Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

Intratumoral microbiota: implications for cancer onset, progression, and therapy

Significant advancements have been made in comprehending the interactions between the microbiome and cancer. However, prevailing research predominantly directs its focus toward the gut microbiome, affording limited consideration to the interactions of intratumoral microbiota and tumors. Within the tumor microenvironment (TME), the intratumoral microbiome and its associated products wield regulatory influence, directing the modulation of cancer cell properties and impacting immune system functionality. However, to grasp a more profound insight into the intratumoral microbiota in cancer, further research into its underlying mechanisms is necessary. In this review, we delve into the intricate associations between intratumoral microbiota and cancer, with a specific focus on elucidating the significant contribution of intratumoral microbiota to the onset and advancement of cancer. Notably, we provide a detailed exploration of therapeutic advances facilitated by intratumoral microbiota, offering insights into recent developments in this burgeoning field.

Unravelling the enigma of the human microbiome: Evolution and selection of sequencing technologies

The human microbiome plays a crucial role in maintaining health, with advances in high-throughput sequencing technology and reduced sequencing costs triggering a surge in microbiome research. Microbiome studies generally incorporate five key phases: design, sampling, sequencing, analysis, and reporting, with sequencing strategy being a crucial step offering numerous options. Present mainstream sequencing strategies include Amplicon sequencing, Metagenomic Next-Generation Sequencing (mNGS), and Targeted Next-Generation Sequencing (tNGS). Two innovative technologies recently emerged, namely MobiMicrobe high-throughput microbial single-cell genome sequencing technology and 2bRAD-M simplified metagenomic sequencing technology, compensate for the limitations of mainstream technologies, each boasting unique core strengths. This paper reviews the basic principles and processes of these three mainstream and two novel microbiological technologies, aiding readers in understanding the benefits and drawbacks of different technologies, thereby guiding the selection of the most suitable method for their research endeavours.

Methionine-producing tumor micro(be) environment fuels growth of solid tumors

BACKGROUND

Recent studies have uncovered the near-ubiquitous presence of microbes in solid tumors of diverse origins. Previous literature has shown the impact of specific bacterial species on the progression of cancer. We propose that local microbial dysbiosis enables certain cancer phenotypes through provisioning of essential metabolites directly to tumor cells.

METHODS

16S rDNA sequencing of 75 patient lung samples revealed the lung tumor microbiome specifically enriched for bacteria capable of producing methionine. Wild-type (WT) and methionine auxotrophic (metA mutant) E. coli cells were used to condition cell culture media and the proliferation of lung adenocarcinoma (LUAD) cells were measured using SYTO60 staining. Further, colony forming assay, Annexin V Staining, BrdU, AlamarBlue, western blot, qPCR, LINE microarray and subcutaneous injection with methionine modulated feed were used to analyze cellular proliferation, cell-cycle, cell death, methylation potential, and xenograft formation under methionine restriction. Moreover, C14-labeled glucose was used to illustrate the interplay between tumor cells and bacteria.

RESULTS/DISCUSSION

Our results show bacteria found locally within the tumor microenvironment are enriched for methionine synthetic pathways, while having reduced S-adenosylmethionine metabolizing pathways. As methionine is one of nine essential amino acids that mammals are unable to synthesize de novo, we investigated a potentially novel function for the microbiome, supplying essential nutrients, such as methionine, to cancer cells. We demonstrate that LUAD cells can utilize methionine generated by bacteria to rescue phenotypes that would otherwise be inhibited due to nutrient restriction. In addition to this, with WT and metA mutant E. coli, we saw a selective advantage for bacteria with an intact methionine synthetic pathway to survive under the conditions induced by LUAD cells. These results would suggest that there is a potential bi-directional cross-talk between the local microbiome and adjacent tumor cells. In this study, we focused on methionine as one of the critical molecules, but we also hypothesize that additional bacterial metabolites may also be utilized by LUAD. Indeed, our radiolabeling data suggest that other biomolecules are shared between cancer cells and bacteria. Thus, modulating the local microbiome may have an indirect effect on tumor development, progression, and metastasis.

