Heterogeneity Of Pancreatic Cancer Research Articles

Phenotypic, genomic, and transcriptomic heterogeneity in a pancreatic cancer cell line.

To evaluate the suitability of the MIA PaCa-2 cell line for studying pancreatic cancer intratumor heterogeneity, we aim to further characterize the nature of MIA PaCa-2 cells' phenotypic, genomic, and transcriptomic heterogeneity. MIA PaCa-2 single-cell clones were established through flow cytometry. For the phenotypic study, we quantified the cellular morphology, proliferation rate, migration potential, and drug sensitivity of the clones. The chromosome copy number and transcriptomic profiles were quantified using SNPa and RNA-seq, respectively. Four MIA PaCa-2 clones showed distinctive phenotypes, with differences in cellular morphology, proliferation rate, migration potential, and drug sensitivity. We also observed a degree of genomic variations between these clones in form of chromosome copy number alterations and single nucleotide variations, suggesting the genomic heterogeneity of the population, and the intrinsic genomic instability of MIA PaCa-2 cells. Lastly, transcriptomic analysis of the clones also revealed gene expression profile differences between the clones, including the uniquely regulated ITGAV, which dictates the morphology of MIA PaCa-2 clones. MIA PaCa-2 is comprised of cells with distinctive phenotypes, heterogeneous genomes, and differential transcriptomic profiles, suggesting its suitability as a model to study the underlying mechanisms behind pancreatic cancer heterogeneity.

HMGA2 overexpression activates IGF2BP2 to stabilize APLP2 via m6A modification and promote pancreatic cancer progression

Pancreatic cancer is a highly aggressive malignancy of the digestive system, with occult onset, rapid progression, and poor prognosis. The genetic heterogeneity of pancreatic cancer contributes to its highly malignant biological behavior. HMGA2 is overexpressed in tumors and is known to regulate tumor progression in various cancers through the HMGA2-IGF2BP2 axis, but its role and mechanism in pancreatic cancer remain unclear. In this study, we demonstrated that HMGA2 promotes pancreatic cancer progression. We further revealed that HMGA2 upregulates IGF2BP2, which stabilizes APLP2 mRNA via m6A modification, thereby promoting pancreatic cancer progression. These results indicate that HMGA2/IGF2BP2/APLP2 signaling axis regulates the progression of pancreatic cancer.

Hourglass, a compass navigating global and regional heterogeneity of pancreatic cancer†.

Advances in the digital pathology field have facilitated the characterization of histology samples for both clinical and preclinical research. However, uncovering subtle correlations between bioimaging, clinical and molecular parameters requires extensive statistical analysis. As a user-friendly software, Hourglass, simplifies multiparametric dataset analysis through intuitive data visualization and statistical tools. Systemic analysis of interleukin-6 (IL-6)/pStat3 signaling pathway through Hourglass revealed differences in regional immune cell composition within tumors. Moreover, these regional disparities were partially mediated by sex. Overall, Hourglass simplifies information extraction from complex datasets, resolving overlooked regional and global spatial tumor differences. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Unfolded Protein-Based Sandwich AIE Probe Imparts High Fluorescent Contrast for Pan-Cancer Surgical Navigation.

Fluorescence imaging-guided navigation for cancer surgery has a promising clinical application. However, pan-cancer encompasses a wide variety of cancer types with significant heterogeneity, resulting in the lack of universal and highly contrasted fluorescent probes for surgical navigation. Here, we developed an aggregation-induced emission (AIE) probe (MI-AIE-TsG, MAT) with dual activation for pan-cancer surgical navigation. MAT weakly activates fluorescence by targeting the SUR1 protein on the endoplasmic reticulum (ER) through the TsG group. Subsequently, the sulfhydryl groups on the unfolded proteins, which are highly enriched in cancer ER, react with the maleimide (MI) of MAT through the thiol-ene click reaction, further enhancing the fluorescence. The formation of a SUR1-MAT-unfolded protein sandwich complex reinforces the restriction of intramolecular motion and eliminates photoinduced electron transfer of MAT, leading to high signal-to-noise (9.2) fluorescence imaging and use for surgical navigation of pan-cancer. The generally high content of unfolded proteins in cancer cells makes MAT imaging generalizable, and it currently has proven feasibility in ovarian, cervical, and breast cancers. Meanwhile, MAT promotes cellular autophagy by hindering protein folding, thereby inhibiting cancer cell proliferation. This generalizable, high-contrast AIE fluorescent probe spans the heterogeneity of pancreatic cancer, enabling precise pancreatic cancer surgery navigation and treatment.

