Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy

Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

The Effects of Radiation Dose Heterogeneity on the Tumor Microenvironment and Anti-Tumor Immunity

Radiotherapy elicits dose- and lineage-dependent effects on immune cell survival, migration, activation, and proliferation in targeted tumor microenvironments. Radiation also stimulates phenotypic changes that modulate the immune susceptibility of tumor cells. This has raised interest in using radiotherapy to promote greater response to immunotherapies. To clarify the potential of such combinations, it is critical to understand how best to administer radiation therapy to achieve activation of desired immunologic mechanisms. In considering the multifaceted process of priming and propagating anti-tumor immune response, radiation dose heterogeneity emerges as a potential means for simultaneously engaging diverse dose-dependent effects in a single tumor environment. Recent work in spatially fractionated external beam radiation therapy demonstrates the expansive immune responses achievable when a range of high to low dose radiation is delivered in a tumor. Brachytherapy and radiopharmaceutical therapies deliver inherently heterogeneous distributions of radiation that may contribute to immunogenicity. This review evaluates the interplay of radiation dose and anti-tumor immune response and explores emerging methodological approaches for investigating the effects of heterogeneous dose distribution on immune responses.

The tumor immune microenvironment remodeling and response to HER2-targeted therapy in HER2-positive advanced gastric cancer

Combination therapy with anti-HER2 agents and immunotherapy has demonstrated significant clinical benefits in gastric cancer (GC), but the underlying mechanism remains unclear. In this study, we used multiplex immunohistochemistry to assess the changes of the tumor microenvironment in 47 advanced GC patients receiving anti-HER2 therapy. Additionally, we performed single-cell transcriptional sequencing to investigate potential cell-to-cell communication and molecular mechanisms in four HER2-positive GC baseline samples. We observed that post-treated the infiltration of NK cells, CD8+ T cells, and B lymphocytes were significantly higher in patients who benefited from anti-HER2 treatment than baseline. Further spatial distribution analysis demonstrated that the interaction scores between NK cells and CD8+ T cells, B lymphocytes and M2 macrophages, B lymphocytes and Tregs were also significantly higher in benefited patients. Cell-cell communication analysis from scRNA sequencing showed that NK cells utilized CCL3/CCL4-CCR5 to recruit CD8+ T cell infiltration. B lymphocytes employed CD74-APP/COPA/MIF to interact with M2 macrophages, and utilized TNF-FAS/ICOS/TNFRSR1B to interact with Tregs. These cell-cell interactions contribute to inhibit the immune resistance of M2 macrophages and Tregs. Our research provides potential guidance for the use of anti-HER2 therapy in combination with immune therapy.

FOXO1 induced fatty acid oxidation in hepatic cells by targeting ALDH1L2

BACKGROUND AND AIM

Lipid metabolism disorder is the primary feature of numerous refractory chronic diseases. Fatty acid oxidation, an essential aerobic biological process, is closely related to the progression of NAFLD. The forkhead transcription factor FOXO1 has been reported to play an important role in lipid metabolism. However, the molecular mechanism through which FOXO1 regulates fatty acid oxidation remains unclear.

METHODS

Transcriptomic analysis was performed to examine the cellular expression profile to determine the functional role of FOXO1 in HepG2 cells with palmitic acid (PA)-induced lipid accumulation. FOXO1-binding motifs at the promoter region of aldehyde dehydrogenase 1 family member L2 (ALDH1L2) were predicted via bioinformatic analysis and confirmed via luciferase reporter assay. Overexpression of ALDH1L2 was induced to recover the impaired fatty acid oxidation in FOXO1-knockout cells.

RESULTS

Knockout of FOXO1 aggravated lipid deposition in hepatic cells. Transcriptomic profiling revealed that knockout of FOXO1 increased the expression of genes associated with fatty acid synthesis but decreased the expression of carnitine palmitoyltransferase1a (CPT1α) and adipose triglyceride lipase (ATGL), which contribute to fatty acid oxidation. Mechanistically, FOXO1 was identified as a transcription factor of ALDH1L2. Knockout of FOXO1 significantly decreased the protein expression of ALDH1L2 and CPT1α in vitro and in vivo. Furthermore, overexpression of ALDH1L2 restored fatty acid oxidation in FOXO1-knockout cells.

CONCLUSION

The findings of this study indicate that FOXO1 modulates fatty acid oxidation by targeting ALDH1L2.

HIV-1 infection promotes neuroinflammation and neuron pathogenesis in novel microglia-containing cerebral organoids

Cerebral organoids (COs) are a valuable tool to study the intricate interplay between glial cells and neurons in brain development and disease, including HIV-associated neuroinflammation. We developed a novel approach to generate microglia containing COs (CO-iMs) by co-culturing hematopoietic progenitors and induced pluripotent stem cells. This approach allowed for the differentiation of microglia within the organoids concomitantly to the neuronal progenitors. CO- iMs exhibited higher efficiency in generation of CD45 + /CD11b + /Iba-1 + microglia cells compared to conventional COs with physiologically relevant proportion of microglia (∼7%). CO-iMs exhibited substantially higher expression of microglial homeostatic and sensome markers as well as markers for the complement cascade. CO-iMs showed susceptibility to HIV infection resulting in a significant increase in several pro-inflammatory cytokines/chemokines and compromised neuronal function, which were abrogated by addition of antiretrovirals. Thus, CO-iM is a robust model to decipher neuropathogenesis, neurological disorders, and viral infections of brain cells in a 3D culture system.

TSWIFT, a novel method for iterative staining of embedded and mounted human brain sections

Comprehensive characterization of protein networks in mounted brain tissue represents a major challenge in brain and neurodegenerative disease research. In this study, we develop a simple staining method, called TSWIFT, to iteratively stain pre-mounted formalin fixed, paraffin embedded (FFPE) brain sections, thus enabling high-dimensional sample phenotyping. We show that TSWIFT conserves tissue architecture and allows for relabeling a single mounted FFPE sample more than 10 times, even after prolonged storage at 4 °C. Our results establish TSWIFT as an efficient method to obtain integrated high-dimensional knowledge of cellular proteomes by analyzing mounted FFPE human brain tissue.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BACKGROUND

The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis.