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Introduction

The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods

To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results

We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion

These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

Global research trends on the links between the gut microbiome and cancer: a visualization analysis

BACKGROUND

Significant links between the microbiota and human health have emerged in the last 20 years. A correlation has recently been demonstrated between changes in the gut microbiota and the development of cancer. This study aimed to use bibliometric analysis of the published gut microbiome and cancer literature to present the research status and summarize the hotspots for frontier studies.

METHODS

A literature search for research on the gut microbiome and cancer research from 2001 to 2020 was conducted using the Scopus database on 20 March 2021. VOSviewer software (version 1.6.16) was used to perform the visualization analysis.

RESULTS

From 2001 to 2020, a total of 2061 publications were retrieved. Annual publication output grew from 10 in 2001 to 486 in 2020. The USA had the largest number of publications, making the largest contribution to the field (n  = 566, 27.46%). Before 2016, most studies focused on the 'effect of probiotics on cancer'. The latest trends showed that 'microbiota composition and gene expression' and 'host-microbiome interaction in cancer immunotherapy' would be more concerned more widely in the future.

CONCLUSIONS

Research on 'microbiota composition and gene expression' and 'host-microbiome interaction in cancer immunotherapy' will continue to be the hotspot. Therefore, this study provides the trend and characteristics of the literature on the gut microbiota and cancer literature, which provided a useful bibliometric analysis for researchers to conduct further research.

Changes in the Vaginal Microbiome and Associated Toxicities Following Radiation Therapy for Gynecologic Cancers

Postmenopausal women often suffer from vaginal symptoms associated with atrophic vaginitis. Additionally, gynecologic cancer survivors may live for decades with additional, clinically significant, persistent vaginal toxicities caused by cancer therapies, including pain, dyspareunia, and sexual dysfunction. The vaginal microbiome (VM) has been previously linked with vaginal symptoms related to menopause (i.e. dryness). Our previous work showed that gynecologic cancer patients exhibit distinct VM profiles from healthy women, with low abundance of lactobacilli and prevalence of multiple opportunistic pathogenic bacteria. Here we explore the association between the dynamics and structure of the vaginal microbiome with the manifestation and persistence of vaginal symptoms, during one year after completion of cancer therapies, while controlling for clinical and sociodemographic factors. We compared cross-sectionally the vaginal microbiome in 134 women, 64 gynecologic patients treated with radiotherapy and 68 healthy controls, and we longitudinally followed a subset of 52 women quarterly (4 times in a year: pre-radiation therapy, 2, 6 and 12 months post-therapy). Differences among the VM profiles of cancer and healthy women were more pronounced with the progression of time. Cancer patients had higher diversity VMs and a variety of vaginal community types (CTs) that are not dominated by Lactobacilli, with extensive VM variation between individuals. Additionally, cancer patients exhibit highly unstable VMs (based on Bray-Curtis distances) compared to healthy controls. Vaginal symptoms prevalent in cancer patients included vaginal pain (40%), hemorrhage (35%), vaginismus (28%) and inflammation (20%), while symptoms such as dryness (45%), lack of lubrication (33%) and dyspareunia (32%) were equally or more prominent in healthy women at baseline. However, 24% of cancer patients experienced persistent symptoms at all time points, as opposed to 12% of healthy women. Symptom persistence was strongly inversely correlated with VM stability; for example, patients with persistent dryness or abnormally high pH have the most unstable microbiomes. Associations were identified between vaginal symptoms and individual bacterial taxa, including: Prevotella with vaginal dryness, Delftia with pain following vaginal intercourse, and Gemillaceaea with low levels of lubrication during intercourse. Taken together our results indicate that gynecologic cancer therapy is associated with reduced vaginal microbiome stability and vaginal symptom persistence.

