Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Tumor-stromal metabolic crosstalk in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a dire prognosis. Standard-of-care chemotherapy regimens offer marginal survival benefit and carry risk of severe toxicity, while immunotherapy approaches have uniformly failed in clinical trials. Extensive desmoplasia in the PDAC tumor microenvironment (TME) disrupts blood flow to and from the tumor, thereby creating a nutrient-depleted, hypoxic, and acidic milieu that suppresses the function of antitumor immune cells and imparts chemotherapy resistance. Additionally, recent seminal studies have demonstrated crucial roles for metabolic crosstalk - the exchange of metabolites between PDAC cells and stromal cell populations in the TME - in establishing and maintaining core malignant behaviors of PDAC: tumor growth, metastasis, immune evasion, and therapy resistance. In this review, we provide a conceptual overview of metabolic crosstalk and how it evolves under various selection pressures in the TME, analyze the landscape of proposed tumorigenic metabolic crosstalk pathways, and highlight potentially druggable nodes.

Innate Immunity and Platelets: Unveiling Their Role in Chronic Pancreatitis and Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal forms of cancer, characterized by a highly desmoplastic tumor microenvironment. One main risk factor is chronic pancreatitis (CP). Progression of CP to PDAC is greatly influenced by persistent inflammation promoting genomic instability, acinar-ductal metaplasia, and pancreatic intraepithelial neoplasia (PanIN) formation. Components of the extracellular matrix, including immune cells, can modulate this progression phase. This includes cells of the innate immune system, such as natural killer (NK) cells, macrophages, dendritic cells, mast cells, neutrophils, and myeloid-derived suppressor cells (MDSCs), either promoting or inhibiting tumor growth. On one hand, innate immune cells can trigger inflammatory responses that support tumor progression by releasing cytokines and growth factors, fostering tumor cell proliferation, invasion, and metastasis. On the other hand, they can also activate immune surveillance mechanisms, which can limit tumor development. For example, NK cells are cytotoxic innate lymphoid cells that are able to kill tumor cells, and active dendritic cells are crucial for a functioning anti-tumor immune response. In contrast, mast cells and MDSCs rather support a pro-tumorigenic tumor microenvironment that is additionally sustained by platelets. Once thought to play a role in hemostasis only, platelets are now recognized as key players in inflammation and cancer progression. By releasing cytokines, growth factors, and pro-angiogenic mediators, platelets help shape an immunosuppressive microenvironment that promotes fibrotic remodeling, tumor initiation, progression, metastasis, and immune evasion. Neutrophils and macrophages exist in different functional subtypes that can both act pro- and anti-tumorigenic. Understanding the complex interactions between innate immune cells, platelets, and early precursor lesions, as well as PDAC cells, is crucial for developing new therapeutic approaches that can harness the immune and potentially also the coagulation system to target and eliminate tumors, offering hope for improved patient outcomes.

Molecular analysis highlights TREM2 as a discriminating biomarker for patients suffering from pancreatic ductal adenocarcinoma

Pancreatic cancer is projected to become the second leading cause of cancer-related deaths by 2030, with its mortality continuing to rise, unlike other common cancers such as breast or colorectal. Late-stage diagnosis, often accompanied by metastatic dissemination, drastically impairs patient survival and underscores the urgent need for improved biomarkers to guide therapeutic strategies. While molecular signatures have been proposed to stratify pancreatic cancer patients, their ability to predict outcomes remains limited. In this study, we applied established molecular signatures to our in-house transcriptomic data from a cohort of pancreatic cancer patients. We took advantage of published datasets to construct comprehensive atlases of cells present in primary and metastatic pancreatic cancers. The atlas of metastasis samples, representative of routinely harvested patient biopsies, revealed that monocyte/macrophage signatures provided superior discriminatory power compared to existing molecular classifications. Notably, the abundance of TREM2-expressing macrophages emerged as a significant parameter for stratifying patients. Our findings position TREM2+ macrophages as a promising biomarker for pancreatic cancer, with potential to enhance patient stratification and inform the development of targeted therapies. This work highlights the critical role of tumor-associated macrophages in pancreatic cancer progression and lays the groundwork for further functional and translational studies.

All-at-once spatial proteome profiling of complex tissue context with single-cell-type resolution by proximity proteomics

Spatial proteomics enables in-depth mapping of tissue architectures, mostly achieved by laser microdissection-mass spectrometry (LMD-MS) and antibody-based imaging. However, trade-offs among sampling precision, throughput, and proteome coverage still limit the applicability of these strategies. Here, we propose proximity labeling for spatial proteomics (PSPro) by combining precise antibody-targeted biotinylation and efficient affinity purification for all-at-once cell-type proteome capture with sub-micrometer resolution from single tissue slice. With fine-tuned labeling parameters, PSPro shows reliable performance in benchmarking against flow cytometry- and LMD-based proteomic workflows. We apply PSPro to tumor and spleen slices, enriching thousands of proteins containing known markers from ten cell types. We further incorporate LMD into PSPro to facilitate comparison of cell subpopulations from the same tissue slice, revealing spatial proteome heterogeneity of cancer cells and immune cells in pancreatic tumor. Collectively, PSPro converts the traditional "antibody-epitope" paradigm to an "antibody-cell-type proteome" for spatial biology in a user-friendly manner. A record of this paper's transparent peer review process is included in the supplemental information.

