Patent Application
20190367995
This application contains a sequence listing, which is submitted electronically via EFS-Web as an ASCII formatted sequence listing with a file name “Sequence_Listing.TXT”, creation date of Aug. 13, 2019, and having a size of about 43 kilobytes. The sequence listing submitted via EFS-Web is part of the specification and is herein incorporated by reference in its entirety.
The present invention relates to biomarkers and methods for predicting the risk of a disease related to microbiota, in particular colorectal cancer (CRC).
Colorectal cancer (CRC) is the third most common form of cancer and the second leading cause of cancer-related death in the Western world (Schetter et al., 2011, “Alterations of microRNAs contribute to colon carcinogenesis,” Semin Oncol., 38:734-742, incorporated herein by reference). A lot of people are diagnosed with CRC and many patients die of this disease each year worldwide. Although current treatment strategies, including surgery, radiotherapy, and chemotherapy, have a significant clinical value for CRC, the relapses and metastases of cancers after surgery have hampered the success of those treatment modalities. Early diagnosis of CRC will help to not only prevent mortality, but also to reduce the costs for surgical intervention.
Current tests of CRC, such as flexible sigmoidoscopy and colonoscopy, are invasive, and patients may find the procedures and the bowel preparation to be uncomfortable or unpleasant.
The development of CRC is a multifactorial process influenced by genetic, physiological, and environmental factors. With regard to environmental factors, lifestyle, particularly dietary intake, may affect the risk of developing CRC. The Western diet, which is rich in animal fat and poor in fiber, is generally associated with an increased risk of CRC. Thus, it has been hypothesized that the relationship between the diet and CRC, may be due to the influence that the diet has on the colon microbiota and bacterial metabolism, making both the colon microbiota and bacterial metabolism relevant factors in the etiology of the disease (McGarr et al., 2005, “Diet, anaerobic bacterial metabolism, and colon cancer,” J Clin Gastroenterol., 39:98-109; Hatakka et al., 2008, “The influence of Lactobacillus rhamnosus LC705 together with Propionibacterium freudenreichii ssp. shermanii JS on potentially carcinogenic bacterial activity in human colon,” Int J Food Microbiol., 128:406-410, both incorporated herein by reference). According to McGarr et al., 2005, clinical studies can now take advantage of the molecular detection techniques used to monitor changes in species of sulfate-reducing bacteria (SRB) with dietary manipulation and medical treatments.
Interactions between the gut microbiota and the immune system have an important role in many diseases both within and outside the gut (Cho et al., 2012, “The human microbiome: at the interface of health and disease,” Nature Rev. Genet. 13, 260-270, incorporated herein by reference). Intestinal microbiota analysis of feces DNA has the potential to be used as a noninvasive test for identifying specific biomarkers that can be used as a screening tool for early diagnosis of patients having CRC, thus leading to longer survival and a better quality of life. According to Cho et al., 2012, microbiome-host interactions may have important bearings on disease susceptibility, and the microbial effects on the balance of host metabolism and immunity provides an excellent model for the broader phenomenon of disease susceptibility. Thus, modifying disease risk by altering metabolic, immunological, or developmental pathways are obvious strategies (Cho et al., 2012).
With the development of molecular biology and its application in microbial ecology and environmental microbiology, an emerging field of metagenomics (environmental genomics or ecogenomics), has been rapidly developed. Metagenomics, comprising extracting total community DNA, constructing a genomic library, and analyzing the library with similar strategies for functional genomics, provides a powerful tool to study uncultured microorganisms in complex environmental habitats. In recent years, metagenomics has been applied to many environmental samples, such as oceans, soils, rivers, thermal vents, hot springs, and human gastrointestinal tracts, nasal passages, oral cavities, skin and urogenital tracts, illuminating its significant value in various areas including medicine, alternative energy, environmental remediation, biotechnology, agriculture and biodefense. For the study of CRC, the inventors performed analysis in the metagenomics field.
Embodiments of the present disclosure seek to solve at least one of the problems existing in the prior art to at least some extent.
The present invention is based on at least the following findings by the inventors:
Assessment and characterization of gut microbiota has become a major research area in human disease, including colorectal cancer (CRC), one of the common causes of death among all types of cancers. To carry out analysis on the gut microbial content of CRC patients, the inventors performed deep shotgun sequencing of the gut microbial DNA from 128 Chinese individuals and conducted a Metagenome-Wide Association Study (MGWAS) using a protocol similar to that described by Qin et al., 2012, “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature, 490, 55-60, the entire content of which is incorporated herein by reference. The inventors identified and validated 140,455 CRC-associated gene markers. To test the potential ability to classify CRC via analysis of gut microbiota, the inventors developed a disease classifier system based on 31 gene markers that are defined as an optimal gene set by a minimum redundancy-maximum relevance (mRMR) feature selection method. For intuitive evaluation of the risk of CRC disease based on these 31 gut microbial gene markers, the inventors calculated a healthy index. The inventors' data provide insight into the characteristics of the gut metagenome corresponding to a CRC risk, a model for future studies of the pathophysiological role of the gut metagenome in other relevant disorders, and the potential for a gut-microbiota-based approach for assessment of individuals at risk of such disorders.
