Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Single-cell transcriptomic analysis of immune cell dynamics in the healthy human endometrium

The microenvironment of the endometrial immune system is crucial to the success of placental implantation and healthy pregnancy. However, the functionalities of immune cells across various stages of the reproductive cycle have yet to be fully comprehended. To address this, we conducted advanced bioinformatic analysis on 230,049 high-quality single-cell transcriptomes from healthy endometrial samples obtained during the proliferative, secretory, early pregnancy, and late pregnancy stages. Our investigation has unveiled that proliferative natural killer (NK) cells, a potential source of endometrial NK cells, exhibit the most robust proliferative and differentiation potential during non-pregnant stages. We have also identified similar differentiation trajectories of NK cells originating from proliferative NK cells across four stages. Notably, during early pregnancy, NK cells demonstrate the highest oxidative phosphorylation metabolism activity, and, in conjunction with macrophages and T cells, exhibit the strongest type II interferon response. With spatial transcriptome data, we have discerned that the most robust immune-non-immune interactions are associated with the promotion and inhibition of cell proliferation, differentiation and migration during four stages. Furthermore, we have compiled lists of stage-specific risk genes implicated in reproductive diseases, which hold promise as potential disease biomarkers. Our study provides insights into the dynamics of the endometrial immune microenvironment during different reproductive cycle stages, thus serving as a reference for detecting pathological changes during pregnancy.

Epithelial-mesenchymal transition in tissue repair and degeneration

Epithelial-mesenchymal transitions (EMTs) are the epitome of cell plasticity in embryonic development and cancer; during EMT, epithelial cells undergo dramatic phenotypic changes and become able to migrate to form different tissues or give rise to metastases, respectively. The importance of EMTs in other contexts, such as tissue repair and fibrosis in the adult, has become increasingly recognized and studied. In this Review, we discuss the function of EMT in the adult after tissue damage and compare features of embryonic and adult EMT. Whereas sustained EMT leads to adult tissue degeneration, fibrosis and organ failure, its transient activation, which confers phenotypic and functional plasticity on somatic cells, promotes tissue repair after damage. Understanding the mechanisms and temporal regulation of different EMTs provides insight into how some tissues heal and has the potential to open new therapeutic avenues to promote repair or regeneration of tissue damage that is currently irreversible. We also discuss therapeutic strategies that modulate EMT that hold clinical promise in ameliorating fibrosis, and how precise EMT activation could be harnessed to enhance tissue repair.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. In this study, we investigated functional drivers of post-treatment recurrent GBM through integrative genomic analyses, genome-wide genetic perturbation screens in patient-derived GBM models and independent lines of validation. Specific genetic dependencies were found consistent across recurrent tumor models, accompanied by increased mutational burden and differential transcript and protein expression compared to its primary GBM predecessor. Our observations suggest a multi-layered genetic response to drive tumor recurrence and implicate PTP4A2 (protein tyrosine phosphatase 4A2) as a modulator of self-renewal, proliferation and tumorigenicity in recurrent GBM. Genetic perturbation or small-molecule inhibition of PTP4A2 acts through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and its downstream molecular players, exploiting a functional dependency on ROBO signaling. Because a pan-PTP4A inhibitor was limited by poor penetrance across the blood-brain barrier in vivo, we engineered a second-generation chimeric antigen receptor (CAR) T cell therapy against ROBO1, a cell surface receptor enriched across recurrent GBM specimens. A single dose of ROBO1-targeted CAR T cells doubled median survival in cell-line-derived xenograft (CDX) models of recurrent GBM. Moreover, in CDX models of adult lung-to-brain metastases and pediatric relapsed medulloblastoma, ROBO1 CAR T cells eradicated tumors in 50-100% of mice. Our study identifies a promising multi-targetable PTP4A-ROBO1 signaling axis that drives tumorigenicity in recurrent GBM, with potential in other malignant brain tumors.

Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts

BACKGROUND

Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.

METHODS

Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.

RESULTS

We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.

