Predicting cancer prognosis and drug response from the tumor microbiome

Whole-Tumor Clearing and Imaging of Intratumor Microbiota in Three Dimensions with miCDaL Strategy

Acquiring detailed spatial information about intratumor microbiota in situ is challenging, which leaves 3D distributions of microbiota within entire tumors largely unexplored. Here, a modified iDISCO-CUBIC tissue clearing and D-amino acid microbiome labeling-based (miCDaL) strategy are proposed, that integrates microbiota in situ labeling, tissue clearing, and whole-mount tissue imaging to enable 3D visualization of indigenous intratumor microbiota. Leveraging whole-mount spatial resolution and centimeter-scale imaging depth, the 3D biogeography of microbiota is successfully charted across various tumors at different developmental stages, providing quantitative spatial insights in relation to host tumors. By incorporating an immunostaining protocol, 3D imaging of the immunologic microenvironment is achieved in both murine and human mammary tumors that is previously assumed to be bacteria-free. Notably, immune infiltrates, including T cells and NK cells, and tertiary lymphoid structures are conspicuously absent in bacteria-colonized regions. This 3D imaging strategy for mapping Indigenous intratumor microbiota offers valuable insights into host-microbiota interactions.

Interactions between microbiota and uterine corpus endometrial cancer: A bioinformatic investigation of potential immunotherapy

Microorganisms in the gut and other niches may contribute to carcinogenesis while also altering cancer immune surveillance and therapeutic response. However, determining the impact of genetic variations and interplay with intestinal microbes' environment is difficult and unanswered. Here, we examined the frequency of thirteen mutant genes that caused aberrant gut in thirty different types of cancer using The Cancer Genomic Atlas (TCGA) database. Substantially, our findings show that all these mutated genes are quite frequent in uterine corpus endometrial cancer (UCEC). Further, these mutant genes are implicated in the infiltration of different subset of immune cells within the Tumor Microenvironment (TME) of UCEC patients. The top-ranking mutant genes that promote immune cell invasion into the TME of UCEC patients were PGLYRP2, OLFM4, and TLR5. In this regard, we used the same deconvolution of the TCGA database to analyze the microbiome that have a strong association with immune cells invasion with TME of UCEC patients. Several bacteria and viruses have been linked to the invasion of immune cells, such as B cell memory and T cell regulatory (Tregs), into the TME of UCEC patients. As a result, our findings pave the way for future research into generating novel immunizations against bacteria or viruses as immunotherapy for UCEC patients.

DRN-CDR: A cancer drug response prediction model using multi-omics and drug features

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

Intratumoral microbiome promotes liver metastasis and dampens adjuvant imatinib treatment in gastrointestinal stromal tumor

Understanding the determinants of long-term liver metastasis (LM) outcomes in gastrointestinal stromal tumor (GIST) patients is crucial. We established the feature selection model of intratumoral microbiome at the surgery, achieving robust predictive accuracies of 0.953 and 0.897 AUCs in discovery (n = 74) and validation (n = 34) cohorts, respectively. Notably, despite the significant reduction in LM occurrence with adjuvant imatinib (AI) treatment, intratumoral microbiome exerted independently stronger effects on post-operative LM. Employing both 16S and full-length rRNA sequencing, we pinpoint intracellular Shewanella algae as a foremost LM risk factor in both AI- and non-AI-treated patients. Experimental validation confirmed S. algae's intratumoral presence in GIST, along with migration/invasion-promoting effects on GIST cells. Furthermore, S. algae promoted LM and impeded AI treatment in metastatic mouse models. Our findings advocate for incorporating intratumoral microbiome evaluation at surgery, and propose S. algae as a therapeutic target for LM suppression in GIST.

Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project

In recent years, a growing number of publications have reported the presence of microbial species in human tumors and of mixtures of microbes that appear to be highly specific to different cancer types. Our recent re-analysis of data from three cancer types revealed that technical errors have caused erroneous reports of numerous microbial species found in sequencing data from The Cancer Genome Atlas (TCGA) project. Here we have expanded our analysis to cover all 5,734 whole-genome sequencing (WGS) data sets currently available from TCGA, covering 25 distinct types of cancer. We analyzed the microbial content using updated computational methods and databases, and compared our results to those from two major recent studies that focused on bacteria, viruses, and fungi in cancer. Our results expand upon and reinforce our recent findings, which showed that the presence of microbes is far smaller than had been previously reported, and that many species identified in TCGA data are either not present at all, or are known contaminants rather than microbes residing within tumors. As part of this expanded analysis, and to help others avoid being misled by flawed data, we have released a dataset that contains detailed read counts for bacteria, viruses, archaea, and fungi detected in all 5,734 TCGA samples, which can serve as a public reference for future investigations.

