The Spatial and Genomic Hierarchy of Tumor Ecosystems Revealed by Single-Cell Technologies

Systematic analysis of lysine lactylation in nucleus pulposus cells

Nucleus pulposus (NP) resides in hypoxic microenvironment and NP cells (NPCs), primarily reply on glycolysis and producing high levels of lactate. Intracellular lactate drives lysine lactylation (Kla) as a newly epigenetic modification. However, the impact of Kla on NPCs remains unknown. Here, single-cell RNA sequencing (scRNA-seq) data suggested an altered balance between glycolysis and aerobic oxidation in intervertebral disc degeneration (IDD). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis displayed 3510 lactylation sites on 1052 non-histone proteins of NPCs isolated from rat cultured in normoxia and hypoxia. Moreover, there are 18 proteins with 129 Kla sites and 117 Kla sites in 27 proteins exclusively detected in normoxia and hypoxia group, respectively. Bioinformatics analysis displayed that these lactylated proteins are tightly related to ribosome, spliceosome and the VEGFA-VEGFA2 signaling pathway. Together, our study reveals that Kla may play an important role in regulating cellular metabolism of NPCs.

Integrated transcriptomics of human blood vessels defines a spatially controlled niche for early mesenchymal progenitor cells

Human blood vessel walls show concentric layers, with the outermost tunica adventitia harboring mesenchymal progenitor cells. These progenitor cells maintain vessel homeostasis and provide a robust cell source for cell-based therapies. However, human adventitial stem cell niche has not been studied in detail. Here, using spatial and single-cell transcriptomics, we characterized the phenotype, potential, and microanatomic distribution of human perivascular progenitors. Initially, spatial transcriptomics identified heterogeneity between perivascular layers of arteries and veins and delineated the tunica adventitia into inner and outer layers. From this spatial atlas, we inferred a hierarchy of mesenchymal progenitors dictated by a more primitive cell with a high surface expression of CD201 (PROCR). When isolated from humans and mice, CD201Low expression typified a mesodermal committed subset with higher osteogenesis and less proliferation than CD201High cells, with a downstream effect on canonical Wnt signaling through DACT2. CD201Low cells also displayed high translational potential for bone tissue generation.

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

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

Optimizing the design of spatial genomic studies

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies-one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor-our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

Exosome derived from tumor-associated macrophages: biogenesis, functions, and therapeutic implications in human cancers

Tumor-associated macrophages (TAMs), one of the most abundant immune cell types in the tumor microenvironment (TME), account for approximately 50% of the local hematopoietic cells. TAMs play an important role in tumorigenesis and tumor development through crosstalk between various immune cells and cytokines in the TME. Exosomes are small extracellular vesicles with a diameter of 50-150 nm, that can transfer biological information (e.g., proteins, nucleic acids, and lipids) from secretory cells to recipient cells through the circulatory system, thereby influencing the progression of various human diseases, including cancer. Recent studies have suggested that TAMs-derived exosomes play crucial roles in malignant cell proliferation, invasion, metastasis, angiogenesis, immune responses, drug resistance, and tumor metabolic reprogramming. TAMs-derived exosomes have the potential to be targeted for tumor therapy. In addition, the abnormal expression of non-coding RNAs and proteins in TAMs-derived exosomes is closely related to the clinicopathological features of patients with cancer, and these exosomes are expected to become new liquid biopsy markers for the early diagnosis, prognosis, and monitoring of tumors. In this review, we explored the role of TAMs-derived exosomes in tumorigenesis to provide new diagnostic biomarkers and therapeutic targets for cancer prevention.

Mapping the topography of spatial gene expression with interpretable deep learning

Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.

Multiplex imaging in immuno-oncology

Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.

