Modern Biochemistry in Research of Human Malignant Cells and Experimental Oncology

Sergey S. Shishkin

Sergey S. Shishkin, Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences. 33, bld. 2 Leninsky Ave., Moscow 119071, Russia

Conflict-of-interest statement: The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.

Correspondence to: Sergey S. Shishkin, Bach Institute of Biochemistry, Research Center of Biotechnology, Russian Academy of Sciences. 33, bld. 2 Leninsky Ave., Moscow 119071, Russia.
Email: sergeyshishkin@yandex.ru
Telephone: +8-495-952-5886

Received: June 27, 2016
Revised: September 5, 2016
Accepted: September 7, 2016
Published online: December 18, 2016


Modern biochemistry or biochemistry of 21th century is developing in many traditional fields: investigations of proteins, nucleic acids, lipids, carbohydrates, metabolites and metabolic processes, etc, however it has gained new features that based on success of Human Genome Project and on application of high performance technologies (post-genomic technologies). As a result, at the turn of the 21st century some new scientific disciplines, named “OMICS” (proteomics, transcriptomics, lipidomics, glycomics, metabolomics, etc) were created through advances in the biochemistry. Currently “OMICS” and post-genomic technologies are involved in mainstream of cancer research, including studies of human malignant cells. Since malignant tumors consist of heterogeneous cancer cells, the cultured cell lines have some advantages over biopsy simples for studies aimed at understanding the molecular basis of carcinogenesis. The malignant human cell lines differ considerably in their origin from tissues or organs, and therefore they vary widely in the differentiation characteristics and the gene expression patterns. Thus, the use of malignant cell lines ex vivo (as models of human cancers) is an important trend in search for key molecules of carcinogenesis in experimental oncology.

Key words: “OMICS”; Systematic analysis; Human malignant cells; 2D and 3D cell culture models

© 2016 The Author(s). Published by ACT Publishing Group Ltd. This is an open access article under the CC BY-NC-ND license (http: //creativecommons.org/licenses/by-nc-nd/4.

Shishkin SS. Modern Biochemistry in Research of Human Malignant Cells and Experimental Oncology. Journal of Biochemistry and Molecular Biology Research 2016; 2(4): 185-193 Available from: URL: http: //www.ghrnet.org/index.php/jbmbr/article/view/1769


The biochemistry has a place of honor among the life sciences largely due to its fantastic success in 20th century. Traditional biochemistry started with the study of chemical components of living organisms and their metabolism that provides life itself, and with description of the main types of biopolymers and other biomolecules. At that time technologies allowing to determine sequence of monomers in complex polymers such as nucleic acids and proteins (sequencing technologies) were designed. As a result, in the 80s of the 20th century the entire human mitochondrial genome was identified, heavy and light mitochondrial DNA strands maps were constructed and their nucleotide sequences were determined[1,2].

Further similar studies were concerned with the complete genome sequencing of different bacteria and some eukaryotes[3-5]. In the last decade of 20th century the DNA sequencing of the human nuclear genome was started that influenced the development of the life sciences[6-8].

To achieve the goal of determining the DNA sequence of the entire human genome the considerable resources of the international community were mobilized and major research projects such as Human Genome Project were realized. After publication of results of human genome identification[9-11], corresponding data were added to publicly available databases (for example, NCBI).

At the turn of the 21st century new stage in development of the life sciences was started known as “post-genomic era”, wherein many medical and biological problems including malignant tumors have been studied[12-16].

One of the distinguishing features of post-genomic era in the development of the life sciences is the emergence of new scientific disciplines (or research directions), that have names ending in “-omics”. The declared goals of “OMICS” disciplines are the specialized studies of different chemical compounds of living organisms. Thereby relationship between biochemistry of 21st century or modern biochemistry and various “OMICS” disciplines deserves special attention.

Modern biochemistry and “OMICS”

In a second half of the 20th century traditional biochemistry was already represented as multidisciplinary science in which various scientific disciplines are being formed and successfully developed. In particular, protein biochemistry, biochemistry of nucleic acids, biochemistry of lipids, carbohydrate biochemistry, biochemistry of metabolites and metabolic processes are considered as separated disciplines.

Traditional biochemistry usually used the research approach based on extraction of one component from biomaterial with further characterization and «step by step» detection of other related compounds. Currently this approach is still used adjusted for higher technological level, however other approach based on parallel study of large amount of molecules in certain organisms was introduced in the last two decades of 20th century. This approach called “systematic analysis” could theoretically allow to receive comprehensive molecular data for species of interest, particularly Homo sapience[17-20]. Apparently, the first manifesto of this approach was published by Anderson N.G. and Anderson L. (1982)[17], who proposed to create the complete catalog of human proteins based on application of two-dimensional electrophoresis.

In the late 1980s, the systematic analysis was already strongly associated with genome-wide studies considered as the basis of a new science known as “genomics”. The introduction of the term “genomics” is generally attributed to T. Roderick (by Primrose, Twyman[21]). However, the earliest mention of this term in Pubmed database belongs to Ferguson-Smith, Ruddle (1988)[22] and Willard (1989)[23].

Genomics was a first “OMICS” science, which had special objects of research (genomes of various organisms) and specific tasks (determination of complete nucleotide sequences of studied genomes and mapping of all identified genes) that were solving by using of sets of special methods and techniques[21].

To the beginning of 21th century the genomes of several hundred viruses, natural plasmids, mitochondria and other organelles, 31 types of microorganisms, 7 species of archaea, etc were decoded[24,9,25]. It is especially important that five eukaryotes including Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, Mus musculus and even Homo sapience have been among the organisms with sequenced genomes.

Soon after the genomics started, the generalization of results of systematic analysis to protein research led to formation of new discipline that was named “proteomics” by analogy with genomics[26,27].

The term “proteome” («PROTEOME: entire PROTEin complement expressed by the genOME») was first defined and introduced in science on the 1994 Siena conference "2D Electrophoresis: from protein maps to genomes"[26,28]. The author of the current article attended this historic meeting and made a report «Two-dimensional electrophoresis and microsequence analysis of proteins in Russian human genome project».