Stromal Cells in Early Inflammation-Related Pancreatic Carcinogenesis-Biology and Its Potential Role in Therapeutic Targeting

The stroma of healthy pancreases contains various non-hematopoietic, non-endothelial mesenchymal cells. It is altered by chronic inflammation which in turn is a major contributor to the development of pancreatic adenocarcinoma (PDAC). In PDAC, the stroma plays a decisive and well-investigated role for tumor progression and therapy response. This review addresses the central role of stromal cells in the early inflammation-driven development of PDAC. It focuses on major subpopulations of pancreatic mesenchymal cells, i.e., fibroblasts, pancreatic stellate cells, and multipotent stroma cells, particularly their activation and functional alterations upon chronic inflammation including the development of different types of carcinoma-associated fibroblasts. In the second part, the current knowledge on the impact of activated stroma cells on acinar-to-ductal metaplasia and the transition to pancreatic intraepithelial neoplasia is summarized. Finally, putative strategies to target stroma cells and their signaling in early pancreatic carcinogenesis are reflected. In summary, the current data show that the activation of pancreatic stroma cells and the resulting fibrotic changes has pro- and anti-carcinogenetic effects but, overall, creates a carcinogenesis-promoting microenvironment. However, this is a dynamic process and the therapeutic targeting of specific pathways and cells requires in-depth knowledge of the molecular interplay of various cell types.

Emerging clinical applications of single-cell RNA sequencing in oncology

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of complex tissues both in health and in disease. Over the past decade, scRNA-seq has been applied to tumour samples obtained from patients with cancer in hundreds of studies, thereby advancing the view that each tumour is a complex ecosystem and uncovering the diverse states of both cancer cells and the tumour microenvironment. Such studies have primarily investigated and provided insights into the basic biology of cancer, although considerable research interest exists in leveraging these findings towards clinical applications. In this Review, we summarize the available data from scRNA-seq studies investigating samples from patients with cancer with a particular focus on findings that are of potential clinical relevance. We highlight four main research objectives of scRNA-seq studies and describe some of the most relevant findings towards such goals. We also describe the limitations of scRNA-seq, as well as future approaches in this field that are anticipated to further advance clinical applicability.

Macrophages and fibroblasts as regulators of the immune response in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the few cancers that has yet to benefit from immunotherapies. This is primarily a result of its characteristic 'cold' tumor microenvironment composed of cancer-associated fibroblasts (CAFs), a dense network of extracellular matrix and several immune cell types, the most abundant of which are the tumor-associated macrophages (TAMs). Advances in single-cell and spatial technologies have elucidated the vast functional heterogeneity of CAFs and TAMs, their symbiotic relationship and their cooperative role in the tumor microenvironment. In this Review, we provide an overview of the heterogeneity of CAFs and TAMs, how they establish an immunosuppressive microenvironment and their collaboration in the remodeling of the extracellular matrix. Finally, we examine why the impact of immunotherapy in PDAC has been limited and how a detailed molecular and spatial understanding of the combined role of CAFs and TAMs is paramount to the design of effective therapies.

CD44+ cells enhance pro-tumor stroma in the spatial landscape of colorectal cancer leading edge

BACKGROUND

The heterogeneity of tumors significantly impacts on colorectal cancer (CRC) progression. However, the influence of this heterogeneity on the spatial architecture of CRC remains largely unknown.

METHODS

Spatial transcriptomic (ST) analysis of AOM/DSS-induced colorectal cancer (CRC), integrated with single-cell RNA sequencing, generated a comprehensive spatial atlas of CRC. Pseudotime trajectory, stemness evaluation, and cell-cell communication analyses explored how CD44+ tumor cells at the leading edge remodel the tumor microenvironment (TME). In vitro experiments and immunofluorescence staining of clinical samples validated pleiotrophin (PTN) signaling in promoting cancer-associated fibroblasts (CAFs) phenotypic transition and CRC progression.

RESULTS

Our findings revealed a distinctive layered ring-like structure within CRC tissues, where CD44+ tumor cells exhibiting high stemness were positioned at the tumor's leading edge. Inflammatory CAFs (iCAFs)-like, myofibroblastic CAFs (myCAFs)-like cells and pro-tumorigenic neutrophils primarily located at the tumor edge, in proximity to CD44+ tumor cells. CD44+ tumor cells then triggered the phenotypic transition of CAFs into iCAF-like and myCAF-like cells through PTN signaling.

CONCLUSIONS

Our results provide distinctive insights into how tumor heterogeneity reshapes the TME at the leading edge of tumor, thereby promoting CRC progression.

The scRNA-sequencing landscape of pancreatic ductal adenocarcinoma revealed distinct cell populations associated with tumor initiation and progression

Pancreatic ductal adenocarcinoma (PDAC) stands as a formidable malignancy characterized by its profound lethality. The comprehensive analysis of the transcriptional landscape holds immense significance in understanding PDAC development and exploring novel treatment strategies. However, due to the firm consistency of pancreatic cancer samples, the dissociation of single cells and subsequent sequencing can be challenging. Here, we performed single-cell RNA sequencing (scRNA-seq) on 8 PDAC patients with different lymph node metastasis status. We first identified the crucial role of MMP1 in the transition from normal pancreatic cells to cancer cells. The knockdown of MMP1 in pancreatic cancer cell lines decreased the expression of ductal markers such as SOX9 while the overexpression of MMP1 in hTERT-HPNE increased the expression of ductal markers, suggesting its function of maintaining ductal identity. Secondly, we found a S100A2 + tumor subset which fueled lymph node metastasis in PDAC. The knockdown of S100A2 significantly reduced the motility of pancreatic cancer cell lines in both wound healing and transwell migration assays. While overexpression of S100A2 led to increased migratory capability. Moreover, overexpression of S100A2 in KPC1199, a mouse pancreatic cancer cell line, caused a larger tumor burden in a hemi-spleen injection model of liver metastasis. In addition, epithelial-mesenchymal transition-related genes were decreased by S100A2 knockdown revealed by bulk RNA sequencing. We also identified several pivotal contributors to the pro-tumor microenvironment, notably OMD + fibroblast and CCL2 + macrophage. As a result, our study provides valuable insights for early detection of PDAC and promising therapeutic targets for combatting lymph node metastasis.

