A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

SIGNIFICANCE

Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for "exogenous sequences in tumors and immune cells" to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches and DNA-amplicon-based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets.

SIGNIFICANCE

The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.

Improving combination therapies: targeting A2B-adenosine receptor to modulate metabolic tumor microenvironment and immunosuppression

BACKGROUND

We investigated the role of A2B-adenosine receptor in regulating immunosuppressive metabolic stress in the tumor microenvironment. Novel A2B-adenosine receptor antagonist PBF-1129 was tested for antitumor activity in mice and evaluated for safety and immunologic efficacy in a phase I clinical trial of patients with non-small cell lung cancer.

METHODS

The antitumor efficacy of A2B-adenosine receptor antagonists and their impact on the metabolic and immune tumor microenvironment were evaluated in lung, melanoma, colon, breast, and epidermal growth factor receptor-inducible transgenic cancer models. Employing electron paramagnetic resonance, we assessed changes in tumor microenvironment metabolic parameters, including pO2, pH, and inorganic phosphate, during tumor growth and evaluated the immunologic effects of PBF-1129, including its pharmacokinetics, safety, and toxicity, in patients with non-small cell lung cancer.

RESULTS

Levels of metabolic stress correlated with tumor growth, metastasis, and immunosuppression. Tumor interstitial inorganic phosphate emerged as a correlative and cumulative measure of tumor microenvironment stress and immunosuppression. A2B-adenosine receptor inhibition alleviated metabolic stress, downregulated expression of adenosine-generating ectonucleotidases, increased expression of adenosine deaminase, decreased tumor growth and metastasis, increased interferon γ production, and enhanced the efficacy of antitumor therapies following combination regimens in animal models (anti-programmed cell death 1 protein vs anti-programmed cell death 1 protein plus PBF-1129 treatment hazard ratio = 11.74 [95% confidence interval = 3.35 to 41.13], n = 10, P < .001, 2-sided F test). In patients with non-small cell lung cancer, PBF-1129 was well tolerated, with no dose-limiting toxicities; demonstrated pharmacologic efficacy; modulated the adenosine generation system; and improved antitumor immunity.

CONCLUSIONS

Data identify A2B-adenosine receptor as a valuable therapeutic target to modify metabolic and immune tumor microenvironment to reduce immunosuppression, enhance the efficacy of immunotherapies, and support clinical application of PBF-1129 in combination therapies.

Comparative Analysis of Tumor Microbiome, Molecular Profile and Immune Cell Abundance by HPV Status in Head and Neck Cancers and Their Impact on Survival

PURPOSE/OBJECTIVE(S)

Traditional clinical and molecular prognostic factors offer valuable insight into the heterogeneous natural history and treatment response of head and neck squamous cell carcinoma (HNSCC) yet fail to explain the full spectrum of observed variability. The tumor microenvironment (TME), comprising microbiome and immune cells can impact treatment response and prognosis. We analyzed The Cancer Genome Atlas (TCGA) to evaluate the association of specific microbes and genes in TME with survival and their differential expression in HPV positive (+) and HPV negative (-) HNSCC.

MATERIALS/METHODS

HNSCC RNA sequencing (RNAseq) samples from TCGA were processed through the Exogenous sequencing in Tumors and Immune Cells (ExoTIC) pipeline to identify gene expression and microbial presence. HPV status was assessed by detection of papillomaviridae family of microbes. Clinical data from TCGA was extracted to compare overall survival (OS) and control for competing variables using Cox proportional hazards regression. Difference in immune cell abundance was evaluated by Kruskal-Wallis test. All statistical analysis was performed using R.

RESULTS

A total of 498 RNAseq samples from TCGA were analyzed. Oral cavity, oropharynx, hypopharynx, and larynx tumors comprised 21.6%, 15%, 1.8%, and 22.2% of specimens, respectively. HPV was detected in 111 patients (22%), most commonly Alpha papillomavirus 9 (90.1%). Of the 5838 enriched microbes, 330 were significantly associated with OS after controlling for tumor stage, smoking, and age. Specifically, the presence of Alpha papillomavirus 9 was associated with significantly improved OS [adjusted HR = 0.60 (95% CI 0.40 - 0.89, p = 0.01)]. Microbial species found in more abundance in HPV- tumors included Citrobacter farmeri, Thermoanaerobacter kivui and Yersinia pestis which are gram negative anaerobes. Genes related to cellular transport and DNA repair were enriched while genes related to proliferation (e.g., SAGE1) were depleted in HPV+ samples. HPV- tumors had a significantly higher number of M0 (p < 0.001) and M2 macrophages (p = 0.035) while HPV+ tumors had more T regulatory cells (p < 0.001) and CD8+ T-cells (p < 0.001).

CONCLUSION

Tumor microenvironment was significantly associated with survival for HNSCC patients, with particular microbes such as Alpha papillomavirus 9 correlating with improved OS. Greater abundance of certain anaerobic microbes was seen in HPV- tumors. These findings suggest TME can be used to predict patient outcomes and potentially guide personalized treatment approaches. We found an abundance of M0 and M2 macrophages in HPV- tumors, which are considered pro-tumorigenic, while anti-tumor M1 macrophages were similar in the two groups. This may help identify mechanism of resistance to immunotherapies and tailor novel immunotherapy combinations in specific patient subgroups. With further prospective research and external validation these findings have the potential to significantly impact the way we treat HNSCC in the future.

Association of medical comorbidities and cardiovascular disease with toxicity and survival among patients receiving checkpoint inhibitor immunotherapy

BACKGROUND AND OBJECTIVES

Medical comorbidities (MC) are highly prevalent among patients with cancer and predict worse outcomes for traditional therapies. This association is poorly understood for checkpoint inhibitor immunotherapy (IO). We aimed to explore the relationship between common MC including cardiovascular disease (CVD), immune-related adverse events (irAEs), and overall survival (OS) among patients receiving IO for advanced cancer.

METHODS

This is a retrospective cohort study of 671 patients with any cancer who received IO at our institution from 2011 to 2018. Clinical data were abstracted via chart review and query of ICD-10 codes and used to calculate modified Charlson comorbidity index (mCCI) scores. The primary outcomes were the association of individual MC with irAEs and OS using bivariate and multivariable analyses. Secondary outcomes included association of mCCI score with irAEs and OS.

RESULTS

Among 671 patients, 62.1% had a mCCI score ≥ 1. No individual MC were associated with irAEs or OS. Increased CCI score was associated with decreased OS (p < 0.01) but not with irAEs. Grade ≥ 3 irAEs were associated with increased OS among patients without CVD (HR 0.37 [95% CI: 0.25, 0.55], p < 0.01), but not among patients with CVD.

