Microbiome In Cancer Research Articles

The cancer microbiome: The discovery and study of tumour-specific cancer microbiomes have given rise to new therapeutic options: The discovery and study of tumour-specific cancer microbiomes have given rise to new therapeutic options.

The discovery that tumours have their own specific microbiome, together with more understanding of the role of the gut microbiome, has great potential for cancer diagnostics and targeted therapies.

Abstract MP30: Metabolomics Analyses Identified Factors Modifying the Association of Aspirin Use With Kidney Function

Introduction: Uncontrolled high dose aspirin use impairs kidney function. Short-term low dose aspirin treatment has also been associated with kidney function in older patients. We aimed to evaluate the association of regular aspirin use with kidney function in middle-aged adults and to identify factors modifying the association. Methods: We performed cross-sectional analyses among 1,261 participants of the 2016 Bogalusa Heart Study (BHS) visit. Regular aspirin use was self-reported and confirmed by serum biomarker, salicyluric glucuronide . Aspirin users were categorized at the median of salicyluric glucuronide levels into low and high dose users. A total of 887 known metabolites were profiled from the serum. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-Epi equation. We tested associations of aspirin use with eGFR and examined interactions of aspirin with serum metabolites on eGFR. Age, sex, race, education, smoking, drinking, physical activity, BMI, blood pressure, and fasting glucose were adjusted in all analyses. Results: The mean age of BHS participants was 48.2, and 133 (10.5%) were self-reported aspirin users. Serum salicyluric glucuronide level was significantly correlated with self-reported aspirin usage (P=2.00х10 -21 ). After adjusting for all covariables, mean eGFR for never, low dose, and high dose aspirin users were 92.9, 90.2, and 89.9 mL/min/1.73m 2 , respectively (P for trend=0.004). After Bonferroni correction, 28 metabolites modified the associations of aspirin use with eGFR (P<5.52х10 -5 ), of which, 26 increased the aspirin's nephrotoxicity effect, while behenoylsphingomyelin and tricosanoylsphingomyelin significantly attenuated the eGFR lowering effect of aspirin (Table). Conclusion: Aspirin use is associated with decreased eGFR among middle-aged adults, and this association was modified by dietary, gut microbiome, and biomarkers of kidney disease, cardiovascular disease, and cancer. The findings may help develop better use strategies for regular users.

Comparison of genomic and transcriptional microbiome analysis in gastric cancer patients and healthy individuals.

Helicobacter pylori and the stomach microbiome play a crucial role in gastric carcinogenesis, and detailed characterization of the microbiome is necessary for a better understanding of the pathophysiology of the disease. There are two common modalities for microbiome analysis: DNA (16S rRNA gene) and RNA (16S rRNA transcript) sequencing. The implications from the use of one or another sequencing approach on the characterization and comparability of the mucosal microbiome in gastric cancer (GC) are poorly studied. To characterize the microbiota of GC using 16S rRNA gene and its transcript and determine difference in the bacterial composition. In this study, 316 DNA and RNA samples extracted from 105 individual stomach biopsies were included. The study cohort consisted of 29 healthy control individuals and 76 patients with GC. Gastric tissue biopsy samples were collected from damaged mucosa and healthy mucosa at least 5 cm from the tumor tissue. From the controls, healthy stomach mucosa biopsies were collected. From all biopsies RNA and DNA were extracted. RNA was reverse transcribed into cDNA. V1-V2 region of bacterial 16S rRNA gene from all samples were amplified and sequenced on an Illumina MiSeq platform. Bray-Curtis algorithm was used to construct sample-similarity matrices abundances of taxonomic ranks in each sample type. For significant differences between groups permutational multivariate analysis of variance and Mann-Whitney test followed by false-discovery rate test were used. Microbial analysis revealed that only a portion of phylotypes (18%-30%) overlapped between microbial profiles obtained from DNA and RNA samples. Detailed analysis revealed differences between GC and controls depending on the chosen modality, identifying 17 genera at the DNA level and 27 genera at the RNA level. Ten of those bacteria were found to be different from the control group at both levels. The key taxa showed congruent results in various tests used; however, differences in 7 bacteria taxa were found uniquely only at the DNA level, and 17 uniquely only at the RNA level. Furthermore, RNA sequencing was more sensitive for detecting differences in bacterial richness, as well as differences in the relative abundance of Reyranella and Sediminibacterium according to the type of GC. In each study group (control, tumor, and tumor adjacent) were found differences between DNA and RNA bacterial profiles. Comprehensive microbial study provides evidence for the effect of choice of sequencing modality on the microbiota profile, as well as on the identified differences between case and control.

