Conserved pan-cancer microenvironment subtypes predict response to immunotherapy

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.

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

Advances of immune-checkpoint inhibition of CTLA-4 in pancreatic cancer

Targeting checkpoints for immune cell activation has been acknowledged known as one of the most effective way to activate anti-tumor immune responses. Among them, drugs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) are approved for clinical treatment though several more are in advanced stages of development, which demonstrated durable response rates and manageable safety profile. However, its therapy efficacy is unsatisfactory in pancreatic cancer (PC), which can be limited by the overall condition of patients, the pathological type of PC, the expression level of tumor related genes, etc. To improve clinical efficiency, various researches have been conducted, and the efficacy of combination therapy showed significantly improvement compared to monotherapy. This review analyzed current strategies based on anti-CTLA-4 combination immunotherapy, providing totally new idea for future research.

Prediction of immunotherapy response using mutations to cancer protein assemblies

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Proteogenomic characterization identifies clinical subgroups in EGFR and ALK wild-type never-smoker lung adenocarcinoma

Patients with lung adenocarcinoma who have never smoked (NSLA) and lack key driver mutations, such as those in the EGFR and ALK genes, face limited options for targeted therapies. They also tend to have poorer outcomes with immune checkpoint inhibitors than lung cancer patients who have a history of smoking. The proteogenomic profile of nonsmoking lung adenocarcinoma patients without these oncogenic driver mutations is poorly understood, which complicates the precise molecular classification of these cancers and highlights a significant area of unmet clinical need. This study analyzed the genome, transcriptome, and LC‒MS/MS-TMT-driven proteome data of tumors obtained from 99 Korean never-smoker lung adenocarcinoma patients. NSLA tumors without EGFR or ALK driver oncogenes were classified into four proteogenomic subgroups: proliferation, angiogenesis, immune, and metabolism subgroups. These 4 molecular subgroups were strongly associated with distinct clinical outcomes. The proliferation and angiogenesis subtypes were associated with a poorer prognosis, while the immune subtype was associated with the most favorable outcome, which was validated in an external lung cancer dataset. Genomic-wide impacts were analyzed, and significant correlations were found between copy number alterations and both the transcriptome and proteome for several genes, with enrichment in the ERBB, neurotrophin, insulin, and MAPK signaling pathways. Proteogenomic analyses suggested several targetable genes and proteins, including CDKs and ATR, as potential therapeutic targets in the proliferation subgroup. Upregulated cytokines, such as CCL5 and CXCL13, in the immune subgroup may serve as potential targets for combination immunotherapy. Our comprehensive proteogenomic analysis revealed the molecular subtypes of EGFR- and ALK-wild-type NSLA with significant unmet clinical needs.

Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity

Although the spatial, cellular and molecular landscapes of resected pancreatic ductal adenocarcinoma (PDAC) are well documented, the characteristics of its metastatic ecology remain elusive. By applying spatially resolved transcriptomics to matched primary and metastatic PDAC samples, we discovered a conserved continuum of fibrotic, metabolic and immunosuppressive spatial ecotypes across anatomical regions. We observed spatial tumor microenvironment heterogeneity spanning beyond that previously appreciated in PDAC. Through comparative analysis, we show that the spatial ecotypes exhibit distinct enrichment between primary and metastatic sites, implying adaptability to the local environment for survival and progression. The invasive border ecotype exhibits both pro-tumorigenic and anti-tumorigenic cell-type enrichment, suggesting a potential immunotherapy target. The ecotype heterogeneity across patients emphasizes the need to map individual patient landscapes to develop personalized treatment strategies. Collectively, our findings provide critical insights into metastatic PDAC biology and serve as a valuable resource for future therapeutic exploration and molecular investigations.

Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation

Fibroblasts, known for their functional diversity, play crucial roles in inflammation and cancer. In this study, we conduct comprehensive single-cell RNA sequencing analyses on fibroblast cells from 517 human samples, spanning 11 tissue types and diverse pathological states. We identify distinct fibroblast subpopulations with universal and tissue-specific characteristics. Pathological conditions lead to significant shifts in fibroblast compositions, including the expansion of immune-modulating fibroblasts during inflammation and tissue-remodeling myofibroblasts in cancer. Within the myofibroblast category, we identify four transcriptionally distinct subpopulations originating from different developmental origins, with LRRC15+ myofibroblasts displaying terminally differentiated features. Both LRRC15+ and MMP1+ myofibroblasts demonstrate pro-tumor potential that contribute to the immune-excluded and immune-suppressive tumor microenvironments (TMEs), whereas PI16+ fibroblasts show potential anti-tumor functions in adjacent non-cancerous regions. Fibroblast-subtype compositions define patient subtypes with distinct clinical outcomes. This study advances our understanding of fibroblast biology and suggests potential therapeutic strategies for targeting specific fibroblast subsets in cancer treatment.

DCLK1 induces a pro-tumorigenic phenotype to drive gastric cancer progression

Doublecortin-like kinase 1 (DCLK1) is a proposed driver of gastric cancer (GC) that phosphorylates serine and threonine residues. Here, we showed that the kinase activity of DCLK1 orchestrated cancer cell-intrinsic and-extrinsic processes that led to pro-invasive and pro-metastatic reprogramming of GC cells. Inhibition of the kinase activity of DCLK1 reduced the growth of subcutaneous xenograft tumors formed from MKN1 human gastric carcinoma cells in mice and decreased the abundance of the stromal markers α-Sma, vimentin, and collagen. Similar effects were seen in mice with xenograft tumors formed from MKN1 cells expressing a kinase-inactive DCLK1 mutant (MKN1D511N). MKN1D511N cells also had reduced in vitro migratory potential and stemness compared with control cells. Mice orthotopically grafted with MKN1 cells overexpressing DCLK1 (MKN1DCLK1) showed increased invasiveness and had a greater incidence of lung metastases compared with those grafted with control MKN1 cells. Mechanistically, we showed that the chemokine CXCL12 acted downstream of DCLK1 in cultured MKN1 cells and in mice subcutaneously implanted with gastric tumors formed by MKN1DCLK1 cells. Moreover, inhibition of the kinase activity of DCLK1 or the expression of DCLK1D511N reversed the pro-tumorigenic and pro-metastatic phenotype. Together, this study establishes DCLK1 as a broadly acting and potentially targetable promoter of GC.