The methodical arsenal of modern proteomics offers a variety of technologies and methods described in numerous publications on those topics. The number of proteomic publications in PubMed NCBI database was gradually increased in the post-genomic era (2001-2015) to the level of more than 8,000 publications per year (Figure 1). It also should be noted that number of proteomic publications dedicated to cancer research nearly reached 2,000 per year in 2014 and 2015.

Figure 1 Dynamics of the growth in number of proteomic publications registered in the PubMed NCBI database during 2001–2015 (columns 1–15, respectively). blue square: annual number of publications with key word “proteomic”; red square: annual number of publications with key words “proteomic cancer”.

The concept of «transcriptome» and «transcriptomics» entered the scientific literature soon after the “proteome” and “proteomics” terms. The term ”transcriptome” was coined by Velculescu et al (1997)[29] to describe the whole set of RNA (transcripts) produced from expressed genes that can be studied using serial analysis of the gene expression. The word ”transcriptomics” apparently appeared for the first time in the journal Nature in 1999 [Nature. 1999 Dec 16; 402(6763): 715]. Two years later the group of French scientists used this term for research of expression profiles (transcripts) at breast cancer[30].

The popularity of genomics, proteomics and transciptomics in 21th century contributed to the development of novel fields of research containing the ending “-omics” which represents the organic relation between genome studies and these disciplines. The neologism “OMICS” is frequently used as a common name for such scientific areas as metabolomics, glycomics, lipidomics and others (alongside with listed above genomics, proteomics and transciptomics). The objectives of metabolomics, glycomics and lipidomics are biomolecules that only indirectly linked with the functioning of genome in contrast to proteomics or transcriptomics. Nevertheless the systematic analysis and mass spectrometry technologies are methodological basis of all of these “OMICS” disciplines[31-33].

It should be noted that the great information banks (in particular of human biomolecules) play a significant role in the development of “OMICS” and of modern biochemistry as a whole. These massive amounts of information are summarized in general or specialized databases that require appropriate software for effective using of containing data. Thereby bioinformatics is considered a relatively new science and an essential part of modern biochemistry.

The scheme of interconnection amongst multidisciplinary traditional biochemistry, modern biochemistry and “OMICS” disciplines with bioinformatics is shown in figure 2.

As shown in figure 2, the bioinformatics allows to summarize the data obtained using different “OMICS” technologies. It is affected on progress of systematic analysis to multi-omics approach which is currently used for various kinds of research including study of human malignant cells[34,35].

Figure 2 The scheme of interconnection between multidisciplinary traditional biochemistry, modern biochemistry and “OMICS” disciplines with bioinformatics.

Modern biochemistry and post-genomic technologies in research of human malignant cells

At the beginning of the 21st century, cancer remains one of leading causes of morbidity and mortality worldwide[36,37]. Respectively, the study of human malignant tumours is important field for application of post-genomic technologies in modern biochemistry. The results of these studies are expected to resolve a number of fundamental and applied problems, including the creation of new efficient methods for diagnostic and target therapy.

In general, malignant tumors have some properties (uncontrolled cell growth, invasiveness, metastatic capacity, and others), related to altered gene expression. Sometimes cancer is even described as a disease of the altered gene expression[38].

Proceeding from the ideas on the mechanism of cancer development, it is supposed that identification of biomolecules that provide the listed properties of malignant cells will allow to find effective diagnostic markers and potential therapeutic targets in cancer. Suitable biomolecules have been looked for a long time in «cancer – noncancer» comparative studies. However a number of difficulties in such studies have been known since traditional biochemistry era.

For example, it has been established that human malignant tumors have various origin, and, therefore, tumor cells significantly differ on types of differentiation and, respectively, on sets of expressed genes.

The most of human malignant tumours fall into one of subsequent main groups according to their origin:

(1) Cancers (carcinomas) – malignant neoplasms of epithelial origin. The most of them are adenocarcinomas, there are also cancers consisting of the partly differentiated cells (follicular carcinoma, squamous cell carcinoma, etc.) and of poorly differentiated cells (small-cell carcinoma, mucinous carcinoma, etc.).

(2) Leukemias and tumors of the hematopoietic and lymphoid tissues, for example, lymphomas. Leukemia (leukosis) – malignant neoplasma of the blood cells in the bone marrow; leukemias are subdivided into two forms – acute and chronic. Lymphomas (lymphosarcoma) – subgroup of blood cell tumors that develop from lymphatic cells of lymph nodes or other lymphoid tissues.

(3) Osteo- and chondrosarcomas – malignant tumors from transformed cells of mesenchymal origin including poorly differentiated cells. Osteosarcoma – malignant neoplasm that arises from primitive mesenchymal cells. Chondrosarcoma – malignant tumor derived from transformed mesenchymal cells that produce cartilage.

(4) Rhabdomyosarcomas and leiomyosarcomas – malignant tumors from skeletal (striated) muscle cells and smooth muscle cells, respectively.

(5) Glioblastomas, neuroblastomas and other malignant tumors of the nervous system that start from the transformed very primitive nerve cells that seem to be very similar to cells found in an embryo or fetus[39].

Besides, tumors have turned out to be composed of cells with heterogenic morphology and differ on many functional properties, in particular on metastatic potential. It is obvious that such heterogeneity needs to be considered at biochemical research of biopsy samples. The problem of intratumoral morphological heterogeneity has been studied in a second half of the 20th century[40,41], and is still actual in the post-genomic era. It is enough to note that several dozen articles about cellular diversity in malignant tumors were published in first half of 2016[37,42-45]. In particular, Gay et al (2016)[42] analyzed and summarized in their review more than 30 publications concerning cellular heterogeneity of various malignant tumors (kidney, lung, colon, mammary gland, prostate, ovary, etc.). The genetic, epigenetic, and phenotypic mechanisms of intra-tumor cell heterogeneity have been described[42,46,47].

