Genetic and stool-based biomarkers enhance early colorectal cancer (CRC) detection

Colorectal cancer (CRC), or bowel cancer, includes colon cancer and rectal cancer. It involves the uncontrolled growth and division of cells in the colon or rectum.

The colon is the longest section of the large intestine, connecting to the anus via the rectum. Colon cancer begins with polyp formation. These polyps are small clumps of cells lining the colon that typically cause no symptoms, and the majority do not progress to cancer.

However, these polyps can occasionally develop into cancer. There are five defined stages of CRC, from Stage 0 to Stage IV. Cancer progresses through these stages as the tumor grows from the inner layer throughout the colon, and potentially throughout the body in the final Stage IV. The chances of survival increase greatly if the cancer is detected early.¹

CRC is the third most commonly diagnosed cancer, accounting for around 10 % of all cancer cases globally. It is also the second leading cause of cancer-related deaths worldwide.¹ CRC predominantly affects older individuals, with most cases occurring in people aged 50 and older.²

Countries implementing population-wide screening have seen a decrease in overall incidence rates. However, recent studies in Europe and the United States analyzing incidence rates by age group have found an increase in colon cancer risk among younger adults.

Millennials (people born between 1981 and 1996) have been found to have twice the risk of colon cancer and quadruple the risk of rectal cancer compared to people born around 1950.

This study also showed that people under the age of 55 are almost 60 % more likely to receive a late-stage diagnosis compared to older adults, reducing the chances of surviving colon cancer and highlighting the need to increase awareness around screening and risk factors in young adults.^3,4

A study on the heritability of various cancers was conducted using a cohort of 45,000 twins. This study estimated that heredity is not the primary risk factor for sporadic cancers, with heritability estimated at 42% for prostate cancer and 35 % for colon cancer. The majority of cases were attributed to chance and environmental factors.⁵

Beyond family history, inflammatory bowel disease is also a notable risk factor for CRC. Statistically significant lifestyle factors linked to increased risk include higher body mass index, smoking cigarettes, and red meat consumption. A high-fiber diet, consumption of fruits and vegetables, and physical exercise were all found to reduce the risk of CRC.⁶

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Genetic mutations as biomarkers of CRC

CRC is a heterogeneous disease at both the cellular and molecular levels. A wide range of genetic and epigenetic mutations, as well as transcriptional and metabolic changes, have been identified as CRC biomarkers.

Exact Sciences’ Cologuard^© multi-target FIT-DNA test includes the detection of a gene mutation known to indicate CRC (KRAS), alongside two methylated regions (NDRG4, BMP3) and a hemoglobin immunoassay.

KRAS is a proto-oncogene found to be mutated in around 40 % of all CRC cases.¹¹A proto-oncogene is a normal gene with the potential to become an oncogene due to mutations or increased expression.

KRAS encodes a compact protein capable of binding to guanosine triphosphate (GTP) and guanosine diphosphate (GDP). This protein relays signals from outside the cell to its nucleus, and its response to extracellular signaling is key to regulating proliferation and differentiation.

Certain mutations lead to constitutive activation of the KRAS protein, continuously triggering downstream signaling pathways such as those involved in cell proliferation. This ultimately drives tumorigenesis.

A number of studies have also demonstrated that patients with mutations in KRAS exhibit much poorer prognosis, meaning that these mutations can be used to predict treatment response.

For instance, mutations in exon 12 (G12V and G12C) were associated with worse survival rates than other mutations. CRC patients with mutations in exon 2, exon 3, or exon 4 were not found to benefit from anti-EGFR therapy, while patients with a mutation in exon 13 (G13D) were found to benefit from chemotherapy plus cetuximab.⁷

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Other stool-based biomarkers

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ncRNA as a biomarker of CRC

The expression of non-coding RNA (ncRNA) and the epigenetic regulation of ncRNA also offer promise as potential biomarkers. MicroRNAs (miRNAs) target specific genes, allowing them to function as post-transcriptional regulators of gene expression. Oncogene-targeting miRNAs, called oncomirs, are upregulated in cancer, while oncogene-silencing miRNAs are downregulated.

This affects the regulation of intracellular signaling networks, inducing cell proliferation, conferring resistance to apoptosis and chemotherapy, and promoting metastasis. A recent meta-analysis identified miR-21 and miR-92a as the most frequently reported fecal-based miRNAs used as CRC biomarkers.

Numerous studies have shown that the upregulation of miR-21 and miR-92a promotes CRC cell migration, invasion, and proliferation, while also inhibiting apoptosis.

