JP-2026514442-A - Discovery, functional analysis, and diagnosis of colorectal adenoma and cancer biomarkers.

Abstract

In various aspects and embodiments, the Disclosure provides methods for evaluating a subject for the presence or absence of colorectal neoplasms, such as colorectal cancer (CRC), colorectal adenoma (CRA), and progressive colorectal adenoma (CRAA), by metagenomic and multi-omics analysis of feces or other biological samples. In other aspects, the Disclosure provides methods for generating machine learning models or “signatures” based on metagenomic and multi-omics analysis of feces or other biological samples to evaluate a subject for the presence or absence of colorectal diseases, including but not limited to CRC, CRA, and CRAA.

Inventors

• キューン トーマス ジェイ．
• ピーターソン スコット エヌ．
• エロシュキン アレクセイ エム．
• コズビアール ピョートル ズィー．
• フローリオ エルマンノ
• キューン グレゴリー ジェイ．

Assignees

• プレサイエント メタバイオミクス ジェイヴィー， エルエルシー

Dates

Publication Date

20260511

Application Date

20240329

Priority Date

20230330

Claims (20)

1. A method for evaluating the presence of colorectal neoplasms in a subject, The quantification of genetic elements from biological samples from the subject, wherein the abundance or prevalence of the genetic elements is associated with colorectal cancer (CRC), colorectal adenoma (CRA), or progressive colorectal adenoma (CRAA), thereby creating an abundance profile of the genetic elements, and the quantification includes elements related to the taxonomic classification of microorganisms and/or genetic elements related to the gene function of one or more microorganisms. The method comprises evaluating the abundance profile of signatures indicating the presence of CRC, CRA, and/or CRAA in the subject, and determining whether the subject is likely to have CRC, CRA, or CRAA.
2. The method according to claim 1, wherein the subject has a low risk of CRC, CRA, CRAA, or colorectal polyps.
3. The method according to claim 1 or 2, wherein the subject does not have a history of CRC, CRA, or CRAA.
4. The method according to claim 2 or 3, wherein the method is performed as an alternative to colonoscopy.
5. The method according to claim 1, wherein the subject is at high or moderate risk of CRC, CRA, CRAA, or colorectal polyps.
6. The method according to claim 5, wherein the subject has a history of CRC, CRA, or CRAA, and/or a family history of CRC.
7. The method according to claim 5 or 6, wherein the method is performed at least once a year, or at least every other year.
8. The method according to any one of claims 1 to 7, wherein the subject is at least 45 years old, or at least 50 years old, or at least 55 years old, or at least 60 years old.
9. The method according to any one of claims 1 to 7, wherein the subject is under 45 years of age or under 50 years of age.
10. The method according to any one of claims 1 to 9, wherein the biological sample is feces, blood, serum, intestinal mucosa, mucosal swab, colonoscopy aspirate, lavage solution, biopsy tissue sample, or other biological sample.
11. The method according to any one of claims 1 to 10, wherein the genetic elements are quantified by a procedure including nucleic acid sequencing, PCR, qPCR, or microarray analysis.
12. The method according to claim 11, wherein the genetic element is quantified by nucleic acid sequencing, and the nucleic acid sequencing comprises sequencing of at least about 20,000,000 reads.
13. The method according to claim 12, wherein the nucleic acid sequencing comprises sequencing of at least about 40,000,000 reads.
14. The method according to any one of claims 11 to 13, wherein the nucleic acid sequencing comprises one or more of shotgun metagenomic sequencing, rDNA sequencing, targeted amplicon nucleic acid sequencing, or hybridization capture probe sequencing.
15. The method according to claim 14, wherein the nucleic acid sequencing comprises 16S rDNA, 18S rDNA, or ITS amplicon nucleic acid sequencing, and further comprises targeted amplicon nucleic acid sequencing or hybridization capture probe sequencing.
16. The method according to claim 14 or 15, wherein one or more genetic elements are quantified by capture from a sequencing library, optionally amplified by PCR, and subsequently sequenced.
17. The method according to any one of claims 1 to 16, wherein the genetic element represents or is associated with colorectal adenoma (CRA).
18. The method according to claim 17, wherein the genetic element includes at least five taxonomic or functional gene features listed in Table 3.
19. The method according to claim 18, wherein the genetic element includes at least about 10, at least about 25, at least about 50, or at least about 100 taxonomic or genetic functional features as listed in Table 3.
20. The method according to claim 19, wherein the genetic element comprises at least one, at least two, at least five, at least about ten, at least about twenty, or at least about fifty taxonomic features listed in Table 3, and at least one, at least two, at least five, at least about ten, at least about twenty, or at least about fifty gene functional features listed in Table 3.