scRNA-seq reveals chemotherapy-induced tumor microenvironment changes in pancreatic ductal adenocarcinoma

Background

Challenges have arisen in finding an effective treatment for pancreatic ductal adenocarcinoma (PDAC). Poor outcomes have fueled ongoing efforts to exploit the tumor microenvironment (TME) in the treatment of PDAC; however, to date, treatment strategies have largely failed. Thus, a comprehensive and deep understanding of the PDAC TME is necessary. The purpose of the present study was to investigate chemotherapy-induced tumor microenvironment changes and to optimize the response to immunotherapy in PDAC.

Methods

We analyzed publicly available single-cell RNA sequencing (scRNA-seq) PDAC (with or without standard chemotherapy) data and performed systematic analyses to elucidate novel mechanisms and to determinate the marker expression alteration induced by the chemotherapy.

Results

Lysozyme (LYZ), which is usually used as a myeloid marker, was significantly increased in the tumor cells, including myeloid cells responding to chemotherapy. Additionally, chemotherapy altered the mechanism of antigen presentation and modulated the TME, which may lead to tumor drug resistance and recurrence. Chemotherapy also affected the receptor of polio virus receptor (PVR) signaling, which shifted from TIGIT in T cells to CD226 in myeloid cells. Thus, PVR (rather than TIGIT) should be the target for treatment.

Conclusions

We described the characteristics of widely expressed markers in different cell types in PDAC. Chemotherapy disrupted the TME balance, altered tumor antigen presentation, and changed PVR signaling. Combining the appropriate chemical and immune therapies could improve the immune therapy response in PDAC.

Decipher the single-cell level responses to chemotherapy in pancreatic ductal adenocarcinoma by a cross-time context graph model

Gemcitabine is commonly used for pancreatic ductal adenocarcinoma (PDAC), one of the most lethal cancer types. However, the drug resistance is a critical challenge for improving the PDAC chemotherapy. Here, we applied single-cell RNA sequencing (scRNA-seq) on PDAC patient-derived xenograft (PDX) models to study the complex cellular responses related to the gemcitabine resistances. To reconstruct dynamic tumor cell responses from these static scRNA-seq snapshots, we proposed scConGraph, a scalable bi-layer graph model that can efficiently integrate cross-time context information. Based on scConGraph, we observed that stemness and endoplasmic reticulum stress contribute to intrinsic resistance. As for acquired resistance, cancer cells may resist or evade gemcitabine treatment by activating the cell cycle, entering quiescence, or inducing epithelial-mesenchymal transition. Notably, GDF15 exhibited recurrent and significant upregulations among acquired-resistance cell subpopulations. Experimental validation confirmed that inhibiting GDF15 sensitizes tumor cells to gemcitabine, suggesting a potential target for gemcitabine-induced chemoresistance.

Dissection of tumoral niches using spatial transcriptomics and deep learning

This study introduces TG-ME, an innovative computational framework that integrates transformer with graph variational autoencoder (GraphVAE) models for dissection of tumoral niches using spatial transcriptomics data and morphological images. TG-ME effectively identifies and characterizes niches in bench datasets and a high resolution NSCLC dataset. The pipeline consists in different stages that include normalization, spatial information integration, morphological feature extraction, gene expression quantification, single cell expression characterization, and tumor niche characterization. For this, TG-ME leverages advanced deep learning techniques that achieve robust clustering and profiling of niches across cancer stages. TG-ME can potentially provide insights into the spatial organization of tumor microenvironments (TME), highlighting specific niche compositions and their molecular changes along cancer progression. TG-ME is a promising tool for guiding personalized treatment strategies by uncovering microenvironmental signatures associated with disease prognosis and therapeutic outcomes.

Spatially Resolved Tumor Ecosystems and Cell States in Gastric Adenocarcinoma Progression and Evolution

SIGNIFICANCE

Integration of spatial transcriptomic (GeoMx Digital Spatial Profiler) and single-cell RNA sequencing data from multiple gastric cancers identifies spatially resolved expression-based intratumoral heterogeneity, associated with distinct immune microenvironments. We uncovered two separate evolutionary trajectories associated with specific molecular subtypes, clinical prognoses, stromal neighborhoods, and genetic drivers. Tumor-stroma interfaces emerged as a unique state of tumor ecology.

Resilience in adversity: Exploring adaptive changes in cancer cells under stress

OBJECTIVE

Cancer cells undergo adaptive processes that favor their survival and proliferation when subjected to different types of cellular stress. These changes are linked to oncogenic processes such as genetic instability, tumor proliferation, therapy resistance, and invasion. Therefore, this study aimed to review studies that discuss possible morphological and genetic changes acquired by neoplastic cells under stressful conditions.

METHODS

The articles used in this integrative review were searched on PubMed, Web of Science, CAPES, BVS and Scopus. Studies that discussed how cells undergo morphogenetic changes as an adaptive response to stress in cancer were included.

