Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) has high relapse and low 5-year survival rates. Single-cell profiling in relapsed HCC may aid in the design of effective anticancer therapies, including immunotherapies. We profiled the transcriptomes of ∼17,000 cells from 18 primary or early-relapse HCC cases. Early-relapse tumors have reduced levels of regulatory T cells, increased dendritic cells (DCs), and increased infiltrated CD8+ T cells, compared with primary tumors, in two independent cohorts. Remarkably, CD8+ T cells in recurrent tumors overexpressed KLRB1 (CD161) and displayed an innate-like low cytotoxic state, with low clonal expansion, unlike the classical exhausted state observed in primary HCC. The enrichment of these cells was associated with a worse prognosis. Differential gene expression and interaction analyses revealed potential immune evasion mechanisms in recurrent tumor cells that dampen DC antigen presentation and recruit innate-like CD8+ T cells. Our comprehensive picture of the HCC ecosystem provides deeper insights into immune evasion mechanisms associated with tumor relapse.

A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer.

Breast cancer is a heterogeneous disease. Tumor cells and associated healthy cells form ecosystems that determine disease progression and response to therapy. To characterize features of breast cancer ecosystems and their associations with clinical data, we analyzed 144 human breast tumor and 50 non-tumor tissue samples using mass cytometry. The expression of 73 proteins in 26 million cells was evaluated using tumor and immune cell-centric antibody panels. Tumors displayed individuality in tumor cell composition, including phenotypic abnormalities and phenotype dominance. Relationship analyses between tumor and immune cells revealed characteristics of ecosystems related to immunosuppression and poor prognosis. High frequencies of PD-L1+ tumor-associated macrophages and exhausted T cells were found in high-grade ER+ and ER- tumors. This large-scale, single-cell atlas deepens our understanding of breast tumor ecosystems and suggests that ecosystem-based patient classification will facilitate identification of individuals for precision medicine approaches targeting the tumor and its immunoenvironment.

Turning cold tumors hot: from molecular mechanisms to clinical applications.

Immune checkpoint blockade (ICB) therapies have achieved clinical benefit, but most 'immune-cold' solid tumors are not responsive. The diversity of immune evasion mechanisms remains a key obstacle in turning nonresponsive 'cold' tumors into responsive 'hot' ones. Therefore, exploring the mechanisms of such transitions and tumor immunotyping can provide significant insights into designing effective therapeutic strategies against cancer. Here, we focus on the latest advances regarding local and systemic regulatory mechanisms of immune responses in cold and hot tumors. We also highlight the necessity for tumor immunotyping through the assessment of multiple immunological variables using various diagnostic techniques and biomarkers. Finally, we discuss the challenges and potential clinical applications of immunophenotyping to turn cold tumors hot, which may further guide combined immunotherapies.

The tumor ecosystem in head and neck squamous cell carcinoma and advances in ecotherapy.

The development of head and neck squamous cell carcinoma (HNSCC) is a multi-step process, and its survival depends on a complex tumor ecosystem, which not only promotes tumor growth but also helps to protect tumor cells from immune surveillance. With the advances of existing technologies and emerging models for ecosystem research, the evidence for cell-cell interplay is increasing. Herein, we discuss the recent advances in understanding the interaction between tumor cells, the major components of the HNSCC tumor ecosystem, and summarize the mechanisms of how biological and abiotic factors affect the tumor ecosystem. In addition, we review the emerging ecological treatment strategy for HNSCC based on existing studies.

Single-cell transcriptomic analysis of gingivo-buccal oral cancer reveals two dominant cellular programs.

Oral squamous cell carcinoma of the gingivo-buccal region (OSCC-GB) is the most common cancer among men in India, and is associated with poor prognosis and frequent recurrence. Cellular heterogeneity in OSCC-GB was investigated by single-cell RNA sequencing of tumors derived from the oral cavity of 12 OSCC-GB patients, 3 of whom had concomitant presence of a precancerous lesion (oral submucous fibrosis [OSMF]). Unique malignant cell types, features, and phenotypic shifts in the stromal cell population were identified in oral tumors with associated submucous fibrosis. Expression levels of FOS, ATP1A, and DUSP1 provided robust discrimination between tumors with or without the concomitant presence of OSMF. Malignant cell populations shared between tumors with and without OSMF were enriched with the expression of partial epithelial-mesenchymal transition (pEMT) or fetal cell type signatures indicative of two dominant cellular programs in OSCC-GB-pEMT and fetal cellular reprogramming. Malignant cells exhibiting fetal cellular and pEMT programs were enriched with the expression of immune-related pathway genes known to be involved in antitumor immune response. In the tumor microenvironment, higher infiltration of immune cells than the stromal cells was observed. The T cell population was large in tumors and diverse subtypes of T cells with varying levels of infiltration were found. We also detected double-negative PLCG2+ T cells and cells with intermediate M1-M2 macrophage polarization. Our findings shed light on unique aspects of cellular heterogeneity and cell states in OSCC-GB.