Sintilimab combined with anlotinib and chemotherapy as second-line or later therapy in extensive-stage small cell lung cancer: a phase II clinical trial

Treatment options for patients with relapsed extensive-stage small cell lung cancer (ES-SCLC) remain scarce. This study aims to evaluate the efficacy and safety of combining anlotinib and sintilimab plus chemotherapy as a second line or later therapy for ES-SCLC patients. This is a phase II clinical trial (ChiCTR2100049390) conducting at Shandong Cancer Hospital. Patients with ES-SCLC and received at least one prior systemic treatment were enrolled. The trial design involved a combination therapy (sintilimab, anlotinib, and nab-paclitaxel) administered over six 21-day cycles, followed by maintenance sintilimab therapy. The primary endpoint was objective response rate (ORR). Circulating tumor DNA sequencing was employed for exploratory analysis. From July 2021 to April 2023, 25 eligible patients were enrolled. The confirmed ORR was 60% (95% CI: 38.7-78.9%) and the DCR was 76% (95% CI: 54.9-90.6%). The mPFS was 6.0 months (95% CI: 5.4-9.7), and the 6-month PFS rate was 49.2%. The mOS was 13.4 months (95% CI: 11.8-NR), with a 12-month survival rate of 62.2%. Treatment-related adverse events (TRAEs) of any grade occurred in 80% of patients, with the most common being fatigue (40%) and nausea (32%). TRAEs of Grade 3 or higher were reported in 12% of patients. ctDNA analysis indicated that low on-treatment blood tumor mutation burden was associated with longer PFS and OS and a potential role of KMT2D mutation in treatment resistance. This combination therapy shows promising efficacy and a manageable safety profile as a second-line or later treatment for ES-SCLC, with genomic insights providing potential biomarkers for treatment response.

Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.

A Phase II, Open-Label, Randomized Trial of Durvalumab With Olaparib or Cediranib in Patients With Mismatch Repair-Proficient Colorectal or Pancreatic Cancer

BACKGROUND

The use of immunotherapy in mismatch repair proficient colorectal cancer (pMMR-CRC) or pancreatic adenocarcinoma (PDAC) is associated with limited efficacy. DAPPER (NCT03851614) is a phase 2, basket study randomizing patients with pMMR CRC or PDAC to durvalumab with olaparib (durvalumab + olaparib) or durvalumab with cediranib (durvalumab + cediranib).

METHODS

PDAC or pMMR-CRC patients were randomized to either durvalumab+olaparib (arm A), or durvalumab + cediranib (arm B). Co-primary endpoints included pharmacodynamic immune changes in the tumor microenvironment (TME) and safety. Objective response rate, progression-free survival (PFS) and overall survival (OS) were determined. Paired tumor samples were analyzed by multiplexed immunohistochemistry and RNA-sequencing.

RESULTS

A total of 31 metastatic pMMR-CRC patients were randomized to arm A (n = 16) or B (n = 15). In 28 evaluable patients, 3 patients had stable disease (SD) (2 patients treated with durvalumab + olaparib and 1 patient treated with durvalumab + cediranib) while 25 had progressive disease (PD). Among patients with PDAC (n = 19), 9 patients were randomized to arm A and 10 patients were randomized to arm B. In 18 evaluable patients, 1 patient had a partial response (unconfirmed) with durvalumab + cediranib, 1 patient had SD with durvalumab + olaparib while 16 had PD. Safety profile was manageable and no grade 4-5 treatment-related adverse events were observed in either arm A or B. No significant changes were observed for CD3+/CD8+ immune infiltration in on-treatment biopsies as compared to baseline for pMMR-CRC and PDAC independent of treatment arms. Increased tumor-infiltrating lymphocytes at baseline, low baseline CD68+ cells and different immune gene expression signatures at baseline were associated with outcomes.

CONCLUSIONS

In patients with pMMR-CRC or PDAC, durvalumab + olaparib and durvalumab + cediranib showed limited antitumor activity. Different immune components of the TME were associated with treatment outcomes.

Immunotherapies targeting the oncogenic fusion gene CLDN18-ARHGAP in gastric cancer

The CLDN18-ARHGAP fusion gene is an oncogenic driver newly discovered in gastric cancer. It was detected in 9% (8/87) of gastric cancer patients in our center. An immunogenic peptide specifically targeting CLDN18-ARHGAP fusion gene was generated to induce neoantigen-reactive T cells, which was proved to have specific and robust anti-tumor capacity both in in vitro coculture models and in vivo xenograft gastric cancer models. Apart from the immunogenic potential, CLDN18-ARHGAP fusion gene was also found to contribute to immune suppression by inducing a regulatory T (Treg) cell-enriched microenvironment. Mechanistically, gastric cancer cells with CLDN18-ARHGAP fusion activate PI3K/AKT-mTOR-FAS signaling, which enhances free fatty acid production of gastric cancer cells to favor the survival of Treg cells. Furthermore, PI3K inhibition could effectively reverse Treg cells upregulation to enhance anti-tumor cytotoxicity of neoantigen-reactive T cells in vitro and reduce tumor growth in the xenograft gastric cancer model. Our study identified the CLDN18-ARHGAP fusion gene as a critical source of immunogenic neoepitopes, a key regulator of the tumor immune microenvironment, and immunotherapeutic applications specific to this oncogenic fusion.

Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored. Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptor expression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Botensilimab plus balstilimab in relapsed/refractory microsatellite stable metastatic colorectal cancer: a phase 1 trial

Microsatellite stable metastatic colorectal cancer (MSS mCRC; mismatch repair proficient) has previously responded poorly to immune checkpoint blockade. Botensilimab (BOT) is an Fc-enhanced multifunctional anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibody designed to expand therapy to cold/poorly immunogenic solid tumors, such as MSS mCRC. BOT with or without balstilimab (BAL; anti-PD-1 antibody) is being evaluated in an ongoing expanded phase 1 study. The primary endpoint is safety and tolerability, which was evaluated separately in the dose-escalation portion of the study and in patients with MSS mCRC (using combined dose-escalation/dose-expansion data). Secondary endpoints include investigator-assessed RECIST version 1.1-confirmed objective response rate (ORR), disease control rate (DCR), duration of response (DOR) and progression-free survival (PFS). Here we present outcomes in 148 heavily pre-treated patients with MSS mCRC (six from the dose-escalation cohort; 142 from the dose-expansion cohort) treated with BOT and BAL, 101 of whom were considered response evaluable with at least 6 months of follow-up. Treatment-related adverse events (TRAEs) occurred in 89% of patients with MSS mCRC (131/148), most commonly fatigue (35%, 52/148), diarrhea (32%, 47/148) and pyrexia (24%, 36/148), with no grade 5 TRAEs reported and a 12% discontinuation rate due to a TRAE (18/148; data fully mature). In the response-evaluable population (n = 101), ORR was 17% (17/101; 95% confidence interval (CI), 10-26%), and DCR was 61% (62/101; 95% CI, 51-71%). Median DOR was not reached (NR; 95% CI, 5.7 months-NR), and median PFS was 3.5 months (95% CI, 2.7-4.1 months), at a median follow-up of 10.3 months (range, 0.5-42.6 months; data continuing to mature). The combination of BOT plus BAL demonstrated a manageable safety profile with no new immune-mediated safety signals and encouraging clinical activity with durable responses. ClinicalTrials.gov identifier: NCT03860272 .