Myofibroblastic cancer-associated fibroblast subtype heterogeneity in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has a fibrotic stroma that has both tumor-promoting and tumor-restraining properties. Different types of cancer-associated fibroblasts (CAFs) have been described. Here, we investigated whether CAFs within the same subtype exhibit heterogeneous functions. We evaluated the gene and protein expression differences in two myofibroblastic CAF (myCAF) lines using single-cell and bulk RNA-sequencing. We utilized proliferation and migration assays to determine the effect of different CAF lines on a tumor cell line. We found that myCAF lines express an activated stroma subtype gene signature, which is associated with a shorter survival in patients. Although both myCAF lines expressed α-smooth muscle actin (α-SMA), platelet-derived growth factor-α (PDGFR-α), fibroblast-activated protein (FAP), and vimentin, we observed heterogeneity between the two lines. Similarly, despite being consistent with myCAF gene expression overall, heterogeneity within specific genes was observed. We found that these differences extended to the functional level where the two myCAF lines had different effects on the same tumor cell line. The myCAF216 line, which had slightly increased inflammatory CAF-like gene expression and higher protein expression of α-SMA, PDGFR-α, and FAP was found to restrain migration of tumor cells. We found that two myCAF lines with globally similar expression characteristics had different effects on the same tumor cell line, one promoting and the other restraining migration. Our study highlights that there may be unappreciated heterogeneity within CAF subtypes. Further investigation and attention to specific genes or proteins that may drive this heterogeneity will be important.

Precision analysis of protein sequence and post-translational modification heterogeneity in pancreatic cancer induced cachexia

Background and Hypothesis:Cachexia is a wasting syndrome commonly occurring in cancer patients that cannot be explained by decreased calorie intake alone. It results in decreased quality of life and increased mortality, and there are currently no effective treatments. To better understand cachexia, we aimed to profile protein heterogeneity seen in pancreatic ductal adenocarcinoma (PDAC) mouse models with cachexia. Since muscle is a readily available protein resource during catabolism, we hypothesize there are significant changes in protein sequence and post-translational modifications (PTMs) of muscle proteins during cachexia progression. As these proteins are scavenged, precise analysis of sequence variation can identify the exact mechanism of protein/muscle breakdown and better elucidate the molecular processes of PDAC-induced cachexia.
 Experimental Design:We performed bioinformatic analysis of two proteomics datasets of cardiac and skeletal muscle samples from PDAC-induced cachexia mouse models. Two proteomics software algorithms, SEQUEST (within Proteome Discoverer (PD)) and PEAKS (a machine-learning algorithm), were used to identify all sample proteins and their PTMs. Because PEAKS can identify more PTMs than PD, we compared the most abundant proteins identified in PD and PEAKS, hypothesizing PEAKS would yield greater protein sequence coverage. Finally, we compiled unpublished PTMs and protein processing events for 25 of the most abundant proteins in both datasets.
 Results:Notably, PEAKS reported greater sequence coverage for cardiac muscle than PD, while the skeletal muscle sample had similar coverage in both algorithms. This discrepancy may suggest cachectic processes degrade skeletal muscle at a greater rate than cardiac muscle, preventing PEAKS from increasing skeletal muscle sequence coverage relative to PD. In addition, several unpublished modifications, including those of actin and acetyl-CoA acetyltransferase, were recorded.
 Potential Impact:These newly discovered protein modifications may indicate previously unknown molecular processes in the course of PDAC-induced cachexia. These modifications may serve as cornerstones of future research to identify novel therapeutic targets in cachexia treatment.