It is believed that gene markers of intestinal microbiota are valuable for improving cancer detection at earlier stages for the following reasons. First, the markers of the present invention are more specific and sensitive as compared to conventional cancer markers. Second, the analysis of stool samples ensures accuracy, safety, affordability, and patient compliance, and stool samples are transportable. As compared to a colonoscopy, which requires bowel preparation, polymerase chain reaction (PCR)-based assays are comfortable and noninvasive, such that patients are more likely to be willing to participate in the described screening program. Third, the markers of the present invention can also serve as a tool for monitoring therapy of cancer patients in order to measure their responses to therapy.
These and other aspects and advantages of the present disclosure will become apparent and more readily appreciated from the following descriptions taken in conjunction with the drawings. It should be understood that the invention is not limited to the precise embodiments shown in the drawings.
In the drawings:
Various publications, articles and patents are cited or described in the background and throughout the specification, each of these references is herein incorporated by reference in its entirety. Discussion of documents, acts, materials, devices, articles or the like which have been included in the present specification is for the purpose of providing context for the present invention. Such discussion is not an admission that any or all of these matters form part of the prior art with respect to any inventions disclosed or claimed.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention pertains. Otherwise, certain terms used herein have the meanings as set in the specification. Terms such as “a”, “an” and “the” are not intended to refer to only a singular entity, but include the general class for which a specific example can be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but its usage does not delimit the invention, except as outlined in the claims.
In one aspect, the present invention relates to a method of obtaining a set of gene markers for predicting the risk of an abnormal condition related to microbiota, comprising
a) identifying abnormal-associated gene markers by a metagenome-wide association study (MGWAS) strategy comprising:
i) collecting a sample from each subject from a population of subjects with the abnormal condition (abnormal) and subjects without the abnormal condition (controls), ii) extracting DNA from each sample, constructing a DNA library from each sample, and carrying out high-throughput sequencing of each DNA library to obtain sequencing reads for each sample;
iii) mapping the sequencing reads to a gene catalog, and deriving a gene profile from the mapping result;
iv) performing a Wilcoxon rank-sum test on the gene profile to identify differential metagenomic gene contents between the abnormal and controls;
b) ranking all of the abnormal-associated gene markers identified in step a) by minimum redundancy-maximum relevance (mRMR) method, and identifying or classifying sequential marker sets therefrom; and
c) for each of the sequential marker set identified or classified from step (b), estimating the error rate by a leave-one-out cross-validation (LOOCV) of a linear discrimination classifier, and selecting an optimal gene marker set with the lowest error rate as the set of gene markers for predicting the risk of the abnormal condition.
In another aspect, the present invention relates to a method of diagnosing whether a subject has an abnormal condition related to microbiota or is at the risk of developing an abnormal condition related to microbiota, comprising:
1) obtaining sequencing reads from sample j of the subject;
2) mapping the sequencing reads to a gene catalog and deriving a gene profile from the mapping result;
3) determining the relative abundance of each gene marker in a set of gene markers, wherein the set of gene markers is obtained using a method according to an embodiment of the invention; and
4) calculating an index of sample j by the following formula:
wherein:
Aij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers in the set of gene markers,
N is a subset of all of abnormal-associated gene markers in selected biomarkers related to the abnormal condition,
M is a subset of all of control-associated gene markers in the selected biomarkers related to the abnormal condition, and
|N| and |M| are numbers (sizes) of the biomarkers in these two subsets, respectively wherein an index greater than a cutoff indicates that the subject has or is at the risk of developing the abnormal condition.
In one embodiment, in a method of the present invention, the metagenome-wide association study (MGWAS) strategy further comprises estimating the false discovery rate (FDR). In one embodiment, the gene catalog is a non-redundant gene set constructed for the related microbiota. In one embodiment, the abnormal condition related to microbiota is an abnormal condition related to environmental microbiota such as soil microbiota, sea microbiota, or river microbiota. In another embodiment, the abnormal condition related to microbiota is a disease related to microbiota present in the animal body or the human body such as microbiota found in the gastrointestinal tract, nasal passages, oral cavities, skin or the urogenital tract, and the sample is a feces sample, a nasal cavity swab, a buccal swab, a skin swab or a vaginal swab. In a preferred embodiment, the abnormal condition related to microbiota is a colorectal disease selected from the group consisting of Colorectal Cancer, Ulcerative Colitis, Crohn's Disease, Irritable Bowel Syndrome (IBS), Diverticular Disease, Hemorrhoids, Anal Fissure, and Bowel Incontinence. In a most preferred embodiment, the abnormal condition related to microbiota is colorectal cancer (CRC).
In one embodiment, the sequencing reads are obtained via steps comprising: 1) collecting the sample j from the subject and extracting DNA from the sample, 2) constructing a DNA library and sequencing the library. In one embodiment, the DNA library is sequenced via a next-generation sequencing method or a next-next-generation sequencing method, preferably using at least one system selected from the group consisting of Hiseq 2000, SOLID, 454, and True Single Molecule Sequencing.