The comprehensive analysis of relationship between gut microbiome and treatment outcome of androgen deprivation therapy (ADT)-based treatment in patients with metastatic castration-sensitive and -resistant prostate cancer.

213 Background: Preclinical and clinical data from various types of cancer show that the gut microbiome can affect the outcome of treatments with immune checkpoint inhibitors or cytotoxic chemotherapies. However, the relationship between the microbiome and treatment outcomes in prostate cancer has remained unclear. Here, we present a comprehensive analysis of the relationship between the gut microbiome and treatment outcomes from a nationwide genome screening project (MONSTAR-SCREEN 1) in patients with metastatic castration-sensitive and -resistant prostate cancer (mCSPC and mCRPC). Methods: We performed 16S rRNA gene sequencing of fecal DNA from 73 mCSPC and 83 mCRPC patients before treatment. The microbiome data were compared using the Wilcoxon-Mann-Whitney test or Fisher’s exact test. Survival status and therapeutic efficacy of ADT based treatment were prospectively collected and estimated using the Kaplan-Meier method, and the log-rank test was used to compare survival in different strata. Results: Fecal samples from mCRPC had more Klebsiella and Enterobacteriaceae than those from mCSPC, whereas mCSPC had more Akkermansia and Bifidobacterium compared to mCRPC. Prior and concurrent usage of anti-biotics did not affect diversity of the amplicon sequence variants (ASVs), KEGG orthology and metabolism pathway representation. Anti-biotics also did not influence time to treatment failure (TTF) in mCRPC (HR:0.81, 95%CI:0.36–1.78) and mCSPC (HR:1.35, 95%CI:0.55–3.34). mCRPC had more diverse KEGG orthology compared to mCSPC ( p=0.0009), however, no statistical differences were observed in the ASV ( p=0.77) and metabolism pathway representation ( p=0.35) between mCRPC and mCSPC. High ASV tended to be associated with longer TTF rather than low ASV in both mCRPC (Adjusted HR:0.63, 95%CI:0.34–1.10) and mCSPC (Adjusted HR:0.66, 95%CI:0.31–1.40). mCRPC patients with high AVS also showed significant longer PSA-progression free survival than patients with low ASV (HR:053, 95%CI:0.28–0.99). Conclusions: This investigation revealed significant differences in the microbiome status of mCRPC compared to mCSPC. These differences and diversity might influence the outcomes of ADT based treatment in prostate cancer.

The Gut Microbiome and Metastatic Renal Cell Carcinoma.

The introduction of targeted therapy (TT) and immuno-oncology (IO) agents have revolutionized the treatment of metastatic renal cell carcinoma (mRCC). However, despite the significant improvements in survival and clinical response yielded by these agents, a significant percentage of patients still experience progressive disease. Evidence now suggests that microorganisms living in the gut (i.e., the gut microbiome) could be used as a biomarker for response and may also have utility in increasing response to these treatments. In this review, we present an overview of the role of the gut microbiome in cancer and its potential implications in the treatment of mRCC.

Leveraging explainable AI for gut microbiome-based colorectal cancer classification

Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.

Colon Cancer Microbiome Landscaping: Differences in Right- and Left-Sided Colon Cancer and a Tumor Microbiome-Ileal Microbiome Association.

