Our body is integrated by a legion of microorganisms that coexist in all our tissues and, notably, with a symbiotic functional purpose. Furthermore, host-microbial interactions are beginning to be recognized for their outstanding influence on well-being or the emergence of diseases such as cancer. The advent of high-throughput (HT) technologies has allowed significant advancements in uncovering these correlations through the diversity and abundance of microorganisms in samples of normal and dysfunctional cohorts of human tissues associated with complex diseases such as obesity, type 2 diabetes, and cancer. For instance, in 2015, Mitra and co-workers reported the characterization of the microbiota at different stages of development of cervical intraepithelial neoplasia, and they observed a strong association between the severity of the disease and the vaginal microbiota diversity (Mitra et al., 2015). Furthermore, the association of the microbiota and obesity has also been explored, with observations of changes in the balance and relative abundances of Bacteroidetes and Firmicutes (Ley et al., 2006). Overall, these and other studies provide a glimpse of the central role that the microbiome has in a variety of biological processes in the human body such as in the regulation of fat storage, lipogenesis, fatty acid oxidation and energy balance (Gérard, 2016).

These findings that associate microbiome and phenotype dysfunctional states have contributed to a change in paradigms regarding the relationship between human body and microorganisms, and suggest elucidating the rules by which this interaction can confer wellness or disease. To this end, some challenges must be overcome. For instance, the development of new computational paradigms that contribute to the coherent interpretation of heterogeneous HT technologies, such as Next Generation Sequencing (NGS) and Metabolomics, and the construction of quantitative schemes capable of influencing clinical decisions in precision medicine.

In this review, we present the forefront of HT technologies and conceptual schemes in bioinformatics and systems biology for surveying the host-microbiome association and cancer progression. We expect that our review will be used as a technical and conceptual guide in human microbiome studies, present and discuss the advances in the field, and establish an introspective analysis of the next steps for linking microbiome studies and precision cancer medicine.

The advent of HT technologies has positively impacted the elucidation of the metabolic and regulatory mechanisms by which hosts and microbes interact to determine a health or disease state in the host. In particular, NGS and techniques related to metabolome analysis such as mass spectrometry (MS) are valuable technologies for analyzing the microbiota composition and exploring the genetic, functional, and metabolic activity of the microbial community. Moreover, the use of these technologies enables us to explore the implications of the human microbiome to induce functional and dysfunctional states in a variety of human tissues. Here, we present the state of the art of these technologies and discuss some key findings to elucidate the relationship between the human microbiome and cancer.

In the emergence of complex diseases such as cancer, the relationship between the environmental influence, the microbiome and cancer appearance can be very entangled. The body offers a suitable and nutrient-rich microenvironment to resident microbes, while the microbiome assists humans in metabolic or immune tasks. Additionally, the microbiota provides humans with non-nutrient essential factors, such as vitamins, and impedes pathogens from establishing (Zitvogel et al., 2015). Differences in microbial and possibly viral compositions between healthy subjects and those affected by diseases have been identified (Blumberg and Powrie, 2012; Koeth et al., 2013; Bultman and Jobin, 2014; Clavel et al., 2014; Tilg and Moschen, 2014). Broadly defined, this imbalance, referred as dysbiosis, imply deviations in the composition of resident commensal communities from the ones found in healthy individuals (Petersen and Round, 2014).

Most of the current research exploring the effects of host-microbe interplay in cancer is focused on colorectal cancer (CRC). By using genomic approaches, some studies have compared the mucosal surface and the intestinal lumen microbiota between healthy patients and those with CRC (Chen et al., 2012; Kostic et al., 2012; Sanapareddy et al., 2012). Although there is no consensus between studies, some taxa are associated with a protective function (e.g., Roseburia) while others are associated with potentially detrimental effects (e.g., Fusobacterium, Klebsiella, and Escherichia/Shigella) (Jobin, 2013; Thomas and Jobin, 2015). This suggests a dysbiotic or differential community composition correlated with CRC development. However, among the open issues about host-microbiome interactions in disease, we ignore the role of the microbiome as a driver or consequence of cancer development (Tjalsma et al., 2012).