PURPOSE

Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach.

METHOD

Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data).

RESULTS

The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes.

CONCLUSION

Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

Congenital heart diseases (CHDs) and forensic investigations: Searching for the cause of death

Congenital Heart Diseases (CHDs) are a group of structural abnormalities or defects of the heart that are present at birth. CHDs could be connected to sudden death (SD), defined by the WHO (World Health Organization) as "death occurring within 24 h after the onset of the symptoms" in an apparently "healthy" subject. These conditions can range from relatively mild defects to severe, life-threatening anomalies. The prevalence of CHDs varies across populations, but they affect millions of individuals worldwide. This article aims to discuss the post-mortem investigation of death related to CHDs, exploring the forensic approach, current methodologies, challenges, and potential advancements in this challenging field. A further goal of this article is to provide a guide for understanding these complex diseases, highlighting the pivotal role of autopsy, histopathology, and genetic investigations in defining the cause of death, and providing evidence about the translational use of autopsy reports. Forensic investigations play a crucial role in understanding the complexities of CHDs and determining the cause of death accurately. Through collaboration between medical professionals and forensic experts, meticulous examinations, and analysis of evidence, valuable insights can be gained. These insights not only provide closure to the families affected but also contribute to the prevention of future tragedies.

Bulk and single-cell RNA sequencing reveal the contribution of laminin γ2 -CD44 to the immune resistance in lymphocyte-infiltrated squamous lung cancer subtype

The high heterogeneity of lung squamous cell carcinomas (LUSC) and the complex tumor microenvironment lead to non-response to immunotherapy in many patients. Therefore, characterizing the heterogeneity of the tumor microenvironment in patients with LUSC and further exploring the immune features and molecular mechanisms that lead to immune resistance will help improve the efficacy of immunotherapy in such patients. Herein, we retrospectively analyzed the RNA sequencing (RNA-seq) data of 513 LUSC samples with other multiomics and single-cell RNA-seq data and validated key features using multiplex immunohistochemistry. We divided these samples into six subtypes (CS1-CS6) based on the RNA-seq data and found that CS3 activates the immune response with a high level of lymphocyte infiltration and gathers a large number of patients with advanced-stage disease but increases the expression of exhausted markers cytotoxic T-lymphocyte associated protein 4, lymphocyte-activation gene 3, and programmed death-1. The prediction of the response to immunotherapy showed that CS3 is potentially resistant to immune checkpoint blockade therapy, and multi-omic data analysis revealed that CS3 specifically expresses immunosuppression-related proteins B cell lymphoma 2, GRB2-associated binding protein, and dual-specificity phosphatase 4 and has a high mutation ratio of the driver gene ATP binding cassette subfamily A member 13. Furthermore, single-cell RNA-seq verified lymphocyte infiltration in the CS3 subtype and revealed a positive relationship between the expression of LAMC2-CD44 and immune resistance. LAMC2 and CD44 are epithelial-mesenchymal transition-associated genes that modulate tumor proliferation, and multicolor immunofluorescence validated the negative relationship between the expression of LAMC2-CD44 and immune infiltration. Thus, we identified a lymphocyte-infiltrated subtype (CS3) in patients with LUSC that exhibited resistance to immune checkpoint blockade therapy, and the co-hyperexpression of LAMC2-CD44 contributed to immune resistance, which could potentially improve immunological efficacy by targeting this molecule pair in combination with immunotherapy.

Anlotinib may enhance the efficacy of anti-PD1 therapy by inhibiting the AKT pathway and promoting the apoptosis of CAFs in lung adenocarcinoma

Although PD-1 inhibitors have revolutionized the treatment paradigm of non-small cell lung cancer (NSCLC), their efficacy in treating NSCLC has remained unsatisfactory. Targeting cancer-associated fibroblasts (CAFs) is a potential approach for improving the immunotherapy response. Multitarget antiangiogenic tyrosine kinase receptor inhibitors (TKIs) can enhance the efficacy of PD-1 inhibitors in NSCLC patients. However, the effects and mechanisms of antiangiogenic TKIs on CAFs have not been elucidated. In this study, we first compared anlotinib with other antiangiogenic TKIs and confirmed the superior efficacy of anlotinib. Furthermore, we established NSCLC-associated CAF models and found that anlotinib impaired CAF viability and migration capacity and contributed to CAF apoptosis and cell cycle arrest in the G2/M phase. Moreover, anlotinib treatment attenuated the capacity of CAFs to recruit lung cancer cells and macrophages. Experiments in animal models suggested that anlotinib could enhance the efficacy of anti-PD1 therapy in NSCLC and affect CAF proliferation and apoptosis. Anlotinib increased the abundance of tumor-infiltrating CD8 + T cells, and PD-1 inhibitor-induced cytotoxicity to tumor cells was achieved through the transformation of the tumor microenvironment (TME) caused by anlotinib, which may partly explain the synergistic antitumor effect of anlotinib and PD-1 inhibitors. Mechanistically, anlotinib affects CAF apoptosis and cell viability at least in part by inhibiting the AKT pathway. In conclusion, our study suggested that anlotinib could regulate the TME, inhibit the AKT pathway and promote CAF apoptosis, providing new insights into the antitumor effect of anlotinib and improving the efficacy of immunotherapy.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method

BACKGROUND

Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.

MATERIAL AND METHODS

We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).

RESULTS

We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).

CONCLUSION

The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

Enhancing AI Research for Breast Cancer: A Comprehensive Review of Tumor-Infiltrating Lymphocyte Datasets

The field of immunology is fundamental to our understanding of the intricate dynamics of the tumor microenvironment. In particular, tumor-infiltrating lymphocyte (TIL) assessment emerges as essential aspect in breast cancer cases. To gain comprehensive insights, the quantification of TILs through computer-assisted pathology (CAP) tools has become a prominent approach, employing advanced artificial intelligence models based on deep learning techniques. The successful recognition of TILs requires the models to be trained, a process that demands access to annotated datasets. Unfortunately, this task is hampered not only by the scarcity of such datasets, but also by the time-consuming nature of the annotation phase required to create them. Our review endeavors to examine publicly accessible datasets pertaining to the TIL domain and thereby become a valuable resource for the TIL community. The overall aim of the present review is thus to make it easier to train and validate current and upcoming CAP tools for TIL assessment by inspecting and evaluating existing publicly available online datasets.

