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

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

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

The Tumor Microbiome Reacts to Hypoxia and Can Influence Response to Radiation Treatment in Colorectal Cancer.

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiation's ability to kill cells. We hypothesized that some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. Therefore, we examined the RNA sequencing data from the Oncology Research Information Exchange Network database of patients with colorectal cancer treated with radiotherapy. We identified microbial RNAs for each tumor and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To experimentally investigate this concept, we implanted CT26 colorectal cancer cells into immune-competent BALB/c and immune-deficient athymic nude mice. After growth, in which tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed a metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-tropic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Thus, hypoxia gene expression scores seem to associate with different microbial populations and elicit an adaptive transcriptional response in intratumoral microbes, potentially influencing clinical outcomes. SIGNIFICANCE: Tumor hypoxia reduces radiotherapy efficacy. In this study, we explored whether some of the clinical effects of hypoxia could be due to interaction with the tumor microbiome. Hypoxic gene expression scores associated with certain microbes and elicited an adaptive transcriptional response in others that could contribute to poor clinical outcomes.

Patient reported outcomes and patient experiences of immune checkpoint modulators for advanced or recurrent melanoma: a mixed methods study.

PURPOSE: Little is known about late and long-term patient-reported outcomes (PROs) of immune checkpoint modulators (ICMs) outside clinical trials. We conducted a cross-sectional, mixed-methods study to describe long-term PROs among advanced melanoma patients who began standard of care treatment with ICMs at least 1 year previously. METHODS: All participants completed the Functional Assessment of Cancer Therapy-Immune Checkpoint Modulator (FACT-ICM), assessing 46 immune-related side effects on a 5-point Likert scale, and a subset completed individual interviews. Descriptive statistics were computed for quantitative data and applied thematic analysis was used to examine qualitative data. RESULTS: Participants (N = 80) had a mean age of 67 years, and the majority were male (66%), non-Hispanic White (96%), and college graduates (61%). Single-agent nivolumab was the most common first (47%) and current/recent ICM (64%). On the FACT-ICM, 98% of participants reported at least one side effect, and 78% reported moderate or severe side effects. The most common moderate or severe side effects were aching joints (43%) and fatigue (38%). In interviews (n = 20), we identified five themes regarding patients' longer-term experiences after ICMs: lasting fatigue or decline in functioning, minimal side effects, manageable thyroid and pituitary dysfunction, skin conditions can be difficult to manage, and treating the cancer is worth the side effects. CONCLUSIONS: Nearly all patients reported side effects of ICMs at least 1 year after starting treatment. Our findings suggest that ICM side effect screening and management-especially for aching joints and fatigue-are indicated during long-term care of people living with advanced melanoma.

Jagged2 targeting in lung cancer activates anti-tumor immunity via Notch-induced functional reprogramming of tumor-associated macrophages.

Signaling through Notch receptors intrinsically regulates tumor cell development and growth. Here, we studied the role of the Notch ligand Jagged2 on immune evasion in non-small cell lung cancer (NSCLC). Higher expression of JAG2 in NSCLC negatively correlated with survival. In NSCLC pre-clinical models, deletion of Jag2, but not Jag1, in cancer cells attenuated tumor growth and activated protective anti-tumor T cell responses. Jag2-/- lung tumors exhibited higher frequencies of macrophages that expressed immunostimulatory mediators and triggered T cell-dependent anti-tumor immunity. Mechanistically, Jag2 ablation promoted Nr4a-mediated induction of Notch ligands DLL1/4 on cancer cells. DLL1/4-initiated Notch1/2 signaling in macrophages induced the expression of transcription factor IRF4 and macrophage immunostimulatory functionality. IRF4 expression was required for the anti-tumor effects of Jag2 deletion in lung tumors. Antibody targeting of Jagged2 inhibited tumor growth and activated IRF4-driven macrophage-mediated anti-tumor immunity. Thus, Jagged2 orchestrates immunosuppressive systems in NSCLC that can be overcome to incite macrophage-mediated anti-tumor immunity.

First-in-Human Stage III/IV Melanoma Clinical Trial of Immune Priming Agent IFx-Hu2.0.