Altered cellular metabolism and inflammation are proposed host dependent hallmarks of cancer (Hanahan and Weinberg, 2011). Even when host-microbiome interactions might not be considered essential for cancer appearance, or its effects are indirect, some cancers, such as CRC, might have an important microbial component. In vitro studies have reported a signaling process between bacterial quorum-sensing peptides (QSPs) and cancer cells. Bacillus derived QSPs are synthesized when there are bacterial stressors and are able to induce tumor cell invasiveness in a process called epithelial-mesenchymal-like (EMT-like) process (involved in CRC metastasis) (Wynendaele et al., 2015). The QSPs contributed both to metastatic and angiogenesis behaviors under these settings (De Spiegeleer et al., 2015; Wynendaele et al., 2015). Furthermore, in other kinds of cancer, the result of microbial activities can reduce the effectiveness of chemotherapy (Wallace et al., 2010) or influence the development of tumors distant from the gut (Iida et al., 2013).

Genetic and environmental factors disrupting the healthy relationship between hosts and microbiomes can provoque dysbiosis and promote cancer development (Figure 3). Lifestyle, diet, and early exposure have been recognized as major players in determining the microbiome composition. Additionally, different metabolites produced by the intestinal microbiota are proposed to play both cancer-promoting and cancer-protecting roles; however, factors determining different outcomes are not completely understood (Bultman and Jobin, 2014). Characterizing bacterial OTUs consistently altered across studies, and attributing to them the presence of specific diseases can be difficult given the inter-individual variations (Zackular et al., 2013). This suggests the need to understand what are the possible roles of the microbiome in this process. In this regard, we will review three major factors that can promote microbial dysbiosis and cancer development: (1) infectious agents, (2) diet- and microbial-derived metabolites; and (3) inflammatory mediators.

Given that cancer can be produced by a myriad of genetic and environmental factors, understanding its mechanisms and designing optimal treatments calls for computational schemes capable of integrating heterogeneous HT data to move toward personalized and predictive medicine. Among these factors, the microbiome composition in patients constitutes an important component to induce carcinogenesis or other dysfunctional states in human tissues (Thomas and Jobin, 2015).

An explanation of how the microbiome contributes to the physiological state in the host emerged by noticing that microbes are metabolic partners, for which the nutritional habits of the host can induce the dysregulation of biological processes and consequently alter the phenotypic state. For instance, foods enriched in phosphatidylcholine, choline or carnitine, such as red meat and fatty foods, can be metabolized by gut microbes to produce trimethylamine. The liver enzymes can further produce trimethylamine-N-oxide (TMAO), and this metabolite has proatherogenic properties (Koeth et al., 2013). Knowledge about the microbiome composition and levels of its derived metabolite TMAO predicted the probability of suffering a cardiovascular problem, by means of platelet hyperresponsiveness. Even more, the thrombosis potential was transmissible as a microbiome-dependent trait (Zhu et al., 2016). In the case of type 2 diabetes, fasting plasma concentrations of branched chain (BCAA) and aromatic amino acids were higher in people who developed diabetes, and this signature was predictive of developing the disease for more than a decade later (Wang et al., 2011). Interestingly, a metagenomic signature identified in fecal samples from patients with diabetes was the enrichment in metabolic pathways for transport of BCAA and oxidative stress (Qin et al., 2012). It to expect in the near future, that identification of cancer biomarkers, microbiome signatures and its implementation in mechanistic models will also aid in predicting cancer risk and prognosis.

Thus, microbiota metabolism is a cornerstone for maintaining human and microbial symbiosis, whose involvement in signaling transduction and transcriptional regulation is capable of inducing wellness or disease in the human body (Chubukov et al., 2014). More importantly, the heterogeneous composition observed in individual microbiota provides evidence for the usefulness of personalized studies in terms of genetic backgrounds, lifestyle, nutrition and environmental factors. Even though these findings are currently supported with experimental evidence, the understanding of how a community of organisms consume and interchange their metabolic and cross-signaling products and how this dynamical behavior influences the phenotypic state of the human host is still an open question.

