Ultralow-dose binary oncolytic/helper-dependent adenovirus promotes antitumor activity in preclinical and clinical studies.

We show that a binary oncolytic/helper-dependent adenovirus (CAdVEC) that both lyses tumor cells and locally expresses the proinflammatory cytokine IL-12 and PD-L1 blocking antibody has potent antitumor activity in humanized mouse models. On the basis of these preclinical studies, we treated four patients with a single intratumoral injection of an ultralow dose of CAdVEC (NCT03740256), representing a dose of oncolytic adenovirus more than 100-fold lower than used in previous trials. While CAdVEC caused no significant toxicities, it repolarized the tumor microenvironment with increased infiltration of CD8 T cells. A single administration of CAdVEC was associated with both locoregional and abscopal effects on metastases and, in combination with systemic administration of immune checkpoint antibodies, induced sustained antitumor responses, including one complete and two partial responses. Hence, in both preclinical and clinical studies, CAdVEC is safe and even at extremely low doses is sufficiently potent to induce significant tumor control through oncolysis and immune repolarization.

Metagenomes of rectal swabs in larger, advanced stage cervical cancers have enhanced mucus degrading functionalities and distinct taxonomic structure.

BACKGROUND: Gut microbiome community composition differs between cervical cancer (CC) patients and healthy controls, and increased gut diversity is associated with improved outcomes after treatment. We proposed that functions of specific microbial species adjoining the mucus layer may directly impact the biology of CC. METHOD: Metagenomes of rectal swabs in 41 CC patients were examined by whole-genome shotgun sequencing to link taxonomic structures, molecular functions, and metabolic pathway to patient's clinical characteristics. RESULTS: Significant association of molecular functions encoded by the metagenomes was found with initial tumor size and stage. Profiling of the molecular function abundances and their distributions identified 2 microbial communities co-existing in each metagenome but having distinct metabolism and taxonomic structures. Community A (Clostridia and Proteobacteria predominant) was characterized by high activity of pathways involved in stress response, mucus glycan degradation and utilization of degradation byproducts. This community was prevalent in patients with larger, advanced stage tumors. Conversely, community B (Bacteroidia predominant) was characterized by fast growth, active oxidative phosphorylation, and production of vitamins. This community was prevalent in patients with smaller, early-stage tumors. CONCLUSIONS: In this study, enrichment of mucus degrading microbial communities in rectal metagenomes of CC patients was associated with larger, more advanced stage tumors.

HydrAd: A Helper-Dependent Adenovirus Targeting Multiple Immune Pathways for Cancer Immunotherapy.

For decades, Adenoviruses (Ads) have been staple cancer gene therapy vectors. Ads are highly immunogenic, making them effective adjuvants. These viruses have well characterized genomes, allowing for substantial modifications including capsid chimerism and therapeutic transgene insertion. Multiple generations of Ad vectors have been generated with reduced or enhanced immunogenicity, depending on their intended purpose, and with increased transgene capacity. The latest-generation Ad vector is the Helper-dependent Ad (HDAd), in which all viral coding sequences are removed from the genome, leaving only the cis-acting ITRs and packaging sequences, providing up to 34 kb of transgene capacity. Although HDAds are replication incompetent, their innate immunogenicity remains intact. Therefore, the HDAd is an ideal cancer gene therapy vector as its infection results in anti-viral immune stimulation that can be enhanced or redirected towards the tumor via transgene expression. Co-infection of tumor cells with an oncolytic Ad and an HDAd results in tumor cell lysis and amplification of HDAd-encoded transgene expression. Here, we describe an HDAd-based cancer gene therapy expressing multiple classes of immunomodulatory molecules to simultaneously stimulate multiple axes of immune pathways: the HydrAd. Overall, the HydrAd platform represents a promising cancer immunotherapy agent against complex solid tumors.

Expansion of Candidate HPV-Specific T Cells in the Tumor Microenvironment during Chemoradiotherapy Is Prognostic in HPV16+ Cancers.

Human papillomavirus (HPV) infection causes 600,000 new cancers worldwide each year. HPV-related cancers express the oncogenic proteins E6 and E7, which could serve as tumor-specific antigens. It is not known whether immunity to E6 and E7 evolves during chemoradiotherapy or affects survival. Using T cells from 2 HPV16+ patients, we conducted functional T-cell assays to identify candidate HPV-specific T cells and common T-cell receptor motifs, which we then analyzed across 86 patients with HPV-related cancers. The HPV-specific clones and E7-related T-cell receptor motifs expanded in the tumor microenvironment over the course of treatment, whereas non-HPV-specific T cells did not. In HPV16+ patients, improved recurrence-free survival was associated with HPV-responsive T-cell expansion during chemoradiotherapy.

Current development in adenoviral vectors for cancer immunotherapy.

