Immune cell profiling in cancer: molecular approaches to cell-specific identification

Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

The Role of the Microbiome in Cancer Therapies: Current Evidence and Future Directions

The microbiome is essential for maintaining human health and is also a key factor in the development and progression of various diseases, including cancer. Growing evidence has highlighted the microbiome's significant impact on cancer development, progression, and treatment outcomes. As research continues to unfold, the microbiome and its modulation stand out as a promising frontier in cancer research and therapy. This review highlights current literature on the interplay between various cancer treatment modalities and human microbiotas, focusing on how the microbiome may affect treatment efficacy and toxicity and its potential as a therapeutic target to enhance future outcomes.

Heterogeneity-Preserving Discriminative Feature Selection for Disease-Specific Subtype Discovery

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on selecting features that differentiate known diseases or cell states, they often struggle to identify features that both preserve heterogeneity and reveal subtypes. To address this, we utilized deep metric learning-based feature embedding to explore the statistical properties of features crucial for preserving heterogeneity. Our analysis indicated that features with a notable difference in interquartile range (IQR) between classes hold important subtype information. Guided by this insight, we developed a statistical method called PHet (Preserving Heterogeneity), which employs iterative subsampling and differential analysis of IQR combined with Fisher's method to identify a small set of features that preserve heterogeneity and enhance subtype clustering quality. Validation on public single-cell RNA-seq and microarray datasets demonstrated PHet's ability to maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods. Furthermore, an analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells identified two distinct basal cell subtypes differentiating towards a luminal secretory phenotype using PHet-based features, demonstrating promising results in a real-data application. These results highlight PHet's potential to enhance our understanding of disease mechanisms and cell differentiation, contributing significantly to the field of personalized medicine.

Machine learning-based screening of asthma biomarkers and related immune infiltration

Introduction

Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma.

Methods

Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryotic reference transcriptome high-throughput sequencing was performed to identify genes expressed in mouse lung tissues.

Results

Thirteen specific asthma genes were obtained from our dataset analysis (LOC100132287, CEACAM5, PRR4, CPA3, POSTN, LYPD2, TCN1, SCGB3A1, NOS2, CLCA1, TPSAB1, CST1, and C7orf26). The GO analysis demonstrated that DEGs linked to asthma were primarily related to positive regulation of guanylate cyclase activity, gpi anchor binding, peptidase activity and arginine binding. The renin-angiotensin system, arginine biosynthesis and arginine and proline metabolism were the key KEGG pathways of DEGs. Additionally, the genes CEACAM5, PRR4, CPA3, POSTN, CLCA1, and CST1 expression levels were positively associated with plasma cells and resting mast cells. The mouse model revealed elevated nos2 and clca1 expression in the asthmatic mouse group compared with that in normal mice, which was consistent with the findings in asthmatic patients.

Discussion

This study identified new marker genes for the prediction and diagnosis of asthma, which can be further validated and applied clinically.

Multicolor Flow Cytometry for Immune Characterization of Omental Metastasis in Peritoneal Carcinomatosis Mice Models

Multicolor fluorescence-activated cell sorting (FACS) has allowed cancer researchers to gain insights into the role of immune cells within the tumor microenvironment. According to this, flow cytometry is widely used to characterize and quantify immune cell infiltration during tumor development in a given tumor implant. In preclinical mouse models of peritoneal carcinomatosis, FACS is usually used to study the peritoneal lavages. However, one of the most relevant tissues to analyze in peritoneal metastases, the omentum, often goes unnoticed. Previous works have demonstrated that flow cytometry analysis of the omentum is critical to understanding the anti-pro-tumoral response of gynecological or gastrointestinal cancer. Here, we set up a detailed protocol to study different cell populations in omental metastasis with specific multicolor FACS panel design, including T cells, natural killer cells, natural killer T cells, and B cells in peritoneal carcinomatosis mice models. In cancer, these cell populations exhibit unique phenotypic profiles and are known to contribute to tumor growth and immune evasion. Nevertheless, immunomodulation of the omentum through intraperitoneal immunotherapy strategies has been demonstrated to be crucial in controlling peritoneal carcinomatosis. The protocol described in this chapter lists the steps to be taken for the localization, extraction, processing, and characterization of different immune cell populations of omental tumor implants in peritoneal carcinomatosis mice models using multicolor flow cytometry. In conclusion, we consider that the immune characterization of omentum cell infiltration is relevant to predicting the antitumor response of different locoregional immunotherapy strategies.

Tumor-Targeted Cell-Penetrating Peptides Reveal That Monomethyl Auristatin E Temporally Modulates the Tumor Immune Microenvironment

Chemotherapies remain standard therapy for cancers but have limited efficacy and cause significant side effects, highlighting the need for targeted approaches. In the progression of cancer, tumors increase matrix metalloproteinase (MMP) activity. Leveraging and therapeutically redirecting tumor MMPs through activatable cell-penetrating peptide (ACPP) technology offers new approaches for tumor-selective drug delivery and for studying how drug payloads engage the tumor immune microenvironment. ACPPs are biosensing peptides consisting of a drug-conjugated polycationic cell-penetrating peptide masked by an autoinhibitory polyanionic peptide through an interlinking peptide linker. Since tumors overexpress MMPs, ACPP tumor-targeting is achieved using an MMP cleavable linker. Monomethyl auristatin E (MMAE) is a potent anti-tubulin and common drug payload in antibody drug conjugates; however there are limited pre-clinical studies on how this clinically effective drug modulates the interplay of cancer cells and the immune system. Here, we report the versatility of ACPP conjugates in syngeneic murine cancer models and interrogate how MMAE temporally alters the tumor immune microenvironment. We show that cRGD-ACPP-MMAE preferentially delivered MMAE to tumors in murine models. Targeted cRGD-ACPP-MMAE demonstrated anti-tumor kill activity that activated the innate and adaptive arms of the immune system. Understanding how targeted MMAE engages tumors can optimize MMAE tumor kill activity and inform rational combinations with other cancer therapeutics.