In another embodiment, the cutoff value is obtained by a Receiver Operator Characteristic (ROC) method, wherein the cutoff corresponds to value when the AUC (Area Under the Curve) is at its maximum.
In yet another aspect, the present invention relates to a method for diagnosing whether a subject has colorectal cancer (CRC) or is at the risk of developing colorectal cancer, comprising:
1) obtaining sequencing reads from sample j of the subject;
2) mapping the sequencing reads to a human gut gene catalog and deriving a gene profile from the mapping result;
3) determining the relative abundance of each of the gene markers listed in SEQ ID NOs: 1-31; and
4) calculating the index of sample j using the following formula:
wherein:
Aij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers listed in SEQ ID NOs 1-31,
N is a subset of all of the CRC-associated gene markers and M is a subset of all of the control-associated gene markers,
wherein the subset of CRC-associated gene markers and the subset of control-associated gene markers are shown in Table 1, and
|N| and |M| are numbers (sizes) of the biomarkers in these two subsets, respectively, wherein an index greater than a cutoff indicates that the subject has or is at the risk of developing colorectal cancer.
In one embodiment, the cutoff value is obtained by a Receiver Operator Characteristic (ROC) method, wherein the cutoff corresponds to the value when the AUC (Area Under the Curve) is at its maximum. In a preferred embodiment, the value of said cutoff is −0.0575.
In another aspect, the present invention relates to a gene marker set for predicting the risk of colorectal cancer (CRC) in a subject, gene marker set consisting of the genes listed in SEQ ID NOs: 1-31.
In another aspect, the present invention relates to a kit for analyzing the gene marker set consisting of the genes listed in SEQ ID NOs: 1-31, comprising primers used for PCR amplification that are designed according to the genes listed in SEQ ID NOs: 1-31.
In another aspect, the present invention relates to a kit for analyzing the gene marker set consisting of the genes listed in SEQ ID NOs: 1-31, comprising one or more probes that are designed according to the genes listed in SEQ ID NOs: 1-31.
In another aspect, the present invention relates to use of the gene marker set consisting of the genes listed in SEQ ID NOs: 1-31 for predicting the risk of colorectal cancer (CRC) in a subject.
In another aspect, the present invention relates to use of the gene marker set consisting of the genes listed in SEQ ID NOs: 1-31 for preparation of a kit for predicting the risk of colorectal cancer (CRC) in a subject.
In one embodiment, the sequencing reads are obtained via steps comprising: 1) collecting the sample j from the subject and extracting DNA from the sample, 2) constructing a DNA library and sequencing the library.
The present invention is further exemplified in the following non-limiting Examples. Unless otherwise stated, parts and percentages are by weight and degrees are in Celsius. As is apparent to one of ordinary skill in the art, these Examples, while indicating preferred embodiments of the invention, are given by way of illustration only, and the agents referenced are all commercially available.
I. Methods for Detecting Biomarkers (Detect Biomarkers by Using MGWAS Strategy)
To define CRC-associated metagenomic markers, the inventors carried out a MGWAS (metagenome-wide association study) strategy (Qin et al., 2012, “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature 490, 55-60, incorporated herein by reference). Using a sequence-based profiling method, the inventors quantified the gut microbiota in samples. On average, with the requirement that there should be ≥90% identity, the inventors could uniquely map paired-end reads to the updated gene catalog. To normalize the sequencing coverage, the inventors used relative abundance instead of the raw read count to quantify the gut microbial genes. However, unlike what is done in a GWAS subpopulation correction, the inventors applied this analysis to microbial abundance rather than to genotype. A Wilcoxon rank-sum test was done on the adjusted gene profile to identify differential metagenomic gene contents between the CRC patients and controls. The outcome of the analyses showed a substantial enrichment of a set of microbial genes that had very small P values, as compared with the expected distribution under the null hypothesis, suggesting that these genes were true CRC-associated gut microbial genes.
The inventors next controlled the false discovery rate (FDR) in the analysis, and defined CRC-associated gene markers from these genes corresponding to a FDR.
II. Methods for Selecting the 31 Best Markers from the Biomarkers (Maximum Relevance Minimum Redundancy (mRMR) Feature Selection Framework)
To identify an optimal gene set, a minimum redundancy-maximum relevance (mRMR) (for detailed information, see Peng et al., 2005, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Trans Pattern Anal Mach Intell, 27, 1226-1238, doi:10.1109/TPAMI.2005.159, which is incorporated herein by reference) feature selection method was used to select from all the CRC-associated gene markers. The inventors used the “sideChannelAttack” package of R software to perform the incremental search and found 128 sequential markers sets. For each sequential set, the inventors estimated the error rate by a leave-one-out cross-validation (LOOCV) of the linear discrimination classifier. The optimal selection of marker sets was the one corresponding to the lowest error rate. In the present study, the inventors made the feature selection on a set of 140,455 CRC-associated gene markers. Since it was computationally prohibitive to perform mRMR using all of the genes, the inventors derived a statistically non-redundant gene set. Firstly, the inventors pre-grouped the 140,455 colorectal cancer associated genes that were highly correlated with each other (Kendall correlation >0.9). Then the inventors chose the longest gene of each group as a representative gene for the group, since longer genes have a higher chance of being functionally annotated and will draw more reads during the mapping procedure. This generated a non-redundant set of 15,836 significant genes. Subsequently, the inventors applied the mRMR feature selection method to the 15,836 significant genes and identified an optimal set of 31 gene biomarkers that are strongly associated with colorectal cancer for colorectal cancer classification, which are shown in Table 1.