CONCLUSIONS

No specific MC predicted risk of irAEs or OS for patients receiving IO. Increased CCI score did not predict risk of irAEs but was associated with shorter OS. This suggests IO is safe for patients with MC, but MC may limit survival benefits of IO. CVD may predict shorter OS in patients with irAEs and should be evaluated among patients receiving IO.

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.

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

Abstract 5904: Intra-tumor microbes identified by RNAseq associated with response to immune checkpoint blockade in metastatic melanoma

The tumor microbiome has recently been shown to play a key role in the context of oncogenesis, cancer immune phenotype, cancer progression and treatment outcomes in a variety of cancers. We investigated the possible associations between tumor microbiome and successful treatment outcomes with immune checkpoint blockade (ICB) in patients with metastatic melanoma.

We evaluated RNAseq from tumor samples, collected prior to the start of treatment with ICB, from 71 patients with metastatic melanoma. Samples were provided by eight members of the Oncology Research Information Exchange Network (ORIEN). Non-response was determined as change in treatment after less than 12 months. Patients maintaining the same treatment regimen for greater than 12 months were classified as responders.

We applied our custom tool, {exotic} (Exogenous sequences in Tumor and Immune Cells), to carefully identify non-human sequences within the RNAseq data. After filtering reads aligning to the human reference genome, reads were further filtered of common laboratory contaminants, taxa inversely correlated with input RNA quantity, and taxa frequently found in the negative controls of microbiome experiments. A differential abundance analysis was performed on the response groups at every taxonomic level utilizing DESeq2. We calculated expression signatures using {tmesig}, and related them to ICB response using {IOSig}.

We observed significantly enriched taxa (p-value &lt; 0.05) with a high (&gt;1.00) fold-difference in abundance between responders and non-responders found within the tumor RNAseq data, including Fusobacterium nucleatum and several viruses in responders, and Delftia lacustris and Fungi in non-responders. These microbes were associated with immune cell expression signatures, including Th17 cells and CD8+ T-cells.

We calculated the gene expression scores of 30 signatures with literature precedence for the ability to predict ICB treatment outcomes in melanoma. The receiver operator characteristic (ROC) curve of the random forest classification model for prediction of response to ICB using the combined expression signature scores resulted in an AUC of 0.8750. Combining expression signature scores with microbe relative abundances at the genus level improved the ability to predict ICB response (AUC = 0.8958).

Combining tumor expression signatures with curated tumor microbiome relative abundances improves the performance of predictive models for treatment outcomes with ICB in melanoma.

Citation Format: Caroline E. Wheeler, Samuel Coleman, Rebecca Hoyd, Afaf Osman, Louis Denko, Aik Choon Tan, Daniel Spakowicz, Ahmad Tarhini. Intra-tumor microbes identified by RNAseq associated with response to immune checkpoint blockade in metastatic melanoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5904.

Abstract A021: Older adult-specific microbes correlate with treatment response and markers of T-cell senescence in NSCLC

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Short Talks from Proffered Abstracts section (PR004) of the Conference Proceedings.

Citation Format: Daniel Spakowicz, Rebecca Hoyd, Caroline E. Wheeler, Nyelia Williams, Amna Bibi, Marium Husain, Srichandhana Rajamouli, Shankar Suman, Joseph Amann, Madison Grogan, Pooja Vibhakar, Dwight H. Owen, David P. Carbone, Ashley Rosko, Christin E. Burd, Carolyn J. Presley. Older adult-specific microbes correlate with treatment response and markers of T-cell senescence in NSCLC [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr A021.

Abstract PR004: Older adult-specific microbes correlate with treatment response and markers of T-cell senescence in NSCLC

The gut microbiome changes with age and affects many aspects of human health, including response to cancer treatments. Cancer survival rates have improved with new treatment options, including immune checkpoint blockade (ICB); however, the objective response rate remains low. Manipulation of the microbiome is a promising approach to improving cancer outcomes, but the effect of age is understudied. Here, we sought to understand whether (1) specific microbes are associated with treatment response in older adults with non-small cell lung cancer (NSCLC) and (2) whether these microbes are the same as for younger adults. Next, we explored the causal effects of the microbiome on ICB response in mouse models and the relationship with blood-based markers of T-cell senescence. We conducted a prospective cohort study of adults ≥60 years with a new diagnosis of NSCLC who received any treatment modality. Stool was collected, and metagenomic whole-genome shotgun sequencing was performed. Blood T-cells were isolated, the RNA purified and then assessed for markers of senescence by nanostring. Response to treatment was determined by RECIST v1.1 criteria. Generalized linear regression was used to relate baseline microbiome abundances to treatment response and non-parametric correlations associated with CDKN2A (p16) expression to microbe abundances. To assess the causal role of the gut microbiome in ICB response, we gavaged gut microbiome samples from responders and non-responders into C57BL/6 mice to create human-microbiome avatar models. The mice were then injected with MC38 cancer cells and treated with anti-PD1 or isotype control antibodies, and tumor volume was measured over time. Biospecimens and best response data at three months were captured from 23 patients, of which five had a complete response, eight had a partial response, eight had stable disease, and two had progressive disease. Over 50 microbes were associated with a response after p-value adjustment. Responder stool was enriched for microbes associated with youth and ICB response (Bifidobacterium adolescentis, p = 2.64e-20). However, microbial taxa associated with response differed from those reported in younger populations (Firmicutes sp. CAG 145, p = 1.58e-20, Oscillibacter sp. 57-20, p = 7.96e-24). Stool from non-responders (NRs) was enriched in taxa previously linked to treatment-related toxicities and shorter progression-free survival (Streptococcus lutetiensis, p = 4.55E-24) but also contained microbes previously linked to response in younger adults (e.g., Roseburia sp. CAG 309, p = 5.16e-15). The T cell senescence marker, p16, correlated with the most enriched taxon in non-responders NRs (Streptococcus thermophilus, r = 0.45, p = 0.02), suggesting a connection between immune aging and the microbiome. Preliminary fecal transplant studies in mice showed improved ICB response in mice engrafted with stool from responders versus non-responders. Together, these data identify potential differences in the gut microbiomes of young and older adult NSCLC patients who respond to ICB.

