In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 3374-3374
Abstract:
Mutations acquired during oncogenesis can result in so-called neoantigens, i.e. mutation-bearing peptides that have the potential to be bound by HLA molecules and recognized by T cells. Neoantigens were suggested to be crucial for the outcome of immune checkpoint therapies and accordingly, the neoantigen load of a tumor is often being used to predict therapy outcome. Current approaches utilize HLA binding predictions to rank peptides, assuming that neoepitopes are ranked towards the top. The neoantigen load of a given tumor is then usually defined as the number of peptides binding above a certain threshold. Using this approach some reports found a very strong correlation of neoantigen load and response to checkpoint blockade therapy while others found a better correlation when mutational load was used instead. However, comparing results between studies is hard because they used different tools and thresholds to identify mutations, and applied different criteria to prioritize their set of mutated peptides. We sought to overcome this lack of uniformity in re-analyzing data from several published checkpoint blockade therapy studies to consistently define the mutational burden and neoantigen load, and correlate them with therapy outcomes We obtained sequencing data from checkpoint blockade studies deposited in NCBI dbGaP and run all datasets through our in-house mapping and variant calling pipelines. Neoepitope candidates were predicted and scored using our previously published pipeline. We applied different scoring schemes to define the neoantigen load which were based on HLA binding predictions with varying thresholds and different metrics used (i.e. predicted IC50 vs. percentile rank). We found that the exact definition of neoantigen load has a major impact on how well this metric performs in predicting checkpoint blockade therapy outcome. Furthermore, we implemented a likelihood score which is based on data of known immunogenic neoantigens and describes the likelihood of a predicted neoantigen to be immunogenic. Based on our preliminary analysis to date, we found that the cumulative likelihood score of a patient is a good predictor of checkpoint blockade therapy outcome, outperforming tumor mutational burden and predicted neoantigen load in several cohorts. Citation Format: Zeynep Kosaloglu-Yalcin, Angela Frentzen, Ashmitaa Logandha Ramamoorthy Premlal, Jason Greenbaum, Alessandro Sette, Bjoern Peters. Assessing the impact of neoantigen load on checkpoint blockade efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3374.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2019-3374
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2019
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
