Gut Microbiome Composition Is Associated with a Pathologic Response After Preoperative Chemoradiation in Patients with Rectal Cancer

MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer

BACKGROUND

Complete response prediction in locally advanced rectal cancer (LARC) patients is generally focused on the radiomics analysis of staging MRI. Until now, omics information extracted from gut microbiota and circulating tumor DNA (ctDNA) have not been integrated in composite biomarkers-based models, thereby omitting valuable information from the decision-making process. In this study, we aim to integrate radiomics with gut microbiota and ctDNA-based genomics tracking during neoadjuvant chemoradiotherapy (nCRT).

METHODS

The main hypothesis of the MOREOVER study is that the incorporation of composite biomarkers with radiomics-based models used in the THUNDER-2 trial will improve the pathological complete response (pCR) predictive power of such models, paving the way for more accurate and comprehensive personalized treatment approaches. This is due to the inclusion of actionable omics variables that may disclose previously unknown correlations with radiomics. Aims of this study are: - to generate longitudinal microbiome data linked to disease resistance to nCRT and postulate future therapeutic strategies in terms of both type of treatment and timing, such as fecal microbiota transplant in non-responding patients. - to describe the genomics pattern and ctDNA data evolution throughout the nCRT treatment in order to support the prediction outcome and identify new risk-category stratification agents. - to mine and combine collected data through integrated multi-omics approaches (radiomics, metagenomics, metabolomics, metatranscriptomics, human genomics, ctDNA) in order to increase the performance of the radiomics-based response predictive model for LARC patients undergoing nCRT on MR-Linac.

EXPERIMENTAL DESIGN

The objective of the MOREOVER project is to enrich the phase II THUNDER-2 trial (NCT04815694) with gut microbiota and ctDNA omics information, by exploring the possibility to enhance predictive performance of the developed model. Longitudinal ctDNA genomics, microbiome and genomics data will be analyzed on 7 timepoints: prior to nCRT, during nCRT on a weekly basis and prior to surgery. Specific modelling will be performed for data harvested, according to the TRIPOD statements.

DISCUSSION

We expect to find differences in fecal microbiome, ctDNA and radiomics profiles between the two groups of patients (pCR and not pCR). In addition, we expect to find a variability in the stability of the considered omics features over time. The identified profiles will be inserted into dedicated modelling solutions to set up a multiomics decision support system able to achieve personalized treatments.

Gut bacteria, host immunity, and colorectal cancer: From pathogenesis to therapy

The emergence of 16S rRNA and metagenomic sequencing has gradually revealed the close relationship between dysbiosis and colorectal cancer (CRC). Recent studies have confirmed that intestinal dysbiosis plays various roles in the occurrence, development, and therapeutic response of CRC. Perturbation of host immunity is one of the key mechanisms involved. The intestinal microbiota, or specific bacteria and their metabolites, can modulate the progression of CRC through pathogen recognition receptor signaling or via the recruitment, polarization, and activation of both innate and adaptive immune cells to reshape the protumor/antitumor microenvironment. Therefore, the administration of gut bacteria to enhance immune homeostasis represents a new strategy for the treatment of CRC. In this review, we cover recent studies that illuminate the role of gut bacteria in the progression and treatment of CRC through orchestrating the immune response, which potentially offers insights for subsequent transformative research.

Role of the microbiota in response to and recovery from cancer therapy

Our understanding of how the microbiota affects the balance between response to and failure of cancer treatment by modulating the tumour microenvironment and systemic immune system has advanced rapidly in recent years. Microbiota-targeting interventions in patients with cancer are an area of intensive investigation. Promisingly, phase I-II clinical trials have shown that interventions such as faecal microbiota transplantation can overcome resistance to immune checkpoint blockade in patients with melanoma, improve therapeutic outcomes in treatment-naive patients and reduce therapy-induced immunotoxicities. Here, we synthesize the evidence showing that the microbiota is an important determinant of both cancer treatment efficacy and treatment-induced acute and long-term toxicity, and we discuss the complex and inter-related mechanisms involved. We also assess the potential of microbiota-targeting interventions, including bacterial engineering and phage therapy, to optimize the response to and recovery from cancer therapy.

The relationship between gastric microbiome features and responses to neoadjuvant chemotherapy in gastric cancer

Background

Emerging evidence demonstrates that the gastrointestinal microbiome has the potential to be a biomarker in neoadjuvant chemoradiotherapy for colorectal cancer (CRC). Yet studies on the impact of the gastric microbiome (GM) on the response to neoadjuvant chemotherapy (NACT) are still scarce.

Methods

Forty-eight patients with gastric cancer participated in this retrospective study, and 16S rRNA sequencing was performed to evaluate formalin-fixed and paraffin-embedded (FFPE) tissue biospecimens and fresh-frozen tissues.

Results

In this study, 16 bacterial taxa at different levels, including Bacillus, Anaerococcus, and Chloroflexi, were identified to be enriched before NACT in response (R) patients in group FFPE. In contrast, 6 bacterial taxa, such as Haemophilus, Veillonellaceae (Veillonella), etc. were enriched after NACT, in which we reported for the first time that the phylum Chloroflexi was enriched before NACT in R patients. Thirty-one bacterial taxa of Coriobacteriaceae, Ruminococcaceae, Veillonellaceae, and Lachnospiraceae were identified in group mucosa as being enriched in R patients. In comparison, 4 bacterial taxa dominated by the phylum Proteobacteria were enriched in NR patients. Notably, the family Veillonellaceae was found in both tissue samples, and the metabolic pathways, including the citrate cycle (TCA cycle) and various amino acids, including alanine, were found to be potentially predictive in both sample species.

Conclusion

There are differences in the features of the GM for different NACT response results. The causal relationship deserves to be confirmed by further investigations.

Effect of Lactobacillus Rhamnosus GG for Regulation of Inflammatory Response in Radiation-Induced Enteritis

The purpose of this study was to investigate the role of Lactobacillus rhamnosus GG (LGG) probiotics in radiation enteritis using in vivo mice. A total of 40 mice were randomly assigned to four groups: control, probiotics, radiotherapy (RT), and RT + probiotics. For the group of probiotics, 0.2 mL of solution that contained 1.0 × 108 colony-forming units (CFU) of LGG was used and orally administered daily until sacrifice. For RT, a single dose of 14 Gy was administered using a 6 mega-voltage photon beam to the abdominopelvic area. Mice were sacrifice at day 4 (S1) and day 7 (S2) after RT. Their jejunum, colon, and stool were collected. A multiplex cytokine assay and 16 s ribosomal RNA amplicon sequencing were then performed. Regarding cytokine concentrations in tissues, pro-inflammatory cytokines, such as tumor necrosis factor-α, interleukin-6 and monocyte chemotactic protein-1, showed significantly decreased protein levels in colon tissues of the RT + probiotics group than in the RT alone group (all p < 0.05). As for comparing microbial abundance through alpha-diversity and beta-diversity, no significant differences were observed between the RT + probiotics and RT alone groups, except for an increase in alpha-diversity in the stool of the RT + probiotics group. Upon analysis of differential microbes based on treatment, the dominance of anti-inflammatory-related microbes, such as Porphyromonadaceae, Bacteroides acidifaciens, and Ruminococcus, was observed in the jejunum, colon, and stool of the RT + probiotics group. With regard to predicted metabolic pathway abundances, the pathways associated with anti-inflammatory processes, such as biosynthesis of pyrimidine nucleotides, peptidoglycans, tryptophan, adenosylcobalamin, and propionate, were differentially identified in the RT + probiotics group compared to the RT alone group. Protective effects of probiotics on radiation enteritis were potentially derived from dominant anti-inflammation-related microbes and metabolites.