Helper T cell subsets: Development, function and clinical role in hypersensitivity reactions in the modern perspective

Helper T cells are traditionally classified into T helper 1 (TH1) and T helper 2 (TH2). The more recent discoveries of T helper 17 (TH17), follicular helper T cells (TFH) and regulatory T cells (Treg) enhanced our understanding on the mechanisms of immune function and hypersensitivity reactions, which shaped the modern perspective on the function and role of these different subsets of helper T cells in hypersensitivity reactions. Each subset of helper T cells has characteristic roles in different types of hypersensitivity reactions, hence giving rise to the respective characteristic clinical manifestations. The roles of helper T cells in allergic contact dermatitis (TH1-mediated), drug rash with eosinophilia and systemic symptoms (DRESS) syndrome (TH2-mediated), and acute generalised exanthematous pustulosis (AGEP) (TH17-mediated) are summarised in this article, demonstrating the correlation between the type of helper T cell involved and the clinical features. TFH plays crucial roles in antibody class-switch recombination; they may be implicated in antibody-mediated hypersensitivity reactions, but further research is warranted to delineate their exact pathogenic roles. The helper T cell subsets and their specific cytokine profiles implicated in different hypersensitivity reactions could be potential treatment targets by biologics, but more clinical trials are warranted to establish their clinical effectiveness.

Spatial interaction and functional status of CD68+SHP2+ macrophages in tumor microenvironment correlate with overall survival of NSCLC

Background

Tumor-associated macrophages (TAMs) constitute a plastic and heterogeneous cell population of the tumor microenvironment (TME) that can regulate tumor proliferation and support resistance to therapy, constituting promising targets for the development of novel anticancer agents. Our previous results suggest that SHP2 plays a crucial role in reprogramming the phenotype of TAMs. Thus, we hypothesized that SHP2+ TAM may predict the treatment efficacy of non-small cell lung cancer NSCLC patients as a biomarker.

Methods

We analyzed cancer tissue samples from 79 NSCLC patients using multiplex fluorescence (mIF) staining to visualize various SHP-2+ TAM subpopulations (CD68+SHP2+, CD68+CD86+, CD68 + 206+, CD68+ CD86+SHP2+, CD68+ CD206+SHP2+) and T cells (CD8+ Granzyme B +) of immune cells. The immune cells proportions were quantified in the tumor regions (Tumor) and stromal regions (Stroma), as well as in the overall tumor microenvironment (Tumor and Stroma, TME). The analysis endpoint was overall survival (OS), correlating them with levels of cell infiltration or effective density. Cox regression was used to evaluate the associations between immune cell subsets infiltration and OS. Correlations between different immune cell subsets were examined by Spearman's tests.

Results

In NSCLC, the distribution of different macrophage subsets within the TME, tumor regions, and stroma regions exhibited inconsistency. The proportions of CD68+ SHP2+ TAMs (P < 0.05) were higher in tumor than in stroma. And the high infiltration of CD68+SHP2+ TAMs in tumor areas correlated with poor OS (P < 0.05). We found that the expression level of SHP2 was higher in M2-like macrophages than in M1-like macrophages. The CD68+SHP2+ subset proportion was positively correlated with the CD68+CD206+ subset within TME (P < 0.0001), tumor (P < 0.0001) and stroma (P < 0.0001).

Conclusions

The high infiltration of CD68+SHP2+ TAMs predict poor OS in NSCLC. Targeting SHP2 is a potentially effective strategy to inhibit M2-phenotype polarization. And it provides a new thought for SHP2 targeted cancer immunotherapy.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics

BACKGROUND

Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context.

METHODS

We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness.

RESULTS

Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response.

CONCLUSION

Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Nivolumab for mismatch-repair-deficient or hypermutated gynecologic cancers: a phase 2 trial with biomarker analyses

Programmed death-1 (PD-1) inhibitors are approved for therapy of gynecologic cancers with DNA mismatch repair deficiency (dMMR), although predictors of response remain elusive. We conducted a single-arm phase 2 study of nivolumab in 35 patients with dMMR uterine or ovarian cancers. Co-primary endpoints included objective response rate (ORR) and progression-free survival at 24 weeks (PFS24). Secondary endpoints included overall survival (OS), disease control rate (DCR), duration of response (DOR) and safety. Exploratory endpoints included biomarkers and molecular correlates of response. The ORR was 58.8% (97.5% confidence interval (CI): 40.7-100%), and the PFS24 rate was 64.7% (97.5% one-sided CI: 46.5-100%), meeting the pre-specified endpoints. The DCR was 73.5% (95% CI: 55.6-87.1%). At the median follow-up of 42.1 months (range, 8.9-59.8 months), median OS was not reached. One-year OS rate was 79% (95% CI: 60.9-89.4%). Thirty-two patients (91%) had a treatment-related adverse event (TRAE), including arthralgia (n = 10, 29%), fatigue (n = 10, 29%), pain (n = 10, 29%) and pruritis (n = 10, 29%); most were grade 1 or grade 2. Ten patients (29%) reported a grade 3 or grade 4 TRAE; no grade 5 events occurred. Exploratory analyses show that the presence of dysfunctional (CD8+PD-1+) or terminally dysfunctional (CD8+PD-1+TOX+) T cells and their interaction with programmed death ligand-1 (PD-L1)+ cells were independently associated with PFS24. PFS24 was associated with presence of MEGF8 or SETD1B somatic mutations. This trial met its co-primary endpoints (ORR and PFS24) early, and our findings highlight several genetic and tumor microenvironment parameters associated with response to PD-1 blockade in dMMR cancers, generating rationale for their validation in larger cohorts.ClinicalTrials.gov identifier: NCT03241745 .

