Next Article in Journal
A Novel Nomenclature for Repeat Motifs in the Thymidylate Synthase Enhancer Region and Its Relevance for Pharmacogenetic Studies
Next Article in Special Issue
Drug Repurposing for Triple-Negative Breast Cancer
Previous Article in Journal
Evidences of CTLA-4 and PD-1 Blocking Agents-Induced Cardiotoxicity in Cellular and Preclinical Models
Previous Article in Special Issue
Circulating miR-1246 Targeting UBE2C, TNNI3, TRAIP, UCHL1 Genes and Key Pathways as a Potential Biomarker for Lung Adenocarcinoma: Integrated Biological Network Analysis
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 10
Issue 4
10.3390/jpm10040180
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Systems Biology and Experimental Model Systems of Cancer

by
Gizem Damla Yalcin
†,
Nurseda Danisik
†,
Rana Can Baygin
† and
Ahmet Acar
*
Department of Biological Sciences, Middle East Technical University, Universiteler Mah. Dumlupınar Bulvarı 1, Çankaya, Ankara 06800, Turkey
*
Author to whom correspondence should be addressed.
†
These authors contributed equally.
J. Pers. Med. 2020, 10(4), 180; https://doi.org/10.3390/jpm10040180
Submission received: 7 September 2020 / Revised: 13 October 2020 / Accepted: 16 October 2020 / Published: 19 October 2020
(This article belongs to the Special Issue Recent Developments in Cancer Systems Biology)
Browse Figure
Versions Notes

Abstract

:
Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

1. Introduction to Cancer Systems Biology

Cancer is an extremely complex disease with heterotypic interactions between cancer cells and neighboring stromal cells that support the proliferation, invasion, and the metastatic cascade of tumor cells [1,2]. Recently, multi-omics approaches empowered by next-generation technologies have enabled genomic characterization and evolutionary histories of both primary and metastatic cancer progression [3,4,5,6]. These technologies that shed light on the genome, transcriptome, metabolome, and proteome of cancer cells corroborate our understanding about systems biology-level approaches in cancer (Figure 1) [7]. Considering the challenges to unify high-throughput data obtained from multi-omics studies, system biology applications in cancer hold a key role to tackle this very problem. For example, cancer as a disease of numerous distinct cell types requires taking into consideration the combination of data derived from these different cell types together with the integration of various layers of genetic and non-genetic data that are forming the cellular systems. Thus, cancer systems biology can simplify the analysis of multi-layer data and offer effective and fast solutions for the development of novel drug technologies and the identification of predictive biomarkers in cancer therapies. Cancer systems biology is an emerging field with accumulating data obtained through network-driven and interdisciplinary science that ultimately aims to tailor better-personalized treatment modalities for patients based on their genetic and non-genetic profiles [8].
The heterogeneous nature of cancers led to studies mapping the (epi)genomic alterations [9,10,11] both in primary [6,12] and metastatic cancers [5]. Through the high-throughput data obtained from cancer patients, it is now possible to combine this information and assess the genotype-to-phenotype link to further characterize the disease onset and clinical outcome. The combination of information derived from the genomic architecture and various gene networks from a single or a group of cells not only determines the fate of these cells during development but also a progression to cancer occurs as a result of the deregulation of these interactions. For example, while the regulation of Notch and Wnt signaling pathways are fine-tuned by each other in normal homeostasis [13], their aberrant expression and deregulation are commonly seen in cancers [14,15]. Therefore, understanding the genetic and epigenetic changes that cause persistent signaling activations and disrupting normal cellular homeostasis is still one of the biggest challenges to address in cancer systems biology.

2. Cancer Systems Biology for Precision Medicine

The vast majority of efforts focus on bridging the “big data” obtained from various multi-omics studies to new computational algorithms to ultimately offer more effective personalized cancer therapies. Despite the advancements in cancer therapy through systems biology approaches, treatment resistance is arguably one of the biggest challenges for better-personalized cancer treatments [16,17]. This is mainly due to the fact that cancer follows distinct evolutionary trajectories in patients compared to their genomic landscapes, not only during the initiation and metastasis cascade of cancer cells but also in response to the treatment in cancer therapies [18,19]. For this reason, the accurate identification of subclonal drivers holds great importance for the timing of the subclonal expansion and its diversity in cancer therapies [20]. This sophisticated subclonal identification tool, empowered by machine learning and population genetics, will potentially lead to developing more comprehensive computational methods by integrating with network-driven approaches for cancer systems biology in the future.
With the rapid developments in next-generation sequencing (NGS) technologies, previous microarray studies have been gradually replaced by massively parallel deep sequencing techniques such as whole-genome, whole-exome, targeted-panel, and RNA sequencing [21]. Initially, the microarray platforms have proved to be a very useful tool for genome-wide association studies (GWAS) in cancer systems biology; however, they demonstrated limitations such as covering only a the small fraction of the genome and failure to take into account more than common genetic risk factors [22]. Later, individual research groups started to apply NGS technologies to identify somatic alterations (single-nucleotide variations, copy-number alterations, structural variations) in cancer driver genes and to determine gene expression changes and open chromatin formations both in coding and non-coding regions of the genome [23,24,25,26,27,28]. Then, individual studies were followed by larger multigroup projects [4,5,6]. One of the remarkable efforts is The International Cancer Genome Consortium/The Cancer Genome Atlas (ICGC/TCGA) Pan-Cancer Analysis of Whole Genomes (PWAG) project, comprising a working group of 700 scientists, which recently reported their findings from 2600 whole-genome samples [4,29,30,31]. In addition to these studies that deciphered the evolutionary trajectories of tumors prospectively, recent technologies have also allowed the monitoring of clonal dynamics using “cellular barcodes” integrated into experimental model systems to map the tumor evolution at the single-cell resolution [32,33].
In addition, investigating cancer genomes at the single-cell resolution has taken a big step forward in the past few years [3]. Initial studies focused on the understanding of the transcriptome of single cells in a plate-based system wherein cells were required to be sorted individually, and thus the system lacked high-throughput capacity [34]. However, recent advances, especially with the use of droplet-based systems, have advanced our understanding about single-cell genomics through an increased capacity to profile thousands of single cells at the same time (single-cell RNA sequencing, scRNA-seq) [35,36]. The scRNA-seq technology provided a high-resolution picture not only of cellular states in developmental biology [37] but also in cancer biology where intratumoral heterogeneity and tumor cell plasticity are highly prevalent [38]. Furthermore, the information obtained from the analysis of single-cell WGS (scWGS) has proved to be informative for understanding intratumor heterogeneity and the evolutionary history of thousands of single cells comprising the bulk tumor population [39,40]. Recently, the high-throughput capacity for scWGS has improved significantly, and clonal/subclonal alterations at the single-cell resolution were reported in thousands of cells [41]. To capture the epigenetic changes at the single-cell level, novel methods to map the single-cell epigenome have also been reported. For example, single-nuclei chromatin accessibility assays (ATAC-seq) inferring the chromatin open or closed states in single cells [42,43]. Lastly, the rapid developments in the single-cell biology have also resulted in novel methods such as parallel sequencing of single-cell genomes and transcriptomes [44] and joint profiling of single-cell chromatin accessibility and gene expression [45]. Various online databases containing cancer systems biology tools to document molecular profiles of cancer types are available and offered for the use of the cancer research community (Table 1). Importantly, various multi-omics data obtained using high-throughput sequencing methods enables the integration of these data into experimental model systems for the identification of the actionable targets in cancer. As such, these molecular data integrated with systems biology applications, for the function of transcriptional and proteomics networks, provide effective solutions for the treatment of cancer. Given that cancer is a systems biology disease, integration of the cellular information with the help of computational and mathematical modeling highlights the need to develop more advanced and sophisticated systems biology applications in cancer. This considerable challenge has especially become evident with a rapid increase in the accumulation of sequencing data over the past decade. Hence, to address this very challenge, systems biology approaches are timely positioned to offer novel solutions to better understand the underlying mechanisms of drug resistance and the identification of biomarkers that can predict the disease outcome and response to targeted therapies. Overall, integrating cellular networks with cancer (epi)genomes in both single and bulk cell populations has paved a way to advance our understanding for developing systems biology approaches for precision therapy to advance clinical decisions for patient benefits.