RESULTS

This article reviewed 82 studies that highlighted multiple types of stress to which cancer can be subjected, such as oxidative, thermal and mechanical stress; glucose and other nutrients deficiency; hypoxia and chemotherapy. Neoplastic cells under stress can undergo adaptive changes that make it possible to overcome this obstacle. In this adaptive process, the acquisition of certain mutations implies cellular morphological changes such as Epithelial-Mesenchymal Transition, polyploidy, mitochondrial and cytoskeletal changes. These adaptive changes occur concomitantly with processes related to oncogenesis such as gene instability, tumor proliferation, resistance to therapy and invasion.

CONCLUSIONS

This study reveals that adaptations to cellular stress promote morphological and functional changes that accompany or accelerate oncogenesis. It has been revised how epithelial-mesenchymal transition, polyploidy and mitochondrial dysfunctions not only reinforce the survival of tumor cells in adverse environments, but also increase therapeutic resistance and invasive capacity. Also noteworthy are the contributions on genomic instability associated with stress and the potential of senescent cells in tumor heterogeneity, both as factors of tumor resistance and progression. These insights suggest new therapeutic targets and prognostic biomarkers, expanding the possibilities for more effective strategies to combat cancer.

Redox signaling in the pancreas in health and disease

This review addresses oxidative stress and redox signaling in the pancreas under healthy physiological conditions as well as in acute pancreatitis, chronic pancreatitis, pancreatic cancer, and diabetes. Physiological redox homeodynamics is maintained mainly by NRF2/KEAP1, NF-κB, protein tyrosine phosphatases, peroxisome proliferator-activated receptor-γ coactivator 1α (PGC1α), and normal autophagy. Depletion of reduced glutathione (GSH) in the pancreas is a hallmark of acute pancreatitis and is initially accompanied by disulfide stress, which is characterized by protein cysteinylation without increased glutathione oxidation. A cross talk between oxidative stress, MAPKs, and NF-κB amplifies the inflammatory cascade, with PP2A and PGC1α as key redox regulatory nodes. In acute pancreatitis, nitration of cystathionine-β synthase causes blockade of the transsulfuration pathway leading to increased homocysteine levels, whereas p53 triggers necroptosis in the pancreas through downregulation of sulfiredoxin, PGC1α, and peroxiredoxin 3. Chronic pancreatitis exhibits oxidative distress mediated by NADPH oxidase 1 and/or CYP2E1, which promotes cell death, fibrosis, and inflammation. Oxidative stress cooperates with mutant KRAS to initiate and promote pancreatic adenocarcinoma. Mutant KRAS increases mitochondrial reactive oxygen species (ROS), which trigger acinar-to-ductal metaplasia and progression to pancreatic intraepithelial neoplasia (PanIN). ROS are maintained at a sufficient level to promote cell proliferation, while avoiding cell death or senescence through formation of NADPH and GSH and activation of NRF2, HIF-1/2α, and CREB. Redox signaling also plays a fundamental role in differentiation, proliferation, and insulin secretion of β-cells. However, ROS overproduction promotes β-cell dysfunction and apoptosis in type 1 and type 2 diabetes.

Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

Spatial cellular context is crucial in shaping intratumor heterogeneity. However, understanding how each tumor establishes its unique spatial landscape and what factors drive the landscape for tumor fitness remains significantly challenging. Here, we analyzed over 2 million cells from 50 tumor biospecimens using spatial single-cell imaging and single-cell RNA sequencing. We developed a deep learning-based strategy to spatially map tumor cell states and the architecture surrounding them, which we referred to as Spatial Dynamics Network (SDN). We found that different tumor cell states may be organized into distinct clusters, or 'villages', each supported by unique SDNs. Notably, tumor cell villages exhibited village-specific molecular co-dependencies between tumor cells and their microenvironment and were associated with patient outcomes. Perturbation of molecular co-dependencies via random spatial shuffling of the microenvironment resulted in destabilization of the corresponding villages. This study provides new insights into understanding tumor spatial landscape and its impact on tumor aggressiveness.

A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes

Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

64Cu Radiolabeled PDGFRβ-Targeting Affibody for PET Imaging in Pancreatic Cancer

Pancreatic cancer is a malignant solid tumor that contains a significant number of cancer-associated fibroblasts (CAFs). Clinical trials have confirmed that CAF-targeted radionuclide therapy can suppress tumor growth and extend the survival of patients; therefore, quantifying CAFs by molecular imaging of CAF biomarkers is helpful for assessing disease progression and therapeutic responses of pancreatic cancer. In our previous study, we found that platelet-derived growth factor receptor beta (PDGFRβ) was highly expressed on various fibroblast cells, and a novel affibody (ZPDGFRβ) with highly specific binding to PDGFRβ had been developed. Herein, we verified the high expression of PDGFRβ on CAFs in pancreatic cancer tissues, and the ZPDGFRβ affibody was radiolabeled with 64Cu to obtain a [64Cu]Cu-NOTA-ZPDGFRβ conjugate with radiochemical purity higher than 95%. Biodistribution studies showed that tumor uptake of [64Cu]Cu-NOTA-ZPDGFRβ reached the peak of 7.28 ± 0.92 at 6 h postinjection, and the tumor-to-pancreas ratio continuously increased to reach the peak of 25.9 ± 8.18 at 24 h postinjection. Positron emission tomography (PET) imaging with [64Cu]Cu-NOTA-ZPDGFRβ showed ideal tumor uptake and imaging capability in mice bearing both subcutaneous xenografts and in situ grafts. Our results demonstrated that the [64Cu]Cu-NOTA-ZPDGFRβ conjugate could be applied as a promising PDGFRβ-targeted radiotracer for PET imaging of pancreatic cancer.