Novel Tumor Organoid-Based Mouse Model to Study Image Guided Radiation Therapy of Rectal Cancer After Noninvasive and Precise Endoscopic Implantation

PURPOSE

Preoperative (neoadjuvant) radiation therapy (RT) is an essential part of multimodal rectal cancer therapy. Recently, total neoadjuvant therapy (TNT), which combines simultaneous radiochemotherapy with additional courses of chemotherapy, has emerged as an effective approach. TNT achieves a pathologic complete remission in approximately 30% of resected patients, opening avenues for treatment strategies that avoid radical organ resection. Furthermore, recent studies have demonstrated that anti-programmed cell death protein 1 immunotherapy can induce clinical complete responses in patients with specific genetic alterations. There is significant potential to enhance outcomes through intensifying, personalizing, and de-escalating treatment approaches. However, the heterogeneous response rates to RT or TNT and strategies to sensitize patients without specific genetic changes to immunotherapy remain poorly understood.

METHODS AND MATERIALS

We developed a novel orthotopic mouse model of rectal cancer based on precisely defined endoscopic injections of tumor organoids that reflect tumor heterogeneity. Subsequently, we employed endoscopic- and computed tomography-guided RT and validated rectal tumor growth and response rates to therapy using small-animal magnetic resonance imaging and endoscopic follow-up.

RESULTS

Rectal tumor formation was successfully induced in all mice after 2 organoid injections. Clinically relevant RT regimens with 5 × 5 Gy significantly delayed clinical signs of tumor progression and significantly improved survival. Consistent with human disease, rectal tumor progression correlated with the development of liver and lung metastases. Notably, long-term survivors after RT showed no evidence of tumor recurrence, as demonstrated by in vivo radiologic tumor staging and histopathologic examination.

CONCLUSIONS

Our novel mouse model combines orthotopic tumor growth via noninvasive and precise rectal organoid injection and small-animal RT. This model holds significant promise for investigating the effect of tumor cell-intrinsic aspects, genetic alterations of the host, and exogenous factors (eg, nutrition or microbiota) on RT outcomes. Furthermore, it allows for the exploration of combination therapies involving chemotherapy, immunotherapy, or novel targeted therapies.

Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning

BACKGROUND

Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pretreatment setting are limited by small data sets and low accuracy.

OBJECTIVE

We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer.

DESIGN

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010 to 2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using an area under the receiver operating characteristic curve.

SETTINGS

This study used a national, multicenter data set.

PATIENTS

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy.

MAIN OUTCOME MEASURES

Pathologic complete response defined as T0/xN0/x.

RESULTS

The data set included 53,684 patients. Pathologic complete response was experienced by 22.9% of patients. Gradient boosting showed the best performance with an area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773-0.781), compared with 0.684 (95% CI, 0.68-0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance.

LIMITATIONS

The models were not externally validated.

CONCLUSIONS

Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning a data set including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch-and-wait strategy. See Video Abstract .

EL CNCER DE RECTO BASADA EN FACTORES PREVIOS AL TRATAMIENTO MEDIANTE EL APRENDIZAJE AUTOMTICO

ANTECEDENTES:La respuesta patológica completa después de la terapia neoadyuvante es un indicador pronóstico importante para el cáncer de recto localmente avanzado y puede dar información sobre qué pacientes podrían ser tratados de forma no quirúrgica en el futuro. Los modelos existentes para predecir la respuesta patológica completa en el entorno previo al tratamiento están limitados por conjuntos de datos pequeños y baja precisión.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más generalizable para la respuesta patológica completa para el cáncer de recto localmente avanzado.DISEÑO:Los pacientes con cáncer de recto localmente avanzado que se sometieron a terapia neoadyuvante seguida de resección quirúrgica se identificaron en la Base de Datos Nacional del Cáncer de los años 2010 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron bosque aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.ÁMBITO:Este estudio utilizó un conjunto de datos nacional multicéntrico.PACIENTES:Pacientes con cáncer de recto localmente avanzado sometidos a terapia neoadyuvante y proctectomía.PRINCIPALES MEDIDAS DE VALORACIÓN:Respuesta patológica completa definida como T0/xN0/x.RESULTADOS:El conjunto de datos incluyó 53.684 pacientes. El 22,9% de los pacientes experimentaron una respuesta patológica completa. El refuerzo de gradiente mostró el mejor rendimiento con un área bajo la curva característica operativa del receptor de 0,777 (IC del 95%: 0,773 - 0,781), en comparación con 0,684 (IC del 95%: 0,68 - 0,688) para la regresión logística. Los predictores más fuertes de respuesta patológica completa fueron la ausencia de invasión linfovascular, la ausencia de invasión perineural, un CEA más bajo, un tamaño más pequeño del tumor y la estabilidad de los microsatélites. Un modelo conciso que incluye las cinco variables principales mostró un rendimiento preservado.LIMITACIONES:Los modelos no fueron validados externamente.CONCLUSIONES:Las técnicas de aprendizaje automático se pueden utilizar para predecir con precisión la respuesta patológica completa para el cáncer de recto localmente avanzado en el entorno previo al tratamiento. Después de realizar ajustes en un conjunto de datos que incluye pacientes tratados de forma no quirúrgica, estos modelos podrían ayudar a los médicos a identificar a los candidatos adecuados para una estrategia de observar y esperar. (Traducción-Dr. Ingrid Melo ).

The role of the gut microbiome in gastrointestinal cancers

The gut microbiota present in the human digestive system is incredibly varied and is home to trillions of microorganisms. The gut microbiome is shaped at birth, while numerous genetic, dietary, and environmental variables primarily influence the microbiome composition. The importance of gut microbiota on host health is becoming more widely acknowledged. Digestion, intestinal permeability, and immunological and metabolism responses can all be affected by changes in the composition and function of the gut microbiota. There is mounting evidence that the microbial population's complex traits are important biomarkers and indicators of patient outcomes in cancer and its therapies. Numerous studies have demonstrated that changed commensal gut microorganisms contribute to the development and spread of cancer through various routes. Despite the ongoing controversy surrounding the gut microbiome and gastrointestinal cancer, accumulating evidence points to a potentially far more intricate connection than a simple cause-and-effect relationship. SIMPLE SUMMARY: Due to their high frequency and fatality rate, gastrointestinal cancers are regarded as a severe public health issue with complex medical and economic burdens. The gut microbiota may directly or indirectly interact with existing therapies like immunotherapy and chemotherapy, affecting how well a treatment works. The gut microbiome influences the immune response's activity, function, and development. Generally, certain gut bacteria impact the antitumor actions during cancer by creating particular metabolites or triggering T-cell responses. Yet, certain bacterial species have been found to promote cellular proliferation and metastasis in cancer, and comprehending these interactions in the context of cancer may help identify possible treatment targets. Notwithstanding the improvements in the field, additional research is still required to comprehend the underlying processes, examine the effects on existing therapies, and pinpoint certain bacteria and immune cells that can cause this interaction.

