Single‐Cell Transcriptome Analysis Uncovers Intratumoral Heterogeneity and Underlying Mechanisms for Drug Resistance in Hepatobiliary Tumor Organoids

Abstract

Molecular heterogeneity of hepatobiliary tumor including intertumoral and intratumoral disparity always leads to drug resistance. Here, seven hepatobiliary tumor organoids are generated to explore heterogeneity and evolution via single‐cell RNA sequencing. HCC272 with high status of epithelia‐mesenchymal transition proves broad‐spectrum drug resistance. By examining the expression pattern of cancer stem cells markers (e.g., PROM1, CD44, and EPCAM), it is found that CD44 positive population may render drug resistance in HCC272. UMAP and pseudo‐time analysis identify the intratumoral heterogeneity and distinct evolutionary trajectories, of which catenin beta‐1 (CTNNB1), glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), and nuclear paraspeckle assembly transcript 1 (NEAT1) advantage expression clusters are commonly shared across hepatobiliary organoids. CellphoneDB analysis further implies that metabolism advantage organoids with enrichment of hypoxia signal upregulate NEAT1 expression in CD44 subgroup and mediate drug resistance that relies on Jak‐STAT pathway. Moreover, metabolism advantage clusters shared in several organoids have similar characteristic genes (GAPDH, NDRG1 (N‐Myc downstream regulated 1), ALDOA, and CA9). The combination of GAPDH and NDRG1 is an independent risk factor and predictor for patient survival. This study delineates heterogeneity of hepatobiliary tumor organoids and proposes that the collaboration of intratumoral heterogenic subpopulations renders malignant phenotypes and drug resistance.

Heterogeneity, Inherent and Acquired Drug Resistance in Patient-Derived Organoids Models of Primary Liver Cancer

“…Sorafenib is the rst-line targeted agent that prolongs the median survival time of patients with advanced HCC by nearly three months; unfortunately, most patients develop resistance to this drug within six months 21 . The mechanisms by which HCC develops resistance to sorafenib are largely unknown, although some molecular events such as Hedgehog signaling, Jak-STAT pathway, oncogene KIF14, and IGF/FGF signaling have been suggested to be related to sorafenib resistance in HCC 10,11,22,23 . Here, we induced acquired sorafenib-resistant organoids from four HCC patients following 3-5 months of culture.…”

“…First, the factors affecting the success rate of establishing organoids are not established, due to small sample size of surgically removed tissues applied in each study. The numbers of patients from whom the organoids developed were eight 7 , eleven 8 , ve 9 , four 10 , and seven 11 in previous studies. Second, the success rate of establishing organoids is low, 26% in PLC 8 .…”

“…Although tumor cell lines have allowed pioneering advances in cancer biology, they might not recapitulate critical characteristics and the heterogeneity of PLC in situ. Recently, PLC patient-derived tumor organoids have been developed [7][8][9][10][11] . These organoids have been proven to retain the morphology, genetic heterogeneity, and expression pattern of the original tumor markers of PLC.…”

Omics and Computational Modeling Approaches for the Effective Treatment of Drug-Resistant Cancer Cells

“…Often, outcomes from (weighted) GCN analysis are further subjected to other computational analyses, for example survival analysis, to validate the biological and/or clinical significance of the candidate genes. As a recent example, Li et al focused on PPP2R2B, encoding serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B beta isoform, as a potential prognostic biomarker for TNBC on the basis of a series of bioinformatic analyses involving a GCN (Li Z. et al, 2021). Kaplan-Meier survival analysis for this gene revealed that patients with a low expression level of PPP2R2B showed shorter survival time than those with a high expression level of PPP2R2B.…”

“…Drug resistance is a phenotypic state that arises as a result of a complex interplay between genetic and non-genetic mechanisms (Marine et al, 2020). Such genetic and non-genetic reprogramming consequently leads to drug resistance through various mechanisms (Gatti and Zunino, 2005;Housman et al, 2014;Zheng, 2017;Lim and Ma, 2019;Vasan et al, 2019;Bukowski et al, 2020), including: drug inactivation, for example by an excessive level of glutathione that detoxifies xenobiotics (Jiang et al, 2017;De Luca et al, 2019); alteration of a drug target by mutations or changes in an expression level (Likhite et al, 2006;Costa et al, 2008); drug efflux by transporters (Giddings et al, 2021); enhanced DNA damage repair system (Harte et al, 2014); development of resistance via dysregulated autophagy (Martin et al, 2017;Cai et al, 2019); epithelial-mesenchymal transition (EMT) (Fischer et al, 2015;Zheng et al, 2015); or heterogeneity of a cancer cell population having cancer stem cells (Seth et al, 2019;Zhao et al, 2021). A state of drug resistance is indeed a highly complex phenotype that requires multidimensional approaches.…”