Insufficient endobronchial ultrasound-guided transbronchial needle aspiration specimens. When and why? The analysis of criteria and reasons behind the insufficient specimens

BACKGROUND

The classification terminology systems for pulmonary cytology specimens have recently emerged. Inadequate samples, classified as "nondiagnostic," raise challenges in determining the threshold of cell numbers and the risk of malignancy (ROM).

METHODS

The study retrospectively reviewed 248 endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) samples: 46 insufficient samples, 60 low cellularity samples, and 142 adequate samples. Characteristics as cellularity, number of benign and malignant cells, and background features were assessed. Receiver operating characteristic curve analysis was performed to establish cell sufficiency thresholds for the diagnosis.

RESULTS

Out of the 248 samples analyzed, 108 were classified as benign, 94 as malignant, and 46 as insufficient. The study found that the cellularity thresholds for diagnosis in cell blocks and cytological samples were ≥50 cells and ≥100 cells, respectively. The thresholds for tumor cell counts were ≥1 - 10 cells for both types of cells, respectively. Considerably, some low cellularity samples were initially classified as insufficient despite meeting the diagnostic thresholds upon revision. The ROM varied across sample categories, with insufficient samples having a ROM of 10.9%, benign samples 15.7%, suspicious samples 92.0%, and malignant samples 100%.

CONCLUSION

Insufficient EBUS-TBNA samples raise challenges in diagnosis and management. This study identified the root cause of insufficient samples, including factors related to humans, diagnostic methods, sampling, and laboratory processing. By understanding the root causes, diagnostic recommendations can be developed to improve the diagnostic process. The findings emphasize the importance of standardized classification and terminology systems for clear communication among healthcare professionals and institutions, ultimately improving patient care and enabling quality assurance measures.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials

BACKGROUND

The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally.

OBJECTIVE

We aimed to optimize AD trial design using simulations.

METHODS

We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens.

RESULTS

We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population.

CONCLUSIONS

This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

Coffee Ring Effect Enhanced Surface Plasmon Resonance Imaging Biosensor via 2-λ Fitting Detection Method

SPR biosensors have been extensively used for investigating protein-protein interactions. However, in conventional surface plasmon resonance (SPR) biosensors, detection is limited by the Brownian-motion-governed diffusion process of sample molecules in the sensor chip, which makes it challenging to detect biomolecule interactions at ultra-low concentrations. Here, we propose a highly sensitive SPR imaging biosensor which exploits the coffee ring effect (CRE) for in situ enrichment of molecules on the sensing surface. In addition, we designed a wavelength modulation system utilizing two LEDs to reduce the system cost and enhance the detection speed. Furthermore, a detection limit of 213 fM is achieved, which amounts to an approximately 365 times improvement compared to traditional SPR biosensors. With further development, we believe that this SPR imaging system with high sensitivity, less sample consumption, and faster detection speed can be readily applied to ultra-low-concentration molecular detection and interaction analysis.

The omics era: a nexus of untapped potential for Mendelian chromatinopathies

The OMICs cascade describes the hierarchical flow of information through biological systems. The epigenome sits at the apex of the cascade, thereby regulating the RNA and protein expression of the human genome and governs cellular identity and function. Genes that regulate the epigenome, termed epigenes, orchestrate complex biological signaling programs that drive human development. The broad expression patterns of epigenes during human development mean that pathogenic germline mutations in epigenes can lead to clinically significant multi-system malformations, developmental delay, intellectual disabilities, and stem cell dysfunction. In this review, we refer to germline developmental disorders caused by epigene mutation as "chromatinopathies". We curated the largest number of human chromatinopathies to date and our expanded approach more than doubled the number of established chromatinopathies to 179 disorders caused by 148 epigenes. Our study revealed that 20.6% (148/720) of epigenes cause at least one chromatinopathy. In this review, we highlight key examples in which OMICs approaches have been applied to chromatinopathy patient biospecimens to identify underlying disease pathogenesis. The rapidly evolving OMICs technologies that couple molecular biology with high-throughput sequencing or proteomics allow us to dissect out the causal mechanisms driving temporal-, cellular-, and tissue-specific expression. Using the full repertoire of data generated by the OMICs cascade to study chromatinopathies will provide invaluable insight into the developmental impact of these epigenes and point toward future precision targets for these rare disorders.

Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma

INTRODUCTION

Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers.

AREAS COVERED

A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma.

EXPERT OPINION

Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Convenient determination of serum HER-2 status in breast cancer patients using Raman spectroscopy

Given the significant therapeutic efficacy of anti-HER-2 treatment, the HER-2 status is a crucial piece of information that must be obtained in breast cancer patients. Currently, as per guidelines, HER-2 status is typically acquired from breast tissue of patients. However, there is growing interest in obtaining HER-2 status from serum and other samples due to the convenience and potential for dynamic monitoring. In this study, we have developed a serum Raman spectroscopy technique that allows for the rapid acquisition of HER-2 status in a convenient manner. The established HER-2 negative and positive classification model achieved an area under the curve of 0.8334. To further validate the reliability of our method, we replicated the process using immunohistochemistry and in situ hybridization. The results demonstrate that serum Raman spectroscopy, coupled with artificial intelligence algorithms, is an effective technical approach for obtaining HER-2 status.

Radionuclide-Labeled Antisilencing Function 1a Inhibitory Peptides for Tumor Identification and Individualized Therapy

Immune checkpoint blockade (ICB) therapy is promising to revolutionize cancer regimens, but the low response rate and the lack of a suitable patient stratification method have impeded universal profit to cancer patients. Noninvasive positron emission tomography (PET) imaging in the whole body, upon coupling with specific biomarkers closely related to the immune response, could provide spatiotemporal information to prescribe cancer therapy. Herein, we demonstrate that antisilencing function 1a (ASF1a) could serve as a biomarker target to delineate tumor immune microenvironments by immune PET (iPET). The iPET radiotracer (68Ga-AP1) is designed to target ASF1a in tumors and predict immune response, and the signal intensity predicts anti-PD-1 (αPD-1) therapy response in a negative correlation manner. The ICB-resistant tumors with a high level of ASF1a as revealed by iPET (ASF1aHigh-iPET) are prescribed to be treated by either the combined 177Lu-labeled AP1 and αPD-1 or the standalone α particle-emitting 225Ac-labeled AP1, both achieving enhanced therapeutic efficacy and prolonged survival time. Our study not only replenishes the iPET arsenal for immune-related response evaluation by designing a reliable biomarker and a facile radiotracer but also provides optional therapeutic strategies for ICB-resistant tumors with versatile radionuclide-labeled AP1 peptides, which is promising for real-time clinical diagnosis and individualized therapy planning simultaneously.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Single-cell RNA sequencing reveals epithelial cells driving brain metastasis in lung adenocarcinoma