III. Gut Healthy Index (CRC Index)
To exploit the potential ability of disease classification by gut microbiota, the inventors developed a disease classifier system based on the gene markers that the inventors defined. For intuitive evaluation of the risk of disease based on these gut microbial gene markers, the inventors calculated a gut healthy index (CRC index).
To evaluate the effect of the gut metagenome on CRC, the inventors defined and calculated the gut healthy index for each individual on the basis of the selected 31 gut metagenomic markers as described above. For each individual sample, the gut healthy index of sample j, denoted by Ij, was calculated by the formula below:
Wherein Aij is the relative abundance of marker i in sample j,
N is a subset of all of the abnormal-associated gene markers in the selected biomarkers related to the abnormal condition (namely, a subset of all of the CRC-associated gene markers in these 31 selected gut metagenomic markers),
M is a subset of all of the control-associated gene markers in the selected biomarkers related to the abnormal condition (namely, a subset of all control-associated markers in these 31 selected gut metagenomic markers), and
|N| and |M| are numbers (sizes) of the biomarkers in these two sets, respectively.
IV. Receiver Operator Characteristic (ROC) Analysis
The inventors applied the ROC analysis to assess the performance of the colorectal cancer classification based on metagenomic markers. Based on the 31 gut metagenomic markers selected above, the inventors calculated the CRC index for each sample. The inventors then used the “Daim” package of R software to draw the ROC curve.
V. Disease Classifier System
After identifying biomarkers using the MGWAS strategy, and the rule that the biomarkers used should yield the highest classification between disease and healthy samples with the least redundancy, the inventors ranked the biomarkers by a minimum redundancy-maximum relevance (mRMR) and found sequential markers sets (the size can be as large as the number of biomarkers). For each sequential set, the inventors estimated the error rate using a leave-one-out cross-validation (LOOCV) of a classifier. The optimal selection of marker sets corresponded to the lowest error rate (In some embodiments, the inventors have selected 31 biomarkers).
Finally, for intuitive evaluation of the risk of disease based on these gut microbial gene markers, the inventors calculated a gut healthy index. The larger the healthy index, the higher the risk of disease. The smaller the healthy index, the more healthy the subjects. The inventors can build an optimal healthy index cutoff using a large cohort. If the healthy index of the test sample is larger than the cutoff, then the subject is at a higher disease risk. If the healthy index of the test sample is smaller than the cutoff, then the subject has a low risk of disease. The optimal healthy index cutoff can be determined using a ROC method when the AUC (Area Under the Curve) is at its maximum.
The following examples are offered to illustrate, but not to limit the claimed invention.
1.1 Sample Collection and DNA Extraction
Stool samples from 128 subjects (cohort I), including 74 colorectal cancer patients and 54 healthy controls (Table 2) were collected in the Prince of Wales Hospital, Hong Kong with informed consent. To be eligible for inclusion in this study, individuals had to fit the following criteria for stool sample collection: 1) no taking of antibiotics or other medications, no special diets (diabetics, vegetarians, etc.), and having a normal lifestyle (without extra stress) for a minimum of 3 months; 2) a minimum of 3 months after any medical intervention; 3) no history of colorectal surgery, any kind of cancer, or inflammatory or infectious diseases of the intestine. Subjects were asked to collect stool samples before a colonoscopy examination in standardized containers at home and store the samples in their home freezer immediately. Frozen samples were then delivered to the Prince of Wales Hospital in insulating polystyrene foam containers and stored at −80° C. immediately until use.
Stool samples were thawed on ice and DNA extraction was performed using the QiagenQIAamp DNA Stool Mini Kit according to the manufacturer's instructions. Extracts were treated with DNase-free RNase to eliminate RNA contamination. DNA quantity was determined using a NanoDrop spectrophotometer, a Qubit Fluorometer (with the Quant-iTTMdsDNA BR Assay Kit) and gel electrophoresis.
1.2 DNA Library Construction and Sequencing
DNA library construction was performed following the manufacturer's instruction (Illumina HiSeq 2000 platform). The inventors used the same workflow as described previously to perform cluster generation, template hybridization, isothermal amplification, linearization, blocking and denaturation, and hybridization of the sequencing primers (Qin, J. et al. (2012), “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature 490, 55-60, incorporated herein by reference).
The inventors constructed one paired-end (PE) library with an insert size of 350 bp for each sample, followed by high-throughput sequencing to obtain around 30 million PE reads of a length of 2×100 bp. High quality reads were extracted by filtering out low quality reads containing ‘N’s in the read, filtering out adapter contamination and human DNA contamination from the raw data, and trimming low quality terminal bases of reads. 751 million metagenomic reads (high quality reads) were generated (5.86 million reads per individual on average, Table 3).