CONCLUSION

By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

KLHL21 suppresses gastric tumourigenesis via maintaining STAT3 signalling equilibrium in stomach homoeostasis

OBJECTIVE

Precancerous metaplasia transition to dysplasia poses a risk for subsequent intestinal-type gastric adenocarcinoma. However, the molecular basis underlying the transformation from metaplastic to cancerous cells remains poorly understood.

DESIGN

An integrated analysis of genes associated with metaplasia, dysplasia was conducted, verified and characterised in the gastric tissues of patients by single-cell RNA sequencing and immunostaining. Multiple mouse models, including homozygous conditional knockout Klhl21-floxed mice, were generated to investigate the role of Klhl21 deletion in stemness, DNA damage and tumour formation. Mass-spectrometry-based proteomics and ribosome sequencing were used to elucidate the underlying molecular mechanisms.

RESULTS

Kelch-like protein 21 (KLHL21) expression progressively decreased in metaplasia, dysplasia and cancer. Genetic deletion of Klhl21 enhances the rapid proliferation of Mist1+ cells and their descendant cells. Klhl21 loss during metaplasia facilitates the recruitment of damaged cells into the cell cycle via STAT3 signalling. Increased STAT3 activity was confirmed in cancer cells lacking KLHL21, boosting self-renewal and tumourigenicity. Mechanistically, the loss of KLHL21 promotes PIK3CB mRNA translation by stabilising the PABPC1-eIF4G complex, subsequently causing STAT3 activation. Pharmacological STAT3 inhibition by TTI-101 elicited anticancer effects, effectively impeding the transition from metaplasia to dysplasia. In patients with gastric cancer, low levels of KLHL21 had a shorter survival rate and a worse response to adjuvant chemotherapy.

CONCLUSIONS

Our findings highlighted that KLHL21 loss triggers STAT3 reactivation through PABPC1-mediated PIK3CB translational activation, and targeting STAT3 can reverse preneoplastic metaplasia in KLHL21-deficient stomachs.

Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer

Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.

Genomic and single-cell characterization of patient-derived tumor organoid models of head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) remains a significant health burden due to tumor heterogeneity and treatment resistance, emphasizing the need for improved biological understanding and tailored therapies. This study enrolled 31 HNSCC patients for the establishment of patient-derived tumor organoids (PDOs), which faithfully maintained genomic features and histopathological traits of primary tumors. Long-term culture preserved key characteristics, affirming PDOs as robust representative models. PDOs demonstrated predictive capability for cisplatin treatment responses, correlating ex vivo drug sensitivity with patient outcomes. Bulk and single-cell RNA sequencing unveiled molecular subtypes and intratumor heterogeneity (ITH) in PDOs, paralleling patient tumors. Notably, a hybrid epithelial-mesenchymal transition (hEMT)-like ITH program is associated with cisplatin resistance and poor patient survival. Functional analyses identified amphiregulin (AREG) as a potential regulator of the hybrid epithelial/mesenchymal state. Moreover, AREG contributes to cisplatin resistance via EGFR pathway activation, corroborated by clinical samples. In summary, HNSCC PDOs serve as reliable and versatile models, offer predictive insights into ITH programs and treatment responses, and uncover potential therapeutic targets for personalized medicine.

Merkel cell polyomavirus pan-T antigen knockdown reduces cancer cell stemness and promotes neural differentiation independent of RB1

Merkel cell carcinoma (MCC) is a highly aggressive skin cancer associated with integration of Merkel cell polyomavirus (MCPyV). MCPyV-encoded T-antigens (TAs) are pivotal for sustaining MCC's oncogenic phenotype, i.e., repression of TAs results in reactivation of the RB pathway and subsequent cell cycle arrest. However, the MCC cell line LoKe, characterized by a homozygous loss of the RB1 gene, exhibits uninterrupted cell cycle progression after shRNA-mediated TA repression. This unique feature allows an in-depth analysis of the effects of TAs beyond inhibition of the RB pathway, revealing the decrease in expression of stem cell-related genes upon panTA-knockdown. Analysis of gene regulatory networks identified members of the E2F family (E2F1, E2F8, TFDP1) as key transcriptional regulators that maintain stem cell properties in TA-expressing MCC cells. Furthermore, minichromosome maintenance (MCM) genes, which encodes DNA-binding licensing proteins essential for stem cell maintenance, were suppressed upon panTA-knockdown. The decline in stemness occurred simultaneously with neural differentiation, marked by the increased expression of neurogenesis-related genes such as neurexins, BTG2, and MYT1L. This upregulation can be attributed to heightened activity of PBX1 and BPTF, crucial regulators of neurogenesis pathways. The observations in LoKe were confirmed in an additional MCPyV-positive MCC cell line in which RB1 was silenced before panTA-knockdown. Moreover, spatially resolved transcriptomics demonstrated reduced TA expression in situ in a part of a MCC tumor characterized by neural differentiation. In summary, TAs are critical for maintaining stemness of MCC cells and suppressing neural differentiation, irrespective of their impact on the RB-signaling pathway.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