Metagenomic Analysis of Intratumoral Microbiome Linking to Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer

PURPOSE

To assess taxonomic and functional characteristics of tumor-bearing microbiota and its association with response to neoadjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer.

METHODS AND MATERIALS

We performed metagenomic sequencing of biopsy tumoral tissues from 73 patients with locally advanced rectal cancer before nCRT. Patients were classified into poor responders (PR) and good responders (GR) according to response to nCRT. Subsequent investigation of network alteration, key community, microbial biomarkers, and function related to nCRT responses were carried out.

RESULTS

The network-driven analysis systematically revealed 2 co-occurring bacteria modules that exhibited opposite relationship with rectal cancer radiosensitivity. In the 2 modules, prominent alteration of global graph properties and community structure was observed between networks of PR and GR group. By quantifying changes in between-group association patterns and abundances, a total of 115 discriminative biomarker species linked to nCRT response were found, and 35 microbial variables were selected to establish the optimal randomForest classifier for nCRT response prediction. It yielded an area under the curve value of 85.5% (95% CI, 73.3%-97.8%) in the training cohort and 88.4% (95% CI, 77.5%-99.4%) in the validation cohort. In a comprehensive consideration, 5 key bacteria showed high relevance with inducing resistance to nCRT, including Streptococcus equinus, Schaalia odontolytica, Clostridium hylemonae, Blautia producta, and Pseudomonas azotoformans. One key hub including several butyrate-formation bacteria involving with driving network alteration from GR to PR indicate that microbiota-derived butyrate may also be involved in reducing the antitumor effects of nCRT, especially Coprococcus. The functional analysis of metagenome linked the nitrate and sulfate-sulfur assimilation, histidine catabolic process, and resistance to cephamycin to the reduced therapeutic response. It also linked to leucine degradation, isoleucine biosynthesis, taurine, and hypotaurine metabolism to the improved response to nCRT.

CONCLUSIONS

Our data offer novel potential microbial factors and shared metagenome function linked to resistance to nCRT.

Microbiota as the unifying factor behind the hallmarks of cancer

The human microbiota is a complex ecosystem that colonizes body surfaces and interacts with host organ systems, especially the immune system. Since the composition of this ecosystem depends on a variety of internal and external factors, each individual harbors a unique set of microbes. These differences in microbiota composition make individuals either more or less susceptible to various diseases, including cancer. Specific microbes are associated with cancer etiology and pathogenesis and several mechanisms of how they drive the typical hallmarks of cancer were recently identified. Although most microbes reside in the distal gut, they can influence cancer initiation and progression in distant tissues, as well as modulate the outcomes of established cancer therapies. Here, we describe the mechanisms by which microbes influence carcinogenesis and discuss their current and potential future applications in cancer diagnostics and management.

Interaction between intratumoral microbiota and tumor mediates the response of neoadjuvant therapy for rectal cancer

Background

Previous observations have demonstrated that the response to neoadjuvant chemoradiotherapy (nCRT) is highly variable in patients with locally advanced rectal cancer (LARC). Recent studies focusing on the intratumoral microbiota of colorectal cancer have revealed its role in oncogenesis and tumor progression. However, limited research has focused on the influence of intratumoral microbiota on the nCRT of LARC.

Methods

We explored the microbial profiles in the tumor microenvironment of LARC using RNA-seq data from a published European cohort. Microbial signatures were characterized in pathological complete response (pCR) and non-pCR groups. Multi-omics analysis was performed between intratumor microbiomes and transcriptomes.

Results

Microbial α and β diversity were significantly different in pCR and non-pCR groups. Twelve differential microbes were discovered between the pCR and non-pCR groups, six of which were related to subclusters of cancer-associated fibroblasts (CAFs) associated with extracellular matrix formation. A microbial risk score based on the relative abundance of seven differential microbes had predictive value for the nCRT response (AUC = 0.820, p < 0.001).

Conclusion

Our study presents intratumoral microbes as potential independent predictive markers for the response of nCRT to LARC and demonstrates the underlying mechanism by which the interaction between intratumoral microbes and CAFs mediates the response to nCRT.

Machine learning approaches in microbiome research: challenges and best practices

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Bile Acids and Microbiota Interplay in Pancreatic Cancer

Evidence suggests the involvement of the microbiota, including oral, intra-tumoral and gut, in pancreatic cancer progression and response to therapy. The gut microbiota modulates the bile acid pool and is associated with maintaining host physiology. Studies have shown that the bile acid/gut microbiota axis is dysregulated in pancreatic cancer. Bile acid receptor expression and bile acid levels are dysregulated in pancreatic cancer as well. Studies have also shown that bile acids can cause pancreatic cell injury and facilitate cancer cell proliferation. The microbiota and its metabolites, including bile acids, are also altered in other conditions considered risk factors for pancreatic cancer development and can alter responses to chemotherapeutic treatments, thus affecting patient outcomes. Altogether, these findings suggest that the gut microbial and/or bile acid profiles could also serve as biomarkers for pancreatic cancer detection. This review will discuss the current knowledge on the interaction between gut microbiota interaction and bile acid metabolism in pancreatic cancer.

Combination of BFHY with Cisplatin Relieved Chemotherapy Toxicity and Altered Gut Microbiota in Mice

Aim

We sought to profile gut microbiota's role in combination of Bu Fei Hua Yu (BFHY) with cisplatin treatment.

Methods

Non-small cell lung cancer (NSCLC) mice model were constructed followed by treatment with cisplatin alone or combined with BFHY. Mice weight and tumor volume were measured during the experiment. And mice cecum were detected by hematoxylin and eosin, cecum contents were collected for Enzyme Linked ImmuneSorbent Assay, and stool were profiled for metagenomic sequencing.

Results

Combination of BFHY with cisplatin treatment decreased the tumor growth and relieved the damage of cecum. Expressions of interleukin-6 (IL-6), interleukin-1β (IL-1β), monocyte chemotactic protein 1 (MCP), and interferon-γ (IFN-γ) were decreased compared with cisplatin treatment alone. Linear discriminant analysis effect size analysis showed that g_Parabacteroides was downregulated and g_Escherichia and g_Blautia were upregulated after cisplatin treatment. After combination with BFHY, g_Bacteroides and g_Helicobacter were decreased. g_Klebsiella, g_Unclssified_Proteobacteria, and g_Unclssified_Clostridiates were increased. Moreover, heatmap results showed that Bacteroides abundance was increased significantly after cisplatin treatment; BFHY combination treatment reversed this state. Function analysis revealed that multiple functions were slightly decreased in cisplatin treatment alone and increased significantly after combination with BFHY.