Brain metastases (BM) of lung adenocarcinoma (LUAD) are the most common intracranial malignancy leading to death. However, the cellular origins and drivers of BM from LUAD have not been clarified. Cellular composition was characterized by single-cell sequencing analysis of primary lung adenocarcinoma (pLUAD), BM and lymph node metastasis (LNM) samples in GSE131907. Our study briefly analyzed the tumor microenvironment (TME), focusing on the role of epithelial cells (ECs) in BM. We have discovered a population of brain metastasis-associated epithelial cells (BMAECs) expressing SPP1, SAA1, and CDKN2A, and it has been observed that this population is mainly composed of aneuploid cells from pLUAD, playing a crucial role in brain metastasis. Our study concluded that both LNM and BM in LUAD originated from pLUAD lesions, but there is currently insufficient evidence to prove a direct association between BM lesions and LNM lesions, which provides inspiration for further investigation of the TME in BM.

Decoding the Cell Atlas and Inflammatory Features of Human Intracranial Aneurysm Wall by Single-Cell RNA Sequencing

BACKGROUND

Intracranial aneurysm (IA) is common and occasionally results in life-threatening hemorrhagic strokes. However, the cell architecture and inflammation in the IA dome remain less understood.

METHODS AND RESULTS

Single-cell RNA sequencing was performed on ruptured and unruptured human IA domes for delineating the cell atlas, gene expression perturbations, and inflammation features. Two external bulk mRNA sequencing-based data sets and serological results of 126 patients were collected for validation. As a result, a total of 21 332 qualified cells were captured. Vascular cells, including endothelial cells, smooth muscle cells, fibroblasts, and pericytes, were assigned in extremely sparse numbers (4.84%), and were confirmed by immunofluorescence staining. Pericytes, characterized by ABCC9 and HIGD1B, were identified in the IA dome for the first time. Abundant immune cells were identified, with the proportion of monocytes/macrophages and neutrophils being remarkably higher in ruptured IA. The lymphocyte compartment was also thoroughly categorized. By leveraging external data sets and machine learning algorithms, macrophages were robustly associated with IA rupture, irrespective of their polarization status. The single nucleotide polymorphism rs2280543, which is identified in East Asian populations, was associated with macrophage metabolic reprogramming through regulating TALDO1 expression.

CONCLUSIONS

This study provides insights into the cellular architecture and inflammatory features in the IA dome and may enlighten novel therapeutics for unruptured IA.

LncRNA XXYLT1-AS2 promotes tumor progression via autophagy inhibition through ubiquitinated degradation of TFEB in hepatocellular carcinoma

PURPOSE

There is compelling evidence that long-stranded non-coding RNAs (lncRNAs) play an important role in the progression of hepatocellular carcinoma (HCC). The aim of this study was to investigate the role of lncRNA XXYLT1 antisense-2 (XXYLT1-AS2) in HCC progression.

METHODS

Real-time PCR was used to assess the levels of XXYLT1-AS2 in plasma from HCC and normal patients. Cell proliferation, apoptosis, migration, and invasion were monitored, and tumor xenografts were established to investigate the biological functions of XXYLT1-AS2 by gain-of-function and loss-of-function studies in vitro and in vivo, the expression of autophagy biomarkers and transcriptional factor EB (TFEB) was examined by immunoprecipitation, ubiquitination assays, and western blotting. Autophagy inhibitor, 3-methyladenine (3MA), and proteasome inhibitor, MG132, were used to verify the role of autophagy in HCC progression and the effect of XXYLT1-AS2 on TFEB ubiquitination, respectively.

RESULTS

In this study, we identified that lncRNA XXYLT1-AS2 is highly expressed in HCC plasma and promotes tumor growth in vivo. In functional studies, it was found that silent expression of XXYLT1-AS2 inhibited HCC proliferation, migration, invasion, and activated autophagy of HCC cells, which were attenuated by autophagy inhibitor, 3MA. Mechanistically, XXYLT1-AS2 decreased the protein level of TFEB through promoting its degradation by ubiquitin proteasome pathway.

CONCLUSION

XXYLT1-AS2 plays an oncogenic role in HCC progression through inhibition of autophagy via promoting the degradation of TFEB, and thus could be a novel target for HCC treatment.

Spatial MS multiomics on clinical prostate cancer tissues

Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.

Non-electrophysiological techniques targeting transient receptor potential (TRP) gene of gastrointestinal tract

Transient receptor potential (TRP) channels are cation channels related to a wide range of physical and chemical stimuli, they are expressed all along the gastrointestinal system, and a myriad of diseases are often associated with aberrant expression or mutation of the TRP gene, suggesting that TRPs are promising targets for drug therapy. Therefore, a better understanding of the information of TRPs in health and disease could facilitate the development of effective drugs for the treatment of gastrointestinal diseases like IBD. But there are very few generalizations about the experimental techniques studied in this field. In view of the promise of TRP as a therapeutic target, we discuss experimental methods that can be used for TRPs including their distribution, function and interaction with other proteins, as well as some promising emerging technologies to provide experimental methods for future studies.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Systemic and local immunosuppression in glioblastoma and its prognostic significance