Complement activation in tumor microenvironment after neoadjuvant therapy and its impact on pancreatic cancer outcomes

Neoadjuvant therapy (NAT) is increasingly being used for pancreatic ductal adenocarcinoma (PDAC). This study investigates how NAT differentially impacts PDAC's carcinoma cells and the tumor microenvironment (TME). Spatial transcriptomics was used to compare gene expression profiles in carcinoma cells and the TME of 23 NAT-treated versus 13 NAT-naïve PDACs. Findings were validated by single-nucleus RNA sequencing (snRNA-seq) analysis. NAT induces apoptosis and inhibits proliferation of carcinoma cells and coordinately upregulates multiple complement genes (C1R, C1S, C3, C4B and C7) within the TME. Higher TME complement expression following NAT is associated with increased immunomodulatory and neurotrophic cancer-associated fibroblasts (CAFs); more CD4+ T cells; reduced immune exhaustion gene expression, and improved overall survival. snRNA-seq analysis demonstrates C3 complement is mainly upregulated in CAFs. These findings suggest that local complement dynamics could serve as a novel biomarker for prognosis, evaluating treatment response, and guiding therapeutic strategies in NAT-treated PDAC patients.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

Human Pancreatic Cancer Single-Cell Atlas Reveals Association of CXCL10+ Fibroblasts and Basal Subtype Tumor Cells

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) patients with tumors enriched for the basal-like molecular subtype exhibit enhanced resistance to standard-of-care treatments and have significantly worse overall survival compared with patients with classic subtype-enriched tumors. It is important to develop genomic resources, enabling identification of novel putative targets in a statistically rigorous manner.

EXPERIMENTAL DESIGN

We compiled a single-cell RNA sequencing (scRNA-seq) atlas of the human pancreas with 229 patient samples aggregated from publicly available raw data. We mapped cell type-specific scRNA-seq gene signatures in bulk RNA-seq (n = 744) and spatial transcriptomics (ST; n = 22) and performed validation using multiplex immunostaining.

RESULTS

Analysis of tumor cells from our scRNA-seq atlas revealed nine distinct populations, two of which aligned with the basal subtype, correlating with worse overall survival in bulk RNA-seq. Deconvolution identified one of the basal populations to be the predominant tumor subtype in nondissociated ST tissues and in vitro tumor cell and patient-derived organoid lines. We discovered a novel enrichment and spatial association of CXCL10+ cancer-associated fibroblasts with basal tumor cells. We identified that besides immune cells, ductal cells also express CXCR3, the receptor for CXCL10, suggesting a relationship between these cell types in the PDAC tumor microenvironment.

CONCLUSIONS

We show that our scRNA-seq atlas (700,000 cells), integrated with ST data, has increased statistical power and is a powerful resource, allowing for expansion of current subtyping paradigms in PDAC. We uncovered a novel signaling niche marked by CXCL10+ cancer-associated fibroblasts and basal tumor cells that could be explored for future targeted therapies.

Clonal Heterogeneity in Human Pancreatic Ductal Adenocarcinoma and Its Impact on Tumor Progression

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer characterized by profound desmoplasia and cellular heterogeneity, which cannot be fully resolved using traditional bulk sequencing approaches. To understand the contribution of this heterogeneity to PDAC biology, we analyzed a large cohort of primary human PDAC samples (n = 62), profiling 443,451 single cells and 53,236 spatial transcriptomic spots using a combined single-cell RNA sequencing and spatial transcriptomics approach. Our analysis revealed significant intratumoral heterogeneity, with multiple genetically distinct neoplastic clones co-existing within individual tumors. These clones exhibited diverse transcriptional states and subtype profiles, challenging the traditional binary classification of PDAC into basal and classical subtypes; instead, our findings support a transcriptional continuum influenced by clonal evolution and spatial organization. Additionally, these clones each interacted uniquely with surrounding cell types in the tumor microenvironment. Phylogenetic analysis uncovered a rare but consistent classical-to-basal clonal transition associated with MYC amplification and immune response depletion, which were validated experimentally, suggesting a mechanism driving the emergence of a more aggressive basal clonal phenotype. Spatial analyses further revealed dispersed clones enriched for epithelial-to-mesenchymal transition (EMT) activity and immune suppression, correlating with metastatic potential and colonization of lymph node niches. These dispersed clones tended to transition toward a basal phenotype, contributing to disease progression. Our findings highlight the critical role of clonal diversity, transcriptional plasticity, and TME interactions in shaping human PDAC biology. This work provides new insights into the molecular and spatial heterogeneity of PDAC and offers potential avenues for therapeutic intervention targeting clonal evolution and the mechanisms driving metastasis.

Oncofetal reprogramming drives phenotypic plasticity in WNT-dependent colorectal cancer

Targeting cancer stem cells (CSCs) is crucial for effective cancer treatment, yet resistance mechanisms to LGR5+ CSC depletion in WNT-driven colorectal cancer (CRC) remain elusive. In the present study, we revealed that mutant intestinal stem cells (SCs) depart from their canonical identity, traversing a dynamic phenotypic spectrum. This enhanced plasticity is initiated by oncofetal (OnF) reprogramming, driven by YAP and AP-1, with subsequent AP-1 hyperactivation promoting lineage infidelity. The retinoid X receptor serves as a gatekeeper of OnF reprogramming and its deregulation after adenomatous polyposis coli (APC) loss of function establishes an OnF 'memory' sustained by YAP and AP-1. Notably, the clinical significance of OnF and LGR5+ states in isolation is constrained by their functional redundancy. Although the canonical LGR5+ state is sensitive to the FOLFIRI regimen, an active OnF program correlates with resistance, supporting its role in driving drug-tolerant states. Targeting this program in combination with the current standard of care is pivotal for achieving effective and durable CRC treatment.