1.3 Reads Mapping
The inventors mapped the high quality reads (Table 3) to a published reference gut gene catalog established from European and Chinese adults (Qin, J. et al. (2012), “A metagenome-wide association study of gut microbiota in type 2 diabetes,” Nature, 490, 55-60, incorporated herein by reference) (identity >=90%), and the inventors then derived the gene profiles using the same method of Qin et al. 2012, supra. From the reference gene catalog, as Qin et al. 2012, supra, the inventors derived a subset of 2,110,489 (2.1M) genes that appeared in at least 6 of the 128 samples.
1.4 Analysis of Factors Influencing Gut Microbiota Gene Profiles
To ensure robust comparison of the gene content of the 128 metagenomes, the inventors generated a set of 2,110,489 (2.1M) genes that were present in at least 6 subjects, and generated 128 gene abundance profiles using these 2.1 million genes. The inventors used the permutational multivariate analysis of variance (PERMANOVA) test to assess the effect of different characteristics, including age, BMI, eGFR, TCHO, LDL, HDL, TG gender, DM, CRC status, smoking status and location, on the gene profiles of the 2.1M genes. The inventors performed the analysis using the “vegan” function of R, and the permuted p-value was obtained after 10,000 permutations. The inventors also corrected for multiple testing using the “p.adjust” function of R with the Benjamini-Hochberg method to get the q-value for each gene.
When the inventors performed permutational multivariate analysis of variance (PERMANOVA) on 13 different covariates, only a CRC status was significantly associated with these gene profiles (q=0.0028, Table 4), showing a stronger association than the second-best determinant, body mass index (q=0.15). Thus, the data suggest an altered gene composition in CRC patient microbiomes.
1.5 CRC-Associated Genes Identified by MGWAS
1.5.1 Identification of colorectal cancer associated genes. The inventors performed a metagenome wide association study (MGWAS) to identify the genes contributing to the altered gene composition in the CRC samples. To identify the association between the metagenomic profile and colorectal cancer, a two-tailed Wilcoxon rank-sum test was used in the 2.1M (2,110,489) gene profiles. The inventors identified 140,455 gene markers, which were enriched in either case or control samples with P<0.01 (
1.5.2 Estimating the false discovery rate (FDR). Instead of a sequential P-value rejection method, the inventors applied the “qvalue” method proposed in a previous study (J. D. Storey and R. Tibshirani (2003), “Statistical significance for genomewide studies,” Proceedings of the National Academy of Sciences of the United States of America, 100, 9440, incorporated herein by reference) to estimate the FDR. In the MGWAS, the statistical hypothesis tests were performed on a large number of features of the 140,455 genes. The false discovery rate (FDR) was 11.03%.
1.6 Gut Microbiota-Based CRC Classification
The inventors proceeded to identify potential biomarkers for CRC from the genes associated with the disease, using the minimum redundancy maximum relevance (mRMR) feature selection method. However, since the computational complexity of this method did not allow them to use all 140,455 genes from the MGWAS approach, the inventors had to reduce the number of candidate genes. First, the inventors selected a stricter set of 36,872 genes with higher statistical significance (P<0.001; FDR=4.147%). Then the inventors identified groups of genes that were highly correlated with each other (Kendall's τ>0.9) and chose the longest gene in each group, generating a statistically non-redundant set of 15,836 significant genes. Finally, the inventors used the mRMR method and identified an optimal set of 31 genes that were strongly associated with CRC status (
The inventors validated the discriminatory power of the CRC classifier using another new independent study group, including 19 CRC patients and 16 non-CRC controls that were also collected in the Prince of Wales Hospital.
For each sample, DNA was extracted and a DNA library was constructed followed by high throughput sequencing as described in Example 1. The inventors calculated the gene abundance profile for these samples using the same method as described in Qin et al. 2012, supra. The relative abundance of each of the gene markers as set forth in SEQ ID NOs: 1-31 was then determined. The index of each sample was then calculated using the following formula:
wherein:
Aij is the relative abundance of marker i in sample j, wherein i refers to each of the gene markers as set forth in SEQ ID NOs 1-31,
N is a subset of all of the abnormal-associated gene markers and M is a subset of all of the control-associated gene markers,
the subset of CRC-associated gene markers and the subset of control-associated gene markers are shown in Table 1, and
|N| and |M| are numbers (sizes) of the biomarkers in these two subsets, respectively, wherein |N| is 13 and |M| is 18.
Table 8 shows the calculated index of each sample, and Table 9 shows the relevant gene relative abundance of a representative sample, V30.
In this assessment analysis, the top 19 samples with the highest gut healthy index were all CRC patients, and all of the CRC patients were diagnosed as CRC individuals (Table 8 and
The inventors have therefore identified and validated a 31 markers set that was determined using a minimum redundancy-maximum relevance (mRMR) feature selection method based on 140,455 CRC-associated markers. The inventors have also developed a gut healthy index to evaluate the risk of CRC disease based on these 31 gut microbial gene markers.