Traditionally tumor cell heterogeneity was explained with a clonal selection and adaptation models. These models propose that genomic instability within the primary tumor results in cell diversity due to mutations in subset of tumor cells (e.g. review Ahmed, Li 2013[48]). In additional, the cancer stem cell theory has been proposed[38,49,50]. Cancer stem cells (CSCs) are a subpopulation of tumor cells that have the capacity to self-renew, provide life-long cell growth and produce distinct cell clones[49-51]. The detailed analysis of properties and methods of isolation of CSCs has been presented in a series of reviews. For example, Moghbeli et al (2014)[52] introduced different protocols used to isolate CSCs from solid tumors of colon, esophagus, liver, breast, brain, and cervix. The isolation of CSCs can be accomplished by selection of tumor cell subpopulations based on expression of one or more of cell surface markers associated with cancer self-renewal such as: CD133, CD166, CD44, CD24, beta1 integrin-CD29 and others[47,53].

The alternative hypothesis suggested that tumor growth could be mediated, at least in part, by the coalescence of multiple tumorigenic foci within a tissue, a process that would contribute to tumor heterogeneity[40,46,54-56]. In particular, mediated coalescence as a possible mechanism for tumor cellular heterogeneity was recently supported by experimental data obtained from 3D matrigel model[46].

One of the significant aspects of the tumor cell heterogeneity associates with the circulated tumor cells (CTCs). CTCs are subpopulation of cancer cells that has been found in patients with cancer and reported since 19th century (according to Andree et al 2016[57]). CTCs are known to be highly heterogeneous, and among them CSCs were detected[58,59]. A large number of studies have been devoted to characteristics and methods of separation of CTCs[60,61]. Collection of blood of cancer patients for isolation and analysis of CTCs is sometimes referred to “liquid tumor biopsy”[61].

As an alternative to traditional heterogeneous biopsy samples, the cultured cell lines derived from various malignant tumors have been actively used since the middle of the 20th century[62,63]. Herewith, the biochemical methods were often used to characterize cultured cell lines, for example, to determine the isoenzyme profiles[64].

In 21st century many cancer cell lines are considered to be important materials for post-genomic research in modern biochemistry[35,65]. Cultured cells are maintained in special biorepositories, for example, American Type Culture Collection, ATCC, or Multidisciplinary Biospecimen Bank[66].

The importance of malignant cell lines as materials for wide range of research has been underscored by the creating of a special database, called Cancer Cell Line Encyclopedia[67]. This large database (CCLE, http://www.broadinstitute.org/ccle/home) contains the gene expression data and some other molecular-biological and biochemical characteristics of 947 human cancer cell lines derived from 36 types of malignant tumors with different level of differentiation. In addition, this database includes the information on the effect of 24 chemotherapeutic agents on 479 cancer cell lines. Thus, according to the creators, CCLE can be useful for predictive modelling of anticancer drug sensitivity. For example, Sonkin et al (2013)[68] used CCLE and systematic analysis for study of functional status of 69 tumor suppressors in 799 cell lines annotated in CCLE.

A necessary condition for the experiments in molecular oncology, including the identification of key molecules in the pathways that lead to carcinogenesis, is the standardization of cancer cell lines[69,70].

Evidently, there is a considerable potential for malignant cell lines in experimental oncology.

Human malignant cells and post-genomic technologies in experimental oncology

Modern experimental oncology uses many different models for solving in the preclinical phase important problems associated with the emergence and growth of malignant tumors, as well as with the screening and evaluation of potential anticancer drugs.

Human and animal malignant cell lines are used as model systems in various experiments both ex vivo and in vivo[71-74]. Along with the cancer cell lines, that relate to continuous cell lines and can be obtained from various cell repositories, the primary cell cultures continue to be used as experimental models for cancers[75,76]. Some main models used in experimental oncology are shown in figure 3.

Figure 3 Some main cellular models used in experimental oncology and relationship with technologies of modern biochemistry.

One of the major goals of experimental oncology is the study of key molecules that involved in carcinogenesis. It is believed that such molecules could be potential biomarkers and/or targets for chemotherapy. The use of malignant cell lines and technologies of modern biochemistry is an effective basis for searching for key molecules ex vivo[77-82].

Ex vivo culture systems in experimental oncology are also used for study of potential anticancer drugs. Several recent reviews have been dedicated to this problem, e.g. Lengyel et al (2014)[72]; Niu, Wang (2015)[73]; Gazdar et al (2016)[74]. The above mentioned Cancer Cell Line Encyclopedia was created as a tool that may help to enable preclinical stratification schemata for anticancer agents[56].

However, the use of ex vivo culture systems is associated with some limitations, for example, see Gazdar et al (2016)[74]. In particular, during repeated passaging of cell lines the oligoclonal selection and substantial genetic differences from the original cells can occur. Besides, cell lines are often grown in a 2D monolayer format with no tumor stroma and blood vessels, and it should be considered in interpretation of the obtained results.

To reduce the role of these limitations, 3D cell culture models were created that more accurately represent the in vivo cells composing malignant tumors[83,84]. Beginning at first publications[85,86] and over the past decade, several types of 3D-cell models have been developed. 3D-cell systems as model tumors can include cells cultured as multicellular aggregates (spheroids) and the cells embedded in constructs made of natural or synthetic matrices[84]. Only in 2015 more than 449 scientific articles on 3D-cell models of cancer were published and annotated in NCBI PubMed.

Ex vivo model systems based on malignant cell lines not allow to estimate the effects of anticancer drugs on other cells and organs of human body. However, the results of such side effects can be critical. As a consequence, extrapolation of data obtained from ex vivo cell models requires great caution.

More adequate results can be obtained from various xenograft tumor models using malignant human cells transplantation into animal hosts[87,88,84].