Global Analysis of Microbiota Signatures in Four Major Types of Gastrointestinal Cancer

The gut microbiota has been previously linked with tumorigenesis and gastrointestinal cancer progression; however, intra-tumor microbiota analysis has just emerged and deserves increasing attention. Based on the public databases of The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA), this study identified the tissue/organ microbial signatures generated from 443 biosamples of four major gastrointestinal cancer types, including esophageal carcinoma (ESCA), which further includes esophageal adenocarcinoma (EAD) and esophageal squamous cell carcinoma (ESCC), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ). According to partial least squares discrimination analysis (PLS-DA), the profile differences in microbial communities between the tumor and normal samples were not particularly noticeable across the four cancer cohorts, whereas paired comparison analyses revealed several specific differences in bacteria between tumor and normal samples in the EAD, STAD, and COAD samples. The taxa classified from the phylum to genus level revealed a trend of distinguishable microbial profiles between upper and lower gastrointestinal tumors. The Bacteroidetes/Firmicutes ratio in lower gastrointestinal tract tumors was nearly three times that in upper gastrointestinal tract tumors. We also determined the relative tissue/organ-prevalent microbes for each of the four cohorts at the order and genus levels. Microbe Alistipes, Blautia, Pasteurellales, and Porphyromonas compositions were correlated with the clinical characteristics of patients with gastrointestinal cancer, particularly colorectal cancer. Taken together, our findings indicate that microbial profiles shift across different gastrointestinal cancer types and that microbial colonization is highly site-specific. Composition of specific microbes can be indicative of cancer stage or disease progression. Overall, this study indicates that the microbial community and abundance in human tissues can be determined using publicly available data, and provides a new perspective for intra-tissue/organ microbiota research.

The Application of High-Throughput Technologies for the Study of Microbiome and Cancer

Human gut microbiome research, especially gut microbiome, has been developing at a considerable pace over the last decades, driven by a rapid technological advancement. The emergence of high-throughput technologies, such as genomics, transcriptomics, and others, has afforded the generation of large volumes of data, and in relation to specific pathologies such as different cancer types. The current review identifies high-throughput technologies as they have been implemented in the study of microbiome and cancer. Four main thematic areas have emerged: the characterization of microbial diversity and composition, microbial functional analyses, biomarker prediction, and, lastly, potential therapeutic applications. The majority of studies identified focus on the microbiome diversity characterization, which is reaching technological maturity, while the remaining three thematic areas could be described as emerging.

Microbiota and cancer: In vitro and in vivo models to evaluate nanomedicines

Nanomedicine implication in cancer treatment and diagnosis studies witness huge attention, especially with the promising results obtained in preclinical studies. Despite this, only few nanomedicines succeeded to pass clinical phase. The human microbiota plays obvious roles in cancer development. Nanoparticles have been successfully used to modulate human microbiota and notably tumor associated microbiota. Taking the microbiota involvement under consideration when testing nanomedicines for cancer treatment might be a way to improve the poor translation from preclinical to clinical trials. Co-culture models of bacteria and cancer cells, as well as animal cancer-microbiota models offer a better representation for the tumor microenvironment and so potentially better platforms to test nanomedicine efficacy in cancer treatment. These models would allow closer representation of human cancer and might smoothen the passage from preclinical to clinical cancer studies for nanomedicine efficacy.

Tissue-associated microbial detection in cancer using human sequencing data

Cancer is one of the leading causes of morbidity and mortality in the globe. Microbiological infections account for up to 20% of the total global cancer burden. The human microbiota within each organ system is distinct, and their compositional variation and interactions with the human host have been known to attribute detrimental and beneficial effects on tumor progression. With the advent of next generation sequencing (NGS) technologies, data generated from NGS is being used for pathogen detection in cancer. Numerous bioinformatics computational frameworks have been developed to study viral information from host-sequencing data and can be adapted to bacterial studies. This review highlights existing popular computational frameworks that utilize NGS data as input to decipher microbial composition, which output can predict functional compositional differences with clinically relevant applicability in the development of treatment and prevention strategies.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Comparison of Bioinformatics Pipelines and Operating Systems for the Analyses of 16S rRNA Gene Amplicon Sequences in Human Fecal Samples