IFx-Hu2.0 was designed to encode part of the Emm55 protein contained within a plasmid in a formulation intended for transfection into mammalian cells. IFx-Hu2.0 promotes both adaptive and innate immune responses in animal studies. Furthermore, previous studies have demonstrated safety/efficacy in equine, canine, and murine species. We present the first-in-human study of IFx-Hu2.0, administered by intralesional injection into melanoma tumors of seven patients with stage III/IV unresectable melanoma. No dose-limiting toxicities attributable to IFx-Hu2.0 were observed. Grade 1/2 injection site reactions were observed in five of seven patients. IgG and IgM responses to Emm55 peptides and known melanoma antigens were seen in the peripheral blood, suggesting that IFx-Hu2.0 acts as an individualized "in situ vaccine." Three of four patients previously refractory to anti-PD1 experienced clinical benefit upon subsequent anti-PD1-based treatment. Therefore, this approach is feasible, and clinical/correlative outcomes warrant further investigation for treating patients with metastatic melanoma with an immune priming agent.

Treatment of Stage III Resectable Melanoma-Adjuvant and Neoadjuvant Approaches.

ABSTRACT: Patients with stage III resectable melanoma carry a high risk of melanoma recurrence that ranges from approximately 40% to 90% at 5 years following surgical management alone. Postoperative systemic adjuvant therapy targets residual micrometastatic disease that could be the source of future recurrence and death from melanoma. Randomized phase III adjuvant trials reported significant improvements in overall survival with high-dose interferon α in 2 of 3 studies (compared with observation and GMK ganglioside vaccine) and with anti-cytotoxic T-lymphocyte antigen 4 ipilimumab at 10 mg/kg compared with placebo and ipilimumab 3 mg/kg compared with high-dose interferon α. In the modern era, more recent phase III trials demonstrated significant recurrence-free survival improvements with anti-programmed cell death protein 1, pembrolizumab, and BRAF-MEK inhibitor combination dabrafenib-trametinib (for BRAF mutant melanoma) versus placebo. Furthermore, anti-programmed cell death protein 1, nivolumab and pembrolizumab have both been shown to significantly improve recurrence-free survival as compared with ipilimumab 10 mg/kg. For melanoma patients with clinically or radiologically detectable locoregionally advanced disease, emerging data support an important role for preoperative systemic neoadjuvant therapy. Importantly, a recent cooperative group trial (S1801) reported superior event-free survival rates with neoadjuvant versus adjuvant therapy. Collectively, current data from neoadjuvant immunotherapy and targeted therapy trials support a future change in clinical practice in favor of neoadjuvant therapy for eligible melanoma patients.

Comparing the rate of immunotherapy treatment change due to toxicity by sex.

BACKGROUND: Immuno-oncology therapy (IO) is associated with a variety of treatment-related toxicities. However, the impact of toxicity on the treatment discontinuation rate between males and females is unknown. We hypothesized that immune-related adverse events would lead to more frequent treatment changes in females since autoimmune diseases occur more frequently in females. AIMS: Our aim was to determine if there was a difference in the rate of immunotherapy treatment change due to toxicity between males and females. METHODS AND RESULTS: The Oncology Research Information Exchange Network Avatar Database collected clinical data from 10 United States cancer centers. Of 1035 patients receiving IO, 447 were analyzed, excluding those who did not have documentation noting if a patient changed treatment (n = 573). Fifteen patients with unknown or gender-specific cancer were excluded. All cancer types and stages were included. The primary endpoint was documented treatment change due to toxicity. Four hundred and forty-seven patients (281 males and 166 females) received IO treatment. The most common cancers treated were kidney, skin, and lung for 99, 84, and 54 patients, respectively. Females had a shorter IO course than males (median 3.7 vs. 5.1 months, respectively, p = .02). Fifty-four patients changed treatment due to toxicity. There was no significant difference between females and males on chi-square test (11.4% vs. 12.5%, respectively, p = 0.75) and multivariable logistic regression (OR 0.924, 95% CI 0.453-1.885, p = .827). Significantly more patients with chronic obstructive pulmonary disease (COPD) changed therapy due to toxicity (OR 2.491, 95% CI 1.025-6.054, p = .044). CONCLUSION: Females received a shorter course of IO than males. However, there was no significant difference in the treatment discontinuation rate due to toxicity between males and females receiving IO. Toxicity-related treatment change was associated with COPD.

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.