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.

Idiopathic pulmonary fibrosis (IPF) is a pathological condition wherein lung injury precipitates the deposition of scar tissue, ultimately leading to a decline in pulmonary function. Existing research indicates a notable exacerbation in the clinical prognosis of IPF patients following infection with COVID-19. This investigation employed bulk RNA-sequencing methodologies to describe the transcriptomic profiles of small airway cell cultures derived from IPF and post-COVID fibrosis patients. Differential gene expression analysis unveiled heightened activation of pathways associated with microtubule assembly and interferon signaling in IPF cell cultures. Conversely, post-COVID fibrosis cell cultures exhibited distinctive characteristics, including the upregulation of pathways linked to extracellular matrix remodeling, immune system response, and TGF-β1 signaling. Notably, BMP signaling levels were elevated in cell cultures derived from IPF patients compared to non-IPF control and post-COVID fibrosis samples. These findings underscore the molecular distinctions between IPF and post-COVID fibrosis, particularly in the context of signaling pathways associated with each condition. A better understanding of the underlying molecular mechanisms holds the promise of identifying potential therapeutic targets for future interventions in these diseases.

Obesity is associated with increased breast cancer risk and poorer cancer outcomes; however, the precise etiology of these observations has not been fully identified. Our previous research suggests that adipose tissue-derived fibroblast growth factor-2 (FGF2) promotes the malignant transformation of epithelial cells through the activation of fibroblast growth factor receptor-1 (FGFR1). FGF2 is increased in the context of obesity, and increased sera levels have been associated with endocrine-resistant breast cancer. Leptin is a marker of obesity and promotes breast carcinogenesis through several mechanisms. In this study, we leverage public gene expression datasets to evaluate the associations between FGFR1, leptin, and the leptin receptor (LepR) in breast cancer. We show a positive association between FGFR1 and leptin protein copy number in primary breast tumors. These observations coincided with a positive association between Janus kinase 2 (Jak2) mRNA with both leptin receptor (LepR) mRNA and FGFR1 mRNA. Moreover, two separate Jak2 inhibitors attenuated both leptin+FGF2-stimulated and mouse adipose tissue-stimulated MCF-10A transformation. These results demonstrate how elevated sera FGF2 and leptin in obese patients may promote cancer progression in tumors that express elevated FGFR1 and LepR through Jak2 signaling. Therefore, Jak2 is a potential therapeutic target for FGFR1 amplified breast cancer, especially in the context of obesity.

Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. Here, we evaluated commonly used machine learning methods and presented TransCell, a two-step deep transfer learning framework that utilized the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell had the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and had comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigated the predictive power of gene expression in six molecular measurement types and developed a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

Motivation: Bispecific antibodies (bsAbs) that bind to two distinct surface antigens on cancer cells are emerging as an appealing therapeutic strategy in cancer immunotherapy. However, considering the vast number of surface proteins, experimental identification of potential antigen pairs that are selectively expressed in cancer cells and not in normal cells is both costly and time-consuming. Recent studies have utilized large bulk RNA-seq databases to propose bispecific targets for various cancers. However, co-expressed pairs derived from bulk RNA-seq do not necessarily indicate true co-expression of both markers in malignant cells. Single-cell RNA-seq (scRNA-seq) can circumvent this issue but the issues in low coverage of transcripts impede the large-scale characterization of co-expressed pairs. Results: We present a computational pipeline for bsAbs target identification which combines the advantages of bulk and scRNA-seq while minimizing the issues associated with using these approaches separately. We select hepatocellular carcinoma (HCC) as a case study to demonstrate the utility of the approach. First, using the bulk RNA-seq samples in the OCTAD database, we identified target pairs that most distinctly differentiate tumor cases from healthy controls. Next, we confirmed our findings on the scRNA-seq database comprising 39 361 healthy cells from vital organs and 18 000 cells from HCC tumors. The top pair was GPC3–MUC13, where both genes are co-expressed on the surface of over 30% of malignant hepatocytes and have very low expression in other cells. Finally, we leveraged the emerging spatial transcriptomic to validate the co-expressed pair in situ. Availability and implementation: A standalone R package (https://github.com/Bin-Chen-Lab/bsAbsFinder).

Most BRAF-mutant melanoma tumors respond initially to BRAFi/MEKi therapy, although few patients have durable long-term responses to these agents. The goal of this study was to utilize an unbiased computational approach to identify inhibitors which reverse an experimentally derived BRAFi resistance gene expression signature. Using this approach, we found that ibrutinib effectively reverses this signature and we demonstrate experimentally that ibrutinib re-sensitizes a subset of BRAFi-resistant melanoma cells to vemurafenib. Ibrutinib is used clinically as a BTK inhibitor; however, neither BTK deletion nor treatment with acalabrutinib, another BTK inhibitor with reduced off-target activity, re-sensitized cells to vemurafenib. These data suggest that ibrutinib acts through a BTK-independent mechanism in vemurafenib re-sensitization. To better understand this mechanism, we analyzed the transcriptional profile of ibrutinib-treated BRAFi-resistant melanoma cells and found that the transcriptional profile of ibrutinib was highly similar to that of multiple SRC kinase inhibitors. Since ibrutinib, but not acalabrutinib, has significant off-target activity against multiple SRC family kinases, it suggests that ibrutinib may be acting through this mechanism. Furthermore, genes either upregulated or downregulated by ibrutinib treatment are enriched in YAP1 target genes and we showed that ibrutinib, but not acalabrutinib, reduces YAP1 activity in BRAFi-resistant melanoma cells. Taken together, these data suggest that ibrutinib, or other SRC family kinase inhibitors, may be useful for treating some BRAFi/MEKi-refractory melanoma tumors.