Amplicon high-throughput sequencing of 16S ribosomal RNA (rRNA) gene is currently the most widely used technique to investigate complex gut microbial communities. Microbial identification might be influenced by several factors, including the choice of bioinformatic pipelines, making comparisons across studies difficult. Here, we compared four commonly used pipelines (QIIME2, Bioconductor, UPARSE and mothur) run on two operating systems (OS) (Linux and Mac), to evaluate the impact of bioinformatic pipeline and OS on the taxonomic classification of 40 human stool samples. We applied the SILVA 132 reference database for all the pipelines. We compared phyla and genera identification and relative abundances across the four pipelines using the Friedman rank sum test. QIIME2 and Bioconductor provided identical outputs on Linux and Mac OS, while UPARSE and mothur reported only minimal differences between OS. Taxa assignments were consistent at both phylum and genus level across all the pipelines. However, a difference in terms of relative abundance was identified for all phyla (p < 0.013) and for the majority of the most abundant genera (p < 0.028), such as Bacteroides (QIIME2: 24.5%, Bioconductor: 24.6%, UPARSE-linux: 23.6%, UPARSE-mac: 20.6%, mothur-linux: 22.2%, mothur-mac: 21.6%, p < 0.001). The use of different bioinformatic pipelines affects the estimation of the relative abundance of gut microbial community, indicating that studies using different pipelines cannot be directly compared. A harmonization procedure is needed to move the field forward.

Long-term high-grain diet alters ruminal pH, fermentation, and epithelial transcriptomes, leading to restored mitochondrial oxidative phosphorylation in Japanese Black cattle

To increase intramuscular fat accumulation, Japanese Black beef cattle are commonly fed a high-grain diet from 10 to 30 months of age. Castrated and fistulated cattle (n = 9) were fed a high-concentrate diets during the early, middle, and late stages consecutively (10-14, 15-22, 23-30 months of age, respectively). Ruminal pH was measured continuously, and rumen epithelium and fluid samples were collected on each stage. The 24-h mean ruminal pH during the late stage was significantly lower than that during the early stage. Total volatile fatty acid (VFA) and lactic acid levels during the late stage were significantly lower and higher, respectively, than those during the early and middle stages. In silico analysis of differentially expressed genes showed that "Oxidative Phosphorylation" was the pathway inhibited most between the middle and early stages in tandem with an inhibited upstream regulator (PPARGC1A, also called PGC-1α) but the most activated pathway between the late and middle stages. These results suggest that mitochondrial dysfunction and thereby impaired cell viability due to acidic irritation under the higher VFA concentration restored stable mitochondrial oxidative phosphorylation and cell viability by higher lactic acid levels used as cellular oxidative fuel under a different underlying mechanism in subacute ruminal acidosis.

Gene Expression Analysis of Human Papillomavirus-Associated Colorectal Carcinoma

PURPOSE

Human papillomavirus (HPV) antigens had been found in colorectal cancer (CRC) tissue, but little evidence demonstrates the association of HPV with oncogene mutations in CRC. We aim to elucidate the mutated genes that link HPV infection and CRC carcinogenesis.

METHODS

Cancerous and adjacent noncancerous tissues were obtained from CRC patients. HPV antigen was measured by using the immunohistochemical (IHC) technique. The differentially expressed genes (DEGs) in HPV-positive and HPV-negative tumor tissues were measured by using TaqMan Array Plates. The target genes were validated with the qPCR method.

RESULTS

15 (31.9%) cases of CRC patients were observed to be HPV positive, in which HPV antigen was expressed in most tumor tissues rather than in adjacent noncancerous tissues. With TaqMan Array Plates analyses, we found that 39 differentially expressed genes (DEGs) were upregulated, while 17 DEGs were downregulated in HPV-positive CRC tissues compared with HPV-negative tissues. Four DEGs (MMP-7, MYC, WNT-5A, and AXIN2) were upregulated in tumor vs. normal tissues, or adenoma vs. normal tissue in TCGA, which was overlapped with our data. In the confirmation test, MMP-7, MYC, WNT-5A, and AXIN2 were upregulated in cancerous tissue compared with adjacent noncancerous tissue. MYC, WNT-5A, and AXIN2 were shown to be upregulated in HPV-positive CRC tissues when compared to HPV-negative tissues.