The cellular trajectories of tumor-associated macrophages decipher the heterogeneity of pancreatic cancer.

Emerging evidence indicates that the interactions and dynamic changes among tumor-associated macrophages (TAMs) are pivotal in molding the tumor microenvironment (TME), thereby influencing diverse clinical outcomes. However, the potential clinical ramifications of these evolutionary shifts in tumor-associated macrophages within pancreatic adenocarcinoma (PAAD) remain largely unexamined. Single-cell RNA sequencing (scRNA-seq) data were retrieved from the Tumor Immune Single-cell Hub. The Seurat and Monocle algorithms were employed to elucidate the progression of TAMs, using non-negative matrix factorization (NMF) to determine molecular classifications. Subsequently, the prognosis, biological characteristics, genomic modifications, and immune landscape across various clusters were interpreted. Furthermore, the sensitivity of potential therapeutic drugs between subtypes was predicted. Cellular experiments were conducted to explore the function of the NR1H3 gene in pancreatic cancer. These experiments encompassed gene knockdown, proliferation assessment, clone formation evaluation, transwell examination, and apoptosis analysis. Trajectory gene expression analysis of tumor-associated macrophages identified three disparate clusters, each associated with different clinical outcomes Compared to clusters C1 and C2, cluster C3 is seemingly at a less advanced pathological stage and associates with a relatively favorable prognosis. Further investigation revealed pronounced genetic instability in cluster C2, whereas cluster C3 demonstrated notable genetic stability. Cluster C1, characterized as "immune-hot," exhibits an abundance of immune cells and elevated immune checkpoint expression, suggesting its suitability for immunotherapy. Furthermore, several potential therapeutic agents have been pinpointed, potentially facilitating the clinical application of these insights. Cell assays indicated that NR1H3 knockdown markedly induced apoptosis and suppressed clonogenesis, migration, and proliferation of pancreatic cancer cells in the PTAU-8988 and PANC-1 cell lines. Overall, our study discerned three clusters with unique characteristics, defined by the evolution of TAMs. We propose customized therapeutic strategies for patients within these specific clusters to improve clinical outcomes and optimize clinical management.

Establishment of organoid models for pancreatic ductal adenocarcinoma and screening of individualized therapy strategy.

Patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgical resection and receive effective chemotherapy have the best chance for long-term survival. Unfortunately, because of the heterogeneity of pancreatic cancer, it is difficult to find a personalized treatment strategy for patients. Organoids are ideal preclinical models for personalized medicine. Therefore, we explore the cultivation conditions and construction methods of PDAC organoid models to screen the individualized therapy strategy. Fresh PDAC tissues from surgical resection were collected and digested with digestive enzymes; then the tumor cells were embedded in Matrigel with a suitable medium to establish the PDAC organoid models. The genetic stability of the organoids was analyzed using whole exon sequencing; hematoxylin and eosin staining and immunohistochemistry of organoids were performed to analyze their consistency with the pathological morphology of the patient's tumor tissue; After 2 days of organoid culture, we selected four commonly used clinical chemotherapy drugs for single or combined treatment to analyze drug sensitivity. Two cases of PDAC organoid models were successfully established, and the results of their pathological characteristics and exome sequencing were consistent with those of the patient's tumor tissue. Both PDAC organoids showed more sensitivity to gemcitabine and cisplatin, and the combined treatment was more effective than monotherapy. Both organoids better retained the pathological characteristics, genomic stability, and heterogeneity with the original tumor. Individual PDAC organoids exhibited different sensitivities to the same drugs. Thus, this study provided ideal experimental models for screening individualized therapy strategy for patients with PDAC.