Conclusion

Our study provided evidence of an efficacy of combination of BFHY with cisplatin on treatment of NSCLC and revealed that gut microbiota plays a role in it. The above results provide new ideas on NSCLC treatment.

The influence of antibiotic administration on the outcomes of head-and-neck squamous cell carcinoma patients undergoing definitive (chemo)radiation

PURPOSE

Effects of antibiotic administration on patients' microbiome may negatively influence cancer outcomes, and adverse prognoses after antibiotic application have been demonstrated for cancer patients receiving immune checkpoint inhibitors. While the microbiome may play an important role also in head-and-neck squamous cell carcinoma (HNSCC), the prognostic value of antibiotic treatment here is largely unknown. We therefore analyzed whether antibiotic prescription is associated with impaired oncological outcomes of HNSCC patients undergoing definitive (chemo)radiation.

METHODS

A cohort of 220 HNSCC patients undergoing definitive (chemo)radiation between 2010 and 2019 was analyzed. The influence of antibiotic administration on locoregional control, progression-free survival (PFS) and overall survival (OS) was determined using Kaplan-Meier and Cox analyses.

RESULTS

A total of 154 patients were treated with antibiotics within 30 days before (chemo)radiation (pretherapeutic) or during (chemo)radiation (peritherapeutic). While antibiotic prescription was not associated with age, ECOG, tumor localization or radiotherapy characteristics, patients treated with antibiotics had significantly higher tumor stages. Peritherapeutic antibiotic administration diminished PFS (HR = 1.397, p < 0.05, log-rank test) and OS (HR = 1.407, p < 0.05), whereas pretherapeutic administration did not. Antibiotic application was an independent prognosticator for OS (HR = 1.703, p < 0.05) and PFS (HR = 1.550, p < 0.05) in the multivariate Cox analysis within the subgroup of patients aged < 75 years.

CONCLUSION

Peritherapeutic antibiotic usage was associated with impaired oncological outcomes in HNSCC patients undergoing (chemo)radiation. Further studies including microbiome analyses are required to elucidate underlying mechanisms.

Gut Microbiome Is Associated With the Response to Chemoradiotherapy in Patients With Non-small Cell Lung Cancer

PURPOSE

To explore the dynamic change of gut microbiota and its predictive role in progression-free survival (PFS) in non-small cell lung cancer (NSCLC) after concurrent chemoradiotherapy (CCRT).

METHODS AND MATERIALS

Forty-one patients with NSCLC in 2 phase 2 trials (NCT02573506 and NCT03006575) were analyzed. A total of 102 fecal samples were collected at 3 time points (T0, before CCRT; T1, 2 weeks after the initiation of CCRT; and T2, the end of CCRT). Gut microbiota composition and functionality were analyzed by 16S rRNA gene sequencing and shotgun metagenomics, respectively. Alpha diversity, taxonomic composition, and KEGG functional pathways were compared between patients in the long-PFS group (PFS ≥11.0 months) and short-PFS group (PFS <11.0 months). A random forest classifier was constructed to identify microbial signature related to PFS. Clinical and microbial factors potentially predictive of PFS were assessed in the univariate and multivariate Cox regression analysis.

RESULTS

The abundance of Bacteroidota and Proteobacteria increased, while the abundance of Firmicutes decreased after CCRT. Shannon index (P = .006) and PD index (P = .022) were significantly higher in the long-PFS group than for those in the short-PFS group at T1. The PFS-prediction microbial signature at T1 included unclassified members of the Lanchospiraceae spp., such as NK4A136 and UCG-003 groups, Dorea sp., various strains from within the Eubacterium hallii and E. siraeum groups, and an unclassified member of the Muribaculaceae, which yielded an area under the ROC curve of 0.87. These discriminatory genera mostly belong to phylum Firmicutes/family Clostridia. Multivariate analysis indicated PD index (HR = 8.036, P = .016) and the abundance of Dorea sp. at T1 (HR = 4.186, P = .043) were independent predictors of PFS. The KEGG pathways at T1 overrepresented in the long-PFS group included fatty acid metabolism, fatty acid biosynthesis, and arginine biosynthesis. Those overrepresented in the short-PFS group included lipopolysaccharide biosynthesis, ascorbate and aldarate metabolism, and biosynthesis of vancomycin group antibiotics.

CONCLUSIONS

Gut microbiota composition and functionality at 2 weeks after the initiation of CCRT were associated with PFS in NSCLC. Further research is needed to confirm these results.

Gut microbiota-mediated nucleotide synthesis attenuates the response to neoadjuvant chemoradiotherapy in rectal cancer

Preoperative neoadjuvant chemoradiotherapy (nCRT) is a standard treatment for locally advanced rectal cancer (LARC) patients, yet little is known about the mediators underlying the heterogeneous patient response. In this longitudinal study, we performed 16S rRNA sequencing on 353 fecal specimens and find reduced microbial diversity after nCRT. Multi-omics data integration reveals that Bacteroides vulgatus-mediated nucleotide biosynthesis associates with nCRT resistance in LARC patients, and nonresponsive tumors are characterized by the upregulation of genes related to DNA repair and nucleoside transport. Nucleosides supplementation or B. vulgatus gavage protects cancer cells from the 5-fluorouracil or irradiation treatment. An analysis of 2,205 serum samples from 735 patients suggests that uric acid is a potential prognosis marker for LARC patients receiving nCRT. Our data unravel the role of intestinal microbiota-mediated nucleotide biosynthesis in the response of rectal tumors to nCRT, and highlight the importance of deciphering the cross-talk between cancer cells and gut microorganisms during cancer therapies.

Clinical Radiobiology for Radiation Oncology

This chapter is focused on radiobiological aspects at the molecular, cellular, and tissue level which are relevant for the clinical use of ionizing radiation (IR) in cancer therapy. For radiation oncology, it is critical to find a balance, i.e., the therapeutic window, between the probability of tumor control and the probability of side effects caused by radiation injury to the healthy tissues and organs. An overview is given about modern precision radiotherapy (RT) techniques, which allow optimal sparing of healthy tissues. Biological factors determining the width of the therapeutic window are explained. The role of the six typical radiobiological phenomena determining the response of both malignant and normal tissues in the clinic, the 6R’s, which are Reoxygenation, Redistribution, Repopulation, Repair, Radiosensitivity, and Reactivation of the immune system, is discussed. Information is provided on tumor characteristics, for example, tumor type, growth kinetics, hypoxia, aberrant molecular signaling pathways, cancer stem cells and their impact on the response to RT. The role of the tumor microenvironment and microbiota is described and the effects of radiation on the immune system including the abscopal effect phenomenon are outlined. A summary is given on tumor diagnosis, response prediction via biomarkers, genetics, and radiomics, and ways to selectively enhance the RT response in tumors. Furthermore, we describe acute and late normal tissue reactions following exposure to radiation: cellular aspects, tissue kinetics, latency periods, permanent or transient injury, and histopathology. Details are also given on the differential effect on tumor and late responding healthy tissues following fractionated and low dose rate irradiation as well as the effect of whole-body exposure.

