Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy

Combined tumor-associated microbiome and immune gene expression profiling predict response to neoadjuvant chemo-radiotherapy in locally advanced rectal cancer

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemo-radiotherapy (nCRT) followed by surgery. A minority of patients show complete response (CR) to nCRT and may avoid surgery and its functional consequences. Instead, most patients show non-complete response (non-CR) and may benefit from additional treatments to increase CR rates. Reliable predictive markers are lacking. Aim of this study was to identify novel signatures predicting nCRT responsiveness. We performed a combined analysis of tumor-associated microbiome and immune gene expression profiling of diagnostic biopsies from 70 patients undergoing nCRT followed by rectal resection, including 16 with CR and 54 with non-CR. Findings were validated by an independent cohort of 49 patients, including 7 with CR and 42 with non-CR. Intratumoral microbiota significantly differed between CR and non-CR groups at genus and species level. Colonization by bacterial species of Ruminococcus genera was consistently associated with CR, whereas abundance of Fusobacterium, Porhpyromonas, and Oscillibacter species predicted non-CR. Immune gene profiling revealed a panel of 59 differentially expressed genes and significant upregulation of IFN-gamma and -alpha response in patients with CR. Integrated microbiome and immune gene profiling analysis unraveled clustering of microbial taxa with each other and with immune cell-related genes and allowed the identification of a combined signature correctly identifying non-CRS in both cohorts. Thus, combined intratumoral microbiome-immune profiling improves the prediction of response to nCRT. Correct identification of unresponsive patients and of bacteria promoting responsiveness might lead to innovative therapeutic approaches based on gut microbiota pre-conditioning to increase nCRT effectiveness in LARC.

Microbiota and Radiotherapy: Unlocking the Potential for Improved Gastrointestinal Cancer Treatment

Radiotherapy (RT) is one of the major cornerstones in managing gastrointestinal (GI) cancers. However, several side effects, such as intestinal inflammation, mucosal injury, and dysbiosis, often compromise this. The gut microbiota increasingly attracts much interest as an essential modulator of RT effects influencing immune responses and tissue repair. Through short-chain fatty acids such as butyrate, representatives of certain bacterial species play a crucial role under normal conditions, keeping the mucosal integrity intact and reducing oxidative stress-mediated damage. Dysbiosis, a state where diminished microbial diversity and increased pathogenic species in the microbiota are seen, amplifies RT-induced toxicity in patients. Clinical investigations highlight that microbiota-targeted interventions, including probiotics, prebiotics, and fecal microbiota transplantation, hold the means to augment RT efficacy and lessen toxicity. Increased microflora diversity and specific microbial profiles have yielded serious patient improvements. Advanced RT methods use stereotactic body radiotherapy combined with microbiota modulation as a promising technique to shield healthy tissue and maximize immune-mediated antitumor effects. Additionally, there is an implication in tumor behavior regulated by the intratumoral microbiota regarding the response to radiotherapy. Notably, the modulation of gut and tumor microbiota provides an avenue to optimize RT benefits in GI cancers, underscoring the importance of personalized therapy.

The Bidirectional Impact of Cancer Radiotherapy and Human Microbiome: Microbiome as Potential Anti-tumor Treatment Efficacy and Toxicity Modulator

Microbiome and radiotherapy represent bidirectionally interacting entities. The human microbiome has emerged as a pivotal modulator of the efficacy and toxicity of radiotherapy; however, a reciprocal effect of radiotherapy on microbiome composition alterations has also been observed. This review explores the relationship between the microbiome and extracranial solid tumors, particularly focusing on the bidirectional impact of radiotherapy on organ-specific microbiome. This article aims to provide a systematic review on the radiotherapy-induced microbial alteration in-field as well as in distant microbiomes. In this review, particular focus is directed to the oral and gut microbiome, its role in the development and progression of cancer, and how it is altered throughout radiotherapy. This review concludes with recommendations for future research, such as exploring microbiome modification to optimize radiotherapy-induced toxicities or enhance its anti-cancer effects.

Rectal Cancer: Exploring Predictive Biomarkers Through Molecular Pathways Involved in Carcinogenesis

In 2022, colorectal cancer (CCR) had the second-highest incidence in Europe, preceded only by breast cancer [...].

Novel radiation and targeted therapy combinations for improving rectal cancer outcomes

Neoadjuvant radiotherapy (RT) is commonly used as standard treatment for rectal cancer. However, response rates are variable and survival outcomes remain poor, highlighting the need to develop new therapeutic strategies. Research is focused on identifying novel methods for sensitising rectal tumours to RT to enhance responses and improve patient outcomes. This can be achieved through harnessing tumour promoting effects of radiation or preventing development of radio-resistance in cancer cells. Many of the approaches being investigated involve targeting the recently published new dimensions of cancer hallmarks. This review article will discuss key radiation and targeted therapy combination strategies being investigated in the rectal cancer setting, with a focus on exploitation of mechanisms which target the hallmarks of cancer.

Construction and validation of a progression prediction model for locally advanced rectal cancer patients received neoadjuvant chemoradiotherapy followed by total mesorectal excision based on machine learning

Background

We attempted to develop a progression prediction model for local advanced rectal cancer(LARC) patients who received preoperative neoadjuvant chemoradiotherapy(NCRT) and operative treatment to identify high-risk patients in advance.