Bibliometric analysis of publications in PubMed NCBI indicates the intensification of studies using xenograft tumor models. The total number of publications on this topic is around 25,000, and more than half of them (14459) have been published in the last 5 years. In many of these works the “OMICS” technologies or even multi-omics approach were used to study of tumor xenograft models[35].

In general, studies of human malignant cells using post-genomic technologies can currently be considered one of the most important and actively developing areas in experimental oncology.

“OMICS” technologies in clinical biochemistry of oncological diseases

In the second decade of the 21st century, “OMICS” technologies are actively used in clinical biochemistry and especially in clinical biochemistry of oncological diseases. General information about scientific publications in PubMed NCBI database related to these fields is shown in table 1.

Table 1 Publication activity (2011-2015) reflecting the application of four main ;OMICS; technologies in clinical biochemistry and in clinical biochemistry of cancer
Fields (keywords that were used to search in PubMed)Years
Clinical genomic8 4499 25110 51613 47114 285
Clinical genomic cancer6613 3033 7925 1945 562
Clinical transcriptomic 104115135207298
Clinical transcriptomic cancer51425385132
Clinical proteomic1 0161 1101 1631 5081 716
Clinical proteomic cancer411394446508630
Clinical metabolomic244308375578712
Clinical metabolomic cancer706779166191

As can be seen from the table, hundreds or even thousands of papers are published each year, dedicated to application of four main “OMICS” technologies in clinical biochemistry and in clinical biochemistry of cancer.

Most of these studies have been concerned with the various known or potential biomarkers which, according to the authors, could be useful for the following clinical goals: (1) establishment and/or verification of diagnosis, including the origin of malignant tumors; (2) identification of disease stage, including the detection of metastases; (3) prediction of survival based on the origin of malignant tumor; (4) evaluation of treatment effectiveness, including the drug-resistance in cancer cells, and monitoring of remission; (5) detection of cancer recurrence.

The vast majority of such biomarker studies used the samples of blood serum, urine, cerebrospinal and other biological fluids as biomaterials. The cellular and molecular biomarkers, including circulating human tumor cells, exosomes, macromolecules (especially proteins, DNA or RNA) and various metabolites, were analyzed.

As noted above, the methods of detection of circulating tumor cells in patients with malignant tumors are being actively developed in the 21st century[57-59], and the “OMICS” technologies are widely used for solving this problem[89,90]. In particular, currently, considerable attention is paid to so-called exosomes produced by malignant cells. Exosomes are small vesicles with a diameter of 30 to 100 nm that are secreted into the bloodstream and urine, have some specific molecular biomarkers and can be used for diagnostic purposes[91-94]. So, Redzic et al (2014)[91] described exosomes from malignant cells of glioblastoma multiforme as stable, membrane-enclosed particles released from the cell surface and accessible in biofluids, such as serum/plasma, urine, cerebrospinal fluid, and saliva. Proteomic analysis and other modern biochemistry technologies revealed that exosomes carry a wide variety of proteins, nucleic acids, lipids, and different metabolites allowing to identify the cell of origin of the vesicles. Accordingly, many authors consider exosomes the potential carriers of cancer diagnostic biomarkers[91-94].

For detection of molecular biomarkers in biological fluids by “OMICS” technologies a very traditional approach is typically used, but on the modern methodical level. This approach consists in comparison of biomarker value in the group of patients with cancer and in healthy control. For example, two samples of urine are separately analyzed by LC-MS/MS[95]. The proteins are identified from fragmented ion spectra. The relative quantification of a protein is obtained from the relative quantification of the corresponding peptides. Each appropriate peptide is quantified by integrating the area under the pseudochromatograms constructed of the precursor ion peak heights generated from the first stage mass analysis of precursor ions (MS1). The essential element in constructing the pseudochromatograms is to obtain peak heights of identical precursor ion masses, expected to represent identical peptides, along the corresponding LC elution period.

The scheme of the application of “OMICS” technologies for detection of cellular and other biomarkers of malignant tumors is shown in figure 4.

Thus, the “OMICS” technologies formed during realization of the fundamental research projects such as the Human Genome Project came to practical application for decision of different clinical problems in oncology. Accordingly, it is possible to think that in the near future the significant progress will be made in this area.

Figure 4 The scheme of the application of “OMICS” technologies for detection of cellular and other biomarkers in biological fluids of patients with malignant tumors.


Since the beginning of the 21st century, the application of systematic analysis to biochemical research has been widely developed due to highly efficient technologies. Besides, the general or specialized databases and methods of bioinformatics are using actively. In fact, biochemical studies made a step change that can be evaluated as a transition from traditional to modern biochemistry. The evidence of this transition is the forming of whole set of new scientific “OMICS” disciplines giving new possibilities for medico-biological studies including studies of malignant tumors. Accordingly, the application of technologies of modern biochemistry (including omics-technologies) for the study of cultured malignant cells can be considered as a promising front in cancer research and experimental oncology.


1. Cantatore P, Attardi G. Mapping of nascent light and heavy strand transcripts on the physical map of HeLa cell mitochondrial DNA. Nucleic Acids Res 1980; 8: 2605-2625. [PMID: 6159578]

2. Anderson S, Bankier AT, Barrell BG, de Bruijn MH, Coulson AR, Drouin J, Eperon IC, Nierlich DP, Roe BA, Sanger F, Schreier PH, Smith AJ, Staden R, Young IG. Sequence and organization of the human mitochondrial genome. Nature 1981; 290: 457-465. [PMID: 7219534]

3. Burland V, Daniels DL, Plunkett G 3rd, Blattner FR. Genome sequencing on both strands: the Janus strategy. Nucleic Acids Res 1993; 21: 3385-3390. [PMID: 8346017]

4. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, Bult CJ, Tomb JF, Dougherty BA, Merrick JM, McKenney K, Sutton G, FitzHugh W, Fields C, Gocayne JD, Scott J, Shirley R, Liu L, Glodek A, Kelley JM, Weidman JF, Phillips CA, Spriggs T, Hedblom E, Cotton MD, Utterback TR, Hanna MC, Nguyen DT, Saudek DM, Brandon RC, Fine LD, Fritchman JL, Geoghagen NSM, Gnehm CL, McDonald LA, Small KV, Fraser CM, Smith HO, Venter JC.Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995; 269: 496-512. [PMID: 7542800]