Exploring the molecular and immune landscape of cellular senescence in lung adenocarcinoma

Introduction

The connection between aging and cancer is complex. Previous research has highlighted the association between the aging process of lung adenocarcinoma (LUAD) cells and the immune response, yet there remains a gap in confirming this through single-cell data validation. Here, we aim to develop a novel aging-related prognostic model for LUAD, and verify the alterations in the genome and immune microenvironment linked to cellular senescence.

Methods

We integrated a comprehensive collection of senescence genes from the GenAge and CellAge databases and employed the least absolute shrinkage and selection operator (LASSO) Cox analysis to construct and validate a novel prognostic model for LUAD. This model was then utilized to examine the relationship between aging, tumor somatic mutations, and immune cell infiltration. Additionally, we explored the heterogeneity of senescence and intercellular communication within the LUAD tumor microenvironment (TME) through single-cell transcriptomic data analysis.

Results

By exploring the expression profiles of 586 cellular senescence-related genes in 428 LUAD patients, we constructed an aging-related genes (ARGs) risk model included 10 ARGs and validated it as an independent prognostic predictor for LUAD patients. Notably, patients with low aging scores (LAS group) exhibited better survival, lower tumor mutation burden (TMB), lower somatic mutation frequency, lower tumor proliferation rate, and an immune activated phenotype compared to patients with high aging scores (HAS group). While the HAS group was enriched in tumor cells and showed a lower infiltration of CD8-CCR7, CD8- CXCL13, CD8-GNLY, FCGR3A NK cells, XCL1 NK cells, plasma cell (PC) and other immune subsets. Furthermore, the SPP1 and TENASCIN pathways, associated with tumor immune escape and tumor progression, were also enriched in the HAS group. Additionally, our study also indicated that senescence levels were heterogeneous in the LUAD tumor microenvironment (TME), especially with tumor cells in the LAS group showing higher age scores compared to those in the HAS group.

Conclusions

Collectively, our findings underscore that ARRS through ARGs serves as a robust biomarker for the prognosis in LUAD.

Tumor microenvironment biomarkers predicting pathological response to neoadjuvant chemoimmunotherapy in locally advanced esophageal squamous cell carcinoma: post-hoc analysis of a single center, phase 2 study

BACKGROUND

Neoadjuvant chemoimmunotherapy has a promising effect on locally advanced esophageal squamous cell carcinoma (ESCC). However, reliable biomarkers robustly predicting therapeutic response are still lacking.

METHODS

Formalin-fixed and paraffin-embedded pre-neoadjuvant chemoimmunotherapy biopsy samples from locally advanced ESCC patients were collected. Cohort 1 composed of 66 locally advanced ESCC patients from a prospective clinical trial (NCT04506138) received two cycles of camrelizumab in combination with nab-paclitaxel and carboplatin every 3 weeks. Cohort 2 included 48 patients receiving various types of immune checkpoint inhibitors with (nab-)paclitaxel and platinum-based chemotherapy as neoadjuvant therapy. Cohort 3 consisted of 27 ESCC patients receiving neoadjuvant treatment of toripalimab with chemotherapy and was used as the external validation dataset. Targeted RNA sequencing, immunohistochemistry for programmed death ligand 1 (PD-L1), and multiplex immunofluorescence (mIF) imaging were performed.

RESULTS

Integration of targeted RNA sequencing, PD-L1 immunohistochemistry, and mIF revealed a significant immune-suppressive microenvironment with higher neutrophil infiltration, enriched TGF-β, and cell cycle pathways in non-pathological complete response (non-pCR) patients. NK, activated CD4+ T cell infiltration, interferon-gamma, antigen processing and presentation, and other immune response signatures were significantly associated with pCR. Based on discovered tumor microenvironmental characteristics and their closely related genes were screened. Consequently, a seven-gene neoadjuvant chemoimmunotherapy risk prediction signature (NCIRPs) model, was constructed. In addition to cohort 1, this model alone or with PD-L1-combined positive score (CPS) demonstrated a higher prediction accuracy of pathological response than PD-L1 CPS or other routinely used immune signatures, such as IFN-γ, in cohorts 2 and 3. Neither prognostic association nor correlation with response to chemoradiotherapy was observed in The Cancer Genome Atlas Program ESCC dataset or in ESCC patients in the neoadjuvant chemoradiotherapy cohort (cohort 4).

CONCLUSION

The NCIRPs model that was developed and validated using treatment-naïve endoscopic samples from the largest ESCC neoadjuvant chemoimmunotherapy dataset represents a robust and clinically meaningful approach to select a putative responder for neoadjuvant chemoimmunotherapy in locally advanced ESCC patients.

Pan-cancer atlas of tumor-resident microbiome, immunity and prognosis

The existence of microbiome in human tumors has been determined widely, but evaluating the contribution of intratumoral bacteria and fungi to tumor immunity and prognosis from a pan-cancer perspective remains absent. We designed an improved microbial analysis pipeline to reduce interference from host sequences, complemented with integration analysis of intratumoral microbiota at species level with clinical indicators, tumor microenvironment, and prognosis across cancer types. We found that intratumoral microbiota is associated with immunophenotyping, with high-immunity subtypes showing greater bacterial and fungal richness compared to low-immunity groups. We also noted that the combination of fungi and bacteria demonstrated promising prognostic value across cancer types. We, thus, present The Cancer Microbiota (TCMbio), an interactive platform that provides the intratumoral bacteria and fungi data, and a comprehensive analysis module for 33 types of cancers. This led to the discovery of clinical and prognostic significance of intratumoral microbes.

