Integrated multiomic approach for identification of novel immunotherapeutic targets in AML

Abstract

Background Immunotherapy of acute myeloid leukemia has experienced considerable advances, however novel target antigens continue to be sought after. To this end, unbiased approaches for surface protein detection are limited and integration with other data types, such as gene expression and somatic mutational burden, are poorly utilized. The Cell Surface Capture technology provides an unbiased, discovery-driven approach to map the surface proteins on cells of interest. Yet, direct utilization of primary patient samples has been limited by the considerable number of viable cells needed. Methods Here, we optimized the Cell Surface Capture protocol to enable direct interrogation of primary patient samples and applied our optimized protocol to a set of samples from patients with acute myeloid leukemia (AML) to generate the AML surfaceome. We then further curated this AML surfaceome to exclude antigens expressed on healthy tissues and integrated mutational burden data from hematologic cancers to further enrich for targets which are likely to be essential to leukemia biology. Finally, we validated our findings in a separate cohort of AML patient samples. Results Our protocol modifications allowed us to double the yield in identified proteins and increased the specificity from 54 to 80.4% compared to previous approaches. Using primary AML patient samples, we were able to identify a total of 621 surface proteins comprising the AML surfaceome. We integrated this data with gene expression and mutational burden data to curate a set of robust putative target antigens. Seventy-six proteins were selected as potential candidates for further investigation of which we validated the most promising novel candidate markers, and identified CD148, ITGA4 and Integrin beta-7 as promising targets in AML. Integrin beta-7 showed the most promising combination of expression in patient AML samples, and low or absent expression on healthy hematopoietic tissue. Conclusion Taken together, we demonstrate the feasibility of a highly optimized surfaceome detection method to interrogate the entire AML surfaceome directly from primary patient samples and integrate this data with gene expression and mutational burden data to achieve a robust, multiomic target identification platform. This approach has the potential to accelerate the unbiased target identification for immunotherapy of AML.

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis

“…Overexpression of CD339/JAG1 in APL was the only consistent finding in the two datasets, in line with previous reports including both gene expression data from RNA-Seq and microarray analyses [ 27 ] or flow cytometry studies [ 28 ] Of note, differential expression in APL and other AML subtypes cannot be deduced from these earlier studies for all CD genes identified by our calculations, such as CD84 [ 29 ], CD88 [ 30 ], CD148 [ 23 , 31 ], CD210A/IL10RA, and CD227 [ 32 , 33 ]. However, all of these genes have been detected in cells from all hematopoietic lineages or AML blasts, and CD148 and CD210 have even been recently suggested as novel immunotherapeutic targets in AML [ 34 ], except forCD261/TNFRSF10A which we did not find in previous publications pointing to implications in AML or APL.…”

The Black Hole: CAR T cell Therapy in AML

“…They identified three promising targets: CD148, ITGA4 and Integrin beta-7. Among these, Integrin beta-7 was the most favorable due to low or absent expression in healthy hematopoietic tissues [61]. In a recent scRNA-sec approach, two antigen targets-CSF1R and CD86 revealed potential targets for CAR T-cell therapy with broad expression on AML blasts accompanied by minimal toxicities toward relevant healthy cells and tissues [62].…”

Big data and single‐cell sequencing in acute myeloid leukemia research

“…42 Lin et al 138 confirmed that Glutamate-cysteine ligase catalytic (GCLC) is essential for cell growth, survival, clonogenicity, and leukemogenesis of AML cells but not for normal hematopoietic stem and progenitor cells (HSPCs), indicating that GCLC is a potential therapeutic target for AML. Köhnke et al 139 demonstrated a surfaceome detection method to investigate the whole AML surfaceome directly from raw patient samples and integrate these data with gene expression and mutational burden data, allowing for unbiased, genome-wide screening for target discovery in AML immunotherapy. Besides, the study of Ravasio et al 140 supported the use of LSD1i in combination therapy.…”

“…Besides, the integration of gene expression data and other relevant information can also yield valuable discoveries. 113,139,159,169 After integration, various analysis tools can be used to perform the analysis easier and more conveniently. Some AML studies utilized tools such as cBioPortal, Metascape, LinkedOmics, 150 and GEPIA, 11 while others require customized models and corresponding statistical or bioinformatics techniques to meet specific research needs.…”

“…Inevitably, we also need to use clinical and demographic information to obtain information about the patient's specific physical manifestations and to investigate external factors, as seen in the studies of Ma et al, 11 Nicora et al, 142 Wang et al, 143 and Shouval et al 157 The studies of Pölönen et al, 168 Malani et al, 147 Wang, et al, 143 and Chen et al 4 showed that the inclusion of molecular profiles and drug profiling data in data integration is also a common demand in AML research. Besides, the integration of gene expression data and other relevant information can also yield valuable discoveries 113,139,159,169 …”