Broadening oncological boundaries: the intratumoral microbiota

The microbiota of solid tumors was identified >100 years ago; however, heterogeneous composition and diversity have been revealed only recently. Growing evidence has suggested that several functional mechanisms of the intratumoral microbiota affect tumorigenesis and progression, suggesting that the intratumoral microbiota is a promising biomarker for multiple cancers. The low biomass of the intratumoral microbiota poses a major challenge to related research, thus necessitating the use of a multiple-modality integrated framework to resolve this dilemma. Advanced techniques such as single-cell sequencing provide significant clues, and the gradual optimization of functional experiments and culture-based methods enables deeper investigation of the underlying mechanisms involved. In this review, we outline the current state of research on the intratumoral microbiota and describe the challenges and comprehensive strategies for future research.

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

SIGNIFICANCE

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Multi-omics-based investigation of Bifidobacterium's inhibitory effect on glioma: regulation of tumor and gut microbiota, and MEK/ERK cascade

Glioma, the most prevalent primary tumor of the central nervous system, is characterized by a poor prognosis and a high recurrence rate. The interplay between microbes, such as gut and tumor microbiota, and the host has underscored the significant impact of microorganisms on disease progression. Bifidobacterium, a beneficial bacterial strain found in the human and animal intestines, exhibits inhibitory effects against various diseases. However, the existing body of evidence pertaining to the influence of Bifidobacterium on glioma remains insufficient. Here, we found that Bifidobacterium reduces tumor volume and prolongs survival time in an orthotopic mouse model of glioma. Experiments elucidated that Bifidobacterium suppresses the MEK/ERK cascade. Additionally, we noted an increase in the α-diversity of the tumor microbiota, along with an augmented relative abundance of Bifidobacterium in the gut microbiota. This rise in Bifidobacterium levels within the intestine may be attributed to a concurrent increase in Bifidobacterium within the glioma. Additionally, Bifidobacterium induced alterations in serum metabolites, particularly those comprised of organonitrogen compounds. Thus, our findings showed that Bifidobacterium can suppress glioma growth by inhibiting the MEK/ERK cascade and regulating tumor, and gut microbiota, and serum metabolites in mice, indicating the promising therapeutic prospects of Bifidobacterium against glioma.

Robustness of cancer microbiome signals over a broad range of methodological variation

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Microbiota Manipulation as an Emerging Concept in Cancer Therapy

Background

The human body is colonized by billions of bacteria that provide nutrients to the host, train our immune system, and importantly affect our heath. It has long been suggested that microbes might play a role in tumor pathogenesis; however, compelling evidence was only provided in the past decades when novel detection methods that do not depend on culturing techniques had been developed.

Summary

The microbiome impacts tumor development and anti-tumor therapies on various levels. Bacteria can promote or suppress tumor growth via direct interactions with cancer cells, production of metabolites that promote or inhibit tumor growth, and via stimulation or suppression of the local and systemic immune response. Cancer patients harbor a distinct microbiome when compared to healthy controls, which could potentially be employed to detect, identify, and treat cancer. Manipulation of the microbiome either via supplementation of single strains, bacterial consortia, fecal microbiota transfer or the use of pre- and probiotics has been suggested as therapeutic approach to directly target tumor growth or to enhance the efficacy of current state-of-the-art treatment options.

Key Messages

(1) Bacteria have a tremendous impact on anti-cancer immune responses. (2) Cancer patients harbor a distinct microbiome when compared to healthy controls. (3) The microbiome seems to be cancer-type specific. (4) Exploitation of bacteria to promote anti-tumor therapy is a novel, very promising venue in cancer treatment.