5. Fraser CM, Fleischmann R.D. Strategies for whole microbial genome sequencing and analysis. Electrophoresis 1997; 18: 1207-1216. [PMID: 9298642]; [DOI: 10.1002/elps.1150180803]

6. Dulbecco R. A turning point in cancer research: sequencing the Human Genome. Science 1986; 231: 1055-1056. [PMID: 3945817]

7. Cantor CR. Orchestrating the Human Genome Project. Science 1990; 248: 49-51 [PMID: 2181666]

8. Baev AA. The Russian human genome program. [Article in Russian]. Mol Biol (Mosk) 1995; 29: 489-499. [PMID: 8552052]

9. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001; 409: 860-921. [PMID: 11237011]; [DOI: 10.1038/35057062]

10. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 2004; 431: 931-945. [PMID: 15496913]; [DOI: 10.1038/nature03001]

11. Venter CJ, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guig? R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J, Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang M, Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X. The Sequence of the Human Genome. Science 2001; 291: 1304-1351. [PMID: 11181995]; [DOI: 10.1126/science.1058040]

12. Vilain E. CYPs, SNPs, and molecular diagnosis in the postgenomic era. Clin Chem 1998; 44: 2403-2404. [PMID: 9836703]

13. Kaelin WG. Choosing anticancer drug targets in the postgenomic era. J Clin Invest 1999; 104: 1503-1506. [PMID: 10587513]; [DOI: 10.1172/JCI8888]

14. Lønning PE, Knappskog S, Staalesen V, Chrisanthar R, Lillehaug JR. Breast cancer prognostication and prediction in the postgenomic era. Ann Oncol 2007; 18: 1293-1306. [PMID: 17317675]; [DOI: 10.1093/annonc/mdm013]

15. Soussi T. The TP53 gene network in a postgenomic era. Hum Mutat 2014; 35: 641-642. [PMID: 24753184]; [DOI: 10.1002/humu.22562]

16. Malm J, Lindberg H, Erlinge D, Appelqvist R, Yakovleva M, Welinder C, Steinfelder E, Fehniger TE, Marko-Varga G. Semi-automated biobank sample processing with a 384 high density sample tube robot used in cancer and cardiovascular studies. Clin Transl Med 2015; Dec;4(1):67. [PMID: 26272727]; [DOI: 10.1186/s40169-015-0067-0]

17. Anderson NG, Anderson L. The Human protein index. Clin Chem 1982; 28: 739-748. [PMID: 7074867]

18. Klose J. Systematic analysis of the total proteins of a mammalian organism: principles, problems and implications for sequencing the human genome. Electrophoresis 1989; 10: 140-152. [PMID: 2659320]; [DOI: 10.1002/elps.1150100208]

19. Celis JE, Gesser B, Rasmussen HH, Madsen P, Leffers H, Dejgaard K., Honore B., Olsen E., Ratz G., Larodsen J., Basse B, Mouritzen S, Hellerrup M, Andersen A, Walbum E, Celis A, Brauw G, Puype M, Van Damme J, Vandekerckhove J. Comprehensive two-dimensional gel protein databases offer a global approach to the analysis of human cells: the transformed amnion cells (AMA) master database and its link to genome DNA sequence data. Electrophoresis 1990; 11: 989-1071. [PMID: 2090460]; [DOI: 10.1002/elps.1150111202]

20. Korenberg JR, Chen XN, Adams MD, Venter JC. Toward a cDNA map of the human genome. Genomics 1995; 29: 364-370. [PMID: 8666383]

21. Primrose SB, Twyman RM. Genomics. Application in Human Biology. Oxford: Blackwell Pulishing 2004. 216p.

22. Ferguson-Smith AC, Ruddle FH. The genomics of human homeobox-containing loci. Pathol Immunopathol Res 1988; 7: 119-126. [PMID: 2906120]

23. Willard HF. The genomics of long tandem arrays of satellite DNA in the human genome. Genome 1989; 31: 737-744. [PMID: 2698839]

24. Goffeau A, Barrell BG, Bussey H., Davis RW, Dujon B, Feldmann H, Galibert F, Hoheisel JD, Jacq C, Johnston M, Louis EJ, Mewes HW, Murakami Y, Philippsen P, Tettelin H, Oliver SG. Life with 6000 genes. Science 1996; 274: 546, 563-567. [PMID: 8849441]

25. Couronne O, Poliakov A, Bray N, Ishkhanov T, Ryaboy D, Rubin E, Pachter L, Dubchak I. Strategies and tools for whole-genome alignments. Genome Res 2003; 13: 73-80. [PMID: 12529308]; [DOI: 10.1101/gr.762503]

26. Dunn MJ, Hochstrasser D, Pallini V, Bini L. Editoral. Electrophoresis 1995; 16: А84-85

27. Wilkins MR, Williams KL, Appel RD, Hochstrasser DF. Proteomic research: new frontiers in functional genomics (principle and practice). Berlin: Springer Verlag. 1997. 464р.