BACKGROUND

Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.

RESULTS

In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.

CONCLUSIONS

Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell-cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell-cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

Modulatory effects of cancer stem cell-derived extracellular vesicles on the tumor immune microenvironment

Cancer stem cells (CSCs), accounting for only a minor cell proportion (< 1%) within tumors, have profound implications in tumor initiation, metastasis, recurrence, and treatment resistance due to their inherent ability of self-renewal, multi-lineage differentiation, and tumor-initiating potential. In recent years, accumulating studies indicate that CSCs and tumor immune microenvironment act reciprocally in driving tumor progression and diminishing the efficacy of cancer therapies. Extracellular vesicles (EVs), pivotal mediators of intercellular communications, build indispensable biological connections between CSCs and immune cells. By transferring bioactive molecules, including proteins, nucleic acids, and lipids, EVs can exert mutual influence on both CSCs and immune cells. This interaction plays a significant role in reshaping the tumor immune microenvironment, creating conditions favorable for the sustenance and propagation of CSCs. Deciphering the intricate interplay between CSCs and immune cells would provide valuable insights into the mechanisms of CSCs being more susceptible to immune escape. This review will highlight the EV-mediated communications between CSCs and each immune cell lineage in the tumor microenvironment and explore potential therapeutic opportunities.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Microenvironment shapes small-cell lung cancer neuroendocrine states and presents therapeutic opportunities

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Dendritic Cells in Shaping Anti-Tumor T Cell Response

Among professional antigen-presenting cells, dendritic cells (DCs) orchestrate innate and adaptive immunity and play a pivotal role in anti-tumor immunity. DCs are a heterogeneous population with varying functions in the tumor microenvironment (TME). Tumor-associated DCs differentiate developmentally and functionally into three main subsets: conventional DCs (cDCs), plasmacytoid DCs (pDCs), and monocyte-derived DCs (MoDCs). There are two major subsets of cDCs in TME, cDC1 and cDC2. cDC1 is critical for cross-presenting tumor antigens to activate cytotoxic CD8+ T cells and is also required for priming earlier CD4+ T cells in certain solid tumors. cDC2 is vital for priming anti-tumor CD4+ T cells in multiple tumor models. pDC is a unique subset of DCs and produces type I IFN through TLR7 and TLR9. Studies have shown that pDCs are related to immunosuppression in the TME through the secretion of immunosuppressive cytokines and by promoting regulatory T cells. MoDCs differentiate separately from monocytes in response to inflammatory cues and infection. Also, MoDCs can cross-prime CD8+ T cells. In this review, we summarize the subsets and functions of DCs. We also discuss the role of different DC subsets in shaping T cell immunity in TME and targeting DCs for potential immunotherapeutic benefits against cancer.

Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease

Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

The tumor-intrinsic role of the m6A reader YTHDF2 in regulating immune evasion

Tumors evade attacks from the immune system through various mechanisms. Here, we identify a component of tumor immune evasion mediated by YTH domain-containing family protein 2 (YTHDF2), a reader protein that usually destabilizes m6A-modified mRNA. Loss of tumoral YTHDF2 inhibits tumor growth and prolongs survival in immunocompetent tumor models. Mechanistically, tumoral YTHDF2 deficiency promotes the recruitment of macrophages via CX3CL1 and enhances mitochondrial respiration of CD8+ T cells by impairing tumor glycolysis metabolism. Tumoral YTHDF2 deficiency promotes inflammatory macrophage polarization and antigen presentation in the presence of IFN-γ. In addition, IFN-γ induces autophagic degradation of tumoral YTHDF2, thereby sensitizing tumor cells to CD8+ T cell-mediated cytotoxicity. Last, we identified a small molecule compound that preferentially induces YTHDF2 degradation, which shows a potent antitumor effect alone but a better effect when combined with anti-PD-L1 or anti-PD-1 antibodies. Collectively, YTHDF2 appears to be a tumor-intrinsic regulator that orchestrates immune evasion, representing a promising target for enhancing cancer immunotherapy.

Long non-coding RNA MALAT1 in hematological malignancies and its clinical applications

ABSTRACT

Metastasis-associated lung adenocarcinoma transcript 1 ( MALAT1 ) is a well-established oncogenic long non-coding RNA, the higher expression of which is strongly correlated with cancer events such as tumorigenesis, progression, metastasis, drug resistance, and treatment outcome in solid cancers. Recently, a series of studies has highlighted its potential role in hematological malignancies in terms of these events. Similar to solid cancers, MALAT1 can regulate various target genes via sponging and epigenetic mechanisms, but the miRNAs sponged by MALAT1 differ from those identified in solid cancers. In this review, we systematically describe the role and underlying mechanisms of MALAT1 in multiple types of hematological malignancies, including regulation of cell proliferation, metastasis, stress response, and glycolysis. Clinically, MALAT1 expression is related to poor treatment outcome and drug resistance, therefore exhibiting potential prognostic value in multiple myeloma, lymphoma, and leukemia. Finally, we discuss the evaluation of MALAT1 as a novel therapeutic target against cancer in preclinical studies.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.

Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis

BACKGROUND

Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.

METHODS

ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.

RESULTS

Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.

CONCLUSIONS

Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions

T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.

Multiple RNA Rapid In Situ Imaging Based on Cas9 Code Key System

Existing RNA in situ imaging strategies mostly utilize parallel repetitive nucleic acid self-assembly to achieve multiple analysis, with limitations of complicated systems and cumbersome steps. Here, a Cas9 code key system with key probe (KP) encoder and CRISPR/Cas9 signal exporter is developed. This system triggers T-protospacer adjacent motif (T-PAM structural transitions of multiple KP encoders to form coding products with uniform single-guide RNA (sgRNA) target sequences as tandem nodes. Only single sgRNA/Cas9 complex is required to cleave multiple coding products, enabling efficient "many-to-one" tandem signaling, and non-collateral cleavage activity-dependent automatic signaling output through active introduction of mismatched bases. Compared with conventional parallel multiple signaling analysis model, the proposed system greatly simplifies reaction process and enhances detection efficiency. Further, a rapid multiple RNA in situ imaging system is developed by combining the Cas9 code key system with a T-strand displacement amplification (T-SDA) signal amplifier. The constructed system is applied to tumor cells and clinicopathology slices, generating clear multi-mRNA imaging profiles in less than an hour with just one step. Therefore, this work provides reliable technical support for clinical tumor typing and molecular mechanism investigation.

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Single-cell Landscape of Malignant Transition: Unraveling Cancer Cell-of-Origin and Heterogeneous Tissue Microenvironment

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients

Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.

Are we getting closer to a successful neoantigen cancer vaccine?

Although significant advances in immunotherapy have revolutionized the treatment of many cancer types over the past decade, the field of vaccine therapy, an important component of cancer immunotherapy, despite decades-long intense efforts, is still transmitting signals of promises and awaiting strong data on efficacy to proceed with regulatory approval. The field of cancer vaccines faces standard challenges, such as tumor-induced immunosuppression, immune response in inhibitory tumor microenvironment (TME), intratumor heterogeneity (ITH), permanently evolving cancer mutational landscape leading to neoantigens, and less known obstacles: neoantigen gain/loss upon immunotherapy, the timing and speed of appearance of neoantigens and responding T cell clonotypes and possible involvement of immune interference/heterologous immunity, in the complex interplay between evolving tumor epitopes and the immune system. In this review, we discuss some key issues related to challenges hampering the development of cancer vaccines, along with the current approaches focusing on neoantigens. We summarize currently well-known ideas/rationales, thus revealing the need for alternative vaccine approaches. Such a discussion should stimulate vaccine researchers to apply out-of-box, unconventional thinking in search of new avenues to deal with critical, often yet unaddressed challenges on the road to a new generation of therapeutics and vaccines.