Subtyping of gastric cancer based on basement membrane genes that stratifies the prognosis, immune infiltration and therapeutic response

Gastric cancer (GC) is highly heterogeneous and prone to metastasis, which are obstacles to the effectiveness of treatment. The basement membrane (BM) acts as a barrier to tumor cell invasion and metastasis. It is critical to investigate the relationship between BM status, metastasis, and patient prognosis. In several large cohorts, we investigated BM gene expression-based molecular classification and risk-prognosis models for GC, examined tumor microenvironment (TME) differences among different molecular subtypes, and developed risk models in predicting prognosis, immunotherapy effectiveness, and chemotherapy resistance. Three GC subtypes (BMclusterA/B/C) based on BM gene expression status were discovered. Each of the three GC subtypes has unique immune infiltration and activated oncogenic signals. Moreover, a 6-gene score (BMscore) predictive model was developed. The low BMscore group had a high tumor mutation burden, high immunogenicity, and low RHOJ expression levels, implying that individuals with GC in this category may be more susceptible to immunotherapy and treatment. The EMT subtype showed a considerably higher BMscore than the other subtypes in the Asian Organization for Research on Cancer (ACRG) molecular classification. Endothelial cells, smooth muscle cells, and fibroblasts may be engaged in regulating BM reorganization in GC progression, according to single-cell transcriptome analyses. In conclusion, we defined a novel molecular classification of GC based on BM genes, developed a prognostic risk model, and elucidated the cell subpopulations involved in BM remodeling at the single-cell level. This study has deepened the understanding of the relationship between GC metastasis and BM alterations, achieved prognostic stratification, and guided therapy.

Targeting treatment resistance: unveiling the potential of RNA methylation regulators and TG-101,209 in pan-cancer neoadjuvant therapy

BACKGROUND

Tumor recurrence and mortality rates remain challenging in cancer patients despite comprehensive treatment. Neoadjuvant chemotherapy and immunotherapy aim to eliminate residual tumor cells, reducing the risk of recurrence. However, drug resistance during neoadjuvant therapy is a significant hurdle. Recent studies suggest a correlation between RNA methylation regulators (RMRs) and response to neoadjuvant therapy.

METHODS

Using a multi-center approach, we integrated advanced techniques such as single-cell transcriptomics, whole-genome sequencing, RNA sequencing, proteomics, machine learning, and in vivo/in vitro experiments. Analyzing pan-cancer cohorts, the association between neoadjuvant chemotherapy/immunotherapy effectiveness and RNA methylation using single-cell sequencing was investigated. Multi-omics analysis and machine learning algorithms identified genomic variations, transcriptional dysregulation, and prognostic relevance of RMRs, revealing distinct molecular subtypes guiding pan-cancer neoadjuvant therapy stratification.

RESULTS

Our analysis unveiled a strong link between neoadjuvant therapy efficacy and RNA methylation dynamics, supported by pan-cancer single-cell sequencing data. Integration of omics data and machine learning algorithms identified RMR genomic variations, transcriptional dysregulation, and prognostic implications in pan-cancer. High-RMR-expressing tumors displayed increased genomic alterations, an immunosuppressive microenvironment, poorer prognosis, and resistance to neoadjuvant therapy. Molecular investigations and in vivo/in vitro experiments have substantiated that the JAK inhibitor TG-101,209 exerts notable effects on the immune microenvironment of tumors, rendering high-RMR-expressing pan-cancer tumors, particularly in pancreatic cancer, more susceptible to chemotherapy and immunotherapy.

CONCLUSIONS

This study emphasizes the pivotal role of RMRs in pan-cancer neoadjuvant therapy, serving as predictive biomarkers for monitoring the tumor microenvironment, patient prognosis, and therapeutic response. Distinct molecular subtypes of RMRs aid individualized stratification in neoadjuvant therapy. Combining TG-101,209 adjuvant therapy presents a promising strategy to enhance the sensitivity of high-RMR-expressing tumors to chemotherapy and immunotherapy. However, further validation studies are necessary to fully understand the clinical utility of RNA methylation regulators and their impact on patient outcomes.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Spatially Resolved Niche and Tumor Microenvironmental Alterations in Gastric Cancer Peritoneal Metastases

BACKGROUND AND AIMS

Peritoneal metastasis (PM) in gastric cancer (GC) is associated with poor prognosis and significant morbidity. We sought to understand the genomic, transcriptomic, and tumor microenvironment (TME) features that contribute to peritoneal organotropism in GC.

METHODS

We conducted a comprehensive multi-omic analysis of 548 samples from 326 patients, including primary tumors, matched normal tissues; peritoneal metastases, and adjacent-normal peritoneal tissues. We used whole exome sequencing, whole transcriptome sequencing, and digital spatial profiling to investigate molecular alterations, gene expression patterns, and TME characteristics associated with PM.

RESULTS

Our analysis identified specific genomic alterations in primary tumors, including mutations in ELF3, CDH1, and PIGR, and TME signatures, such as stromal infiltration and M2 macrophage enrichment, associated with increased risk of PM. We observed distinct transcriptional programs and immune compositions in GCPM compared with liver metastases, highlighting the importance of the TME in transcoelomic metastasis. We found differential expression of therapeutic targets between primary tumors and PM, with lower CLDN18.2 and FGFR2b expression in PM. We unravel the roles of the TME in niche reprogramming within the peritoneum, and provide evidence of pre-metastatic niche conditioning even in early GC without clinical PM. These findings were further validated using a humanized mouse model, which demonstrated niche remodeling in the peritoneum during transcoelomic metastasis.

CONCLUSION

Our study provides a comprehensive molecular characterization of GCPM and unveils key biological principles underlying transcoelomic metastasis. The identified predictive markers, therapeutic targets, and TME alterations offer potential avenues for targeted interventions and improved patient outcomes.

Gene panel predicts neoadjuvant chemoimmunotherapy response and benefit from immunotherapy in HER2-negative breast cancer

BACKGROUND

It is encountering the dilemma of lacking precise biomarkers to predict the response to neoadjuvant chemoimmunotherapy (NACI) and determine whether patients should use immune checkpoint inhibitors (ICIs) in early breast cancer (BC). We aimed to develop a gene signature to predict NACI response for BC patients and identify individuals suitable for adding ICIs.

PATIENTS AND METHODS

Two I-SPY2 cohorts and one West China Hospital cohort of patients treated with NACI were included. Machine learning algorithms were used to identify key genes. Principal component analysis was used to calculate the ImPredict (IP) score. The interaction effects between biomarkers and treatment regimens were examined based on the logistic regression analysis. The relationship between the IP score and immune microenvironment was investigated through immunohistochemistry (IHC) and multiplex IHC.

RESULTS

The area under the curves of the IP score were 0.935, 0.865, and 0.841 in the discovery cohort, validation cohort 1, and in-house cohort. Marker-treatment interaction tests indicated that the benefits from immunotherapy significantly varied between patients with high and low IP scores (p for interaction <0.001), and patients with high IP scores were more suitable for immunotherapy addition.

CONCLUSIONS

Our IP model shows favorable performance in predicting NACI response and is an effective tool for identifying BC patients who will benefit from ICIs. It may help clinicians optimize treatment strategies and guide clinical decision-making.