Identifying important microbial biomarkers for the diagnosis of colon cancer using a random forest approach

Colon cancer is one of the most common cancers, with 30-50 % of patients returning or metastasizing within 5 years of treatment. Increasingly, researchers have highlighted the influence of microbes on cancer malignant activity, while no studies have explored the relationship between colon cancer and the microbes in tumors. Here, we used tissue and blood samples from 67 colon cancer patients to identify pathogenic microorganisms associated with the diagnosis and prediction of colon cancer and evaluate the predictive performance of each pathogenic marker and its combination based on the next-generation sequencing data by using random forest algorithms. The results showed that we constructed a database of 13,187 pathogenic microorganisms associated with human disease and identified 2 pathogenic microorganisms (Synthetic.construct_32630 and Dicrocoelium.dendriticum_57078) associated with colon cancer diagnosis, and the constructed diagnostic prediction model performed well for tumor tissue samples and blood samples. In summary, for the first time, we provide new molecular markers for the diagnosis of colon cancer based on the expression of pathogenic microorganisms in order to provide a reference for improving the effective screening rate of colon cancer in clinical practice and ameliorating the personalized treatment of colon cancer patients.

Intratumoral microorganisms in tumors of the digestive system

Tumors of the digestive system pose a significant threat to human health and longevity. These tumors are associated with high morbidity and mortality rates, leading to a heavy economic burden on healthcare systems. Several intratumoral microorganisms are present in digestive system tumors, and their sources and abundance display significant heterogeneity depending on the specific tumor subtype. These microbes have a complex and precise function in the neoplasm. They can facilitate tumor growth through various mechanisms, such as inducing DNA damage, influencing the antitumor immune response, and promoting the degradation of chemotherapy drugs. Therefore, these microorganisms can be targeted to inhibit tumor progression for improving overall patient prognosis. This review focuses on the current research progress on microorganisms present in the digestive system tumors and how they influence the initiation, progression, and prognosis of tumors. Furthermore, the primary sources and constituents of tumor microbiome are delineated. Finally, we summarize the application potential of intratumoral microbes in the diagnosis, treatment, and prognosis prediction of digestive system tumors. Video Abstract.

Association between gut microbiota and CpG island methylator phenotype in colorectal cancer

The intestinal microbiota is an important environmental factor implicated in CRC development. Intriguingly, modulation of DNA methylation by gut microbiota has been reported in preclinical models, although the relationship between tumor-infiltrating bacteria and CIMP status is currently unexplored. In this study, we investigated tumor-associated bacteria in 203 CRC tumor cases and validated the findings using The Cancer Genome Atlas datasets. We assessed the abundance of Bacteroides fragilis, Escherichia coli, Fusobacterium nucleatum, and Klebsiella pneumoniae through qPCR analysis and observed enrichment of all four bacterial species in CRC samples. Notably, except for E. coli, all exhibited significant enrichment in cases of CIMP. This enrichment was primarily driven by a subset of cases distinguished by high levels of these bacteria, which we labeled as "Superhigh". The bacterial Superhigh status showed a significant association with CIMP (odds ratio 3.1, p-value = 0.013) and with MLH1 methylation (odds ratio 4.2, p-value = 0.0025). In TCGA CRC cases (393 tumor and 45 adj. normal), bacterial taxa information was extracted from non-human whole exome sequencing reads, and the bacterial Superhigh status was similarly associated with CIMP (odds ratio 2.9, p < 0.001) and MLH1 methylation (odds ratio 3.5, p < 0.001). Finally, 16S ribosomal RNA gene sequencing revealed high enrichment of Bergeyella spp. C. concisus, and F. canifelinum in CIMP-Positive tumor cases. Our findings highlight that specific bacterial taxa may influence DNA methylation, particularly in CpG islands, and contribute to the development and progression of CIMP in colorectal cancer.

The human microbiome and cancer: a diagnostic and therapeutic perspective

Recent evidence has shown that the human microbiome is associated with various diseases, including cancer. The salivary microbiome, fecal microbiome, and circulating microbial DNA in blood plasma have all been used experimentally as diagnostic biomarkers for many types of cancer. The microbiomes present within local tissue, other regions, and tumors themselves have been shown to promote and restrict the development and progression of cancer, most often by affecting cancer cells or the host immune system. These microbes have also been shown to impact the efficacy of various cancer therapies, including radiation, chemotherapy, and immunotherapy. Here, we review the research advances focused on how microbes impact these different facets and why they are important to the clinical care of cancer. It is only by better understanding the roles these microbes play in the diagnosis, development, progression, and treatment of cancer, that we will be able to catch and treat cancer early.