Adenoviruses are well characterized and thus easily modified to generate oncolytic vectors that directly lyse tumor cells and can be "armed" with transgenes to promote lysis, antigen presentation, and immunostimulation. Oncolytic adenoviruses (OAds) are safe, versatile, and potent immunostimulants in patients. Since transgene expression is restricted to the tumor, adenoviral transgenes overcome the toxicities and short half-life of systemically administered cytokines, immune checkpoint blockade molecules, and bispecific T cell engagers. While OAds expressing immunostimulatory molecules ("armed" OAds) have demonstrated anti-tumor potential in preclinical solid tumor models, the efficacy has not translated into significant clinical outcomes as a monotherapy. However, OAds synergize with established standards of care and novel immunotherapeutic agents, providing a multifaceted means to address complexities associated with solid tumors. Critically, armed OAds revitalize endogenous and adoptively transferred immune cells while simultaneously enhancing their anti-tumor function. To properly evaluate these novel vectors and reduce the gap in the cycle between bench-to-bedside and back, improving model systems must be a priority. The future of OAds will involve a multidimensional approach that provides immunostimulatory molecules, immune checkpoint blockade, and/or immune engagers in concert with endogenous and exogenous immune cells to initiate durable and comprehensive anti-tumor responses.

Diversity and composition of gut microbiome of cervical cancer patients: Do results of 16S rRNA sequencing and whole genome sequencing approaches align?

BACKGROUND: Next generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. RESULTS: Comparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all P-value>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables. CONCLUSION: Diversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S.

A prospective study of the adaptive changes in the gut microbiome during standard-of-care chemoradiotherapy for gynecologic cancers.

BACKGROUND: A diverse and abundant gut microbiome can improve cancer patients' treatment response; however, the effect of pelvic chemoradiotherapy (CRT) on gut diversity and composition is unclear. The purpose of this prospective study was to identify changes in the diversity and composition of the gut microbiome during and after pelvic CRT. MATERIALS AND METHODS: Rectal swabs from 58 women with cervical, vaginal, or vulvar cancer from two institutions were prospectively analyzed before CRT (baseline), during CRT (weeks 1, 3, and 5), and at first follow-up (week 12) using 16Sv4 rRNA gene sequencing of the V4 hypervariable region of the bacterial 16S rRNA marker gene. 42 of these patients received antibiotics during the study period. Observed operational taxonomic units (OTUs; representative of richness) and Shannon, Simpson, Inverse Simpson, and Fisher diversity indices were used to characterize alpha (within-sample) diversity. Changes over time were assessed using a paired t-test, repeated measures ANOVA, and linear mixed modeling. Compositional changes in specific bacteria over time were evaluated using linear discriminant analysis effect size. RESULTS: Gut microbiome richness and diversity levels continually decreased throughout CRT (mean Shannon diversity index, 2.52 vs. 2.91; all P <0.01), but were at or near baseline levels in 60% of patients by week 12. Patients with higher gut diversity at baseline had the steepest decline in gut microbiome diversity. Gut microbiome composition was significantly altered during CRT, with increases in Proteobacteria and decreases in Clostridiales, but adapted after CRT, with increases in Bacteroides species. CONCLUSION: After CRT, the diversity of the gut microbiomes in this population tended to return to baseline levels by the 12 week follow-up period, but structure and composition remained significantly altered. These changes should be considered when designing studies to analyze the gut microbiome in patients who receive pelvic CRT for gynecologic cancers.

Gut microbiome diversity is an independent predictor of survival in cervical cancer patients receiving chemoradiation.

Diversity of the gut microbiome is associated with higher response rates for cancer patients receiving immunotherapy but has not been investigated in patients receiving radiation therapy. Additionally, current studies investigating the gut microbiome and outcomes in cancer patients may not have adjusted for established risk factors. Here, we sought to determine if diversity and composition of the gut microbiome was independently associated with survival in cervical cancer patients receiving chemoradiation. Our study demonstrates that the diversity of gut microbiota is associated with a favorable response to chemoradiation. Additionally, compositional variation among patients correlated with short term and long-term survival. Short term survivor fecal samples were significantly enriched in Porphyromonas, Porphyromonadaceae, and Dialister, whereas long term survivor samples were significantly enriched in Escherichia Shigella, Enterobacteriaceae, and Enterobacteriales. Moreover, analysis of immune cells from cervical tumor brush samples by flow cytometry revealed that patients with a high microbiome diversity had increased tumor infiltration of CD4+ lymphocytes as well as activated subsets of CD4 cells expressing ki67+ and CD69+ over the course of radiation therapy. Modulation of the gut microbiota before chemoradiation might provide an alternative way to enhance treatment efficacy and improve treatment outcomes in cervical cancer patients.

Tumor microbial diversity and compositional differences among women in Botswana with high-grade cervical dysplasia and cervical cancer.

