Multispectral imaging for quantitative and compartment-specific immune infiltrates reveals distinct immune profiles that classify lung cancer patients

Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma

Immune checkpoint blockade (ICB) is the standard of care for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC), yet efficacy remains low. The combined positive score (CPS) for PD-L1 is the only biomarker approved to predict response to ICB and has limited performance. Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. To elucidate the impact of TLS spatial biology in response to ICB, we utilized pre-ICB tumor tissue sections from 9 responders (complete response, partial response, or stable disease) and 11 non-responders (progressive disease) classified via RECISTv1.1. A custom multi-immunofluorescence (mIF) staining assay was applied to characterize tumor cells (pan-cytokeratin), T cells (CD4, CD8), B cells (CD19, CD20), myeloid cells (CD16, CD56, CD163), dendritic cells (LAMP3), fibroblasts (α Smooth Muscle Actin), proliferative status (Ki67) and immunoregulatory molecules (PD1). A machine learning model was employed to measure the effect of spatial metrics on achieving a response to ICB. A higher density of B cells (CD20+) was found in responders compared to non-responders to ICB (p = 0.022). The presence of TLS within 100 µm of the tumor was associated with improved overall (p = 0.04) and progression-free survival (p = 0.03). A multivariate machine learning model identified TLS density as a leading predictor of response to ICB with 80% accuracy. Immune cell densities and TLS spatial location play a critical role in the response to ICB in HNSCC and may potentially outperform CPS as a predictor of response.

Spatial distribution of tertiary lymphoid structures in the molecular and clinical context of non-small cell lung cancer

INTRODUCTION

Tertiary lymphoid structures (TLS) are lymphocyte aggregates resembling secondary lymphoid organs and are pivotal in cancer immunity. The ambiguous morphological definition of TLS makes it challenging to ascertain their clinical impact on patient survival and response to immunotherapy.

OBJECTIVES

This study aimed to characterize TLS in hematoxylin-eosin tissue sections from lung cancer patients, assessing their occurrence in relation to the local immune environment, mutational background, and patient outcome.

METHODS

Two pathologists evaluated one whole tissue section from resection specimens of 680 NSCLC patients. TLS were spatially quantified within the tumor area or periphery and further categorized based on the presence of germinal centers (mature TLS). Metrics were integrated with immune cell counts, genomic and transcriptomic data, and correlated with clinical parameters.

RESULTS

TLS were present in 86% of 536 evaluable cases, predominantly in the tumor periphery, with a median of eight TLS per case. Mature TLS were found in 24% of cases. TLS presence correlated positively with increased plasma cell (CD138+) and lymphocytic cell (CD3+, CD8+, FOXP3+) infiltration. Tumors with higher tumor mutational burden exhibited higher numbers of peripheral TLS. The overall TLS quantity was independently associated with improved patient survival, irrespective of TLS maturation status. This prognostic association held true for peripheral TLS but not for tumor TLS.

CONCLUSION

TLS in NSCLC is common and their correlation with a specific immune phenotype suggests biological relevance in the local immune reaction. The prognostic significance of this scoring system on routine hematoxylin-eosin sections has the potential to augment diagnostic algorithms for NSCLC patients.

Deciphering the Immunomodulatory Function of GSN+ Inflammatory Cancer-Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan-Cancer Single-Cell Landscape and Spatial Transcriptomics Analysis

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

Selective Depletion of CCR8+Treg Cells Enhances the Antitumor Immunity of Cytotoxic T Cells in Lung Cancer by Dendritic Cells

INTRODUCTION

Accumulation of regulatory T (Treg) cells, an immunosuppressive population, limits the efficacy of immunotherapy in NSCLC. C-C motif chemokine receptor 8 (CCR8) is selectively expressed in tumor-infiltrating Treg cells and is, therefore, considered an ideal target.

METHODS

The efficacy and safety of anti-CCR8 monotherapy and its combination with programmed cell death protein-1 (PD1) inhibitor were evaluated in four NSCLC-bearing mice models. To track the dynamic changes in tumor microenvironment, we performed the single-cell RNA sequencing, the single-cell T-cell receptor sequencing analysis, the flow cytometry, the multi-color immunofluorescence, and the Luminex assay on tumors after three, seven, 14, and 21 days of different treatment regimens. Then, in vitro and in vivo experiments were applied to validate our findings and explore molecular mechanisms of the synergistic effects.

RESULTS

Across four NSCLC-bearing mice models, the combination of CCR8 antibody and PD1 inhibitor significantly reduced tumor growth (p < 0.05) without obvious mouse body weight drops and systemic cytokine storm. The anti-CCR8 therapy synergizes with PD1 blockade by remodeling the tumor microenvironment and disrupting CCR8+Treg-C-C motif chemokine ligand 5 (CCL5)+ dendritic cells (DC) interaction. Mechanistically, therapeutic depletion of CCR8+Treg cells combined with PD1 inhibitor extremely increased interleukin-12 secretion by the Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway activation on CCL5+ DCs, thereby promoting cytotoxic activity of CD8+ T cells. The therapeutic potential of the CCR8 antibody LM-108 in combination with immunotherapy was observed in clinical patients with advanced NSCLC.

CONCLUSION

Overall, CCR8 expression on tumor-infiltrating Treg cells is correlated with immunosuppressive function on DCs and CD8+ T cells, thus impeding antitumor immunity.

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.

Molecular characterization of EBV-associated primary pulmonary lymphoepithelial carcinoma by multiomics analysis

BACKGROUND

Primary pulmonary lymphoepithelial carcinoma (pLEC) is a subtype of non-small cell lung cancer (NSCLC) characterized by Epstein-Barr virus (EBV) infection. However, the molecular pathogenesis of pLEC remains poorly understood.

METHODS

In this study, we explored pLEC using whole-exome sequencing (WES) and RNA-whole-transcriptome sequencing (RNA-seq) technologies. Datasets of normal lung tissue, other types of NSCLC, and EBV-positive nasopharyngeal carcinoma (EBV+-NPC) were obtained from public databases. Furthermore, we described the gene signatures, viral integration, cell quantification, cell death and immune infiltration of pLEC.

