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Background: Incorporating gene fusions into a comprehensive profile is critical not only because very effective therapies targeting oncogenic fusion proteins exist but they may also negate the response to therapy of an actionable SNV and InDel mutations. We examined the co-occurrence of actionable gene fusions detected by RNA-seq with actionable SNVs/InDels and with the immunotherapy response biomarker, PD-L1. Methods: In 2021, 5341 FFPE samples were analyzed by our clinical laboratory using a novel hybridization-based RNA sequencing assay. DNA (SNV/Indels) mutations were detected with a clinical grade NGS assay and PD-L1 protein expression was determined by IHC using an appropriate LDT or FDA approved assay. De-identified data were analyzed following an approved IRB protocol. Results: Of the 5341 patients tested for gene fusions only 0.7% were profiled with a comprehensive fusion detection panel for 250 clinically relevant fusion genes. Conversely, 67% of all patients were tested only for NTRK gene fusions. The prevalence of the following most relevant fusions was: ALK=3.16% (in particular EML4-ALK), NTRKs=0.85%, FGFR2=5.60%, RET=1.34% and ROS1=1.05%. Among NTRK fusions, NTRK3 was detected in 52.9% of the positive cases; in particular, dominant fusions are ETV6-NTRK3 (26%) and EML4-NTRK3 (20%). In the 163 fusion positive cases, 37 cases had available DNA mutation testing and PD-L1 expression testing. These patients presented 24 different actionable fusions, including ALK (2), FGFR1/2 (5), NTRK 1/2/3 (10), NRG1 (3), RET (3), and ROS1 (3) fusions. Interestingly, while 73% (27/37) of those tumors were PD-L1 positive, similar to the 75% found on the fusion negative samples, PD-L1 was positive in 88% (15/17) of the lung samples with pathogenic mutations from this subset. This was strikingly higher than the 45% (192/424) found in the fusion negative cohort. Finally, excluding TP53 mutations, NTRK fusions frequently co-occurred with RNF43, FBXW7, TERT promoter, and ARID1A pathogenic mutations. FGFR fusions co-occurred with BAP1, PBRM1, or KRAS mutations, which correlates with the fact that both FGFR2 fusions and swi/snf alterations are enriched in intrahepatic cholangiocarcinoma, where the FGFR fusions were detected. RET fusions co-occurred with ARID1A and TERT. ROS1 fusions co-occurred with SMARCA4 and KRAS pathogenic mutations simultaneously. NRG1 fusions co-occurred with ARID1A and FBXW7 mutations. EML4-ALK fusions co-occurred with FGFR2 and KMT2D variants of unknown significance. Conclusion: Actionable cancer-driving gene fusions were detected by RNA-sequencing and co-occurred with other biomarkers that can guide the selection of therapies, such as immune checkpoint inhibitors (ICI) and olaparib. The data suggest that gene fusion testing is an important addition to the genomic profiling for therapy selection in solid tumors. Citation Format: Miyoung Shin, Paris Petersen, Steven Lau-Rivera, Segun Jung, Sally Agersborg, Jacyln Hechtman, Fernando Lopez-Diaz, Vincent Funari. Actionable fusions detected by RNA-seq co-occur with PD-L1 expression and driver mutations in solid tumors patients [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 1254.

Stratification of cancer patients into different molecular groups is essential for developing targeted therapies. High-throughput technologies, such as microarrays and next-generation sequencing, have been extensively used for generating multi-omics data. Indeed, The Cancer Genome Atlas (TCGA) consortium, one of the most comprehensive and popular databases of cancer, has been accumulating large volumes of invaluable data for more than 30 cancer types, offering unprecedented opportunity to attain new insights in cancer biology. Despite the technological advances, analyzing, integrating and translating the gene signatures across different platforms remains a computationally challenging task. Here, we developed a novel computational framework that integrates genomic and clinical data to stratify cancer patients into different molecular subgroups and predict clinically applicable phenotypes, such as survival. Application of this user-friendly framework derives platform-independent isoform-level gene signatures that can be translated from high-dimensional platforms (e.g., RNA-Seq) to clinically adaptable low-dimensional molecular assays (e.g., RT-PCR) for prognostic stratification. We applied the pipeline on two TCGA lung cancer datasets—Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). Using independent test data, we achieved about 93% (LUAD) and 98% (LUSC) classification accuracy using less than 70 isoform-level gene signature. The proposed informatics platform is applicable to other cancer types in TCGA data portal. Citation Format: Segun Jung, Yingtao Bi, Ramana V. Davuluri. Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-47.

Stratification of cancer patients into different molecular groups is essential for developing targeted therapies. High-throughput technologies, such as microarrays and next-generation sequencing, have been extensively used for generating multi-omics data. Indeed, The Cancer Genome Atlas (TCGA) consortium, one of the most comprehensive and popular databases of cancer, has been accumulating large volumes of invaluable data for more than 30 cancer types, offering unprecedented opportunity to attain new insights in cancer biology. Despite the technological advances, analyzing, integrating and translating the gene signatures across different platforms remains a computationally challenging task. Here, we developed a novel computational framework that integrates genomic and clinical data to stratify cancer patients into different molecular subgroups and predict clinically applicable phenotypes, such as survival. Application of this user-friendly framework derives platform-independent isoform-level gene signatures that can be translated from high-dimensional platforms (e.g., RNA-Seq) to clinically adaptable low-dimensional molecular assays (e.g., RT-PCR) for prognostic stratification. We applied the pipeline on two TCGA lung cancer datasets_Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). Using independent test data, we achieved about 93% (LUAD) and 98% (LUSC) classification accuracy using less than 70 isoform-level gene signature. The proposed informatics platform is applicable to other cancer types in TCGA data portal. Note: This abstract was not presented at the meeting. Citation Format: Segun Jung, Yingtao Bi, Ramana V. Davuluri. Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2185. doi:10.1158/1538-7445.AM2015-2185