Based on the sequencing reads of the 128 microbiomes from cohort I in Example 1, the inventors examined the taxonomic differences between control and CRC-associated microbiomes to identify microbial taxa contributing to the dysbiosis. For this, the inventors used taxonomic profiles derived from three different methods, as supporting evidence from multiple methods would strengthen an association. First, the inventors mapped metagenomic reads to 4650 microbial genomes in the IMG database (version 400) and estimated the abundance of microbial species included in that database (denoted IMG species). Second, the inventors estimated the abundance of species-level molecular operational taxonomic units (mOTUs) using universal phylogenetic marker genes. Third, the inventors organized the 140,455 genes identified by MGWAS into metagenomic linkage groups (MLGs) that represent clusters of genes originating from the same genome, and they annotated the MLGs at the species level using the IMG database whenever possible, grouped the MLGs based on these species annotations, and estimated the abundance of these species (denoted MLG species).
3.1 Species Annotation of IMG Genomes
For each IMG genome, using the NCBI taxonomy identifier provided by IMG, the inventors identified the corresponding NCBI taxonomic classification at the species and genus levels using NCBI taxonomy dump files. The genomes without corresponding NCBI species names were left with their original IMG names, most of which were unclassified.
3.2 Data Profile Construction
3.2.1 Gene Profiles
The inventors mapped their high-quality reads to a published reference gut gene catalog established from European and Chinese adults (identity >=90%), and the inventors then derived the gene profiles using the same method of Qin et al. 2012, supra.
3.2.2 mOTU Profile
Clean reads (high quality reads, as in Example 1) were aligned to the mOTU reference (79268 sequences total) with default parameters (S. Sunagawa et al. (2013), “Metagenomic species profiling using universal phylogenetic marker genes,” Nature methods, 10, 1196, incorporated herein by reference). 549 species-level mOTUs were identified, including 307 annotated species and 242 mOTU linkage groups without representative genomes, the latter of which were putatively Firmicutes or Bacteroidetes.
3.2.3 IMG-Species and IMG-Genus Profiles
Bacterial, archaeal and fungal sequences were extracted from the IMG v400 reference database (V. M. Markowitz et al. (2012), “IMG: the Integrated Microbial Genomes database and comparative analysis system,” Nucleic acids research, 40, D115, incorporated herein by reference) downloaded from http://ftp.jgi-psf.org. 522,093 sequences were obtained in total, and a SOAP reference index was constructed based on 7 equal-sized segments of the original file. Clean reads were aligned to the reference using a SOAP aligner (R. Li et al. (2009), “SOAP2: an improved ultrafast tool for short read alignment,” Bioinformatics, 25, 1966, incorporated herein by reference) version 2.22, with the parameters “-m 4 -s 32 -r 2 -n 100 -x 600 -v 8 -c 0.9 -p 3”. SOAP coverage software was then used to calculate the read coverage of each genome, normalized by genome length, and further normalized to the relative abundance for each individual sample. The profile was generated based on uniquely-mapped reads only.
3.3 Identification of Colorectal Cancer-Associated MLG Species
Based on the identified 140,455 colorectal cancer associated maker genes profile, the inventors constructed the colorectal cancer-associated MLGs using the method described in the previous type 2 diabetes study (Qin et al. 2012, supra). All of the genes were aligned to the reference genomes of the IMG database v400 to obtain genome-level annotation. An MLG was assigned to a genome if >50% constitutive genes were annotated to that genome, otherwise the genome was labeled unclassified. A constitutive gene is a gene that is transcribed continually as opposed to a facultative gene, which is only transcribed when needed. A total of 87 MLGs with a gene number over 100 were selected as colorectal cancer-associated MLGs. These MLGs were grouped based on the species annotations of these genomes to construct MLG species.
To estimate the relative abundance of an MLG species, the inventors estimated the average abundance of the genes of the MLG species, after removing the genes with the 5% lowest and 5% highest abundance. The relative abundance of the IMG species was estimated by summing the abundance of the IMG genomes belonging to that species.
These analyses identified 30 IMG species, 21 mOTUs and 86 MLG species that were significantly associated with CRC status (Wilcoxon rank-sum test, q<0.05; see Tables 10, 11). Eubacterium ventriosum was consistently associated with or enriched in the control microbiomes using all three methods (Wilcoxon rank-sum tests—IMG: q=0.0414; mOTU: q=0.012757; MLG: q=5.446×10−4), and Eubacterium eligens was enriched according to two methods (Wilcoxon rank-sum tests—IMG: q=0.069; MLG: q=0.00031). Conversely, Parvimonas micra (q<1.80×10−5), Peptostreptococcus stomatis (q<1.80×10−5), Solobacterium moorei (q<0.004331) and Fusobacterium nucleatum (q<0.004565) were consistently associated with or enriched in CRC patient microbiomes using all three methods (
3.4 Species Marker Identification
In order to evaluate the predictive power of these taxonomic associations, the inventors used the random forest ensemble learning method (D. Knights, E. K. Costello, R. Knight (2011), “Supervised classification of human microbiota,” FEMS microbiology reviews, 35, 343, incorporated herein by reference) to identify key species markers in the species profiles from the three different methods.
