Project 1 - Structure-informed dissection of cancer-specific intracellular and paracrine networks.

Investigators: Barry Honig (Project Leader), Mohammed Al Quraishi, Diana Murray, Andrea Califano, Gordana Vunjak-Novakovic

Understanding the cell-autonomous behavior of cancer cells and their interaction with the tumor microenvironment (TME) represents a critical challenge to the identification and therapeutic targeting of mechanism-based tumor dependencies. Addressing this challenge is increasingly dependent on the availability of accurate, comprehensive, and cell context-specific network models representing both intra-cellular and cell-cell molecular interactions. 

To further improve modeling of intra and extra-cellular interactions, Project 1 will build on the methodologies and results generated during the previous CSBC funding period. These include: (a) expanding structure-informed prediction of protein-protein interaction (PPI) networks by leveraging novel deep learning techniques, such as AlphaFold, (b) critically improving our understanding of signal transduction networks, based on the analysis of time-dependent drug perturbation assays, and (c) elucidating ligand/receptor-mediated paracrine interaction networks that mediate recruitment—and possibly reprogramming—of healthy cells to the TME, to create a promalignant (e.g., immunosuppressive) environment, thus also supporting the studies described in Project 2. To accomplish these goals, without loss of generality, we will focus on two aggressive tumors—colon adenocarcinoma (COAD) and pancreatic ductal adenocarcinoma (PDAC)—for which data, models, reagents, and analytical tools are already available from our previous CaST-related studies, thus providing continuity to our investigations. Our goals include:

• Improving the structure-based prediction of PPI networks and development of structure-based Cancer Hallmark signaling maps. Interactions between protein structured domains and peptides will be predicted by novel algorithms based on Natural Language Processing (1) and integrated with structure-informed protein-protein interactions to create the CaST Center proteome-wide Reference Interactome. By leveraging the OncoSig algorithm (2), the Reference Interactome will support a concerted effort to extend our understanding of Cancer Hallmarks and tumorigenic processes mediated by context-specific signal transduction networks, using a repertoire of 3D structure-enabled methodologies.
• Reverse engineering and interrogation of the integrated transcriptional and signal transduction networks that mediate the cell’s response to drug perturbations. Multi-omics, time-dependent profiles, including phosphoproteomic and transcriptional profiles of COAD cell lines, following perturbation with clinically relevant small molecule inhibitors, will provide improved context-specific signaling networks for the study of downstream effects due to paracrine interactions.
• Achieving a more holistic view of tumors as complex, neomorphic organs comprising multiple, co-existing, mutually interacting subpopulations of malignant and healthy cells. We will develop a detailed, structure- and function-based model of the ligand/receptor-mediated molecular interactions between malignant and stromal cells. For proof of concept, we will leverage PDAC as the archetypal, stroma-rich tumor. Validation of relevant paracrine interactions driving critical pro-malignant processes will be performed using an innovative Organs-on-a-chip (OOC) technology (3, 4), which allows modulation of individual variables in co-existing tumor and stromal cell subpopulations.

Taken together, these studies will create critical missing knowledge to improve the study of cancer phenotypes via network-based methodologies. Thus, the overall deliverable of Project 1 is a novel, integrated, and extensively validated predictive framework for modeling ligand/receptor and drug-mediated interactions in cancer, as well as the complex signaling cascades they induce.

References

1. Cunningham JM, Koytiger G, Sorger PK, AlQuraishi M. Biophysical prediction of protein-peptide interactions and signaling networks using machine learning. Nat Methods. 2020;17(2):175-83. Epub 20200106. doi: 10.1038/s41592-019-0687-1. PubMed PMID: 31907444; PMCID: PMC7004877.

2. Broyde J, Simpson DR, Murray D, Paull EO, Chu BW, Tagore S, Jones SJ, Griffin AT, Giorgi FM, Lachmann A, Jackson P, Sweet-Cordero EA, Honig B, Califano A. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nat Biotechnol. 2021;39(2):215-24. Epub 20200914. doi: 10.1038/s41587-020-0652-7. PubMed PMID: 32929263; PMCID: PMC7878435.

3. Vunjak-Novakovic G, Ronaldson-Bouchard K, Radisic M. Organs-on-a-chip models for biological research. Cell. 2021;184(18):4597-611. doi: 10.1016/j.cell.2021.08.005. PubMed PMID: 34478657; PMCID: PMC8417425.

4. Ronaldson-Bouchard K, Vunjak-Novakovic G. Organs-on-a-Chip: A Fast Track for Engineered Human Tissues in Drug Development. Cell Stem Cell. 2018;22(3):310-24. doi: 10.1016/j.stem.2018.02.011. PubMed PMID: 29499151; PMCID: PMC5837068.