Introduction
Cancer is the leading cause of death among people under the age of 70 in most countries. According to the statistics from the International Agency for Research on Cancer (IARC), there were 19.29 million new cancer cases worldwide in 2020, resulting in the death of 9.96 million patients (Wild, Weiderpass, & Stewart, 2020). To satisfy rapid growth and proliferation, tumor cells require a large amount of nutrients to meet their material and energy needs (Zhu & Thompson, 2019). Tumorigenesis is broadly dependent on the reprogramming of cellular metabolism as both direct and indirect consequence of oncogenic mutations. Tumor cells mainly rely on aerobic glycolysis (also known as the Warburg effect) for rapid energy and precursor supply, which is accompanied by the production of large amounts of lactate (Vander Heiden, Cantley, & Thompson, 2009). Apart from the glucose as a bioenergetic substrate, the conversion of glutamine to α-ketoglutarate and subsequent entering the tricarboxylic acid cycle (TCA cycle) is often necessary for tumor cell growth (Son et al., 2013). Also, recent findings showed that accumulation and recycling of lactate and ammonium can contribute to the progression and occurrence of cancer (Martínez‐Monge et al., 2018; Spinelli et al., 2017). In addition, the nucleotide metabolic pathway of tumor cells is highly efficient and nucleotide imbalance further induces tumor-associated mutations (Aird & Zhang, 2015). In 2000, Dr. Weinberg and colleagues summarized the six characteristics of cancer cells, i.e., self-sufficiency in growth signals, evading apoptosis, insensitivity to anti-growth signals, sustained angiogenesis, tissue invasion and metastasis, as well as limitless replicative potential (Hanahan & Weinberg, 2000). In 2011, they supplemented the characteristics of tumors with two emerging hallmarks, namely, reprogramming of energy metabolism and evading immune destruction (Hanahan & Weinberg, 2011). Very recently, Pavlova and Thompson have organized known cancer-associated metabolic changes into six hallmarks (Pavlova & Thompson, 2016). Based on the complex genetic diversities and epigenetic differences of tumor cells, there are great differences between the individual differences of different patients and the tissue heterogeneity of the same patient are huge, which seriously hinders the effective treatment of tumors (Hensley et al., 2016).
Cervical cancer is the second leading cause of deaths in female cancer patients of childbearing age, and there were 600 thousand new cases and 340 thousand deaths worldwide in 2020 (Sengupta & Honey, 2020). Cervical cancer has no obvious symptoms in the early stage and is prone to be ignored, and the cure rate is low in the middle and late stages (Serkies & Jassem, 2018). At present, surgery, radiotherapy and chemotherapy are the most commonly used methods for the treatment of cervical cancer; however, surgery cannot remove all cancer cells and the metastasis of cancer cells exacerbates the difficulty of treatment, while radiotherapy and chemotherapy have serious side effects and induce drug resistance (Keshavarz-Fathi & Rezaei, 2021). For example, 5-FU, an uracil analog, is a commonly used clinical drug for cancer treatment, which targets thymidylate synthase (TYMS) to affect the de novo nucleotide synthesis pathway (Longley, Harkin, & Johnston, 2003). Although 5-FU has a good therapeutic effect for cancer patients in the early stage, the evolution of chemoresistance towards antitumor drug hampers its clinical use. For example, Giacchetti et al. showed that the overall response rate of 5-FU for patients with advanced colon cancer was only 10~15% (Giacchetti et al., 2000). Vishnoi et al. found that 5-FU resistant cervical cell lines displayed elevated expression of markers (e.g., Survivin, ABCG2) related to chemoresistance and epithelial mesenchymal transformation (EMT), meanwhile human papillomavirus oncoprotein (HPV16 E6) promoted the EMT process and induced drug resistance (Vishnoi et al., 2016). In 5-FU resistant cervical cancer cells, Ma et al. found that the glycolysis rate was significantly increased, and inhibition of HPV16 E6/E7 can down-regulate the glycolysis pathway to overcome 5-FU resistance (Ma, Huang, & Song, 2019).