To decode this bewildering complexity and uncover their underlying mechanisms, combined strategies with available data coming from different HT technologies and conceptual schemes from systems biology have been employed. Currently, in systems biology, some paradigms have been suggested to reach this combined description, including genome scale metabolic reconstructions and constraints-based modeling (Bordbar et al., 2014). The implementation of this paradigm has made it possible to explore the metabolic phenotypes of isolated microorganisms and has successfully contributed to areas such as in vitro microbial evolution and organisms with biotechnological and therapeutic applications (Resendis-Antonio et al., 2007; Bordbar et al., 2014). More fundamentally, these schemes have served as a guide to characterize the metabolic activity of human tissues and explore the metabolic phenotypes in cancer (Resendis-Antonio et al., 2010; Lewis et al., 2012). Remarkably, genome scale metabolic reconstruction and computational modeling have extended the scope. Currently, it is possible to model the metabolic interaction between different tissues in the human body (Bordbar et al., 2011), and new approaches are currently pointing toward the integration of models for human-microbiome interaction to explore the metabolic activity in a community of microorganisms (Heinken and Thiele, 2015; Shoaie et al., 2015). Notably, these approaches pave the path toward quantitative models able to predict the metabolic profile in a community of microorganisms and exploring the mechanisms by which their metabolic products could drive the development of cancer.

Although this is a titanic enterprise, systems biology is a cornerstone in precision medicine for moving toward: (1) the coherent interpretation of heterogeneous HT data; (2) identification of potential biomarkers in cancer; and (3) the optimal design of personalized treatments in clinical trials (Wang R.-S. et al., 2015). Among the immediate challenges needing to be overcome to materialize those aims, the development of integrative conceptual schemes of HT data is important. Nonetheless, its capacity to provide meaningful biological insight will be the proof of concept. The development of methods for a coherent interpretation of data is particularly important in cancer studies where massive genome characterization of a variety of cancers have been reported (Cancer Genome Atlas Network, 2015). The accumulation of enormous quantities of molecular data has led to the emergence of systems biology as a set of principles that underlie the base functional properties of living organisms, evaluating and interpreting interactions between molecules (Kristensen et al., 2014). From a systems biology perspective, the use of genomic technologies and computational procedures may provide molecular approaches to early disease detection and opportunities for identifying high-risk individuals, thus contributing to opportune diagnosis (Stewart et al., 2015). In terms of cancer and the microbiome, the computational platform from systems biology should be able to integrate HT data such as metagenome and metatranscriptome data for building hypotheses of host-microbiota metabolic activity, and eventually evaluate its role in cancer development (Bäckhed et al., 2012). The hypothesis generated using this approach can be contextualized with the nutritional information of patients, genetic variability, immune status or clinical record. As stated before, integrating microbiome analysis and host data has the potential to predict the disease outcome and has recently been explored in microbiome-related diseases.

Studying the microbiome variation over time offers an exceptional window to understand the properties leading to health and disease states. To date few longitudinal microbiome studies have been conducted on humans, mainly using 16S rRNA sequencing and observing changes in microbial diversity for over a year (Caporaso et al., 2011; David et al., 2014). Results from whole shotgun metagenomics over time are consistent with 16S studies, indicating both small taxonomic and functional variation over time in the absence of perturbations (Voigt et al., 2015). Although those whole shotgun metagenomic studies are scarce, it is expected that price reduction on sequencing will promote their application. From early exposition at birth to adulthood, factors such as diet, immunological tolerance, environment and microbe-microbe interactions can account preferred taxonomic compositions (Wu et al., 2011; Costello et al., 2012; Nutsch et al., 2016). Despite these factors can include an stochastic component, robustness is observed in tissue-specific microbiome identities maintained over time (Caporaso et al., 2011). Notably, when the community suffers a perturbation, taxonomically related bacteria are preferred as substitutes and subject-specific proportions are maintained within the same taxa (David et al., 2014).