Exploiting dietary fibre and the gut microbiota in pelvic radiotherapy patients

With an ageing population, there is an urgent need to find alternatives to current standard-of-care chemoradiation schedules in the treatment of pelvic malignancies. The gut microbiota may be exploitable, having shown a valuable role in improving patient outcomes in anticancer immunotherapy. These bacteria feed on dietary fibres, which reach the large intestine intact, resulting in the production of beneficial metabolites, including short-chain fatty acids. The gut microbiota can impact radiotherapy (RT) treatment responses and itself be altered by the radiation. Evidence is emerging that manipulation of the gut microbiota by dietary fibre supplementation can improve tumour responses and reduce normal tissue side effects following RT, although data on tumour response are limited to date. Both may be mediated by immune and non-immune effects of gut microbiota and their metabolites. Alternative approaches include use of probiotics and faecal microbiota transplantation (FMT). Current evidence will be reviewed regarding the use of dietary fibre interventions and gut microbiota modification in improving outcomes for pelvic RT patients. However, data regarding baseline (pre-RT) gut microbiota of RT patients and timing of dietary fibre manipulation (before or during RT) is limited, heterogenous and inconclusive, thus more robust clinical studies are required before these strategies can be applied clinically.

Evaluation of the ability of fatty acid metabolism signature to predict response to neoadjuvant chemoradiotherapy and prognosis of patients with locally advanced rectal cancer

Neoadjuvant chemoradiotherapy (nCRT) is widely used to treat patients with locally advanced rectal cancer (LARC), and treatment responses vary. Fatty acid metabolism (FAM) is closely associated with carcinogenesis and cancer progression. In this study, we investigated the vital role of FAM on the gut microbiome and metabolism in the context of cancer. We screened 34 disease-free survival (DFS)-related, FAM-related, and radiosensitivity-related genes based on the Gene Expression Omnibus database. Subsequently, we developed a five-gene FAM-related signature using the least absolute shrinkage and selection operator Cox regression model. The FAM-related signature was also validated in external validation from Fujian Cancer Hospital for predicting nCRT response, DFS, and overall survival (OS). Notably, patients with a low-risk score were associated with pathological complete response and better DFS and OS outcomes. A comprehensive evaluation of the tumor microenvironment based on the FAM-related signature revealed that patients with high-risk scores were closely associated with activating type I interferon response and inflammation-promoting functions. In conclusion, our findings indicate the potential ability of FAM to predict nCRT response and the prognosis of DFS and OS in patients with LARC.

Characteristics of gut microbiota in patients with gastric cancer by surgery, chemotherapy and lymph node metastasis

BACKGROUND

Gastric cancer (GC) is a malignant gastrointestinal tumor that can result in high mortality. Surgery and chemotherapy are often used for the effective treatment of GC. In addition, lymph node metastasis is a significant factor affecting the therapy of GC. Current researches have revealed that gut microbiota has the potential as biomarkers to distinguish healthy people and GC patients. However, the relationship between surgery, chemotherapy, and lymph node metastasis is still unclear.

METHODS

In this study, 16S rRNA sequencing was used to investigate 157 GC fecal samples to identify the role of surgery, chemotherapy, and lymph node metastasis. Immunohistochemical analysis was used to value the expression of Ki67, HER2 in GC patient tissues.

RESULTS

There exist some gut microbiotas which can distinguish surgery from non-surgery GC patients, including Enterococcus, Megasphaera, Corynebacterium, Roseburia, and Lachnospira. Differences between lymph node metastasis and chemotherapy in GC patients are not significant. Moreover, we found the abundance of Blautia, Ruminococcus, Oscillospira were related to the expression of Ki67 and the abundance of Prevotella, Lachnospira, Eubacterium, Desulfovibiro were correlated with the expression of HER2.

CONCLUSIONS

The choice of treatment has a certain impact on the intestinal flora of patients with gastric cancer. Our research shows that surgery has a great effect on the intestinal flora of patients with gastric cancer. However, there were no significant differences in the characteristics of intestinal flora in patients with gastric cancer whether they received chemotherapy or whether they had lymph node metastasis. In addition, the association of gut microbiota with Ki67 and HER2 indicators is expected to provide the possibility of gut microbiota as a tumor prognostic marker.

Changes of gut microbiota in colorectal cancer patients with Pentatrichomonas hominis infection

Pentatrichomonas hominis is a parasitic trichomonads protozoa that parasitizes in the colon and cecum of humans and other animals. Our previous studies have demonstrated that infection with P. hominis is associated with the incidence of colon cancer (37.93%). However, the mechanism by which P. hominis infections increase the incidence of colon cancer remains unclear. Previous studies have suggested that certain parasites promote colon cancer by regulating gut microbiota. This study aimed to elucidate whether the association between P. hominis infections and the increased incidence of colon cancer is related to changes in gut microbiota. Therefore, the gut microbiota patients with colon cancer who were infected with P. hominis and uninfected patients with colon cancer were analyzed by 16S rRNA high-throughput sequencing. The results demonstrated that patients with colon cancer who were not infected with P. hominis showed increased gut bacterial diversity, a higher relative abundance of Alcaligenes sp., Leucobacter sp., Paraprevotella sp., Ruminococcaceae UCG-002, and a significant reduction in the abundance of Veillonella sp., compared to individuals without colon cancer. Additionally, the relative abundance of the Ruminococcaceae UCG-002 and the Eubacterium eligens groups was reduced, while the relative abundance of bacteria associated with colon cancer, including Flavonifractor sp., Lachnoclostridium sp., and the Ruminococcus gnavus group, increased significantly in patients with colon cancer who were infected with P. hominis, compared to those of uninfected patients with colon cancer. In conclusion, these results suggested that P. hominis infections may aggravate the development of colon cancer and the findings provide new insights for subsequent in-depth studies on the pathogenesis, diagnosis, and prevention of colon cancer.

Patient Derived Organoids Confirm That PI3K/AKT Signalling Is an Escape Pathway for Radioresistance and a Target for Therapy in Rectal Cancer

Objectives

Partial or total resistance to preoperative chemoradiotherapy occurs in more than half of locally advanced rectal cancer patients. Several novel or repurposed drugs have been trialled to improve cancer cell sensitivity to radiotherapy, with limited success. We aimed to understand the mechanisms of resistance to chemoradiotherapy in rectal cancer using patient derived organoid models.