3. Experimental Model Systems of Cancer

Although cancer mortality rates are gradually diminishing, it is still one of the deadliest diseases in the world [65]. To develop more effective therapeutic solutions, cancer cell lines, 3D spheroids, in vivo patient-derived xenografts (PDXs), and ex vivo patient-derived organoids (PDOs) have been studied by various groups [66,67,68]. Due to the advances in the development of experimental model systems, there has been remarkable progress in understanding the underlying mechanisms of initiation, progression, and the metastatic cascade of cancer cells [69]. In addition to the advantages of each model system, traditional model systems have failed to recapitulate the response to drugs that are observed in the clinic. For instance, targeted therapies and chemotherapeutic agents that work well in preclinical model systems fail to proceed into clinical trials since specific model systems were unable to recapitulate the disease progression [70]. Therefore, in this section of the review, we sought to discuss current preclinical model systems used in cancer research and their role in predicting how cancer will progress and respond to the therapy when these model systems are integrated with system biology approaches.

4. Cell Line-Based Model Systems

Since the first human cancer cell line was established in 1951, 2D monolayer systems have provided major advantages in the understanding of tumor biology and cancer therapy [71]. Over the decades, 2D monolayer systems offered several advantages such as being easy to expand and hence allowing long-term culture times, being manipulated by gene insertions and deletions, and requiring inexpensive material for culturing [72]. On the other hand, this platform has many drawbacks, mainly its inability to mimic the 3D nature of tumor growth. The inadequacies of the 2D monolayer systems also include a lack of cell-to-extracellular matrix (ECM) contact that has been reported as responsible for the accurate detection of cell viability/death, drug metabolism, and expression of certain genes and protein in tumors [73]. Another major limitation of 2D monolayer systems is their inaccurate utility of oxygen and nutrients when compared to 3D culture systems that have proven to be more successful in mimicking real tumor masses [74]. Collectively, 2D monolayer systems have played a major role in understanding and designing cancer therapies for systems biology approaches; however, due to their insufficiency to predict real tumor outcomes in patients, more suitable model systems such as 3D culture systems have been developed.
The first 3D culture was performed using a soft agar solution by Hamburger and Salmon in 1977 [75]. Since that time, several 3D culture methods have been documented. Depending on the material used, the 3D culture systems can be divided into three categories: (i) cultured onto non-adherent plates, (ii) embedded into matrigel-like substances, and (iii) seeded into scaffold-based systems. The general approach for 3D culture systems is based on the formation of a spheroid structure in which cancer cells can form various layers. The 3D nature of spheroids has been demonstrated as a successful system in mimicking the features of the solid tumor mass [73]. Three-dimensional spheroids can also mimic tissue-specific functional characteristics in developmental processes. For example, cardiomyocyte spheroids can exhibit heart-like rhythms, and hepatocyte spheroids exhibit biochemical functions of the liver [76,77]. Three-dimensional culture systems have also been shown to mimic in vivo-like microenvironments via the establishment of complex cell-to-cell and cell-to-ECM communications. These interactions result in cellular signal transduction events similar to tumor tissues that can mediate their cell shape and proliferation [78]. In addition, drug response assays in 3D culture systems were shown to resemble in vivo studies more than 2D culture systems in terms of their success rates in preclinical studies [79,80]. In another study, sensitivities of the same cell line against different chemotherapeutic agents were reported as different in 2D vs. 3D culture systems [81]. For instance, in this study, HCT-116 cells grown in both 2D and 3D model systems and their sensitivities against four commonly used anticancer agents (melphalan, 5-Fluorouracil, oxaliplatin, and irinotecan) were tested. The efficacy of these inhibitors was higher in the 2D than the 3D culture system, suggesting that phenotypic differences and distinct cell-to-cell interactions between these model systems might be responsible for observing the differences in drug sensitivities.