Many reported gene targets of miR-21 are implicated in CRC malignancy. For example, the phosphatase and tensin homolog (PTEN) is frequently silenced, leading to activation of the PI3K/AKT pathway and the induction of tumor formation.

miR-92a has also been shown to enhance tumorigenesis and interfere with the expression of tumor suppressor genes involved in the PI3K/AKT, WNT/β-catenin, and BMP/Smad pathways.⁸

Evidence also suggests that miRNA dysregulation plays a role in chemotherapy resistance. For example, miR-26b, miR-148a, and several other miRNAs have been shown to be significantly downregulated in response to FOLFOX—a mixture of folic acid (FOL), 5-fluorouracil (F), and oxaliplatin (OX).⁹

Epigenetic regulation of miRNAs

It has also been suggested that epigenetic alterations occur more frequently than genetic mutations and that these affect multiple cellular processes by activating oncogenes or silencing tumor suppressor genes.

Recent studies have shown that microRNAs are frequently dysregulated in CRC via aberrant DNA methylation. This suggests that methylation quantification may prove to be a more sensitive diagnostic biomarker than measuring miRNA expression.

miR-124a was one of the first CRC-related microRNAs shown to be silenced via epigenetic methylation. Silencing miR-124a through hypermethylation results in the phosphorylation of a tumor suppressor gene (retinoblastoma) and the activation of an oncogene (CDK6).¹⁰

In many tumors, including CRC, miR-137 is downregulated via promoter hypermethylation. Methylation of miR-137 can also be used to differentiate ulcerative colitis (UC) patients at high risk of developing CRC.

miR-34 is a tumor-suppressive microRNA family that has been shown to be directly regulated by the p53 tumor suppressor. miR-34 is regularly methylated in CRC tissues and, to a lesser extent, in adjacent normal tissues. These findings suggest that miR-34a-5p could serve as a prognostic marker to predict cancer aggressiveness in stage II and III CRCs.⁹

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Fecal metagenomics for CRC

Bacteria are considered causative agents for cancer due to several mechanisms, including the production of cancer-promoting or cancer-inhibiting metabolites and the induction of chronic inflammation.

For instance, Helicobacter pylori has been found to cause adenocarcinoma of the distal stomach (gastric cancer) through chronic inflammation.

Bacteria metabolize food to produce a range of molecules. In the context of colon cancer, fiber fermentation produces short-chain fatty acids (SCFAs), while cholesterol metabolism produces secondary bile acids (SBAs).

Microbial-derived SCFAs have been shown to regulate populations of regulatory T cells through interactions with cell surface receptors and the inhibition of host histone deacetylases (HDAC). This influences cell attachment, cytokine production, immune cell migration, chemotaxis, and programmed cell death.¹⁰

Multiple species have also been shown to convert secondary bile acids into cytotoxic compounds. These compounds increase colonic cell proliferation,¹¹ meaning that an increase in SCFA-producing bacteria is typically associated with a lower risk of CRC. Conversely, an increase in SBA-producing bacteria is common in patients with CRC, which aligns with findings that low-fiber, high-fat diets are CRC risk factors.

Zeller et al. (2014) was the first group to investigate whether CRC microbial biomarkers could be identified in the gut microbiome. This was achieved via metagenomic sequencing of fecal samples, enabling the identification of taxonomic markers that distinguished tumor-free controls from CRC patients.

The team developed a classification algorithm based on the abundance of 22 microbial species. At least 50 % of the model’s predictive power was attributed to the abundance of the four most discriminative species: two Fusobacterium species, Porphyromonas asaccharolytica, and Peptostreptococcus stomatis—all of which are enriched in CRC.

Fusobacterium species have been shown to accelerate tumorigenesis, with some shifts in the microbial community also attributable to metabolic influences. Species known to efficiently produce butyrate from carbohydrates were found to be consistently depleted in CRC, including Roseburia hominis, R. intestinalis, Anaerostipes hadrus, and Faecalibacterium prausnitzii.

It was observed that the accuracy of metagenomic CRC detection was comparable to that of the standard fecal occult blood test (FOBT). Combining both approaches maintained specificity while improving sensitivity by more than 45 % compared to the FOBT.¹²

Multiple recent studies combining fecal metabolomics and metagenomics have identified their potential as diagnostic tools for CRC.

One group identified bacterial and metabolic differences between early-onset and late-onset CRC. Late-onset CRC (LO-CRC) was characterized by short-chain fatty acid depletion and Fusobacterium nucleatum enrichment. Reduced microbial GABA biosynthesis was also observed, along with a shift in acetate/acetaldehyde metabolism toward acetyl-CoA production.