A micropeptide TREMP encoded by lincR-PPP2R5C promotes Th2 cell differentiation by interacting with PYCR1 in allergic airway inflammation

BACKGROUND

Allergic asthma is largely dominated by Th2 lymphocytes. Micropeptides in Th2 cells and asthma remain unmasked. Here, we aimed to demonstrate a micropeptide, T-cell regulatory micropeptide (TREMP), in Th2 cell differentiation in asthma.

METHODS

TREMP translated from lincR-PPP2R5C was validated using Western blotting and mass spectrometry. TREMP knockout mice were generated using CRISPR/Cas9. Coimmunoprecipitation revealed that TREMP targeted pyrroline-5-carboxylate reductase 1 (PYCR1), which was further explored in vitro and in vivo. The levels of TREMP and PYCR1 in Th2 cells from clinical samples were determined by flow cytometry.

RESULTS

TREMP, encoded by lincR-PPP2R5C, was in the mitochondrion. The lentivirus encoding TREMP promoted Th2 cell differentiation. In contrast, Th2 differentiation was suppressed in TREMP-/- CD4+ T cells. In the HDM-induced model of allergic airway inflammation, TREMP was increased in pulmonary tissues. Allergic airway inflammation was relieved in TREMP-/- mice treated with HDM. Mechanistically, TREMP interacted with PYCR1, which regulated Th2 differentiation via glycolysis. Glycolysis was decreased in Th2 cells from TREMP-/- mice and PYCR1-/- mice. Similar to TREMP-/- mice, allergic airway inflammation was mitigated in HDM-challenged PYCR1-/- mice. Moreover, we measured TREMP and PYCR1 in asthma patients. And we found that, compared with those in healthy controls, the levels of TREMP and PYCR1 in Th2 cells were significantly increased in asthmatic patients.

CONCLUSIONS

The micropeptide TREMP encoded by lincR-PPP2R5C promoted Th2 differentiation in allergic airway inflammation by interacting with PYCR1 and enhancing glycolysis. Our findings highlight the importance of neglected micropeptides from noncoding RNAs in allergic diseases.

Cell-specific AHR-driven differential gene expression in the mouse liver cell following acute TCDD exposure

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that disrupts hepatic function leading to steatotic liver disease (SLD)-like pathologies, such as steatosis, steatohepatitis, and fibrosis. These effects are mediated by the aryl hydrocarbon receptor following changes in gene expression. Although diverse cell types are involved, initial cell-specific changes in gene expression have not been reported. In this study, differential gene expression in hepatic cell types was examined in male C57BL/6 mice gavaged with 30 µg/kg of TCDD using single-nuclei RNA-sequencing. Ten liver cell types were identified with the proportions of most cell types remaining unchanged, except for neutrophils which increased at 72 h. Gene expression suggests TCDD induced genes related to oxidative stress in hepatocytes as early as 2 h. Lipid homeostasis was disrupted in hepatocytes, macrophages, B cells, and T cells, characterized by the induction of genes associated with lipid transport, steroid hormone biosynthesis, and the suppression of β-oxidation, while linoleic acid metabolism was altered in hepatic stellate cells (HSCs), B cells, portal fibroblasts, and plasmacytoid dendritic cells. Pro-fibrogenic processes were also enriched, including the induction retinol metabolism genes in HSCs and the early induction of anti-fibrolysis genes in hepatocytes, endothelial cells, HSCs, and macrophages. Hepatocytes also had gene expression changes consistent with hepatocellular carcinoma. Collectively, these findings underscore the effects of TCDD in initiating SLD-like phenotypes and identified cell-specific gene expression changes related to oxidative stress, steatosis, fibrosis, cell proliferation and the development of HCC.

Triple Negative Breast Cancer Cells Acquire Lymphocyte Proteins and Genomic DNA During Trogocytosis with T Cells

Trogocytosis is the process by which a recipient cell siphons small membrane fragments and proteins from a donor cell and may be utilized by cancer cells to avoid immune detection. We observed lymphocyte specific protein expressed by TNBC cells via immunofluorescence imaging of patient samples. Image analysis of CD45RA expression, a T cell specific protein, revealed that all stages of TNBCs express CD45RA. Flow cytometry revealed TNBC cells trogocytose CD45 protein from T cells. We also showed that the acquisition of these lymphoid markers is contact dependent. Confocal and super-resolution imaging further revealed CD45 + spherical structures containing T cell genomic DNA inside TNBC cells after co-culture. Trogocytosis between T cells and TNBC cells altered cancer cell gene expression. Our results revealed that CD45 is obtained by TNBC cells from T cells via trogocytosis and that TNBC cells express CD45 intracellularly and on the membrane.

Teaser

TNBC cells acquire small spherical structures from T cells containing lymphocyte-specific membrane proteins and genomic DNA.

Cancer Nanovaccines: Nanomaterials and Clinical Perspectives

Cancer nanovaccines represent a promising frontier in cancer immunotherapy, utilizing nanotechnology to augment traditional vaccine efficacy. This review comprehensively examines the current state-of-the-art in cancer nanovaccine development, elucidating innovative strategies and technologies employed in their design. It explores both preclinical and clinical advancements, emphasizing key studies demonstrating their potential to elicit robust anti-tumor immune responses. The study encompasses various facets, including integrating biomaterial-based nanocarriers for antigen delivery, adjuvant selection, and the impact of nanoscale properties on vaccine performance. Detailed insights into the complex interplay between the tumor microenvironment and nanovaccine responses are provided, highlighting challenges and opportunities in optimizing therapeutic outcomes. Additionally, the study presents a thorough analysis of ongoing clinical trials, presenting a snapshot of the current clinical landscape. By curating the latest scientific findings and clinical developments, this study aims to serve as a comprehensive resource for researchers and clinicians engaged in advancing cancer immunotherapy. Integrating nanotechnology into vaccine design holds immense promise for revolutionizing cancer treatment paradigms, and this review provides a timely update on the evolving landscape of cancer nanovaccines.

Integrative genomic analysis of the lung tissue microenvironment in SARS-CoV-2 and NL63 patients

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects. In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus. Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients. Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data.