In the current era of precision oncology, it is widely acknowledged that CRC is a heterogeneous disease entity. Tumor location (right- or left-sided colon cancer or rectal cancer) is a crucial factor in determining disease progression as well as prognosis and influences disease management. In the last decade, numerous works have reported that the microbiome is an important element of CRC carcinogenesis, progression and therapy response. Owing to the heterogeneous nature of microbiomes, the findings of these studies were inconsistent. The majority of the studies combined colon cancer (CC) and rectal cancer (RC) samples as CRC for analysis. Furthermore, the small intestine, as the major site for immune surveillance in the gut, is understudied compared to the colon. Thus, the CRC heterogeneity puzzle is far from being solved, and more research is necessary for prospective trials that separately investigate CC and RC. Our prospective study aimed to map the colon cancer landscape using 16S rRNA amplicon sequencing in biopsy samples from the terminal ileum, healthy colon tissue, healthy rectal tissue and tumor tissue as well as in preoperative and postoperative stool samples of 41 patients. While fecal samples provide a good approximation of the average gut microbiome composition, mucosal biopsies allow for detecting subtle variations in local microbial communities. In particular, the small bowel microbiome has remained poorly characterized, mainly because of sampling difficulties. Our analysis revealed the following: (i) right- and left-sided colon cancers harbor distinct and diverse microbiomes, (ii) the tumor microbiome leads to a more consistent cancer-defined microbiome between locations and reveals a tumor microbiome-ileal microbiome association, (iii) the stool only partly reflects the microbiome landscape in patients with CC, and (iv) mechanical bowel preparation and perioperative antibiotics together with surgery result in major changes in the stool microbiome, characterized by a significant increase in the abundance of potentially pathogenic bacteria, such as Enterococcus. Collectively, our results provide new and valuable insights into the complex microbiome landscape in patients with colon cancer.

Racial disparities in tumor microbiome in young-onset and average-onset colorectal cancer.

13 Background: The occurrence of young-onset colorectal cancer (yoCRC) is rising alarmingly, with a disproportionate incidence in Black patients as compared to White. We and others have previously shown differences in tumor microbiomes between yoCRC and average-onset colorectal cancer (aoCRC), but it is unknown if these differences contribute to racial disparities. We therefore analyzed racial differences in the intra-tumoral microbiome of CRC in young adults (<50 years). Methods: We analyzed 277 samples of histologically confirmed cases of stage I-IV CRC patients identified as either non-Hispanic Black or non-Hispanic White, who underwent surgical resection at Cleveland Clinic from year 2000-2020, who consented to a prospective biorepository. Fresh frozen specimens from the primary tumor with paired adjacent nonmalignant tissue were analyzed. The 16s rRNA gene was amplified and sequenced using V4 region primers and Illumina’s MiSeq system. Using DADA2, the quality filtered 16S rRNA amplicon reads were clustered and annotated as amplicon sequence variants (ASVs). The abundance count matrix was analyzed for alpha and beta diversity and differential abundance analysis (DAA) using Phyloseq package. Statistical tests included analysis of variance (ANOVA), permutational multivariate analysis (PERMANOVA), linear regression and Wilcoxon test. DAA and correlation analysis were controlled for false discovery rate using Benjamini Hochberg correction. Results: Differential abundance analysis highlighted key differences in the microbiome composition of Black and White patients with yoCRC. Amongst Black patients, the most prevalent taxa were Limosilactobacillus, Bacillus, Staphylococcus, Listeria and Akkermansia, whereas amongst White patients the most prevalent taxa were Enterococcus and Escherichia-Shigella (Table). Microbial profiles were also significantly different between Black patients with yoCRC and aoCRC and between White patients with yoCRC and aoCRC. At ASV level, the microbiome of Black yoCRC is more similar (R2 =0.87) to Black aoCRC, in comparison to white yoCRC (R2=0.57). Conclusions: We found significant differences between the intra-tumoral microbiome of yoCRC in the United States by race. Future epidemiologic studies and public health interventions need to account for these differences to reduce risk of yoCRC across various US populations. [Table: see text]

MultiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Understanding the role of the gut microbiome in gastrointestinal cancer: A review.