It is better to light a candle than to curse the darkness: single-cell transcriptomics sheds new light on pancreas biology and disease

Recent advances in single-cell RNA sequencing and bioinformatics have drastically increased our ability to interrogate the cellular composition of traditionally difficult to study organs, such as the pancreas. With the advent of these technologies and approaches, the field has grown, in just a few years, from profiling pancreas disease states to identifying molecular mechanisms of therapy resistance in pancreatic ductal adenocarcinoma, a particularly deadly cancer. Single-cell transcriptomics and related spatial approaches have identified previously undescribed epithelial and stromal cell types and states, how these populations change with disease progression, and potential mechanisms of action which will serve as the basis for designing new therapeutic strategies. Here, we review the recent literature on how single-cell transcriptomic approaches have changed our understanding of pancreas biology and disease progression.

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

Optimizing the design of spatial genomic studies

Spatially-resolved genomic technologies have shown promise for studying the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. Thus, when performing spatial genomics experiments using multiple tissue slices, there is a need to select the tissue cross sections that will be maximally informative for the purposes of the experiment. In this work, we formalize the problem of experimental design for spatial genomics experiments, which we generalize into a problem class that we call structured batch experimental design. We propose approaches for optimizing these designs in two types of spatial genomics studies: one in which the goal is to construct a spatially-resolved genomic atlas of a tissue and another in which the goal is to localize a region of interest in a tissue, such as a tumor. We demonstrate the utility of these optimal designs, where each slice is a two-dimensional plane, on several spatial genomics datasets.

Oncofetal reprogramming in tumour development and progression

Embryonic development is characterized by rapidly dividing cells, cellular plasticity and a highly vascular microenvironment. These features are similar to those of tumour tissue, in that malignant cells are characterized by their ability to proliferate and exhibit cellular plasticity. The tumour microenvironment also often includes immunosuppressive features. Reciprocal communication between various cellular subpopulations enables fetal and tumour tissues to proliferate, migrate and escape immune responses. Fetal-like reprogramming has been demonstrated in the tumour microenvironment, indicating extraordinary cellular plasticity and bringing an additional layer of cellular heterogeneity. More importantly, some of these features are also present during inflammation. This Perspective discusses the similarity between embryogenesis, inflammation and tumorigenesis, and describes the mechanisms of oncofetal reprogramming that enable tumour cells to escape from immune responses, promoting tumour growth and metastasis.

Lactate metabolism in human health and disease

The current understanding of lactate extends from its origins as a byproduct of glycolysis to its role in tumor metabolism, as identified by studies on the Warburg effect. The lactate shuttle hypothesis suggests that lactate plays an important role as a bridging signaling molecule that coordinates signaling among different cells, organs and tissues. Lactylation is a posttranslational modification initially reported by Professor Yingming Zhao’s research group in 2019. Subsequent studies confirmed that lactylation is a vital component of lactate function and is involved in tumor proliferation, neural excitation, inflammation and other biological processes. An indispensable substance for various physiological cellular functions, lactate plays a regulatory role in different aspects of energy metabolism and signal transduction. Therefore, a comprehensive review and summary of lactate is presented to clarify the role of lactate in disease and to provide a reference and direction for future research. This review offers a systematic overview of lactate homeostasis and its roles in physiological and pathological processes, as well as a comprehensive overview of the effects of lactylation in various diseases, particularly inflammation and cancer.

Prognostic/predictive markers in systemic therapy resistance and metastasis in breast cancer

Breast cancer is a highly heterogeneous group of diseases posing a significant challenge in biomarker-driven research and the development of effective targeted therapies. Especially the treatment of metastatic breast cancer poses even more challenges, as we still lose more than 42,000 women and men each year in the United States alone. New biological insight helps to improve breast cancer treatment through early detection, adaptation to chemotherapy resistance, and tailoring to find the right size of care. This review focuses on existing and new areas of predictive biomarkers under development to tailor the management of breast cancer and the application of integrative approaches that have resulted in the promising candidate biomarker discovery. Furthermore, we review new methods to detect metastatic progression using imaging, and blood-based assays. We hope to increase the attention and awareness of a new generation of therapeutic development strategies in metastatic breast cancer.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Tyramide-conjugated DNA barcodes enable signal amplification for multiparametric CODEX imaging