Understanding the principles that rule the microbiome dynamics is an important challenge for system biology, nonetheless new paradigms capable to integrate data bases (Hood et al., 2014; Integrative HMP Research Network Consortium, 2014), empirically dissected patterns (Caporaso et al., 2011; David et al., 2014), and computational models (Stein et al., 2013; Mcgeachie et al., 2016) can aid to reach this enterprise. An hypothesis to explore in future is the idea of early warning signals that could link the dynamical microbiome behavior preceding the progression of a human disease (Faust et al., 2015). The advance in this aim will have a strong impact to translate basic knowledge into precision medicine.

In summary, systems biology suggests that human diseases are fundamentally a system issue at which our phenotype (functional or dysfunctional) is an emergent property that results from host-microbiome interactions. Understanding how this property emerges at a molecular level is valuable to reach one of the aims in precision medicine: the desire for more effective treatments in cancer based on personalized genetic background and lifestyle. In this context, HT technologies and biochemical, physiological and clinical data can be organized and evaluated using a network approach that can be useful for predicting disease expression or response to therapies (Loscalzo and Barabasi, 2011). Finally, addressing these aims will contribute positively to understanding the biological mechanisms in human diseases, and providing the right treatment for the right patients at the right moment with clinical strategies based on genomic, proteomics, metabolomics, and taking into account the behavioral and environment background information of individual patients. All these schemes aim to improve diagnostic power.

The development of diagnostic tests using biomarkers to be applied for early detection is likely a key aspect for precision medicine. For example, the immunosignature approach leverages the response of antibodies to disease-related changes and can be used for the simultaneous classification of multiple cancers (Stafford et al., 2014). In addition, researchers have evaluated the potential of the fecal microbiota for early-stage detection of CRC and as a screening tool to differentiate between healthy, adenoma, and carcinoma clinical groups (Zackular et al., 2014). Using metagenomic sequencing, it is possible to identify microbiome signatures able to distinguish CRC patients from tumor-free controls (Zeller et al., 2014).

Conversely, germ-free status and treatment with antibiotics has been shown to lead to a reduction of the numbers of tumors in genetic experimental models of CRC, suggesting the use of antibiotics to knock out cancer-promoting gut microbes (Schwabe and Jobin, 2013; Thomas and Jobin, 2015). For instance, cefoxitin treatment resulted in complete clearance of enterotoxigenic Bacteroides fragilis, a microbe that causes IL17A-dependent colon tumors. Bacteroides fragilis eradication reduced tumorigenesis and decreased mucosal IL-17A expression (DeStefano Shields et al., 2016). Nonetheless, clinical studies must be developed to probe the clinical effectiveness and the potential effect on the whole human microbiome.

Other players must be taken into account in shaping the microbiome. From environmental studies, it has been established that bacteriophages shape bacterial community structure and function via predation and gene transfer (Chibani-Chennoufi et al., 2004). In contrast to antibiotics, lytic phages are fairly specific, usually only targeting a subgroup of strains within one bacterial species, for treating bacterial human diseases. For instance, when a bacteriophage cocktail was used to treat Shigella sonnei in a mouse model, bacteriophage administration significantly reduced Shigella colonization without deleterious side effects and distortions in the gut microbiota (Mai et al., 2015). Taking this into account, using bacteriophages has been proposed to target specific strains of bacteria that are implicated in cancer, while leaving the rest of the microbiome unchanged (DeWeerdt, 2015).

In addition, with diet being a key determinant shaping the gut microbiome, dietary interventions and probiotics that promote the development of microorganisms providing health benefits are an attractive way to prevent or treat diseases such as cancer. Dietary interventions, such as a curcumin-supplemented diet increased survival and entirely eliminated tumor burden in a mouse model of colitis-associated colorectal cancer. The beneficial effect of curcumin on tumorigenesis was associated with the maintenance of a more diverse colonic microbial ecology (Mcfadden et al., 2015). Furthermore, dietary intervention with polyphenol extracts modulate the human gut microbiota toward a more healthy profile increasing the relative abundance of bifidobacteria and lactobacilli (Marchesi et al., 2015). The beneficial effects of natural polyphenols and their synthetic derivatives are extensively studied in context of cancer prophylaxis and therapy (Lewandowska et al., 2016).