Gastrointestinal cancer represents one of the most diagnosed types of cancer. Cancer is a genetic and multifactorial disease, influenced by the host and environmental factors. It has been stated that 20% of cancer is caused by microorganisms such as Helicobacter pylori, hepatitis B and C virus, and human papillomavirus. In addition to these well-known microorganisms associated with cancer, it has been shown differences in the composition of the microbiota between healthy individuals and cancer patients. Some studies have suggested the existence of the selected microorganisms and their metabolites that can promote or inhibit tumorigenesis via some mechanisms. Recent findings have shown that gut microbiome and their metabolites can act as cancer promotors or inhibitors. It has been shown that gastrointestinal cancer can be caused by a dysregulation of the expression of non-coding RNA (ncRNA) through the gut microbiome. This review will summarize the latest reports regarding the relationship among gut microbiome, ncRNAs, and gastrointestinal cancer. The potential applications of diagnosing and cancer treatments will be discussed.

The lung, the niche, and the microbe: Exploring the lung microbiome in cancer and immunity.

The lung is a complex and unique organ system whose biology is strongly influenced by environmental exposure, oxygen abundance, connection to extrapulmonary systems via a dense capillary network, and an array of immune cells that reside in the tissue at steady state. The lung also harbors a low biomass community of commensal microorganisms that are dynamic during both health and disease with the capacity to modulate regulatory immune responses during diseases such as cancer. Lung cancer is the third most common cancer worldwide with the highest mortality rate amongst cancers due to the difficulty of an early diagnosis. This review discusses the current body of work addressing the interactions between the lung microbiota and the immune system, and how these two components of the pulmonary system are linked to lung cancer development and outcomes. Bringing in lessons from broader studies examining the effects of the gut microbiota on cancer outcomes, we highlight many challenges and gaps in this nascent field.

Breast cancer patients from the Midwest region of the United States have reduced levels of short-chain fatty acid-producing gut bacteria

As geographical location can impact the gut microbiome, it is important to study region-specific microbiome signatures of various diseases. Therefore, we profiled the gut microbiome of breast cancer (BC) patients of the Midwestern region of the United States. The bacterial component of the gut microbiome was profiled utilizing 16S ribosomal RNA sequencing. Additionally, a gene pathway analysis was performed to assess the functional capabilities of the bacterial microbiome. Alpha diversity was not significantly different between BC and healthy controls (HC), however beta diversity revealed distinct clustering between the two groups at the species and genera level. Wilcoxon Rank Sum test revealed modulation of several gut bacteria in BC specifically reduced abundance of those linked with beneficial effects such as Faecalibacterium prausnitzii. Machine learning analysis confirmed the significance of several of the modulated bacteria found by the univariate analysis. The functional analysis showed a decreased abundance of SCFA (propionate) production in BC compared to HC. In conclusion, we observed gut dysbiosis in BC with the depletion of SCFA-producing gut bacteria suggesting their role in the pathobiology of breast cancer. Mechanistic understanding of gut bacterial dysbiosis in breast cancer could lead to refined prevention and treatment.

Metaproteomic Analysis of an Oral Squamous Cell Carcinoma Dataset Suggests Diagnostic Potential of the Mycobiome.

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy, with an estimated 5-year survival rate of only 40-50%, largely due to late detection and diagnosis. Emerging evidence suggests that the human microbiome may be implicated in OSCC, with oral microbiome studies putatively identifying relevant bacterial species. As the impact of other microbial organisms, such as fungi and viruses, has largely been neglected, a bioinformatic approach utilizing the Trans-Proteomic Pipeline (TPP) and the R statistical programming language was implemented here to investigate not only bacteria, but also viruses and fungi in the context of a publicly available, OSCC, mass spectrometry (MS) dataset. Overall viral, bacterial, and fungal composition was inferred in control and OSCC patient tissue from protein data, with a range of proteins observed to be differentially enriched between healthy and OSCC conditions, of which the fungal protein profile presented as the best potential discriminator of OSCC within the analysed dataset. While the current project sheds new light on the fungal and viral spheres of the oral microbiome in cancer in silico, further research will be required to validate these findings in an experimental setting.