28. Wasinger VC, Cordwell SJ, Cerpa-Poljak A, Yan JX, Gooley AA, Wilkins MR, Duncan MW, Harris R, Williams KL, Humphery-Smith I. Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 1995; 16: 1090-1094. [PMID: 7498152]

29. Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE Jr, Hieter P, Vogelstein B, Kinzler KW. Characterization of the yeast transcriptome. Cell 1997; 88: 243-251. [PMID: 9008165]

30. Bertucci F, Houlgatte R, Nguyen C, Benziane A, Nasser V, Granjeaud S, Tagett B, Loriod B, Giaconia A, Jacquemier J, Viens P, Birnbaum D. Molecular typing of breast cancer: transcriptomics and DNA microarrays. [Article in French] Bull Cancer 2001; 88: 277-286. [PMID: 11313205]

31. Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ, Westerhoff HV, van Dam K, Oliver SG. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 2001; 19: 45-50. [PMID: 11135551]; [DOI: 10.1038/83496]

32. Hirabayashi J, Arata Y, Kasai K. Glycome project: concept, strategy and preliminary application to Caenorhabditis elegans. Proteomics 2001; 1: 295-303. [PMID: 11680876]; [DOI: 10.1002/1615-9861(200102)1:2<295::AID-PROT295>3.0.CO;2-C]

33. Han X., Gross R.W. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J Lipid Res 2003; 44: 1071-1079. [PMID: 12671038]; [DOI: 10.1194/jlr.R300004-JLR200]

34. Inagaki Y, Song P, Kokudo N, Tang W. New 'multi-omics' approach and its contribution to hepatocellular carcinoma in China. Chin J Cancer Res 2014; 26: 639-640. [PMID: 25561758]; [DOI: 10.3978/j.issn.1000-9604.2014.11.07]

35. Pavel AB, Sonkin D, Reddy A. Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity. BMC Syst Biol 2016; Feb 11;10(1):16. [PMID: 26864072]; [DOI: 10.1186/s12918-016-0260-9]

36. Chang CY, Leu JD, Lee YJ. The actin depolymerizing factor (ADF)/cofilin signaling pathway and DNA damage responses in cancer. Int J Mol Sci 2015; 16: 4095-4120. [PMID: 25689427]; [DOI: 10.3390/ijms16024095]

37. Bandhavkar S. Cancer stem cells: a metastasizing menace! Cancer Med 2016; 5: 649-655. [PMID: 26773710]; [DOI: 10.1002/cam4.629]

38. Shipitsin M, Polyak K. The cancer stem cell hypothesis: in search of definitions, markers, and relevance. Lab Invest 2008; 88: 459-463. [PMID: 18379567]; [DOI: 10.1038/labinvest.2008.14]

39. Osborn AG, Salzman KL, Thurnher MM, Rees JH, Castillo M. The new World Health Organization Classification of Central Nervous System Tumors: what can the neuroradiologist really say? AJNR Am J Neuroradiol 2012; 33: 795-802. [PMID: 21835942]; [DOI: 10.3174/ajnr.A2583]

40. Slaughter DP, Southwick HW, Smejkal W. Field cancerization in oral stratified squamous epithelium; clinical implications of multicentric origin. Cancer 1953; 6: 963-968. [PMID: 13094644]

41. Fidler IJ. Tumor heterogeneity and the biology of cancer invasion and metastasis. Cancer Res 1978; 38: 2651-2660. [PMID: 354778]

42. Gay L, Baker AM, Graham TA. Tumour Cell Heterogeneity. F1000Res 2016; Feb 29;5. pii: F1000 Faculty Rev-238. [PMID: 26973786]; [DOI: 10.12688/f1000research.7210.1]

43. Mack SC, Hubert CG, Miller TE, Taylor MD, Rich JN. An epigenetic gateway to brain tumor cell identity. Nat Neurosci 2016; 19: 10-19. [PMID: 26713744]; [DOI: 10.1038/nn.4190]

44. Cho SY, Kang W, Han JY, Min S, Kang J, Lee A., Kwon J.Y., Lee C., Park H. An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts. Mol Cells 2016; 39: 77-86. [PMID: 26831452]; [DOI: 10.14348/molcells.2016.2350]

45. Naik RR, Singh AK, Mali AM, Khirade MF, Bapat SA. A tumor deconstruction platform identifies definitive end points in the evaluation of drug responses. Oncogene 2016; 35:727-737. [PMID: 25915841]; [DOI: 10.1038/onc.2015.130]

46. Ambrose J, Livitz M, Wessels D, Kuhl S, Lusche DF, Scherer A, Voss E, Soll DR. Mediated coalescence: a possible mechanism for tumor cellular heterogeneity. Am J Cancer Res 2015; 5: 3485-3504. [PMID: 26807328]

47. Rostoker R, Abelson S, Genkin I, Ben-Shmuel S, Sachidanandam R, Scheinman EJ, Bitton-Worms K, Orr ZS, Caspi A, Tzukerman M0, LeRoith D. CD24(+) cells fuel rapid tumor growth and display high metastatic capacity. Breast Cancer Res 2015; Jun 4;17:78. [PMID: 26040280]; [DOI: 10.1186/s13058-015-0589-9]

48. Ahmed M, Li LC. Adaptation and clonal selection models of castration-resistant prostate cancer: current perspective. Int J Urol 2013; 20: 362-371. [PMID: 23163774]; [DOI: 10.1111/iju.12005]

49. Dick JE. Stem cell concepts renew cancer research. Blood 2008; 112: 4793-4807. [PMID: 19064739]; [DOI: 10.1182/blood-2008-08-077941]

50. Sugihara E, Saya H. Complexity of cancer stem cells. Int J Cancer 2013; 132: 1249-1259. [PMID: 23180591]; [DOI: 10.1002/ijc.27961]

51. Basu S, Haase G, Ben-Ze'ev A. Wnt signaling in cancer stem cells and colon cancer metastasis. F1000Res 2016; Apr 19;5. pii: F1000 Faculty Rev-699. [PMID: 27134739]; [DOI: 10.12688/f1000research.7579.1]

52. Moghbeli M, Moghbeli F, Forghanifard MM, Abbaszadegan MR. Cancer stem cell detection and isolation. Med Oncol 2014; Sep;31(9):69. [PMID: 25064729]; [DOI: 10.1007/s12032-014-0069-6]

53. Cherciu I, Bărbălan A, Pirici D, Mărgăritescu C, Săftoiu A. Stem cells, colorectal cancer and cancer stem cell markers correlations. Curr Health Sci J 2014; 40: 153-161. [PMID: 25729599]; [DOI: 10.12865/CHSJ.40.03.01]

