Search

Four independent international groups have demonstrated that the pre-treatment gut microbiome of cancer patients is associated with the subsequent response to treatment with Immune Checkpoint Inhibitors (ICI) [1-4]. However, each study identified different bacteria as being linked to outcome, which has limited the development of drug response biomarkers and novel microbiome-based therapeutics. Here we describe the identification of a microbial signature predictive of response to ICI across multiple melanoma studies, and a derived Live Bacterial Therapeutic with potent anti-tumour activity. MELRESIST is a single centre, prospective melanoma patient data and biosample collection research study. We collected longitudinal stool samples from 69 patients with advanced melanoma who received standard anti-PD-1+/- anti-CTLA-4 antibodies. Shotgun metagenomic sequencing analysis of the baseline stool microbiome was done using Microbiotica's platform, which comprises the world's leading Reference Genome Database to give the most comprehensive and precise mapping of gut microbiomes. Using 6 months progression-free survival as our cut-off for response, the analysis revealed a small but discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing another 3 melanoma patient stool sample sequence datasets [1-3] using the Microbiotica platform, and a machine learning-based bioinformatic model. The resultant bacterial signature accurately predicted response when all 4 studies when combined (91%), as well as when the cohorts were analysed individually (82-100%). We validated the model using independent cohorts and the signature using NSCLC and Renal Cell Carcinoma (RCC) datasets [4]. The latter indicated the bacteria associated with response may differ slightly between indications. At the core of the signature was 9 bacteria that were all overrepresented in patients that responded to ICI treatment. Notably as a consortium, these 9 bacteria demonstrated tumor growth inhibition when dosed in a syngeneic mouse model. These strains also stimulate primary immune cells in vitro leading to tumor cell killing. In summary, we have identified a microbiome biomarker that is predictive of response to ICI treatment in multiple clinical studies from different countries. In addition, a unique set of bacteria derived from the signature has great therapeutic potential in combination with ICIs. References 1 Matson V et al Science (2018) 359:104 2 Gopalakrishnan V Science (2018) 359:97 3 Frankel AE et al Neoplasia (2017) 19:848 4 Routy B et al Science (2018) 359:91 Citation Format: Matthew J. Robinson, Kevin Vervier, Simon Harris, Roy Rabbie, Doreen Milne, Catherine Booth, Christine Parkinson, Sarah J. Welsh, David Bruce, Emily Barker, David Adams, Pippa Corrie, Trevor D. Lawley. Precision microbiome profiling identifies a novel biomarker predictive of Immune Checkpoint Inhibitor response in multiple cohorts and a potent therapeutic consortium of bacteria [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1783.

Purpose: Adjuvant therapies prolong survival in patients with stage III melanoma. However, biomarkers are needed to stratify patients with primary melanoma at highest risk for metastases which could help minimize exposure to potentially irreversible toxicities and allow for rational clinical trial designs in the adjuvant setting. Methods: We analyzed data from 194 RNA-sequenced primary cutaneous melanomas from patients with stage IIB-IIIC disease recruited to the multicenter AVAST-M phase III randomized trial. By undertaking covariate-corrected differential expression between patients experiencing distant metastasis (n=89) versus no-metastases (n=105), we identified metastasis-associated genes of which 121 were externally validated and made up our predictive signature, “Cam_121”. Several machine learning classification models were trained using nested leave-one-out cross validation (LOOCV) to test the signature's capacity to predict metastases. Univariate and multivariate Cox proportional hazard regression survival analyses were performed. The signatures' predictive accuracy was further externally validated in an independent population-controlled cohort study measuring melanoma-specific survival (Leeds Melanoma Cohort, n=687). Results: The signature distinguished patients with distant recurrence from those without across multiple machine learning models (sensitivity=0.64, specificity=0.79, accuracy=0.72, kappa=0.43) and performed significantly better than any of the models trained with the clinical covariates alone (pAccuracy =4.92x10-3), as well as those trained with predictive signatures selected from two published datasets (Decision-Dx MelanomaTM and Leeds Melanoma Cohort 150 genes). The signature also correlated with progression-free survival (PFS), overall survival (OS) and melanoma-specific survival (MSS) while retaining its predictive accuracy following multivariate correction (PFS: HR=0.49 (0.35-0.69), p=2.8x10-5, OS: HR=0.6 (0.42-0.86), p=0.005 and MSS: HR=0.57, p=8x10-5). Importantly, we found that the median signature expression score positively correlated with measures of immune cell infiltration, with a lower score representing a poorer tumor lymphocytic infiltration and worse long-term prognosis. Conclusions: We have identified Cam_121 a primary melanoma expression signature that outperforms currently available predictive signatures. The signature confirms (using unbiased approaches) the central prognostic importance of immune cell infiltration in long-term patient outcomes and could help identify primary melanoma patients at highest risk of metastases and poor survival who might benefit most from adjuvant therapies. Citation Format: Manik Garg, Dominique-Laurent Courturier, Nuno A. Fonseca, Matthew Wongchenko, Yibing Yan, Jeremie Nsengimana, Tim Bishop, Julia Newton-Bishop, Mark Middleton, Pippa Corrie, David J. Adams, Alvis Brazma, Roy Rabbie. Primary tumor gene expression signature predicts long-term outcomes in primary melanoma: A prospective multicenter study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-274.