Oral microbiome and risk of incident head and neck cancer: A nested case-control study

ObjectivesThis nested case-control study in the NIH-AARP Diet and Health Study was carried out to prospectively investigate the relationship of oral microbiome with head and neck cancer (HNC). Materials and Methods56 incident HNC cases were identified, and 112 controls were incidence-density matched to cases. DNA extracted from pre-diagnostic oral wash samples was whole-genome shotgun metagenomic sequenced to measure the overall oral microbiome. ITS2 gene qPCR was used to measure the presence of fungi. ITS2 gene sequencing was performed on ITS2 gene qPCR positive samples. We computed taxonomic and functional alpha-diversity and beta-diversity metrics. The presence and relative abundance of groups of red-complex (e.g., Porphyromonas gingivalis) and/or orange-complex (e.g., Fusobacterium nucleatum) periodontal pathogens were compared between cases and controls using conditional logistic regression models and MiRKAT. ResultsParticipants with higher taxonomic microbial alpha-diversity had a non-statistically significant decreased risk of HNC. No case-control differences were found for beta diversity by MiRKAT model (all p > 0.05). A greater relative abundance of red-complex periodontal pathogens (OR = 0.51, 95 % CI = 0.26–1.00), orange-complex (OR = 0.38, 95 % CI = 0.18–0.83), and both complexes’ pathogens (OR = 0.32, 95 % CI = 0.14–0.75), were associated with reduced risk of HNC. The presence of oral fungi was also strongly associated with reduced risk of HNC compared with controls (OR = 0.39, 95 % CI = 0.17–0.92). ConclusionGreater taxonomic alpha-diversity, the presence of oral fungi, and the presence or relative abundance of multiple microbial species, including the red- and orange-complex periodontal pathogens, were associated with reduced risk of HNC. Future studies with larger sample sizes are needed to evaluate these associations.

Special tissue microbiota such as Cyanobacteria are associated with the immune microenvironment of lung adenocarcinoma.

Lung cancer is the leading prevalent form of human cancer and has the highest mortality rate among all cancer types. The role and potential mechanism of the lung microbiome in lung cancer is still unknown. This study aims to investigate the microbiomes of lung cancer patients possessing different levels of infiltrated CD8+ T cells and programmed cell death-1 (PD-1) receptors, and further assess the correlation between specific microbes and the immune environment of lung tumor. We analyzed the microbiomes of lung cancer tissues from patients with different levels of infiltrated CD8+ T cells and PD-1 expression using 16S rRNA gene sequencing. The relative abundance of dominant phyla and genera was compared, and the correlation between microbial composition and immune markers was explored. Our results showed that lung cancer tissues displayed similar microbiome profiles, including Proteobacteria, Bacteroidetes, and Actinobacteria as the dominant phyla; and Chryseobacterium, Triticum aestivum (bread wheat), and Acinetobacter as the dominant genera. We found that the relative abundance of Chryseobacterium was positively correlated with CD8+ T cell infiltration and the level of PD-1 expression, while the relative abundance of Acinetobacter was negatively associated with the PD-1 level. In addition, higher beta diversity was identified in samples with low CD8+ T cell infiltration, but no significant correlation between beta diversity and PD-1 expression was observed. Furthermore, the relative abundance of Cyanobacteria was significantly higher in both the CD8 high and PD-1 high groups. Our study indicated that the lung microbiota played an indispensable role in the CD8+ T cell-mediated tumor immune response. These findings shed light on valuable insights into the intricate interplay between the lung microbiome and the immune system in the progression of lung cancer, offing potential therapeutic strategies targeting the lung microbiome.