Design

To understand the mechanisms underlying this resistance, we compared the pre-treatment transcriptomes of patient-derived organoids (PDO) with measured radiotherapy sensitivity to identify biological pathways involved in radiation resistance coupled with single cell sequencing, genome wide CRISPR-Cas9 and targeted drug screens.

Results

RNA sequencing enrichment analysis revealed upregulation of PI3K/AKT/mTOR and epithelial mesenchymal transition pathway genes in radioresistant PDOs. Single-cell sequencing of pre & post-irradiation PDOs showed mTORC1 and PI3K/AKT upregulation, which was confirmed by a genome-wide CRSIPR-Cas9 knockout screen using irradiated colorectal cancer (CRC) cell lines. We then tested the efficiency of dual PI3K/mTOR inhibitors in improving cancer cell sensitivity to radiotherapy. After irradiation, significant AKT phosphorylation was detected (p=0.027) which was abrogated with dual PI3K/mTOR inhibitors and lead to significant radiosensitisation of the HCT116 cell line and radiation resistant PDO lines.

Conclusions

The PI3K/AKT/mTOR pathway upregulation contributes to radioresistance and its targeted pharmacological inhibition leads to significant radiosensitisation in CRC organoids, making it a potential target for clinical trials.

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.

Tumor Microbiome in Nasopharyngeal Carcinoma and Its Association With Prognosis

Introduction

Previous studies have reported a close relationship between cancer and microbes, particularly gut and tumor microbiota; however, the presence of tumor microbiome in nasopharyngeal carcinoma (NPC) and its role in the prognosis of NPC remain unclear.

Methods

We collected 64 samples including tissues from 50 patients with NPC (NPC group) and 14 patients with chronic nasopharyngitis (control group) receiver operating characteristics and we applied 16S ribosome RNA gene sequencing of all samples to assess microbiome profiles and immunohistochemistry to detect tumor microbiome in NPC.

Results

Patients in the control group harbored higher species diversity than those in the NPC group; however, the beta diversity was more distinct in the NPC group. In total, three genera with statistically significant differences between the two groups were identified. The area under the receiver operating characteristics (ROC) curve (AUC) was calculated using the relative abundance of these three significant genera, and a value of 0.842 was achieved. Furthermore, Turicibacter was confirmed as a potentially independent prognostic factor for NPC patients, and the progression-free survival (PFS) was markedly prolonged in patients with a low relative abundance of Turicibacter compared to patients with a high relative abundance of this genus (cutoff: 0.0046, hazard ratio: 5.10, 95% confidence interval: 2.04–12.77, p = 0.004).

Conclusions

The present study provided strong evidence of a correlation between tumor microbiome and NPC; the tumor microbiome may be considered a biomarker for early NPC diagnosis. Turicibacter potentially served as a independently prognostic indicator for NPC patients.

Biomarkers of Favorable vs. Unfavorable Responses in Locally Advanced Rectal Cancer Patients Receiving Neoadjuvant Concurrent Chemoradiotherapy

Colorectal cancer is the second leading cause of cancer death globally. The gold standard for locally advanced rectal cancer (LARC) nowadays is preoperative concurrent chemoradiation (CCRT). Approximately three quarters of LARC patients do not achieve pathological complete response and hence suffer from relapse, metastases and inevitable death. The exploration of trustworthy and timely biomarkers for CCRT response is urgently called for. This review focused upon a broad spectrum of biomarkers, including circulating tumor cells, DNA, RNA, oncogenes, tumor suppressor genes, epigenetics, impaired DNA mismatch repair, patient-derived xenografts, in vitro tumor organoids, immunity and microbiomes. Utilizing proper biomarkers can assist in categorizing appropriate patients by the most efficient treatment modality with the best outcome and accompanied by minimal side effects. The purpose of this review is to inspect and analyze accessible data in order to fully realize the promise of precision oncology for rectal cancer patients.

Predicting response to neoadjuvant chemoradiotherapy in rectal cancer: from biomarkers to tumor models

Colorectal cancer (CRC) is a major contributor to cancer-associated morbidity worldwide and over one-third of CRC is located in the rectum. Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is commonly applied to treat locally advanced rectal cancer (LARC). In this review, we summarize current and novel concepts of neoadjuvant therapy for LARC such as total neoadjuvant therapy and describe how these developments impact treatment response. Moreover, as response to nCRT is highly divergent in rectal cancers, we discuss the role of potential predictive biomarkers. We review recent advances in biomarker discovery, from a clinical as well as a histopathological and molecular perspective. Furthermore, the role of emerging predictive biomarkers derived from the tumor environment such as immune cell composition and gut microbiome is presented. Finally, we describe how different tumor models such as patient-derived cancer organoids are used to identify novel predictive biomarkers for chemoradiotherapy (CRT) in rectal cancer.

Microenvironmental Metabolites in the Intestine: Messengers between Health and Disease

The intestinal mucosa is a highly absorptive organ and simultaneously constitutes the physical barrier between the host and a complex outer ecosystem. Intestinal epithelial cells (IECs) represent a special node that receives signals from the host and the environment and translates them into corresponding responses. Specific molecular communication systems such as metabolites are known to transmit information across the intestinal boundary. The gut microbiota or food-derived metabolites are extrinsic factors that influence the homeostasis of the intestinal epithelium, while mitochondrial and host-derived cellular metabolites determine the identity, fitness, and regenerative capacity of IECs. Little is known, however, about the role of intrinsic and extrinsic metabolites of IECs in the initiation and progression of pathological processes such as inflammatory bowel disease and colorectal cancer as well as about their impact on intestinal immunity. In this review, we will highlight the most recent contributions on the modulatory effects of intestinal metabolites in gut pathophysiology, with a particular focus on metabolites in promoting intestinal inflammation or colorectal tumorigenesis. In addition, we will provide a perspective on the role of newly identified oncometabolites from the commensal and opportunistic microbiota in shaping response and resistance to antitumor therapy.

Host microbiomes in tumor precision medicine: how far are we?

The human gut microbiome has received a crescendo of attention in recent years, due to the countless influences on human pathophysiology, including cancer. Research on cancer and anticancer therapy is constantly looking for new hints to improve the response to therapy while reducing the risk of relapse. In this scenario, the gut microbiome and the plethora of microbial-derived metabolites are considered a new opening in the development of innovative anticancer treatments for a better prognosis. This narrative review summarizes the current knowledge on the role of the gut microbiome in the onset and progression of cancer, as well as in response to chemo-immunotherapy. Recent findings regarding the tumor microbiome and its implications for clinical practice are also commented on. Current microbiome-based intervention strategies (i.e., prebiotics, probiotics, live biotherapeutics and fecal microbiota transplantation) are then discussed, along with key shortcomings, including a lack of long-term safety information in patients who are already severely compromised by standard treatments. The implementation of bioinformatic tools applied to microbiomics and other omics data, such as machine learning, has an enormous potential to push research in the field, enabling the prediction of health risk and therapeutic outcomes, for a truly personalized precision medicine.