Comparative RNA-sequencing analysis of the prostate in a mouse model of benign prostatic hyperplasia with bladder outlet obstruction

In ageing men, benign prostatic hyperplasia (BPH) is a chronic disease that leads to progressive lower urinary tract symptoms (LUTS) caused by obstruction of the bladder outlet (BOO). Patients with LUTS (such as increased frequency and urgency of urination) and complications of BOO (such as hydronephrosis and bladder stones) are at risk of serious health problems. BPH causes a rapidly rising burden of LUTS far exceeding that of other urological conditions. Treatment outcomes are unsatisfactory for BPH largely due to the lacking of fully understanding of the pathogenesis. Hormonal imbalances related to androgen and oestrogen can cause BPH, but the exact mechanism is still unknown, even the animal model is not fully understood. Additionally, there are no large-scale data to explain this mechanism. A BPH mouse model was established using mixed slow-release pellets of testosterone (T) and estradiol (E2), and we measured gene expression in mouse prostate tissue using RNA-seq, verified the results using qRT‒PCR, and used bioinformatics methods to analyse the differentially expressed genes (DEGs).

Major data analysis errors invalidate cancer microbiome findings

IMPORTANCE

Recent reports showing that human cancers have a distinctive microbiome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid.

Refined control of CRISPR-Cas9 gene editing in Clostridium sporogenes: the creation of recombinant strains for therapeutic applications

Despite considerable clinical success, the potential of cancer immunotherapy is restricted by a lack of tumour-targeting strategies. Treatment requires systemic delivery of cytokines or antibodies at high levels to achieve clinically effective doses at malignant sites. This is exacerbated by poor penetration of tumour tissue by therapeutic antibodies. High-grade immune-related adverse events (irAEs) occur in a significant number of patients (5-15%, cancer- and therapeutic-dependent) that can lead to lifelong issues and can exclude from treatment patients with pre-existing autoimmune diseases. Tumour-homing bacteria, genetically engineered to produce therapeutics, is one of the approaches that seeks to mitigate these drawbacks. The ability of Clostridium sporogenes to form spores that are unable to germinate in the presence of oxygen (typical of healthy tissue) offers a unique advantage over other vectors. However, the limited utility of existing gene editing tools hinders the development of therapeutic strains. To overcome the limitations of previous systems, expression of the Cas9 protein and the gRNA was controlled using tetracycline inducible promoters. Furthermore, the components of the system were divided across two plasmids, improving the efficiency of cloning and conjugation. Genome integrated therapeutic genes were assayed biochemically and in cell-based functional assays. The potency of these strains was further improved through rationally-conceived gene knock-outs. The new system was validated by demonstrating the efficient addition and deletion of large sequences from the genome. This included the creation of recombinant strains expressing two pro-inflammatory cytokines, interleukin-2 (IL-2) and granulocyte macrophage-colony stimulating factor (GM-CSF), and a pro-drug converting enzyme (PCE). A comparative, temporal in vitro analysis of the integrant strains and their plasmid-based equivalents revealed a substantial reduction of cytokine activity in chromosome-based constructs. To compensate for this loss, a 7.6 kb operon of proteolytic genes was deleted from the genome. The resultant knock-out strains showed an 8- to 10-fold increase in cytokine activity compared to parental strains.

