases, identifying three M6A modification patterns linked to TME cell infiltration. Principal component analysis (PCA) yielded an M6A score for individual tumors, utilizing patient gene expression profiles and CNV data from TCGA. M6A modification patterns play a crucial role in SKCM development and progression, influencing tumor attributes such as inflammatory stage, subtype, TME interstitial activity, and genetic mutations. The M6A score independently predicts patient outcomes and correlates with improved response to immunotherapy, validated across anti-PD-1 and anti-PD-L1 therapy cohorts. M6A modifications significantly impact the TME landscape, with the M6A score serving as a predictive marker for immunotherapy response. Integrating M6A-related information into clinical practice could revolutionize SKCM management and treatment strategies.

Lighting the torch: intratumoural T cell-to-stroma enrichment score as a predictor of immunotherapy response in urothelial carcinoma.

Lighting the torch: intratumoural T cell-to-stroma enrichment score as a predictor of immunotherapy response in urothelial carcinoma.

Tumor lymphangiogenesis index reveals the immune landscape and immunotherapy response in lung adenocarcinoma.

Lymphangiogenesis (LYM) has an important role in tumor progression and is strongly associated with tumor metastasis. However, the clinical application of LYM has not progressed as expected. The potential value of LYM needs to be further developed in lung adenocarcinoma (LUAD) patients. The Sequencing data and clinical characteristics of LUAD patients were downloaded from The Cancer Genome Atlas and GEO databases. Multiple machine learning algorithms were used to screen feature genes and develop the LYM index. Immune cell infiltration, immune checkpoint expression, Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and drug sensitivity analysis were used to explore the correlation of LYM index with immune profile and anti-tumor therapy. We screened four lymphangiogenic feature genes (PECAM1, TIMP1, CXCL5 and PDGFB) to construct LYM index based on multiple machine learning algorithms. We divided LUAD patients into the high LYM index group and the low LYM index group based on the median LYM index. LYM index is a risk factor for the prognosis of LUAD patients. In addition, there was a significant difference in immune profile between high LYM index and low LYM index groups. LUAD patients in the low LYM index group seemed to benefit more from immunotherapy based on the results of TIDE algorithm. Overall, we confirmed that the LYM index is a prognostic risk factor and a valuable predictor of immunotherapy response in LUAD patients, which provides new evidence for the potential application of LYM.

New knowledge on anti-IgLON5 disease.

Anti-IgLON5 disease is characterized by a distinctive sleep disorder, associated with a heterogeneous spectrum of neurological symptoms. Initial autopsies showed a novel neuronal tauopathy predominantly located in the tegmentum of the brainstem. Recently, new diagnostic red flags, biomarkers predictors of response to immunotherapy, and novel insights into the autoimmune pathogenesis of the disease have been reported. Patients with diagnosis of neurodegenerative dementia, progressive supranuclear palsy (PSP) or with motor-neuron disease (MND)-like syndrome have been reported to have IgLON5 antibodies, which are the hallmark of anti-IgLON5 disease. Second, low levels of neurofilament light chain in serum and cerebrospinal fluid of patients at disease onset could be a predictor of immunotherapy response. Recent neuropathological studies indicate that the neuronal tau deposits occur late in the course of the disease. Moreover, IgLON5 antibodies induce cytoskeletal changes in cultured hippocampal neurons suggesting that the tauopathy could be secondary of the IgLON5 antibody effects. Anti-IgLON5 disease can mimic and should be considered in atypical presentations of MND, neurodegenerative dementia and PSP. Neurofilament light chain levels seem promising biomarker for disease prognosis. Finally, the neuropathological and in vitro experimental studies strengthen the autoimmune hypothesis of the disease.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BackgroundAberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.MethodsWe performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.ResultsWe identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.ConclusionsOur findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Mitochondrial ribosomal protein S24 is associated with immunosuppressive microenvironment and cold tumor in lung adenocarcinoma

ObjectiveMRPS24 (Mitochondrial Ribosomal Protein S24) belongs to the mitochondrial ribosomal protein family, which participates in the protein synthesis of the mitochondrion. However, the relationship of MRPS24 with lung adenocarcinoma (LUAD) remained unknown. We aimed to identify its immunological and functional mechanisms in LUAD. MethodsThe analysis of MRPS24 expression, clinical features, diagnosis, prognosis, function analysis, genetic alteration, copy number variations, methylation, and tumor microenvironment was investigated by the TCGA, UCSC Xena, GEO, HPA, GEPIA, cBioPortal, MethSurv, TIMER, TIMER2.0, and TISIDB databases. ResultsMRPS24 was found to be more abundant in LUAD tumor tissue than in normal tissue. High levels of MRPS24 expression were found to be an independent prognostic factor by multivariate analysis. Functional analysis revealed that MRPS24 expression was associated with the immune, cell cycle and methylation. MRPS24 methylation level was inversely linked with its expression (p < 0.001). Patients with low MRPS24 methylation had a worse prognosis than those with high methylation (p < 0.05). In addition, the result revealed that the MRPS24 expression was inversely linked to the immune cell infiltration in LUAD. Finally, the validations of the expression level, prognosis, and immune cell infiltration of MRPS24 were in accordance with our previous results. ConclusionsThis study systematically explored that MRPS24 expression was significantly correlated with prognosis, tumorigenesis, genetic alteration, copy number variations, methylation, and immune cell infiltration in LUAD. MRPS24 might be a potential immune-related biomarker in the development and treatment of LUAD, thereby acting as a promising predictor of immunotherapy response in LUAD.