Ovarian cancer is especially deadly, challenging to treat, and has proven refractory to known immunotherapies. Cytokine therapy is an attractive strategy to drive a proinflammatory immune response in immunologically cold tumors such as many high grade ovarian cancers; however, this strategy has been limited in the past due to severe toxicity. We previously demonstrated the use of a layer‐by‐layer (LbL) nanoparticle (NP) delivery vehicle in subcutaneous flank tumors to reduce the toxicity of interleukin‐12 (IL‐12) therapy upon intratumoral injection. However, ovarian cancer cannot be treated by local injection as it presents as dispersed metastases. Herein, we demonstrate the use of systemically delivered LbL NPs using a cancer cell membrane‐binding outer layer to effectively target and engage the adaptive immune system as a treatment in multiple orthotopic ovarian tumor models, including immunologically cold tumors. IL‐12 therapy from systemically delivered LbL NPs shows reduced severe toxicity and maintained anti‐tumor efficacy compared to carrier‐free IL‐12 or layer‐free liposomal NPs leading to a 30% complete survival rate.
- Award ID(s):
- 1660703
- PAR ID:
- 10276098
- Date Published:
- Journal Name:
- APL bioengineering
- ISSN:
- 2473-2877
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Motivation Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.
Results Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells.
Availability and implementation R codes can be found at https://github.com/petraf01/TSNet.
Supplementary information Supplementary data are available at Bioinformatics online.
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Accepted Manuscript1.0
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