Citation Format: Daniel Spakowicz, Rebecca Hoyd, Caroline E. Wheeler, Nyelia Williams, Amna Bibi, Marium Husain, Srichandhana Rajamouli, Shankar Suman, Joseph Amann, Madison Grogan, Pooja Vibhakar, Dwight H. Owen, David P. Carbone, Ashley Rosko, Christin E. Burd, Carolyn J. Presley. Older adult-specific microbes correlate with treatment response and markers of T-cell senescence in NSCLC [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr PR004.

Association between Tumor Microbiome and Hypoxia across Anatomic Subsites of Head and Neck Cancers

Purpose/Objective(s): Microbiome has been shown to affect tumorigenesis by promoting inflammation. However, the association between the upper aerodigestive microbiome and head and neck squamous cell carcinoma (HNSCC) is not well established. Hypoxia is a modifiable factor associated with poor radiation response. Our study analyzed the HNSCC tumor samples from The Cancer Genome Atlas (TCGA) to investigate the relationship between different HNSCC tumor subsites, hypoxia, and local tumor microbiome composition. Results: A total of 357 patients were included [Oral cavity (OC) = 226, Oropharynx (OPx) = 53, and Larynx/Hypopharynx (LHPx) = 78], of which 12.8%, 71.7%, and 10.3%, respectively, were HPV positive. The mean (SD) hypoxia scores were 30.18 (11.10), 24.31 (14.13), and 29.53 (12.61) in OC, OPx, and LHPx tumors, respectively, with higher values indicating greater hypoxia. The hypoxia score was significantly higher for OC tumors compared to OPx (p = 0.044) and LHPx (p = 0.002). There was no significant correlation between hypoxia and HPV status. Pseudomonas sp. in OC, Actinomyces sp. and Sulfurimonas sp. in OPx, and Filifactor, Pseudomonas and Actinomyces sp. in LHPx had the strongest association with the hypoxia score. Materials/Methods: Tumor RNAseq samples from TCGA were processed, and the R package “tmesig” was used to calculate gene expression signature, including the Buffa hypoxia (BH) score, a validated hypoxia signature using 52 hypoxia-regulated genes. Microbe relative abundances were modeled with primary tumor location and a high vs. low tertile BH score applying a gamma-distributed generalized linear regression using the “stats” package in R, with adjusted p-value < 0.05 considered significant. Conclusions: In our study, oral cavity tumors were found to be more hypoxic compared to other head and neck subsites, which could potentially contribute to their radiation resistance. For each subsite, distinct microbial populations were over-represented in hypoxic tumors in a subsite-specific manner. Further studies focusing on an association between microbiome, hypoxia, and patient outcomes are warranted.

Abstract 3523: A Black Raspberry dietary intervention to modify the gut microbiome and improve the response to immune checkpoint inhibitors

Lung cancer kills more people annually worldwide than any other cancer. Outcomes have improved with the use of immune checkpoint inhibitor (ICI) treatment, however, only about 20% of tumors respond. Emerging data demonstrate that responses to ICI may depend on the host microbiome. The challenge is to identify strategies to manipulate the gut microbiome to improve response to ICIs. Here we explore a targeted dietary intervention to modify the microbiome and determine the response to ICIs. Studies in a preclinical murine model showed that freeze-dried black raspberry powder (AIN-76A synthetic diet containing 5% lyophilized black raspberry powder) increased the abundance of Akkermansia muciniphila, which has been associated with improved response to ICIs in melanoma. Next, we conducted a human intervention trial called the BEWELL Study (Black raspberry nEctar Working to prEvent Lung cancer NCT04267874). This placebo-controlled, randomized, cross-over trial examined the impact of 2x 80 mL black raspberry (BRB) nectar drink boxes per day for 4 weeks. There were 96 participants recruited and classified as being at high risk of developing lung cancer (eligibility criteria: &gt;30 pack-year smoking history and 55-77 years old) in an attempt to match the phenotype of typical lung cancer patients but allowing us to clearly assess the impact of the intervention on the microbiome. Pre- and post-dietary intervention gut microbiome, blood, and urine samples were collected. Black raspberry dietary supplementation was not associated with a significant change in A. muciniphila (logistic regression with negative binomial Wald test p-value 0.056), however, changes in other taxa were observed. Finally, stool from participants in the BEWELL study was gavaged into C57BL/6J mice to create human microbiome avatar models. Mouse colon cancer cells (mc38) were injected subcutaneously and treated with anti-PD1 Ab (5mg/kg mouse; clone RMP1-14) or isotype control (clone 2A3). Preliminary experiments using avatar mice with post-BRB human microbiomes showed smaller tumors relative to mice receiving stool from that same individual pre-BRB dietary intervention, relative to isotype control (t-test, p-value 0.05). These results suggest that black raspberry nectar may modify the human gut microbiome in a way that promotes an improved response to immunotherapy.

Citation Format: Amna Bibi, Aaditya Pallerla, Nyelia Williams, Caroline Wheeler, Rebecca Hoyd, Shankar Suman, Joseph Amann, Mounika Goruganthu, Tamio Okimoto, Yangyang Liu, Marisa Bittoni, Ni Shi, Shiqi Zhang, Alvin Anand, Kristen Heitman, Maxine Mendelson, Elizabeth M. Grainger, Madison Grogan, Carolyn J. Presley, Fred K. Tabung, Lang Li, Yael Vodovotz, Jiangjiang Zhu, David P. Carbone, Tong Chen, Steven K. Clinton, Daniel Spakowicz. A Black Raspberry dietary intervention to modify the gut microbiome and improve the response to immune checkpoint inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3523.

The effect of the microbiome on immune checkpoint inhibitor toxicity in patients with melanoma