Description

This priority application claims priority and benefits thereof to U.S. Provisional Application No. 63/455,698, filed on 30 March 2023, which is incorporated herein by reference in its entirety. This sequence listing application includes a sequence listing submitted in XML format via EFS-Web. The XML copy named "MBI-003PC_108458-5003_Sequence_Listing" was created on March 29, 2024, and is 73,728 bytes in size, and its entire contents are incorporated herein by reference. Colorectal cancer is one of the most common cancers worldwide, with approximately 1.8 million new cases of colorectal cancer and over 700,000 cases of rectal cancer reported in 2018. (Bray et al., Global cancer statistics 2018: GLOBOCAN estimates of incident and mortality worldwide for 36 cancers in 185 countries, CA Cancer J Clin. 2018;68(6):394-424). Furthermore, colorectal cancer is currently the leading cause of death for men under 50 years of age. (Siegel RL, et al.) Cancer Statistics, 2024, CA:A Cancer Journal for Clinicians (2024). Despite strong evidence showing that screening individuals with average CRC risk reduces mortality, compliance among individuals is limited due to the invasiveness, discomfort, and fear associated with colonoscopy. Lauby-Secretan et al., The IARC Perspective on Colorectal Cancer Screening, N Engl J Med. 2018;378(18):1734-1740. This creates a significant gap between healthcare systems and, in particular, CRC prevention, highlighting the need for highly sensitive, accurate, and non-invasive diagnostics to detect colon adenomas and cancers. This disclosure fills this gap by providing biomarkers derived from microbial species present in feces and other samples. A is a principal coordinate analysis (PCoA) plot of the microbiome profiles derived from samples within the analyzed trials. B is a PCoA plot of the microbiome profiles derived from samples for each disease class. C is a PCoA plot of the microbiome profiles derived from samples within the analyzed trials after supervised normalization. D is a PCoA plot of the microbiome profiles derived from samples for each disease class after supervised normalization.This is a cross-correlation plot. CRC models were trained using samples from the trials listed at the top. These models were then used to predict the samples (test sets) from each trial.This workflow describes two different feature selection methods, feature importance rank ensembling (FIRE) and statistical estimation of associations between microbial communities and host phenotypes (SIAMCAT), either separately or in combination.This figure shows the independent feature selection method SIAMCAT. Features are ranked according to their significance score. Box plots displaying the relative abundance of the sample are shown to visualize the contribution of features to the difference representation, magnification change, prevalence shift, and area under the curve (AUC).This figure shows performance evaluations obtained using mean AUC based on a combination of taxonomic and genetic features (KEGG ortholog (KO) group, or taxonomic groups associated with colorectal adenoma (CRA), progressive colorectal adenoma (CRAA), or colorectal cancer (CRC)). Here, the feature selection methods FIRE and SIAMCAT are applied separately and together.These are Venn diagrams showing the top 800, 500, 200, 100, 50, and 20 features generated from combinations of FIRE and SIAMCAT for CRA, CRAA, and CRC. The total number and percentage of overlapping features across disease classes are shown. The number of features corresponding to taxonomic (T) and genetic (K) features are shown separately. The number of analyzed features is shown in descending order from left to right and top to bottom (800, 500, 200, 100, 50, and 20 features).This is a Venn diagram showing the direction of change in characteristics. The central plot shows a Venn diagram of the number of overlapping features among 800 taxonomic and genetic features generated from the combination of FIRE and SIAMCAT for CRA, CRAA, and CRC. The surrounding diagrams consider the direction of change in a pairwise manner to show similarities and differences between features common to the disease classes. (FC) = magnification change.This figure shows the differential representation of bacterial taxonomic classes between disease classes. The number of features at the class level was summed as either an overrepresentation (positive value) or underrepresentation (negative value) compared to the control sample.This figure shows the differential representation of bacterial families between disease classes. The number of features at the family level was summed as either an overrepresentation (positive value) or underrepresentation (negative value) compared to the control sample. The family distinguishes CRAA from the control and other disease classes.This cladogram shows important taxonomic features that illustrate the differential representation in CRA, CRAA, and CRC.This figure shows the expre