5. Patient Sample-Based Model Systems

Patient-derived xenografts (PDXs) are preclinical models established by directly transplanting patient-derived tumor specimens into immunodeficient mice [82]. PDXs have been accepted as promising preclinical model systems that successfully mimic the testing of anticancer drugs [66]. This system provides several advantages, such as the preservation of tumor heterogeneity, molecular subtypes and the clinicopathological features of the tumors obtained from patients [83]. In addition, PDXs have been shown to successfully predict the drug response in the preclinical setting to test the effectiveness of therapeutic agents [84]. While PDXs offer several advantages as a preclinical model system, an increasing body of evidence suggests there are limitations [85]. Firstly, a significant proportion of tumor samples engrafted in mice may not successfully grow due to the host mouse environment causing a bottleneck. Secondly, engraftment times can be long so that the maintenance costs associated with each PDX prove prohibitive. Thirdly, there is still no standardized method for choosing the type of mouse or engraftment technique specific for each cancer type, which raises the possibility of obtaining non-reproducible results between different studies. Studies that overcome these limitations have shed light on the mechanisms of acquired drug resistance, especially in metastatic colorectal cancer (mCRC). For instance, a series of seminal studies published by the Bertotti Lab has demonstrated the use of a large PDX biobank to investigate the underlying mechanisms of drug resistance in mCRC [86,87,88,89]. Importantly, one of these studies played a critical role in assessing the genomic landscape of anti-EGFR antibody blockage in PDXs and functional consequences linked to clinical data in cancer patients [87]. Thus, PDXs have paved a way to develop a platform for the systematic analysis and evaluation of cancer therapies.
Patient-derived tumor organoids (PDOs) are ex vivo three-dimensional structures of tumors obtained from cancer patients and grown in the presence of an extracellular matrix [90]. Accumulating evidence suggests that PDOs can successfully predict the drug response in cancer patients in the clinic in addition to preserving the genetic and transcriptomic heterogeneity of the original tumor [67]. In addition, studies focused on comparing the histopathological features of tumors with PDOs revealed that the PDOs maintain similar morphological characteristics as the original tumor [90,91]. Importantly, PDOs also mimic the genomic and transcriptomic features of the tumors that they have derived from even after long ex vivo culture times [91,92,93]. To date, PDOs have been established from different cancer types including colorectal [93], gastrointestinal [91], pancreatic [94], prostate [95], bladder [96], breast [97], glioblastoma [98], and ovarian [99]. Three-dimensional cultures of PDOs that predict the outcome of drug treatment in cancer patients can be considered an important milestone for personalized medicine for the benefits of cancer patient [100]. When PDOs are established from individual patients in a short time, they can provide a window of opportunity to test therapeutic agents in parallel to the clinic, and thus the outcome of drug testing in the laboratory can prove informative for the decision making of treatment for patients.
Amongst the key studies about PDOs, van de Wetering et al. (2015) is the first study that reported a well-established and characterized PDO biobank from 20 primary CRC patients [93]. In this study, whole-exome sequencing (WES) and the RNA sequencing of samples resulted in preserved genetic heterogeneity and molecular cancer subtypes both in the primary tumor tissue and PDOs. In addition, the genetic heterogeneity of the primary tumor was mostly preserved during the establishment and long culture times of organoids in ex vivo. The histopathological assessment of samples suggested a very high similarity in terms of the phenotypic heterogeneity between PDOs and the parental tumor. In this important study, PDOs were treated with 58 chemotherapeutic agents, and those with TP53 loss of function mutation were resistant to MDM2 inhibitors and as a consequence acquired RAS mutations and therefore decreased sensitivity to an EGFR inhibitor. Importantly, in this study, colon tumor organoids carrying the RNF43 mutation were dramatically sensitive to Wnt inhibitors.
In another significant study, PDOs were examined for the first time to investigate whether PDOs as a preclinical model could predict the drug response seen in the gastrointestinal cancer patients in the clinic [91]. In this study, a living organoid biobank was established from metastatic gastrointestinal cancer patients who were previously recruited for phase I or II clinical trials. According to the phenotypic and genotypic profiling of organoid and patient tumor samples, both of them exhibited highly similar profiles. Then, this led the authors to assess drug responses of PDOs in the laboratory setting in parallel to the clinic. High-throughput drug screening of PDOs with Food and Drug Administration (FDA)-approved drugs was shown to be successful with a positive predictive value (predicting that a certain drug worked) of 88% and a negative predictive value (predicting that a certain drug did not work) of 100%. This suggests a promising forecasting potential for PDOs in terms of the treatment response.

6. Conclusions

Extensive (epi)genetic heterogeneity in cancer has been demonstrated in several studies. As a result of the aberrantly activated and sustained complex signaling networks both in cancer cells and neighboring tumor microenvironment, examples of the hallmarks of cancer were presented. To address genomic aberrations and signaling network complexity, there has been a growing need to develop more sophisticated approaches for cancer systems biology. Cancer systems biology can deliver solutions for the better understanding of intratumor heterogeneity and therapeutic opportunities. Specifically, improved cancer systems biology approaches integrated not only with multi-omics data from tumors but also with comprehensive patient-derived experimental model systems can guide clinicians for their decision-making to offer better therapeutic solutions with an ultimate aim to overcome treatment failure in cancer.

Author Contributions

Conceptualization, all authors; writing—original draft preparation, all authors; writing—review and editing, all authors. All authors have read and agreed to the published version the manuscript.

Funding

This research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) grant number 118C197. And the APC was funded by the same grant.