54. Japaze H, Garcia-Bunuel R, Woodruff JD. Primary vulvar neoplasia: a review of in situ and invasive carcinoma, 1935-1972. Obstet Gynecol 1977; 49: 404-411. [PMID: 854243]

55. Poulsen HS, Jensen J, Hermansen C. Human breast cancer: heterogeneity of estrogen binding sites. Cancer 1981; 48: 1791-1793. [PMID: 6269725]

56. Fujii T, Ishida E, Shimada K, Hirao K, Tanaka N, Fujimoto K, Konishi N. Computer-assisted three-dimensional analysis of multifocal/multicentric prostate cancer. Cancer Invest 2014; 32: 303-310. [PMID: 24827595]; [DOI: 10.3109/07357907.2014.911878]

57. Andree KC, van Dalum G, Terstappen LW. Challenges in circulating tumor cell detection by the CellSearch system. Mol Oncol 2016; 10: 395-407. [PMID: 26795350]; [DOI: 10.1016/j.molonc.2015.12.002]

58. Yang MH, Imrali A, Heeschen C. Circulating cancer stem cells: the importance to select. Chin J Cancer Res 2015; 27: 437-449. [PMID: 26543330]; [DOI: 10.3978/j.issn.1000-9604.2015.04.08]

59. Brouwer A, De Laere B, Peeters D, Peeters M., Salgado R., Dirix L, Van Laere S. Evaluation and consequences of heterogeneity in the circulating tumor cell compartment. Oncotarget 2016; Mar 9. [PMID: 26980749]; [DOI: 10.18632/oncotarget.8015]

60. Low WS, Wan Abas WA. Benchtop technologies for circulating tumor cells separation based on biophysical properties. Biomed Res Int 2015; 2015:239362. [PMID: 25977918]; [DOI: 10.1155/2015/239362]

61. Schmidt F, Efferth T. Tumor Heterogeneity, Single-Cell Sequencing, and Drug Resistance. Pharmaceuticals (Basel) 2016 Jun 16; 9(2). pii: E33. [PMID: 27322289]; [DOI: 10.3390/ph9020033]

62. Clark RL. Cancer 1980: achievements, challenges, and prospects. Cancer 1982; 49: 1739-1745. [PMID: 6176312]

63. Park JG, Gazdar AF. Biology of colorectal and gastric cancer cell lines. J Cell Biochem (Suppl) 1996; 24: 131-141. [PMID: 8806095]

64. Woods LK, Morgan RT, Quinn LA, Moore GE, Semple TU, Stedman KE. Comparison of four new cell lines from patients with adenocarcinoma of the ovary. Cancer Res 1979; 39: 4449-4459. [PMID: 498076]

65. Akil H, Perraud A, Jauberteau MO, Mathonnet M. Tropomyosin-related kinase B/brain derived-neurotrophic factor signaling pathway as a potential therapeutic target for colorectal cancer. World J Gastroenterol 2016; 22: 490-500. [PMID: 26811602]; [DOI: 10.3748/wjg.v22.i2.490]

66. Ho TH, Nateras RN, Yan H, Park JG, Jensen S, Borges C, Lee JH, Champion MD, Tibes R, Bryce AH, Carballido EM, Todd MA, Joseph RW, Wong WW, Parker AS, Stanton ML, Castle EP. A Multidisciplinary Biospecimen Bank of Renal Cell Carcinomas Compatible with Discovery Platforms at Mayo Clinic, Scottsdale, Arizona. PLoS One 2015 Jul 16;10(7):e0132831. [PMID: 26181416]; [DOI: 10.1371/journal.pone.0132831]

67. Barretina J, Caponigro G, Stransky N., Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jané-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P Jr, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483: 603-607. [PMID: 22460905]; [DOI: 10.1038/nature11003]

68. Sonkin D, Hassan M, Murphy DJ, Tatarinova TV. Tumor suppressors status in cancer cell line Encyclopedia. Mol Oncol 2013; 7: 791-798. [PMID: 23639312]; [DOI: 10.1016/j.molonc.2013.04.001]

69. Rhodes A. Developing a cell line standard for HER2/neu. Cancer Biomark 2005; 1: 229-232. [PMID: 17192046]

70. Freedman L.P., Gibson M.C., Ethier S.P., Soule HR, Neve R, Reid YA. Reproducibility: changing the policies and culture of cell line authentication. Nat Methods 2015; 12: 493-497. [PMID: 26020501]; [DOI: 10.1038/nmeth.3403]

71. Lopez-Barcons LA. Serially heterotransplanted human prostate tumours as an experimental model. J Cell Mol Med 2010; 14: 1385-1395. [PMID: 19874422]; [DOI: 10.1111/j.1582-4934.2009.00957.x]

72. Lengyel E., Burdette J.E., Kenny H.A, Matei D, Pilrose J, Haluska P, Nephew KP, Hales DB, Stack MS. Epithelial ovarian cancer experimental models. Oncogene 2014; 33: 3619-3633. [PMID: 23934194]; [DOI: 10.1038/onc.2013.321]

73. Niu N, Wang L. In vitro human cell line models to predict clinical response to anticancer drugs. Pharmacogenomics 2015; 16: 273-285. [PMID: 25712190]; [DOI: 10.2217/pgs.14.170]

74. Gazdar AF, Hirsch FR, Minna JD. From Mice to Men and Back: An Assessment of Preclinical Model Systems for the Study of Lung Cancers. J Thorac Oncol 2016; 11: 287-299. [PMID: 26723239]; [DOI: 10.1016/j.jtho.2015.10.009]

75. Ravi M, Paramesh V, Kaviya SR, Anuradha E, Solomon FD. 3D cell culture systems: advantages and applications. J Cell Physiol 2015; 230: 16-26. [PMID: 24912145]; [DOI: 10.1002/jcp.24683]