Multiparametric imaging allows researchers to measure the expression of many biomarkers simultaneously, allowing detailed characterization of cell microenvironments. One such technique, CODEX, allows fluorescence imaging of >30 proteins in a single tissue section. In the commercial CODEX system, primary antibodies are conjugated to DNA barcodes. This modification can result in antibody dysfunction, and development of a custom antibody panel can be very costly and time consuming as trial and error of modified antibodies proceeds. To address these challenges, we developed novel tyramide-conjugated DNA barcodes that can be used with primary antibodies via peroxidase-conjugated secondary antibodies. This approach results in signal amplification and imaging without the need to conjugate primary antibodies. When combined with commercially available barcode-conjugated primary antibodies, we can very quickly develop working antibody panels. We also present methods to perform antibody staining using a commercially available automated tissue stainer and in situ hybridization imaging on a CODEX platform. Future work will include application of the combined tyramide-based and regular CODEX approach to image specific tumors with their immune cell infiltrates, including biomarkers that are currently difficult to image by regular CODEX.

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

Background

High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.

Methods

Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival.

Conclusions

Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintaining specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory networks and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field.

Museum of spatial transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

Computational elucidation of spatial gene expression variation from spatially resolved transcriptomics data

Recent advances in spatially resolved transcriptomics (SRT) have revolutionized biological and medical research and enabled unprecedented insight into the functional organization and cell communication of tissues and organs in situ. Identifying and elucidating gene spatial expression variation (SE analysis) is fundamental to elucidate the SRT landscape. There is an urgent need for public repositories and computational techniques of SRT data in SE analysis alongside technological breakthroughs and large-scale data generation. Increasing efforts to use in silico techniques in SE analysis have been made. However, these attempts are widely scattered among a large number of studies that are not easily accessible or comprehensible by both medical and life scientists. This study provides a survey and a summary of public resources on SE analysis in SRT studies. An updated systematic overview of state-of-the-art computational approaches and tools currently available in SE analysis are presented herein, emphasizing recent advances. Finally, the present study explores the future perspectives and challenges of in silico techniques in SE analysis. This study guides medical and life scientists to look for dedicated resources and more competent tools for characterizing spatial patterns of gene expression.

Spatial components of molecular tissue biology

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.

Spatial omics: Navigating to the golden era of cancer research

The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high-throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi-modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

Cellular-Resolution Imaging of Bystander Payload Tissue Penetration from Antibody-Drug Conjugates

After several notable clinical failures in early generations, antibody-drug conjugates (ADCs) have made significant gains with seven new FDA-approvals within the last 3 years. These successes have been driven by a shift towards mechanistically informed ADC design, where the payload, linker, drug-to-antibody ratio, and conjugation are increasingly tailored to a specific target and clinical indication. However, fundamental aspects needed for design, such as payload distribution, remain incompletely understood. Payloads are often classified as 'bystander' or 'non-bystander' depending on their ability to diffuse out of targeted cells into adjacent cells that may be antigen negative or more distant from tumor vessels, helping to overcome heterogeneous distribution. Seven of the eleven FDA-approved ADCs employ these bystander payloads, but the depth of penetration and cytotoxic effects as a function of physicochemical properties and mechanism of action have not been fully characterized. Here, we utilized tumor spheroids and pharmacodynamic marker staining to quantify tissue penetration of the three major classes of agents: microtubule inhibitors, DNA-damaging agents, and topoisomerase inhibitors. PAMPA data and co-culture assays were performed to compare to the 3D tissue culture data. The results demonstrate a spectrum in bystander potential and tissue penetration depending on the physicochemical properties and potency of the payload. Generally, directly targeted cells show a greater response even with bystander payloads, consistent with the benefit of deeper ADC penetration. These results are compared to computational simulations to help scale the data from in vitro and preclinical animal models to the clinic.