Spatial Transcriptomics Reveals Cancer and Stromal Cell Heterogeneity Between Center and Invasive Front of Pancreatic Cancer

Intratumor heterogeneity is considered a major cause of treatment failure in pancreatic ductal adenocarcinoma (PDAC). In recent years, marked heterogeneity at the genomic and transcriptional level has been revealed, but the spatial distribution of the heterogeneous cell populations has not been considered. Yet, it is assumed that cancer cells at the invasive front are endowed with enhanced migratory and invasive properties, although evidence is scanty, and cancer-associated fibroblasts (CAFs) in this location have not been characterized. In this study, digital spatial profiling was used to compare the transcriptional profiles of cancer cells and CAFs in the tumor center versus the invasive front of human PDAC. Four well-differentiated PDACs with conventional morphology were investigated with the GeoMx system (Nanostring). Regions of interest were analyzed in the tumor center and at the invasive front using a whole transcriptome assay in the cancer cell and CAF segments separately. Three of the PDACs harbored mutated KRAS, whereas the fourth case was confirmed wild-type KRAS. Substantial inter-regional heterogeneity was identified, with increased activity of pathways associated with cellular stress (including TNFα-signaling via NFκB, hypoxia, P53 pathway), proliferation (MYC targets, mitotic spindle), glycolysis, and epithelial-mesenchymal transition (EMT) at the invasive front in both the cancer cell and CAF segments compared with the center of the tumor. Immunohistochemical validation on 17 PDACs of well, moderate, and poor differentiation confirmed significant inter-regional heterogeneity in the expression level of markers of EMT and glycolysis. The results of this study show that in PDAC, transcriptional profiles of both cancer cells and CAFs differ between the center of the tumor and the invasive front.

Stromal architecture and fibroblast subpopulations with opposing effects on outcomes in hepatocellular carcinoma

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

Cancer-associated fibroblasts as therapeutic targets for cancer: advances, challenges, and future prospects

Research into cancer treatment has been mainly focused on developing therapies to directly target cancer cells. Over the past decade, extensive studies have revealed critical roles of the tumour microenvironment (TME) in cancer initiation, progression, and drug resistance. Notably, cancer-associated fibroblasts (CAFs) have emerged as one of the primary contributors in shaping TME, creating a favourable environment for cancer development. Many preclinical studies have identified promising targets on CAFs, demonstrating remarkable efficacy of some CAF-targeted treatments in preclinical models. Encouraged by these compelling findings, therapeutic strategies have now advanced into clinical evaluation. We aim to provide a comprehensive review of relevant subjects on CAFs, including CAF-related markers and targets, their multifaceted roles, and current landscape of ongoing clinical trials. This knowledge can guide future research on CAFs and advocate for clinical investigations targeting CAFs.

CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data

Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and have many arbitrary parameter choices. Methods that can model scRNA-seq data as non-discrete "gene expression programs" (GEPs) can better preserve the data's structure, but currently, they are often not scalable, not consistent across repeated runs, and lack an established method for choosing key parameters. Here, we developed a GPU-based unsupervised learning approach, "consensus and scalable inference of gene expression programs" (CSI-GEP). We show that CSI-GEP can recover ground truth GEPs in real and simulated atlas-scale scRNA-seq datasets, significantly outperforming cutting-edge methods, including GPT-based neural networks. We applied CSI-GEP to a whole mouse brain atlas of 2.2 million cells, disentangling endothelial cell types missed by other methods, and to an integrated scRNA-seq atlas of human tumors and cell lines, discovering mesenchymal-like GEPs unique to cancer cells growing in culture.

Genetics and biology of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) poses a grim prognosis for patients. Recent multidisciplinary research efforts have provided critical insights into its genetics and tumor biology, creating the foundation for rational development of targeted and immune therapies. Here, we review the PDAC genomic landscape and the role of specific oncogenic events in tumor initiation and progression, as well as their contributions to shaping its tumor biology. We further summarize and synthesize breakthroughs in single-cell and metabolic profiling technologies that have illuminated the complex cellular composition and heterotypic interactions of the PDAC tumor microenvironment, with an emphasis on metabolic cross-talk across cancer and stromal cells that sustains anabolic growth and suppresses tumor immunity. These conceptual advances have generated novel immunotherapy regimens, particularly cancer vaccines, which are now in clinical testing. We also highlight the advent of KRAS targeted therapy, a milestone advance that has transformed treatment paradigms and offers a platform for combined immunotherapy and targeted strategies. This review provides a perspective summarizing current scientific and therapeutic challenges as well as practice-changing opportunities for the PDAC field at this major inflection point.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

SPathDB: a comprehensive database of spatial pathway activity atlas

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689 868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114 998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics

Molecular imaging using positron emission tomography (PET) provides sensitive detection and mapping of molecular targets. While cancer-associated fibroblasts and integrins have been proposed as targets for imaging of pancreatic ductal adenocarcinoma (PDAC), herein, spatial transcriptomics and proteomics of human surgical samples are applied to select PDAC targets. We find that selected cancer cell surface markers are spatially correlated and provide specific cancer localization, whereas the spatial correlation between cancer markers and immune-related or fibroblast markers is low. Claudin-4 expression increases ~16 fold in cancer as compared with normal pancreas, and tight junction localization confers low background for imaging in normal tissue. We develop a peptide-based molecular imaging agent targeted to claudin-4 with accumulation to ~25% injected activity per cubic centimeter (IA/cc) in metastases and ~18% IA/cc in tumors. Our work motivates a data-driven approach to selection of molecular targets.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Emerging strategies to investigate the biology of early cancer