Single-cell RNA-sequencing dissects cellular heterogeneity and identifies two tumor-suppressing immune cell subclusters in HPV-related cervical adenosquamous carcinoma.

The intratumor heterogeneity of human papillomavirus (HPV)-related cervical cancer remains poorly defined. We performed single-cell RNA sequencing on 18 046 individual cells derived from two HPV-related cervical adenosquamous carcinoma samples to analyze the transcriptional heterogeneity of both epithelial and immune constituents, identifying seven epithelial (Epi1-7) and 11 immune subclusters. Based on expression of known cervical cancer markers, Epi1-2 primarily displayed features of adenocarcinoma, whereas Epi3-6 were instead characterized by features of squamous carcinoma. Our analyses also revealed that hypoxia and Kirsten rat sarcoma viral oncogene signaling were highly represented within Epi1; metabolic pathways mediating glycolysis and oxidative phosphorylation were enriched in Epi2-4; while Epi5 was enriched in p53 pathway components and features of epithelial-mesenchymal transition. Moreover, CD8+ FGFBP2+ T cells and FGFBP2+ natural killer cells were found to display high levels of cytotoxic effectors (GZMA, GZMB, GNLY, and PRF1) and low levels of inhibitory markers (PDCD1, TIGIT, and CTLA4), such that tumor infiltration by these populations was positively associated with survival in a cohort of n = 165 patients with HPV-related cervical cancer from The Cancer Genome Atlas database (p = 0.017 and 0.014, respectively). These results shed new light on the intratumor heterogeneity of HPV-related cervical adenosquamous carcinoma, which will help to refine diagnostic and treatment approaches.

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.

Objective: This study aimed to establish a method to predict the overall survival (OS) of patients with stage I-III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods: We retrospectively identified 161 consecutive patients with stage I-III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction. Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433-12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646-4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289-8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804-0.822 in the training cohort; 0.758, 95% CI: 0.751-0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness. Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I-III CRC patients.

Engineering chimeric antigen receptor-T cells for cancer treatment.

Intratumor heterogeneity of tumor clones and an immunosuppressive microenvironment in cancer ecosystems contribute to inherent difficulties for tumor treatment. Recently, chimeric antigen receptor (CAR) T-cell therapy has been successfully applied in the treatment of B-cell malignancies, underscoring its great potential in antitumor therapy. However, functional challenges of CAR-T cell therapy, especially in solid tumors, remain. Here, we describe cancer-immunity phenotypes from a clonal-stromal-immune perspective and elucidate mechanisms of T-cell exhaustion that contribute to tumor immune evasion. Then we assess the functional challenges of CAR-T cell therapy, including cell trafficking and infiltration, targeted-recognition and killing of tumor cells, T-cell proliferation and persistence, immunosuppressive microenvironment and self-control regulation. Finally, we delineate tumor precision informatics and advancements in engineered CAR-T cells to counteract inherent challenges of the CAR-T cell therapy, either alone or in combination with traditional therapeutics, and highlight the therapeutic potential of this approach in future tumor precision treatment.

Breast cancer complexity: implications of intratumoral heterogeneity in clinical management.

Generation of intratumoral phenotypic and genetic heterogeneity has been attributed to clonal evolution and cancer stem cells that together give rise to a tumor with complex ecosystems. Each ecosystem contains various tumor cell subpopulations and stromal entities, which, depending upon their composition, can influence survival, therapy responses, and global growth of the tumor. Despite recent advances in breast cancer management, the disease has not been completely eradicated as tumors recur despite initial response to treatment. In this review, using data from clinically relevant breast cancer models, we show that the fates of tumor stem cells/progenitor cells in the individual tumor ecosystems comprising a tumor are predetermined to follow a limited (unipotent) and/or unlimited (multipotent) path of differentiation which create conditions for active generation and maintenance of heterogeneity. The resultant dynamic systems respond differently to treatments, thus disrupting the delicate stability maintained in the heterogeneous tumor. This raises the question whether it is better then to preserve stability by preventing takeover by otherwise dormant ecosystems in the tumor following therapy. The ultimate strategy for personalized therapy would require serial assessments of the patient's tumor for biomarker validation during the entire course of treatment that is combined with their three-dimensional mapping to the tumor architecture and landscape.