Diagnostic and prognostic potential of the resident non-small cell lung cancer microbiome.

The data of a comprehensive comparative study of the taxonomic composition of the resident microbiome of tumors from 26 patients with non-small cell lung cancer are presented. Analysis of taxonomic diversity revealed 10 types, 280 genera and 788 species of microorganisms. The analysis of the relative content and prognostic significance was carried out for 62 dominant genera. Differences in the relative abundance of bacteria of the genera Acinetobacter, Halomonas, and Chryseobacterium between tumor and conditionally normal lung tissue were found, but their diagnostic potential was not confirmed. The correlation analysis did not reveal any relationship between the content of various genera of bacteria and the histological type of the tumor, its localization, and the age of the patients. Differences were found in the content of the studied bacteria depending on the stage of the disease, the presence of regional metastases and tumor differentiation. The prognostic significance of bacteria of the genera Variovorax and Pseudoclavibacter in non-small cell lung cancer was established. The results obtained can be used in the development of new effective methods for the diagnosis and prognosis of non-small cell lung cancer.

The Cytokine Network in Colorectal Cancer: Implications for New Treatment Strategies.

Colorectal cancer (CRC) is one of the most frequent tumor entities worldwide with only limited therapeutic options. CRC is not only a genetic disease with several mutations in specific oncogenes and/or tumor suppressor genes such as APC, KRAS, PIC3CA, BRAF, SMAD4 or TP53 but also a multifactorial disease including environmental factors. Cancer cells communicate with their environment mostly via soluble factors such as cytokines, chemokines or growth factors to generate a favorable tumor microenvironment (TME). The TME, a heterogeneous population of differentiated and progenitor cells, plays a critical role in regulating tumor development, growth, invasion, metastasis and therapy resistance. In this context, cytokines from cancer cells and cells of the TME influence each other, eliciting an inflammatory milieu that can either enhance or suppress tumor growth and metastasis. Additionally, several lines of evidence exist that the composition of the microbiota regulates inflammatory processes, controlled by cytokine secretion, that play a role in carcinogenesis and tumor progression. In this review, we discuss the cytokine networks between cancer cells and the TME and microbiome in colorectal cancer and the related treatment strategies, with the goal to discuss cytokine-mediated strategies that could overcome the common therapeutic resistance of CRC tumors.

The oral cancer microbiome contains tumor space-specific and clinicopathology-specific bacteria.

The crosstalk between the oral microbiome and oral cancer has yet to be characterized. This study recruited 218 patients for clinicopathological data analysis. Multiple types of specimens were collected from 27 patients for 16S rRNA gene sequencing, including 26 saliva, 16 swabs from the surface of tumor tissues, 16 adjacent normal tissues, 22 tumor outer tissue, 22 tumor inner tissues, and 10 lymph nodes. Clinicopathological data showed that the pathogenic bacteria could be frequently detected in the oral cavity of oral cancer patients, which was positively related to diabetes, later T stage of the tumor, and the presence of cervical lymphatic metastasis. Sequencing data revealed that compared with adjacent normal tissues, the microbiome of outer tumor tissues had a greater alpha diversity, with a larger proportion of Fusobacterium, Prevotella, and Porphyromonas, while a smaller proportion of Streptococcus. The space-specific microbiome, comparing outer tumor tissues with inner tumor tissues, suggested minor differences in diversity. However, Fusobacterium, Neisseria, Porphyromonas, and Alloprevotella were more abundant in outer tumor tissues, while Prevotella, Selenomonas, and Parvimonas were enriched in inner tumor tissues. Clinicopathology-specific microbiome analysis found that the diversity was markedly different between negative and positive extranodal extensions, whereas the diversity between different T-stages and N-stages was slightly different. Gemella and Bacillales were enriched in T1/T2-stage patients and the non-lymphatic metastasis group, while Spirochaetae and Flavobacteriia were enriched in the extranodal extension negative group. Taken together, high-throughput DNA sequencing in combination with clinicopathological features facilitated us to characterize special patterns of oral tumor microbiome in different disease developmental stages.