RESULTS

Compared with other types of NSCLC and EBV+-NPC, pLEC patients exhibited a lower somatic mutation burden and extensive copy number deletions, including 1p36.23, 3p21.1, 7q11.23, and 11q23.3. Integration of EBV associated dysregulation of gene expression, with CNV-altered regions coinciding with EBV integration sites. Specifically, ZBTB16 and ERRFI1 were downregulated by CNV loss, and the FOXD family genes were overexpressed with CNV gain. Decreased expression of the FOXD family might be associated with a favorable prognosis in pLEC patients, and these patients exhibited enhanced cytotoxicity.

CONCLUSION

Compared with other types of NSCLC and NPC, pLEC has distinct molecular characteristics. EBV integration, the aberrant expression of genes, as well as the loss of CNVs, may play a crucial role in the pathogenesis of pLEC. However, further research is needed to assess the potential role of the FOXD gene family as a biomarker.

The Human Pathology Atlas for deciphering the prognostic features of human cancers

BACKGROUND

Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding and the development of personalized treatments. The present study aims to establish a solid association between gene expression and patient survival outcomes to enhance the utility of the Human Pathology Atlas for cancer research.

METHODS

In this updated analysis, we examined the expression profiles of 6918 patients across 21 cancer types. We integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. We applied systems biology approach to identify the association between gene expression profiles and patient survival outcomes. We further constructed prognostic regulatory networks for kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC), which elucidate the molecular underpinnings associated with patient survival in these cancers.

FINDINGS

We observed that gene expression during the transition from normal to tumorous tissue exhibited diverse shifting patterns in their original tissue locations. Significant correlations between gene expression and patient survival outcomes were identified in KIRC and LIHC among the major cancer types. Additionally, the prognostic regulatory network established for these two cancers showed the indicative capabilities of the Human Pathology Atlas and provides actionable insights for cancer research.

INTERPRETATION

The updated Human Pathology Atlas provides a significant foundation for precision oncology and the formulation of personalized treatment strategies. These findings deepen our understanding of cancer biology and have the potential to advance targeted therapeutic approaches in clinical practice.

FUNDING

The Knut and Alice Wallenberg Foundation (72110), the China Scholarship Council (Grant No. 202006940003).

PD-L1 expression and immune profiling cannot predict osimertinib efficacy in lung cancer with EGFR T790 M mutation: A translational study

BACKGROUND

PD-L1 is associated with poor efficacy of first- or second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in untreated EGFR-mutant non-small-cell lung cancer (NSCLC). Whether PD-L1 is also predictive of osimertinib efficacy in pre-treated patients with an acquired EGFR T790 M mutation is unclear.

PATIENTS AND METHODS

PD-L1 expression and tumor microenvironments were evaluated in tumors from EGFR-mutant T790 M + NSCLC patients treated with osimertinib. In vitro and in vivo experiments were also performed to examine the effect of PD-L1 overexpression on osimertinib susceptibility in EGFR T790 M + cells.

RESULTS

A total of 134 pre-treated EGFR T790 M + patients were enrolled, of whom 72 had del19, 58 had L858R, and 4 had G719X as initial EGFR mutation subtype. Positive PD-L1 expression (TC ≥ 1%) was found in 21 of 134 (15.7%) patients. PD-L1 expression did not differ across different biopsied sites and among different EGFR mutation subgroups. Kaplan-Meier estimate revealed no significant difference in progression-free survival (PFS) in PD-L1-positive versus PD-L1-negative patients. Multivariate analysis using the Cox proportional hazard model found that older age and L858R mutation were independent predictive factors. Multiplex IHC showed that immune cell infiltration was not associated with PD-L1 expression or osimertinib treatment response. By overexpressing PD-L1 in EGFR T790 M + cells, we found that PD-L1 did not result in osimertinib resistance in in vitro and xenograft models.

CONCLUSIONS

PD-L1 expression in pre-treated EGFR T790 M + lung adenocarcinoma is not predictive of osimertinib efficacy, as demonstrated by in vitro, xenograft, and clinical case studies.

Immunohistochemistry effect on diagnostic reliability for paediatric cancer at Mwanza region, Tanzania: a laboratory descriptive study

Introduction

In nations with poor and intermediate incomes, cancer is one of the main causes of mortality. Immunohistochemistry (IHC) is crucial for an accurate cancer evaluation, prognosis and treatment decision-making. To use IHC, a significant amount of facilities and capacity growth are needed. Because of this, it is crucial to comprehend the potential effects of IHC and identify the most essential reagents required to distinguish between typical diagnoses in our environment.

Objective

Employing IHC, this study aims to assess how well paediatric cancer diagnoses in Tanzania can be made and to identify the most widely used biomarkers for diagnostic distinction.

Methods

Pathology samples from kids who were given cancer diagnoses in 2018 at the Bugando Medical Centre in Mwanza, Tanzania, were examined using H&E staining. Basic demographic information from the histology form was gathered in addition to the reported histopathology results from Bugando Medical Centre, including patient age, sex and sample collection. Muhimbili National Hospital received tissue from the histology block for the IHC examination. It was determined which reagents/biomarkers were required for diagnostic confirmation by comparing the histopathology data for diagnostic agreement, variations in diagnosis and other factors.

Results

The examinations included 105 (105) patients with paediatric cancer. 55.2% of the population, who had a median age of 6 years (IQR 3-9), were female. Burkitt and NHL-DLBCL were the paediatric diagnoses with the greatest pathology. The correlation between H&E and IHC histology was 51.0%. 17.6% (n = 18) of diagnoses had enhanced diagnostic specificity (e.g., NHL to diffuse large B cell lymphoma), and 31.4% of diagnoses were altered as a result of IHC.