Early-onset CRC (EO-CRC) was associated with Flavonifractor plautii and increased bile acid, tryptophan, and choline metabolism. Using a predictive model based on metabolomic, metagenomic, and functional gene markers, the study was able to accurately distinguish EO-CRC from controls.¹³

A further study involved the successful development of a classification algorithm based on combined metagenomics and metabolism. This algorithm was able to distinguish between colorectal adenomas, CRC, and healthy subjects, potentially enabling the early diagnosis of colorectal neoplasia.¹⁴

VOC-based detection

One CRC screening method involves the detection of volatile organic compounds (VOCs), which are carbon-containing molecules that exhibit high vapor pressure and a larger proportion of gas molecules.

VOCs are present in stools or can diffuse into the bloodstream via the lungs into breath or into urine via the kidneys. They can be derived from the environment (for example, smoking and diet) or be products of microbial or human cell metabolism.

Neoplasms in the colon have been linked to changes in microbial cellular metabolism, and many studies have highlighted that it is possible to screen for CRC by detecting specific VOCs or a VOC fingerprint.

Discriminatory compounds include p-cresol, 3(4H)-DBZ, and 1H-indole, p-cresol 3(4H)-dibenzofuranone, with area under the curve (AUC) values ranging from 70 % to 90 %.¹⁵

Other groups have used urine-based VOC analysis and identified similar molecules. One study found that butanol was the most discriminatory VOC, with an AUC of 0.98.¹⁶ Thanks to the ease of sample collection, urinary VOC analysis offers some advantages over fecal analysis.

Expensive equipment and trained personnel can be used for VOC detection, or this can be done using dogs as disease sensors. Canines have superior olfactory senses compared to humans, with some reports suggesting that a dog’s sense of smell is 100,000 times more sensitive.

Detection, or sniffer, dogs are trained to detect diseases by "sniffing" or sampling feces, urine, blood, breath, and tissue. A range of studies has highlighted the ability of scent-trained dogs to detect COVID-19 with 94 % to 96 % accuracy and a detection time of 5–10 seconds.¹⁷ Studies have shown, however, that these dogs must be retrained as new variants emerge.¹⁸

An earlier colon cancer study involved a trained Labrador Retriever analyzing the breath and stool samples of healthy controls and CRC patients enrolled in a clinical trial. After smelling, the dog would sit down to indicate a positive sample.

Breath detection sensitivity was found to be 0.91, with a specificity of 0.99, while stool sample sensitivity was found to be 0.97, with a specificity of 0.99. The accuracy of canine scent detection was found to be high, even for early-stage cancer.

It is also important to note that canine scent detection was not hindered by current smoking, benign colorectal disease, or inflammatory disease.¹⁹

Norgen products

Norgen offers a comprehensive workflow, from stool preservation to the extraction of nucleic acids and NGS. The company’s preservation kits make sampling straightforward, offering the ability to keep DNA and RNA stable for 2 years and 7 days, respectively.

Extraction kits for DNA, RNA, and nucleic acids are available in a range of formats, including spin columns, magnetic beads, and 96-well plates. The preserved stool is compatible with metabolite analysis, and Norgen also offers a variety of metagenomic library preparation kits and associated products.