Acknowledgments

Ahmet Acar’s laboratory is currently supported by the International Fellowship for Outstanding Researchers Program administered by The Scientific and Technological Research Council of Turkey (TUBITAK). We are grateful to Daniel Nichol for critical reading of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Pietras, K.; Östman, A. Hallmarks of cancer: Interactions with the tumor stroma. Exp. Cell Res. 2010, 316, 1324–1331. [Google Scholar] [CrossRef]
  3. Han, X.; Zhou, Z.; Fei, L.; Sun, H.; Wang, R.; Chen, Y.; Chen, H.; Wang, J.; Tang, H.; Ge, W.; et al. Construction of a human cell landscape at single-cell level. Nature 2020, 581, 303–309. [Google Scholar] [CrossRef]
  4. Campbell, P.J.; Getz, G.; Korbel, J.O.; Stuart, J.M.; Jennings, J.L.; Stein, L.D.; Perry, M.D.; Nahal-Bose, H.K.; Ouellette, B.F.F.; Li, C.H.; et al. Pan-cancer analysis of whole genomes. Nature 2020, 578, 82–93. [Google Scholar] [CrossRef] [Green Version]
  5. Zehir, A.; Benayed, R.; Shah, R.H.; Syed, A.; Middha, S.; Kim, H.R.; Srinivasan, P.; Gao, J.; Chakravarty, D.; Devlin, S.M.; et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 2017, 23, 703–713. [Google Scholar] [CrossRef] [PubMed]
  6. Jamal-Hanjani, M.; Wilson, G.A.; McGranahan, N.; Birkbak, N.J.; Watkins, T.B.K.; Veeriah, S.; Shafi, S.; Johnson, D.H.; Mitter, R.; Rosenthal, R.; et al. Tracking the evolution of non–small-cell lung cancer. N. Eng. J. Med. 2017, 376, 2109–2121. [Google Scholar] [CrossRef] [Green Version]
  7. Chakraborty, S.; Hosen, M.I.; Ahmed, M.; Shekhar, H.U. Onco-Multi-OMICS Approach: A New Frontier in Cancer Research. BioMed Res. Int. 2018, 2018. [Google Scholar] [CrossRef] [Green Version]
  8. Filipp, F.V. Precision medicine driven by cancer systems biology. Cancer Metastasis Rev. 2017, 36, 91–108. [Google Scholar] [CrossRef] [Green Version]
  9. McGranahan, N.; Swanton, C. Clonal heterogeneity and tumor evolution: Past, present, and the future. Cell 2017, 168, 613–628. [Google Scholar] [CrossRef]
  10. Hinohara, K.; Polyak, K. Intratumoral Heterogeneity: More Than Just Mutations. Trends Cell Biol. 2019, 29, 569–579. [Google Scholar] [CrossRef]
  11. Guo, M.; Peng, Y.; Gao, A.; Du, C.; Herman, J.G. Epigenetic heterogeneity in cancer. Biomark. Res. 2019, 7, 23. [Google Scholar] [CrossRef] [PubMed]
  12. Turajlic, S.; Xu, H.; Litchfield, K.; Rowan, A.; Chambers, T.; Lopez, J.I.; Nicol, D.; O’Brien, T.; Larkin, J.; Horswell, S.; et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 2018, 173, 581–594.e12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Acar, A.; Hidalgo-Sastre, A.; Leverentz, M.K.; Mills, C.G.; Woodcock, S.; Baron, M.; Collu, G.M.; Brennan, K. Inhibition of Wnt signalling by Notch via two distinct mechanisms. bioRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
  14. Collu, G.M.; Hidalgo-Sastre, A.; Brennan, K. Wnt-Notch signalling crosstalk in development and disease. Cell. Mol. Life Sci. 2014, 71, 3553–3567. [Google Scholar] [CrossRef]
  15. Stylianou, S.; Clarke, R.B.; Brennan, K. Aberrant activation of Notch signaling in human breast cancer. Cancer Res. 2006, 66, 1517–1525. [Google Scholar] [CrossRef] [Green Version]
  16. Marusyk, A.; Janiszewska, M.; Polyak, K. Intratumor heterogeneity: The Rosetta Stone of therapy resistance. Cancer Cell 2020, 37, 471–484. [Google Scholar] [CrossRef] [PubMed]
  17. Werner, H.M.J.; Mills, G.B.; Ram, P.T. Cancer systems biology: A peek into the future of patient care? Nat. Rev. Clin. Oncol. 2014, 11, 167–176. [Google Scholar] [CrossRef] [Green Version]
  18. Turajlic, S.; Sottoriva, A.; Graham, T.; Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 2019, 20, 406–416. [Google Scholar] [CrossRef]
  19. Greaves, M.; Maley, C.C. Clonal evolution in cancer. Nature 2012, 481, 306–313. [Google Scholar] [CrossRef]
  20. Caravagna, G.; Heide, T.; Williams, M.J.; Zapata, L.; Nichol, D.; Chkhaidze, K.; Cross, W.; Cresswell, G.D.; Werner, B.; Acar, A.; et al. Subclonal reconstruction of tumors by using machine learning and population genetics. Nat. Genet. 2020, 52, 898–907. [Google Scholar] [CrossRef]
  21. Levy, S.E.; Myers, R.M. Advancements in next-generation sequencing. Annu. Rev. Genom. Hum. Genet. 2016, 17, 95–115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Antman, E.; Weiss, S.; Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip. Rev. Syst. Biol. Med. 2012, 4, 367–383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Barry, P.; Vatsiou, A.; Spiteri, I.; Nichol, D.; Cresswell, G.D.; Acar, A.; Trahearn, N.; Hrebien, S.; Garcia-Murillas, I.; Chkhaidze, K.; et al. The spatiotemporal evolution of lymph node spread in early breast cancer. Clin. Cancer Res. 2018, 24, 4763–4770. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Spiteri, I.; Caravagna, G.; Cresswell, G.D.; Vatsiou, A.; Nichol, D.; Acar, A.; Ermini, L.; Chkhaidze, K.; Werner, B.; Mair, R.; et al. Evolutionary dynamics of residual disease in human glioblastoma. Ann. Oncol. 2019, 30, 456–463. [Google Scholar] [CrossRef] [Green Version]
  25. Cross, W.; Kovac, M.; Mustonen, V.; Temko, D.; Davis, H.; Baker, A.M.; Biswas, S.; Arnold, R.; Chegwidden, L.; Gatenbee, C.; et al. The evolutionary landscape of colorectal tumorigenesis. Nat. Ecol. Evol. 2018, 2, 1661–1672. [Google Scholar] [CrossRef] [Green Version]
  26. Kelso, T.W.R.; Porter, D.K.; Amaral, M.L.; Shokhirev, M.N.; Benner, C.; Hargreaves, D.C. Chromatin accessibility underlies synthetic lethality of SWI/SNF subunits in ARID1A-mutant cancers. eLife 2017, 6. [Google Scholar] [CrossRef]
