Tumor Environment Influences Cell Behavior and Treatment Response in Pancreatic Cancer

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Summary

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for oncologists as prognostic RNA expression states are incompletely understood. To address this gap, researchers performed single-cell profiling of metastatic biopsies and matched organoid models. In vivo, they discovered a previously unknown intermediate PDAC transcriptional cell state, which revealed distinct tumor microenvironments (TMEs) varying by site and state specificity. To better understand the mechanisms of transcriptional plasticity, models were benchmarked against a reference map. Researchers found strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. By adding back in vivo-relevant factors, researchers were able to restore expression state heterogeneity and reveal plasticity in culture models. The findings highlight the importance of non-genetic modulation of cell state in influencing drug responses and uncovering state-specific vulnerabilities, which may inform the development of novel therapeutic approaches.

Fig.1 Graphical abstract. (Raghavan, 2021)

Research Criteria

The purpose of this study is to systematically profile single cells from metastatic pancreatic cancer biopsies and matched organoid models to gain insight into cellular states.

Fig.2 Experimental design. (Raghavan, 2021)

Sample Type

Cells from human PDAC tissues

Result—Single-Cell Profiling of Metastatic PDAC and Matched Organoids Models

The results underscore the imperative to develop novel methodologies for discerning determinants of cancer cell states in vivo and in model systems. Given the intricacy of cell states, which incorporate both cell-intrinsic and TME-dependent features, multiple mechanisms, such as clonal selection or plasticity, might contribute to the discrepancies between in vivo and ex vivo expression patterns. The researchers posited that a single-cell resolution dataset facilitating matched comparisons of in vivo and ex vivo attributes would yield an improved comprehension of cell state drivers, stability, and functional significance. Consequently, a pipeline was established to generate matched scRNA-seq profiles and organoid models using core needle biopsies from metastatic PDAC patients. The pipeline generated a substantial number of high-quality single cells per biopsy and successful early-passage organoid cultures for the majority of patient samples. The study identified malignant cells and 11 unique non-malignant cell types, thereby establishing a robust pipeline that procured high-quality malignant and non-malignant single-cell transcriptomes from metastatic PDAC needle biopsies and matched organoids.

Fig.3 scRNA-seq of metastatic PDAC and matched organoids. (Raghavan, 2021)

Result—Single-Cell Resolution Identifies an IC Cancer Cell State in Metastatic PDAC

The study demonstrates that a significant subset of PDAC cells manifests weak expression for previously identified signatures, prompting an unbiased analysis of the single-cell dataset to elucidate the in vivo expression states. Through this analysis, scBasal and scClassical signatures emerged, revealing that basal-like cells are associated with TGF-β signaling, WNT signaling, EMT, and cell cycle progression, while classical-like cells display epithelial and pancreatic lineage programs. The scRNA-seq data revealed scBasal and scClassical programs are not mutually exclusive, and identified an intermediate cell state enriched for developmental, RAS signaling, and inflammation/stress response gene sets, termed the IC state. This tripartite cell state framework encompasses committed basal and classical phenotypes, with considerable signature co-expression in single cells. Patient specimens exhibited significant heterogeneity at the cellular level, suggesting that the IC state may serve as a transition between scBasal and scClassical poles.

Fig.4 An intermediate co-expressor state bridges basal and classical phenotypes. (Raghavan, 2021)

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The cellular heterogeneity and diversity of gene expression present a significant challenge for understanding molecular mechanisms at the single-cell level. The analysis of the transcriptome, which is crucial to gain insight into cellular states and functions, can be achieved by single-cell RNA sequencing (scRNA-seq). For researchers looking for high-quality scRNA-seq services, Creative Biolabs provides end-to-end solutions, from sample preparation to data analysis, ensuring rapid turnaround times, maximal flexibility, and data accuracy.

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Reference

1. Raghavan, S.; et al. Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell. 2021, 184(25): 6119-6137.