The promise of the human microbiome in cancer research

Trillions of microbes in the human body could play a crucial role in cancer detection and treatment

The promise of the human microbiome in cancer research

Trillions of microorganisms are scattered throughout the human body, outnumbering human cells by a ratio of 10 to one. These living creatures have become an emerging target for cancer treatment.

In a recent U of T-affiliated review, scientists from the Princess Margaret Cancer Centre and the University Health Network have explored research on the complex relationship between the human microbiome and cancer. 

What is the human microbiome?

The human microbiome is the collection of genomes present in the microbes that live on or in humans. These microbes include not only bacteria, but also fungi, viruses, and prokaryotes, wrote Dr. Brian Coburn, a co-author of the study and a professor at U of T’s Department of Laboratory Medicine & Pathobiology.

Most microbes are concentrated in the genital tract and mucosal surfaces, which are the membranes that cover internal organs and various body cavities. A human’s microbiome develops from birth and is influenced by the mother’s microbiota, as well as individual genetic makeup and environmental exposure. 

The microbiome has been suggested to be directly causing cancer through inflammation of mucosal surfaces, systemic impairment of metabolism and the immune system, and influencing the effectiveness of anti-cancer therapy.

Certain bacterial species have been identified as more abundant in patients that respond to certain types of anti-cancer therapies than others, explained Coburn.

Researchers have also found that many tissues have their own distinct bacterial, viral, and fungal populations. Cancerous tissue itself appears to have an altered microbiome. Further evidence shows differences in the microbial composition of specific cancers. For example, scientists have found distinct microbial composition patterns in subtypes of breast cancer.  

How the microbiome could be a target for anti-cancer therapies

Positive responses to anti-cancer therapy are typically “defined by a reduction in the size of their tumor of 30% or more,” wrote co-author Dr. Aaron Hansen, an oncologist at the Princess Margaret Cancer Centre, to The Varsity. “This is a desirable outcome for treatment. Patients who do not respond to therapy typically have a growth of the tumor by 20% or they develop new metastasis.”

Metastasis is the growth of cancer at a secondary site, away from the initial location of cancer. 

Emerging evidence has also described tumour activity as closely related to abnormal microbiota in adjacent tissues. Scientists have suggested that microbiota may cause inflammation ⁠— or altered inflammatory signaling ⁠— in these tissues, which could promote the growth and spread of tumors. However, more research on tissue samples from cancerous and non-cancerous patients is needed to validate the results.

Scientists are looking at ways to manipulate the microbiome in humans, and often use animal subjects to test their hypothesis. These continue to be an important but imperfect tool for testing research hypotheses for human diseases.

“There is some evidence from observational studies and animal models that the gut microbiota is associated with response to some cancer therapies,” wrote Coburn, “but causality [sic] in humans is a difficult thing to definitively prove and this remains an untested hypothesis.”

Probiotics have been considered as a form of additional treatment for approved cancer therapies. In research involving mice, scientists have shown that probiotic supplementation could decrease the number of tumour cells and their proliferation.

Other researchers have found that the introduction of the bacteria Lactobacillus to mice decreased tumour size and improved survival rates, suggesting that altering the microbiome may have an impact on suppressing tumour growth.

The lasting challenge of research in this field

Research in this field has proven challenging because individuals vary in their responses to antibiotics, probiotics, and other interventions that affect the microbiome. There is a wide range of different types of probiotics, noted Coburn, and they can have vastly different effects on different patients, or they can have no effect at all.

“We don’t know what to use, how much, when or for how long and in which patients – it remains a very large research challenge [that] will take decades to thoroughly investigate,” he explained.

Furthermore, animal models “will always be limited in how they are applied to human disease and treatment,” he wrote. “In the end, only human studies (especially randomized controlled trials) can prove or disprove that a new type of therapy is safe or effective.”

Research on the effect of the microbiome on cancer is relatively new. Well-designed, controlled, and structured observational and interventional studies would shed light on this growing field. Such studies would enable clinicians to assess the link between the microbiome and cancer, as well as the microbiome’s potential in cancer diagnosis and treatment.

New research, wrote Coburn, could help clinicians “determine if the relationship is causal or simply coincidence and whether [the microbiome] is a useful therapeutic target.”

A new tumour analysis technique could improve predictions for pancreatic cancer outcomes

Method could assist in developing treatment plans, reduce health care costs, says U of T-affiliated paper

A new tumour analysis technique could improve predictions for pancreatic cancer outcomes

A new tumour analysis technique has been created to tackle the most common type of pancreatic cancer, in a new U of T-affiliated study. This method could improve physicians’ ability to better predict how a patient will be affected by the cancer, as well as reduce the health care costs of this type of analysis.

The researchers investigated pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related deaths in the world. It is predicted by scientists that PDAC could become the second leading cause of death by cancer by 2030.

The current system for analyzing tumours is flawed

Physicians currently use a three-tiered grading system to analyze tumours from PDAC. The system relies on the classification of tumours into three groups: well, moderate, and poorly differentiated.

The best tumours for pathological analysis are well differentiated, while the worst are poorly differentiated, according to Dr. Sangeetha Kalimuthu, an Assistant Professor at U of T’s Department of Laboratory Medicine & Pathobiology, in an interview with The Varsity.

Tumours in the middle of the scale are considered moderately differentiated. “Think of a well-formed ice cream,” she continued. “When it starts to melt, it gets all ugly and not really pretty to look at, so that’s in essence how a tumour behaves.

But a major problem with this current grading system is that most tumours from PDAC are identified as moderately differentiated. Tumours with this classification are of limited clinical utility, explained Kalimuthu, as they provide little useful prognostic information about the patient.

Recently, largescale studies have identified prognostically significant molecular subtypes in PDAC. Different subtypes of PDAC are associated with differing clinical outcomes.

However, direct identification of a patient’s molecular subtype of PDAC through molecular analysis is expensive and not readily available worldwide. 

New study offers cost-effective solution for overcoming limitation

The U of T-affiliated study, co-authored by Kalimuthu, identified specific structural — or morphological — patterns in PDAC tumours and presented a novel tumour classification system based on these patterns.

The new classification system presented in the paper correlates morphological patterns with the known subtypes of PDAC. This enabled physicians to identify the molecular subtype of PDAC without using costly molecular analysis.

Kalimuthu added that looking at tissue stains “is the standard bread and butter of pathology.”

“Taking a tiny piece of tissue that you get from a much larger tumour and sequencing it doesn’t give you a representation of the tumour,” she said.

“We look at these stains so we can actually get an idea of the tumour — nothing [such as other techniques like sequencing] gives you a better picture of [it].”

Future potential to integrate technique with artificial intelligence

An established procedure that provides precedent for this newly developed classification system for PDAC tumours is an existing grading system for prostate cancer tumours.

Researchers devised the system, called Gleason grading, in 1966. It similarly gave prognostic information for colon cancer patients.

Kalimuthu and her co-authors hope that their new tumour grading system can fulfill a similar role for PDAC.

In the future, Kalimuthu and her co-authors hope to validate the PDAC-based grading system with a larger cohort of pathologists, before incorporating the grading system into a clinical setting.

If they can achieve this, the improved system of prognosis could help guide physicians in developing treatment plans for patients with PDAC.

Since the new classification system is based on patterns, Kalimuthu believes it could one day be integrated with artificial intelligence.

“This classification system could be directly applicable with deep learning algorithms — so that’s the long-term goal.”