Conclusion

Considering that the diagnosis of juvenile cancer changes in about 30% of cases, IHC is crucial for accurate diagnosis. IHC retraining is crucial, and developing nations can successfully adopt a modest shared biomarker panel to improve therapy allocation.

Spatial heterogeneity of infiltrating immune cells in the tumor microenvironment of non-small cell lung cancer

Tumor-infiltrating lymphocytes (TILs) are essential components of the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC). Still, it is difficult to describe due to their heterogeneity. In this study, five cell markers from NSCLC patients were analyzed. We segmented tumor cells (TCs) and TILs using Efficientnet-B3 and explored their quantitative information and spatial distribution. After that, we simulated multiplex immunohistochemistry (mIHC) by overlapping continuous single chromogenic IHCs slices. As a result, the proportion and the density of programmed cell death-ligand 1 (PD-L1)-positive TCs were the highest in the core. CD8+ T cells were the closest to the tumor (median distance: 41.71 μm), while PD-1+T cells were the most distant (median distance: 62.2μm), and our study found that most lymphocytes clustered together within the peritumoral range of 10-30 μm where cross-talk with TCs could be achieved. We also found that the classification of TME could be achieved using CD8+ T-cell density, which is correlated with the prognosis of patients. In addition, we achieved single chromogenic IHC slices overlap based on CD4-stained IHC slices. We explored the number and spatial distribution of cells in heterogeneous TME of NSCLC patients and achieved TME classification. We also found a way to show the co-expression of multiple molecules economically.

HNRNPD is a prognostic biomarker in non-small cell lung cancer and affects tumor growth and metastasis via the PI3K-AKT pathway

Heterogeneous nuclear ribonucleoprotein D (HNRNPD) can regulate expression of key proteins in various cancers. However, the prognostic predictive value and biology function of HNRNPD in non-small cell lung cancer (NSCLC) is unknown. First, we used the TCGA and GEO datasets to determine that HNRNPD predicts the prognosis of NSCLC patients. Following that, we knocked down HNRNPD in NSCLC cell lines in vitro and validated its biological function using CCK-8, transwell assays, wound healing tests, and Western blotting. Finally, we constructed tissue microarrays (TMAs) from 174 NSCLC patients and verified our findings using immunohistochemistry staining for HNRNPD from public databases. In both the public datasets, NSCLC tissues with elevated HNRNPD expression had shorter overall survival (OS). In addition, HNRNPD knockdown NSCLC cell lines showed significantly reduced proliferation, invasion, and metastatic capacity via the PI3K-AKT pathway. Finally, elevated HNRNPD expression in NSCLC TMAs was linked to a poorer prognosis and decreased PD-L1 expression levels. HNRNPD is associated with a poorer prognosis in NSCLC and affects tumor growth and metastasis via the PI3K-AKT pathway.

Tumor Cell Communications as Promising Supramolecular Targets for Cancer Chemotherapy: A Possible Strategy

Fifty-two years have passed since President Nixon launched the "War on Cancer". Despite unparalleled efforts and funds allocated worldwide, the outlined goals were not achieved because cancer treatment approaches such as chemotherapy, radiation therapy, hormonal and targeted therapies have not fully met the expectations. Based on the recent literature, a new direction in cancer therapy can be proposed which targets connections between cancer cells and their microenvironment by chemical means. Cancer-stromal synapses such as immunological synapses between cancer and immune cells provide an attractive target for this approach. Such synapses form ligand-receptor clusters on the interface of the interacting cells. They share a common property of involving intercellular clusters of spatially proximate and cooperatively acting proteins. Synapses provide the space for the focused intercellular signaling molecules exchange. Thus, the disassembly of cancer-stromal synapses may potentially cause the collapse of various tumors. Additionally, the clustered arrangement of synapse components offers opportunities to enhance treatment safety and precision by using targeted crosslinking chemical agents which may inactivate cancer synapses even in reduced concentrations. Furthermore, attaching a cleavable cell-permeable toxic agent(s) to a crosslinker may further enhance the anti-cancer effect of such therapeutics. The highlighted approach promises to be universal, relatively simple and cost-efficient. We also hope that, unlike chemotherapeutic and immune drugs that interact with a single target, by using supramolecular large clusters that include many different components as a target, the emergence of a resistance characteristic of chemo- and immunotherapy is extremely unlikely.

Modified spatial architecture of regulatory T cells after neoadjuvant chemotherapy in non-small cell lung cancer patients

It is crucial to decipher the modulation of regulatory T cells (Tregs) in tumor microenvironment (TME) induced by chemotherapy, which may contribute to improving the efficacy of neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer (NSCLC). We retrospectively collected specimens from patients with II-III NSCLC, constituting two cohorts: a neoadjuvant chemotherapy (NAC) cohort (N = 141) with biopsy (N = 58) and postoperative specimens (N = 141), and a surgery-only cohort (N = 122) as the control group. Then, the cell density (Dens), infiltration score (InS), and Treg-cell proximity score (TrPS) were conducted using a panel of multiplex fluorescence staining (Foxp3, CD4, CD8, CK, CD31, ɑSMA). Subsequently, the association of Tregs with cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) was analyzed. Patients with NAC treatment have a higher density of Tregs in both paired (P < 0.001) and unpaired analysis (P = 0.022). Additionally, patients with NAC treatment showed higher infiltration score (paired, P < 0.001; unpaired, P = 0.014) and more CD8+T cells around Tregs (paired/unpaired, both P < 0.001). Subgroup analysis indicated that tumors with a diameter of ≤ 5 cm exhibited increase in both Dens(Treg) and InS(Treg), and gemcitabine, pemetrexed and taxel enhanced Dens(Treg) and TrPS(CD8) following NAC. Multivariate analysis identified that the Dens(Tregs), InS(Tregs) and TrPS(CD8) were significantly associated with better chemotherapy response [OR = 8.54, 95%CI (1.69, 43.14), P = 0.009; OR = 7.14, 95%CI (1.70, 30.08), P = 0.024; OR = 5.50, 95%CI (1.09, 27.75), P = 0.039, respectively] and positive recurrence-free survival [HR = 3.23, 95%CI (1.47, 7.10), P = 0.004; HR = 2.70; 95%CI (1.27, 5.72); P = 0.010; HR = 2.55, 95%CI (1.21, 5.39), P = 0.014, respectively]. Moreover, TrPS(CD8) and TrPS(CD4) were negatively correlated with the CMVs and CAFs. These discoveries have deepened our comprehension of the immune-modulating impact of chemotherapy and underscored that the modified spatial landscape of Tregs after chemotherapy should be taken into account for personalized immunotherapy, aiming to ultimately improve clinical outcomes in patients with NSCLC.