3.4.1 MLG Species Marker Identification
Based on the constructed 87 MLGs with gene numbers over 100, the inventors performed the Wilcoxon rank-sum test on each MLG using a Benjamini-Hochberg adjustment, and 86 MLGs were selected as colorectal-associated MLGs with q<0.05. To identify MLG species markers, the inventors used the “randomForest 4.5-36” function of R vision 2.10 to analyze the 86 colorectal cancer-associated MLG species. Firstly, the inventors sorted all of the 86 MLG species by the importance given by the “randomForest” method. MLG marker sets were constructed by creating incremental subsets of the top ranked MLG species, starting from 1 MLG species and ending at 86 MLG species.
For each MLG marker set, the inventors calculated the false predication ratio in the 128 Chinese cohorts (cohort I). Finally, the MLG species sets with the lowest false prediction ratio were selected as MLG species markers. Furthermore, the inventors drew the ROC curve using the probability of illness based on the selected MLG species markers.
3.4.2 IMG Species and mOTU Species Markers Identification
Based on the IMG species and mOTU species profiles, the inventors identified the colorectal cancer-associated IMG species and mOTU species with q<0.05 (Wilcoxon rank-sum test with 6Benjamini-Hochberg adjustment). Subsequently, the IMG species markers and the mOTU species markers were selecting using the random forest approach as in the MLG species markers selection.
This analysis revealed that 16 IMG species, 10 species-level mOTUs and 21 MLG species were highly predictive of CRC status (Tables 12, 13), with a predictive power of 0.86, 0.90 and 0.94 in ROC analysis, respectively (
3.5 MLG, IMG and mOTU Species Stage Enrichment Analysis
Encouraged by the consistent species associations with CRC status, and to take advantage of the records of disease stages of the CRC patients (Table 2), the inventors explored the species profiles for specific signatures identifying early stages of CRC. The inventors hypothesized that such an effort might even reveal stage-specific associations that are difficult to identify in a global analysis. To identify which species were associated with or enriched in the four colorectal cancer stages or in healthy controls, the inventors carried out a Kruskal test for the MLG species with a gene number over 100, and all of the IMG species and mOTU species with q<0.05 (Wilcoxon rank-sum test with Benjamini-Hochberg adjustment) to obtain the species enrichment information using the highest rank mean among the four CRC stages and the control. The inventors also compared the significance between every two groups by a pair-wise Wilcoxon Rank sum test.
In Chinese cohort I, several species showed significantly different abundances in the different CRC stages. Among these, the inventors did not identify any species enriched in stage I compared to the other CRC stages and the control samples. Peptostreptococcus stomatis, Prevotella nigrescens and Clostridium symbiosum were enriched in stage II or later compared to the control samples, suggesting that they colonize the colon/rectum after the onset of CRC (
The 31 gene biomarkers were derived using the admittedly expensive deep metagenome sequencing approach. Translating them into diagnostic biomarkers would require reliable detection using more simple and less expensive methods such as quantitative PCR (TaqMan probe-based qPCR). Primers and probes were designed using Primer Express v3.0 (Applied Biosystems, Foster City, Calif., USA). The qPCR was performed on an ABI7500 Real-Time PCR System using the TaqMan® Universal PCR Master Mixreagent (Applied Biosystems). Universal 16S rDNA was used as an internal control, and the abundance of gene markers were expressed as relative levels to 16S rDNA.
To validate the test, the inventors selected two case-enriched gene markers (m482585 and m1704941) and measured their abundance by qPCR in a subset of 100 samples (55 cases and 45 controls). Quantification of each of the two genes using the two platforms (metagenomic sequencing and qPCR) showed strong correlations (Spearman r=0.93-0.95,
Next, in order to validate the markers in previously unseen samples, the inventors measured the abundance of these two gene markers using qPCR in 164 fecal samples (51 cases and 113 controls) from an independent Chinese cohort (cohort II). Two case-enriched gene markers significantly associated with CRC status, at significance levels of q=6.56×10−9 (m1704941, butyryl-CoA dehydrogenase from F. nucleatum), and q=0.0011 (m482585, RNA-directed DNA polymerase from an unknown microbe). The gene from F. nucleatum was present in only 4 out of 113 control microbiomes, suggesting a potential for developing specific diagnostic tests for CRC using fecal samples. The CRC index based on the combined qPCR abundance of the two case-enriched gene markers separated the CRC samples from control samples in cohort II (Wilcoxon rank-sum test, P=4.01×10−7;
Another gene from P. micra was the highly conserved rpoB gene (namely m1696299, with identity of 99.78%) encoding RNA polymerase subunit (3, often used as a phylogenetic marker (F. D. Ciccarelli et al. (2006), “Toward automatic reconstruction of a highly resolved tree of life,” Science, 311, 1283, incorporated herein by reference). Since the inventors repeatedly identified P. micra as a novel biomarker for CRC using several strategies including species-agnostic procedures, the inventors performed an additional qPCR experiment for this marker gene on Chinese cohort II as described above and found a significant enrichment in CRC patient microbiomes (Wilcoxon rank-sum test, P=2.15×10−15). When the inventors combined this gene with the two qPCR-validated genes, the CRC index from these three genes clearly separated case from control samples in Chinese cohort II (Wilcoxon rank-sum test, P=5.76×10−13,
Sequence Information for the primers and probes for the selected 3 gene markers:
Bacteroides sp.