Engineering Tumor Stroma Morphogenesis Using Dynamic Cell-Matrix Spheroid Assembly

The tumor microenvironment consists of resident tumor cells organized within a compositionally diverse, three-dimensional (3D) extracellular matrix (ECM) network that cannot be replicated in vitro using bottom-up synthesis. We report a new self-assembly system to engineer ECM-rich 3D MatriSpheres wherein tumor cells actively organize and concentrate microgram quantities of decellularized ECM dispersions which modulate cell phenotype. 3D colorectal cancer (CRC) MatriSpheres were created using decellularized small intestine submucosa (SIS) as an orthotopic ECM source that had greater proteomic homology to CRC tumor ECM than traditional ECM formulations such as Matrigel. SIS ECM was rapidly concentrated from its environment and assembled into ECM-rich 3D stroma-like regions by mouse and human CRC cell lines within 4-5 days via a mechanism that was rheologically distinct from bulk hydrogel formation. Both ECM organization and transcriptional regulation by 3D ECM cues affected programs of malignancy, lipid metabolism, and immunoregulation that corresponded with an in vivo MC38 tumor cell subpopulation identified via single cell RNA sequencing. This 3D modeling approach stimulates tumor specific tissue morphogenesis that incorporates the complexities of both cancer cell and ECM compartments in a scalable, spontaneous assembly process that may further facilitate precision medicine.

Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

Integrated proteogenomic characterization of glioblastoma evolution

The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.

Comparative analysis of single-cell transcriptome reveals heterogeneity and commonality in the immune microenvironment of colorectal cancer and inflammatory bowel disease

Background

During aging, chronic inflammation can promote tumor development and metastasis. Patients with chronic inflammatory bowel diseases (IBD) are at an increased risk of developing colorectal cancer (CRC). However, the molecular mechanism underlying is still unclear.

Methods

We conducted a large-scale single-cell sequencing analysis comprising 432,314 single cells from 92 CRC and 24 IBD patients. The analysis focused on the heterogeneity and commonality of CRC and IBD with respect to immune cell landscape, cellular communication, aging and inflammatory response, and Meta programs.

Results

The CRC and IBD had significantly different propensities in terms of cell proportions, differential genes and their functions, and cellular communication. The progression of CRC was mainly associated with epithelial cells, fibroblasts, and monocyte-macrophages, which displayed pronounced metabolic functions. In particular, monocyte-macrophages were enriched for the aging and inflammation-associated NF-κB pathway. And IBD was enriched in immune-related functions with B cells and T cells. Cellular communication analysis in CRC samples displayed an increase in MIF signaling from epithelial cells to T cells, and an increase in the efferent signal of senescence-associated SPP1 signaling from monocyte-macrophages. Notably, we also found some commonalities between CRC and IBD. The efferent and afferent signals showed that the pro-inflammatory cytokine played an important role. And the activity of aging and inflammatory response with AUCell analysis also showed a high degree of commonality. Furthermore, using the Meta programs (MPs) with the NMF algorithm, we found that the CRC non-malignant cells shared a substantial proportion of the MP genes with CRC malignant cells (68% overlap) and IBD epithelial cells (52% overlap), respectively. And it was extensively involved in functions of cell cycle and immune response, revealing its dual properties of inflammation and cancer. In addition, CRC malignant and non-malignant cells were enriched for the senescence-related cell cycle G2M phase transition and the p53 signaling pathway.