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Background

Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.

Methods

The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.

Results

A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field.

Conclusion

In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

RPS24 alternative splicing is a marker of cancer progression and epithelial-mesenchymal transition

Although alternative splicing (AS) is a major mechanism that adds diversity to gene expression patterns, its precise role in generating variability in ribosomal proteins, known as ribosomal heterogeneity, remains unclear. The ribosomal protein S24 (RPS24) gene, encoding a ribosomal component, undergoes AS; however, in-depth studies have been challenging because of three microexons between exons 4 and 6. We conducted a detailed analysis of RPS24 AS isoforms using a direct approach to investigate the splicing junctions related to these microexons, focusing on four AS isoforms. Each of these isoforms showed tissue specificity and relative differences in expression among cancer types. Significant differences in the proportions of these RPS24 AS isoforms between cancerous and normal tissues across diverse cancer types were also observed. Our study highlighted a significant correlation between the expression levels of a specific RPS24 AS isoform and the epithelial-mesenchymal transition process in lung and breast cancers. Our research contributes to a better understanding of the intricate regulatory mechanisms governing AS of ribosomal protein genes and highlights the biological implications of RPS24 AS isoforms in tissue development and tumorigenesis.

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Scientific figures interpreted by ChatGPT: strengths in plot recognition and limits in color perception

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting basic bioinformatics data analyses. The recent feature of accepting image inputs by ChatGPT, also known as GPT-4V(ision), motivated us to explore its efficacy in deciphering bioinformatics scientific figures. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when color perception and quantitative analysis of visual elements were involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer

Strategies are needed to better identify patients that will benefit from immunotherapy alone or who may require additional therapies like chemotherapy or radiotherapy to overcome resistance. Here we employ single-cell transcriptomics and spatial proteomics to profile triple negative breast cancer biopsies taken at baseline, after one cycle of pembrolizumab, and after a second cycle of pembrolizumab given with radiotherapy. Non-responders lack immune infiltrate before and after therapy and exhibit minimal therapy-induced immune changes. Responding tumors form two groups that are distinguishable by a classifier prior to therapy, with one showing high major histocompatibility complex expression, evidence of tertiary lymphoid structures, and displaying anti-tumor immunity before treatment. The other responder group resembles non-responders at baseline and mounts a maximal immune response, characterized by cytotoxic T cell and antigen presenting myeloid cell interactions, only after combination therapy, which is mirrored in a murine model of triple negative breast cancer.

Tumor-associated macrophages and PD-L1 in prostate cancer: a possible key to unlocking immunotherapy efficacy

PURPOSE

Prostate cancer (PCa) is often considered as a "cold" tumor with low responsiveness to immunotherapy. Recent evidence suggests the activation of specific immune cells, such as tumor-associated macrophages (TAMs), could potentially influence the efficacy of immunotherapy in PCa. However, the relationship between TAMs and PD-L1, a significant regulator in immunotherapy, within PCa remains unexplored.

METHODS

In this study, we assessed TAM infiltration and PD-L1 expression levels in a local cohort of 95 PCa tissue samples and two publicly available PCa datasets. We employed a combination of bioinformatics and experimental techniques, including gene set enrichment analysis, CIBERSORTx, tissue microarray, immunohistochemistry staining, and analysis of single-cell sequencing datasets, to provide a comprehensive understanding of the association between PD-L1 and TAMs in the PCa microenvironment.

RESULTS

The study showed that CD68+ TAMs and CD163+ TAMs (M2-TAMs) were more abundant in the tumor microenvironment than in non-cancerous surrounding tissues. The infiltration of CD163+ TAMs was significantly associated with the Gleason score and risk stratification of PCa. Importantly, elevated PD-L1 expression correlated significantly with high infiltration of CD163+ TAMs. Furthermore, patients displaying high levels of CD163+ TAMs and PD-L1 expression exhibited shorter times to biochemical recurrence-free survival.

CONCLUSION

Our study suggests that CD163+ TAMs are closely associated with PD-L1 expression and can act as a valuable prognostic indicator for PCa. The high infiltration of M2-TAMs, coupled with the overexpression of PD-L1, may contribute to immune escape mechanisms in PCa, thereby influencing disease prognosis.

Distinct microRNA Signature and Suppression of ZFP36L1 Define ASCL1-Positive Lung Adenocarcinoma

Achaete-scute family bHLH transcription factor 1 (ASCL1) is a master transcription factor involved in neuroendocrine differentiation. ASCL1 is expressed in approximately 10% of lung adenocarcinomas (LUAD) and exerts tumor-promoting effects. Here, we explored miRNA profiles in ASCL1-positive LUADs and identified several miRNAs closely associated with ASCL1 expression, including miR-375, miR-95-3p/miR-95-5p, miR-124-3p, and members of the miR-17∼92 family. Similar to small cell lung cancer, Yes1 associated transcriptional regulator (YAP1), a representative miR-375 target gene, was suppressed in ASCL1-positive LUADs. ASCL1 knockdown followed by miRNA profiling in a cell culture model further revealed that ASCL1 positively regulates miR-124-3p and members of the miR-17∼92 family. Integrative transcriptomic analyses identified ZFP36 ring finger protein like 1 (ZFP36L1) as a target gene of miR-124-3p, and IHC studies demonstrated that ASCL1-positive LUADs are associated with low ZFP36L1 protein levels. Cell culture studies showed that ectopic ZFP36L1 expression inhibits cell proliferation, survival, and cell-cycle progression. Moreover, ZFP36L1 negatively regulated several genes including E2F transcription factor 1 (E2F1) and snail family transcriptional repressor 1 (SNAI1). In conclusion, our study revealed that suppression of ZFP36L1 via ASCL1-regulated miR-124-3p could modulate gene expression, providing evidence that ASCL1-mediated regulation of miRNAs shapes molecular features of ASCL1-positive LUADs.