Heterogeneity analysis of pancreatic cancer and identification of molecular subtypes of tumor cells based on CEACAM5, LGALS1 and CENPF gene expression

To explore the heterogeneity of pancreatic cancer and new methods for tumor cell molecular subtyping and identify the signature genes in pancreatic cancer progression. Based on the single-cell sequencing data of 16 pancreatic cancer tissues from the GSE155698 dataset, the single pancreatic cancer cells were classified according to EPCAM gene expression after preliminary clustering, re-clustering, and subgrouping to identify the signature genes, followed by pathway enrichment analysis and pseudo-time analysis. The key genes identified were validated using the clinical and tissue gene and protein expression data from 179 pancreatic cancer patients and 171 healthy controls. The impact of CEACAM5, LGALS1, and CENPF on proliferation, migration and invasion of pancreatic cancer cells were analyzed. Analysis of 48 570 cells from 16 pancreatic cancer samples revealed a total of 22 clusters, including 5 clusters of pancreatic cancer cells, which were classified into Subtype 1, Subtype 2, and Subtype 3, each exhibiting distinct gene expression patterns and functions. The signature genes were enriched in negatively regulated protein metabolic processes, ferroptosis, and antigen processing and presentation-related pathways in Subtype 1 pancreatic cancer cells; in peptide synthesis processes, translation, and ribosome-related pathways in Subtype 2; and in ATP metabolic processes, glycolysis/gluconeogenesis, and cell cyclerelated pathways in Subtype 3. Subtypes 2 and 3 were potentially derived from Subtype 1, and Subtype 3 possibly represented the final developmental stage of pancreatic cancer cells. The key signature genes (CEACAM5, LGALS1, and CENPF) also exhibited different expression patterns in the developmental trajectory and showed high expressions in pancreatic cancer in association with poor prognoses. In pancreatic cancer cells, downregulation of CEACAM5, LGALS1, and CENPF significantly inhibited the proliferation, migration, and invasion abilities of the cells (P<0.05). Pancreatic cancer cells exhibit significant heterogeneity, and CEACAM5, LGALS1, and CENPF gene expressions, which affect pancreatic cancer cell proliferation, invasion, and metastasis, can be used to identify distinct molecular subtypes during tumor cell development.

Characterization of anoikis-based molecular heterogeneity in pancreatic cancer and pancreatic neuroendocrine tumor and its association with tumor immune microenvironment and metabolic remodeling.

Accumulating evidence suggests that anoikis plays a crucial role in the onset and progression of pancreatic cancer (PC) and pancreatic neuroendocrine tumors (PNETs); nevertheless, the prognostic value and molecular characteristics of anoikis in cancers are yet to be determined. We gathered and collated the multi-omics data of several human malignancies using the TCGA pan-cancer cohorts. We thoroughly investigated the genomics and transcriptomics features of anoikis in pan-cancer. We then categorized a total of 930 patients with PC and 226 patients with PNETs into distinct clusters based on the anoikis scores computed through single-sample gene set enrichment analysis. We then delved deeper into the variations in drug sensitivity and immunological microenvironment between the various clusters. We constructed and validated a prognostic model founded on anoikis-related genes (ARGs). Finally, we conducted PCR experiments to explore and verify the expression levels of the model genes. Initially, we identified 40 differentially expressed anoikis-related genes (DE-ARGs) between pancreatic cancer (PC) and adjacent normal tissues based on the TCGA, GSE28735, and GSE62452 datasets. We systematically explored the pan-cancer landscape of DE-ARGs. Most DE-ARGs also displayed differential expression trends in various tumors, which were strongly linked to favorable or unfavorable prognoses of patients with cancer, especially PC. Cluster analysis successfully identified three anoikis-associated subtypes for PC patients and two anoikis-associated subtypes for PNETs patients. The C1 subtype of PC patients showed a higher anoikis score, poorer prognosis, elevated expression of oncogenes, and lower level of immune cell infiltration, whereas the C2 subtype of PC patients had the exact opposite characteristics. We developed and validated a novel and accurate prognostic model for PC patients based on the expression traits of 13 DE-ARGs. In both training and test cohorts, the low-risk subpopulations had significantly longer overall survival than the high-risk subpopulations. Dysregulation of the tumor immune microenvironment could be responsible for the differences in clinical outcomes between low- and high-risk groups. These findings provide fresh insights into the significance of anoikis in PC and PNETs. The identification of subtypes and construction of models have accelerated the progress of precision oncology.