Microbial Biomarkers for Lung Cancer: Current Understandings and Limitations.

As our "hidden organ", microbes widely co-exist at various sites on the human body. These microbes are collectively referred to as the microbiome. A considerable number of studies have already proven that the microbiome has significant impacts on human health and disease progression, including cancers. The recent discovery of cancer-specific microbiomes renders these cancer-associated microbes as potential biomarkers and therapeutic targets. While at low biomass levels, the lung microbiome still dramatically influences the initiation, progression and treatment of lung cancers. However, research on lung cancer-associated microbiomes is emerging, and most profiling studies are performed within three years. Unfortunately, there are substantial inconsistencies across these studies. Variations in microbial diversity were observed, and different microbial biomarkers for lung cancer have been proposed. In this review, we summarized the current findings of lung cancer microbiome studies and attempt to explain the potential reasons for the dissimilarities. Other than lung microbiomes, oral and airway microbiomes are highly related to lung microbiomes and are therefore included as well. In addition, several lung cancer-associated bacterial genera have been detected by different independent studies. These bacterial genera may not be perfect biomarkers, but they still serve as promising risk factors for lung cancers and show great prognostic value.

Abstract A008: AI-driven image-based microbiome analysis reveals correlation of higher pre-treatment microbiome level with complete response in QUILT 3032 trial patients

Abstract The human microbiome, both systemic and tumor microenvironment (TME)-associated, exerts a profound influence on cancer development, progression, and response to treatment. To facilitate microbiome analysis in cancer, we developed an AI-driven computational pathology system that determines relative microbiome levels in H&E-stained slides from formalin-fixed paraffin- embedded tissue specimens. We utilized available whole-genome and whole-transcriptome sequencing data for microbiome detection in TCGA bladder cancer [Poore et al. 2020; Nature 579], for which images were available, to define microbiome-low and -high labels. The TCGA bladder cancer cohort (n = 408) was distributed into training (66.7%, n = 272), validation (8.3%, n = 34) and test (25.0%, n = 102) sets; each with equal numbers of microbiome-low and -high patients. The cohort featured an average of 1.05 diagnostic (DX) images per patient, with 429 DX images in total. We trained a deep network using a total of 303,104 patches (of size 100 x 100 microns, equivalent to 400 x 400 pixels at 40X magnification) randomly selected from 296 DX images in the training set. Testing on untouched patient data (n = 102) gave area under the ROC curve of 0.74 and F1 score of 0.74. The developed system was then used to determine correlations between TCGA bladder cancer patient survival and microbiome level. Treatment-naïve bladder cancer patients identified as microbiome-low by our image-based system had higher survival rates, with hazard ratio of 1.45. Further, we assessed correlations between pathologic complete response (pCR) at 12 or 27 weeks and microbiome level in non-muscle invasive bladder cancer patients in ImmunityBio, Inc.’s QUILT-3.032 trial. Patients that achieved a pCR with treatment were found to have significantly higher probability of microbiome-high pre-treatment, as compared to those who did not achieve a pCR. Reductions in predicted microbiome levels after treatment were also observed in pCR patients. Conversely, 6 of 7 non-responders were found to have an increase in microbiome-high probability with treatment. These findings suggest our novel AI-driven image-based computational pathology system has the potential to provide data that may not only inform clinical decision-making, but also allow further investigation into the role of the TME microbiome in cancer. Citation Format: Mustafa I. Jaber, Kamil Wnuk, Patricia Spilman, Bing Song, David Trianni, Megan Huang, Paul Bhar, Patrick Soon-Shiong, Sandeep Reddy. AI-driven image-based microbiome analysis reveals correlation of higher pre-treatment microbiome level with complete response in QUILT 3032 trial patients [abstract]. In: Proceedings of the Second Biennial NCI Meeting: Translational Advances in Cancer Prevention Agent Development (TACPAD); 2022 Sep 7-9. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_2): Abstract nr A008.