Key promoters of tumor hallmarks

Evolution of tumor hallmarks is a result of accommodation of tumor cells with their nearby milieu called tumor microenvironment (TME). Accommodation or adaptive responses is highly important for a cell to survive, without which no cell is allowed to take any further steps in tumorigenesis. Metabolism of cancer cells is largely depended on stroma. Composition and plasticity of cells within the stroma is highly affected from inflammatory setting of TME. Hypoxia which is a common event in many solid cancers, is known as one of the key hallmarks of chronic inflammation and the master regulator of metastasis. Transforming growth factor (TGF)-β is produced in the chronic inflammatory and chronic hypoxic settings, and it is considered as a cardinal factor for induction of all tumor hallmarks. Aging, obesity and smoking are the main predisposing factors of cancer, acting mainly through modulation of TME.

Atlas of clinically distinct cell states and ecosystems across human solid tumors

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

In situ functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and non-cancer cell populations. Microdomain-specific network signaling coevolves with a continuum of cell states and functional plasticity associated with disease progression and therapeutic responses. We present LEAPH, an unsupervised machine learning algorithm for identifying cell phenotypes, which applies recursive steps of probabilistic clustering and spatial regularization to derive functional phenotypes (FPs) along a continuum. Combining LEAPH with pointwise mutual information and network biology analyses enables the discovery of outcome-associated microdomains visualized as distinct spatial configurations of heterogeneous FPs. Utilization of an immunofluorescence-based (51 biomarkers) image dataset of colorectal carcinoma primary tumors (n = 213) revealed microdomain-specific network dysregulation supporting cancer stem cell maintenance and immunosuppression that associated selectively with the recurrence phenotype. LEAPH enables an explainable artificial intelligence platform providing insights into pathophysiological mechanisms and novel drug targets to inform personalized therapeutic strategies.

Single-Cell Analysis Reveals Spatial Heterogeneity of Immune Cells in Lung Adenocarcinoma

The impacts of the tumor microenvironment (TME) on tumor evolvability remain unclear. A challenge for nearly all cancer types is spatial heterogeneity, providing substrates for the emergence and evolvability of drug resistance and leading to unfavorable prognosis. Understanding TME heterogeneity among different tumor sites would provide deeper insights into personalized therapy. We found 9,992 cell profiles of the TME in human lung adenocarcinoma (LUAD) samples at a single-cell resolution. By comparing different tumor sites, we discovered high TME heterogeneity. Single-sample gene set enrichment analysis (ssGSEA) was utilized to explore functional differences between cell subpopulations and between the core, middle and edge of tumors. We identified 8 main cell types and 27 cell subtypes of T cells, B cells, fibroblasts and myeloid cells. We revealed CD4+ naive T cells in the tumor core that express high levels of immune checkpoint molecules and have a higher activity of immune-exhaustion signaling. CD8+ T cell subpopulations in the tumor core correlate with the upregulated activity of transforming growth factor-β (TGF-β) and fibroblast growth factor receptor (FGFR) signaling and downregulated T cell activity. B cell subtypes in the tumor core downregulate cytokine production. In this study, we revealed that there was immunological heterogeneity in the TME of patients with LUAD that have different ratios of immune cells and stromal cells, different functions, and various degrees of activation of immune-related pathways in different tumor parts. Therefore, clarifying the spatial heterogeneity of the tumor in the immune microenvironment can help clinicians design personalized treatments.

Therapeutic Synergy in Esophageal Cancer and Mesothelioma Is Predicted by Dynamic BH3 Profiling