  27. Kim, H.; Zheng, S.; Amini, S.S.; Virk, S.M.; Mikkelsen, T.; Brat, D.J.; Grimsby, J.; Sougnez, C.; Muller, F.; Hu, J.; et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 2015, 25, 316–327. [Google Scholar] [CrossRef] [Green Version]
  28. Zhang, X.; Choi, P.S.; Francis, J.M.; Imielinski, M.; Watanabe, H.; Cherniack, A.D.; Meyerson, M. Identification of focally amplified lineage-specific super-enhancers in human epithelial cancers. Nat. Genet. 2016, 48. [Google Scholar] [CrossRef]
  29. Calabrese, C.; Davidson, N.R.; Demircioğlu, D.; Fonseca, N.A.; He, Y.; Kahles, A.; van Lehmann, K.; Liu, F.; Shiraishi, Y.; Soulette, C.M.; et al. Genomic basis for RNA alterations in cancer. Nature 2020, 578. [Google Scholar] [CrossRef] [Green Version]
  30. Reyna, M.A.; Haan, D.; Paczkowska, M.; Verbeke, L.P.C.; Vazquez, M.; Kahraman, A.; Pulido-Tamayo, S.; Barenboim, J.; Wadi, L.; Dhingra, P.; et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nat. Commun. 2020, 11. [Google Scholar] [CrossRef]
  31. Rheinbay, E.; Nielsen, M.M.; Abascal, F.; Wala, J.A.; Shapira, O.; Tiao, G.; Hornshøj, H.; Hess, J.M.; Juul, R.I.; Lin, Z.; et al. Analyses of non-coding somatic drivers in 2658 cancer whole genomes. Nature 2020, 578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Bhang, H.E.C.; Ruddy, D.A.; Radhakrishna, V.K.; Caushi, J.X.; Zhao, R.; Hims, M.M.; Singh, A.P.; Kao, I.; Rakiec, D.; Shaw, P.; et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 2015, 21, 440–448. [Google Scholar] [CrossRef] [PubMed]
  33. Acar, A.; Nichol, D.; Fernandez-Mateos, J.; Cresswell, G.D.; Barozzi, I.; Hong, S.P.; Trahearn, N.; Spiteri, I.; Stubbs, M.; Burke, R.; et al. Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. Nat. Commun. 2020, 11, 1923. [Google Scholar] [CrossRef] [Green Version]
  34. Tang, F.; Barbacioru, C.; Wang, Y.; Nordman, E.; Lee, C.; Xu, N.; Wang, X.; Bodeau, J.; Tuch, B.B.; Siddiqui, A.; et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 2009, 6, 377–382. [Google Scholar] [CrossRef] [PubMed]
  35. Macosko, E.Z.; Basu, A.; Satija, R.; Nemesh, J.; Shekhar, K.; Goldman, M.; Tirosh, I.; Bialas, A.R.; Kamitaki, N.; Martersteck, E.M.; et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015, 161, 1202–1214. [Google Scholar] [CrossRef] [Green Version]
  36. Klein, A.M.; Mazutis, L.; Akartuna, I.; Tallapragada, N.; Veres, A.; Li, V.; Peshkin, L.; Weitz, D.A.; Kirschner, M.W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015, 161, 1187–1201. [Google Scholar] [CrossRef] [Green Version]
  37. Baron, C.S.; van Oudenaarden, A. Unravelling cellular relationships during development and regeneration using genetic lineage tracing. Nat. Rev. Mol. Cell Biol. 2019, 20, 753–765. [Google Scholar] [CrossRef]
  38. González-Silva, L.; Quevedo, L.; Varela, I. Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies. Trends Cancer 2020, 6, 13–19. [Google Scholar] [CrossRef] [Green Version]
  39. Navin, N.; Kendall, J.; Troge, J.; Andrews, P.; Rodgers, L.; McIndoo, J.; Cook, K.; Stepansky, A.; Levy, D.; Esposito, D.; et al. Tumour evolution inferred by single-cell sequencing. Nature 2011, 472, 90–94. [Google Scholar] [CrossRef] [Green Version]
  40. Kim, C.; Gao, R.; Sei, E.; Brandt, R.; Hartman, J.; Hatschek, T.; Crosetto, N.; Foukakis, T.; Navin, N.E. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 2018, 173, 879–893.e13. [Google Scholar] [CrossRef] [Green Version]
  41. Laks, E.; McPherson, A.; Zahn, H.; Lai, D.; Steif, A.; Brimhall, J.; Biele, J.; Wang, B.; Masud, T.; Ting, J.; et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 2019, 179, 1207–1207.e22. [Google Scholar] [CrossRef] [PubMed]
  42. Cusanovich, D.A.; Daza, R.; Adey, A.; Pliner, H.A.; Christiansen, L.; Gunderson, K.L.; Steemers, F.J.; Trapnell, C.; Shendure, J. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 2015, 348, 910–914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Granja, J.M.; Klemm, S.; McGinnis, L.M.; Kathiria, A.S.; Mezger, A.; Corces, M.R.; Parks, B.; Gars, E.; Liedtke, M.; Zheng, G.X.Y.; et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 2019, 37, 1458–1465. [Google Scholar] [CrossRef] [PubMed]
  44. Macaulay, I.C.; Haerty, W.; Kumar, P.; Li, Y.I.; Hu, T.X.; Teng, M.J.; Goolam, M.; Saurat, N.; Coupland, P.; Shirley, L.M.; et al. G&T-seq: Parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 2015, 12, 519–522. [Google Scholar] [CrossRef] [PubMed]
  45. Cao, J.; Cusanovich, D.A.; Ramani, V.; Aghamirzaie, D.; Pliner, H.A.; Hill, A.J.; Daza, R.M.; McFaline-Figueroa, J.L.; Packer, J.S.; Christiansen, L.; et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 2018, 361, 1380–1385. [Google Scholar] [CrossRef] [Green Version]
  46. Cao, Q.; Zhou, M.; Wang, X.; Meyer, C.A.; Zhang, Y.; Chen, Z.; Li, C.; Liu, X.S. CaSNP: A database for interrogating copy number alterations of cancer genome from SNP array data. Nucleic Acids Res. 2011, 39, D968–D974. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. de Anda-Jáuregui, G.; Hernández-Lemus, E. Computational oncology in the multi-omics era: State of the Art. Front. Oncol. 2020, 10, 423. [Google Scholar]
  48. Huret, J.L.; Ahmad, M.; Arsaban, M.; Bernheim, A.; Cigna, J.; Desangles, F.; Guignard, J.C.; Jacquemot-Perbal, M.C.; Labarussias, M.; Leberre, V.; et al. Atlas of genetics and cytogenetics in oncology and haematology in 2013. Nucleic Acids Res. 2013, 41, D920–D924. [Google Scholar] [CrossRef]