9568

Background: Immune-checkpoint inhibitor (ICI) immunotherapy has increased survival in patients with melanoma. However, only half of the patients respond, and many experience immune-related adverse events (irAEs). Recent evidence suggests that modification of the gut microbiome may increase response to ICIs and decrease toxicity. Here we describe the first results of a clinical trial to determine if the microbiome can predict the response or toxicity during the first 16 weeks of ICI treatment. Methods: We enrolled patients aged 18 or older in a prospective observational cohort study at The Ohio State University Comprehensive Cancer Center Skin Cancer Clinic (OSUCCC-SCC) who were to receive treatment with pembrolizumab or nivolumab alone or in combination with other treatments (e.g. nivolumab and ipilimumab) for melanoma. Patients receiving systemic or oral corticosteroids at the start of ICI cycle 1 were excluded but were eligible if receiving adrenal physiologic replacement. Patients collected stool samples at baseline, within 2 days of an adverse event (if applicable), and at 12 weeks. The response to ICIs was evaluated by Response Evaluation Criteria in Solid Tumors (RECIST v1.1) at a 12-week computed tomography scan. Metagenomic whole-genome shotgun sequencing was performed on an Illumina NovaSeq 6000 and then classified using HUMAnN3. The effect of microbe relative abundances on potential irAEs was modeled by logistic regression with the R package glmm. Results: In total, 88 patients consented to the trial. Pre-treatment microbiome samples were collected from 49 patients. Potential irAEs were observed in 16 out of the 49 patients for whom pre-treatment microbiome samples were collected. There was no significant difference in the ages (p = 0.150, genders (p = 0.2), stages (p = 0.2) or treatments (p = 0.07) of those who developed potential irAEs. Pretreatment abundance of the family Ruminococaceae was most strongly associated with the development of a potential irAE (p = 0.03), followed by a taxon in an unclassified order within the phylum Firmicutes (p = 0.05). The family Bacteroidaceae was most strongly associated with no potential irAE (p = 0.05). Conclusions: Longitudinal and event-driven biospecimen collection in the context of treatment with immunotherapies was feasible in the OSUCCC-SCC. The abundance of the two high-taxonomic rank microbe groups was significantly associated with potential irAEs. The association with Ruminococaceae is consistent with previous studies where it was associated with response to ICIs and, in separate studies, development of an irAE was associated with a better response. The unclassified taxon is potentially a new biomarker for the prediction of toxicity and a therapeutic target to reduce treatment side effects. Future analyses will associate microbes with treatment response and test for consistent microbiome changes at the time of irAE development. Clinical trial information: NCT05102773.

Pan-cancer analysis of exogenous (microbial) sequences in tumor transcriptome data from the ORIEN consortium and their association with cancer and tumor microenvironment

3113

Background: The tumor microbiome holds great potential for its ability to characterize various aspects of cancer biology and as a target for rational manipulation. For many cancer types, little is known about the role of microbes and in what contexts they affect clinical outcomes. Non-human (i.e. exogenous) sequences can be observed in low abundance within high throughput sequencing data of tumors. Here, we describe a collaboration among members of The Oncology Research Information Exchange Network (ORIEN) to leverage tumor biopsy RNAseq data collected under a shared protocol and generated at a single site to better understand the tumor microbiome, its association with prognostic features of the tumor microenvironment (TME) such as hypoxia, and how it may be used to improve clinical outcomes. Methods: Tumor RNAseq samples from 10 primary source locations including the tissues colon, lung, pancreas, and skin from ORIEN and similar cancers from The Cancer Genome Atlas (TCGA) were processed through the exoTIC (exogenous sequencing in tumors and immune cells) pipeline to identify and count exogenous sequences, filter contaminants, and normalize across datasets. Gene expression signatures of the TME, such as hypoxia, were calculated using ‘tmesig’. Microbe relative abundances were modeled with primary tumor location and hypoxia score using a gamma-distributed generalized linear regression via the stats package in R. Results: We analyzed RNAseq data of 2892 and 2720 tumors from ORIEN and TCGA, respectively. Patients’ ages were significantly greater in the ORIEN than the TCGA dataset (62 vs 58 yo, t-test p<0.001). The ORIEN data contained more sarcoma samples than TCGA (n = 691 vs 259) with roughly equivalent numbers in other cancer types. Fewer microbes were significantly associated with the hypoxia score than with cancer type (n = 32 vs 210). This trend was observed in both the ORIEN and TCGA datasets. The largest effect sizes were observed between microbes and small cell lung cancer. Conclusions: We found microbial sequences in all ORIEN and TCGA tumor RNAseq samples tested. Cancer type showed more significant associations with microbes than a hypoxia signature. These observations merit further investigation into the interaction between microbes and the TME. [Table: see text]

A Geriatric Assessment Intervention to Reduce Treatment Toxicity Among Older Adults With Advanced Lung Cancer: A Subgroup Analysis From a Cluster Randomized Controlled Trial

Introduction

More older adults die from lung cancer worldwide than breast, prostate, and colorectal cancers combined. Current lung cancer treatments may prolong life, but can also cause considerable treatment-related toxicity.

Objective

This study is a secondary analysis of a cluster-randomized clinical trial which evaluated whether providing a geriatric assessment (GA) summary and GA-guided management recommendations can improve grade 3-5 toxicity among older adults with advanced lung cancer.

Methods

We analyzed participants aged ≥70 years(y) with stage III & IV (advanced) lung cancer and ≥1 GA domain impairment starting a new cancer treatment with high-risk of toxicity within the National Cancer Institute's Community Oncology Research Program. Community practices were randomized to the intervention arm (oncologists received GA summary & recommendations) versus usual care (UC: no summary or recommendations given). The primary outcome was grade 3-5 toxicity through 3 months post-treatment initiation. Secondary outcomes included 6-month (mo) and 1-year overall survival (OS), treatment modifications, and unplanned hospitalizations. Outcomes were analyzed using generalized linear mixed and Cox proportional hazards models with practice site as a random effect. Trial Registration: NCT02054741.

Results & Conclusion

Among 180 participants with advanced lung cancer, the mean age was 76.3y (SD 5.1), 39.4% were female and 82.2% had stage IV disease. The proportion of patients who experienced grade 3-5 toxicity was significantly lower in the intervention arm vs UC (53.1% vs 71.6%, P=0.01). More participants in the intervention arm received lower intensity treatment at cycle 1 (56.3% vs 35.3%; P<0.01). Even with a cycle 1 dose reduction, OS at 6mo and 1 year was not significantly different (adjusted hazard ratio [HR] intervention vs. UC: 6mo HR=0.90, 95% CI: 0.52-1.57, P=0.72; 1 year HR=0.89, 95% CI: 0.58-1.36, P=0.57). Frequent toxicity checks, providing education and counseling materials, and initiating direct communication with the patient's primary care physician were among the most common GA-guided management recommendations. Providing a GA summary and management recommendations can significantly improve tolerability of cancer treatment among older adults with advanced lung cancer.

837 Preliminary insights into the impact of tumor microbiome in head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous set of distinct malignancies. Recognized prognostic factors rely on clinical and biological features, consisting mainly of stage, site of disease, performance status, comorbidities, smoking history and human papilloma virus (HPV) status. However, patients clustered by these parameters still differ in their clinical behavior and therapy response. The impact of the tumor microbiome on human disease has been explored and discussed extensively. Evaluating the tumor microbiome is a promising new approach that could be used as a prognostic and predictive tool in HNSCC, with the potential for improved treatment options and better clinical outcomes.