Approximately 20,000 patients per year are diagnosed with esophageal adenocarcinoma (EAC) and malignant pleural mesothelioma (MPM); fewer than 20% survive 5 years. Effective therapeutic strategies are limited although patients receive a combination of chemotherapeutics. These tumors harbor thousands of mutations that contribute to tumor development. Downstream of oncogenic driving mutations, altered tumor mitochondria promote resistance to apoptosis. Dynamic Bcl-2 homology-3 profiling (DBP) is a functional assay of live cells that identifies the mitochondrial proteins responsible for resistance to apoptosis. We hypothesized that DBP will predict which protein to target to overcome resistance thereby enhancing combinatorial therapy.DBP predicted that targeting either Mcl-1 or Bcl-xL increases the efficacy of the chemotherapeutic agent, cisplatin, whereas targeting Bcl-2 does not. We performed these assays by treating EAC and MPM cells with a combination of Bcl-2 homology-3 (BH3) mimetics and cisplatin. Following treatments, we performed efficacy assessments including apoptosis assays, IC50 calculations, and generation of a combinatorial index. DBP confirmed that targeting mitochondria with BH3 mimetics alters the threshold of apoptosis. These apoptotic effects were abolished when the mitochondrial pathway was disrupted. We validated our findings by developing knockdown models of antiapoptotic proteins Mcl-1, Bcl-xL, and the mitochondrial effector proteins Bax/Bak. Knockdown of Mcl-1 or Bcl-xL recapitulated the results of BH3 mimetics. In addition, we report an approach for BH3 profiling directly from patient tumor samples. We demonstrate that the DBP assay on living tumor cells measures the dynamic changes of resistance mechanisms, assesses response to combinatorial therapy, and provides results in a clinically feasible time frame.

Exploring tissue architecture using spatial transcriptomics

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug

Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells’ growth in a population influences their susceptibility to anticancer drugs.

The updated landscape of tumor microenvironment and drug repurposing

Accumulating evidence shows that cellular and acellular components in tumor microenvironment (TME) can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Cancer research and treatment have switched from a cancer-centric model to a TME-centric one, considering the increasing significance of TME in cancer biology. Nonetheless, the clinical efficacy of therapeutic strategies targeting TME, especially the specific cells or pathways of TME, remains unsatisfactory. Classifying the chemopathological characteristics of TME and crosstalk among one another can greatly benefit further studies exploring effective treating methods. Herein, we present an updated image of TME with emphasis on hypoxic niche, immune microenvironment, metabolism microenvironment, acidic niche, innervated niche, and mechanical microenvironment. We then summarize conventional drugs including aspirin, celecoxib, β-adrenergic antagonist, metformin, and statin in new antitumor application. These drugs are considered as viable candidates for combination therapy due to their antitumor activity and extensive use in clinical practice. We also provide our outlook on directions and potential applications of TME theory. This review depicts a comprehensive and vivid landscape of TME from biology to treatment.

Nanotherapeutics for Antimetastatic Treatment

Tumor metastases, that is, the development of secondary tumors in organs distant from the primary tumor, and their treatment remain a serious problem in cancer therapy. The unique challenges for tracking and treating tumor metastases lie in the small size, high heterogeneity, and wide dispersion to distant organs of metastases. Recently, nanomedicines, with the capacity to precisely deliver therapeutic agents to both primary and secondary tumors, have demonstrated many potential benefits for metastatic cancer theranostics. Given the remarkable progression in emerging nanotherapeutics for antimetastatic treatment, it is timely to summarize the latest advances in this field. This review highlights the rationale, advantages, and challenges for integrating biomedical nanotechnology with cancer biology to develop antimetastatic nanotherapeutics.

CRISPR screen in mechanism and target discovery for cancer immunotherapy

CRISPR/Cas-based genetic perturbation screens have emerged as powerful tools for large-scale identification of new targets for cancer immunotherapy. Various strategies of CRISPR screen have been used for immune-oncology (IO) target discovery. The genomic sequences targeted by CRISPR/Cas system range from coding sequences to non-coding RNA/DNA, including miRNAs, LncRNAs, circRNAs, promoters, and enhancers, which may be potential targets for future pharmacological and therapeutic interventions. Rapid progresses have been witnessed in finding novel targets for enhancing tumor antigen presentation, sensitizing of tumor cells to immune-mediated cytotoxicity, and reinvigorating tumor-specific T cells by using CRISPR technologies. In combination with other strategies, the detailed characteristics of the targets for immunotherapy have been obtained by CRISPR screen. In this review, we present an overview of recent progresses in the development of CRISPR-based screens for IO target identification and discuss the challenges and possible solutions in this rapidly growing field.