  49. Gorohovski, A.; Tagore, S.; Palande, V.; Malka, A.; Raviv-Shay, D.; Frenkel-Morgenstern, M. ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions. Nucleic Acids Res. 2017, 45, D790–D795. [Google Scholar] [CrossRef]
  50. Halling-Brown, M.D.; Bulusu, K.C.; Patel, M.; Tym, J.E.; Al-Lazikani, B. canSAR: An integrated cancer public translational research and drug discovery resource. Nucleic Acids Res. 2012, 40, D947–D956. [Google Scholar] [CrossRef] [Green Version]
  51. Su, W.H.; Chao, C.C.; Yeh, S.H.; Chen, D.S.; Chen, P.J.; Jou, Y.S. OncoDB.HCC: An integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci. Nucleic Acids Res. 2007, 35. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Forbes, S.A.; Beare, D.; Boutselakis, H.; Bamford, S.; Bindal, N.; Tate, J.; Cole, C.G.; Ward, S.; Dawson, E.; Ponting, L.; et al. COSMIC: Somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017, 45, D777–D783. [Google Scholar] [CrossRef]
  53. Samur, M.K.; Yan, Z.; Wang, X.; Cao, Q.; Munshi, N.C.; Li, C.; Shah, P.K. canEvolve: A web portal for integrative oncogenomics. PLoS ONE 2013, 8, e56228. [Google Scholar] [CrossRef] [PubMed]
  54. Tyagi, A.; Tuknait, A.; Anand, P.; Gupta, S.; Sharma, M.; Mathur, D.; Joshi, A.; Singh, S.; Gautam, A.; Raghava, G.P.S. CancerPPD: A database of anticancer peptides and proteins. Nucleic Acids Res. 2015, 43, D837–D843. [Google Scholar] [CrossRef] [Green Version]
  55. Cutts, R.J.; Gadaleta, E.; Hahn, S.A.; Crnogorac-Jurcevic, T.; Lemoine, N.R.; Chelala, C. The pancreatic expression database: 2011 update. Nucleic Acids Res. 2011, 39, D1023–D1028. [Google Scholar] [CrossRef] [Green Version]
  56. Hudson, T.J.; Anderson, W.; Aretz, A.; Barker, A.D.; Bell, C.; Bernabé, R.R.; Bhan, M.K.; Calvo, F.; Eerola, I.; Gerhard, D.S.; et al. International network of cancer genome projects. Nature 2010, 464, 993–998. [Google Scholar]
  57. He, X.; Chang, S.; Zhang, J.; Zhao, Q.; Xiang, H.; Kusonmano, K.; Yang, L.; Sun, Z.S.; Yang, H.; Wang, J. MethyCancer: The database of human DNA methylation and cancer. Nucleic Acids Res. 2008, 36, D836–D841. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Whiteaker, J.R.; Halusa, G.N.; Hoofnagle, A.N.; Sharma, V.; MacLean, B.; Yan, P.; Wrobel, J.A.; Kennedy, J.; Mani, D.R.; Zimmerman, L.J.; et al. CPTAC Assay Portal: A repository of targeted proteomic assays. Nat. Methods 2014, 11, 703–704. [Google Scholar] [CrossRef] [Green Version]
  59. Perez-Llamas, C.; Gundem, G.; Lopez-Bigas, N. Integrative Cancer Genomics (IntOGen) in Biomart. Database 2011, 2011. [Google Scholar] [CrossRef] [Green Version]
  60. Parkinson, H.; Kapushesky, M.; Shojatalab, M.; Abeygunawardena, N.; Coulson, R.; Farne, A.; Holloway, E.; Kolesnykov, N.; Lilja, P.; Lukk, M.; et al. ArrayExpress—A public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 2007, 35. [Google Scholar] [CrossRef]
  61. Liu, S.H.; Shen, P.C.; Chen, C.Y.; Hsu, A.N.; Cho, Y.C.; Lai, Y.L.; Chen, F.H.; Li, C.Y.; Wang, S.C.; Chen, M.; et al. DriverDBv3: A multi-omics database for cancer driver gene research. Nucleic Acids Res. 2020, 48. [Google Scholar] [CrossRef] [Green Version]
  62. Thomas, J.K.; Kim, M.S.; Balakrishnan, L.; Nanjappa, V.; Raju, R.; Marimuthu, A.; Radhakrishnan, A.; Muthusamy, B.; Khan, A.A.; Sakamuri, S.; et al. Pancreatic Cancer Database: An integrative resource for pancreatic cancer. Cancer Biol. Ther. 2014, 15. [Google Scholar] [CrossRef] [Green Version]
  63. Kumar, R.; Chaudhary, K.; Gupta, S.; Singh, H.; Kumar, S.; Gautam, A.; Kapoor, P.; Raghava, G.P.S. CancerDR: Cancer drug resistance database. Sci. Rep. 2013, 3. [Google Scholar] [CrossRef] [PubMed]
  64. Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. Platinum: A database of experimentally measured effects of mutations on structurally defined protein-ligand complexes. Nucleic Acids Res. 2015, 43. [Google Scholar] [CrossRef]
  65. Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Thun, M.J. Cancer Statistics, 2009. CA Cancer J. Clin. 2009, 59. [Google Scholar] [CrossRef]
  66. Hidalgo, M.; Amant, F.; Biankin, A.V.; Budinská, E.; Byrne, A.T.; Caldas, C.; Clarke, R.B.; de Jong, S.; Jonkers, J.; Mælandsmo, G.M.; et al. Patient-derived Xenograft models: An emerging platform for translational cancer research. Cancer Discov. 2014, 4. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Sachs, N.; Clevers, H. Organoid cultures for the analysis of cancer phenotypes. Curr. Opin. Genet. Dev. 2014, 24, 68–73. [Google Scholar] [CrossRef]
  68. Ben-David, U.; Beroukhim, R.; Golub, T.R. Genomic evolution of cancer models: Perils and opportunities. Nat. Rev. Cancer 2019, 19, 97–109. [Google Scholar] [CrossRef]
  69. Dugger, S.A.; Platt, A.; Goldstein, D.B. Drug development in the era of precision medicine. Nat. Rev. Drug Discov. 2018, 17, 183–196. [Google Scholar] [CrossRef] [PubMed]
  70. Dhandapani, M.; Goldman, A. Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools. J. Mol. Biomark. Diagn. 2017, 8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Gey, G.O.; Coffmann, W.D.; Kubicek, M.T. Tissue culture studies of the proliferative capacity of cervical carcinoma and normal epithelium. Cancer Res. 1952, 12, 264–265. [Google Scholar]
  72. Masters, J.R.W. Human cancer cell lines: Fact and fantasy. Nat. Rev. Mol. Cell Biol. 2000, 1, 233–236. [Google Scholar] [CrossRef]
  73. Kapałczyńska, M.; Kolenda, T.; Przybyła, W.; Zajączkowska, M.; Teresiak, A.; Filas, V.; Ibbs, M.; Bliźniak, R.; Łuczewski, Ł.; Lamperska, K. 2D and 3D cell cultures—A comparison of different types of cancer cell cultures. Arch. Med. Sci. 2018, 14. [Google Scholar] [CrossRef] [PubMed]