Bioinformatics analysis of the tumor microenvironment in melanoma - Constructing a prognostic model based on CD8+ T cell-related genes: An observational study

This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. The model was validated using the GSE65904 and GSE35640 datasets. Multi-omics analysis was conducted utilizing the maftools, limma, edgeR, ChAMP, and clusterProfiler packages. The examination of single-cell sequencing data revealed the presence of 8 cell types, with the transcription factors RFXAP, CLOCK, MGA, RBBP, and ZNF836 exhibiting notably high expression levels in CD8+ T cells as determined by the SCENIC package. Utilizing these transcription factors and their associated target genes, a prognostic model was developed through COX and LASSO analyses, incorporating the genes GPR171, FAM174A, and BPI. This study validated the model with independent datasets and conducted additional analysis involving multi-omics and immune infiltration to identify a more favorable prognosis for patients in the low-risk group. The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.

Recent developments in immunotherapy for gastrointestinal tract cancers

The past few decades have witnessed the rise of immunotherapy for Gastrointestinal (GI) tract cancers. The role of immune checkpoint inhibitors (ICIs), particularly programmed death protein 1 (PD-1) and PD ligand-1 antibodies, has become increasingly pivotal in the treatment of advanced and perioperative GI tract cancers. Currently, anti-PD-1 plus chemotherapy is considered as first-line regimen for unselected advanced gastric/gastroesophageal junction adenocarcinoma (G/GEJC), mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) colorectal cancer (CRC), and advanced esophageal cancer (EC). In addition, the encouraging performance of claudin18.2-redirected chimeric antigen receptor T-cell (CAR-T) therapy in later-line GI tract cancers brings new hope for cell therapy in solid tumour treatment. Nevertheless, immunotherapy for GI tumour remains yet precise, and researchers are dedicated to further maximising and optimising the efficacy. This review summarises the important research, latest progress, and future directions of immunotherapy for GI tract cancers including EC, G/GEJC, and CRC.

Immunogenomic profiles and therapeutic options of the pan-programmed cell death-related lncRNA signature for patients with bladder cancer

Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network

INTRODUCTION

Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

OBJECTIVES

Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

METHODS

The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer.

RESULTS

Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu.

CONCLUSION

IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

Nicotinamide metabolism face-off between macrophages and fibroblasts manipulates the microenvironment in gastric cancer

Immune checkpoint blockade has led to breakthroughs in the treatment of advanced gastric cancer. However, the prominent heterogeneity in gastric cancer, notably the heterogeneity of the tumor microenvironment, highlights the idea that the antitumor response is a reflection of multifactorial interactions. Through transcriptomic analysis and dynamic plasma sample analysis, we identified a metabolic "face-off" mechanism within the tumor microenvironment, as shown by the dual prognostic significance of nicotinamide metabolism. Specifically, macrophages and fibroblasts expressing the rate-limiting enzymes nicotinamide phosphoribosyltransferase and nicotinamide N-methyltransferase, respectively, regulate the nicotinamide/1-methylnicotinamide ratio and CD8+ T cell function. Mechanistically, nicotinamide N-methyltransferase is transcriptionally activated by the NOTCH pathway transcription factor RBP-J and is further inhibited by macrophage-derived extracellular vesicles containing nicotinamide phosphoribosyltransferase via the SIRT1/NICD axis. Manipulating nicotinamide metabolism through autologous injection of extracellular vesicles restored CD8+ T cell cytotoxicity and the anti-PD-1 response in gastric cancer.

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

The prognostic and immune significance of SLAMF9 in pan-cancer and validation of its role in colorectal cancer

SLAMF9, a member of the conserved lymphocyte activation molecules family (SLAMF), has been less investigated compared to other SLAMs, especially concerning its implications across various cancer types. In our systematic pan-cancer investigation, we observed elevated SLAMF9 expression in various tumor tissues, which was correlated with reduced patient survival across most malignancies. Correlation analyses further revealed significant associations between SLAMF9 expression and immune cell infiltrates, immune checkpoint inhibitors, tumor mutation load, microsatellite instability, and epithelial-mesenchymal transition (EMT) scores. Cell-based assays demonstrated that SLAMF9 knockdown attenuated the proliferative, motile, and invasive capacities of colorectal cancer (CRC) cells. In a nude mouse xenograft model, suppression of SLAMF9 expression substantially inhibited tumor growth. These findings highlight the potential of SLAMF9 as a prognostic and therapeutic biomarker across tumors, with notable implications for CRC cell proliferation and migration.

Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine learning: bioinformatics multi-omics study

BACKGROUND

Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through complex mechanisms with other types of cells in the tumour microenvironment. However, essential fibroblasts-related genes (FRG) in bladder cancer still need to be explored, and there is a shortage of an ideal predictive model or molecular subtype for the progression and immune therapeutic assessment for bladder cancer, especially muscular-invasive bladder cancer based on the FRG.

MATERIALS AND METHODS

CAF-related genes of bladder cancer were identified by analysing single-cell RNA sequence datasets, and bulk transcriptome datasets and gene signatures were used to characterize them. Then, 10 types of machine learning algorithms were utilised to determine the hallmark FRG and construct the FRG index (FRGI) and subtypes. Further molecular subtypes combined with CD8+ T-cells were established to predict the prognosis and immune therapy response.

RESULTS

Fifty-four BLCA-related FRG were screened by large-scale scRNA-sequence datasets. The machine learning algorithm established a 3-genes FRGI. High FRGI represented a worse outcome. Then, FRGI combined clinical variables to construct a nomogram, which shows high predictive performance for the prognosis of bladder cancer. Furthermore, the BLCA datasets were separated into two subtypes - fibroblast hot and cold types. In five independent BLCA cohorts, the fibroblast hot type showed worse outcomes than the cold type. Multiple cancer-related hallmark pathways are distinctively enriched in these two types. In addition, high FRGI or fibroblast hot type shows a worse immune therapeutic response. Then, four subtypes called CD8-FRG subtypes were established under the combination of FRG signature and activity of CD8+ T-cells, which turned out to be effective in predicting the prognosis and immune therapeutic response of bladder cancer in multiple independent datasets. Pathway enrichment analysis, multiple gene signatures, and epigenetic alteration characterize the CD8-FRG subtypes and provide a potential combination strategy method against bladder cancer.

CONCLUSIONS

In summary, the authors established a novel FRGI and CD8-FRG subtype by large-scale datasets and organised analyses, which could accurately predict clinical outcomes and immune therapeutic response of BLCA after surgery.