The effectiveness of tumor therapy, especially immunotherapy and oncolytic virotherapy, critically depends on the activity of the host immune cells. However, various local and systemic mechanisms of immunosuppression operate in cancer patients. Tumor-associated immunosuppression involves deregulation of many components of immunity, including a decrease in the number of T lymphocytes (lymphopenia), an increase in the levels or ratios of circulating and tumor-infiltrating immunosuppressive subsets [e.g., macrophages, microglia, myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs)], as well as defective functions of subsets of antigen-presenting, helper and effector immune cell due to altered expression of various soluble and membrane proteins (receptors, costimulatory molecules, and cytokines). In this review, we specifically focus on data from patients with glioblastoma/glioma before standard chemoradiotherapy. We discuss glioblastoma-related immunosuppression at baseline and the prognostic significance of different subsets of circulating and tumor-infiltrating immune cells (lymphocytes, CD4+ and CD8+ T cells, Tregs, natural killer (NK) cells, neutrophils, macrophages, MDSCs, and dendritic cells), including neutrophil-to-lymphocyte ratio (NLR), focus on the immune landscape and prognostic significance of isocitrate dehydrogenase (IDH)-mutant gliomas, proneural, classical and mesenchymal molecular subtypes, and highlight the features of immune surveillance in the brain. All attempts to identify a reliable prognostic immune marker in glioblastoma tissue have led to contradictory results, which can be explained, among other things, by the unprecedented level of spatial heterogeneity of the immune infiltrate and the significant phenotypic diversity and (dys)functional states of immune subpopulations. High NLR is one of the most repeatedly confirmed independent prognostic factors for shorter overall survival in patients with glioblastoma and carcinoma, and its combination with other markers of the immune response or systemic inflammation significantly improves the accuracy of prediction; however, more prospective studies are needed to confirm the prognostic/predictive power of NLR. We call for the inclusion of dynamic assessment of NLR and other blood inflammatory markers (e.g., absolute/total lymphocyte count, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, and systemic immune response index) in all neuro-oncology studies for rigorous evaluation and comparison of their individual and combinatorial prognostic/predictive significance and relative superiority.

Highly Sensitive Labeling, Clickable Functionalization, and Glycoengineering of the MUC1 Neighboring System

This study introduces a novel wash-type affinity-primed proximity labeling (WAPL) strategy for labeling and surface engineering of the MUC1 protein neighboring system. The strategy entails the utilization of peroxidase in conjunction with a MUC1-selective aptamer, facilitating targeted binding to MUC1 and inducing covalent labeling of the protein neighboring system. This study reveals a novel finding that the WAPL strategy demonstrates superior labeling efficiency in comparison to nonwash-type affinity-primed proximity labeling, marking the first instance of such observations. The WAPL strategy provides signal amplification by converting a single recognition event into multiple covalent labeling events, thereby improving the detection sensitivity for subtle changes in MUC1. The WAPL platform employs two levels of labeling upgrades, modifying the biotin handles of the conventional labeling substrate, biotin-phenol. The first level involves a range of clickable molecules, facilitating dibenzoazacyclooctynylation, alkynylation, and trans-cyclooctenylation of the protein neighboring system. The second level utilizes lactose as a post-translational modification model, enabling rapid and reliable glycoengineering of the MUC1 neighboring system while remaining compatible with cell-based assays. The implementation of the WAPL strategy in protein neighboring systems has resulted in the establishment of a versatile platform that can effectively facilitate diverse monitoring and regulation techniques. This platform offers valuable insights into the regulation of relevant signaling pathways and promotes the advancement of novel therapeutic approaches, thereby bringing substantial implications for human health.

Extended correlation functions for spatial analysis of multiplex imaging data

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

Optimized combination of MALDI MSI and immunofluorescence for neuroimaging of lipids within cellular microenvironments

Proper neurological function relies on the cellular and molecular microenvironment of the brain, with perturbations of this environment leading to neurological disorders. However, studying the microenvironments of neurological tissue has proven difficult because of its inherent complexity. Both the cell type and metabolomic underpinnings of the cell have crucial functional roles, thus making multimodal characterization methods key to acquiring a holistic view of the brain's microenvironment. This study investigates methods for combining matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and immunofluorescence (IF) microscopy to enable the concurrent investigation of cell types and lipid profiles on the same sample. In brief, 1,5-diaminonaphthalene (DAN), α-cyano-4-hydroxy-cinnamic acid (CHCA), and 2,5-dihydroxybenzoic acid (DHB) were tested in addition to instrument-specific parameters for compatibility with IF. Alternatively, the effects of IF protocols on MALDI MSI were also tested, showing significant signal loss with all tested permutations. Ultimately, the use of CHCA for MALDI MSI resulted in the best IF images, while the use of DAN gave the lowest quality IF images. Overall, increasing the laser power and number of shots per laser burst resulted in the most tissue ablation. However, optimized parameter settings allowed for minimal tissue ablation while maintaining sufficient MALDI MSI signal.

High contrast breast cancer biomarker semi-quantification and immunohistochemistry imaging using upconverting nanoparticles

Breast cancer is the second leading cause of cancer death in women. Current clinical treatment stratification practices open up an avenue for significant improvements, potentially through advancements in immunohistochemistry (IHC) assessments of biopsies. We report a high contrast upconverting nanoparticles (UCNP) labeling to distinguish different levels of human epidermal growth factor receptor 2 (HER2) in HER2 control pellet arrays (CPAs) and HER2-positive breast cancer tissue. A simple Fourier transform algorithm trained on CPAs was sufficient to provide a semi-quantitative HER2 assessment tool for breast cancer tissues. The UCNP labeling had a signal-to-background ratio of 40 compared to the negative control.

Antigen loss following CAR-T cell therapy: Mechanisms, implications, and potential solutions

Chimeric Antigen Receptor T-cell (CAR-T cell) therapy has emerged as a groundbreaking immunotherapeutic approach for treating various hematological malignancies. CAR-T cells are engineered to express synthetic receptors that target specific antigens on cancer cells, leading to their eradication. While the therapy has shown remarkable efficacy, a significant challenge that has been observed in 30%-70% of patients showing recurrent disease is antigen loss or downregulation. We searched PubMed/MEDLINE, EMBASE, and Google scholar for articles on antigen loss/escape following Chimeric antigen receptor T-cell therapy in malignancies. Antigen loss refers to the loss or reduction in the expression of the target antigen on cancer cells, rendering CAR-T cells ineffective. This phenomenon poses a significant clinical concern, as it can lead to disease relapse and limited treatment options. This review explores the mechanisms underlying antigen loss following CAR-T cell therapy, its implications on treatment outcomes, and potential strategies to overcome the problem.