IMPLICATIONS

Our study revealed unique miRNA profiles of ASCL1-positive LUADs and identified ASCL1-regulated miRNAs with functional relevance.

CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

A Novel Nucleic Acid Sensing-related Genes Signature for Predicting Immunotherapy Efficacy and Prognosis of Lung Adenocarcinoma

BACKGROUND

As a novel pillar for lung adenocarcinoma (LUAD) treatment, immunotherapy has limited efficiency in LUAD patients. The nucleic acid sensing (NAS) pathways are critical in the anti-tumor immune response, but their role in LUAD remains controversial.

OBJECTIVE

The study aims to develop a classification system to identify immune subtypes of LUAD based on nucleic acid sensing-related genes so that it can help screen patients who may respond to immunotherapy.

METHODS

We performed a comprehensive bioinformatics analysis of the NAS molecule expression profiles across multiple public datasets. Using qRT-PCR to verify the NAS genes in multiple lung cancer cell lines. Molecular docking was performed to screen drug candidates.

RESULTS

The NAS-activated subgroup and NAS-suppressed subgroup were validated based on the different patterns of gene expression and pathways enrichment. The NAS-activated subgroup displayed a stronger immune infiltration and better prognosis of patients. Moreover, we constructed a seven nucleic acid sensing-related risk score (NASRS) model for the convenience of clinical application. The predictive values of NASRS in prognosis and immunotherapy were subsequently fully validated in the lung adenocarcinoma dataset and the uroepithelial carcinoma dataset. Additionally, five potential drugs binding to the core target of the NAS signature were predicted through molecular docking.

CONCLUSION

We found a significant correlation between nucleic acid sensing function and the immune treatment efficiency in LUAD. The NASRS can be used as a robust biomarker for the predicting of prognosis and immunotherapy efficiency and may help in clinical decisions for LUAD patients.

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly

Emerging studies underscore the promising capabilities of large language model-based chatbots in conducting fundamental bioinformatics data analyses. The recent feature of accepting image-inputs by ChatGPT motivated us to explore its efficacy in deciphering bioinformatics illustrations. Our evaluation with examples in cancer research, including sequencing data analysis, multimodal network-based drug repositioning, and tumor clonal evolution, revealed that ChatGPT can proficiently explain different plot types and apply biological knowledge to enrich interpretations. However, it struggled to provide accurate interpretations when quantitative analysis of visual elements was involved. Furthermore, while the chatbot can draft figure legends and summarize findings from the figures, stringent proofreading is imperative to ensure the accuracy and reliability of the content.

Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare

Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.

Machine learning-based risk model incorporating tumor immune and stromal contexture predicts cancer prognosis and immunotherapy efficacy

The immune and stromal contexture within the tumor microenvironment (TME) interact with cancer cells and jointly determine disease process and therapeutic response. We aimed at developing a risk scoring model based on TME-related genes of squamous cell lung cancer to predict patient prognosis and immunotherapeutic response. TME-related genes were identified through exploring genes that correlated with immune scores and stromal scores. LASSO-Cox regression model was used to establish the TME-related risk scoring (TMErisk) model. A TMErisk model containing six genes was established. High TMErisk correlated with unfavorable OS in LUSC patients and this association was validated in multiple NSCLC datasets. Genes involved in pathways associated with immunosuppressive microenvironment were enriched in the high TMErisk group. Tumors with high TMErisk showed elevated infiltration of immunosuppressive cells. High TMErisk predicted worse immunotherapeutic response and prognosis across multiple carcinomas. TMErisk model could serve as a robust biomarker for predicting OS and immunotherapeutic response.

Personal immune profiles: Diversity and prognostic value for oral tongue squamous cell carcinoma evaluated by comprehensive immune parameter analyses with multiplex immunofluorescence

OBJECTIVES

Understanding the tumor immune microenvironment is becoming increasingly necessary for risk prediction and treatment selection. In particular, oral cancer has various immunosuppressive characteristics in the tumor microenvironment. Therefore, we comprehensively assessed the immune profiles of oral tongue squamous cell carcinoma (OTSCC).

MATERIALS AND METHODS

Multiplex immunofluorescence and tissue imaging analyses were performed to evaluate immune profiles at the invasive tumor front of 60 OTSCC surgical specimens. We analyzed 58 immune parameters including the density and proportion (%) of total leukocytes (Leu) and T cells, six subsets of T and myeloid cells, and the expression of programmed cell death-1 (PD-1) and PD-1 ligand 1 (PD-L1).

RESULTS

The density, proportion, and location of CD45⁺ Leu, three T cell subsets (CD8⁺, Foxp3^-CD4⁺ conventional, and Foxp3⁺CD4⁺ regulatory T cells), CD163^-CD68⁺ M1 and CD163⁺CD68⁺ M2 macrophages, and neutrophils were highly variable at the individual level. The density and proportion of M2 macrophages were significantly lower in the T1 stage group. Risk prediction analyses for recurrence and/or metastasis (R/M) showed that R/M (+) T1 cases had significantly higher M2 density and percentages.

CONCLUSIONS

The immune profiles of OTSCC patients are diverse and cannot be predicted from clinicopathological information alone. The M2 macrophage abundance is a potential candidate biomarker for R/M in the early stage of OTSCC. Personal immune profiling may provide beneficial information for risk prediction and treatment selection.

