Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma

Peng, Junya; Sun, Bao‐Fa; Chen, Chuanyuan; Zhou, Jiayi; Chen, Yu‐Sheng; Chen, Hao; Liu, Lulu; Huang, Dan; Jiang, Jialin; Cui, Guanshen; Yang, Ying; Wang, Wenze; Guo, Dan; Dai, Menghua; Guo, Junchao; Zhang, Taiping; Liao, Quan; Liu, Yi; Zhao, Yongliang; Han, Dali; Zhao, Yupei; Wu, Wenming

doi:10.1038/s41422-019-0195-y

Cited by 844 publications

(1,044 citation statements)

References 92 publications

Supporting

Mentioning

922

Contrasting

Order By: Relevance

“…The application of this algorithm to histopathologically unclassifiable tumors identifies two patient groups with significantly different overall survival. We therefore hypothesize that the algorithm is capable of re-identifying the dominant features of the QM and non-QM molecular subtypes in CT images and that radiomics-based phenotyping may thus offer a clinically usable classification advantageous over histopathology in the notoriously heterogenous entity of PDAC [18][19][20]. This notion is reinforced by the fact that histopathological samples are by default a significant underrepresentation of the tumor, since they are derived from a small sub-section of the tissue [21], and regions of differing molecular subtype are likely to coexist within the same tumor [22].…”

Section: Discussionmentioning

confidence: 99%

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2020

JCM

View full text Add to dashboard Cite

To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Kaissis

Ziegelmayer

Lohöfer

et al. 2020

JCM

View full text Add to dashboard Cite

show abstract

“…In a recent work, Peng et al (Peng et al, 2019) ports a study on single cell transcriptome analysis of a 312 total of 57,530 cells from 24 primary PDAC tumors and 11 control pancreases. They found that PDAC 313 tumor mass is highly heterogeneous and composed of diverse malignant and stromal cell types as 314 expected.…”

mentioning

confidence: 99%

Basal-like and Classical cells coexistence in pancreatic cancer revealed by single cell analysis

Juiz

El-Kaoutari

Bigonnet

et al. 2020

Preprint

View full text Add to dashboard Cite

26Pancreatic ductal adenocarcinoma (PDAC) is composed of stromal, immune and epithelial 27 cells. Transcriptomic analysis of the epithelial compartment allows a binary classification into 28 mainly two phenotypic subtypes, classical and basal-like. However, little is known about the 29 intra-tumor heterogeneity of the epithelial component. Growing evidences suggest that this 30 two side phenotypic segregation is not so clear and that both could coexist in a single tumor. In 31 order to elucidate this hypothesis, we performed single-cell transcriptomic analyses using 32 combinational barcoding on epithelial cells from 6 different classical PDAC obtained by 33 Endoscopic Ultrasound (EUS) with Fine Needle Aspiration (FNA). In order to purify the 34 epithelial compartment, PDAC were grown as Biopsy Derived Pancreatic Cancer Organoids. 35 Single cell transcriptomic analysis allowed the identification of 4 main cell clusters present in 36 different proportions in all tumors. Remarkably, although these tumors were classified as 37 Classical, one of the clusters corresponded to a basal-like. These results depict the 38 unanticipated high heterogeneity of pancreatic cancers and demonstrated that basal-like cells 39 with a high aggressive phenotype are more widespread than expected. 40 41 42With a survival rate of 5 years in less than 8% of the cases (Siegel et al., 2018) pancreatic 43 ductal adenocarcinoma (PDAC) is still one of the most lethal cancers. A principal problem 44 facing this disease is its heterogeneity that results as a consequence of the combination of 45 genetic, epigenetic, and micro-environmental factors (Lomberk et al., 2019(Lomberk et al., , 2018 Yachida and 46 Iacobuzio-Donahue, 2013). Recently, two main PDAC subtypes have been identified by 47 molecular characterization: 1-classical, that are more frequently resectables, presenting a 48 higher level of differentiation, often associated with fibrosis and inflammation; and 2-basal-49 like, with a poorest clinical outcome and a loss of differentiation (Moffitt et al., 2015; Nicolle 50 et al., 2017). This well-established binary classification could be controverted if cells from a 51 unique tumor contain both phenotypes at the same time. In fact, in addition to the tumor 52 differences between patients that argues in favor of stratification for personalizing PDAC 53 treatments, it is also absolutely necessary to consider the intra-tumor differences since they are 54 playing key roles in the evolution of tumors (i.e.: they can conduce to clonal selection of 55 resistant cells and to the relapse so frequently observed after the first line of chemotherapy). 56Single-cell analysis by transcriptomics is nowadays a powerful strategy to determine the intra-57 tumor heterogeneity, however we need to bypass two main challenging difficulties: The first 58 one is to obtain pure epithelial transformed cells. To do that, we specifically amplified these 59 cells by a few passages in three dimensional (3D) ex vivo culture (Tiriac et al., 2019). Three ...