CONCLUSION

HPV-encoding genome may integrate into the tumor genomes that involved in multiple signaling pathways. Further genomic and proteomic investigation is necessary for obtaining a more comprehensive knowledge of signaling pathways associated with the CRC carcinogenesis.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Cooperative and Escaping Mechanisms between Circulating Tumor Cells and Blood Constituents

Metastasis is the leading cause of cancer-related deaths and despite measurable progress in the field, underlying mechanisms are still not fully understood. Circulating tumor cells (CTCs) disseminate within the bloodstream, where most of them die due to the attack of the immune system. On the other hand, recent evidence shows active interactions between CTCs and platelets, myeloid cells, macrophages, neutrophils, and other hematopoietic cells that secrete immunosuppressive cytokines, which aid CTCs to evade the immune system and enable metastasis. Platelets, for instance, regulate inflammation, recruit neutrophils, and cause fibrin clots, which may protect CTCs from the attack of Natural Killer cells or macrophages and facilitate extravasation. Recently, a correlation between the commensal microbiota and the inflammatory/immune tone of the organism has been stablished. Thus, the microbiota may affect the development of cancer-promoting conditions. Furthermore, CTCs may suffer phenotypic changes, as those caused by the epithelial–mesenchymal transition, that also contribute to the immune escape and resistance to immunotherapy. In this review, we discuss the findings regarding the collaborative biological events among CTCs, immune cells, and microbiome associated to immune escape and metastatic progression.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.

The significance of microbiome in personalized medicine

Considering the important role of microbiome, many of current investigations have focused on its beneficial aspects. Although, research explores new dimensions of the impact of microbiome and examines the differences in patients and healthy individuals for identifying biomarker patterns, but limited information is available, and investigation in this field seems to be of great value. On the other hand, new therapeutic approaches, called personalized medicine, have opened a new window in medical science, and the association between microbiome and personalized medicine seems to be one of the most interesting aspects of the subsequent research, and has a pivotal perspective on the treatment of diseases such as cancer. Accordingly, given the novelty of the relationship between these two axes, there are very few studies in this regard. The presence of specific strains may have the ability to modulate cancer progression and therapeutics; this increases the likelihood of precision medicine in relation to microbiota, in terms of treatment and prognosis, and therefore, microbiota is a next generation medicine and may develop a novel therapeutic action in this field.

The power of small changes: Comprehensive analyses of microbial dysbiosis in breast cancer

Disparate occurrence of breast cancer remains an intriguing question since only a subset of women with known risk factors develop cancer. Recent studies suggest an active role of local and distant microbiota in breast cancer initiation, progression, and overall prognosis. A dysbiotic microbiota predisposes the body to develop cancer by inducing genetic instability, initiating DNA damage and proliferation of the damaged progeny, eliciting favorable immune response, metabolic dysregulation and altered response to therapy. In this review, we present our analyses of the existing datasets and discuss the local dysbiosis observed in breast cancer patients and different aspects of breast carcinogenesis that can be potentially influenced by local breast microbiota. Striking differences between microbial community compositions in breast of cancer patients compared to healthy individuals were noted. Differences in microbiome were also apparent between benign and malignant disease and between nipple aspirate fluid of healthy individuals and breast survivors. We also discuss the identification of distinct bacterial, fungal, viral as well as parasite signatures for breast cancer. These microbes are capable of producing numerous secondary metabolites that can act as signaling mediators effecting breast cancer progression. We review how microbes potentially alter response to therapy affecting drug metabolism, pharmacokinetics, anti-tumor effects and toxicity. In conclusion, breast harbors a community of microbes that can communicate with the host cells inducing downstream signaling pathways and modulating various aspects of breast cancer growth and metastatic progression and an improved understanding of microbial dysbiosis can potentially reduce breast cancer risk and improve outcomes of breast cancer patients. The human microbiome, now referred to as, the "forgotten organ" contains a metagenome that is 100-fold more diverse compared to the human genome, thereby, is critically associated with human health [1,2]. With the revelations of the human microbiome project and advent of deep sequencing techniques, a plethora of information has been acquired in recent years. Body sites like stomach, bladder and lungs, once thought to be sterile, are now known to harbor millions of indigenous microbial species. Approximately 80% of the healthy microbiome consists of Firmicutes and Bacteroidetes accompanied by Verrucomicrobia, Actinobacteria, Proteobacteria, Tenericutes and Cyanobacteria [2-7]. The role of microbiome in diabetes, obesity and even neurodegenerative diseases was greatly appreciated in the last decade [1,7-14] and now it has been established that microbiome significantly contributes to many organ specific cancers [1,15,16].