High density and proximity of CD8+ T cells to tumor cells are correlated with better response to nivolumab treatment in metastatic pleural mesothelioma

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) in pleural mesothelioma has recently been established. The response to ICIs can be predicted by quantitative analysis of cells and their spatial distribution in the tumor microenvironment (TME). However, the detailed composition of the TME in pleural mesothelioma has not been reported. We evaluated the association between the TME and response to ICIs in this cancer.

METHODS

A retrospective analysis of 22 pleural mesothelioma patients treated with nivolumab in different centers was performed using surgical specimens. Four patients had a partial response to nivolumab (response group) and 18 patients had stable or progressive disease (nonresponse group). The number of CD4, CD8, FoxP3, CK, and PD-L1 positive cells, cell density, and cell-to-cell distance were analyzed by multiplex immunofluorescence.

RESULTS

PD-L1 expression did not differ significantly between the response and nonresponse groups. The density of total T cells and of CD8⁺ T cells was significantly higher in the response than in the nonresponse group. CD8⁺ T cells were more clustered and located closer to tumor cells, whereas regulatory T cells were located further from tumor cells in the response than in the nonresponse group.

CONCLUSIONS

High density and spatial proximity of CD8⁺ T cells to tumor cells were associated with better response to nivolumab, whereas the proximity of regulatory T cells to tumor cells was associated with worse response, suggesting that the distinct landscape of the TME could be a potential predictor of ICI efficacy in pleural mesothelioma.

Efficacy of PD-1/PD-L1 inhibitors in advanced gastroesophageal cancer based on characteristics: a meta-analysis

Objective: Evaluate the potency of anti-PD-1/PD-L1 antibodies in advanced gastroesophageal cancer patients with different clinical features. Methods: Randomized, controlled trials comparing anti-PD-1/PD-L1 antibodies with chemotherapy in individuals with gastroesophageal cancer were retrieved. Results: 15 trials involving 9194 individuals were included. PD-1/PD-L1 inhibitors significantly improved overall survival (OS) but not progression-free survival. Significantly improved OS was observed in PD-L1 combined positive score ≥1, primary esophageal cancer, primary gastric cancer and Asian patients. Subgroup analysis revealed significant OS benefit achieved for esophageal squamous cell carcinoma, but not for esophageal adenocarcinoma. Conclusion: PD-1/PD-L1 inhibitors improved OS in advanced gastroesophageal carcinoma, especially in patients with esophageal cancer. Race, primary tumor sites and PD-L1 combined positive score can be used to predict the potency of immune checkpoint inhibitors.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer

INTRODUCTION

Immune cells in the tumour microenvironment are associated with prognosis and response to therapy. We aimed to comprehensively characterise the spatial immune phenotypes in the mutational and clinicopathological background of non-small cell lung cancer (NSCLC).

METHODS

We established a multiplexed fluorescence imaging pipeline to spatially quantify 13 immune cell subsets in 359 NSCLC cases: CD4 effector cells (CD4-Eff), CD4 regulatory cells (CD4-Treg), CD8 effector cells (CD8-Eff), CD8 regulatory cells (CD8-Treg), B-cells, natural killer cells, natural killer T-cells, M1 macrophages (M1), CD163+ myeloid cells (CD163), M2 macrophages (M2), immature dendritic cells (iDCs), mature dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs).

RESULTS

CD4-Eff cells, CD8-Eff cells and M1 macrophages were the most abundant immune cells invading the tumour cell compartment and indicated a patient group with a favourable prognosis in the cluster analysis. Likewise, single densities of lymphocytic subsets (CD4-Eff, CD4-Treg, CD8-Treg, B-cells and pDCs) were independently associated with longer survival. However, when these immune cells were located close to CD8-Treg cells, the favourable impact was attenuated. In the multivariable Cox regression model, including cell densities and distances, the densities of M1 and CD163 cells and distances between cells (CD8-Treg-B-cells, CD8-Eff-cancer cells and B-cells-CD4-Treg) demonstrated positive prognostic impact, whereas short M2-M1 distances were prognostically unfavourable.

CONCLUSION

We present a unique spatial profile of the in situ immune cell landscape in NSCLC as a publicly available data set. Cell densities and cell distances contribute independently to prognostic information on clinical outcomes, suggesting that spatial information is crucial for diagnostic use.

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

BACKGROUND

The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.

RECENT FINDINGS

Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker.

CONCLUSIONS

The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

Transcriptional profiles and common genes link lung cancer with the development and severity of COVID-19

Lung cancer patients with COVID-19 present an increased risk of developing severe disease and, consequently, have poor outcomes. Determining SARS-CoV-2-host interactome in lung cancer cells and tissues, infected or uninfected with SARS-CoV-2, may reveal molecular mechanisms associated with COVID-19 development and severity in lung cancer patients. Here, we integrated transcriptome data of lung tumors from patients with small- or non-small cell lung cancer (SCLC and NSCLC) and normal lung and lung cancer cells infected with SARS-CoV-2. We aimed to characterize molecular mechanisms potentially associated with COVID-19 development and severity in lung cancer patients and to predict the SARS-CoV-2-host cell interactome. We found that the gene expression profiles of lung cell lines infected with SARS-CoV-2 resemble more primary lung tumors than non-malignant lung tissues. In addition, the transcriptomic-based interactome analysis of SCLC and NSCLC revealed increased expression of cancer genes BRCA1 and CENPF, whose proteins are known or predicted to interact with the SARS-CoV-2 spike glycoprotein and helicase, respectively. We also found that TRIB3, a gene coding a putative host-SARS-CoV-2 interacting protein associated with COVID-19 infection, is co-expressed with the up-regulated genes MTHFD2, ADM2, and GPT2 in all tested conditions. Our analysis identified biological processes such as amino acid metabolism and angiogenesis and 22 host mediators of SARS-CoV-2 infection and replication that may contribute to the development and severity of COVID-19 in lung cancers.

