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.