Eavesdropping on altered cell-to-cell signaling in cancer by secretome profiling

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

A robust strategy for proteomic identification of biomarkers of invasive phenotype complexed with extracellular heat shock proteins

As an extension of their orchestration of intracellular pathways, secretion of extracellular heat shock proteins (HSPs) is an emerging paradigm of homeostasis imperative to multicellular organization. Extracellular HSP is axiomatic to the survival of cells during tumorigenesis; proportional representation of specific HSP family members is indicative of invasive potential and prognosis. Further significance has been added by the knowledge that all cancer-derived exosomes have surface-exposed HSPs that reflect the membrane topology of cells that secrete them. Extracellular HSPs are also characteristic of chronic inflammation and sepsis. Accordingly, interrogation of extracellular HSPs secreted from cell culture models may represent a facile means of identifying translational biomarker signatures for targeting in situ. In the current study, we evaluated a simple peptide-based multivalent HSP affinity approach using the Vn96 peptide for low speed pelleting of HSP complexes from bioreactor cultures of cell lines with varying invasive phenotype in xenotransplant models: U87 (glioblastoma multiforme; invasive); HELA (choriocarcinoma; minimally invasive); HEK293T (virally transformed immortalized; embryonic). Proteomic profiling by bottom-up mass spectrometry revealed a comprehensive range of candidate biomarkers including primary HSP ligands. HSP complexes were associated with additional chaperones of prognostic significance such as protein disulfide isomerases, as well as pleiotropic metabolic enzymes, established as proportionally reflective of invasive phenotype. Biomarkers of inflammatory and mechanotransductive phenotype were restricted to the most invasive cell model U87, including chitinase CHI3L1, lamin C, amyloid derivatives, and histone isoforms.

Cell-Cell Mechanical Communication in Cancer

Communication between cancer cells enables cancer progression and metastasis. While cell-cell communication in cancer has primarily been examined through chemical mechanisms, recent evidence suggests that mechanical communication through cell-cell junctions and cell-ECM linkages is also an important mediator of cancer progression. Cancer and stromal cells remodel the ECM through a variety of mechanisms, including matrix degradation, cross-linking, deposition, and physical remodeling. Cancer cells sense these mechanical environmental changes through cell-matrix adhesion complexes and subsequently alter their tension between both neighboring cells and the surrounding matrix, thereby altering the force landscape within the microenvironment. This communication not only allows cancer cells to communicate with each other, but allows stromal cells to communicate with cancer cells through matrix remodeling. Here, we review the mechanisms of intercellular force transmission, the subsequent matrix remodeling, and the implications of this mechanical communication on cancer progression.

Melanoma exosomes deliver a complex biological payload that upregulates PTPN11 to suppress T lymphocyte function

As exosomes are emerging as a new mode of intercellular communication, we hypothesized that the payload contained within exosomes is shaped by somatic evolution. To test this, we assayed the impact on primary CD8+ T-cell function, a key mechanism for antitumor immunity, of exosomes derived from three melanoma-related cell lines. While morphologically similar, exosomes from each cell line were functionally different, as B16F0 exosomes dose-dependently suppressed T-cell proliferation. In contrast, Cloudman S91 exosomes promoted T-cell proliferation and Melan-A exosomes had a negligible effect on primary CD8+ T cells. Mechanistically, transcript profiling suggested that exosomal mRNA is enriched for full-length mRNAs that target immune-related pathways. Interestingly, B16F0 exosomes were unique in that they contained both protein and mRNA for PTPN11, which inhibited T-cell proliferation. Collectively, the results suggest that upregulation of PTPN11 by B16F0 exosomes to tumor infiltrating lymphocytes would bypass the extracellular control of the immune checkpoints.

Inferring the Impact of Regulatory Mechanisms that Underpin CD8+ T Cell Control of B16 Tumor Growth In vivo Using Mechanistic Models and Simulation

A major barrier for broadening the efficacy of immunotherapies for cancer is identifying key mechanisms that limit the efficacy of tumor infiltrating lymphocytes. Yet, identifying these mechanisms using human samples and mouse models for cancer remains a challenge. While interactions between cancer and the immune system are dynamic and non-linear, identifying the relative roles that biological components play in regulating anti-tumor immunity commonly relies on human intuition alone, which can be limited by cognitive biases. To assist natural intuition, modeling and simulation play an emerging role in identifying therapeutic mechanisms. To illustrate the approach, we developed a multi-scale mechanistic model to describe the control of tumor growth by a primary response of CD8+ T cells against defined tumor antigens using the B16 C57Bl/6 mouse model for malignant melanoma. The mechanistic model was calibrated to data obtained following adenovirus-based immunization and validated to data obtained following adoptive transfer of transgenic CD8+ T cells. More importantly, we use simulation to test whether the postulated network topology, that is the modeled biological components and their associated interactions, is sufficient to capture the observed anti-tumor immune response. Given the available data, the simulation results also provided a statistical basis for quantifying the relative importance of different mechanisms that underpin CD8+ T cell control of B16F10 growth. By identifying conditions where the postulated network topology is incomplete, we illustrate how this approach can be used as part of an iterative design-build-test cycle to expand the predictive power of the model.