Reversal of cancer gene expression identifies repurposed drugs for diffuse intrinsic pontine glioma

Diffuse intrinsic pontine glioma (DIPG) is an aggressive incurable brainstem tumor that targets young children. Complete resection is not possible, and chemotherapy and radiotherapy are currently only palliative. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in subcutaneous xenograft mice models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.

Deciphering COVID-19 host transcriptomic complexity and variations for therapeutic discovery against new variants

The molecular manifestations of host cells responding to SARS-CoV-2 and its evolving variants of infection are vastly different across the studied models and conditions, imposing challenges for host-based antiviral drug discovery. Based on the postulation that antiviral drugs tend to reverse the global host gene expression induced by viral infection, we retrospectively evaluated hundreds of signatures derived from 1,700 published host transcriptomic profiles of SARS/MERS/SARS-CoV-2 infection using an iterative data-driven approach. A few of these signatures could be reversed by known anti-SARS-CoV-2 inhibitors, suggesting the potential of extrapolating the biology for new variant research. We discovered IMD-0354 as a promising candidate to reverse the signatures globally with nanomolar IC50 against SARS-CoV-2 and its five variants. IMD-0354 stimulated type I interferon antiviral response, inhibited viral entry, and down-regulated hijacked proteins. This study demonstrates that the conserved coronavirus signatures and the transcriptomic reversal approach that leverages polypharmacological effects could guide new variant therapeutic discovery.

Abstract 1198: Discovering novel bispecific antibody targets through the mining of large-scale bulk and single cell RNA-seq databases

Bispecific antibodies (bsAbs) that bind two distinct surface proteins in cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. Among thousands of surface proteins, experimentally identifying the best target pairs that are expressed only in cancer cells, but not in normal cells, is costly and time-consuming. The open bulk RNASeq and single-cell (sc)RNASeq offers a great resource to identify novel bispecific targets. Bulk RNASeq has been widely explored to identify therapeutic targets and biomarkers, resulting in voluminous data for various cancers and normal tissues, but the mixture of cell types in bulk RNASeq could not characterize the precise expression of targets in cancer cells, resulting in considerable false positives. scRNASeq provides a high resolution of target expression in individual cells, however, the challenges in solving the dropout issue and cell type classification hinder the direct use of scRNASeq in bsAbs target identification. Utilizing the OCTAD database consisting of over 20,000 bulk RNASeq samples, we proposed an approach that identifies target pairs that separate tumors from healthy the most, taking into account cluster heterogeneity, the distance between tumors and healthy as well as the angle between potential markers. Among the top pairs in Hepatocellular Carcinoma (HCC), CD33~PLVAP, for example, was a false positive because CD33 is myeloid and lymphoid cell lineage-specific. We thus assembled a scRNAseq database of healthy vital organs containing 39361 cells to aid selection. By comparing their expression with the expression of 18000 malignant cells predicted from 72000 cells of eight HCC samples, we identified target pairs that mostly express on the surface of the malignant HCC cells and had low or zero expression in vital organs. The most promising marker pair was GPC3~MUC13, presenting on the surface of over 30% of malignant HCC cells, with very low expression in vital organs. We further developed an R package to navigate the bsAbs target selection from open bulk RNASeq and scRNASeq.

Citation Format: Eugene Chekalin, Shreya Paithankar, Bin Chen. Discovering novel bispecific antibody targets through the mining of large-scale bulk and single cell RNA-seq databases [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 1198.