Gut Microbiome and Its Associations With Acute and Chronic Gastrointestinal Toxicities in Cancer Patients With Pelvic Radiation Therapy: A Systematic Review

Aim

Pelvic radiation therapy (RT) can impact the gut microbiome in patients with cancer and result in gastrointestinal (GI) toxicities. The purpose of this systematic review was to describe the effects of RT on the gut microbiome and the associations between the gut microbiome and GI toxicities in patients treated with pelvic RT.

Methods

PubMed, Embase, and Web of Science databases were searched from their earliest records to August 2020. The articles screening process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Mixed Method Assessment Tool was used to assess the methodological quality for each included study. All study findings were synthesized and presented in narrative format. Thirteen studies were included. The gut microbiome of fecal samples was analyzed using 16S rRNA sequencing approaches.

Results

There were disparities in alpha and beta diversities that existed across the studies. Divergent results were found among various phyla, including Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Cyanobacteria, Fusobacteria, and Deinococcus-Thermus. Moreover, alteration in the gut microbiome diversity and abundance related to cancer treatment was associated with pelvic toxicities, specifically diarrhea. Following treatment, increases in the abundance of Bacteroides was associated with diarrhea and radiation enteritis.

Conclusions

Pelvic RT can disrupt the diversity and abundance of commensal gut microorganisms. A dysbiotic gut microbiome showed a promising association with radiation enteritis through alterations of the intestinal barrier function, innate immunity, and intestinal repair mechanisms; however, confounders, such as diet, were not thoroughly addressed.

Biomarkers and cell-based models to predict the outcome of neoadjuvant therapy for rectal cancer patients

Rectal cancer constitutes approximately one-third of all colorectal cancers and contributes to considerable mortality globally. In contrast to colon cancer, the standard treatment for localized rectal cancer often involves neoadjuvant chemoradiotherapy. Tumour response rates to treatment show substantial inter-patient heterogeneity, indicating a need for treatment stratification. Consequently researchers have attempted to establish new means for predicting tumour response in order to assist in treatment decisions. In this review we have summarized published findings regarding potential biomarkers to predict neoadjuvant treatment response for rectal cancer tumours. In addition, we describe cell-based models that can be utilized both for treatment prediction and for studying the complex mechanisms involved.

The Gut Microbiome and Gastrointestinal Toxicities in Pelvic Radiation Therapy: A Clinical Review

Simple Summary

A substantial proportion of cancer patients receive radiotherapy (RT) during their cancer trajectory. One of the most challenging pelvic RT-related toxicities are gastrointestinal (GI) toxicities (e.g., abdominal pain, rectal bleeding, faecal incontinence, and diarrhoea) which impair the quality of life (QoL) of patients. Mounting evidence suggests that gut microbiota plays a pivotal role in health and disease, including cancer. Our current clinical review aims to assess the impact of RT on gut microbiota and GI toxicities in cancer patients to provide useful information, in addition to standard care, for clinicians and patients.

Abstract

Background: Gastrointestinal (GI) toxicities are common adverse effects of pelvic radiotherapy (RT). Several recent studies revealed that toxicity of RT is associated with dysbiosis of the gut microbiome. Method: A literature search was conducted in electronic databases Medline, PubMed, and ScienceDirect, with search terms “microbiome and/or microbiota” and “radiotherapy (RT) and/or chemoradiation therapy (CRT)” and “cancer”, and the relevant literature were selected for use in this article. Results: Eight prospective cohort studies were selected for review with a total of 311 participants with a range of 15–134 participants within these studies. The selected studies were conducted in patients with gynaecological (n = 3), rectal (n = 2), or prostate cancers (n = 1), or patients with various types of malignancies (n = 2). Three studies reported that cancer patients had significantly lower alpha diversity compared with healthy controls. Seven studies found that lower alpha diversity and modulated gut microbiome were associated with GI toxicities during and after pelvic RT (n = 5) and CRT (n = 2), whereas one study found that beta diversity was related to a complete response following CRT. Two further studies reported that fatigue was associated with dysbiosis of the gut microbiome and low alpha diversity during and after RT, and with dysbiosis of the gut microbiome and diarrhoea, respectively. Conclusion: Gut microbiome profiles are associated with GI toxicities and have the potential to predict RT/CRT-induced toxicities and quality of life (QoL) in patients undergoing those treatments. Further robust randomized controlled trials (RCTs) are required to elucidate the effect of gut microbiome profiles on RT-related adverse effects and responses to RT.

Gut Microbiota as Potential Biomarker and/or Therapeutic Target to Improve the Management of Cancer: Focus on Colibactin-Producing Escherichia coli in Colorectal Cancer

Simple Summary

Gut microbiota is emerging as new diagnostic and prognostic marker and/or therapeutic target to improve the management of cancer. This review aims to summarize microbial signatures that have been associated with digestive and other cancers. We report the clinical relevance of these microbial markers to predict the response to cancer therapy. Among these biomarkers, colibactin-producing E. coli are prevalent in the colonic mucosa of patients with colorectal cancer and they promote colorectal carcinogenesis in several pre-clinical models. Here we discuss the promising use of colibactin-producing E. coli as a new predictive factor and a therapeutic target in colon cancer management.

Abstract

The gut microbiota is crucial for physiological development and immunological homeostasis. Alterations of this microbial community called dysbiosis, have been associated with cancers such colorectal cancers (CRC). The pro-carcinogenic potential of this dysbiotic microbiota has been demonstrated in the colon. Recently the role of the microbiota in the efficacy of anti-tumor therapeutic strategies has been described in digestive cancers and in other cancers (e.g., melanoma and sarcoma). Different bacterial species seem to be implicated in these mechanisms: F. nucleatum, B. fragilis, and colibactin-associated E. coli (CoPEC). CoPEC bacteria are prevalent in the colonic mucosa of patients with CRC and they promote colorectal carcinogenesis in susceptible mouse models of CRC. In this review, we report preclinical and clinical data that suggest that CoPEC could be a new factor predictive of poor outcomes that could be used to improve cancer management. Moreover, we describe the possibility of using these bacteria as new therapeutic targets.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Radiotherapy and the gut microbiome: facts and fiction