Advances in spatial multi-omics in tumors.

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

From immune equilibrium to tumor ecodynamics.

Objectives: There is no theory to quantitatively describe the complex tumor ecosystem. At the same time, cancer immunotherapy is considered a revolution in oncology, but the methods used to describe tumors and the criteria used to evaluate efficacy are not keeping pace. The purpose of this study is to establish a new theory for quantitatively describing the tumor ecosystem, innovating the methods of tumor characterization, and establishing new efficacy evaluation criteria for cancer immunotherapy. Methods: Based on the mathematization of immune equilibrium theory and the establishment of immunodynamics in a previous study, the method of reverse immunodynamics was used, namely, the immune braking force was regarded as the tumor ecological force and the immune force was regarded as the tumor ecological braking force, and the concept of momentum in physics was applied to the tumor ecosystem to establish a series of tumor ecodynamic equations. These equations were used to solve the fundamental and applied problems of the complex tumor ecosystem. Results: A series of tumor ecodynamic equations were established. The tumor ecological momentum equations and their component factors could be used to distinguish disease progression, pseudoprogression, and hyperprogression in cancer immunotherapy. On this basis, the adjusted tumor momentum equations were established to achieve the equivalence of tumor activity (including immunosuppressive activity and metabolic activity) and tumor volume, which could be used to calculate individual disease remission rate and establish new efficacy evaluation criteria (ieRECIST) for immunotherapy of solid tumor based on tumor ecodynamics. At the same time, the concept of moving cube-to-force square ratio and its expression were proposed to calculate the area under the curve of tumor ecological braking force of blood required to achieve an individual disease remission rate when the adjusted tumor ecological momentum was known. Conclusions: A new theory termed tumor ecodynamics emphasizing both tumor activity and tumor volume is established to solve a series of basic and applied problems in the complex tumor ecosystem. It can be predicted that the future will be the era of cancer immune ecotherapy that targets the entire tumor ecosystem.

Single-Cell Characterization of the Tumor Ecosystem in Liver Cancer.

Tumor heterogeneity along with the complex landscape of the tumor microenvironment create critical challenges for effective liver cancer interventions. Characterizing the tumor ecosystem at the single-cell level may provide insight into the collective behaviors of tumor cells and their interplays with stromal and immune cells. Here we introduce the experimental protocol and computational methods for the single-cell study of liver cancer, which may be essential for a mechanistic understanding of the tumor ecosystem in liver cancer and further pave the way for developing novel therapeutics.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response.

BACKGROUND & AIMS: Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC). METHODS: We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes. RESULTS: Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155. CONCLUSIONS: Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Dissecting Intra-Tumoral Changes Following Immune Checkpoint Blockades in Intrahepatic Cholangiocarcinoma via Single-Cell Analysis.

Purpose: To dissect the tumor ecosystem following immune checkpoint blockades (ICBs) in intrahepatic cholangiocarcinoma (ICC) at a single-cell level. Methods: Single-cell RNA sequencing (scRNA-seq) data of 10 ICC patients for the ICB clinical trial were extracted from GSE125449 and systematically reanalyzed. Bulk RNA-seq data of 255 ICC patients were analyzed. Infiltration levels of SPP1+CD68+ tumor-associated macrophages (TAMs) were examined by dual immunofluorescence (IF) staining in 264 resected ICC samples. The correlation between SPP1+ TAMs and clinicopathological features as well as their prognostic significance was evaluated. Results: Among the 10 patients, five received biopsy at baseline, and others were biopsied at different timings following ICBs. Single-cell transcriptomes for 5,931 cells were obtained. A tighter cellular communication network was observed in ICB-treated ICC. We found a newly emerging VEGF signaling mediated by PGF-VEGFR1 between cancer-associated fibroblasts (CAFs) and endothelial cells in ICC following ICBs. SPP1 expression was dramatically upregulated, and SPP1+ TAM gene signatures were enriched in TAMs receiving ICB therapy. We also identified SPP1+ TAMs as an independent adverse prognostic indicator for survival in ICC. Conclusion: Our analyses provide an overview of the altered tumor ecosystem in ICC treated with ICBs and highlight the potential role of targeting CAFs and SPP1+TAMs in developing a more rational checkpoint blockade-based therapy for ICC.

Targeting CAFs to overcome anticancer therapeutic resistance.