In terms of reducing gastrointestinal inflammation and preventing CRC, beneficial roles of probiotics have been demonstrated. Moreover, a novel probiotic mixture suppressed hepatocellular carcinoma growth in mice; shotgun-metagenome sequencing revealed the crosstalk between gut microbial metabolites and hepatocellular carcinoma development (Li et al., 2016). Probiotics shifted the gut microbial community toward certain beneficial bacteria, including the genera Prevotella and Oscillibacter, which are producers of anti-inflammatory metabolites (Li et al., 2016; Figure 4).

Another area of clinical implications of the microbiome relates to its influence on the host's immune system response against pathogens and cancer (Abt et al., 2012; Belkaid and Hand, 2014). For instance, using antibiotics, the reduction of intestinal microbes ablated the effect not only of the immunotherapy directed to TLRs but also the effectiveness of platinum chemotherapy (Iida et al., 2013). Another type of immunotherapy against cancer relies on immune-checkpoint blockers (ICB). Both Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) and Programmed Death-1 (PD-1) are receptors that dampen T cell responses, and blocking these receptors with antibodies is approved for patients with advanced melanoma to enhance its recognition and elimination (Rotte et al., 2015). In vivo studies have shown that CTLA-4 blockade reduces tumor growth in specific pathogen-free mice but not in germ-free mice. This effect relied on the presence of the gut intestinal microbiota and the activation of both CD4+ T_H1 cells and dendritic cells (DCs). Moreover, in melanoma patients who responded to anti-CTLA-4 treatment, the abundance in the stool of Bacteroides thetaiotaomicron and B. fragilis correlated with the response to therapy. This protective effect can be transferred to mice by a fecal microbial transplant (FMT) (Vétizou et al., 2015). In addition, Sivan et al. found that differences in the gut intestinal microbiome composition in mice of the same strain can alter the response to PD-1 blockade, with stool samples enriched in Bifidobacterium spp. having a robust CD8+ T cell tumor infiltration and DC activation. The protective effect was also transferred between mice by means of FMT (Sivan et al., 2015).

Those results indicate an important modulatory role of both the microbiota activities and composition on the immune response to cancer. Importantly, the effectiveness of treatment in those experiments depended on the integrity of the gut intestinal microbiome, but its effects extend to the systemic level. From a translational point of view, the manipulation of the microbiome composition by means of probiotics, prebiotics or even FMT can have therapeutic benefits in cancer treatment.

Analysis of massive amounts of data generated by HT technology and personal clinical records requires computational capacities to handle this data, and, as a consequence, unveil the biological information of interest. Data collection, storage, and handling, and privacy policies of personalized genome data becomes a central issue that must be solved. Information management faces different challenges that can be classified into three aspects: storage, structural organization, and safety. The storage problems have been solved by the buying or leasing of space in the cloud hosting systems of large technology companies that simultaneously have been developing applications for data analysis. The structural organization involves the appropriate classification of personal records and HT data and the development of an efficient and optimized mechanism to look for the desired information through heterogeneous sources of biological databases.

Finally, given the social, ethical and legal implications of personalized information, it should be stored in a protected way. To this end, the management system can include protocols for prevention and protection, access control and a plan of action to prevent the loss of information when some event endangers the integrity and security of the data (https://www.whitehouse.gov/sites/whitehouse.gov/files/documents/PMI_Security_Principles_and_Framework_FINAL_022516.pdf).

Compositional and functional alterations of the human microbiome have been related to the development of complex diseases such as cancer, type 2 diabetes and obesity. As previously mentioned, host-microbiome interactions play a major role in determining the metabolic phenotype in the host, and, more importantly, their particular composition can serve as a potential measurement for establishing wellness and monitoring the evolution of diseases. This notion has not only changed our paradigm of how our body works, like a superorganism, but also unveiled the outstanding role that microorganisms play in establishing wellness or disease states. Although outstanding breakthroughs have been accomplished to discover its connection, new conceptual schemes able to integrate innovative HT technologies and computational modeling are required to improve our measurements and elucidate their fundamental mechanisms.