Apart from the drug resistance, the lack of preclinical model further aggravates the attrition rate in the clinical drug development. It has been estimated that the drug attrition rates are as high as 95% tested in phase I clinical trials; 67% and 33% of all drugs that enter Phase II and Phase III clinical trials fail to transit into the next stage, respectively (Santo et al., 2017). Since the 1950s, monolayer cells have been used for in vitro drug screening due to its simple, cheap and repeatable features (Hickman et al., 2014). However, monolayer cells cannot completely reproduce the physiological and pathological microenvironment of tumor cells in vivo , and thus could not accurately characterize the function and phenotype of tumor cellsin vivo (Badea et al., 2019). The use of preclinical models that are unable to fully recapitulate the complexity of tumors would seriously affects the transition of new anticancer treatment to the clinic. The traditional patient-derived tumor xenograft (PDX) model could better retain the tumor tissue characteristics of patients, but with high cost and long periods (Ali, Anand, Tangella, Ramkumar, & Saif, 2015). Therefore, the construction of efficient in vitrotumor model is of great significance to improve the efficiency of drug screening and the accuracy of clinical application. In 1970, Sutherland and colleagues first proposed the 3D multicellular tumor spheroid (MTS), and pointed out that the physiological characteristics of MTSs were similar to the avascular tumor nodules, tumor micro-metastasis and intervascular areas of tumor (Inch, Mccredie, & Sutherland, 1970). Compared with the 2D monolayer culture, MTSs behave closer microenvironment to tumors in vivo , which could more accurately characterize the function and phenotype of tumor tissues, with closer gene expression profiles, protein expression profiles, and metabolite profiles to the counterparts of the PDX model (Lauschke, Shafagh, Hendriks, & Ingelman-Sundberg, 2019; Zietarska et al., 2007). Numerous reports have shown that MTSs were more resistant to radiotherapy and chemotherapy, and even the aggregates of 25~50 tumor cells also have shown enhanced resistance than 2D monolayer cells (Yu, Chen, & Cheung, 2010).
Multi-omics analysis of the effect of antitumor drug on tumor cell metabolism renders a more comprehensive exploration of the mechanism of drug resistance (Guang et al., 2018). Systems biology research has contributed to clinical drug development, judgment of patient prognosis and personalized treatment (Veenstra, 2021). Genomics, transcriptomics, proteomics and metabolomics are most powerful systems biology tools, which have been used in combination or alone to elucidate the mechanism of tumor resistance. For example, based on transcriptome and proteomic techniques, Lu et al. found that inhibition of STAT3 signal transduction contributed to inhibiting the epithelial mesenchymal transformation (EMT) and down-regulating the expression of stem genes, thereby inhibiting tumor progression, metastasis and chemical resistance (Lu, Bankhead, Ljungman, & Neamati, 2019). Nonetheless, to the best of our knowledge, numerous studies were carried out with 2D monolayer cultures, while multi-omics studies are rarely performed with 3D in vitrotumor models (Kalfe, Telfah, Lambert, & Hergenroder, 2015; Schroll, LaBonia, Ludwig, & Hummon, 2017; Seker et al., 2019).
Although MTSs are capable of accurately describing the effects of drugs in vivo , the use of 3D model is still limited due to the limit in preparation and analysis methods, and only less than 30% of researchers used 3D model for anticancer drug screening, evaluation and other relevant studies (Hutmacher, 2010). Therefore, in order to improve the predictability and availability for screening anticancer drug candidates for preclinical trials, in the present study we established a rapid, reproducible and standardized MTSs culture method. Furthermore, to investigate the phenotypic differences of Hela carcinoma cells with the 5-FU treatment under both 2D monolayer cultures and 3D MTS, we leveraged multi-omics analysis across transcript, protein and metabolite levels to better understand the key regulatory genes and related metabolic pathways responsible for the drug resistance mechanism.