Conclusion

Our study highlights the characteristics of aging, inflammation and tumor in CRC and IBD at the single-cell level, and the dual property of inflammation-cancer in CRC non-malignant cells may provide a more up-to-date understanding of disease transformation.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Plastic persisters: revival stem cells in colorectal cancer

Colorectal cancer (CRC) is traditionally considered to be a genetically driven disease. However, nongenetic plasticity has recently emerged as a major driver of tumour initiation, metastasis, and therapy response in CRC. Central to these processes is a recently discovered cell type, the revival colonic stem cell (revCSC). In contrast to traditional proliferative CSCs (proCSCs), revCSCs prioritise survival over propagation. revCSCs play an essential role in primary tumour formation, metastatic dissemination, and nongenetic chemoresistance. Current evidence suggests that CRC tumours leverage intestinal stem cell plasticity to both proliferate (via proCSCs) when unchallenged and survive (via revCSCs) in response to cell-extrinsic pressures. Although revCSCs likely represent a major source of therapeutic failure in CRC, our increasing knowledge of this important stem cell fate provides novel opportunities for therapeutic intervention.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Key processes in tumor metastasis and therapeutic strategies with nanocarriers: a review

Cancer metastasis is the leading cause of cancer-related death. Metastasis occurs at all stages of tumor development, with unexplored changes occurring at the primary site and distant colonization sites. The growing understanding of the metastatic process of tumor cells has contributed to the emergence of better treatment options and strategies. This review summarizes a range of features related to tumor cell metastasis and nanobased drug delivery systems for inhibiting tumor metastasis. The mechanisms of tumor metastasis in the ideal order of metastatic progression were summarized. We focus on the prominent role of nanocarriers in the treatment of tumor metastasis, summarizing the latest applications of nanocarriers in combination with drugs to target important components and processes of tumor metastasis and providing ideas for more effective nanodrug delivery systems.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.

SCAR: Single-cell and Spatially-resolved Cancer Resources

Advances in sequencing and imaging technologies offer a unique opportunity to unravel cell heterogeneity and develop new immunotherapy strategies for cancer research. There is an urgent need for a resource that effectively integrates a vast amount of transcriptomic profiling data to comprehensively explore cancer tissue heterogeneity and the tumor microenvironment. In this context, we developed the Single-cell and Spatially-resolved Cancer Resources (SCAR) database, a combined tumor spatial and single-cell transcriptomic platform, which is freely accessible at http://8.142.154.29/SCAR2023 or http://scaratlas.com. SCAR contains spatial transcriptomic data from 21 tumor tissues and single-cell transcriptomic data from 11 301 352 cells encompassing 395 cancer subtypes and covering a wide variety of tissues, organoids, and cell lines. This resource offers diverse functional modules to address key cancer research questions at multiple levels, including the screening of tumor cell types, metabolic features, cell communication and gene expression patterns within the tumor microenvironment. Moreover, SCAR enables the analysis of biomarker expression patterns and cell developmental trajectories. SCAR also provides a comprehensive analysis of multi-dimensional datasets based on 34 state-of-the-art omics techniques, serving as an essential tool for in-depth mining and understanding of cell heterogeneity and spatial location. The implications of this resource extend to both cancer biology research and cancer immunotherapy development.

A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma

To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.

Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance

Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.

Solid tumor growth depends on an intricate equilibrium of malignant cell states

Control of cell identity and number is central to tissue function, yet principles governing organization of malignant cells in tumor tissues remain poorly understood. Using mathematical modeling and candidate-based analysis, we discover primary and metastatic pancreatic ductal adenocarcinoma (PDAC) organize in a stereotypic pattern whereby PDAC cells responding to WNT signals (WNT-R) neighbor WNT-secreting cancer cells (WNT-S). Leveraging lineage-tracing, we reveal the WNT-R state is transient and gives rise to the WNT-S state that is highly stable and committed to organizing malignant tissue. We further show that a subset of WNT-S cells expressing the Notch ligand DLL1 form a functional niche for WNT-R cells. Genetic inactivation of WNT secretion or Notch pathway components, or cytoablation of the WNT-S state disrupts PDAC tissue organization, suppressing tumor growth and metastasis. This work indicates PDAC growth depends on an intricately controlled equilibrium of functionally distinct cancer cell states, uncovering a fundamental principle governing solid tumor growth and revealing new opportunities for therapeutic intervention.