Methods

We utilized The Cancer Genome Atlas (TCGA) database to obtain RNA sequencing (RNAseq) data to identify microbes in HNSCC samples. We utilized ExoTIC, ”Exogenous sequences in Tumors and Immune cells,” a tool recently developed by Spakowicz et al. ExoTIC takes raw RNAseq reads and carefully aligns them to both human and non-human reference genomes to identify low-abundance microbes. We performed Cox proportional hazards regression to identify the microbes associated with overall survival (OS), controlling for age, stage, and smoking status.

Results

We evaluated 498 RNAseq samples from TCGA (table 1). ExoTIC identified 5838 microbes including bacteria, viruses and fungi, of which 330 were statistically associated with OS. Interestingly, 20% (n=100) of samples had HPV virus which was significantly associated with improved OS (HR 0.59, CI 0.4–0.9, p<0.01). There were also several other viruses and bacteria associated with significantly improved OS.

Abstract 837 Table 1

Patient characteristics of TCGA dataset

Conclusions

We found the presence of certain microbes in tumor biopsies statistically correlated with OS in HNSCC patients. This supports further study into the presence and correlation of specific microbes with tumor subsite and outcomes. Assessing individual characteristics of a HNSCC subtype with its particular microenvironment (e.g., microbes) can lead to personalized treatment insights and improved outcomes. Our future research will validate and correlate the microbial profile of HNSCC subtypes with clinical outcomes retrospectively and prospectively.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

In this paper, we propose and describe a robust and flexible modeling framework called MhealthCI based on the Bayesian structural time series, for which we have found to excel at analyzing diverse biosensor data. While Bayesian modeling is often employed in various fields such as finance, marketing, and weather forecasting, it is rarely used in biomedicine, specifically for biosensor and wearable data relating to human health and behavior. We use and apply this framework with the goal of interpreting and quantifying the causal impact of an intervention, a widespread goal of biomedicine. We describe the diversity of data types to which it could apply, provide intuition to its mechanics, collect relevant data in various fields, provide a wrapper tool around well-known R packages that prepares and registers diverse biosensor data to be analyzed, and finally apply the method to showcase its strength in quantifying the impact of interventions.

Microbiome signature, global methylation and immune landscape in early onset colorectal cancer

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Background: The incidence of colorectal cancer (CRC) in young adults ( < 50 years old) has been rapidly increasing by 2% per year since early 1990. Approximately 20% of early-onset (EO) CRC cases are due to germline gene mutations. However, the etiology of sporadic EO CRC remains poorly understood. Research suggests that environmental factors such as the Western diet (high in fat and low in fiber) may be associated with an increased incidence of sporadic EO CRC. The gut microbiota decompose and ferment dietary fibers to produce microbial metabolites, which play pivotal roles in maintaining the integrity of intestinal epithelium as well as the immune cell homeostasis. Also, these microbial metabolites may influence the host epigenome by altering either the activity of epigenetic enzymes or by modifying the availability of cofactors needed for epigenetic modifications. The aim of our research is to associate intratumoral microbiota with methylation pattern and immune cell composition in EO CRC. Methods: A total of 358 CRC cases, including 54 cases of EO CRC (age < 50 years) and 304 cases of late onset (LO) CRC (age ≥ 50 years), with matched methylation array (Infinium HM450), RNA-sequencing (Illumina HiSeq) from colon adenocarcinomas (COAD) and rectal adenocarcinomas (READ), and clinicopathological information of each patient, were extracted from the Cancer Genome Atlas (TCGA). We characterized and compared the intra-tumoral microbiota composition, tumor-infiltrating lymphocytes (TILs), and methylation profile between EO and LO CRC. Results: We found that there is a distinct microbial distribution, gene expression and methylation pattern in the EO CRC when compared with LO CRC. Non-human sequences from several kingdoms including bacteria, fungi and viruses were found and the incidences were consistent with reported values by other methods, e.g. Fusobacterium incidence. The EO CRC cases showed global hypomethylation, even though hypermethylation pattern is expected in the young chronological age group (known as Horvath’s clock). Pathway overrepresentation analysis of differentially expressed genes showed significant activation of p53 and pentose phosphate pathways and de novo nucleotide synthesis in EO CRC. Integration across datasets showed positive correlations between microbes and inflammasome pathway, positive correlation with regulatory T cells (Tregs), and negative correlations with CD4 memory T cells. Conclusions: These data suggest a mechanism by which the colorectal cancer-associated microbiota may be associated with epigenetic regulation and host immune response.

Investigating intra-tumor microbes, blood microbes, and CEA for development of non-invasive biomarkers in colorectal cancer

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Background: The development of non-invasive biomarkers has the potential to revolutionize clinical care for colorectal cancer (CRC) patients. The presence of bacteria in CRC tumor biopsies has been shown to contribute to CRC development. In a previous study, our group showed some intra-tumor microbes in CRC tumor biopsies correlated with overall survival in CRC patients. However, the correlations between microbes in tumor vs blood, and between non-invasive serum marker carcinoembryonic antigen (CEA) and microbes are unknown. We hypothesize that tumor microbes will also be found in blood, and that CEA will correlate with certain microbes. Methods: We obtained RNA-seq data from CRC tumor biopsies from patients treated at The Ohio State University Comprehensive Cancer Center as part of the Oncology Research Information Exchange Network (ORIEN). Reads were aligned to human and exogenous genomes using TopHat2 and Kraken2/Bracken, respectively. RNA-seq from CRC tumor biopsies as well as peripheral blood at the Cancer Genome Atlas (TCGA) consortium were processed by the same method. Results: The analyzed ORIEN cohort included 93 CRC patients with an age range from 30-83 years, 60.2% male, 87.1% adenocarcinoma, and 47.3% with metastatic CRC. The TGCA cohort included 495 CRC patients with an age range from 31-90 years, 53.3% male, 85.1% adenocarcinoma, and 15.5% with metastatic CRC. Over fifteen exogenous phyla (including bacteria, viruses, fungi) were observed in both ORIEN and TCGA cohorts. Several of the samples were dominated by viral sequences while others by bacteria, suggesting considerable tumor microbiome heterogeneity. Evaluation of the fraction of microbes in tumor and blood showed that nearly all the microbes found in blood (97.6%) were also observable in tumor in the TCGA cohort. Microbial abundances of various taxa, including Fusobacterium, significantly correlated between blood and tumor. Several bacteria including members of the genera Bacillus and Staphylococcus were positively associated with tumor stage (metastatic vs non-metastatic), but microbial relative abundances were not correlated with the location of tumor in colon (right, left, transverse colon). Certain microbial species from the ORIEN cohort were found to positively correlate with CEA, (including from the genera Fusobacterium, Lactobacillus, Pseudomonas, Vibrio, Clostridium) and these associations remained when adjusted for alcohol and smoking by multivariate analysis. Conclusions: Nearly all the microbes found in blood were found in tumor and abundances of various taxa were significantly correlated, suggesting that blood-based cancer microbiome analysis has great potential. Serum CEA has a low diagnostic ability when used alone, but combining this with blood microbiome could improve diagnostic/prognostic utility as a non-invasive biomarker.