State-Of-The-Art Advancements on Cancer Vaccines and Biomarkers

The origins of cancer vaccines date back to the 1800s. Since then, there have been significant efforts to generate vaccines against solid and hematologic malignancies using a variety of platforms. To date, these efforts have generally been met with minimal success. However, in the era of improved methods and technological advancements, supported by compelling preclinical and clinical data, a wave of renewed interest in the field offers the promise of discovering field-changing paradigms in the management of established and resected disease using cancer vaccines. These include novel approaches to personalized neoantigen vaccine development, as well as innovative immune-modulatory vaccines (IMVs) that facilitate activation of antiregulatory T cells to limit immunosuppression caused by regulatory immune cells. This article will introduce some of the limitations that have affected cancer vaccine development over the past several decades, followed by an introduction to the latest advancements in neoantigen vaccine and IMV therapy, and then conclude with a discussion of some of the newest technologies and progress that are occurring across the cancer vaccine space. Cancer vaccines are among the most promising frontiers for breakthrough innovations and strategies poised to make a measurable impact in the ongoing fight against cancer.

Molecular insights into clinical trials for immune checkpoint inhibitors in colorectal cancer: Unravelling challenges and future directions

Colorectal cancer (CRC) is a complex disease with diverse etiologies and clinical outcomes. Despite considerable progress in development of CRC therapeutics, challenges remain regarding the diagnosis and management of advanced stage metastatic CRC (mCRC). In particular, the five-year survival rate is very low since mCRC is currently rarely curable. Over the past decade, cancer treatment has significantly improved with the introduction of cancer immunotherapies, specifically immune checkpoint inhibitors. Therapies aimed at blocking immune checkpoints such as PD-1, PD-L1, and CTLA-4 target inhibitory pathways of the immune system, and thereby enhance anti-tumor immunity. These therapies thus have shown promising results in many clinical trials alone or in combination. The efficacy and safety of immunotherapy, either alone or in combination with CRC, have been investigated in several clinical trials. Clinical trials, including KEYNOTE-164 and CheckMate 142, have led to Food and Drug Administration approval of the PD-1 inhibitors pembrolizumab and nivolumab, respectively, for the treatment of patients with unresectable or metastatic microsatellite instability-high or deficient mismatch repair CRC. Unfortunately, these drugs benefit only a small percentage of patients, with the benefits of immunotherapy remaining elusive for the vast majority of CRC patients. To this end, primary and secondary resistance to immunotherapy remains a significant issue, and further research is necessary to optimize the use of immunotherapy in CRC and identify biomarkers to predict the response. This review provides a comprehensive overview of the clinical trials involving immune checkpoint inhibitors in CRC. The underlying rationale, challenges faced, and potential future steps to improve the prognosis and enhance the likelihood of successful trials in this field are discussed.

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.

Development of a customizable mouse backbone spectral flow cytometry panel to delineate immune cell populations in normal and tumor tissues

Introduction

In vivo studies of cancer biology and assessment of therapeutic efficacy are critical to advancing cancer research and ultimately improving patient outcomes. Murine cancer models have proven to be an invaluable tool in pre-clinical studies. In this context, multi-parameter flow cytometry is a powerful method for elucidating the profile of immune cells within the tumor microenvironment and/or play a role in hematological diseases. However, designing an appropriate multi-parameter panel to comprehensively profile the increasing diversity of immune cells across different murine tissues can be extremely challenging.

Methods

To address this issue, we designed a panel with 13 fixed markers that define the major immune populations -referred to as the backbone panel- that can be profiled in different tissues but with the option to incorporate up to seven additional fluorochromes, including any marker specific to the study in question.

Results

This backbone panel maintains its resolution across different spectral flow cytometers and organs, both hematopoietic and non-hematopoietic, as well as tumors with complex immune microenvironments.

Discussion

Having a robust backbone that can be easily customized with pre-validated drop-in fluorochromes saves time and resources and brings consistency and standardization, making it a versatile solution for immuno-oncology researchers. In addition, the approach presented here can serve as a guide to develop similar types of customizable backbone panels for different research questions requiring high-parameter flow cytometry panels.

Tissue biomarkers of immune checkpoint inhibitor therapy

Cancer immunotherapy has been rejuvenated by the growing understanding of the immune system's role in tumor activity over the past two decades. During cancer initiation and progression, tumor cells employ various mechanisms that resemble peripheral immune tolerance to evade the antitumor responses of the immune system. Immune checkpoint molecules are the major mechanism of immune resistance that are exploited by tumor cells to inhibit T-cell activation and suppress immune responses. The targeting of immune checkpoint pathways has led to substantial improvements in survival rates in a number of solid cancers. However, a lack of understanding of the heterogeneity of the tumor microenvironment (TME) has resulted in inefficient therapy responses. A greater understanding of the TME is needed to identify patients likely to respond, and those that will have resistance to immune checkpoint inhibitors (ICIs). Advancement in spatial single-cell technologies has allowed deeper insight into the phenotypic and functional diversities of cells in the TME. In this review, we provide an overview of ICI biomarkers and highlight how high-dimensional spatially resolved, single-cell approaches provide deep molecular insights into the TME and allow for the discovery of biomarkers of clinical benefit.

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.

Remodeling of the gastric environment in Helicobacter pylori-induced atrophic gastritis