Rare Earth Nanoprobes for Targeted Delineation of Triple Negative Breast Cancer and Enhancement of Radioimmunotherapy

Radiotherapy demonstrates a synergistic effect with immunotherapy by inducing a transformation of "immune cold" tumors into "immune hot" tumors in triple negative breast cancer (TNBC). Nevertheless, the effectiveness of immunotherapy is constrained by low expression of tumor-exposed antigens, inadequate inflammation, and insufficient tumor infiltrating lymphocyte (TILs). To address this predicament, novel lutecium-based rare earth nanoparticles (RENPs) are synthesized with the aim of amplifying radiation effect and tumor immune response. The nanoprobe is characterized by neodymium-based down-conversion fluorescence, demonstrating robust photostability, biocompatibility, and targetability. The conjugation of RENPs with a CXCR4 targeted drug enables precise delineation of breast tumors using a near-infrared imaging system and improves radiation efficacy via lutetium-based radio-sensitizer in vivo. Furthermore, the study shows a notable enhancement of immune response through the induction of immunogenic cell death and recruitment of TILs, resulting in the inhibition of tumor progression both in vitro and in vivo models following the administration of nanoparticles. Hence, the novel multifunctional nanoprobes incorporating various lanthanide elements offer the potential for imaging-guided tumor delineation, radio-sensitization, and immune activation post-radiation, thus presenting an efficient radio-immunotherapeutic approach for TNBC.

Overexpression of COX7A1 Promotes the Resistance of Gastric Cancer to Oxaliplatin and Weakens the Efficacy of Immunotherapy

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. Presently, the overall response rate to immunotherapy is low, and current methods for predicting the prognosis of GC are not optimal. Therefore, novel biomarkers with accuracy, efficiency, stability, performance ratio, and wide clinical application are needed. Based on public data sets, the chemotherapy cohort and immunotherapy cohort from Sun Yat-sen University Cancer Center, a series of bioinformatics analyses, such as differential expression analysis, survival analysis, drug sensitivity prediction, enrichment analysis, tumor immune dysfunction and exclusion analysis, single-sample gene set enrichment analysis, stemness index calculation, and immune cell infiltration analysis, were performed for screening and preliminary exploration. Immunohistochemical staining and in vitro experiments were performed for further verification. Overexpression of COX7A1 promoted the resistance of GC cells to Oxaliplatin. COX7A1 may induce immune escape by regulating the number of fibroblasts and their cellular communication with immune cells. In summary, measuring the expression levels of COX7A1 in the clinic may be useful in predicting the prognosis of GC patients, the degree of chemotherapy resistance, and the efficacy of immunotherapy.

Progress in reeducating tumor-associated macrophages in tumor microenvironment

Malignant tumor, one of the most threatening diseases to human health, has been comprehensively treated with surgery, radiotherapy, chemotherapy and targeted therapy, but the prognosis has not always been ideal. In the past decade, immunotherapy has shown increased efficacy in tumor treatment; however, for immunotherapy to achieve its fullest potential, obstacles are to be conquered, among which tumor microenvironment (TME) has been widely investigated. In remodeling the tumor immune microenvironment to inhibit tumor progression, macrophages, as the most abundant innate immune population, play an irreplaceable role in the immune response. Therefore, how to remodel TME and alter the recruitment and polarization status of tumor-associated macrophages (TAM) has been of wide interest. In this context, nanoparticles, photodynamic therapy and other therapeutic approaches capable of affecting macrophage polarization have emerged. In this paper, we categorize and organize the existing means and methods for reprogramming TAM to provide ideas for clinical application of novel tumor-related therapies.

Novel molecular subtypes of METex14 non-small cell lung cancer with distinct biological and clinical significance

Not all MET exon 14 skipping (METex14) NSCLC patients benefited from MET inhibitors. We hypothesized an inter-tumoral heterogeneity in METex14 NSCLC. Investigations at genomic and transcriptomic level were conducted in METex14 NSCLC samples from stage I-III and recurrent/metastatic patients as discovery and validation cohort. Four molecular subtypes were discovered. MET-Driven subtype, with the worst prognosis, displayed MET overexpression, enrichment of MET-related pathways, and higher infiltration of fibroblast and regulatory T cells. Immune-Activated subtype having the most idea long-term survival, had higher tertiary lymphoid structures, spatial co-option of PD-L1+ cancer cells, and GZMK+ CD8+ T cell. FGFR- and Bypass-Activated subtypes displayed FGFR2 overexpression and enrichments of multiple oncogenic pathways respectively. In the validation cohort, patients with MET-Driven subtype had better response to MET inhibitors than those with MET overexpression. Thus, molecular subtypes of METex14 NSCLC with distinct biological and clinical significance may indicate more precise therapeutic strategies for METex14 NSCLC patients.

PD-L2 mediates tobacco smoking-induced recruitment of regulatory T cells via the RGMB/NFκB/CCL20 cascade

Programmed cell death ligand 2 (PD-L2), a ligand for the receptor programmed cell death 1 (PD-1), has an identity of 34% with its twin ligand PD-L1 and exhibits higher binding affinity with PD-1 than PD-L1. However, the role of PD-L2 in non-small cell lung cancer (NSCLC) progression, especially tobacco-induced cancer progression, has not been fully understood. Here, we found that PD-L2 promoted tumor growth in murine models with recruitment of regulatory T cells (Tregs). In patients with NSCLC, PD-L2 expression level in tumor samples was higher than in counterpart normal controls and was positively associated with patients' response to anti-PD-1 treatment. Mechanismly, PD-L2 bound its receptor Repulsive guidance molecule B (RGMB) on cancer cells and activated extracellular signal-regulated kinase (Erk) and nuclear factor κB (NFκB), leading to increased production of chemokine CCL20, which recruited Tregs and contributed to NSCLC progression. Consistently, knockdown of RGMB or NFκB p65 inhibited PD-L2-induced CCL20 production, and silencing of PD-L2 repressed Treg recruitment by NSCLC cells. Furthermore, cigarette smoke and carcinogen benzo(a)pyrene (BaP) upregulated PD-L2 in lung epithelial cells via aryl hydrocarbon receptor (AhR)-mediated transcription activation, whose deficiency markedly suppressed BaP-induced PD-L2 upregulation. These results suggest that PD-L2 mediates tobacco-induced recruitment of Tregs via the RGMB/NFκB/CCL20 cascade, and targeting this pathway might have therapeutic potentials in NSCLC.

Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy

Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITF+SPARCL1+ and CENPF+ melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.

The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer

Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP's function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.

