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Poster session 01

117P - BiomeOne: Multi-centric validation of a novel microbiome-based biomarker to predict response to cancer immunotherapy

Date

10 Sep 2022

Session

Poster session 01

Topics

Global Cancer Statistics;  Tumour Immunology;  Immunotherapy

Tumour Site

Non-Small Cell Lung Cancer

Presenters

Irina Robinson

Citation

Annals of Oncology (2022) 33 (suppl_7): S27-S54. 10.1016/annonc/annonc1037

Authors

I. Robinson1, M. Schmidinger2, M.J. Hochmair1, L. Ay3, G. Absenger4, M. Pichler4, V.A.F. Nguyen5, E. Richtig6, B. Rainer6, C. Jansen7, B. Sladek7, A. Knabl7, N. Gasche7, A. Valipour3

Author affiliations

  • 1 Respiratory Oncology Unit, Department Of Respiratory And Critical Care Medicine, Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, 1210 - Vienna/AT
  • 2 Department Of Urology, Comprehensive Cancer Center, Vienna General Hospital (AKH), Vienna Medical University, 1090 - Vienna/AT
  • 3 Respiratory Oncology Unit, Department Of Respiratory And Critical Care Medicine, Karl Landsteiner Institute of Lung Research and Pulmonary Oncology, Klinik Floridsdorf, Vienna/AT
  • 4 Clinical Department Of Internal Medicine, Division Of Oncology, Medical University of Graz, 8036 - Graz/AT
  • 5 Dermatology Department, Landeskrankenhaus - Universitaetskliniken Innsbruck, 6020 - Innsbruck/AT
  • 6 Dermatology Dept., Universitätsklinik für Dermatologie und Venerologie, 8036 - Graz/AT
  • 7 Scientific Team, Biome Diagnostics GmbH, 1200 - Wien/AT
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Abstract 117P

Background

The intestinal microbiome has a substantial influence on the cancer related response to immune checkpoint inhibitor (ICI ) therapy. Biome Diagnostics (BiomeDx) has developed a microbiome-based prediction test to ICI response. The biomarker was developed using datasets from stool samples of previously studied patients with non-small-lung-cancer (NSCLC), renal cell cancer (RCC), melanoma, and a healthy cohort (n=8000).

Methods

Stool samples were taken prior to ICI treatment initiation with a sampling kit using Norgen® tubes. Genetic sequencing of intestinal microbiota was performed with Illumina MiSeq® at a central laboratory. 1400 species were identified which were then transformed into a robust low-dimensional representation using an autoencoder. Machine learning (ML) algorithms were applied to determine a specific microbial signature, named BiomeOne®. Clinical response was assessed at the end of first-line therapy. In this study, responders were classified as complete and partial responders. Best response was compared with the outcome of BiomeOne® analysis.

Results

In this multi-centric study, 67 patients (29 f, 38m) encompassing NSCLC (42), RCC (15) and Melanoma (10) patients were enrolled. Patients were treated with ICIs targeting CTLA-4, PD-1 or PD-L1 as well as combinations with TKIs. Antibiotic therapy within 30 days prior to treatment was not allowed. Overall, 63 including 42 responders and 21 non-responders completed first-line treatment. BiomeOne® was able to identify responders to ICI treatment with a sensitivity of 81% and a positive predictive value of 77%. ML analysis did not depict significant differences between the 3 tumour types, suggesting that the biomarker is tumour agnostic.

Conclusions

The presented research demonstrates the potential of BiomeOne® as a novel, non-invasive test to predict the outcome of ICI therapy. Further research will be necessary to expand the scope of BiomeOne® to other tumour entities.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Biome Diagnostics GmbH.

Disclosure

A. Valipour: Non-Financial Interests, Personal, Advisory Board: Biome Diagnostics. All other authors have declared no conflicts of interest.

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