We here report a large-scale comparative meta-analysis of genomics and gene expression signatures of immune checkpoint blockade (ICB) responses in over 3,500 patients across 12 tumor types. Non-synonymous tumor mutational burden (nsTMB), as well as 22 out of 39 reported gene expression signatures, were significantly associated with pan-cancer ICB responses. Strikingly, the predictive value of a de novo gene expression signature (PredictIO_100), composed of the top 100 genes most significantly associated with ICB responses at pan-cancer level, was superior to nsTMB and other gene expression signatures. Two genes within PredictIO_100, F2RL1 (encoding protease-activated receptor-2) and RBFOX2 (encoding RNA binding motif protein 9), were concurrently associated with worse ICB clinical outcomes, T cell dysfunction in ICB-naive patients and resistance to dual PD-1/CTLA-4 blockade in preclinical models. Our study underlines the relative impact of candidate biomarkers of ICB responses and identifies F2RL1 and RBFOX2 as potential therapeutic targets to overcome ICB resistance.
Abstract Purpose: We developed Label-It, a new web-application for rapid review of radiotherapy (RT) target volumes, and used it to evaluate the relationship between target delineation compliance to the international guidelines and treatment outcomes of nasopharyngeal carcinoma (NPC) patients undergoing definitive RT. Methods and Materials: Our radiographic image database consists of anonymized simulation CT scans, RT structures, and treatment data of 3,211 head and neck cancer patients treated between July 2005 and August 2017. We used 332 patients treated with intensity-modulated RT for pathologically confirmed NPC as the study cohort and imported intermediate risk clinical target volumes of the primary tumor (IR-CTVp) receiving 56 Gy into Label-It. We determined inclusion of anatomic sites within IR-CTVp in accordance with 2018 International guideline for CTV delineation for NPC and correlated the results with time to local failure (TTLF) using Cox-regression. Results: At a median follow-up of 5.6 years, 5-year TTLF and overall survival rates were 93.1% and 85.9% respectively. The most frequently non-compliant anatomic sites were sphenoid sinus (n = 69, 20.8%), followed by cavernous sinus (n = 38, 11.4%), left and right petrous apices (n = 37 and 32, 11.1% and 9.6%), clivus (n = 14, 4.2%), and right and left foramen rotundum (n = 14 and 12, 4.2% and 3.6%). Among 23 patients with a local failure (6.9%), the number of non-compliant cases were 8 for sphenoid sinus, 7 cavernous sinus, 4 left and 3 right petrous apices, and 2 clivus. In Cox regression analysis, T4 disease (p = 0.003), RT alone (p = 0.007), cavernous sinus non-conformity (p = 0.020) were independent prognostic factors for TTLF. Conclusions: Label-It was an effective tool for rapid review of target volumes in a large patient cohort. Despite a high compliance to the international guidelines, inadequate coverage of cavernous sinus was correlated with decreased TTLF. Citation Format: Jun Won Kim, Joseph Marsilla, Michal Kazmierski, Denis Tkachuk, Benjamin Haibe-Kains, Andrew Hope. Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-051.
This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, ToxicoGx (https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html), and are available publicly in the ToxicoDB web application (www.toxicodb.ca).
This data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413. The data have been curated and analyzed using our open-source R package, ToxicoGx (cran.r-project.org/web/packages/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca).
In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.
This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, ToxicoGx (http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html), and are available publicly in the ToxicoDB web application (www.toxicodb.ca).
This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, ToxicoGx (http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html), and are available publicly in the ToxicoDB web application (www.toxicodb.ca).
This data set contains the anonymised PDX growth curves from the paper KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts. bioRxiv 2020.09.08.287573.
This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, ToxicoGx (http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html) and are available publicly in the ToxicoDB web application (www.toxicodb.ca).