A repository of microbial marker genes related to human health and diseases for host phenotype prediction using microbiome data

The microbiome research is going through an evolutionary transition from focusing on the characterization of reference microbiomes associated with different environments/hosts to the translational applications, including using microbiome for disease diagnosis, improving the effcacy of cancer treatments, and prevention of diseases (e.g., using probiotics). Microbial markers have been identified from microbiome data derived from cohorts of patients with different diseases, treatment responsiveness, etc, and often predictors based on these markers were built for predicting host phenotype given a microbiome dataset (e.g., to predict if a person has type 2 diabetes given his or her microbiome data). Unfortunately, these microbial markers and predictors are often not published so are not reusable by others. In this paper, we report the curation of a repository of microbial marker genes and predictors built from these markers for microbiome-based prediction of host phenotype, and a computational pipeline called Mi2P (from Microbiome to Phenotype) for using the repository. As an initial effort, we focus on microbial marker genes related to two diseases, type 2 diabetes and liver cirrhosis, and immunotherapy efficacy for two types of cancer, non-small-cell lung cancer (NSCLC) and renal cell carcinoma (RCC). We characterized the marker genes from metagenomic data using our recently developed subtractive assembly approach. We showed that predictors built from these microbial marker genes can provide fast and reasonably accurate prediction of host phenotype given microbiome data. As understanding and making use of microbiome data (our second genome) is becoming vital as we move forward in this age of precision health and precision medicine, we believe that such a repository will be useful for enabling translational applications of microbiome data.

Modeling the Role of the Microbiome in Evolution

There is undeniable evidence showing that bacteria have strongly influenced the evolution and biological functions of multicellular organisms. It has been hypothesized that many host-microbial interactions have emerged so as to increase the adaptive fitness of the holobiont (the host plus its microbiota). Although this association has been corroborated for many specific cases, general mechanisms explaining the role of the microbiota in the evolution of the host are yet to be understood. Here we present an evolutionary model in which a network representing the host adapts in order to perform a predefined function. During its adaptation, the host network (HN) can interact with other networks representing its microbiota. We show that this interaction greatly accelerates and improves the adaptability of the HN without decreasing the adaptation of the microbial networks. Furthermore, the adaptation of the HN to perform several functions is possible only when it interacts with many different bacterial networks in a specialized way (each bacterial network participating in the adaptation of one function). Disrupting these interactions often leads to non-adaptive states, reminiscent of dysbiosis, where none of the networks the holobiont consists of can perform their respective functions. By considering the holobiont as a unit of selection and focusing on the adaptation of the host to predefined but arbitrary functions, our model predicts the need for specialized diversity in the microbiota. This structural and dynamical complexity in the holobiont facilitates its adaptation, whereas a homogeneous (non-specialized) microbiota is inconsequential or even detrimental to the holobiont's evolution. To our knowledge, this is the first model in which symbiotic interactions, diversity, specialization and dysbiosis in an ecosystem emerge as a result of coevolution. It also helps us understand the emergence of complex organisms, as they adapt more easily to perform multiple tasks than non-complex ones.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.