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

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

Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.

There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches, such as the Dirichlet multinomial mixture model, in three key respects: we incorporate feature selection, learn the appropriate number of clusters from the data, and integrate information on the tree structure relating the observed features. We compare the performance of our proposed method to existing methods on simulated data designed to mimic real microbiome data. We then illustrate results obtained for our motivating data set, a clinical study aimed at characterizing the tumor microbiome of pancreatic cancer patients.

Abstract C084: Oncogenic KRAS signaling drives the activation of tissue-resident fibroblasts and is required to maintain CAF heterogeneity in pancreatic cancer

Abstract The complexity of the tumor microenvironment (TME) is one of the distinguishing features of pancreatic ductal adenocarcinoma (PDA) and is responsible for patients’ poor response to therapies. The heterogeneity of cancer-associated fibroblasts (CAFs) has been correlated to the key features of the stroma that contribute to making PDA the third-leading cause of cancer-related deaths in the United States. The recent development of FDA-approved drugs against oncogenic KRAS opened a new therapeutic avenue for the treatment of tumors that have KRAS as their driver oncogene, such as PDA. However, the precise mechanisms driving the development of the TME and the contribution of KRAS to these processes have yet to be elucidated. This poses a challenge for the prediction of the effects of KRAS inhibition on established PDA tumors. By employing spatial transcriptomic technologies on various murine models recapitulating different stages of tumor initiation and progression, from acute and chronic inflammation to PanIN and overt PDA, we were able to observe distinctive changes in the activation status of pancreatic fibroblasts. These resident activated fibroblasts (RAFs) display expression of discrete markers brought upon by inflammation (inflammatory RAFs, iRAFs) or specific to oncogenic KRAS activation (myofibroblastic RAFs, myRAFs). Importantly, these RAF populations are maintained in established tumors and are identifiable in human PDA. To assess the consequences of the disruption of KRAS signaling on CAFs and RAFs, we employed a PDA mouse model that allows for the irreversible excision of KrasG12V, the FPC model (KrasFrt-LSL-G12V-Frt; p53LSL-R172H; PDX-CRE; Rosa26FlpOERT2). Through a combination of single-cell RNA sequencing (scRNA-seq), spatial transcriptomics and fluorescent in situ hybridization combined with immunofluorescence (immunoFISH) on FPC mice before and after KrasG12V deletion, we demonstrate the profound impact of Kras ablation on the TME composition. A deep remodeling of the stroma as well as significant changes in the proportion of CAF subtypes were evident. Interestingly, we observed a decrease in CAFs with a concomitant increase in RAFs. Our results suggest that the activation of oncogenic Kras in pre-neoplastic lesions drives a unique paracrine signaling that shapes the TME and is required to maintain the CAF population in PDA. Citation Format: Giuseppina Caligiuri, Jennifer S. Thalappillil, Mojdeh Shakiba, Sandeep Nadella, Juliene Hinds, Elise Courtois, William F. Flynn, Brinda Alagesan, Georgi N. Yordanov, Benjamin Kaminow, Paul Robson, Pedro A. Perez-Mancera, Jonathan Preall, Alexander Dobin, Youngku Park, David A. Tuveson. Oncogenic KRAS signaling drives the activation of tissue-resident fibroblasts and is required to maintain CAF heterogeneity in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C084.