The Immune Landscape of Pheochromocytoma and Paraganglioma: Current Advances and Perspectives

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from neural crest cells from adrenal medullary chromaffin tissues and extra-adrenal paraganglia, respectively. Although the current treatment for PPGLs is surgery, optimal treatment options for advanced and metastatic cases have been limited. Hence, understanding the role of the immune system in PPGL tumorigenesis can provide essential knowledge for the development of better therapeutic and tumor management strategies, especially for those with advanced and metastatic PPGLs. The first part of this review outlines the fundamental principles of the immune system and tumor microenvironment, and their role in cancer immunoediting, particularly emphasizing PPGLs. We focus on how the unique pathophysiology of PPGLs, such as their high molecular, biochemical, and imaging heterogeneity and production of several oncometabolites, creates a tumor-specific microenvironment and immunologically "cold" tumors. Thereafter, we discuss recently published studies related to the reclustering of PPGLs based on their immune signature. The second part of this review discusses future perspectives in PPGL management, including immunodiagnostic and promising immunotherapeutic approaches for converting "cold" tumors into immunologically active or "hot" tumors known for their better immunotherapy response and patient outcomes. Special emphasis is placed on potent immune-related imaging strategies and immune signatures that could be used for the reclassification, prognostication, and management of these tumors to improve patient care and prognosis. Furthermore, we introduce currently available immunotherapies and their possible combinations with other available therapies as an emerging treatment for PPGLs that targets hostile tumor environments.

Immuno-oncologic profiling of pediatric brain tumors reveals major clinical significance of the tumor immune microenvironment

With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay and used it to profile a diverse range of 1382 samples with detailed clinical and molecular annotation. In low-grade gliomas we identify distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors. In high-grade gliomas, we observe immune activation and T-cell infiltrates in tumors that have historically been considered immune cold, as well as genomic correlates of inflammation levels. In mismatch repair deficient high-grade gliomas, we find that high tumor inflammation signature is a significant predictor of response to immune checkpoint inhibition, and demonstrate the potential for multimodal biomarkers to improve treatment stratification. Importantly, while overall patterns of immune activation are observed for histologically and genetically defined tumor types, there is significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

A-to-I RNA co-editing predicts clinical outcomes and is associated with immune cells infiltration in hepatocellular carcinoma

Aberrant RNA editing has emerged as a pivotal factor in the pathogenesis of hepatocellular carcinoma (HCC), but the impact of RNA co-editing within HCC remains underexplored. We used a multi-step algorithm to construct an RNA co-editing network in HCC, and found that HCC-related RNA editings are predominantly centralized within the network. Furthermore, five pairs of risk RNA co-editing events were significantly correlated with the overall survival in HCC. Based on presence of risk RNA co-editings resulted in the categorization of HCC patients into high-risk and low-risk groups. Disparities in immune cell infiltrations were observed between the two groups, with the high-risk group exhibiting a greater abundance of exhausted T cells. Additionally, seven genes associated with risk RNA co-editing pairs were identified, whose expression effectively differentiates HCC tumor samples from normal ones. Our research offers an innovative perspective on the etiology and potential therapeutics for HCC.

Spatiotemporal single-cell analysis decodes cellular dynamics underlying different responses to immunotherapy in colorectal cancer

Expanding the efficacy of immune checkpoint blockade (ICB) in colorectal cancer (CRC) presses for a comprehensive understanding of treatment responsiveness. Here, we analyze multiple sequential single-cell samples from 22 patients undergoing PD-1 blockade to map the evolution of local and systemic immunity of CRC patients. In tumors, we identify coordinated cellular programs exhibiting distinct response associations. Specifically, exhausted T (Tex) or tumor-reactive-like CD8+ T (Ttr-like) cells are closely related to treatment efficacy, and Tex cells show correlated proportion changes with multiple other tumor-enriched cell types following PD-1 blockade. In addition, we reveal the less-exhausted phenotype of blood-associated Ttr-like cells in tumors and find that their higher abundance suggests better treatment outcomes. Finally, a higher major histocompatibility complex (MHC) II-related signature in circulating CD8+ T cells at baseline is linked to superior responses. Our study provides insights into the spatiotemporal cellular dynamics following neoadjuvant PD-1 blockade in CRC.

Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome

BACKGROUND

The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS.

METHODS

The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice.

RESULTS

FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2.

CONCLUSIONS

Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

LILRB4 in acute myeloid leukemia: From prognostic biomarker to immunotherapeutic target

ABSTRACT

Leukocyte immunoglobulin-like receptor (LILR) B4 (also known as ILT3/CD85k) is an immune checkpoint protein that is highly expressed in solid tumors and hematological malignancies and plays a significant role in the pathophysiology of cancer. LILRB4 is highly expressed in acute myeloid leukemia (AML), and this phenotype is associated with adverse patient outcomes. Its differential expression in tumors compared to normal tissues, its presence in tumor stem cells, and its multifaceted roles in tumorigenesis position it as a promising therapeutic target in AML. Currently, several immunotherapies targeting LILRB4 are undergoing clinical trials. This review summarizes advancements made in the study of LILRB4 in AML, focusing on its structure, ligands, expression, and significance in normal tissues and AML; its protumorigenic effects and mechanisms in AML; and the application of LILRB4-targeted therapies in AML. These insights highlight the potential advantages of LILRB4 as an immunotherapeutic target in the context of AML.

Biomarkers to predict efficacy of immune checkpoint inhibitors in colorectal cancer patients: a systematic review and meta-analysis

Immune checkpoint inhibitors (ICIs) are approved to treat colorectal cancer (CRC) with mismatch-repair gene deficiency, but the response rate remains low. Value of current biomarkers to predict CRC patients' response to ICIs is unclear due to heterogeneous study designs and small sample sizes. Here, we aim to assess and quantify the magnitude of multiple biomarkers for predicting the efficacy of ICIs in CRC patients. We systematically searched MEDLINE, Embase, the Cochrane Library, and Web of Science databases (to June 2023) for clinical studies examining biomarkers for efficacy of ICIs in CRC patients. Random-effect models were performed for meta-analysis. We pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) for biomarkers predicting response rate and survival. 36 studies with 1867 patients were included in systematic review. We found that a lower pre-treatment blood neutrophil-to-lymphocyte ratio (n=4, HR 0.37, 95%CI 0.21-0.67) predicts good prognosis, higher tumor mutation burden (n=10, OR 4.83, 95%CI 2.16-10.78) predicts response to ICIs, and liver metastasis (n=16, OR 0.32, 95%CI 0.16-0.63) indicates resistance to ICIs, especially when combined with VEGFR inhibitors. But the predictive value of tumor PD-L1 expression (n=9, OR 1.01, 95%CI 0.48-2.14) was insignificant in CRC. Blood neutrophil-to-lymphocyte ratio, tumor mutation burden, and liver metastasis, but not tumor PD-L1 expression, function as significant biomarkers to predict efficacy of ICIs in CRC patients. These findings help stratify CRC patients suitable for ICI treatments, improving efficacy of immunotherapy through precise patient management. (PROSPERO, CRD42022346716).

