Background: Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lags in disease-specific RNA biomarkers due in part to its paucity of large discrete tumors. A strategy to overcome this challenge is to identify blood-based RNA biomarkers that are minimally invasive and reflect the state of both the diseased breast tissue and the patient's immune response. Here, we identified IBC-specific RNA biomarkers by thermostable group II intron reverse transcriptase sequencing (TGIRT-seq), a recently developed comprehensive RNA-seq technology that enables simultaneous profiling of all RNA biotypes from small amounts of starting material. We used these biomarkers to develop novel disease classification models for IBC based on coding and non-coding RNAs from FFPE tumor slices, PBMCs, and plasma. Methods: We obtained biological samples including FFPE, PBMC, and plasma from a cohort of ten patients with IBC and compared them to samples from six patients with non-IBC and sixteen healthy donors using TGIRT-seq technology. Results: TGIRT-seq of FFPE tumor slices identified differentially expressed mRNAs and miRNAs found previously to distinguish IBC from non-IBC tumors, as well as numerous additional differentially expressed mRNAs and small non-coding RNAs characteristic of IBC. Surprisingly, TGIRT-seq revealed that the differentially expressed protein-coding gene transcripts fall into two categories: mature mRNAs with reads confined to exons, and pre-mRNAs-derived transcripts with reads distributed across exons and introns, to our knowledge, a distinction not made previously for any cancer type. Differentially expressed miRNAs included both mature miRNAs and other transcripts of miRNA loci. IBC PBMCs showed a characteristic inflammatory response not seen in PBMCs from non-IBC patients, as well as differentially expressed tRNAs, snoRNAs, and other sncRNAs, while plasma samples, although of variable quality, included coding and non-coding RNAs distinctive of IBC. Classification models using panels consisting of sets of 50 selected biomarkers profiled by TGIRT-seq achieved a high degree of accuracy under cross-validation, with models based on PBMCs and plasma RNAs correlating with those based on tumor RNAs, and models using both coding and non-coding RNA biomarkers outperforming those based on either alone. Conclusions: Our findings are the first to define a distinct IBC profile across three different tissue types and advance TGIRT-seq as a promising method for high-resolution RNA biomarker profiling of both primary tumors and liquid biopsies with potentially broad utility for diagnosing and defining treatment response in IBC and other cancers. COI: Thermostable group II intron reverse transcriptase (TGIRT) enzymes and methods for their use are the subject of patents and patent applications that have been licensed by the University of Texas to InGex, LLC. A.M.L., some former and present members of the Lambowitz laboratory, and the University of Texas are minority equity holders in InGex, and receive royalty payments from the sale of TGIRT enzymes and kits and from sublicensing of intellectual property to other companies.

Citation Format: Dennis C. Wylie, Xiaoping Wang, Jun Yao, Hengyi Xu, Toshiaki Iwase, Savitri Krishnamurthy, Naoto T. Ueno, Alan M. Lambowitz. Disease classification modeling of inflammatory breast cancer based on simultaneous profiling of coding and non-coding RNAs in tumor and blood samples by TGIRT-sequencing [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-07-03.