Helicobacter pylori colonization of the human stomach is a strong risk factor for gastric cancer. To investigate H. pylori-induced gastric molecular alterations, we used a Mongolian gerbil model of gastric carcinogenesis. Histologic evaluation revealed varying levels of atrophic gastritis (a premalignant condition characterized by parietal and chief cell loss) in H. pylori-infected animals, and transcriptional profiling revealed a loss of markers for these cell types. We then assessed the spatial distribution and relative abundance of proteins in the gastric tissues using imaging mass spectrometry and liquid chromatography with tandem mass spectrometry. We detected striking differences in the protein content of corpus and antrum tissues. Four hundred ninety-two proteins were preferentially localized to the corpus in uninfected animals. The abundance of 91 of these proteins was reduced in H. pylori-infected corpus tissues exhibiting atrophic gastritis compared with infected corpus tissues exhibiting non-atrophic gastritis or uninfected corpus tissues; these included numerous proteins with metabolic functions. Fifty proteins localized to the corpus in uninfected animals were diffusely delocalized throughout the stomach in infected tissues with atrophic gastritis; these included numerous proteins with roles in protein processing. The corresponding alterations were not detected in animals infected with a H. pylori ∆cagT mutant (lacking Cag type IV secretion system activity). These results indicate that H. pylori can cause loss of proteins normally localized to the gastric corpus as well as diffuse delocalization of corpus-specific proteins, resulting in marked changes in the normal gastric molecular partitioning into distinct corpus and antrum regions.IMPORTANCEA normal stomach is organized into distinct regions known as the corpus and antrum, which have different functions, cell types, and gland architectures. Previous studies have primarily used histologic methods to differentiate these regions and detect H. pylori-induced alterations leading to stomach cancer. In this study, we investigated H. pylori-induced gastric molecular alterations in a Mongolian gerbil model of carcinogenesis. We report the detection of numerous proteins that are preferentially localized to the gastric corpus but not the antrum in a normal stomach. We show that stomachs with H. pylori-induced atrophic gastritis (a precancerous condition characterized by the loss of specialized cell types) exhibit marked changes in the abundance and localization of proteins normally localized to the gastric corpus. These results provide new insights into H. pylori-induced gastric molecular alterations that are associated with the development of stomach cancer.

Good Cop, Bad Cop: Profiling the Immune Landscape in Multiple Myeloma

Multiple myeloma (MM) is a dyscrasia of plasma cells (PCs) characterized by abnormal immunoglobulin (Ig) production. The disease remains incurable due to a multitude of mutations and structural abnormalities in MM cells, coupled with a favorable microenvironment and immune suppression that eventually contribute to the development of drug resistance. The bone marrow microenvironment (BMME) is composed of a cellular component comprising stromal cells, endothelial cells, osteoclasts, osteoblasts, and immune cells, and a non-cellular component made of the extracellular matrix (ECM) and the liquid milieu, which contains cytokines, growth factors, and chemokines. The bone marrow stromal cells (BMSCs) are involved in the adhesion of MM cells, promote the growth, proliferation, invasion, and drug resistance of MM cells, and are also crucial in angiogenesis and the formation of lytic bone lesions. Classical immunophenotyping in combination with advanced immune profiling using single-cell sequencing technologies has enabled immune cell-specific gene expression analysis in MM to further elucidate the roles of specific immune cell fractions from peripheral blood and bone marrow (BM) in myelomagenesis and progression, immune evasion and exhaustion mechanisms, and development of drug resistance and relapse. The review describes the role of BMME components in MM development and ongoing clinical trials using immunotherapeutic approaches.

Combining Mass Cytometry Data by CyTOFmerge Reveals Additional Cell Phenotypes in the Heterogeneous Ovarian Cancer Tumor Microenvironment: A Pilot Study

The prognosis of high-grade serous ovarian carcinoma (HGSOC) is poor, and treatment selection is challenging. A heterogeneous tumor microenvironment (TME) characterizes HGSOC and influences tumor growth, progression, and therapy response. Better characterization with multidimensional approaches for simultaneous identification and categorization of the various cell populations is needed to map the TME complexity. While mass cytometry allows the simultaneous detection of around 40 proteins, the CyTOFmerge MATLAB algorithm integrates data sets and extends the phenotyping. This pilot study explored the potential of combining two datasets for improved TME phenotyping by profiling single-cell suspensions from ten chemo-naïve HGSOC tumors by mass cytometry. A 35-marker pan-tumor dataset and a 34-marker pan-immune dataset were analyzed separately and combined with the CyTOFmerge, merging 18 shared markers. While the merged analysis confirmed heterogeneity across patients, it also identified a main tumor cell subset, additionally to the nine identified by the pan-tumor panel. Furthermore, the expression of traditional immune cell markers on tumor and stromal cells was revealed, as were marker combinations that have rarely been examined on individual cells. This study demonstrates the potential of merging mass cytometry data to generate new hypotheses on tumor biology and predictive biomarker research in HGSOC that could improve treatment effectiveness.

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases

BACKGROUND

Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma.

METHODS

We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy.

FINDINGS

We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival.

INTERPRETATION

Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy.

FUNDING

See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

Identification and validation of biomarkers for epithelial-mesenchymal transition-related cells to estimate the prognosis and immune microenvironment in primary gastric cancer by the integrated analysis of single-cell and bulk RNA sequencing data

BACKGROUND

The epithelial-mesenchymal transition (EMT) is associated with gastric cancer (GC) progression and immune microenvironment. To better understand the heterogeneity underlying EMT, we integrated single-cell RNA-sequencing (scRNA-seq) data and bulk sequencing data from GC patients to evaluate the prognostic utility of biomarkers for EMT-related cells (ERCs), namely, cancer-associated fibroblasts (CAFs) and epithelial cells (ECs).

METHODS

scRNA-seq data from primary GC tumor samples were obtained from the Gene Expression Omnibus (GEO) database to identify ERC marker genes. Bulk GC datasets from the Cancer Genome Atlas (TCGA) and GEO were used as training and validation sets, respectively. Differentially expressed markers were identified from the TCGA database. Univariate Cox, least-absolute shrinkage, and selection operator regression analyses were performed to identify EMT-related cell-prognostic genes (ERCPGs). Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses were adopted to evaluate the prognostic utility of the ERCPG signature. An ERCPG-based nomogram was constructed by integrating independent prognostic factors. Finally, we evaluated the correlations between the ERCPG signature and immune-cell infiltration and verified the expression of ERCPG prognostic signature genes by in vitro cellular assays.

RESULTS

The ERCPG signature was comprised of seven genes (COL4A1, F2R, MMP11, CAV1, VCAN, FKBP10, and APOD). Patients were divided into high- and low-risk groups based on the ERCPG risk scores. Patients in the high-risk group showed a poor prognosis. ROC and calibration curves suggested that the ERCPG signature and nomogram had a good prognostic utility. An immune cell-infiltration analysis suggested that the abnormal expression of ERCPGs induced the formation of an unfavorable tumor immune microenvironment. In vitro cellular assays showed that ERCPGs were more abundantly expressed in GC cell lines compared to normal gastric tissue cell lines.