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

Recent studies have identified a unique subtype of cancer-associated fibroblasts (CAFs) termed antigen-presenting CAFs (apCAFs), which remain the least understood CAF subtype. To gain a comprehensive understanding of the origin and function apCAFs, we construct a fibroblast molecular atlas across 14 types of solid tumors. Our integration study unexpectedly reveals two distinct apCAF lineages present in most cancer types: one associated with mesothelial-like cells and the other with fibrocytes. Using a high-resolution single-cell spatial imaging platform, we characterize the spatial niches of these apCAF lineages. We find that mesothelial-like apCAFs are located near cancer cells, while fibrocyte-like apCAFs are associated with tertiary lymphoid structures. Additionally, we discover that both apCAF lineages can up-regulate the secreted protein SPP1, which facilitates primary tumor formation and peritoneal metastasis. Taken together, this study offers an unprecedented resolution in analyzing apCAF lineages and their spatial niches.

Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

BACKGROUND

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.

METHODS

We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties.

FINDINGS

Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells.

INTERPRETATION

This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism.

FUNDING

National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity

Although the spatial, cellular and molecular landscapes of resected pancreatic ductal adenocarcinoma (PDAC) are well documented, the characteristics of its metastatic ecology remain elusive. By applying spatially resolved transcriptomics to matched primary and metastatic PDAC samples, we discovered a conserved continuum of fibrotic, metabolic and immunosuppressive spatial ecotypes across anatomical regions. We observed spatial tumor microenvironment heterogeneity spanning beyond that previously appreciated in PDAC. Through comparative analysis, we show that the spatial ecotypes exhibit distinct enrichment between primary and metastatic sites, implying adaptability to the local environment for survival and progression. The invasive border ecotype exhibits both pro-tumorigenic and anti-tumorigenic cell-type enrichment, suggesting a potential immunotherapy target. The ecotype heterogeneity across patients emphasizes the need to map individual patient landscapes to develop personalized treatment strategies. Collectively, our findings provide critical insights into metastatic PDAC biology and serve as a valuable resource for future therapeutic exploration and molecular investigations.

Spatial transcriptomics in pancreatic cancer: Advances, prospects and challenges

Pancreatic cancer remains one of the deadliest malignancies with an overall 5-year survival rate of 13 %. This dismal fact can be partly attributed to currently limited understanding of tumor heterogeneity and immune microenvironment. Traditional bulk-sequencing techniques overlook the diversity of tumor cells, while single-cell sequencing disorganizes the position localizing of cells in tumor microenvironment. The advent of spatial transcriptomics (ST) presents a novel solution by integrating location and whole transcript expression information. This technology allows for detailed observation of spatio-temporal changes across various cell subtypes within the pancreatic tumor microenvironment, providing insights into their potential functions. This review offers an overview of recent studies implementing ST in pancreatic cancer research, highlighting its instrumental role in investigating the heterogeneity and functions of tumor cells, stromal cells, and immune cells. On the basis, we also prospected and summarized the clinical application scenarios, technical limitations and challenges of ST technology in pancreatic cancer.

Defining precancer: a grand challenge for the cancer community

The term 'precancer' typically refers to an early stage of neoplastic development that is distinguishable from normal tissue owing to molecular and phenotypic alterations, resulting in abnormal cells that are at least partially self-sustaining and function outside of normal cellular cues that constrain cell proliferation and survival. Although such cells are often histologically distinct from both the corresponding normal and invasive cancer cells of the same tissue origin, defining precancer remains a challenge for both the research and clinical communities. Once sufficient molecular and phenotypic changes have occurred in the precancer, the tissue is identified as a 'cancer' by a histopathologist. While even diagnosing cancer can at times be challenging, the determination of invasive cancer is generally less ambiguous and suggests a high likelihood of and potential for metastatic disease. The 'hallmarks of cancer' set out the fundamental organizing principles of malignant transformation but exactly how many of these hallmarks and in what configuration they define precancer has not been clearly and consistently determined. In this Expert Recommendation, we provide a starting point for a conceptual framework for defining precancer, which is based on molecular, pathological, clinical and epidemiological criteria, with the goal of advancing our understanding of the initial changes that occur and opportunities to intervene at the earliest possible time point.

Targeting a chemo-induced adaptive signaling circuit confers therapeutic vulnerabilities in pancreatic cancer

Advanced pancreatic ductal adenocarcinomas (PDACs) respond poorly to all therapies, including the first-line treatment, chemotherapy, the latest immunotherapies, and KRAS-targeting therapies. Despite an enormous effort to improve therapeutic efficacy in late-stage PDAC patients, effective treatment modalities remain an unmet medical challenge. To change the status quo, we explored the key signaling networks underlying the universally poor response of PDAC to therapy. Here, we report a previously unknown chemo-induced symbiotic signaling circuit that adaptively confers chemoresistance in patients and mice with advanced PDAC. By integrating single-cell transcriptomic data from PDAC mouse models and clinical pathological information from PDAC patients, we identified Yap1 in cancer cells and Cox2 in stromal fibroblasts as two key nodes in this signaling circuit. Co-targeting Yap1 in cancer cells and Cox2 in stroma sensitized PDAC to Gemcitabine treatment and dramatically prolonged survival of mice bearing late-stage PDAC, whereas simultaneously inhibiting Yap1 and Cox2 only in cancer cells was ineffective. Mechanistically, chemotherapy triggers non-canonical Yap1 activation by nemo-like kinase in 14-3-3ζ-overexpressing PDAC cells and increases secretion of CXCL2/5, which bind to CXCR2 on fibroblasts to induce Cox2 and PGE2 expression, which reciprocally facilitate PDAC cell survival. Finally, analyses of PDAC patient data revealed that patients who received Statins, which inhibit Yap1 signaling, and Cox2 inhibitors (including Aspirin) while receiving Gemcitabine displayed markedly prolonged survival compared to others. The robust anti-tumor efficacy of Statins and Aspirin, which co-target the chemo-induced adaptive circuit in the tumor cells and stroma, signifies a unique therapeutic strategy for PDAC.