Methods

Data from 272 LARC patients who received NCRT and total mesorectal excision(TME) from 2011 to 2018 at the Fourth Hospital of Hebei Medical University were collected. Data from 161 patients with rectal cancer (each sample with one target variable (progression) and 145 characteristic variables) were included. One Hot Encoding was applied to numerically represent some characteristics. The K-Nearest Neighbor (KNN) filling method was used to determine the missing values, and SmoteTomek comprehensive sampling was used to solve the data imbalance. Eventually, data from 135 patients with 45 characteristic clinical variables were obtained. Random forest, decision tree, support vector machine (SVM), and XGBoost were used to predict whether patients with rectal cancer will exhibit progression. LASSO regression was used to further filter the variables and narrow down the list of variables using a Venn diagram. Eventually, the prediction model was constructed by multivariate logistic regression, and the performance of the model was confirmed in the validation set.

Results

Eventually, data from 135 patients including 45 clinical characteristic variables were included in the study. Data were randomly divided in an 8:2 ratio into a data set and a validation set, respectively. Area Under Curve (AUC) values of 0.72 for the decision tree, 0.97 for the random forest, 0.89 for SVM, and 0.94 for XGBoost were obtained from the data set. Similar results were obtained from the validation set. Twenty-three variables were obtained from LASSO regression, and eight variables were obtained by considering the intersection of the variables obtained using the previous four machine learning methods. Furthermore, a multivariate logistic regression model was constructed using the data set; the ROC indicated its good performance. The ROC curve also verified the good predictive performance in the validation set.

Conclusions

We constructed a logistic regression model with good predictive performance, which allowed us to accurately predict whether patients who received NCRT and TME will exhibit disease progression.

Methylglyoxal from gut microbes boosts radiosensitivity and radioimmunotherapy in rectal cancer by triggering endoplasmic reticulum stress and cGAS-STING activation

BACKGROUND

Preoperative radiation therapy (preRT) is a fundamental aspect of neoadjuvant treatment for rectal cancer (RC), but the response to this treatment remains unsatisfactory. The combination of radiation therapy (RT) and immunotherapy (iRT) presents a promising approach to cancer treatment, though the underlying mechanisms are not yet fully understood. The gut microbiota may influence the response to RT and immunotherapy. Therefore, we aimed to identify the metabolism of gut microbiota to reverse radioresistance and enhance the efficacy of iRT.

METHODS

Fecal and serum samples were prospectively collected from patients with locally advanced rectal cancer (LARC) who had undergone pre-RT treatment. Candidate gut microbiome-derived metabolites linked with radiosensitization were screened using 16s rRNA gene sequencing and ultrahigh-performance liquid chromatography-mass coupled with mass spectrometry. In vitro and in vivo studies were conducted to assess the radiosensitizing effects of the metabolites including the syngeneic CT26 tumor model and HCT116 xenograft tumor model, transcriptomics and immunofluorescence. The CT26 abscopal effect modeling was employed to evaluate the combined effects of metabolites on iRT.

RESULTS

We initially discovered the gut microbiota-associated metabolite, methylglyoxal (MG), which accurately predicts the response to preRT (Area Under Curve (AUC) value of 0.856) among patients with LARC. Subsequently, we observed that MG amplifies the RT response in RC by stimulating intracellular reactive oxygen species (ROS) and reducing hypoxia in the tumor in vitro and in vivo. Additionally, our study demonstrated that MG amplifies the RT-induced activation of the cyclic guanosine monophosphate AMP synthase-stimulator of interferon genes pathway by elevating DNA double-strand breaks. Moreover, it facilitates immunogenic cell death generated by ROS-mediated endoplasmic reticulum stress, consequently leading to an increase in CD8+ T and natural killer cells infiltrated in the tumor immune microenvironment. Lastly, we discovered that the combination of anti-programmed cell death protein 1 (anti-PD1) therapy produced long-lasting complete responses in all irradiated tumor sites and half of the non-irradiated ones.

CONCLUSIONS

Our research indicates that MG shows promise as a radiosensitizer and immunomodulator for RC. Furthermore, we propose that combining MG with iRT has great potential for clinical practice.

Leveraging circulating microbiome signatures to predict tumor immune microenvironment and prognosis of patients with non-small cell lung cancer

BACKGROUND

Accumulating evidence supports the significant role of human microbiome in development and therapeutic response of tumors. Circulating microbial DNA is non-invasive and could show a general view of the microbiome of host, making it a promising biomarker for cancers. However, whether circulating microbiome is associated with prognosis of non-small cell lung cancer (NSCLC) and its potential mechanisms on tumor immune microenvironment still remains unknown.

METHODS

The blood microbiome data and matching tumor RNA-seq data of TCGA NSCLC patients were obtained from Poore's study and UCSC Xena. Univariate and multivariate Cox regression analysis were used to identify circulating microbiome signatures associated with overall survival (OS) and construct the circulating microbial abundance prognostic scoring (MAPS) model. Nomograms integrating clinical characteristics and circulating MAPS scores were established to predict OS rate of NSCLC patients. Joint analysis of blood microbiome data and matching tumor RNA-seq data was used to deciphered the tumor microenvironment landscape of patients in circulating MAPS-high and MAPS-low groups. Finally, the predictive value of circulating MAPS on the efficacy of immunotherapy and chemotherapy were assessed.

RESULTS

A circulating MAPS prediction model consisting of 14 circulating microbes was constructed and had an independent prognostic value for NSCLC. The integration of circulating MAPS into nomograms may improve the prognosis predictive power. Joint analysis revealed potential interactions between prognostic circulating microbiome and tumor immune microenvironment. Especially, intratumor plasma cells and humoral immune response were enriched in circulating MAPS-low group, while intratumor CD4 + Th2 cells and proliferative related pathways were enriched in MAPS-high group. Finally, drug sensitivity analysis indicated the potential of circulating MAPS as a predictor of chemotherapy efficacy.

CONCLUSION

A circulating MAPS prediction model was constructed successfully and showed great prognostic value for NSCLC. Our study provides new insights of interactions between microbes, tumors and immunity, and may further contribute to precision medicine for NSCLC.