CONCLUSIONS

We constructed and validated an ERCPG signature using scRNA-seq and bulk sequencing data from ERCs of GC patients. Our findings support the estimation of patient prognosis and tumor treatment in future clinical practice.

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.

Immunology of Oral Squamous Cell Carcinoma-A Comprehensive Insight with Recent Concepts

This review aims to understand the concept of oral cancer immunology through the notion of immune profiling, immunoediting and immunotherapy, and to gain knowledge regarding its application for the management of oral cancer patients. Oral cancer is an immunogenic tumor where the cells of the tumor microenvironment play an important role in tumorigenesis. Understanding the mechanism of these modulations can help design immunotherapeutic strategies in oral cancer patients. This article gives an overview of immunomodulation in the oral cancer tumor microenvironment, with concepts of immune profiling, immunoediting and immunotherapy. English literature searches via Google Scholar, Web of Science, EBSCO, Scopus, and PubMed database were performed with the key words immunology, tumor microenvironment, cells, cross talk, immune profiling, biomarkers, inflammation, gene expression, techniques, immunoediting, immunosurveillance, tumor escape, immunotherapy, immune checkpoint inhibitors, vaccines in cancer, oral cancer, and head and neck cancer. Original research articles, reviews, and case reports published from 2016-2021 (n = 81) were included to appraise different topics, and were discussed under the following subsections. Literature published on oral cancer immunology reveals that oral cancer immune profiling with appropriate markers and techniques and knowledge on immunoediting concepts can help design and play an effective role in immunotherapeutic management of oral cancer patients. An evaluation of oral cancer immunology helps to determine its role in tumorigenesis, and immunotherapy could be the emerging drift in the effective management of oral cancer.

Triple negative breast cancer (TNBC): Non-genetic tumor heterogeneity and immune microenvironment: Emerging treatment options

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intra-tumoral heterogeneity, and frequently develops resistance to therapies. Tumor heterogeneity and lack of biomarkers are thought to be some of the most difficult challenges driving therapeutic resistance and relapse. This review will summarize current therapy for TNBC, studies in treatment resistance and relapse, including data from recent single cell sequencing. We will discuss changes in both the transcriptome and epigenome of TNBC, and we will review mechanisms regulating the immune microenvironment. Lastly, we will provide new perspective in patient stratification, and treatment options targeting transcriptome dysregulation and the immune microenvironment of TNBC patients.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

Monomethyl auristatin antibody and peptide drug conjugates for trimodal cancer chemo-radio-immunotherapy

Locally advanced cancers remain therapeutically challenging to eradicate. The most successful treatments continue to combine decades old non-targeted chemotherapies with radiotherapy that unfortunately increase normal tissue damage in the irradiated field and have systemic toxicities precluding further treatment intensification. Therefore, alternative molecularly guided systemic therapies are needed to improve patient outcomes when applied with radiotherapy. In this work, we report a trimodal precision cytotoxic chemo-radio-immunotherapy paradigm using spatially targeted auristatin warheads. Tumor-directed antibodies and peptides conjugated to radiosensitizing monomethyl auristatin E (MMAE) specifically produce CD8 T cell dependent durable tumor control of irradiated tumors and immunologic memory. In combination with ionizing radiation, MMAE sculpts the tumor immune infiltrate to potentiate immune checkpoint inhibition. Here, we report therapeutic synergies of targeted cytotoxic auristatin radiosensitization to stimulate anti-tumor immune responses providing a rationale for clinical translational of auristatin antibody drug conjugates with radio-immunotherapy combinations to improve tumor control.

The Landscape of Immune Microenvironments in Racially Diverse Breast Cancer Patients

BACKGROUND

Immunotherapy is a rapidly evolving treatment option in breast cancer; However, the breast cancer immune microenvironment is understudied in Black and younger (<50 years) patients.

METHODS

We used histologic and RNA-based immunoprofiling methods to characterize the breast cancer immune landscape in 1,952 tumors from the Carolina Breast Cancer Study (CBCS), a population-based study that oversampled Black (n = 1,030) and young women (n = 1,039). We evaluated immune response leveraging markers for 10 immune cell populations, compared profiles to those in The Cancer Genome Atlas (TCGA) Project [n = 1,095 tumors, Black (n = 183), and young women (n = 295)], and evaluated in association with clinical and demographic variables, including recurrence.

RESULTS

Consensus clustering identified three immune clusters in CBCS (adaptive-enriched, innate-enriched, or immune-quiet) that varied in frequency by race, age, tumor grade and subtype; however, only two clusters were identified in TCGA, which were predominantly comprised of adaptive-enriched and innate-enriched tumors. In CBCS, the strongest adaptive immune response was observed for basal-like, HER2-positive (HER2+), triple-negative breast cancer (TNBC), and high-grade tumors. Younger patients had higher proportions of adaptive-enriched tumors, particularly among estrogen receptor (ER)-negative (ER-) cases. Black patients had higher frequencies of both adaptive-enriched and innate-enriched tumors. Immune clusters were associated with recurrence among ER- tumors, with adaptive-enriched showing the best and innate-enriched showing the poorest 5-year recurrence-free survival.

CONCLUSIONS

These data suggest that immune microenvironments are intricately related to race, age, tumor subtype, and grade.

IMPACT

Given higher mortality among Black and young women, more defined immune classification using cell-type-specific panels could help explain higher recurrence and ultimately lead to targetable interventions.

