In:
Cancer Immunology Research, American Association for Cancer Research (AACR), Vol. 8, No. 4_Supplement ( 2020-04-01), p. IA09-IA09
Kurzfassung:
The emergence of immuno-oncology (IO) agents has led to unprecedented successes in the treatment of deadly cancers and sometimes even cures. These agents act by “releasing the brakes” and thereby unleashing the full potential of cells that fight tumors. Sadly, not every patient benefits from these new treatments. We need to know who will benefit and who may not. Here we approached the problem by designing a customized workflow by using high-dimensional single-cell mass cytometry (CyTOF) combined with machine-learning bioinformatics for the in-depth characterization of single immune cells in predicting and monitoring immune responses. 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. We tested our workflow on two studies: a) Predict response to anti-PD-1 immunotherapy in melanoma. In our discovery cohort peripheral blood mononuclear cells (PBMCs) from 20 patients with stage IV melanoma before and after 12 weeks of anti-PD-1 therapy were 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 cytometry in an independent exploratory cohort of 31 patients before therapy. Finally, we correlated enhanced monocyte frequencies before therapy initiation with clinical response and could show association with lower hazard, extended progression-free and overall survival. b) Monitoring the immune response in anti-PD-1 immunotherapy-refractory non-small cell lung cancer to a novel combination immunotherapy of anti-PD-1 and an IL-15 superagonist. Twenty-one patients with non-small cell lung cancer who mostly were refractory to previous anti-PD-1 treatment received a novel combination therapy of IL-15 superagonist plus anti-PD-1. A response in the CD8+ T cell compartment was observed. Unexpectedly, our high-dimensional approach 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, select the right drug combination, and identify new druggable cell populations. Citation Format: Luis Cardenas, Silvia Guglietta, John Wrangle, Mark Rubinstein, Mark Robinson, Carsten Krieg. Using high-dimensional machine-assisted analysis for biomarkers detection and guidance of immunotherapy to cancer [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr IA09.
Materialart:
Online-Ressource
ISSN:
2326-6066
,
2326-6074
DOI:
10.1158/2326-6074.TUMIMM18-IA09
Sprache:
Englisch
Verlag:
American Association for Cancer Research (AACR)
Publikationsdatum:
2020
ZDB Id:
2732517-9
