Colorectal cancer (CRC) is the second most deadly cancer globally, and 30% of these cancers occur in the rectum. The primary treatment for CRC is surgery, often radiotherapy with adjuvant chemotherapy is used before or following surgical resection.
Treatment carries with it a high cost and side effect burden while response rates remain unpredictable. Approximately 20% of patients have total tumour regression post chemoradiotherapy; however, most patients receive only partial or no benefit from treatment. The ability to predict which patients would benefit from standard treatment and those who should be directed to an alternative treatment or an accelerated pathway to surgery would potentially avoid lengthy and costly treatments that may only cause side effects for patients, improving survival rates and quality of life.
In this study, the microbiome, immune cells and patient gene expression were evaluated for their use as predictive biomarkers for response to chemoradiotherapy in rectal cancer patients. Tumour and adjacent normal tissue biopsies were taken before treatment and had DNA and RNA extracted and sequenced.
First, the methodology for analysing microbiomes via shotgun sequencing data was evaluated and improved, increasing taxonomic assignment accuracy by 11% and potentially decreasing analysis time more than nine-fold. Secondly, the sequencing technologies, Oxford Nanopore, 16S rRNA and RNA-sequencing, were evaluated for their ability to assess the microbiome. The results demonstrated that platforms had concordance with one another; however, this was reduced at the species level.
Third, microbial transcription was used to assess rectal cancer microbiomes, correlating them with response rates. The results showed that microbial diversity did not contribute to radiotherapy response, but that individual microbes may influence response. It was hypothesised that species such as Hungatella hathewayi, Fusobacterium nucleatum, Butyricimonas faecalis, Alistipes finegoldii, Bacteroides thetaiotaomicron, and B. fragilis may contribute to tumour regression by modulating metabolism and immune responses.
Third, the abundance of infiltrating immune cells was predicted using RNA-Sequencing data. Analysis indicated that the abundance of M1 macrophages and resting mast cells were correlated with response, while microbial transcription was correlated with the abundance of allergic and anti-tumour effector cells, as well as antigen-presenting cells. It was hypothesised that the microbiome might modulate anti-tumour immune responses directly, and indirectly by altering the tumour microenvironment. Microbes may help maintain a population of anti-tumour effector and antigen-presenting cells for tumour-antigen presentation during tumour cell death and neo-antigen uptake, which may be otherwise exhausted by targeting aspects of the inflammatory tumour microenvironment (i.e., lipid phagocytosis, anti-bacterial and allergic responses).
Lastly, machine-learning was employed to establish a panel of molecular biomarkers predictive of response, including microbial transcription, immune cells and gene expression. The final model demonstrated the ability to predict response with a 7% overall error rate, and that predicting response relied mostly on normal and tumour tissue gene expression, and tumour infiltrating immune cells.
This study provides a panel of prognostic biomarkers which could be utilised to predict patient response. Additionally, it provides evidence for microbial-immune interactions that could be manipulated to enhance treatment and increase response rates