The role of microbiota in esophageal squamous cell carcinoma: A review of the literature

Esophageal squamous cell carcinoma (ESCC) exhibits high incidence with poor prognosis. Alcohol drinking, cigarette smoking, and betel nut chewing are well-known risk factors. Dysbiosis, an imbalance of the microbiota residing in a local environment, is known to be associated with human diseases, especially cancer. This article reviews the current evidence of esophageal microbiota in ESCC carcinogenesis, including initiation, progression, and drug resistance. Articles involving the esophageal microbiota, diagnosis, treatment, and the progression of esophageal cancer were acquired using a comprehensive literature search in PubMed in recent 10 years. Based on 16S rRNA sequencing of human samples, cell, and animal studies, current evidence suggests dysbiosis of the esophagus promotes ESCC progression and chemotherapy resistance, leading to a poor prognosis. Smoking and drinking are associated with esophageal dysbiosis. Specific bacteria have been reported to promote carcinogenesis, involving either progression or drug resistance in ESCC, for example Porphyromonas gingivalis and Fusobacterium nucleatum. These bacteria promote ESCC cell proliferation and migration via the TLR4/NF-κB and IL-6/STAT3 pathways. F. nucleatum induces cisplatin resistance via the enrichment of immunosuppressive myeloid-derived suppressor cells (MDSCs). Correcting the dysbiosis and reducing the abundance of specific esophageal pathogens may help in suppressing cancer progression. In conclusion, esophageal dysbiosis is associated with ESCC progression and chemoresistance. Screening the oral and esophageal microbiota is a potential diagnostic tool for predicting ESCC development or drug-resistance. Repairing esophageal dysbiosis is a novel treatment for ESCC. Clinical trials with probiotics in addition to current chemotherapy are warranted to study the therapeutic effects.

Analyzing omics data by feature combinations based on kernel functions

Defining meaningful feature (molecule) combinations can enhance the study of disease diagnosis and prognosis. However, feature combinations are complex and various in biosystems, and the existing methods examine the feature cooperation in a single, fixed pattern for all feature pairs, such as linear combination. To identify the appropriate combination between two features and evaluate feature combination more comprehensively, this paper adopts kernel functions to study feature relationships and proposes a new omics data analysis method KF-[Formula: see text]-TSP. Besides linear combination, KF-[Formula: see text]-TSP also explores the nonlinear combination of features, and allows hybridizing multiple kernel functions to evaluate feature interaction from multiple views. KF-[Formula: see text]-TSP selects [Formula: see text] > 0 top-scoring pairs to build an ensemble classifier. Experimental results show that KF-[Formula: see text]-TSP with multiple kernel functions which evaluates feature combinations from multiple views is better than that with only one kernel function. Meanwhile, KF-[Formula: see text]-TSP performs better than TSP family algorithms and the previous methods based on conversion strategy in most cases. It performs similarly to the popular machine learning methods in omics data analysis, but involves fewer feature pairs. In the procedure of physiological and pathological changes, molecular interactions can be both linear and nonlinear. Hence, KF-[Formula: see text]-TSP, which can measure molecular combination from multiple perspectives, can help to mine information closely related to physiological and pathological changes and study disease mechanism.

Intratumoral Microbiota Composition Regulates Chemoimmunotherapy Response in Esophageal Squamous Cell Carcinoma

Neoadjuvant chemoimmunotherapy (NACI) has shown promise in the treatment of resectable esophageal squamous cell carcinoma (ESCC). The microbiomes of patients can impact therapy response, and previous studies have demonstrated that intestinal microbiota influences cancer immunotherapy by activating gut immunity. Here, we investigated the effects of intratumoral microbiota on the response of patients with ESCC to NACI. Intratumoral microbiota signatures of β-diversity were disparate and predicted the treatment efficiency of NACI. The enrichment of Streptococcus positively correlated with GrzB+ and CD8+ T-cell infiltration in tumor tissues. The abundance of Streptococcus could predict prolonged disease-free survival in ESCC. Single-cell RNA sequencing demonstrated that responders displayed a higher proportion of CD8+ effector memory T cells but a lower proportion of CD4+ regulatory T cells. Mice that underwent fecal microbial transplantation or intestinal colonization with Streptococcus from responders showed enrichment of Streptococcus in tumor tissues, elevated tumor-infiltrating CD8+ T cells, and a favorable response to anti-PD-1 treatment. Collectively, this study suggests that intratumoral Streptococcus signatures could predict NACI response and sheds light on the potential clinical utility of intratumoral microbiota for cancer immunotherapy.

SIGNIFICANCE

Analysis of intratumoral microbiota in patients with esophageal cancer identifies a microbiota signature that is associated with chemoimmunotherapy response and reveals that Streptococcus induces a favorable response by stimulating CD8+ T-cell infiltration. See related commentary by Sfanos, p. 2985.