Proton pump inhibitor use (PPI) in patients treated with immune checkpoint inhibitors (ICI) for advanced cancer: Survival and prior therapy

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Background: Emerging data suggest that concomitant medications (CM) influence response to ICI. CM impact the host microbiome which may mitigate tumor-immune responsiveness. PPI use in patients treated with ICI has been associated with worse survival. Few data exist regarding the effects of PPI use in terms of prior chemotherapy or in risk for immune related adverse events (irAE) (e.g., colitis). Methods: This retrospective study of patients with advanced cancer treated with ICI between 2011 and 2019 was conducted at The Ohio State University. Patients who received ICI as either single agent or combination were included. Clinical data was abstracted from chart review, including CM, toxicity, and survival. Overall survival (OS) was evaluated to date of death or last contact. Associations between OS and proton pump inhibitor (PPI) use were studied using log-rank tests and Cox regression analyses overall and by the groups of whether prior chemotherapy was administered and timing from chemotherapy to ICI. The associations between PPI and incidence of irAE (overall and colitis) were assessed by chi-square tests. Results: We identified 1,091 patients treated with ICI, of whom 415 (38%) received PPI at time of ICI. Most common cancers were NSCLC and melanoma; most common therapy was PD1/L1 (Table). PPI use was associated with shorter OS in patients treated as first line therapy (HR = 1.46, 95% CI = [1.11, 1.91], p=0.006) and in second line and beyond (HR = 1.30, 95% CI = [1.10, 1.53], p=0.002). PPI use was associated with shorter OS in patients treated with ICI for those without prior chemotherapy (HR = 1.47, 95% CI = [1.17, 1.86], p=0.001). When evaluated by timing from chemotherapy to ICI, PPI use was associated with shorter OS only in patients where last chemotherapy was > 1 year from ICI (HR = 1.99, 95% CI [1.15, 3.45], p=0.014) but not for patients with chemotherapy within 1 year of ICI (HR = 1.01, 95% CI = [0.79, 1.29], p=0.960). The use of PPI was not associated with incidence of irAE (p=0.317) or colitis in particular (p=0.781). Conclusions: PPI use was associated with shorter survival in patients treated with ICI across a broad variety of cancers and in first line of therapy or beyond. In patients with recent chemotherapy (<1 year), PPI use was not associated with survival, which may be due to disruption of the microbiome by chemotherapy. Further study is needed to determine the impact of CM (e.g, PPI), on outcomes of patients treated with ICI.[Table: see text]

The sarcoma microbiome as a diagnostic and therapeutic target

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Background: Sarcoma is a heterogeneous group of malignant tumors that consist of distinct histological and molecular subtypes, each with unique features. Despite immunotherapy’s promise in many cancers, immunotherapeutic approaches for sarcoma have had variable response rates. Evaluating the tumor microbiome is a promising new approach that aims to improve our understanding of the immunogenicity of sarcoma subtypes, leading to improved treatment options and better clinical outcomes. Methods: We utilized The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) database to obtain RNA sequencing (RNAseq) data to identify microbes in sarcoma samples (all subtypes available). Due to the large number of sarcoma subtypes, we focused on three groups: dedifferentiated liposarcoma (DDLPS), leiomyosarcoma (LMS) and “other,” representing all other sarcoma subtypes. We utilized ExoTIC, “Exogenous sequences in Tumors and Immune cells,” a tool recently developed by Dr. Daniel Spakowicz and Dr. Xaiokui Mo. ExoTIC takes raw RNAseq reads and carefully aligns to both human and non-human reference genomes to identify low-abundance microbes. Models of association were analyzed based on each of the three groups as well as all the samples: “All” group. We performed Cox proportional hazards regression to identify the microbes associated with overall survival (OS). Results: We evaluated 97 LMS, 56 DDLPS and 100 “other” RNAseq samples (Table). ExoTIC identified 1304 microbes, of which 431 were statistically associated with OS in the “All” group. Of these, 50 microbes were statistically associated only with DDLPS, 54 only with LMS (e.g., Candida dubliniensis, Mycobacterium avium, Streptococcus sp. Z15), and 46 with “other.” The presence of no organism was associated with improved survival. Median hazard ratios were largest in DDLPS (2.3), followed by “other” (2.1) and LMS (1.9). Only 18 microbes were found in the DDLPS, LMS and “All” groups, including Bacillus sp., Streptococcus lutetiensis, Clostridium tetani, and Pseudomonas sp. LTJR-52. Each was negatively correlated with survival with a median hazard ratio of 2.5. Conclusions: We found a specific relationship between microbial presence and histological sarcoma subtype (DDLPS, LMS), which also statistically correlated with OS. Assessing individual characteristics of a sarcoma histological subtype with its particular microenvironment (e.g., microbes) can lead to personalized treatment insights and improvements in outcomes. Our future research will consist of validating and correlation of the microbial profile of sarcoma subtypes with clinical outcomes retrospectively and prospectively. [Table: see text]

Checkpoint inhibitor immunotherapy toxicity and overall survival among older adults with advanced cancer

OBJECTIVES

Despite growing evidence that checkpoint inhibitor immunotherapy (IO) toxicity is associated with improved treatment response, the relationship between immune-related adverse events (irAEs) and overall survival (OS) among older adults [age ≥ 70 years (y)] remains unknown. The study goal was to determine differences in OS based on age and ≥ grade 3 (G3) irAEs.

MATERIALS AND METHODS

This was a retrospective cohort study of 673 patients with advanced cancer. Patients who received ≥1 dose of IO at our institution from 2011 to 2018 were eligible. The primary outcome was OS from the start of first line of IO treatment, compared between four patient groups stratified by age and ≥ G3 irAEs with adjustment for patient characteristics using a Cox proportional hazards model.