76. Sandercock AM, Rust S, Guillard S, Sachsenmeier KF, Holoweckyj N, Hay C, Flynn M, Huang Q, Yan K, Herpers B, Price LS, Soden J, Freeth J, Jermutus L, Hollingsworth R, Minter R. Identification of anti-tumour biologics using primary tumour models, 3-D phenotypic screening and image-based multi-parametric profiling. Mol Cancer 2015; Jul 31;14:147. [PMID: 26227951]; [DOI: 10.1186/s12943-015-0415-0]

77. Taniguchi S. Suppression of cancer phenotypes through a multifunctional actin-binding protein, calponin, that attacks cancer cells and simultaneously protects the host from invasion. Cancer Sci 2005; 96: 738-746. [PMID: 16271067]; [DOI: 10.1111/j.1349-7006.2005.00118.x]

78. Miyamoto S, Yagi H, Yotsumoto F, Kawarabayashi T, Mekada E. Heparin-binding epidermal growth factor-like growth factor as a novel targeting molecule for cancer therapy. Cancer Sci 2006; 97: 341-347. [PMID: 16630129]; [DOI: 10.1111/j.1349-7006.2006.00188.x]

79. Datta A, Adelson ME, Mogilevkin Y, Mordechai E, Sidi AA, Trama JP. Oncoprotein DEK as a tissue and urinary biomarker for bladder cancer. BMC Cancer 2011 Jun 10;11:234. [PMID: 21663673]; [DOI: 10.1186/1471-2407-11-234]

80. Shishkin S, Kovaleva M, Ivanov A, Eryomina L, Lisitskaya K, Toropugin I, Kovalev L, Okhritz V, Loran O. Comparative proteomic study of proteins in prostate cancer and benign hyperplasia cells. J Cancer Sci Ther 2011; S1. [DOI: 10.4172/1948-5956.S1-003]

81. Voth B, Nagasawa DT, Pelargos PE, Chung LK, Ung N, Gopen Q, Tenn S, Kamei DT, Yang I. Transferrin receptors and glioblastoma multiforme: Current findings and potential for treatment. J Clin Neurosci 2015; 22: 1071-1076. [PMID: 25891893]; [DOI: 10.1016/j.jocn.2015.02.002]

82. Schrödter S, Braun M, Syring I, Klümper N, Deng M, Schmidt D, Perner S, Müller SC, Ellinger J. Identification of the dopamine transporter SLC6A3 as a biomarker for patients with renal cell carcinoma. Mol Cancer 2016; Feb 2;15:10. [PMID: 26831905]; [DOI: 10.1186/s12943-016-0495-5]

83. White EA, Kenny HA, Lengyel E. Three-dimensional modeling of ovarian cancer. Adv Drug Deliv Rev 2014; Dec 15;79-80:184-92. [PMID: 25034878]; [DOI: 10.1016/j.addr.2014.07.003]

84. Guiro K, Arinzeh TL. Bioengineering Models for Breast Cancer Research. Breast Cancer (Auckl) 2016; 9 (Suppl 2): 57-70. [PMID: 26792996]; [DOI: 10.4137/BCBCR.S29424]

85. Abbott A. Cell culture: biology’s new dimension. Nature 2003; 424: .870–872. [PMID: 12931155]; [DOI: 10.1038/424870a]

86. Nelson CM, Bissell MJ. Modeling dynamic reciprocity: engineering three-dimensional culture models of breast architecture, function, and neoplastic transformation. Semin Cancer Biol 2005; 15: 342-352. [PMID: 15963732]; [DOI: 10.1016/j.semcancer.2005.05.001]

87. Bundred NJ, Chan K, Anderson NG. Studies of epidermal growth factor receptor inhibition in breast cancer. Endocr Relat Cancer 2001; 8: 183-189. [PMID: 11566609]

88. Voskoglou-Nomikos T, Pater JL, Seymour L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin Cancer Res 2003; 9: 4227-4239. [PMID: 14519650]

89. Zhang Y, Tang Y, Sun S, Wang Z, Wu W, Zhao X, Czajkowsky DM, Li Y1, Tian J2, Xu L2, Wei W3, Deng Y, Shi Q. Single-cell codetection of metabolic activity, intracellular functional proteins, and genetic mutations from rare circulating tumor cells. Anal Chem 2015; 87: 9761-9768. [PMID: 26378744]; [DOI: 10.1021/acs.analchem.5b01901]

90. Levy B, Hu ZI, Cordova KN, Close S, Lee K, Becker D. Clinical Utility of Liquid Diagnostic Platforms in Non-Small Cell Lung Cancer. Oncologist 2016; Jul 7. pii: theoncologist.2016-0082. [PMID: 27388233]; [DOI: 10.1634/theoncologist.2016-0082]

91. Redzic JS, Ung TH, Graner MW. Glioblastoma extracellular vesicles: reservoirs of potential biomarkers. Glioblastoma extracellular vesicles: reservoirs of potential biomarkers. Pharmgenomics Pers Med 2014; 7: 65-77. [PMID: 24634586]; [DOI: 10.2147/PGPM.S39768]

92. Boukouris S, Mathivanan S. Exosomes in bodily fluids are a highly stable resource of disease biomarkers. Proteomics Clin Appl 2015; 9: 358-367. [PMID: 25684126]; [DOI: 10.1002/prca.201400114]

93. Erb U, Zöller M. Progress and potential of exosome analysis for early pancreatic cancer detection. Expert Rev Mol Diagn 2016; 16: 757-767. [PMID: 27206554]; [DOI: 10.1080/14737159.2016.1187563]

94. Saini S. PSA and beyond: alternative prostate cancer biomarkers. Cell Oncol (Dordr) 2016; 39: 97-106. [PMID: 26790878]; [DOI: 10.1007/s13402-016-0268-6]

95. Pisitkun T, Johnstone R, Knepper MA. Discovery of urinary biomarkers. Mol Cell Proteomics 2006; 5: 1760-1771. [PMID: 16837576]; [DOI: 10.1074/mcp.R600004-MCP200]

Peer reviewer: Xavier Valldeperas


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