The intratumor microbiome is associated with microsatellite instability

Intratumoral microbes may have multifunctional roles in carcinogenesis. Microsatellite instability (MSI) is associated with higher tumor immunity and mutational burden. Using whole transcriptome and whole genome sequencing microbial abundance data, we investigated associations of intratumoral microbes with MSI, survival, and MSI-relevant tumor molecular characteristics across multiple cancer types including colorectal cancer (CRC), stomach adenocarcinoma, and endometrial carcinoma. Among 451 CRC patients, our key finding was strong associations of multiple CRC-associated genera, including Dialister and Casatella, with MSI. Dialister and Casatella abundance was associated with improved overall survival (hazard ratiomortality = 0.56, 95% confidence interval = 0.34 to 0.92, and hazard ratiomortality = 0.44, 95% confidence interval = 0.27 to 0.72), respectively, comparing higher relative to lower quantiles. Multiple intratumor microbes were associated with immune genes and tumor mutational burden. Diversity of oral cavity-originating microbes was also associated with MSI among CRC and stomach adenocarcinoma patients. Overall, our findings suggest the intratumor microbiota may differ by MSI status and play a role in influencing the tumor microenvironment.

Major data analysis errors invalidate cancer microbiome findings

We re-analyzed the data from a recent large-scale study that reported strong correlations between microbial organisms and 33 different cancer types, and that created machine learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (1) errors in the genome database and the associated computational methods led to millions of false positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (2) errors in transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well.

Immune Resistance Mechanisms and the Road to Personalized Immunotherapy

What does the future of cancer immunotherapy look like and how do we get there? Find out where we've been and where we're headed in A Report on Resistance: The Road to personalized immunotherapy.

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival ≥24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

Intratumour microbiome of pancreatic cancer

Pancreatic cancer is a high mortality malignancy with almost equal mortality and morbidity rates. Both normal and tumour tissues of the pancreas were previously considered sterile. In recent years, with the development of technologies for high-throughput sequencing, a variety of studies have revealed that pancreatic cancer tissues contain small amounts of bacteria and fungi. The intratumour microbiome is being revealed as an influential contributor to carcinogenesis. The intratumour microbiome has been identified as a crucial factor for pancreatic cancer progression, diagnosis, and treatment, chemotherapy resistance, and immune response. A better understanding of the biology of the intratumour microbiome of pancreatic cancer contributes to the establishment of better early cancer screening and treatment strategies. This review focuses on the possible origins of the intratumour microbiome in pancreatic cancer, the intratumour localization, the interaction with the tumour microenvironment, and strategies for improving the outcome of pancreatic cancer treatment. Thus, this review offers new perspectives for improving the prognosis of pancreatic cancer.

Location and condition based reconstruction of colon cancer microbiome from human RNA sequencing data

BACKGROUND

The association between microbes and cancer has been reported repeatedly; however, it is not clear if molecular tumour properties are connected to specific microbial colonisation patterns. This is due mainly to the current technical and analytical strategy limitations to characterise tumour-associated bacteria.

METHODS

Here, we propose an approach to detect bacterial signals in human RNA sequencing data and associate them with the clinical and molecular properties of the tumours. The method was tested on public datasets from The Cancer Genome Atlas, and its accuracy was assessed on a new cohort of colorectal cancer patients.

RESULTS

Our analysis shows that intratumoural microbiome composition is correlated with survival, anatomic location, microsatellite instability, consensus molecular subtype and immune cell infiltration in colon tumours. In particular, we find Faecalibacterium prausnitzii, Coprococcus comes, Bacteroides spp., Fusobacterium spp. and Clostridium spp. to be strongly associated with tumour properties.

CONCLUSIONS

We implemented an approach to concurrently analyse clinical and molecular properties of the tumour as well as the composition of the associated microbiome. Our results may improve patient stratification and pave the path for mechanistic studies on microbiota-tumour crosstalk.