Abstract A007: Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs

Abstract Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of single cell RNA-seq profiles showed that most PDA tumors comprise three coexisting lineages whose aberrant transcriptional state is mechanistically controlled by distinct regulatory programs. These lineages were characterized by the aberrant activation of either gastrointestinal lineage markers (GLS), transcriptional effectors of morphogen pathways (MOS) and acinar to ductal metaplasia markers (ALS). Each lineage was characterized by cells in two different cell states determined by the differential activation of MEK signaling (M+/M-) and high cellular plasticity. These states were confirmed in multiple cohorts, cell lines, PDX models and harmonized with bulk profile analyses. Master regulators (MRs) of GLS and MOS state were predictive of patient’s survival in bulk profiles. Cross-state plasticity was confirmed by lineage tracing assays, while pooled CRISPR/Cas9 assays confirmed the essentiality of identified MR proteins. Finally, mechanistic MR-mediated cell state control was confirmed by MR expression-mediated reprogramming of MOS cells to a GLS state. Our work provided a mechanistic model of pancreatic cancer heterogeneity and testable hypothesis to target cell state-specific pancreatic cancer dependencies. Citation Format: Pasquale Laise, Mikko Turunen, Hans Carlo Maurer, Alvaro Curiel Garcia, Ela Elyada, Bernhard Schmierer, Lorenzo Tomassoni, Jeremy Worley, Mariano J. Alvarez, Jordan Kesner, Xiangtian Tan, Somnath Tagore, Ester Calvo Fernandez, Kelly Wong, Alexander L. E. Wang, Sabrina Ge, Alina C. Iuga, Aaron T. Griffin, Winston Wong, Gulam A. Manji, Faiyaz Notta, David A. Tuveson, Kenneth P. P. Olive, Andrea Califano. Pancreatic cancer comprises co-existing transcriptional states regulated by distinct master regulator programs [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A007.

The Right Treatment Strategy for the Right Patient: A Biomarker-Driven Approach to Neoadjuvant vs. Surgery-First Management of Resectable and Borderline Resectable Pancreatic Cancer.

Simple SummaryRoutine neoadjuvant therapy for resectable and borderline resectable pancreatic cancer is gaining popularity, but its true oncological benefit remains disputed. Whilst the genotypic and phenotypic heterogeneity of pancreatic cancer is becoming increasingly appreciated, there is currently no method to determine whether certain patients will benefit from a neoadjuvant approach and whether others will benefit from a surgery-first approach. In this study, a previously validated prognostic triple biomarker panel is shown to predict genetic subtypes and clinical phenotypes of pancreatic cancer and also the optimal treatment strategy (neoadjuvant vs. surgery-first) for patients with resectable and borderline resectable pancreatic cancer.The genomic heterogeneity of pancreatic ductal adenocarcinoma (PDAC) is becoming increasingly appreciated. We aimed to evaluate the ability of a triple biomarker panel (S100A4, Ca-125, and mesothelin) to predict: (i) genetic PDAC subtypes; (ii) clinical phenotypes; and (iii) the optimal treatment strategy (neoadjuvant vs. surgery-first) in resectable and borderline resectable PDAC. Patients who underwent resection for resectable and borderline resectable PDAC were included from one single-institutional cohort and one multi-institutional cohort from the Australian Pancreatic Genome Initiative (APGI). Tumors were immunohistochemically evaluated for S100A4, Ca-125, and mesothelin, and a subset from the APGI cohort underwent RNA sequencing. This study included 252 and 226 patients from the single institution and the APGI cohorts, respectively. Triple-negative biomarker status correlated with non-squamous PDAC genotypes (p = 0.020), lower rates of distant recurrence (p = 0.002), and longer median overall survival (mOS) with the surgery-first approach compared with neoadjuvant treatment (33.3 vs. 22.2 mths, p = 0.038) in resectable PDAC. In contrast, the triple-positive disease was associated with longer mOS with neoadjuvant treatment compared with the surgery-first approach (29.5 vs. 13.7 mths, p = 0.021) in resectable and borderline resectable PDAC. In conclusion, the triple biomarker panel predicts genetic PDAC subtypes, clinical phenotypes, and optimal treatment strategies in resectable and borderline resectable PDAC.

GREM1 Restricts Plasticity and Maintains Heterogeneity in Pancreatic Cancer

Pancreatic cancer cellular heterogeneity is enforced via paracrine signaling from mesenchymal cells.

