In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 4225-4225
Abstract:
Checkpoint inhibitors have significantly accelerated cancer treatment but still a majority of patients do not respond. Biomarker driven patient stratification early to the right immunotherapeutic might enhance response and patient survival. Here we used high-dimensional mass cytometry (CyTOF) combined with machine-learning bioinformatics for the in-depth characterization of immune responses before and during anti-PD-1 immunotherapy. CyTOF allows us to monitor protein expression of 34 markers on a single cell while running 20 samples simultaneously. The analysis is data driven, can be adapted to high throughput approaches and can model arbitrary trial designs such as batch effects and paired designs and is quantitative over millions of events. Using CyTOF as a precision medicine tool we could predict response to anti-PD-1 using liquid blood biopsies. Biobanked peripheral blood mononuclear cells (PBMCs) from 51 patients with stage IV melanoma before and after 12 weeks of anti-PD-1 therapy was analyzed. We observed a clear T cell response on therapy. The most evident difference in responders before therapy was an enhanced frequency of CD14+ CD16+HLA-DRhi classical monocytes. We validated our results using conventional flow and found a clear correlation of enhanced monocyte frequencies before therapy initiation with clinical response such as lower hazard and extended progression-free and overall survival. In a second study we used CyTOF to monitor immune response in 21 non small cell lung cancer (NSCLC) patients that initially responded and then progressed under anti-PD-1 to a novel combination immunotherapy of anti-PD-1 plus an IL-15 super-agonist (ALT-803). In this phase Ib clinical study a response in the CD8+ T cell compartment was observed. Unexpected our high dimensional unbiased analysis was able to detect and characterize a strong expansion of innate tumor-reactive effector NK cells starting around day 4 of therapy. Taken together, our unbiased artificial intelligence driven immune workflow might support patient selection prior to therapy, and serve as a novel tool for precision medicine to select the right drug combination and identify new drug-able cell populations. Citation Format: Carsten Krieg, Luis Cardenas, Silvia Guglietta, John Wrangle, Mark Rubinstein, Mark Robinson. Is biomarker-driven precision medicine possible by using high dimensional augmented intelligence assisted analysis of cancer immune responses [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 4225.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2019-4225
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