  74. Pampaloni, F.; Reynaud, E.G.; Stelzer, E.H.K. The third dimension bridges the gap between cell culture and live tissue. Nat. Rev. Mol. Cell Biol. 2007, 8, 839–845. [Google Scholar] [CrossRef] [PubMed]
  75. Hamburger, A.W.; Salmon, S.E. Primary bioassay of human tumor stem cells. Science 1977, 197. [Google Scholar] [CrossRef] [PubMed]
  76. Fukuda, J.; Nakazawa, K. Orderly arrangement of hepatocyte spheroids on a microfabricated chip. Tissue Eng. 2005, 11, 1254–1262. [Google Scholar] [CrossRef]
  77. Desroches, B.R.; Zhang, P.; Choi, B.R.; King, M.E.; Maldonado, A.E.; Li, W.; Rago, A.; Liu, G.; Nath, N.; Hartmann, K.M.; et al. Functional scaffold-free 3-D cardiac microtissues: A novel model for the investigation of heart cells. Am. J. Physiol. Heart Circ. Physiol. 2012, 302. [Google Scholar] [CrossRef] [Green Version]
  78. Achilli, T.M.; Meyer, J.; Morgan, J.R. Advances in the formation, use and understanding of multi-cellular spheroids. Exp. Opin. Biol. Ther. 2012, 12, 1347–1360. [Google Scholar] [CrossRef] [Green Version]
  79. Lee, J.; Cuddihy, M.J.; Kotov, N.A. Three-dimensional cell culture matrices: State of the art. Tissue Eng. Part B Rev. 2008, 14, 61–68. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Schilsky, R.L. Personalized medicine in oncology: The future is now. Nat. Rev. Drug Discov. 2010, 9, 363–366. [Google Scholar] [CrossRef]
  81. Karlsson, H.; Fryknäs, M.; Larsson, R.; Nygren, P. Loss of cancer drug activity in colon cancer HCT-116 cells during spheroid formation in a new 3-D spheroid cell culture system. Exp. Cell Res. 2012, 318. [Google Scholar] [CrossRef]
  82. Lai, Y.; Wei, X.; Lin, S.; Qin, L.; Cheng, L.; Li, P. Current status and perspectives of patient-derived xenograft models in cancer research. J. Hematol. Oncol. 2017, 10, 106. [Google Scholar] [CrossRef]
  83. Chdiwa, T.; Kawai, K.; Noguchi, A.; Sato, H.; Hayashi, A.; Cho, H.; Shiozawa, M.; Kishida, T.; Morinaga, S.; Yokose, T.; et al. Establishment of patient-derived cancer xenografts in immunodeficient NOG mice. Int. J. Oncol. 2015, 47. [Google Scholar] [CrossRef] [Green Version]
  84. Jhan, J.R.; Andrechek, E.R. Effective personalized therapy for breast cancer based on predictions of cell signaling pathway activation from gene expression analysis. Oncogene 2017, 36. [Google Scholar] [CrossRef] [PubMed]
  85. Byrne, A.T.; Alférez, D.G.; Amant, F.; Annibali, D.; Arribas, J.; Biankin, A.V.; Bruna, A.; Budinská, E.; Caldas, C.; Chang, D.K.; et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 2017, 17, 254–268. [Google Scholar] [CrossRef]
  86. Lupo, B.; Sassi, F.; Pinnelli, M.; Galimi, F.; Zanella, E.R.; Vurchio, V.; Migliardi, G.; Gagliardi, P.A.; Puliafito, A.; Manganaro, D.; et al. Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell–like phenotype. Sci. Transl. Med. 2020, 12, eaax8313. [Google Scholar] [CrossRef]
  87. Bertotti, A.; Papp, E.; Jones, S.; Adleff, V.; Anagnostou, V.; Lupo, B.; Sausen, M.; Phallen, J.; Hruban, C.A.; Tokheim, C.; et al. The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 2015, 526. [Google Scholar] [CrossRef]
  88. Bertotti, A.; Migliardi, G.; Galimi, F.; Sassi, F.; Torti, D.; Isella, C.; Corà, D.; di Nicolantonio, F.; Buscarino, M.; Petti, C.; et al. A molecularly annotated platform of patient- derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 2011, 1. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  89. Lazzari, L.; Corti, G.; Picco, G.; Isella, C.; Montone, M.; Arcela, P.; Durinikova, E.; Zanella, E.R.; Novara, L.; Barbosa, F.; et al. Patient-derived xenografts and matched cell lines identify pharmacogenomic vulnerabilities in colorectal cancer. Clin. Cancer Res. 2019, 25. [Google Scholar] [CrossRef] [Green Version]
  90. Yang, H.; Sun, L.; Liu, M.; Mao, Y. Patient-derived organoids: A promising model for personalized cancer treatment. Gastroenterol. Rep. 2018, 6, 243–245. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Vlachogiannis, G.; Hedayat, S.; Vatsiou, A.; Jamin, Y.; Fernández-Mateos, J.; Khan, K.; Lampis, A.; Eason, K.; Huntingford, I.; Burke, R.; et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 2018, 359. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Weeber, F.; van de Wetering, M.; Hoogstraat, M.; Dijkstra, K.K.; Krijgsman, O.; Kuilman, T.; Gadellaa-Van Hooijdonk, C.G.M.; van der Velden, D.L.; Peeper, D.S.; Cuppen, E.P.J.G.; et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc. Natl. Acad. Sci. USA 2015, 112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. van de Wetering, M.; Francies, H.E.; Francis, J.M.; Bounova, G.; Iorio, F.; Pronk, A.; van Houdt, W.; van Gorp, J.; Taylor-Weiner, A.; Kester, L.; et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 2015, 161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Boj, S.F.; Hwang, C.I.; Baker, L.A.; Chio, I.I.C.; Engle, D.D.; Corbo, V.; Jager, M.; Ponz-Sarvise, M.; Tiriac, H.; Spector, M.S.; et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 2015, 160. [Google Scholar] [CrossRef] [Green Version]
  95. Gao, D.; Vela, I.; Sboner, A.; Iaquinta, P.J.; Karthaus, W.R.; Gopalan, A.; Dowling, C.; Wanjala, J.N.; Undvall, E.A.; Arora, V.K.; et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 2014, 159. [Google Scholar] [CrossRef]
  96. Lee, S.H.; Hu, W.; Matulay, J.T.; Silva, M.V.; Owczarek, T.B.; Kim, K.; Chua, C.W.; Barlow, L.M.J.; Kandoth, C.; Williams, A.B.; et al. Tumor evolution and drug response in patient-derived organoid models of bladder cancer. Cell 2018, 173. [Google Scholar] [CrossRef] [Green Version]