An immune score reflecting pro- and anti-tumoural balance of tumour microenvironment has major prognostic impact and predicts immunotherapy response in solid cancers

BACKGROUND

Cancer immunity is based on the interaction of a multitude of cells in the spatial context of the tumour tissue. Clinically relevant immune signatures are therefore anticipated to fundamentally improve the accuracy in predicting disease progression.

METHODS

Through a multiplex in situ analysis we evaluated 15 immune cell classes in 1481 tumour samples. Single-cell and bulk RNAseq data sets were used for functional analysis and validation of prognostic and predictive associations.

FINDINGS

By combining the prognostic information of anti-tumoural CD8⁺ lymphocytes and tumour supportive CD68⁺CD163⁺ macrophages in colorectal cancer we generated a signature of immune activation (SIA). The prognostic impact of SIA was independent of conventional parameters and comparable with the state-of-art immune score. The SIA was also associated with patient survival in oesophageal adenocarcinoma, bladder cancer, lung adenocarcinoma and melanoma, but not in endometrial, ovarian and squamous cell lung carcinoma. We identified CD68⁺CD163⁺ macrophages as the major producers of complement C1q, which could serve as a surrogate marker of this macrophage subset. Consequently, the RNA-based version of SIA (ratio of CD8A to C1QA) was predictive for survival in independent RNAseq data sets from these six cancer types. Finally, the CD8A/C1QA mRNA ratio was also predictive for the response to checkpoint inhibitor therapy.

INTERPRETATION

Our findings extend current concepts to procure prognostic information from the tumour immune microenvironment and provide an immune activation signature with high clinical potential in common human cancer types.

FUNDING

Swedish Cancer Society, Lions Cancer Foundation, Selanders Foundation, P.O. Zetterling Foundation, U-CAN supported by SRA CancerUU, Uppsala University and Region Uppsala.

Cross-talk between cuproptosis and ferroptosis regulators defines the tumor microenvironment for the prediction of prognosis and therapies in lung adenocarcinoma

Cuproptosis, a newly identified form of programmed cell death, plays vital roles in tumorigenesis. However, the interconnectivity of cuproptosis and ferroptosis is poorly understood. In our study, we explored genomic alterations in 1162 lung adenocarcinoma (LUAD) samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) cohort to comprehensively evaluate the cuproptosis regulators. We systematically performed a pancancer genomic analysis by depicting the molecular correlations between the cuproptosis and ferroptosis regulators in 33 cancer types, indicating cross-talk between cuproptosis and ferroptosis regulators at the multiomic level. We successfully identified three distinct clusters based on cuproptosis and ferroptosis regulators, termed CuFeclusters, as well as the three distinct cuproptosis/ferroptosis gene subsets. The tumor microenvironment cell-infiltrating characteristics of three CuFeclusters were highly consistent with the three immune phenotypes of tumors. Furthermore, a CuFescore was constructed and validated to predict the cuproptosis/ferroptosis pathways in individuals and the response to chemotherapeutic drugs and immunotherapy. The CuFescore was significantly associated with the expression of miRNA and the regulation of post-transcription. Thus, our research established an applied scoring scheme, based on the regulators of cuproptosis/ferroptosis to identify LUAD patients who are candidates for immunotherapy and to predict patient sensitivity to chemotherapeutic drugs.

Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.

The Clinical Relevance and Immune Correlation of SLC10 Family Genes in Liver Cancer

Background and Aim

This study was aimed to reveal the clinical relevance and immune correlation of the SLC10 family genes in liver cancer.

Methods

A comprehensive bioinformatics analysis was utilized to determine the gene expression, genetic alterations, DNA methylation, clinical significance, survival association and immune correlation of seven SLC10 family genes in liver cancer. The multiplexed immunohistochemical technique was applied to determine the association between SLC10A3 protein expression and immune cells, and the correlation between SLC10A3 protein and immune checkpoints (PD1 and PD-L1) in a cohort of 32 individuals with liver cancer.

Results

The expression of SLC10 family genes was different between normal liver tissues and malignant liver tissues. SLC10A5 showed the highest alteration rate (8%), followed by SLC10A3 (2.8%). Low expression of SLC10A1 was indicative of poor tumor grade and advanced tumor stage in liver cancer. Scatter plots uncovered that expression of SLC10A3 was inversely associated with SLC10A1 and SLC10A5 expression in liver cancer. The expression of SLC10A1 and SLC10A5 was strongly associated with their DNA methylation. SLC10A1 expression was a reliable genetic biomarker for the prediction of survival outcomes in liver cancer population. Expression of SLC10 family genes was remarkably linked with the abundance of most immune infiltrating cells in liver cancer, and SLC10A3 was the most significant member. The multiplexed immunohistochemical technique confirmed that there existed the significant correlations between SLC10A3 protein expression and CD4 T cells, CD20 B cells and the close association with PD-1 in the stromal area from malignant tissues.

Conclusion

The expressions of SLC10 family genes were different between normal liver tissues and malignant liver tissues, and they were correlated with each other in liver cancer. SLC10A1 possesses the most significant correlation with survival outcomes. SLC10A3 exhibited the most significant relationship with immune cells, as revealed by bioinformatics analysis and multispectral imaging technique.

A prognostic and therapeutic hallmark developed by the integrated profile of basement membrane and immune infiltrative landscape in lung adenocarcinoma

Basement membranes (BMs) are specialised extracellular matrices that maintain cellular integrity and resist the breaching of carcinoma cells for metastases while regulating tumour immunity. The tumour immune microenvironment (TME) is essential for tumour growth and the response to and benefits from immunotherapy. In this study, the BM score and TME score were constructed based on the expression signatures of BM-related genes and the presence of immune cells in lung adenocarcinoma (LUAD), respectively. Subsequently, the BM-TME classifier was developed with the combination of BM score and TME score for accurate prognostic prediction. Further, Kaplan-Meier survival estimation, univariate Cox regression analysis and receiver operating characteristic curves were used to cross-validate and elucidate the prognostic prediction value of the BM-TME classifier in several cohorts. Findings from functional annotation analysis suggested that the potential molecular regulatory mechanisms of the BM-TME classifier were closely related to the cell cycle, mitosis and DNA replication pathways. Additionally, the guiding value of the treatment strategy of the BM-TME classifier for LUAD was determined. Future clinical disease management may benefit from the findings of our research.