RESULTS AND CONCLUSION

Among all 673 patients, 35.4% were ≥ 70y, 39.8% had melanoma, and 45.6% received single-agent nivolumab. Incidence and types of ≥G3 irAEs did not differ by age. Median OS was significantly longer for all patients with ≥G3 irAEs (unadjusted 21.7 vs. 11.9 months, P = 0.007). There was no difference in OS among patients ≥70y with ≥G3 irAEs (HR 0.94, 95% CI 0.61-1.47, P = 0.79) in the multivariable analysis. Patients <70y with ≥G3 irAEs had significantly increased OS (HR 0.33, 95% CI 0.21-0.52, P < 0.001). Younger patients, but not older adults, with high-grade irAEs experience strong survival benefit. This difference may be due to the toll of irAEs themselves or the effects of treatments for irAEs, such as corticosteroids. Factors impacting OS of older adults after irAEs must be determined and optimized.

Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNA-seq) of sputum samples can be challenging to interpret due to the complex and heterogeneous mixtures of human cells and exogenous (microbial) material. In this study, we develop a pipeline that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. LDA(Latent Dirichlet allocation)-link connects microbes to genes using reduced-dimensionality LDA topics. We validate our method with single-cell RNA-seq and microscopy and then apply it to the sputum of asthmatic patients to find known and novel relationships between microbes and genes.

Intra-tumoral microbes and overall survival in colorectal cancer patients

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Background: The presence of certain bacteria among or adjacent to tumor cells may contribute to colorectal cancer (CRC) development. However, the effect of the tumor microbiome on survival in CRC patients undergoing treatment is poorly understood. We hypothesize that intra-tumoral microbes correlate with overall survival (OS) in CRC patients. Methods: We obtained RNA-seq data from CRC tumor biopsies from patients treated at The Ohio State University Comprehensive Cancer Center as part of the Oncology Research Information Exchange Network (ORIEN). Reads were aligned to human and exogenous genomes using TopHat2 and Kraken2/Bracken, respectively. Results: The analyzed cohort included 99 CRC patients with an age range from 31-83 years, 62% female, and 44% with metastatic CRC. Therapies received prior to sample collection were grouped into chemotherapy with or without radiation (37%), antiVEGF/EGFR therapies (33%), no systemic therapy (23%), immunotherapy (3%); 3% were unknown. Overall, eleven bacteria were significantly associated with shorter OS, including a species in the genus Clostridium and Vibrio. Conversely, five other bacteria including several commensal gut microbes, were associated with longer OS. In patients who received chemotherapy with or without radiation (n = 38), several microbes were significantly associated with shorter OS, including a member of the genus Streptomyces. Only three bacteria were significantly associated with longer OS. In the patients who received antiVEGF/EGFR therapies (bevacizumab, cetuximab, panitumumab) (n = 33), several bacterial taxa were associated with shorter OS. In addition, bacteria including a member of the genera Bacillus and Staphylococcus were significantly associated with metastatic CRCs. (p < 0.05 for all, Fisher’s Exact tests). Conclusions: This study suggests that demonstrating the presence or absence of certain microbes in tumor biopsies could have important therapeutic implications for CRC patients. Only bacteria (no fungi, viruses, archaea, etc.) were found to significantly associate with OS across the entire cohort and within treatment subsets. The presence of bacteria was mostly, but not always, associated with worse OS. Antibiotics targeted towards bacterial species associated with negative outcomes could have the potential to improve OS in CRC patients.

Platelets impact the responsiveness of immune checkpoint blockade therapy in solid tumors

e15023

Background: A growing body of evidence has linked platelets (plts) with immune suppression in the tumor microenvironment (TME). Our group has demonstrated, plts are the dominant source for TGF-β in TME and pharmacologic inhibition of plt function enhances multiple forms of immunotherapy (Sc Immunol 2017 PMID 28763790; J Immunol 2019 PMID 31358658; Sci Transl Med 2020 PMID 31915300). To further delineate this relationship, we examined the roles of plts on T cell exhaustion in a preclinical model and on response to immune checkpoint inhibitors (ICI) in a large cohort of patients (pts) with stage 4 cancers. Methods: The mouse MC-38 colon adenocarcinoma model was used in age-matched female C57Bl/6J mice. Antiplatelet therapy (APT) consisting of aspirin and clopidogrel (both at 30 mg/kg daily) was delivered p.o. and anti-PD-1 antibody (100 mg/mouse every 3 days) was administered i.p. starting on days 5 and 9 respectively. TME analysis via multispectral histology or flow cytometry was performed. Retrospective analysis was carried out on 826 pts who received ICI from 2011-2017 at the Ohio State University. Baseline plt count was collected within 7 days before initiating ICI. Repeat plt count was obtained prior to initiation of cycle 2. Normal plt counts were defined as 150,000 – 450,000/µl blood, and thrombocytosis as plt ≥450,000/µl blood. Kaplan Meier and log-rank analysis were performed to estimate median survival and determine the association with plt count. Results: In pre-clinical models, pretreatment with APT (1) synergized with PD-1 blockade to enhance T cell infiltration into MC-38 tumors resulting in immediate tumor reduction, and (2) decreased tumor infiltrating CD8+ T cell TOX expression, a transcription factor associated with T cell exhaustion (Nat Immunol 2019 PMID: 31427776). Among pts receiving ICIs, 46 (5.6%) pts with thrombocytosis had a significantly reduced median OS vs pts with normal plt counts: 6.0 (95 CI: 1.5—10.6) months (mos) vs 11.6 (95 CI: 9.7—13.4) mos (p = 0.002). Fluctuations ≥50,000 plt/mL in either direction between cycle 1 and 2 were associated with a significant reduction in median OS: 8.3 (95 CI: 6.4—10.1) mos vs 13.6 (95 CI: 11.2-16.1) mos (p < 0.001). Conclusions: There is a strong association between plts and failure of ICI in both the preclinical and clinical settings, likely via modifying the amount of active CD8+ T cells infiltrating into tumors. These findings merit further study to delineate the underlying mechanism for plt-mediated immune suppression and strategies to overcome it.

Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications

BACKGROUND

The microbiome has been shown to affect the response to Immune Checkpoint Inhibitors (ICIs) in a small number of cancers and in preclinical models. Here, we sought to broadly survey cancers to identify those in which the microbiome may play a prognostic role using retrospective analyses of patients with advanced cancer treated with ICIs.

METHODS

We conducted a retrospective analysis of 690 patients who received ICI therapy for advanced cancer. We used a literature review to define a causal model for the relationship between medications, the microbiome, and ICI response to guide the abstraction of electronic health records. Medications with precedent for changes to the microbiome included antibiotics, corticosteroids, proton pump inhibitors, histamine receptor blockers, non-steroid anti-inflammatories and statins. We tested the effect of medication timing on overall survival (OS) and evaluated the robustness of medication effects in each cancer. Finally, we compared the size of the effect observed for different classes of antibiotics to taxa that have been correlated to ICI response using a literature review of culture-based antibiotic susceptibilities.