INTRODUCTION: We characterized the cervical 16S rDNA microbiome of patients in Botswana with high-grade cervical dysplasia and locally advanced cervical cancer. METHODS: This prospective study included 31 patients: 21 with dysplasia and 10 with cancer. The Shannon diversity index was used to evaluate alpha (intra-sample) diversity, while the UniFrac (weighted and unweighted) and Bray-Curtis distances were employed to evaluate beta (inter-sample) diversity. The relative abundance of microbial taxa was compared among samples using linear discriminant analysis effect size. RESULTS: Alpha diversity was significantly higher in patients with cervical cancer than in patients with cervical dysplasia (P<0.05). Beta diversity also differed significantly (weighted UniFrac Bray-Curtis, P<0.01). Neither alpha diversity (P=0.8) nor beta diversity (P=0.19) varied by HIV status. The results of linear discriminant analysis effect size demonstrated that multiple taxa differed significantly between patients with cervical dysplasia vs cancer. Lachnospira bacteria (in the Clostridia class) were particularly enriched among cervical dysplasia patients, while Proteobacteria (members of the Firmicutes phyla and the Comamonadaceae family) were enriched in patients with cervical cancer. DISCUSSION: The results of our study suggest that differences exist in the diversity and composition of the cervical microbiota between patients with cervical dysplasia and patients with cervical cancer in Botswana. Additional studies are warranted to validate these findings and elucidate their clinical significance among women living in sub-Saharan Africa, as well as other regions of the world.

Microbial Diversity and Composition Is Associated with Patient-Reported Toxicity during Chemoradiation Therapy for Cervical Cancer.

PURPOSE: Patients receiving pelvic radiation for cervical cancer experience high rates of acute gastrointestinal (GI) toxicity. The association of changes in the gut microbiome with bowel toxicity from radiation is not well characterized. METHODS AND MATERIALS: Thirty-five patients undergoing definitive chemoradiation therapy (CRT) underwent longitudinal sampling (baseline and weeks 1, 3, and 5) of the gut microbiome and prospective assessment of patient-reported GI toxicity. DNA was isolated from stool obtained at rectal examination and analyzed with 16S rRNA sequencing. GI toxicity was assessed with the Expanded Prostate Cancer Index Composite instrument to evaluate frequency, urgency, and discomfort associated with bowel function. Shannon diversity index was used to characterize alpha (within sample) diversity. Weighted UniFrac principle coordinates analysis was used to compare beta (between sample) diversity between samples using permutational multivariate analysis of variance. Linear discriminant analysis effect size highlighted microbial features that best distinguish categorized patient samples. RESULTS: Gut microbiome diversity continuously decreased over the course of CRT, with the largest decrease at week 5. Expanded Prostate Cancer Index Composite bowel function scores also declined over the course of treatment, reflecting increased symptom burden. At all individual time points, higher diversity of the gut microbiome was linearly correlated with better patient-reported GI function, but baseline diversity was not predictive of eventual outcome. Patients with high toxicity demonstrated different compositional changes during CRT in addition to compositional differences in Clostridia species. CONCLUSIONS: Over time, increased radiation toxicity is associated with decreased gut microbiome diversity. Baseline diversity is not predictive of end-of-treatment bowel toxicity, but composition may identify patients at risk for developing high toxicity.

CancerDiscover: an integrative pipeline for cancer biomarker and cancer class prediction from high-throughput sequencing data.

Accurate identification of cancer biomarkers and classification of cancer type and subtype from High Throughput Sequencing (HTS) data is a challenging problem because it requires manual processing of raw HTS data from various sequencing platforms, quality control, and normalization, which are both tedious and time-consuming. Machine learning techniques for cancer class prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. To date, great research efforts have been taken for cancer biomarker identification and cancer class prediction. However, currently available tools and pipelines lack flexibility in data preprocessing, running multiple feature selection methods and learning algorithms, therefore, developing a freely available and easy-to-use program is strongly demanded by researchers. Here, we propose CancerDiscover, an integrative open-source software pipeline that allows users to automatically and efficiently process large high-throughput raw datasets, normalize, and selects best performing features from multiple feature selection algorithms. Additionally, the integrative pipeline lets users apply different feature thresholds to identify cancer biomarkers and build various training models to distinguish different types and subtypes of cancer. The open-source software is available at https://github.com/HelikarLab/CancerDiscover and is free for use under the GPL3 license.

Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers.

Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent "OMICS" studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier. Given a sample of a specified tissue type, the single-tissue models classified samples as cancer or normal with a testing accuracy between 85.29% and 100%. Given a sample of non-specific tissue type, the multi-tissue bi-class model classified the sample as cancer versus normal with a testing accuracy of 97.89%. Given a sample of non-specific tissue type, the multi-tissue multi-class model classified the sample as cancer versus normal and as a specific tissue type with a testing accuracy of 97.43%. Given a normal sample of any of the nine tissue types, the multi-tissue normal model classified the sample as a particular tissue type with a testing accuracy of 97.35%. The machine learning classifiers developed in this study identify potential cancer biomarkers with sensitivity and specificity that exceed those of existing biomarkers and pointed to pathways that are critical to tissue-specific tumor development. This study demonstrates the feasibility of predicting the tissue origin of carcinoma in the context of multiple cancer classes.