A robust and versatile host-guest peptide toolbox for developing highly stable and specific quantum dot-based peptide probes for imaging extracellular matrices and cells

Multiplex fluorescence imaging plays a vital role in precision medicine for targeting complex diseases with diverse biomolecular signatures. Quantum dot (QD) probes with vibrant colors are promising candidates for multiplex imaging, but their stability and specificity are frequently compromised by the current tedious post-modification process. We have herein developed a robust and versatile host-guest peptide (HGP) toolbox for creating highly stable and specific QD-based peptide probes for imaging extracellular matrices and cells. The HGP system comprises a host peptide and a guest peptide with a shared sequence pattern of cysteine and negatively charged amino acids, allowing for QD stabilization and specificity towards targeted biomarkers. HGP has been demonstrated as a convenient one-step approach to construct hydrophilic QD-based peptide probes with superior stability under various conditions. Six multicolor HGP-modified QDs have been developed to specifically target extracellular matrix proteins such as collagen, laminin, and nidogen, as well as major cellular elements like the membrane, nucleus, and cytoplasm, providing an efficient tool for real-time monitoring of high-resolution interactions between cancer cells and the extracellular matrix. The HGP system represents a next-generation approach to developing QDs with unprecedented stability and specificity, holding great potential in multiplex imaging and precision medicine.

Association between spatial distribution of leukocyte subsets and clinical presentation of head and neck squamous cell carcinoma

Background

Interactions between tumor cells and cells in the microenvironment contribute to tumor development and metastasis. The spatial arrangement of individual cells in relation to each other influences the likelihood of whether and how these cells interact with each other.

Methods

This study investigated the effect of spatial distribution on the function of leukocyte subsets in the microenvironment of human head and neck squamous cell carcinoma (HNSCC) using multiplex immunohistochemistry (IHC). Leukocyte subsets were further classified based on analysis of two previously published HNSCC single-cell RNA datasets and flow cytometry (FC).

Results

IHC revealed distinct distribution patterns of leukocytes differentiated by CD68 and CD163. While CD68hiCD163lo and CD68hiCD163hi cells accumulated near tumor sites, CD68loCD163hi cells were more evenly distributed in the tumor stroma. PD-L1hi and PD-1hi cells accumulated predominantly around tumor sites. High cell density of PD-L1hi CD68hiCD163hi cells or PD-1hi T cells near the tumor site correlated with improved survival. FC and single cell RNA revealed high variability within the CD68/CD163 subsets. CD68hiCD163lo and CD68hiCD163hi cells were predominantly macrophages (MΦ), whereas CD68loCD163hi cells appeared to be predominantly dendritic cells (DCs). Differentiation based on CD64, CD80, CD163, and CD206 revealed that TAM in HNSCC occupy a broad spectrum within the classical M1/M2 polarization. Notably, the MΦ subsets expressed predominantly CD206 and little CD80. The opposite was observed in the DC subsets.

Conclusion

The distribution patterns and their distinct interactions via the PD-L1/PD-1 pathway suggest divergent roles of CD68/CD163 subsets in the HNSCC microenvironment. PD-L1/PD-1 interactions appear to occur primarily between specific cell types close to the tumor site. Whether PD-L1/PD-1 interactions have a positive or negative impact on patient survival appears to depend on both the spatial localization and the entity of the interacting cells. Co-expression of other markers, particularly CD80 and CD206, supports the hypothesis that CD68/CD163 IHC subsets have distinct functions. These results highlight the association between spatial leukocyte distribution patterns and the clinical presentation of HNSCC.

Data Science for Health Image Alignment: A User-Friendly Open-Source ImageJ/Fiji Plugin for Aligning Multimodality/Immunohistochemistry/Immunofluorescence 2D Microscopy Images

Most of the time, the deep analysis of a biological sample requires the acquisition of images at different time points, using different modalities and/or different stainings. This information gives morphological, functional, and physiological insights, but the acquired images must be aligned to be able to proceed with the co-localisation analysis. Practically speaking, according to Aristotle's principle, "The whole is greater than the sum of its parts", multi-modal image registration is a challenging task that involves fusing complementary signals. In the past few years, several methods for image registration have been described in the literature, but unfortunately, there is not one method that works for all applications. In addition, there is currently no user-friendly solution for aligning images that does not require any computer skills. In this work, DS4H Image Alignment (DS4H-IA), an open-source ImageJ/Fiji plugin for aligning multimodality, immunohistochemistry (IHC), and/or immunofluorescence (IF) 2D microscopy images, designed with the goal of being extremely easy to use, is described. All of the available solutions for aligning 2D microscopy images have also been revised. The DS4H-IA source code; standalone applications for MAC, Linux, and Windows; video tutorials; manual documentation; and sample datasets are publicly available.

Inhibition of CYP1A1 Alleviates Colchicine-Induced Hepatotoxicity

Colchicine, a natural compound extracted from Colchicum autumnale, is a phytotoxin, but interestingly, it also has multiple pharmacological activities. Clinically, colchicine is widely used for the treatment of gouty arthritis, familial Mediterranean fever, cardiovascular dysfunction and new coronary pneumonia. However, overdose intake of colchicine could cause lethal liver damage, which is a limitation of its application. Therefore, exploring the potential mechanism of colchicine-induced hepatotoxicity is meaningful. Interestingly, it was found that CYP1A1 played an important role in the hepatotoxicity of colchicine, while it might also participate in its metabolism. Inhibition of CYP1A1 could alleviate oxidative stress and pyroptosis in the liver upon colchicine treatment. By regulating CYP1A1 through the CASPASE-1-GSDMD pathway, colchicine-induced liver injury was effectively relieved in a mouse model. In summary, we concluded that CYP1A1 may be a potential target, and the inhibition of CYP1A1 alleviates colchicine-induced liver injury through pyroptosis regulated by the CASPASE-1-GSDMD pathway.

