scholarly journals 673 Precision microbiome mapping identifies a microbiome signature predictive of Immune checkpoint inhibitor response across multiple research study cohorts

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A711-A711
Author(s):  
Matthew Robinson ◽  
Kevin Vervier ◽  
Simon Harris ◽  
David Adams ◽  
Doreen Milne ◽  
...  

BackgroundThe gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies.MethodsLongitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform.ResultsAnalysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders.ConclusionsAnalysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs.Ethics ApprovalThe study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312.ReferencesMatson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108.Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103.Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855.Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21546-e21546
Author(s):  
Mat Robinson ◽  
Kevin Vervier ◽  
Amy Popple ◽  
Simon Harris ◽  
Robyne Hudson ◽  
...  

e21546 Background: Four independent groups have demonstrated that the pre-treatment gut microbiome of cancer patients impacts the subsequent response to Immune Checkpoint Inhibitor (ICIs) therapy [1-4]. However, the patient’s outcome was linked to different bacteria in each study, which has limited the development of drug response biomarkers and clinic-first design of novel microbiome-based therapeutics. Methods: The Cambridge (UK) MELRESIST study includes a cohort of advanced melanoma patients receiving approved ICIs. Pretreatment stool samples from MELRESIST were analysed by Microbiotica using shotgun metagenomic sequencing. Microbiotica’s platform comprises the world’s leading Reference Genome Database to give the most comprehensive and precise mapping of the gut microbiome. Results: MELRESIST samples showed an overall difference in the microbiome composition between advanced melanoma patients and healthy donors, but not between patients who did or did not respond to ICIs. However, we did identify a discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing three published melanoma cohorts [1-3] using the Microbiotica platform, and a propriety bioinformatic model. The resultant bacterial signature was very accurate at predicting response in all 4 published studies combined (91%), and each cohort individually (82-100%). We validated the model using independent validation cohorts and the signature using lung and renal cancer studies [4]. At the core of our microbiome signature was 9 bacteria most significantly associated with ICI efficacy. All 9 were overrepresented in patients who responded to immunotherapy suggesting high abundance of these bacteria is a central driver of ICI response. A consortium comprised of all 9 strains had very potent anti-tumor efficacy in a cancer syngeneic mouse model. The bacteria also demonstrate multiple interactions with primary human immune cells in vitro leading to dendritic cells activation, Cytotoxic T lymphocyte activation and tumor cell killing. These validate the potential of this consortium as a novel therapy for use in combination with ICIs. Conclusions: We have identified a unique microbiome signature predictive of ICI response in 4 independent melanoma cancer cohorts. This removes a major challenge to the field, and could represent a new highly accurate biomarker with clinical application. Nine core bacteria appear to be driving response, and demonstrate anti-tumor activity in vivo and in vitro. This consortium holds great potential as a co-therapy with ICIs. References:1 Matson V et al, Science (2018) 359:104; 2 Gopalakrishnan V et al, Science (2018) 359:97; 3 Frankel AE et al, Neoplasia (2017) 19:848; 4 Routy B et al, Science (2018) 359:91.


2022 ◽  
Vol 12 ◽  
Author(s):  
Wenjing Zhang ◽  
Yujia Kong ◽  
Yuting Li ◽  
Fuyan Shi ◽  
Juncheng Lyu ◽  
...  

BackgroundImmune checkpoint inhibitor (ICI) therapy dramatically prolongs melanoma survival. Currently, the identified ICI markers are sometimes ineffective. The objective of this study was to identify novel determinants of ICI efficacy.MethodsWe comprehensively curated pretreatment somatic mutational profiles and clinical information from 631 melanoma patients who received blockade therapy of immune checkpoints (i.e., CTLA-4, PD-1/PD-L1, or a combination). Significantly mutated genes (SMGs), mutational signatures, and potential molecular subtypes were determined. Their association with ICI responses was assessed simultaneously.ResultsWe identified 27 SMGs, including four novel SMGs (COL3A1, NRAS, NARS2, and DCC) that are associated with ICI efficacy and well-known driver genes. COL3A1 mutations were associated with improved ICI overall survival (hazard ratio (HR): 0.64, 95% CI: 0.45–0.91, p = 0.012), whereas immune resistance was observed in patients with NRAS mutations (HR: 1.42, 95% CI: 1.10–1.82, p = 0.006). The presence of the tobacco smoking-related signature was significantly correlated with inferior prognoses (HR: 1.42, 95% CI: 1.11–1.82, p = 0.005). In addition, the signature resembling that of alkylating agents and a newly discovered signature both exhibited extended prognoses (both HR < 1, p < 0.05). Based on the activities of the extracted 6 mutational signatures, we identified one immune subtype that was significantly associated with better ICI outcomes (HR: 0.44, 95% CI: 0.23–0.87, p = 0.017).ConclusionWe uncovered several novel SMGs and re-annotated mutational signatures that are linked to immunotherapy response or resistance. In addition, an immune subtype was found to exhibit favorable prognoses. Further studies are required to validate these findings.


2019 ◽  
Vol 58 (1) ◽  
pp. 18-24 ◽  
Author(s):  
Sok-Ja Janket ◽  
Leland K. Ackerson ◽  
Eleftherios P. Diamandis

Abstract As the largest immune organ, human gut microbiome could influence the efficacy of immune checkpoint inhibitor therapy (ICI). However, identifying contributory microbes from over 35,000 species is virtually impossible and the identified microbes are not consistent among studies. The reason for the disparity may be that the microbes found in feces are markers of other factors that link immune response and microbiotas. Notably, gut microbiome is influenced by stool consistency, diet and other lifestyle factors. Therefore, the ICI and microbiotas relationship must be adjusted for potential confounders and analyzed longitudinally. Moreover, a recent study where 11 low-abundance commensal bacteria induced interferon-γ-producing CD8 T cells, challenges the validity of the abundance-oriented microbiotas investigations. This study also confirmed the hierarchy in immunogenic roles among microbiotas. Fecal transplantation trials in germ-free mice provided “the proof of principle” that germ-free mice reproduce the donor’s microbiome and corresponding ICI efficacy. However, species-specific biological differences prevent direct extrapolation between the results in murine and human models. Fecal transplantation or supplementation with microbes found in ICI responders requires caution due to potential adverse events.


PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230306 ◽  
Author(s):  
Thomas T. DeLeon ◽  
Daniel R. Almquist ◽  
Benjamin R. Kipp ◽  
Blake T. Langlais ◽  
Aaron Mangold ◽  
...  

2018 ◽  
Vol 41 (3) ◽  
pp. 101-108 ◽  
Author(s):  
Parul Tandon ◽  
Samuel Bourassa-Blanchette ◽  
Kirles Bishay ◽  
Simon Parlow ◽  
Scott A. Laurie ◽  
...  

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