Tumor Immunology and Immunotherapy of Non-Small-Cell Lung Cancer

Historically, non-small-cell lung cancer (NSCLC) has been regarded as a nonimmunogenic tumor; however, recent studies have shown that NSCLCs are among the most responsive cancers to monoclonal antibody immune checkpoint inhibitors (ICIs). ICIs have dramatically improved clinical outcomes for a subset of patients (∼20%) with locally advanced and metastatic NSCLC, and they have also demonstrated promise as neoadjuvant therapy for early-stage resectable disease. Nevertheless, the majority of patients with NSCLC are refractory to ICIs for reasons that are poorly understood. Thus, major questions are: how do we initially identify the patients most likely to derive significant clinical benefit from these therapies; how can we increase the number of patients benefiting; what are the mechanisms of primary and acquired resistance to immune-based therapies; are there additional immune checkpoints besides PD-1/PD-L1 and CTLA-4 that can be targeted to provide greater clinical benefit to patients; and how do we best combine ICI therapy with surgery, radiotherapy, chemotherapy, and targeted therapy? To answer these questions, we need to deploy the latest technologies to study tumors and their microenvironment and how they interact with components of the innate and adaptive immune systems. There is also a need for new preclinical model systems to investigate the molecular mechanisms of resistance to treatment and identify novel therapeutic targets. Recent advances in technology are beginning to shed new light on the immune landscape of NSCLC that may uncover biomarkers of response and maximize the clinical benefit of immune-based therapies. Identification of the mechanisms of resistance should lead to the identification of novel targets and the generation of new therapeutic strategies that improve outcomes for a greater number of patients. In the sections below, we discuss the results of studies examining the immune microenvironment in NSCLC, summarize the clinical experience with immunotherapy for NSCLC, and review candidate biomarkers of response to these agents in NSCLC.

Differential immune landscapes in appendicular versus axial skeleton

Roughly 400,000 people in the U.S. are living with bone metastases, the vast majority occurring in the spine. Metastases to the spine result in fractures, pain, paralysis, and significant health care costs. This predilection for cancer to metastasize to the bone is seen across most cancer histologies, with the greatest incidence seen in prostate, breast, and lung cancer. The molecular process involved in this predilection for axial versus appendicular skeleton is not fully understood, although it is likely that a combination of tumor and local micro-environmental factors plays a role. Immune cells are an important constituent of the bone marrow microenvironment and many of these cells have been shown to play a significant role in tumor growth and progression in soft tissue and bone disease. With this in mind, we sought to examine the differences in immune landscape between axial and appendicular bones in the normal noncancerous setting in order to obtain an understanding of these landscapes. To accomplish this, we utilized mass cytometry by time-of-flight (CyTOF) to examine differences in the immune cell landscapes between the long bone and vertebral body bone marrow from patient clinical samples and C57BL/6J mice. We demonstrate significant differences between immune populations in both murine and human marrow with a predominance of myeloid progenitor cells in the spine. Additionally, cytokine analysis revealed differences in concentrations favoring a more myeloid enriched population of cells in the vertebral body bone marrow. These differences could have clinical implications with respect to the distribution and permissive growth of bone metastases.

CFTR-mediated monocyte/macrophage dysfunction revealed by cystic fibrosis proband-parent comparisons

Cystic fibrosis (CF) is an inherited disorder caused by biallelic mutations of the CF transmembrane conductance regulator (CFTR) gene. Converging evidence suggests that CF carriers with only 1 defective CFTR copy are at increased risk for CF-related conditions and pulmonary infections, but the molecular mechanisms underpinning this effect remain unknown. We performed transcriptomic profiling of peripheral blood mononuclear cells (PBMCs) of CF child-parent trios (proband, father, and mother) and healthy control (HC) PBMCs or THP-1 cells incubated with the plasma of these participants. Transcriptomic analyses revealed suppression of cytokine-enriched immune-related genes (IL-1β, CXCL8, CREM), implicating lipopolysaccharide tolerance in innate immune cells (monocytes) of CF probands and their parents. These data suggest that a homozygous as well as a heterozygous CFTR mutation can modulate the immune/inflammatory system. This conclusion is further supported by the finding of lower numbers of circulating monocytes in CF probands and their parents, compared with HCs, and the abundance of mononuclear phagocyte subsets, which correlated with Pseudomonas aeruginosa infection, lung disease severity, and CF progression in the probands. This study provides insight into demonstrated CFTR-related innate immune dysfunction in individuals with CF and carriers of a CFTR mutation that may serve as a target for personalized therapy.

High-dimensional role of AI and machine learning in cancer research

The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).

scMRMA: single cell multiresolution marker-based annotation

Single-cell RNA sequencing has become a powerful tool for identifying and characterizing cellular heterogeneity. One essential step to understanding cellular heterogeneity is determining cell identities. The widely used strategy predicts identities by projecting cells or cell clusters unidirectionally against a reference to find the best match. Here, we develop a bidirectional method, scMRMA, where a hierarchical reference guides iterative clustering and deep annotation with enhanced resolutions. Taking full advantage of the reference, scMRMA greatly improves the annotation accuracy. scMRMA achieved better performance than existing methods in four benchmark datasets and successfully revealed the expansion of CD8 T cell populations in squamous cell carcinoma after anti-PD-1 treatment.

Prognostic value and predictive biomarkers of phenotypes of tumor-associated macrophages in colorectal cancer

BACKGROUND

The roles of different subtypes of tumor-associated macrophages (TAMs) in predicting the prognosis of colorectal cancer (CRC) remain controversial. In this study, different subtypes of TAMs were investigated as prognostic and predictive biomarkers for CRC.

METHODS

Expressions of CD68, CD86 and CD163 were investigated by immunohistochemistry (IHC) and immunofluorescence (IF), and the correlation between the expression of CD86 and CD163 was calculated in colorectal cancer tissues from 64 CRC patients.

RESULTS

The results showed that high expressions of CD86⁺ and CD68⁺ CD86⁺ TAMs as well as low expression of CD163⁺ and CD68⁺ CD163⁺ TAMs were significantly associated with favorable overall survival (OS). The level of CD86 protein expression showed a negative correlation with CD163 protein expression. In addition, CD86 protein expression remarkably negative correlated with tumor differentiation and tumor node metastasis (TNM) stage, while CD163 protein expression significantly positive correlated with tumor differentiation and tumor size. As an independent risk factor, high expression of CD86 TAMs had prominently favorable prognostic efficacy while high expression of CD68⁺ CD163⁺ TAMs had significantly poor prognostic efficacy.

CONCLUSIONS

These results indicate that CD86⁺ and CD68⁺ CD163⁺ TAMs as prognostic and predictive biomarkers for CRC.