The view of cancer as a tumor cell-centric disease is now replaced by our understanding of the interconnection and dependency of tumor stroma. Cancer-associated fibroblasts (CAFs), the most abundant stromal cells in the tumor microenvironment (TME), are involved in anticancer therapeutic resistance. As we unearth more solid evidence on the link between CAFs and tumor progression, we gain insight into the role of CAFs in establishing resistance to cancer therapies. Herein, we review the origin, heterogeneity, and function of CAFs, with a focus on how CAF subsets can be used as biomarkers and can contribute to therapeutic resistance in cancer. We also depict current breakthroughs in targeting CAFs to overcome anticancer therapeutic resistance and discuss emerging CAF-targeting modalities.

Dynamic Cancer Cell Heterogeneity: Diagnostic and Therapeutic Implications.

Though heterogeneity of cancers is recognized and has been much discussed in recent years, the concept often remains overlooked in different routine examinations. Indeed, in clinical or biological articles, reviews, and textbooks, cancers and cancer cells are generally presented as evolving distinct entities rather than as an independent heterogeneous cooperative cell population with its self-oriented biology. There are, therefore, conceptual gaps which can mislead the interpretations/diagnostic and therapeutic approaches. In this short review, we wish to summarize and discuss various aspects of this dynamic evolving heterogeneity and its biological, pathological, clinical, diagnostic, and therapeutic implications, using thyroid carcinoma as an illustrative example.

Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma.

Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies. The source of dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas by spatially resolved transcriptomics, metabolomics, and proteomics. By deciphering regionally shared transcriptional programs across patients, we infer that glioblastoma is organized by spatial segregation of lineage states and adapts to inflammatory and/or metabolic stimuli, reminiscent of the reactive transformation in mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.

["Adaptation of the tumour and its ecosystem to radiotherapies: Mechanisms, imaging and therapeutic approaches" XIVth edition of the workshop organised by the "Vectorisation, Imagerie, Radiothérapies" network of the Cancéropôle Grand-Ouest, 22-25 September 2021, Le Bono, France]. / « Adaptation of the tumour and its ecosystem to radiotherapies: Mechanisms, imaging and therapeutic approaches ¼ XIVe édition du workshop organisé par le réseau « Vectorisation, Imagerie, Radiothérapies ¼ du Cancéropôle Grand-Ouest, 22­25 septembre 2021, Le Bono, France.

The fourteenth edition of the workshop covered the latest advances in internal and external radiotherapy obtained through a better understanding of the adaptive capacity of the tumor and its microenvironment, from different disciplinary angles, chemistry, biology, physics, and medicine, paving the way for numerous technological innovations. The biological aspects and the contribution of imaging in monitoring and understanding the adaptation of tumors to radiotherapy were presented, before focusing on innovative radiotherapy strategies and machine learning and data-driven techniques. Finally, the challenges were explored in the radiobiology of targeted radionuclide therapy as well as data science and machine learning in radiomics.

Long non-coding RNA and non-coding nucleic acids: Signaling players in the networks of the tumor ecosystem.

Recent findings have revealed that human genome encodes tens of thousands long noncoding RNAs (lncRNAs), which play essential roles in broad spectrum of cellular processes. Emerging evidence has uncovered a new archetype of lncRNAs which functions as key components of cell signaling pathways. In this review, we describe how lncRNAs interact with proteins to regulate cancer intracellular signaling and intercellular signaling in the tumor microenvironment (TME), which enable cancer cells to acquire malignant hallmarks. Moreover, besides lncRNAs, non-coding nucleic acids, such as neutrophil extracellular trap-DNA (NET-DNA), endogenous DNA and RNA, can act as signal molecules to connect cells from distant organs and trigger systemic responses in the macroenvironment of tumor-bearing hosts. Overall, the widely observed dysregulation of non-coding nucleic acids in cancer alters signaling networks in the tumor ecosystem, providing a rich resource for the identification of cancer biomarkers and therapeutic targets.

The theory of tumor ecosystem.

Cancer cells can be conceived as "living organisms" interacting with cellular or non-cellular components in the host internal environment, not only the local tumor microenvironment but also the distant organ niches, as well as the immune, nervous and endocrine systems, to construct a self-sustainable tumor ecosystem. With increasing evidence for the systemic tumor-host interplay, we predict that a new era of cancer therapy targeting the ecosystemic vulnerability of human malignancies has come. Revolving around the tumor ecosystem scoped as different hierarchies of primary, regional, distal and systemic onco-spheres, we comprehensively review the tumor-host interaction among cancer cells and their local microenvironment, distant organ niches, immune, nervous and endocrine systems, highlighting material and energy flow with tumor ecological homeostasis as an internal driving force. We also substantiate the knowledge of visualizing, modelling and subtyping this dynamically intertwined network with recent technological advances, and discuss ecologically rational strategies for more effective cancer therapies.