References and further reading

1. Sawicki, T., et al. (2021). A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers, (online) 13(9), p.2025. https://doi.org/10.3390/cancers13092025.
2. World Health Organization (2023). Colorectal cancer. (online) World Health Organization. Available at: https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer.
3. Siegel, R.L., et al. (2017). Colorectal Cancer Incidence Patterns in the United States, 1974-2013. Journal of the National Cancer Institute, (online) 109(8), p.djw322. https://doi.org/10.1093/jnci/djw322.
4. Vuik, F.E., et al. (2019). Increasing incidence of colorectal cancer in young adults in Europe over the last 25 years. Gut, (online) 68(10), pp.1820–1826. https://doi.org/10.1136/gutjnl-2018-317592.
5. Lichtenstein, P., et al. (2000). Environmental and Heritable Factors in the Causation of Cancer — Analyses of Cohorts of Twins from Sweden, Denmark, and Finland. New England Journal of Medicine, (online) 343(2), pp.78–85. https://doi.org/10.1056/nejm200007133430201.
6. Johnson, C.M., et al. (2013). Meta-analyses of colorectal cancer risk factors. Cancer Causes & Control, 24(6), pp.1207–1222. https://doi.org/10.1007/s10552-013-0201-5.
7. National Cancer Institute (2021). Tests to Detect Colorectal Cancer and Polyps. (online) National Cancer Institute. Available at: https://www.cancer.gov/types/colorectal/screening-fact-sheet.
8. Hirai, H.W., et al. (2016). Systematic review with meta-analysis: faecal occult blood tests show lower colorectal cancer detection rates in the proximal colon in colonoscopy-verified diagnostic studies. Alimentary Pharmacology & Therapeutics, 43(7), pp.755–764. https://doi.org/10.1111/apt.13556.
9. Imperiale, T.F., et al. (2014). Multitarget Stool DNA Testing for Colorectal-Cancer Screening. New England Journal of Medicine, 370(14), pp.1287–1297. https://doi.org/10.1056/nejmoa1311194.
10. Vijan, S. (2012). Adherence to Colorectal Cancer Screening. Archives of Internal Medicine, 172(7), p.575. https://doi.org/10.1001/archinternmed.2012.332.
11. Zhu, G., et al. (2021). Role of oncogenic KRAS in the prognosis, diagnosis and treatment of colorectal cancer. Molecular Cancer, (online) 20(1). https://doi.org/10.1186/s12943-021-01441-4.
12. Müller, D. and Győrffy, B. (2022). DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1877(3), p.188722. https://doi.org/10.1016/j.bbcan.2022.188722.
13. Melotte, V., et al (2010). The N‐myc downstream regulated gene (NDRG) family: diverse functions, multiple applications. The FASEB Journal, 24(11), pp.4153–4166. https://doi.org/10.1096/fj.09-151464.
14. Yau, T.O., et al. (2019). Faecal microRNAs as a non-invasive tool in the diagnosis of colonic adenomas and colorectal cancer: A meta-analysis. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45570-9.
15. Baharudin, R., et al. (2022). MicroRNA Methylome Signature and Their Functional Roles in Colorectal Cancer Diagnosis, Prognosis, and Chemoresistance. International Journal of Molecular Sciences, 23(13), p.7281. https://doi.org/10.3390/ijms23137281.
16. Harada, T., et al (2014). Analysis of DNA Methylation in Bowel Lavage Fluid for Detection of Colorectal Cancer. Cancer Prevention Research, 7(10), pp.1002–1010. https://doi.org/10.1158/1940-6207.capr-14-0162.
17. Mirzaei, R., et al. (2021). Role of microbiota-derived short-chain fatty acids in cancer development and prevention. Biomedicine & Pharmacotherapy, 139, p.111619. https://doi.org/10.1016/j.biopha.2021.111619.
18. Yang, R. and Li, Q. (2022). Research on Gut Microbiota-Derived Secondary Bile Acids in Cancer Progression. 21, p.153473542211141-153473542211141. https://doi.org/10.1177/15347354221114100.
19. Wu, X., et al. (2023). Taxonomic and functional profiling of fecal metagenomes for the early detection of colorectal cancer. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1218056.
20. Kong, C., et al. (2023). Integrated metagenomic and metabolomic analysis reveals distinct gut-microbiome-derived phenotypes in early-onset colorectal cancer. Gut, (online) 72(6), pp.1129–1142. https://doi.org/10.1136/gutjnl-2022-327156.
21. Olabisi Oluwabukola Coker, Liu, C., et al. (2022). Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers. 10(1). https://doi.org/10.1186/s40168-021-01208-5.
22. Alustiza, M., et al. (2023). A Novel Non-Invasive Colorectal Cancer Diagnostic Method: Volatile Organic Compounds As Biomarkers. Clinica Chimica Acta, (online) p.117273. https://doi.org/10.1016/j.cca.2023.117273.
23. L.S.A, E., et al. (2023). Urinary volatile organic compounds for colorectal cancer screening: A systematic review and meta-analysis. European journal of cancer, 186, pp.69–82. https://doi.org/10.1016/j.ejca.2023.03.002.
24. Mutesa, L., et al. (2022). Use of trained scent dogs for detection of COVID-19 and evidence of cost-saving. Frontiers in Medicine, 9. https://doi.org/10.3389/fmed.2022.1006315.
25. Kantele, A., et al. (2022). Scent dogs in detection of COVID-19: triple-blinded randomised trial and operational real-life screening in airport setting. BMJ Global Health, (online) 7(5), p.e008024. https://doi.org/10.1136/bmjgh-2021-008024.
26. Sonoda, H., et al. (2011). Colorectal cancer screening with odour material by canine scent detection. Gut, 60(6), pp.814–819. https://doi.org/10.1136/gut.2010.218305.

Acknowledgments

Produced from materials originally authored by Norgen Biotek Corporation.

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