Intratumoral microbiota is associated with prognosis in patients with adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy. Recent studies have discovered a pivotal role of the intratumoral microbiota in various cancers, yet it remains elusive in ACC. Here, we explored the intratumoral microbiome data derived from in silico identification, further validated in an in-house cohort by bacterial 16S rRNA fluorescence in situ hybridization and lipopolysaccharide staining. Unsupervised clustering determined two naturally distinct clusters of the intratumoral microbiome in ACC, which was associated with overall survival. The incorporation of microbial signatures enhanced the prognostic performance of the clinical stage in an immunity-dependent manner. Genetic and transcriptomic association analyses identified significant upregulation of the cell cycle and p53 signaling pathways associated with microbial signatures for worsened prognosis. Our study not only supports the presence of intratumoral bacteria but also implies a prognostic and biological role of intratumoral microbiota in ACC, which can advance a better understanding of the biology of ACC.

Probiotics enhances anti-tumor immune response induced by gemcitabine plus cisplatin chemotherapy for urothelial cancer

Chemotherapy drugs, such as gemcitabine and cisplatin (GC), are frequently administered to patients with advanced urothelial carcinoma, however the influence of the gut microbiota on their action is unclear. Thus, we investigated the effects of GC on the gut microbiome and determined whether oral supplementation with a probiotics mixture of Lactobacillus casei Shirota and Bifidobacterium breve enhanced the anti-tumor immune response. After subcutaneous inoculation with MBT2 murine bladder cancer cells, syngenic C3H mice were randomly allocated into eight groups. The gut microbiome cluster pattern was altered in both the GC and oral probiotics groups (p = 0.025). Both tumor-bearing conditions (no treatment) and GC chemotherapy influenced Pseudoclostridium, Robinsoniella, Merdimonas, and Phocea in the gut. Furthermore, comparison of the GC-treated and GC + probiotics groups revealed an association of four methyltransferase family enzymes and two short-change fatty acid-related enzymes with oral probiotics use. A significant difference in tumor volume was observed between the GC and GC + probiotics groups at week 2 of treatment. Additionally, decreased recruitment of cancer-associated fibroblasts and regulatory T cells, and activation of CD8⁺ T cells and dendritic cells were observed in the tumor microenvironment. Our findings reveal the positive effects of a probiotics mixture of Lactobacillus and Bifidobacterium in enhancing anti-tumor effects through the gut-tumor immune response axis. Future clinical trials are needed to evaluate the full benefits of this novel supplement with oral probiotics in patients with advanced urothelial carcinoma.

The Role of Intratumor Microbiomes in Cervical Cancer Metastasis

BACKGROUND

Intratumor microbiomes can influence tumorigenesis and progression. The relationship between intratumor microbiomes and cervical cancer metastasis, however, remains unclear.

METHODS

We examined 294 cervical cancer samples together with information on microbial expression, identified metastasis-associated microbiomes, and used machine learning methods to validate their predictive ability on tumor metastasis. The tumors were subsequently typed based on differences in microbial expression. Differentially expressed genes in different tumor types were combined to construct a tumor-prognostic risk score model and a multiparameter nomogram model. In addition, we performed a functional enrichment analysis of differentially expressed genes to infer the mechanism of action between microbiomes and tumor cells.

RESULTS

Based on the 15 differentially expressed microbiomes, machine learning models were able to correctly predict the risk of cervical cancer metastasis. In addition, both the risk score and the nomogram model accurately predicted tumor prognosis. Differences in the expression of endogenous genes in tumors can influence the distribution of the intracellular microbiomes.

CONCLUSIONS

Intratumoral microbiomes in cervical cancer are associated with tumor metastasis and influence disease prognosis. A change in gene expression within tumor cells is responsible for differences in the microbial populations within the tumor.

Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review

It is well known that lung cancer relies on a number of genes aberrantly expressed because of somatic lesions. Indeed, the lungs, based on their anatomical features, are organs at a high risk of development of extremely heterogeneous tumors due to the exposure to several environmental toxic agents. In this context, the microbiome identifies the whole assemblage of microorganisms present in the lungs, as well as in distant organs, together with their structural elements and metabolites, which actively interact with normal and transformed cells. A relevant amount of data suggest that the microbiota plays a role not only in cancer disease predisposition and risk but also in its initiation and progression, with an impact on patients’ prognosis. Here, we discuss the mechanistic insights of the complex interaction between lung cancer and microbiota as a relevant component of the microenvironment, mainly focusing on novel diagnostic and therapeutic objectives.