Non-neoplastic cells as prognostic biomarkers in diffuse large B-cell lymphoma: A system review and meta-analysis

The microenvironment of diffuse large B-cell lymphoma (DLBCL) is composed of various components, including immune cells and immune checkpoints, some of which have been correlated with the prognosis of DLBCL, but their results remain controversial. Therefore, we conducted a systematic review and meta-analysis to investigate the association between the microenvironment and prognosis in DLBCL. We searched PubMed, Web of Science, and EMBASE for relevant articles between 2001 and 2022. Twenty-five studies involving 4495 patients with DLBCL were included in the analysis. This meta-analysis confirmed that high densities of Foxp3+Tregs and PD-1+T cells are good indicators for overall survival (OS) in DLBCL, while high densities of programmed cell death protein ligand1(PD-L1)-positive expression cells and T-cell immunoglobulin-and mucin domain-3-containing molecule 3 (TIM-3)-positive expression tumor-infiltrating cells (TILs) play a contrary role in OS. Additionally, higher numbers of T-cell intracytoplasmic antigen-1(TIA-1)-positive expression T cells imply better OS and progression-free survival (PFS), while high numbers of lymphocyte activation gene(LAG)-positive expression TILs predict bad OS and PFS. Various non-tumoral cells in the microenvironment play important roles in the prognosis of DLBCL.

Spatial features of specific CD103+CD8+ tissue-resident memory T cell subsets define the prognosis in patients with non-small cell lung cancer

BACKGROUND

Tissue-resident memory T (TRM) cells can reside in the tumor microenvironment and are considered the primary response cells to immunotherapy. Heterogeneity in functional status and spatial distribution may contribute to the controversial role of TRM cells but we know little about it.

METHODS

Through multiplex immunofluorescence (mIF) (CD8, CD103, PD-1, Tim-3, GZMB, CK), the quantity and spatial location of TRM cell subsets were recognized in the tissue from 274 patients with NSCLC after radical surgery. By integrating multiple machine learning methods, we constructed a TRM-based spatial immune signature (TRM-SIS) to predict the prognosis. Furthermore, we conducted a CD103-related gene set enrichment analysis (GSEA) and verified its finding by another mIF panel (CD8, CD103, CK, CD31, Hif-1α).

RESULTS

The density of TRM cells was significantly correlated with the expression of PD-1, Tim-3 and GZMB. Four types of TRM cell subsets was defined, including TRM1 (PD-1-Tim-3-TRM), TRM2 (PD-1+Tim-3-TRM), TRM3 (PD-1-Tim-3+TRM) and TRM4 (PD-1+Tim-3+TRM). The cytotoxicity of TRM2 was the strongest while that of TRM4 was the weakest. Compare with TRM1 and TRM2, TRM3 and TRM4 had better infiltration and stronger interaction with cancer cells. The TRM-SIS was an independent prognostic factor for disease-free survival [HR = 2.43, 95%CI (1.63-3.60), P < 0.001] and showed a better performance than the TNM staging system for recurrence prediction. Furthermore, by CD103-related GSEA and mIF validation, we found a negative association between tumor angiogenesis and infiltration of TRM cells.

CONCLUSIONS

These findings reveal a significant heterogeneity in the functional status and spatial distribution of TRM cells, and support it as a biomarker for the prognosis of NSCLC patients. Regulating TRM cells by targeting tumor angiogenesis may be a potential strategy to improve current immunotherapy.

A multiplex immunoprofiling approach for detecting the co-localization of breast cancer biomarkers using a combination of Alexafluor - Quantum dot conjugates and a panel of chromogenic dyes

There is a plethora of information embedded in a tissue section that the conventional IHC understands only partially. Predictive biomarkers for precision immuno-oncology heavily dependent on the spatial arrangement of cells and the co-expression patterns in the tissue sections. Here we have explored the versatility of indirect multiplex immunofluorescence (mIF) and indirect multiplex immunohistochemistry (mIHC) for the labeling of breast cancer prognostic markers in routinely processed, formalin-fixed paraffin-embedded (FFPE) tissues at high resolution. The multiplex immunohistochemistry protocol utilized sequential staining for the chromogenic immunolabelling of Estrogen Receptor α (ERα) or Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Nucleoside diphosphate kinase 1 (NM23) by multicolor chromogens in different combinations. A feasible workflow for multiplex immunofluorescence was also effectively standardized for ERα, PR, and HER2 using combinations of commercially available Alexa Fluor and Quantum dots semiconductor nanocrystal conjugated secondary antibodies. Multiplex chromogenic immunolabeling revealed differential expression of the markers on the same slide. Kappa statistics revealed perfect agreement with uniplex immunohistochemistry. For multiplex fluorescence approach, surface receptor detection using Quantum dots and Alexa fluor dyes for cytoplasmic or nuclear markers performed well for profiling multiple co-localized biomarkers on a single paraffin tissue section. The technique developed reveals additional information such as co-expression, spatial relationships, and tumor heterogeneity, providing a deeper insight into developing combinatorial therapeutic strategies in clinical care. This high throughput workflow complements the outcomes of traditional IHC while saving tissue, time, labour, and reagents.

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.

Trace detection of canine distemper virus based on Michelson-interferometer sensing probe

A single-mode-fiber (SMF)-multimode-fiber (MMF)-tri-core-fiber (TCF) Michelson probe structure is proposed for trace detection of canine distemper virus (CDV). One end of the TCF is cut flat and fused with the multimode fiber, and the other end is coated with a silver film to enhance the reflection, and an optic-fiber sensing probe with SMF-MMF-TCF structure is obtained. The (PDDA/PSS)3 multilayer film is modified on the surface of the fiber by layer-by-layer self-assembly method as a polyelectrolyte binder to immobilize CDV antibodies to form a (PDDA/PSS)3 /CDV antibody composite membrane for specific detection of CDV antigens. The response-recovery test of the sensor is performed to verify its repeatability. The detection limit, the sensitivity, and the linear fitting degree for CDV antigen are 0.1236 pg/mL, 1.1776 dB/(pg/mL), and 0.9899, respectively. At the same time, the stability, selectivity, and clinical samples of the sensors were also verified.