Constitutive type-1 interferons signaling activity in malignant gliomas

PURPOSE

Recent studies revealed a pro-tumor effect of constitutive Type-1 interferons (IFN-I) production and the downstream signaling activity in several malignancies. In contrast, heterogeneity and clinical significance of the signaling activity in gliomas remain unknown. Thus, we aimed to depict the heterogeneity and clinical significance of constitutive Type-1 interferon (IFN-I) production and the downstream signaling activity in gliomas.

METHODS

We utilized multiplex immunofluorescence (mIF) on a 364 gliomas tissue microarray from our cohort. Moreover, we conducted bioinformatic analyses on the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases to investigate the heterogeneity and clinical significance of constitutive IFN-I signaling activity in gliomas.

RESULTS

We observed high heterogeneity of the constitutive IFN-I signaling activity among glioma subtypes. Signaling increased with the WHO malignancy grade while decreasing in the gliomas with IDH mutations. Additionally, high IFN-I activity served as an independent predictor of unfavorable outcomes, and global DNA hypermethylation in IDH-mutant gliomas was associated with decreased IFN-I signaling activity. Positive correlations were observed between the IFN-I activity and glioma-associated inflammation, encompassing both anti-tumor and pro-tumor immune responses. Furthermore, the IFN-I activity varied significantly among tumor and immune cells in the glioma microenvironment (GME). Notably, a distinct pattern of IFN-I signaling activity distribution in GME cells was observed among glioma subtypes, and the pattern was independently associated with patient overall survival.

CONCLUSIONS

Constitutive IFN-I signaling activity varies significantly among glioma subtypes and represents a potential indicator for increased glioma inflammation and unfavorable clinical outcomes.

Tumor-associated macrophages restrict CD8+ T cell function through collagen deposition and metabolic reprogramming of the breast cancer microenvironment

Tumor progression is accompanied by fibrosis, a condition of excessive extracellular matrix accumulation, which is associated with diminished antitumor immune infiltration. Here we demonstrate that tumor-associated macrophages (TAMs) respond to the stiffened fibrotic tumor microenvironment (TME) by initiating a collagen biosynthesis program directed by transforming growth factor-β. A collateral effect of this programming is an untenable metabolic milieu for productive CD8+ T cell antitumor responses, as collagen-synthesizing macrophages consume environmental arginine, synthesize proline and secrete ornithine that compromises CD8+ T cell function in female breast cancer. Thus, a stiff and fibrotic TME may impede antitumor immunity not only by direct physical exclusion of CD8+ T cells but also through secondary effects of a mechano-metabolic programming of TAMs, which creates an inhospitable metabolic milieu for CD8+ T cells to respond to anticancer immunotherapies.

Hydrogen Therapy Reverses Cancer-Associated Fibroblasts Phenotypes and Remodels Stromal Microenvironment to Stimulate Systematic Anti-Tumor Immunity

Tumor microenvironment (TME) plays an important role in the tumor progression. Among TME components, cancer-associated fibroblasts (CAFs) show multiple tumor-promoting effects and can induce tumor immune evasion and drug-resistance. Regulating CAFs can be a potential strategy to augment systemic anti-tumor immunity. Here, the study observes that hydrogen treatment can alleviate intracellular reactive oxygen species of CAFs and reshape CAFs' tumor-promoting and immune-suppressive phenotypes. Accordingly, a controllable and TME-responsive hydrogen therapy based on a CaCO3 nanoparticles-coated magnesium system (Mg-CaCO3) is developed. The hydrogen therapy by Mg-CaCO3 can not only directly kill tumor cells, but also inhibit pro-tumor and immune suppressive factors in CAFs, and thus augment immune activities of CD4+ T cells. As implanted in situ, Mg-CaCO3 can significantly suppress tumor growth, turn the "cold" primary tumor into "hot", and stimulate systematic anti-tumor immunity, which is confirmed by the bilateral tumor transplantation models of "cold tumor" (4T1 cells) and "hot tumor" (MC38 cells). This hydrogen therapy system reverses immune suppressive phenotypes of CAFs, thus providing a systematic anti-tumor immune stimulating strategy by remodeling tumor stromal microenvironment.

Molecular subtyping of skin cutaneous melanoma based on inflammatory response

The inflammatory response plays a crucial role in determining the prognosis and therapeutic response of skin cutaneous melanoma (SKCM). However, the molecular subtypes based on the inflammatory response and their clinical significance in SKCM have not been extensively studied. Clustering analyses to identify inflammation subtypes of SKCM based on the expression levels of inflammation response gene. We identified three subtypes: Inflammation_H, Inflammation_M, and Inflammation_L, which offer a more nuanced understanding of the complex relationship between inflammation and SKCM. The Inflammation_H subtype is associated with the most favourable prognosis, and is characterised by high levels of immune infiltrates and PD-L1 expression, low levels of stemness, high differentiation, and high genomic stability. In contrast, the Inflammation_L subtype has the least favourable prognosis, with the lowest levels of immune infiltrates and PD-L1 expression, high levels of stemness, low differentiation, and low genomic stability. In addition, the Inf-score, which is a linear risk scoring model based on the expression levels of inflammatory response genes, can be a useful tool for clinicians to assess SKCM prognosis and guide therapeutic decisions. This scoring model shows promise for clinical use in predicting patient outcomes and helps clinicians tailor treatments for individual patients. In conclusion, these findings represent a significant contribution to our understanding of the molecular subtypes of SKCM based on the levels of inflammatory response genes and their potential clinical significance. However, further studies are necessary to validate these findings and explore the underlying mechanisms of different subtypes.

Axl as a potential therapeutic target for adamantinomatous craniopharyngiomas: Based on single nucleus RNA-seq and spatial transcriptome profiling

Craniopharyngiomas (CPs), particularly Adamantinomatous Craniopharyngiomas (ACPs), often exhibit a heightened risk of postoperative recurrence and severe complications of the endocrine and hypothalamic function. The primary objective of this study is to investigate potential novel targeted therapies within the microenvironment of ACP tumors. Cancer-Associated Fibroblasts (CAFs) were identified in the craniopharyngioma microenvironment, notably in regions characterized by cholesterol clefts, wet keratin, ghost cells, and fibrous stroma in ACPs. CAFs, alongside ghost cells, basaloid-like epithelium cells and calcifications, were found to secrete PROS1 and GAS6, which can activate AXL receptors on the surface of tumor epithelium cells, promoting immune suppression and tumor progression in ACPs. Additionally, the AXL inhibitor Bemcentinib effectively inhibited the proliferation organoids and enhanced the immunotherapeutic efficacy of Atezolizumab. Furthermore, neural crest-like cells were observed in the glial reactive tissue surrounding finger-like protrusions. Overall, our results revealed that the AXL might be a potentially effective therapeutic target for ACPs.