RESULTS

Of the medications assessed, only antibiotics and corticosteroids significantly associated with shorter OS. The hazard ratios (HRs) for antibiotics and corticosteroids were highest near the start of ICI treatment but remained significant when given prior to ICI. Antibiotics and corticosteroids remained significantly associated with OS even when controlling for multiple factors such as Eastern Cooperative Oncology Group performance status, Charlson Comorbidity Index score, and stage. When grouping antibiotics by class, β-lactams showed the strongest association with OS across all tested cancers.

CONCLUSIONS

The timing and strength of the correlations with antibiotics and corticosteroids after controlling for confounding factors are consistent with the microbiome involvement with the response to ICIs across several cancers.

Abstract B30: Intratumoral microbes correlate with tumor-infiltrating lymphocytes in lung cancer RNAseq

Nonhuman sequences have been found in many tumors, but their effect on outcomes remains poorly understood. We hypothesize that intratumoral microbes affect the recruitment of immune cells through local immunostimulatory effects including activation nucleic acid sensing pathways. We obtained RNA-seq data from 480 tumor biopsies including melanoma (16), bladder (104), colorectal (20), renal cell carcinoma (20), sarcoma (118), and lung (202) from patients treated at The Ohio State University Comprehensive Cancer Center as part of the Oncology Research Information Exchange Network (ORIEN). Reads were aligned to human and exogenous genomes using TopHat2 and Kraken2/Bracken, respectively. Human gene expression was deconvolved to absolute abundances of immune cells using CIBERSORT. An average of 99.87% of reads aligned to the human reference genome across all samples. Inferred counts of tumor-infiltrating lymphocytes (TILs), and particularly the immune cell types CD8+ T-cells and M1 macrophages, were significantly enriched in lung cancer relative to the other tested cancers (p-values &lt;0.0001). TILs significantly correlated with both RIG-I and cGAS expression (p-values &lt;0.0001). Exogenous reads aligned to 1328 genomes of bacteria, archaea, viruses, and fungi after filtering. The most abundant phyla within exogenous counts were the Proteobacteria (39.5%), Firmicutes (20.1%), and Actinobacteria (5.16%). Lung cancer samples showed positive correlations between Acinetobacter junii and CD8+ T-cells (p-value=0.035) and between Acidipropionibacterium virtanenii and activated NK cells (p-value=0.035). This suggests a mechanism by which intratumoral microbes may affect immune cell infiltration and cancer outcomes.

Citation Format: Daniel Spakowicz, Rebecca Hoyd, YunZhou Liu, Janhavi Sahasrabudhe, Malvenderjit J. Singh, Isaac Arefi, Andrew Denney, David Carbone, Xiaokui Mo. Intratumoral microbes correlate with tumor-infiltrating lymphocytes in lung cancer RNAseq [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B30.

Is immunotherapy toxicity associated with improved overall survival among older adults with advanced cancer?

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Background: There is growing evidence that checkpoint inhibitor immunotherapy (IO) toxicity is associated with improved treatment response. There is a paucity of evidence examining the relationship between toxicity and overall survival (OS) in older adults. Methods: We performed a single institution retrospective cohort study of adults who received IO for advanced cancer from 2011-2017. Baseline clinical characteristics were abstracted from the electronic health record. Immune-related toxicities were graded by physicians based on Common Terminology for Adverse Events criteria, v4.0. Bivariate analysis with chi-squared statistics was used to describe baseline characteristics of patients ≥70 years (y) vs. <70y. Survival outcomes were estimated by the Kaplan-Meier method (time zero = start of first-line IO) and compared using the log-rank test. The association of age and ≥ grade 3 toxicity with OS was tested with a Cox proportional hazards model. Results: Among 676 patients treated with IO, 238 (35.4%) were ≥70y. There was no difference in baseline characteristics of each age group except cancer type (P<0.01). The incidence of ≥ grade 3 toxicity did not differ by age (<70y: 14.5% vs. ≥70y: 13.5%, P=0.71). Median OS was significantly longer for adults <70y (16.4 vs. 13.2 months, P<0.01) or those with ≥ grade 3 toxicity (18.3 vs. 14.7 months, P<0.01). When stratified by age and toxicity, patients <70y with ≥ grade 3 toxicity had longer OS vs. those without ≥ grade 3 toxicity (P<0.01). However, there was no OS difference among adults ≥70y with vs. without ≥ grade 3 toxicity (P=0.78). Adjusted hazard ratios with an interaction term are below. Conclusions: Though the incidence of ≥ grade 3 toxicity did not significantly differ by age, there was no significant OS advantage for older adults with ≥ grade 3 toxicity as compared to younger adults. Caution should be used in considering a toxicity-survival relationship in older adults.[Table: see text]

Re-evaluating the neutrophil-to-lymphocyte ratio: Machine learning-based variable selection for predicting survival at twelve months in late-stage cancer patients receiving immunotherapy

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Background: Neutrophil to Lymphocyte Ratio (NLR) is prognostic for cancer patients treated with immune checkpoint inhibitors (ICI). We showed the change in NLR early during treatment to be a stronger, curvilinear predictor, i.e. patients with an intermediate change in NLR performed better than those with large decreases or increases. This led us to re-examine whether NLR is an optimal predictor of overall survival (OS). Methods: A retrospective review of 467 patients with advanced cancer who received ICIs from 2011 to 2017 at the Ohio State University was performed with IRB approval. NLR was collected at the initiation of ICI and on-treatment (median 21, IQR 8 days) and calculated as ratio of absolute neutrophil to lymphocyte counts. Variable selection machine-learning algorithms included fast and frugal decision trees and random forest, performed in R. Results: The machine-learning algorithm fast and frugal decision trees identified the ratio of NLR on treatment to baseline NLR, the NLR on treatment, the change in NLR and the cubic change in NLR to be the most informative predictors of survival at 12 months. A random forest algorithm identified the same four variables as the most important for prediction accuracy. Age, sex and cancer type were the least informative predictors in the model, suggesting the on-treatment NLR variables are of value across wide range of demographics. Conclusions: NLR measured during treatment, and its derivative values of the ratio to baseline, change from baseline, and the cubic change from baseline, hold more predictive value than NLR measured at baseline. Common control variables such as age, and sex showed little effect on the model, suggesting on-treatment NLR is useful across wide demographic space. [Table: see text]