Clostridium hathewayi
Clostridium hathewayi
Clostridium hathewayi
Clostridium symbiosum
Fusobacterium sp. 7_1
Fusobacterium nucleatum
vincentii ATCC 49256
Parvimonas micra ATCC
Parvimonas micra ATCC
Peptostreptococcus
anaerobius 653-L
Peptostreptococcus
anaerobius 653-L
Burkholderiales bacterium
Coprobacillus sp.
Eubacterium ventriosum
Faecalibacterium cf.
prausnazii KLE1255
Faecalibacterium
prausnitzii A2-165
Faecalibacterium
prausnazii L2-6
Faecalibacterium
prausnazii L2-6
Bacteroides clarus YIT
Roseburia intestinalis
Coprococcus catus GD/7
Peptostreptococcus stomatis
Parvimonas micra
Parvimonas sp. oral taxon 393
Parvimonas sp. oral taxon 110
Gemella morbillorum
Burkholderia mallei
Fusobacterium sp. oral taxon 370
Fusobacterium nucleatum
Leptotrichia buccalis
Beggiatoa sp. PS
Prevotella intermedia
Streptococcus dysgalactiae
Streptococcus pseudoporcinus
Paracoccus denitriflcans
Solobacterium moorei
Streptococcus constellatus
Crenothrix polyspora
Filifactor alocis
Sulfurovum sp. SCGC AAA036-O23
Clostridium hathewayi
Peptostreptococcus anaerobius
Streptococcus equi
Streptococcus anginosus
Leptotrichia hofstadii
Peptoniphilus indolicus
Eubacterium ventriosum
Adhaeribacter aquaticus
Eubacterium eligens
Haemophilus sputorum
Parvimonas micra
Peptostreptococcus stomatis
Gemella morbillorum
Clostridium symbiosum
Solobacterium moorei
Fusobacterium nucleatum
Clostridium ramosum
Bacteroides fragilis
Clostridium bolteae
Eubacterium ventriosum
Parvimonas micra
Fusobacterium nucleatum
Solobacterium moorei
Clostridium symbiosum
Clostridium hathewayi
Clostridium clostridioforme
Clostridium sp. HGF2
Bacteroides fragilis
Desulfovibrio sp. 6_1_46AFAA
Coprobacillus sp. 3_3_56FAA
Cloacibacillus evryensis
Fusobacterium varium
Clostridium bolteae
Subdoligranulum sp. 4_3_54A2FAA
Clostridium citroniae
Streptococcus equinus
Dorea formicigenerans
Synergistes sp. 3_1 syn1
Alistipes indistinctus
Coprococcus sp. ART55/1
Haemophilus parainfluenzae
Eubacterium eligens
Clostridium sp. L2-50
Eubacterium ventriosum
Bacteroides clarus
Eubacterium biforme
Faecalibacterium prausnitzii
Ruminococcus obeum
Roseburia intestinalis
Ruminococcus torques
Klebsiella pneumoniae
Eubacterium rectale
Peptostreptococcus stomatis
Parvimonas micra
Parvimonas sp. oral taxon 393
Parvimonas sp. oral taxon 110
Gemella morbillorum
Fusobacterium sp. oral taxon 370
Burkholderia mallei
Fusobacterium nucleatum
Leptotrichia buccalis
Prevotella intermedia
Beggiatoa sp. PS
Crenothrix polyspora
Clostridium hathewayi
Eubacterium ventriosum
Haemophilus sputorum
Peptostreptococcus stomatis
Parvimonas micra
Gemella morbillorum
Solobacterium moorei
Parvimonas micra
Fusobacterium nucleatum
Solobacterium moorei
Clostridium hathewayi
Clostridium sp. HGF2
Bacteroides fragilis
Clostridium citroniae
Dorea formicigenerans
Haemophilus parainfluenzae
Although explanatory embodiments have been shown and described, it would be appreciated by those skilled in the art that the above embodiments can not be construed to limit the present disclosure, and changes, alternatives, and modifications can be made to the embodiments without departing from the nature, principles and scope of the present disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| PCT/CN2013/080872 | Aug 2013 | CN | national |
The present patent application is a continuation of U.S. patent application Ser. No. 15/015,358, filed Feb. 4, 2016, which is a continuation-in-part of PCT Patent Application No. PCT/CN2014/083663, filed Aug. 5, 2014, which was published in the English language on Feb. 12, 2015, under International Publication No. WO 2015/018307 A1, which claims priority to PCT Patent Application No. PCT/CN2013/080872, filed Aug. 6, 2013, and the disclosure of both prior applications is incorporated herein by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 15015358 | Feb 2016 | US |
| Child | 16541439 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/CN2014/083663 | Aug 2014 | US |
| Child | 15015358 | US |