BNIP3+ fibroblasts associated with hypoxia and inflammation predict prognosis and immunotherapy response in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors that lacks effective treatment options. Cancer-associated fibroblasts (CAFs), an important component of the tumor microenvironment, associated with tumor progression, prognosis, and treatment response. This work aimed to explore the novel CAFs-associated target to improve treatment strategies in PDAC.

METHODS

The PDAC single-cell sequencing data (CRA001160, n = 35) were downloaded and integrated based on GSA databases to classify fibroblasts into fine subtypes. Functional enrichment analysis and coexpression regulatory network analysis were used to identify the functional phenotypes and biological properties of the different fibroblast subtypes. Fibroblast differentiation trajectories were constructed using pseudochronological analysis to identify initial and terminally differentiated subtypes of fibroblasts. The changes in the proportions of different fibroblast subtypes before and after PDAC immunotherapy were compared in responsive and nonresponding patients, and the relationships between fibroblast subtypes and PDAC immunotherapy responsiveness were determined based on GSA and GEO database. Using molecular biology methods to confirm the effects of BNIP3 on hypoxia and inflammation in CAFs. CAFs were co cultured with pancreatic cancer cells to detect their effects on migration and invasion of pancreatic cancer.

RESULTS

Single-cell data analysis divided fibroblasts into six subtypes. The differentiation trajectory suggested that BNIP3+ Fibro subtype exhibited terminal differentiation, and the expression of genes related to hypoxia and the inflammatory response increased gradually with differentiation time. The specific overexpressed genes in the BNIP3+ Fibro subtype were significantly associated with overall and disease progression-free survival in the patients with PDAC. Interestingly, the greater the proportion of the BNIP3+ Fibro subtype was, the worse the response of PDAC patients to immunotherapy, and the CRTL treatment regimen effectively reduced the proportion of the BNIP3+ Fibro subtype. After knocking out BNIP3, the hypoxia markers and inflammatory factors of CAFs were inhibited. Co-culture of CAFs with pancreatic cancer cells can increase the migration and invasion of pancreatic cancer, but this could be reversed by knocking out BNIP3.

CONCLUSIONS

This study revealed the BNIP3+ Fibro subtype associated with hypoxia and inflammatory responses, which was closely related to the poor prognosis of patients with PDAC, and identified signature genes that predict the immunotherapy response in PDAC.

Tumour evolution and microenvironment interactions in 2D and 3D space

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

Panoramic tumor microenvironment in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is notorious for its resistance to various treatment modalities. The genetic heterogeneity of PDAC, coupled with the presence of a desmoplastic stroma within the tumor microenvironment (TME), contributes to an unfavorable prognosis. The mechanisms and consequences of interactions among different cell types, along with spatial variations influencing cellular function, potentially play a role in the pathogenesis of PDAC. Understanding the diverse compositions of the TME and elucidating the functions of microscopic neighborhoods may contribute to understanding the immune microenvironment status in pancreatic cancer. As we delve into the spatial biology of the microscopic neighborhoods within the TME, aiding in deciphering the factors that orchestrate this intricate ecosystem. This overview delineates the fundamental constituents and the structural arrangement of the PDAC microenvironment, highlighting their impact on cancer cell biology.

Advances in spatial multi-omics in tumors

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

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

A spatiotemporal comparative analysis on tumor immune microenvironment characteristics between neoadjuvant chemotherapy and preoperative immunotherapy for ESCC

The average five-year survival rate for esophageal cancer, a common malignant tumor of the digestive system, is barely 20%. The majority of esophageal squamous cell carcinoma (ESCC) patients had already progressed to a locally advanced or even advanced stage at initial diagnosis, making routine surgery ineffective. Chemotherapy and immunotherapy are important neoadjuvant treatments for ESCC, however, it remains unknown how treatment will affect the immunological microenvironment, especially at the spatial level. Here, we presented the TME characters of ESCC from the temporal and spatial dimensions using scRNA-seq and ST, investigated the changes of immune cell clusters in the TME under neoadjuvant chemotherapy and preoperative immunotherapy, and explored the potential mechanisms. It was found that compared with chemotherapy, immunotherapy combined with chemotherapy increased the level of T cell proliferation, partially restored the function of exhausted T cells, induced the expansion of specific exhausted CD8 T cells, increased the production of dendritic cells (DCs), and supported the immune hot microenvironment of the tumor. We also found that CD52 and ID3 have potential as biomarkers of ESCC. Particularly, CD52 may be served as a predictor of the efficacy to screen the advantaged population of different regimens. Through multiple pathways, CAF2 and CAF5's antigen-presenting role affected the other fibroblast clusters, resulting in malignant transformation. We analyzed the immune microenvironment differences between the two regimens to provide a more thorough description of the ESCC microenvironment profile and serve as a foundation for customized neoadjuvant treatment of ESCC.