Evidence of Antitumor and Antimetastatic Potential of Induced Pluripotent Stem Cell-Based Vaccines in Cancer Immunotherapy

Cancer is maintained by the activity of a rare population of self-renewing "cancer stem cells" (CSCs), which are resistant to conventional therapies. CSCs over-express several proteins shared with induced pluripotent stem cells (iPSCs). We show here that allogenic or autologous murine iPSCs, combined with a histone deacetylase inhibitor (HDACi), are able to elicit major anti-tumor responses in a highly aggressive triple-negative breast cancer, as a relevant cancer stemness model. This immunotherapy strategy was effective in preventing tumor establishment and efficiently targeted CSCs by inducing extensive modifications of the tumor microenvironment. The anti-tumoral effect was correlated with the generation of CD4+, CD8+ T cells, and CD44+ CD62L- CCR7low CD127low T-effector memory cells, and the reduction of CD4+ CD25+FoxP3+ Tregs, Arg1⁺ CD11b+ Gr1+, and Arg1⁺ and CD11b+ Ly6+ myeloid-derived suppressor cell populations within the tumor. The anti-tumoral effect was associated with a reduction in metastatic dissemination and an improvement in the survival rate. These results demonstrate for the first time the clinical relevance of using an off-the-shelf allogeneic iPSC-based vaccine combined with an HDACi as a novel pan-cancer anti-cancer immunotherapy strategy against aggressive tumors harboring stemness features with high metastatic potential.

Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer

Background

Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types.

Methods

In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction.

Results

We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway.

Conclusions

Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03179-5.

Mammary tumor-derived transplants as breast cancer models to evaluate tumor-immune interactions and therapeutic responses

In breast cancer, the type and distribution of infiltrating immune cells are associated with clinical outcome. Moreover, infiltrated cancers with abundant tumor infiltrating lymphocytes (TIL) are more likely to respond to immunotherapy, while those in which CD8+ T cells are completely absent (deserts) or excluded are less likely to respond. Detailed understanding of this biology is limited by a lack of preclinical breast cancer models that recapitulate TIL distributions and their associated biology. Here we established mammary tumor-derived transplants (mTDT) from 12 Trp53 null mammary tumors in syngeneic BALB/cJ mice and examined the stability of their growth rate, TIL distribution, and transcriptomic profiles. All mTDT were estrogen receptor negative. Half of the parental tumors were classified as infiltrated, and the rest were divided between excluded and desert phenotypes. After two orthotopic passages, most (70%) mTDT from infiltrated parents recapitulated this pattern, whereas the desert or excluded parental patterns were maintained in about half of daughter mTDT. Approximately 30% of mTDT gave rise to lung or liver metastases, but metastasis was not associated with a TIL phenotype. Unsupervised transcriptomic analysis clustered mTDT according to their TIL spatial patterns. Infiltrated mTDT transplanted subcutaneously and orthotopically were resistant to anti-PD-L1. Profiling implicated prolonged antigen stimulation and loss of effector function of lymphocytes rather than T cell exhaustion in the lack of response of infiltrated mTDT to checkpoint blockade. In summary, the molecular diversity and immune complexity of mTDT will facilitate the dissection of mechanisms of breast cancer response to immunotherapies.

Uncovering potential single nucleotide polymorphisms, copy number variations and related signaling pathways in primary Sjogren's syndrome

Primary Sjogren’s syndrome (pSS) is a complex systemic autoimmune disease, which is difficult to accurately diagnose due to symptom diversity in patients, especially at earlier stages. We tried to find potential single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and related signaling pathways. Genomic DNA was extracted from peripheral blood of 12 individuals (7 individuals from 3 pSS pedigrees and 5 sporadic cases) for whole-exome sequencing (WES) analysis. SNPs and CNVs were identified, followed by functional annotation of genes with SNPs and CNVs. Gene expression profile (involving 64 normal controls and 166 cases) was downloaded from the Gene Expression Omnibus database (GEO) dataset for differentially expression analysis. Sanger sequencing and in vitro validation was used to validate the identified SNPs and differentially expressed genes, respectively. A total of 5 SNPs were identified in both pedigrees and sporadic cases, such as FES, PPM1J, and TRAPPC9. A total of 3402 and 19 CNVs were identified in pedigrees and sporadic cases, respectively. Fifty-one differentially expressed genes were associated with immunity, such as BATF3, LAP3, BATF2, PARP9, and IL15RA. AMPK signaling pathway and cell adhesion molecules (CAMs) were the most significantly enriched signaling pathways of identified SNPs. Identified CNVs were associated with systemic lupus erythematosus, mineral absorption, and HTLV-I infection. IL2-STAT5 signaling, interferon-gamma response, and interferon-alpha response were significantly enriched immune related signaling pathways of identified differentially expressed genes. In conclusion, our study found some potential SNPs, CNVs, and related signaling pathways, which could be useful in understanding the pathological mechanism of pSS.

High-dimensional immune-profiling in cancer: implications for immunotherapy

Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.

Immunity Depletion, Telomere Imbalance, and Cancer-Associated Metabolism Pathway Aberrations in Intestinal Mucosa upon Short-Term Caloric Restriction

Systems cancer biology analysis of calorie restriction (CR) mechanisms and pathways has not been carried out, leaving therapeutic benefits unclear. Using metadata analysis, we studied gene expression changes in normal mouse duodenum mucosa (DM) response to short-term (2-weeks) 25% CR as a biological model. Our results indicate cancer-associated genes consist of 26% of 467 CR responding differential expressed genes (DEGs). The DEGs were enriched with over-expressed cell cycle, oncogenes, and metabolic reprogramming pathways that determine tissue-specific tumorigenesis, cancer, and stem cell activation; tumor suppressors and apoptosis genes were under-expressed. DEG enrichments suggest telomeric maintenance misbalance and metabolic pathway activation playing dual (anti-cancer and pro-oncogenic) roles. The aberrant DEG profile of DM epithelial cells is found within CR-induced overexpression of Paneth cells and is coordinated significantly across GI tract tissues mucosa. Immune system genes (ISGs) consist of 37% of the total DEGs; the majority of ISGs are suppressed, including cell-autonomous immunity and tumor-immune surveillance. CR induces metabolic reprogramming, suppressing immune mechanics and activating oncogenic pathways. We introduce and argue for our network pro-oncogenic model of the mucosa multicellular tissue response to CR leading to aberrant transcription and pre-malignant states. These findings change the paradigm regarding CR's anti-cancer role, initiating specific treatment target development. This will aid future work to define critical oncogenic pathways preceding intestinal lesion development and biomarkers for earlier adenoma and colorectal cancer detection.