For instance, single-cell genomics has the potential to assemble the genomes of viruses and microorganisms that are at low frequencies, thus contributing to a better characterization of the biological samples (Gawad et al., 2016). There has been extraordinary progress in single-cell DNA and RNA sequencing for cancer research, specifically regarding evolution, diversity of cells in tumor progression, and intra-tumor heterogeneity depending on spatial localization of single cancer cells in tissue sections (Crosetto et al., 2015; Navin, 2015; Gawad et al., 2016). In the context of host-microbiome interactions, using the spatial information of the surrounding microbiome state and measurements of intra-tumor genetic heterogeneity might have prognostic utility for predicting which patients will be more likely to show poor response to therapy, higher probability of metastasis, or poor overall survival (Burrell et al., 2013; Murugaesu et al., 2013; Almendro et al., 2014). These and other HT technologies permit us to characterize the microbiome composition and open the possibility of therapeutic applications with a focus on precision medicine: the notion of a precision medicine with treatments applied at the right time, at the right dose, and for the right patient.

Precision medicine based on powerful HT technologies for characterizing patients, such as genomics, proteomics and metabolomics, and computational tools for analyzing large sets of data will integrate the discovery of biomarkers and the electronic medical records to provide evidence for the improvement of clinical practice. The big challenge of data analysis of HT technologies is the development of new computational algorithms to improve the integration of the information from different platforms. In this context, deep learning and machine learning have been proposed as good alternatives to perform these tasks (Eddy, 2009; https://arxiv.org/pdf/1603.06430.pdf). In addition, ambitious projects in precision medicine need to leverage important resources, such as research cohort biobanks for longitudinal research studies, and an efficient bioinformatics system that aids in the translation from biomedical research to molecular targeting and identification of biomarkers that correlate with the disease state. Intensive investigations are being conducted to illustrate how microbiome profiles, taking into account relationships with the host, could be used as biomarkers to revolutionize prognostication in cancer.

However, the interindividual variations in microbiome composition can potentially influence cancer evolution and the effectiveness of treatment. Cross sectional studies in large cohorts showed no evidence of a “core” of OTUs shared among healthy subjects (Huse et al., 2012). This highlights geography, ancestry, diet and age as crucial factors shaping the microbiome composition (Yatsunenko et al., 2012). On the other hand, at the metagenomic level, several functions or pathways are more consistent across individuals than taxonomic composition (HMP Consortium, 2012b; Knights et al., 2014). These evidence suggests both a robustness in functions and membership redundancy in the microbiome.

Harnessing the microbiome to improve cancer diagnosis and treatment is challenging given this interindividual variation. As we are still uncovering the mechanisms behind the emergent phenotypes from host-microbiome interactions, profiling the microbiome composition and its functional properties (i.e., metatranscriptomics and metabolomics) can provide more insight on the significance of different compositions in different states (Shade and Handelsman, 2012; Shafquat et al., 2014). Nonetheless, identifying conserved or consistently altered functions of the microbiome can also be elusive. A comparison of functional alterations at the metagenomic level revealed some overlapping, but no universal biomarkers between cohorts with type 2 diabetes (Qin et al., 2012; Karlsson et al., 2013). Thus, characterizing microbiome biomarkers should take into account the specific traits of the populations under study.

Finally, precision medicine faces many other challenges that will be addressed not only from a scientific point of view but also from a social and ethical point of view, including the proper distribution of the benefits of these technologies across most regions of the world and the development of reliable computational platforms that allow data to be stored confidentially, private and protected. Likewise, coordination with ethics committees and regulation will also be necessary for the use of information without the risk of infringement of the patient's rights and to understand and regulate the legal, social, and economic implications.

All authors contributed extensively to the design and discussions of the material presented in this review. All the authors participated to wrote the final version of the paper.

The authors thank the financial support from the Research Chair on Systems Biology-FUNTEL and Instituto Nacional de Medicina Genomica, Mexico.