GREM1 is required to maintain cellular heterogeneity in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) shows pronounced epithelial and mesenchymal cancer cell populations1-4. Cellular heterogeneity in PDAC is an important feature in disease subtype specification3-5, but how distinct PDAC subpopulations interact, and the molecular mechanisms that underlie PDAC cell fate decisions, are incompletely understood. Here we identify the BMP inhibitor GREM16,7 as a key regulator of cellular heterogeneity in pancreatic cancer in human and mouse. Grem1 inactivation in established PDAC in miceresulted in a direct conversion of epithelial into mesenchymal PDAC cells within days, suggesting that persistent GREM1 activity is required to maintain the epithelial PDAC subpopulations. By contrast, Grem1 overexpression caused an almost complete 'epithelialization' of highly mesenchymal PDAC, indicating that high GREM1 activity is sufficient to revert the mesenchymal fate of PDAC cells. Mechanistically, Grem1 was highly expressed in mesenchymal PDAC cells and inhibited the expression of the epithelial-mesenchymal transition transcription factors Snai1 (also known as Snail) and Snai2 (also known as Slug) in the epithelial cell compartment, therefore restricting epithelial-mesenchymal plasticity. Thus, constant suppression of BMP activity is essential to maintain epithelial PDAC cells, indicating that the maintenance of the cellular heterogeneity of pancreatic cancer requires continuous paracrine signalling elicited by a single soluble factor.

Lipocalin 2 may be a key factor regulating the chemosensitivity of pancreatic cancer to gemcitabine

Owing to the high heterogeneity of pancreatic cancer, patient-derived xenografts (PDX) can compensate for the defects of cell line-derived xenografts (CDX) and also better preserve the heterogeneity and tumor microenvironment of primary tumors. Further, gemcitabine, which is used for the treatment of various cancers, is prone to tumor drug resistance, and this limits its sustained efficacy. Therefore, in this study, our objective was to screen appropriate individual therapeutic drugs for pancreatic cancer. To this end, we established pancreatic cancer PDX models from different patients and screened gemcitabine sensitivity regulatory molecules via high-throughput transcriptome sequencing and bioinformatics analysis. Based on the results obtained, gemcitabine was identified as the most suitable chemotherapeutic drug in a variety of PDX models. Additionally, our results indicated that Lipocalin 2 (LCN 2) may play an important role in the sensitivity of pancreatic cancer to gemcitabine treatment. Thus, the study provides a new potential intervention target for the treatment of pancreatic cancer in clinical practice.

Molecular regulation of CD8 T cell activity and heterogeneity in pancreatic cancer

Abstract T cell dysfunction or ‘exhaustion’ is a hallmark of solid malignancies. Despite the critical importance of CD8 T lymphocytes in controlling tumor growth and immunotherapy efficacy, the mechanisms governing T cell exhaustion remain unclear. Here, we focused on resolving the heterogeneity and molecular programming of exhausted CD8 T cell states in the context of pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a discouraging 5-year survival rate of approximately 8%. PDAC tumors commonly present as immunologically ‘cold’, devoid of protective T cell populations, and are typically resistant to promising immunotherapies such as immune checkpoint blockade or adoptive cell therapy. Therefore, understanding the unique microenvironmental and CD8 T cell-intrinsic signals governing T cell exhaustion and accumulation in PDAC tumors may reveal crucial insight for engineering CD8 T cells for optimal immunotherapy efficacy. Here, we developed preclinical PDAC models for profiling tumor-specific murine CD8 T cell responses. Through application of spectral flow cytometry and multimodal single-cell sequencing approaches, we have uncovered impressively diverse antigen-specific CD8 T cell states in PDAC tumors. Select CD8 T cell populations identified within the PDAC tumor microenvironment included canonical populations of terminally-exhausted and progenitor-exhausted CD8 T cells as well as unexpected and uncharacterized CD8 T cell populations. Accordingly, we also have identified critical transcription factors controlling key features of CD8 T cell heterogeneity and persistence in PDAC tumors. These findings may ultimately provide insight for tailoring CD8 T cell fate and function in PDAC. Supported by 2022 AAI Intersect Fellowship for Computational Scientists and Immunologists

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.