LCAT1 is an oncogenic LncRNA by stabilizing the IGF2BP2-CDC6 axis

Long non-coding RNAs (lncRNAs) is known to play vital roles in modulating tumorigenesis. We previously reported that LCAT1, a novel lncRNA, promotes the growth and metastasis of lung cancer cells both in vitro and in vivo. However, the underlying mechanism(s) of LCAT1 as an oncogenic regulator remains elusive. Here, we showed that LCAT1 physically interacts with and stabilizes IGF2BP2, an m⁶A reader protein, by preventing its degradation via autolysosomes. IGF2BP2 is overexpressed in lung cancer tissues, which is associated with poor survival of non-small cell lung cancer patients, suggesting its oncogenic role. Biologically, IGF2BP2 depletion inhibits growth and survival as well as the migration of lung cancer cells. Mechanistically, the LCAT1/IGF2BP2 complex increased the levels of CDC6, a key cell cycle regulator, by stabilizing its mRNA in an m⁶A-dependent manner. Like IGF2BP2, CDC6 is also overexpressed in lung cancer tissues with poor patient survival, and CDC6 knockdown has oncogenic inhibitory activity. Taken together, the LCAT1-IGF2BP2-CDC6 axis appears to play a vital role in promoting the growth and migration of lung cancer cells, and is a potential therapeutic target for lung cancer. Importantly, our finding also highlights a previously unknown critical role of LCAT1 in m⁶A-dependent gene regulation by preventing autolytic degradation of IGF2BP2.

Emerging Biomaterials Imaging Antitumor Immune Response

Immunotherapy is one of the most promising clinical modalities for the treatment of malignant tumors and has shown excellent therapeutic outcomes in clinical settings. However, it continues to face several challenges, including long treatment cycles, high costs, immune-related adverse events, and low response rates. Thus, it is critical to predict the response rate to immunotherapy by using imaging technology in the preoperative and intraoperative. Here, the latest advances in nanosystem-based biomaterials used for predicting responses to immunotherapy via the imaging of immune cells and signaling molecules in the immune microenvironment are comprehensively summarized. Several imaging methods, such as fluorescence imaging, magnetic resonance imaging, positron emission tomography imaging, ultrasound imaging, and photoacoustic imaging, used in immune predictive imaging, are discussed to show the potential of nanosystems for distinguishing immunotherapy responders from nonresponders. Nanosystem-based biomaterials aided by various imaging technologies are expected to enable the effective prediction and diagnosis in cases of tumors, inflammation, and other public diseases.

A feature selection-based framework to identify biomarkers for cancer diagnosis: A focus on lung adenocarcinoma

Lung cancer (LC) represents most of the cancer incidences in the world. There are many types of LC, but Lung Adenocarcinoma (LUAD) is the most common type. Although RNA-seq and microarray data provide a vast amount of gene expression data, most of the genes are insignificant to clinical diagnosis. Feature selection (FS) techniques overcome the high dimensionality and sparsity issues of the large-scale data. We propose a framework that applies an ensemble of feature selection techniques to identify genes highly correlated to LUAD. Utilizing LUAD RNA-seq data from the Cancer Genome Atlas (TCGA), we employed mutual information (MI) and recursive feature elimination (RFE) feature selection techniques along with support vector machine (SVM) classification model. We have also utilized Random Forest (RF) as an embedded FS technique. The results were integrated and candidate biomarker genes across all techniques were identified. The proposed framework has identified 12 potential biomarkers that are highly correlated with different LC types, especially LUAD. A predictive model has been trained utilizing the identified biomarker expression profiling and performance of 97.99% was achieved. In addition, upon performing differential gene expression analysis, we could find that all 12 genes were significantly differentially expressed between normal and LUAD tissues, and strongly correlated with LUAD according to previous reports. We here propose that using multiple feature selection methods effectively reduces the number of identified biomarkers and directly affects their biological relevance.

Quantitative high-throughput analysis of tumor infiltrating lymphocytes in breast cancer

In breast cancer (BC), the development of cancer immunotherapy including immune checkpoint inhibitors has progressed. Tumor infiltrating lymphocytes (TILs) is one of the important factors for an immune response between tumor cells and immune cells in the tumor microenvironment, and the presence of TILs has been identified as predictors of response to chemotherapy. However, because complex mechanisms underlies the crosstalk between immune cells and cancer cells, the relationship between immune profiles in the tumor microenvironment and the efficacy of the immune checkpoint blocked has been unclear. Moreover, in many cases of breast cancer, the quantitative analysis of TILs and immuno-modification markers in a single tissue section are not studied. Therefore, we quantified detailed subsets of tumor infiltrating lymphocytes (TILs) from BC tissues and compared among BC subtypes. The TILs of BC tissues from 86 patients were classified using multiplex immunohistochemistry and an artificial intelligence-based analysis system based on T-cell subset markers, immunomodification markers, and the localization of TILs. The levels of CD4/PD1 and CD8/PD1 double-positive stromal TILs were significantly lower in the HER2- BC subtype (p <0.01 and p <0.05, respectively). In triple-negative breast cancer (TNBC), single marker-positive intratumoral TILs did not affect prognosis, however CD4/PDL1, CD8/PD1, and CD8/PDL1 double-positive TILs were significantly associated with TNBC recurrence (p<0.05, p<0.01, and p<0.001, respectively). TIL profiles differed among different BC subtypes, suggesting that the localization of TILs and their tumor-specific subsets influence the BC microenvironment.