Abstract 2713: RAS pathway activity inference using activity measurement tools with RNA-seq in blood cancer
Authors: Yoo Seung-Joo , Shin Dong-Yeop , Byun Ja Min , Koh Youngil , Yoon Sung-Soo 
Published: June 2022

It is well known that variations of RAS oncogene appear in various cancers. Most variations in the RAS make role of GTPase abnormal active. Accordingly, many studies have been rencently conducted and inhibitor of KRAS G12C has been approved by Food and Drug Administration(FDA). However, further research on RAS pathway is still needed due to the limitation of cancer types and. In addition, treatment of the RAS pathway in blood cancer, as well as solid carcinoma, is necessary. To search for novel therapeutic targets, we first investigated hyperactivation of the pathways of RAS-mutated patients using activity measurement tools. We used normalized expression information from RNA-seq of AML and MM patients from the public database; The Cancer Genome Atlas (TCGA) project, Multiple Myeloma Research Foundation(MMRF) project. We identified the degree of RAS pathway and other downstream pathway activation. Among various pathway activity inference tools, we used two R-based tools named PROGENy(based on experimental results) and pathifier(based on principal component analysis and Kyoto Encyclopedia of Genes and Genomes database) and compared scores from the tools in same samples. In addition, applying mutation profiling data, the activity scores were cross-checked against status of whether RAS is altered or not. In TCGA-LAML(n=179) and MMRF dataset(n=762), there were 23 and 155 samples that matched with RNA-sequencing and also have RAS (KRAS and NRAS) variation. Setting a cutoff for ‘high score’ as same as a percentage of RAS-altered samples in each dataset(LAML=12.8%, MMRF=20.3%), we found that many RAS-altered samples were not included in group of the high score. Only 26% and 21.7% (PROGENy, pathifier) of the RAS-altered samples were classified into high scores in TCGA-LAML, and 29.0% and 18.7% (PROGENy, pathifier) in MMRF. Also, we found several cases where PROGENy and pathifier showed opposite scores even though they were calculated using the same sample. We counted the number of cases where PROGENy and pathifier corresponded to the outer range of the first and third quantiles and had opposite values ​​to each other; with MAPK(6.1% and 21.5%), PI3K(2.7% and 12.3%) and JAK-STAT(13.9% and 11.6%) in the TCGA and MMRF datasets. Accordingly, combination of PROGENy and pathifier resulted in the more enrichment of high score samples (LAML: 43.4% and MMRF: 44.5%, with MAPK pathway) in RAS-altered samples suggesting potential of ensemble to develop new classifier. In conclusion, we identified the pathway activity scores of the AML and RAS patient based on RAS mutation. Comparing the scores from different two activity inference tools, we confirmed that each tool could display different scores according to the pathway and that not all RAS-altered samples had higher activity scores in related pathways. Considering these results, we think that a more accurate inference approach is required by combining the measurement tools.

Citation Format: Seung-Joo Yoo, Dong-Yeop Shin, Ja Min Byun, Youngil Koh, Sung-Soo Yoon. RAS pathway activity inference using activity measurement tools with RNA-seq in blood cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2713.