The intrinsic immunogenic properties of cancer cell lines, immunogenic cell death, and how these influence host antitumor immune responses

Modern cancer therapies often involve the combination of tumor-directed cytotoxic strategies and generation of a host antitumor immune response. The latter is unleashed by immunotherapies that activate the immune system generating a more immunostimulatory tumor microenvironment and a stronger tumor antigen-specific immune response. Studying the interaction between antitumor cytotoxic therapies, dying cancer cells, and the innate and adaptive immune system requires appropriate experimental tumor models in mice. In this review, we discuss the immunostimulatory and immunosuppressive properties of cancer cell lines commonly used in immunogenic cell death (ICD) studies being apoptosis or necroptosis. We will especially focus on the antigenic component of immunogenicity. While in several cancer cell lines the epitopes of endogenously expressed tumor antigens are known, these intrinsic epitopes are rarely determined in experimental apoptotic or necroptotic ICD settings. Instead by far the most ICD research studies investigate the antigenic response against exogenously expressed model antigens such as ovalbumin or retroviral epitopes (e.g., AH1). In this review, we will argue that the immune response against endogenous tumor antigens and the immunopeptidome profile of cancer cell lines affect the eventual biological readouts in the typical prophylactic tumor vaccination type of experiments used in ICD research, and we will propose additional methods involving immunopeptidome profiling, major histocompatibility complex molecule expression, and identification of tumor-infiltrating immune cells to document intrinsic immunogenicity following different cell death modalities.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

BACKGROUND

The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC.

METHODS

The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis.

RESULTS

8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK.

CONCLUSION

The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.

Whole Transcriptome Analysis of Myeloid Dendritic Cells Reveals Distinct Genetic Regulation in Patients with Allergies

Dendritic cells (DCs) play critical roles in atopic diseases, orchestrating both innate and adaptive immune systems. Nevertheless, limited information is available regarding the mechanism through which DCs induce hyperresponsiveness in patients with allergies. This study aims to reveal novel genetic alterations and future therapeutic target molecules in the DCs from patients with allergies using whole transcriptome sequencing. Transcriptome sequencing of human BDCA-3+/CD11c+ DCs sorted from peripheral blood monocytes obtained from six patients with allergies and four healthy controls was conducted. Gene expression profile data were analyzed, and an ingenuity pathway analysis was performed. A total of 1638 differentially expressed genes were identified at p-values < 0.05, with 11 genes showing a log2-fold change ≥1.5. The top gene network was associated with cell death/survival and organismal injury/abnormality. In validation experiments, amphiregulin (AREG) showed consistent results with transcriptome sequencing data, with increased mRNA expression in THP-1-derived DCs after Der p 1 stimulation and higher protein expression in myeloid DCs obtained from patients with allergies. This study suggests an alteration in the expression of DCs in patients with allergies, proposing related altered functions and intracellular mechanisms. Notably, AREG might play a crucial role in DCs by inducing the Th2 immune response.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Tumor-Infiltrating Lymphocytes and Their Prognostic Value in Cutaneous Melanoma

Recent breakthroughs in tumor immunotherapy such as immune checkpoint blockade (ICB) antibodies, have demonstrated the capacity of the immune system to fight cancer in a number of malignancies such as melanoma and lung cancer. The numbers, localization and phenotypes of tumor-infiltrating lymphocytes (TIL) are not only predictive of response to immunotherapy but also key modulators of disease progression. In this review, we focus on TIL profiling in cutaneous melanoma using histopathological approaches and highlight the observed prognostic value of the primary TIL subsets. The quantification of TIL in formalin-fixed tumor samples ranges from visual scoring of lymphocytic infiltrates in H&E to multiplex immunohistochemistry and immunofluorescence followed by enumeration using image analysis software. Nevertheless, TIL enumeration in the current literature primarily relies upon single marker immunohistochemistry analyses of major lymphocyte subsets such as conventional T cells (CD3, CD4, CD8), regulatory T cells (FOXP3) and B cells (CD20). We review key studies in the literature on associations between TIL subsets and patient survival. We also cover recent findings with respect to the existence of ectopic lymphoid aggregates found in the TME which are termed tertiary lymphoid structures (TLS) and are generally a positive prognostic feature. In addition to their prognostic significance, the existence of various TIL sub-populations has also been reported to predict a patient's response to ICB. Thus, the literature on the predictive potential of TIL subsets in melanoma patients receiving ICB has also been discussed. Finally, we describe recently developed state-of-the-art profiling approaches for tumor infiltrating immune cells such as digital pathology scoring algorithms (e.g., Immunoscore) and multiplex proteomics-based immunophenotyping platforms (e.g., imaging mass cytometry). Translating these novel technologies have the potential to revolutionize tumor immunopathology leading to altering our current understanding of cancer immunology and dramatically improving outcomes for patients.

Earlier-Phased Cancer Immunity Cycle Strongly Influences Cancer Immunity in Operable Never-Smoker Lung Adenocarcinoma

Exome and transcriptome analyses of clinically homogeneous early-stage never-smoker female patients with lung adenocarcinoma were performed to understand tumor-T cell interactions and immune escape points. Using our novel gene panels of eight functional categories in the cancer-immunity cycle, three distinct subgroups were identified in this immune checkpoint blockade-refractory cohort by defective gene expression in two major domains, i.e., type I interferon production/signaling pathway and antigen-presenting machinery. Our approach could play a critical role in understanding immune evasion mechanisms, developing a method for effective selection of rare immune checkpoint blockade responders, and finding new treatment strategies.