show abstract

“…These fibroblast cells were categorized into two distinct subclusters (Fig. 4A), including myofibroblastic CAFs (myCAFs) with periglandular FAP+ αSMA high and apCAFs with high level of MHC class II family members [24]. Compared to the myCAFs, the apCAFs showed relative higher expression level of CD74 and the MHC II molecules while the expression level of DCN and LUM was relatively lower (Fig.…”

Section: Resultsmentioning

confidence: 99%

Single-cell RNA-seq reveals identity and heterogeneity of malignant osteoblast cells and TME in osteosarcoma

Zhou

2020

Preprint

View full text Add to dashboard Cite

25Osteosarcoma (OS) has high heterogeneity and poor prognosis. In order to explore the 26 molecular mechanism of OS and the tumor micro-environment (TME) on OS, we 27 employed single-cell RNA-sequencing (scRNA-seq) on 110,745 individual cells from 28 65 chromosomal lesions, including structural variations (SVs) and copy number 66 alterations (CNAs); however, few recurrent point mutations in protein-encoding genes 67 have been identified in OS [11][12][13][14][15].Low expression of immune-associated genes is 68 another significant phenotype for OS [16]. How to convert the immunosuppressive 69 microenvironment into the one that favors the induction of antitumor immunity is 70 indispensable for effective cancer immunotherapy. 71Here, we employed single cell transcriptome approach to dissect the 72 heterogeneity of OS cells. We analyzed the transcriptomic profiles of a total of 73 110745 cells from 7 primary tumors, 2 lung metastatic and 2 recurrent OS tissues. We 74 first divided the OS cells into 5 sub-clusters and osteoclast into 3 subtypes. The 75 profiles of OS, OC and immune-system cells were analyzed. We found that the TME 76 of the recurrent and lung metastatic OS exhibited more significant suppressivity than 77 primary tumor tissue. Thus, the results in this study improve the understanding of the 78 immune-suppressivity observed in advanced OS including distant metastasis and 79 recurrence, and are potentially valuable in novel treatment strategy for OS. 80Of importance, we are the first to uncover that Treg cells in OS expressed 81 TIGIT. TIGIT is a coinhibitory receptor expressed on effector T cells, natural killer 82 (NK) cells, T regulatory cells (Treg) and T follicular helper (TFH) cells. It has gained 83 attention as a potential therapeutic target in wide variety of tumors[17-19]. The 84 antibodies of TIGIT, named BGB-A1217, had registered and recruited on August 85 2019. Here we explored the preclinical significance of blocking of TIGIT. 86 Method 87 Patients 88 The eleven patients for scRNA-seq analysis enrolled in this study were hospitalized 89 during the period of The study was approved by Shanghai Sixth People's Hospital Ethics Committee. Each 91 patient was provided a written signed consent. All patients were diagnosed according 92 to the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines 93 in Oncology with the terms of Bone Cancer (Version 2.2019). Among 11 patients for 94 scRNA-seq, 7 were derived from the primary sites of patients who received traditional 95 first line combination chemotherapy for OS, including Adriamycin(ADM), 96 cisplatin(DDP), methotrexate(MTX) and Ifosfamide(IFO) and surgical therapy. 2 lung 97 metastatic patients and 2 recurrent patients all received the gemcitabine combined 98 with Docetaxel(GT) chemotherapy treatment. The BC17, one of the lung metastasis 99 patients, had enrolled in clinical trial NCT03676985 and undergone the anti-PD-L1 100 treatment for 6 times. For this clinical trial, all enrolled patients had finished the 101 neoadjuvant...

show abstract

Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma

Cited by 844 publications

References 92 publications

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

Basal-like and Classical cells coexistence in pancreatic cancer revealed by single cell analysis

Single-cell RNA-seq reveals identity and heterogeneity of malignant osteoblast cells and TME in osteosarcoma

Contact Info

Product

Resources

About