An ever-growing body of evidence has linked the gut microbiome with both the effectiveness and the toxicity of cancer therapies. Radiotherapy is an effective way to treat tumors, although large variations exist among patients in tumor radio-responsiveness and in the incidence and severity of radiotherapy-induced side effects. Relatively little is known about whether and how the microbiome regulates the response to radiotherapy. Gut microbiota may be an important player in modulating “hot” versus “cold” tumor microenvironment, ultimately affecting treatment efficacy. The interaction of the gut microbiome and radiotherapy is a bidirectional function, in that radiotherapy can disrupt the microbiome and those disruptions can influence the effectiveness of the anticancer treatments. Limited data have shown that interactions between the radiation and the microbiome can have positive effects on oncotherapy. On the other hand, exposure to ionizing radiation leads to changes in the gut microbiome that contribute to radiation enteropathy. The gut microbiome can influence radiation-induced gastrointestinal mucositis through two mechanisms including translocation and dysbiosis. We propose that the gut microbiome can be modified to maximize the response to treatment and minimize adverse effects through the use of personalized probiotics, prebiotics, or fecal microbial transplantation. 16S rRNA sequencing is the most commonly used approach to investigate distribution and diversity of gut microbiome between individuals though it only identifies bacteria level other than strain level. The functional gut microbiome can be studied using methods involving metagenomics, metatranscriptomics, metaproteomics, as well as metabolomics. Multiple ‘-omic’ approaches can be applied simultaneously to the same sample to obtain integrated results. That said, challenges and remaining unknowns in the future that persist at this time include the mechanisms by which the gut microbiome affects radiosensitivity, interactions between the gut microbiome and combination treatments, the role of the gut microbiome with regard to predictive and prognostic biomarkers, the need for multi “-omic” approach for in-depth exploration of functional changes and their effects on host-microbiome interactions, and interactions between gut microbiome, microbial metabolites and immune microenvironment.

Neoadjuvant Therapy for Locally Advanced Rectal Cancer: Recent Advances and Ongoing Challenges

Locally advanced rectal cancer has a rising global incidence. Over the last 4 decades, advances first in surgery and later in radiotherapy and chemoradiotherapy have improved outcomes, particularly with regard to local recurrence. Unfortunately, distant metastases remain a significant problem. In clinical trials of patients with stage II and III disease, distant relapse occurs in 25% to 30% of patients regardless of the treatment approach. Recent phase 3 trials have therefore focused on intensification of systemic therapy for localized disease, with an aim of reducing the distant relapse rate. Early results of trials of total neoadjuvant therapy with combination systemic therapy provided in the neoadjuvant setting are promising; for the first time, a significant improvement in the rate of distant relapse has been noted. Longer-term follow-up is eagerly awaited. On the other hand, trimodal therapy with chemotherapy, radiotherapy, and surgery is toxic. Several trials are currently assessing the feasibility of a watch-and-wait approach, omitting surgery in those with complete response to neoadjuvant treatment, in an attempt to reduce the burden of treatment on patients. The future for rectal cancer patients is likely to be highly personalized, with more intense approaches for high-risk patients and omission of unnecessary therapy for those whose disease responds well to initial treatment. Biomarkers such as circulating tumor DNA will help to more accurately stratify patients into risk groups. Improvements in survival and quality of life are expected as the results of ongoing research become available throughout the next decade.

The Gut Microbiome Is Associated With Therapeutic Responses and Toxicities of Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients-A Pilot Study

Responses to neoadjuvant chemoradiotherapy (nCRT) and therapy-related toxicities in rectal cancer vary among patients. To provide the individualized therapeutic option for each patient, predictive markers of therapeutic responses and toxicities are in critical need. We aimed to identify the association of gut microbiome with and its potential predictive value for therapeutic responses and toxicities. In the present study, we collected fecal microbiome samples from patients with rectal cancer at treatment initiation and just after nCRT. Taxonomic profiling via 16S ribosomal RNA gene sequencing was performed on all samples. Patients were classified as responders versus non-responders. Patients were grouped into no or mild diarrhea and severe diarrhea. STAMP and high-dimensional class comparisons via linear discriminant analysis of effect size (LEfSe) were used to compare the compositional differences between groups. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was utilized to predict differences in metabolic function between groups. Ten patients were classified as responders and 12 patients were classified as non-responders. Fourteen patients experienced no or mild diarrhea and 8 patients experienced severe diarrhea. Several bacteria taxa with significantly different relative abundances before and after nCRT were identified. Similarly, several baseline bacteria taxa and predicted pathways with significantly different relative abundances between responders and non-responders or between patients no or mild diarrhea and severe diarrhea were identified. Specifically, Shuttleworthia was identified as enriched in responders and several bacteria taxa in the Clostridiales order etc. were identified as enriched in non-responders. Pathways including fatty acid metabolism were predicted to be enriched in responders. In addition, Bifidobacterium, Clostridia, and Bacteroides etc. were identified as enriched in patients with no or mild diarrhea. Pathways including primary bile acid biosynthesis were predicted to be enriched in patients with no or mild diarrhea. Together, the microbiota and pathway markers identified in this study may be utilized to predict the therapeutic responses and therapy-related toxicities of nCRT in patients with rectal cancer. More patient data is needed to verify the current findings and the results of metagenomic, metatranscriptomic, and metabolomic analyses will further mine key biomarkers at the compositional and functional level.

Gut Microbiome Components Predict Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Prospective, Longitudinal Study

PURPOSE

The gut microbiome is involved in antitumor immunotherapy and chemotherapy responses; however, evidence-based research on the role of gut microbiome in predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) remains scarce. This prospective, longitudinal study aimed to evaluate the feasibility of the gut microbiome in predicting nCRT responses.

EXPERIMENTAL DESIGN

We collected 167 fecal samples from 84 patients with LARC before and after nCRT and 31 specimens from healthy individuals for 16S rRNA sequencing. Patients were divided into responders and nonresponders according to pathologic response to nCRT. After identifying microbial biomarkers related to nCRT responses, we constructed a random forest classifier for nCRT response prediction of a training cohort of baseline samples from 37 patients and validated the classifier in another cohort of 47 patients.

RESULTS

We observed significant microbiome alterations represented by a decrease in LARC-related pathogens and an increase in Lactobacillus and Streptococcus during nCRT. Furthermore, a prominent microbiota difference between responders and nonresponders was noticed in the baseline samples. Microbes related with butyrate production, including Roseburia, Dorea, and Anaerostipes, were overrepresented in responders, whereas Coriobacteriaceae and Fusobacterium were overrepresented in nonresponders. Ten biomarkers were selected for the response-prediction classifier, including Dorea, Anaerostipes, and Streptococcus, which yielded an area under the curve value of 93.57% [95% confidence interval (CI), 85.76%-100%] in the training cohort and 73.53% (95% CI, 58.96%-88.11%) in the validation cohort.

CONCLUSIONS

The gut microbiome offers novel potential biomarkers for predicting nCRT responses, which has important manifestations in the clinical management of these patients.

Elucidating the gut microbiota composition and the bioactivity of immunostimulatory commensals for the optimization of immune checkpoint inhibitors

Accumulating evidence from preclinical studies and human trials demonstrated the crucial role of the gut microbiota in determining the effectiveness of anticancer therapeutics such as immunogenic chemotherapy or immune checkpoint blockade. In summary, it appears that a diverse intestinal microbiota supports therapeutic anticancer responses, while a dysbiotic microbiota composition that lacks immunostimulatory bacteria or contains overabundant immunosuppressive species causes treatment failure. In this review, we explore preclinical and translational studies highlighting how eubiotic and dysbiotic microbiota composition can affect progression-free survival in cancer patients.