  97. Sachs, N.; de Ligt, J.; Kopper, O.; Gogola, E.; Bounova, G.; Weeber, F.; Balgobind, A.V.; Wind, K.; Gracanin, A.; Begthel, H.; et al. A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity. Cell 2018, 172. [Google Scholar] [CrossRef] [Green Version]
  98. Hubert, C.G.; Rivera, M.; Spangler, L.C.; Wu, Q.; Mack, S.C.; Prager, B.C.; Couce, M.; McLendon, R.E.; Sloan, A.E.; Rich, J.N. A three-dimensional organoid culture system derived from human glioblastomas recapitulates the hypoxic gradients and cancer stem cell heterogeneity of tumors found in vivo. Cancer Res. 2016, 76. [Google Scholar] [CrossRef] [Green Version]
  99. Nelson, L.; Tighe, A.; Golder, A.; Littler, S.; Bakker, B.; Moralli, D.; Murtuza Baker, S.; Donaldson, I.J.; Spierings, D.C.J.; Wardenaar, R.; et al. A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity. Nat. Commun. 2020, 11. [Google Scholar] [CrossRef]
  100. Aboulkheyr Es, H.; Montazeri, L.; Aref, A.R.; Vosough, M.; Baharvand, H. Personalized Cancer Medicine: An Organoid Approach. Trends Biotechnol. 2018, 36, 358–371. [Google Scholar] [CrossRef]
Jpm 10 00180 g001 550
Figure 1. Comprehensive picture of systems biology approaches and experimental model systems constituting the core components of the biology of cancer.
Figure 1. Comprehensive picture of systems biology approaches and experimental model systems constituting the core components of the biology of cancer.
Jpm 10 00180 g001
Table
Table 1. A collection of databases.
Table 1. A collection of databases.
NameDescriptionWebsiteReference
CaSNPCaSNP performs quantitative analysis of copy number variation from SNP arrays in multiple cancer typeshttps://bioinformaticshome.com/tools/cnv/descriptions/CaSNP.html[46]
OncoLandOncoLand provides oncology data access in sample and gene directions.https://omicsoftdocs.github.io/ArraySuiteDoc/tutorials/OncoLand/Introduction/[47]
AGCOHThe Atlas of Genetics, Cytogenetics in Oncology and Hematology perform comprehensive genomic characterization and analysis of multiple cancer types http://atlasgeneticsoncology.org/BackpageAbout.html[48]
PCWAGPCWAG—Pan-cancer Analysis of Whole Genomes provides common patterns of mutations from more than 2600 cancer whole genomeshttp://dcc.icgc.org/pcawg[4]
ChiTaRSChiTaRS contains chimeric transcripts and RNA-Seq datahttp://chitars.bioinfo.cnio.es/[49]
CanSARCanSAR provides information about translational research and drug discovery knowledgebase https://cansarblack.icr.ac.uk/[50]
OncoDB.HCCOncogenomics Database of Hepatocellular Carcinoma provides genomic, transcriptomic, and proteomic datahttp://oncodb.hcc.ibms.sinica.edu.tw/index.htm[51]
COSMICCOSMIC performs a comprehensive database of somatic mutation in multiple cancer typeshttps://cancer.sanger.ac.uk/cosmic[52]
canEvolvecanEvolve is a comprehensive database including genes, miRNA, and protein expression profiles; copy number changes for a variety of cancer types and protein–protein interactionshttp://www.canevolve.org/AnalysisResults/AnalysisResults.html[53]
CancerPPDCancerPPD provides information about anticancer peptides and proteins in multiple cancer typeshttp://crdd.osdd.net/raghava/cancerppd/[54]
PEDThe Pancreatic Expression Database performs a comprehensive meta-analysis of pancreatic cancerhttp://www.pancreasexpression.org/[55]
CGPCancer Genome Project provides genotype and copy number changes information in tumors https://www.sanger.ac.uk/group/cancer-genome-project[56]
MethyCancerMethyCancer provides information about DNA methylation and gene expression in a variety of cancer typeshttp://methycancer.psych.ac.cn/[57]
CPTACClinical Proteomic Tumor Analysis Consortium is a database containing an integration of genomic and proteomic data https://proteomics.cancer.gov/[58]
intOGenIntegrative Onco Genomics performs comprehensive genomic data of multiple cancer typeshttps://www.intogen.org/search[59]
ArrayExpressArrayExpress focuses on microarray gene expression datahttps://www.ebi.ac.uk/arrayexpress/[60]
DriverDBv3DriverDBv3 is a database of cancer omicshttp://driverdb.tms.cmu.edu.tw/[61]
PCDBThe Pancreatic Cancer Database provides genetic information in pancreatic cancerhttp://www.pancreaticcancerdatabase.org[62]
CancerDRCancerDR contains anticancer drugs and their effectiveness against a variety of cell lineshttp://crdd.osdd.net/raghava/cancerdr/[63]
PlatinumPlatinum provides knowledge about missense mutations on ligand–proteome interactionshttp://biosig.unimelb.edu.au/platinum/[64]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yalcin, G.D.; Danisik, N.; Baygin, R.C.; Acar, A. Systems Biology and Experimental Model Systems of Cancer. J. Pers. Med. 2020, 10, 180. https://doi.org/10.3390/jpm10040180

AMA Style

Yalcin GD, Danisik N, Baygin RC, Acar A. Systems Biology and Experimental Model Systems of Cancer. Journal of Personalized Medicine. 2020; 10(4):180. https://doi.org/10.3390/jpm10040180

Chicago/Turabian Style

Yalcin, Gizem Damla, Nurseda Danisik, Rana Can Baygin, and Ahmet Acar. 2020. "Systems Biology and Experimental Model Systems of Cancer" Journal of Personalized Medicine 10, no. 4: 180. https://doi.org/10.3390/jpm10040180

APA Style

Yalcin, G. D., Danisik, N., Baygin, R. C., & Acar, A. (2020). Systems Biology and Experimental Model Systems of Cancer. Journal of Personalized Medicine, 10(4), 180. https://doi.org/10.3390/jpm10040180

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop