Biology subjects

Klinke, D. J.

Publications and source records attributed to Klinke, D. J..

11 recordsLinked to original sources

bioRxiv2026

Cell Communication Network factor 4 promotes tumor-induced immunosuppression in breast cancer.

Cell Communication Network factor 4 (CCN4/WISP1) is a matricellular protein secreted by cancer cells that is upregulated in essentially all invasive breast cancers and promotes immunosuppression in melanoma. Recent work suggests that limited anti-tumor immunity also associates with poor patient outcomes in patients with breast cancer. Motivated by increased CCN4 correlating with dampened anti-tumor immunity in primary breast cancer, we test for a direct causal link by knocking out CCN4 (CCN4 KO) in the Py230 and Py8119 mouse breast cancer models. Tumor growth is reduced when CCN4 KO breast cancer cells are implanted in immunocompetent but not in immunodeficient mice. Correspondingly in size-matched tumors, CD4+ and CD8+ T cells are significantly increased in CCN4 KO tumors while the myeloid compartment is shifted from polymorphonuclear- to monocytic-myeloid-derived suppressor cells (MDSC). This shift in the MDSC compartment is also associated with a significant reduction in splenomegaly. Among mechanisms linked to local immunosuppression, CCN4 knockout has a similar impact on the secretome of both breast cancer and melanoma cell lines. Overall, our results suggest that CCN4 promotes tumor-induced immunosuppression in the context of breast cancer and is a potential target for therapeutic combinations with immunotherapies.

cancer biology↗

bioRxiv2025

A functional comparison of two transplantable syngeneic mouse models of melanoma: B16F0 and YUMM1.7

The B16 murine melanoma cell lines are considered the gold standard for testing melanoma immunotherapies due to low treatment success rates. However, the clinical relevance of these models has been questioned due to a mutational landscape void of driver mutations typically seen in human melanomas and a tendency to form necrotic cores at high tumor volumes. Creating the YUMM1.7 line addressed these limitations by providing an additional contextually consistent model with a more clinically relevant genetic background. The combined use of both models can generate stronger studies in melanoma immunology and immunotherapy. However, to date, there have been no direct functional comparisons of the characteristics of these two models to inform the design of such studies. To address this, we conducted a series of functional experiments to characterize the kinetics of tumor growth, chemotherapeutic sensitivity, and immunogenicity of these models. We found that the B16F0 model had faster intrinsic tumor growth rates, was more susceptible to lysis by tumor-specific CD8+ T cells, and secreted higher levels of the angiogenic factors VEGF and Ang2. Meanwhile, the YUMM1.7 model was more sensitive to chemotherapeutic treatment, secreted higher levels of chemokines CCL2, CXCL1, and CX3CL1, and showed higher infiltration of lymphocyte and myeloid subsets at the same tumor size. Overall, YUMM1.7 model may be better suited for in vivo studies of mechanisms that require a wider observation window and intervention than the B16F0 model, such as immune response. However, angiogenesis and immunotherapy studies may benefit from a more in-depth comparative analyses of both models.

cancer biology↗

bioRxiv2022

Head-to-head comparison of CCN4, DNMT3A, PTPN11, and SPARC as suppressors of anti-tumor immunity

BackgroundEffective communication between innate and adaptive immunity is essential for mounting an effective antitumor immune response. Emergent cancer cells likely secrete factors that inhibit this communication. To identify such factors, we applied an in vitro workflow that coupled a functional assay with proteomics to a syngeneic mouse model of melanoma known to be resistant to immunotherapies. Collectively, these in vitro results suggested CCN4, DNMT3A, PTPN11, and SPARC as secreted factors that potentially mediate immunosuppression and could potentially become easily accessible targets for novel immunotherapies. The objective of this study was to test for consistent clinical correlates in existing human data and to verify in vivo whether knocking out tumor cell production of these secreted factors improved immune-mediated control of tumor growth. ResultsAnalyses of available human data indicate that high CCN4 expression is associated with reduced survival in primary melanoma patients. High DNMT3A, PTPN11, and SPARC expression showed no association with overall survival. In addition, scRNAseq data from patients with melanoma confirmed CCN4 is expressed by the tumor cells, as opposed to other cells in the tumor microenvironment. An experimental system was created using a CRISPR/Cas9 approach to generate knock out cell lines for each of the four genes of interest using the B16F0 mouse melanoma cell line. In a immunocompetent C57BL/6, DNMT3A, PTPN11, and SPARC knockouts had no effect on overall survival compared to mice challenged with wildtype B16F0 cells while the CCN4 knockout significantly increased survival. This increase in overall survival was lost when the CCN4 knockout cells were injected into severely immunocompromised NSG hosts, indicating that CCN4s effect on the tumor microenvironment is immune mediated. A kinetic analysis leveraging a Markov Chain Monte Carlo approach quantified the various knockouts effect on cells intrinsic growth rate. The analysis shows CCN4 is the only knockout tested that decreased the net proliferation rate in immunocompetent mice compared to wildtype cells. ConclusionsThe results suggest that CCN4 is a mediator of immunosuppression in the melanoma tumor microenvironment and a potential collateral immunotherapy target.

cancer biology↗

bioRxiv2021

Cell Communication Network factor 4 promotes tumor-induced immunosuppression in melanoma

Immune cell composition within the tumor microenvironment is regulated by tumor-derived factors. Cell Communication Network factor 4 (CCN4/WISP1) is a matricellular protein secreted by cancer cells that promotes metastasis by inducing the epithelial-mesenchymal transition. While metastatic dissemination limits patient survival, the absence of anti-tumor immunity also associates with poor patient out-comes with recent work suggesting these two clinical correlates are linked. Motivated by finding that CCN4 was associated with a dampened anti-tumor immune contexture in patients diagnosed with primary melanoma, we tested for a direct causal link by knocking out CCN4 (CCN4-KO) in the B16F0 and YUMM1.7 mouse models for melanoma. Tumor growth was significantly reduced when CCN4-KO melanoma cells were implanted subcutaneously in immunocompetent C57BL/6 mice but not in immunodeficient NSG mice. Correspondingly, the frequency of total CD45+ tumor-infiltrating leukocytes was significantly increased in CCN4-KO tumors, with increased natural killer (NK) and effector CD8+ T cells and reduced myeloid-derived suppressor cells (MDSC). Additionally, the absence of tumor-derived CCN4 was associated with an impaired splenic generation of suppressive granulocytic MDSC. Among mechanisms linked to local immunosuppression, we found CCN4 directly suppressed antigen-induced IFN{gamma} release by CD8+ T cells, promoted glycolysis and consequent lactate release by melanoma cells, and enhanced tumor secretion of MDSC-attracting chemokines like CCL2 and CXCL1. Finally, CCN4-KO in B16F0 and YUMM1.7 melanoma cells complemented the anti-tumor effect of immune checkpoint blockade (ICB) therapy. Overall, our results suggest that CCN4 promotes tumor-induced immunosuppression and is a potential target for therapeutic combinations with ICB. Statement of SignificanceGiven emerging interest in understanding the interplay between functional plasticity and anti-tumor immunity, Cell Communication Network factor 4, a secreted matricellular protein linked to promoting metastasis in melanoma, also suppresses anti-tumor immunity.

cancer biology↗

bioRxiv2020

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

Discovering and developing pharmaceutical drugs increasingly relies on mechanistic mathematical modeling and simulation. In immuno-oncology, models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity provide an important complement to wet experiments, given the cellular complexity and dynamics within tumors. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation by experts, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms have been developed to limit a priori biases in formulating mechanistic models. To realize an equivalent approach for cell-level networks, we combined digital cytometry with Bayesian network inference to generate causal models that link an increase in gene expression associated with onco-genesis with alterations in stromal and immune cell subsets directly from bulk transcriptomic datasets. To illustrate, we predicted how an increase in expression of Cell Communication Network factor 4 (CCN4/WISP1) altered the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Digital cytometry and network inference predictions were then tested using two immunocompetent mouse models for melanoma, which provided consistent experimental results.

systems biology↗

bioRxiv2019

An unsupervised feature extraction and selection strategy for identifying epithelial-mesenchymal transition state metrics in breast cancer and melanoma

Digital cytometry is opening up new avenues to better understand the heterogeneous cell types present within the tumor microenvironment. While the focus is towards elucidating immune and stromal cells as clinical correlates, there is still a need to better understand how a change in tumor cell phenotype, such as the epithelial-mesenchymal transition, influences the immune contexture. To complement existing digital cytometry methods, our objective was to develop an unsupervised gene signature capturing a change in differentiation state that is tailored to the specific cellular context of breast cancer and melanoma, as a illustrative example. Towards this aim, we used principal component analysis coupled with resampling to develop unsupervised gene expression-based state metrics specific for the cellular context that characterize the state of cellular differentiation within an epithelial to mesenchymal-like state space and independently correlate with metastatic potential. First developed using cell line data, the orthogonal state metrics were refined to exclude the contributions of normal fibroblasts and to provide tissue-level state estimates based on bulk tissue RNA-seq measures. The resulting gene expression-based metrics for differentiation state aim to inform a more holistic view of how the malignant cell phenotype influences the immune contexture within the tumor microenvironment.

cancer biology↗

bioRxiv2019

An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets

BackgroundHost immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously.\n\nResultsElastic-net logistic regression, a type of machine learning, was used to construct separate classifiers for ten different types of immune cell and for five T helper cell subsets. The resulting classifiers were then used to develop gene signatures that best discriminate among immune cell types and T helper cell subsets using RNA-seq datasets. We validated the approach using single-cell RNA-seq (scRNA-seq) datasets, which gave consistent results. In addition, we classified cell types that were previously unannotated. Finally, we benchmarked the proposed gene signatures against other existing gene signatures.\n\nConclusionsDeveloped classifiers can be used as priors in predicting the extent and functional orientation of the host immune response in diseases, such as cancer, where transcriptomic profiling of bulk tissue samples and single cells are routinely employed. Information that can provide insight into the mechanistic basis of disease and therapeutic response. The source code and documentation are available through GitHub: https://github.com/KlinkeLab/ImmClass2019.

bioinformatics↗

bioRxiv2019

Interleukin-12 elicits a non-canonical response in B16 melanoma cells to enhance survival

Oncogenesis rewires signaling networks to confer a fitness advantage to malignant cells. For instance, the B16F0 melanoma cell model creates a cytokine sink for Interleukin-12 (IL-12) to deprive neighboring cells of this important anti-tumor immune signal. While a cytokine sink provides an indirect fitness advantage, does IL-12 provide an intrinsic advantage to B16F0 cells? Functionally, stimulation with IL-12 enhanced B16F0 cell survival but not normal Melan-A melanocytes that were challenged with a cytotoxic agent. To identify a mechanism, we assayed the phosphorylation of proteins involved in canonical IL-12 signaling, STAT4, and cell survival, Akt. In contrast to T cells that exhibited a canonical response to IL-12 by phosphorylating STAT4, IL-12 stimulation of B16F0 cells predominantly phosphorylated Akt. Mechanistically, the differential response in B16F0 cells is explained by both ligand-dependent and ligand-independent aspects to initiate PI3K-AKT signaling upon IL12RB2 homodimerization. Namely, IL-12 promotes IL12RB2 homodimerization with low affinity and IL12RB2 overexpression promotes homodimerization via molecular crowding on the plasma membrane. Collectively, the data suggest that B16F0 cells shifted the intra-cellular response to IL-12 from engaging immune surveillance to favoring cell survival. Identifying how signaling networks are rewired in model systems of spontaneous origin can inspire therapeutic strategies in humans. Statement of SignificanceWhile Interleukin-12 is a key cytokine that promotes anti-tumor immunity, thinking of cancer as an evolutionary process implies that an immunosuppressive tumor microenvironment could arise during oncogenesis by interfering with endogenous anti-tumor immune signals, like IL-12. Previously, we found that B16F0 cells, a cell line derived from a spontaneous melanoma, interrupts this heterocellular signal by sequestering IL-12, which provides an indirect fitness advantage. Here, we report that B16F0 cells gain an intrinsic advantage by rewiring the canonical response to IL-12 to instead initiate PI3K-AKT signaling, which promotes cell survival. The data suggest a model where overexpressing one component of the IL-12 receptor, IL12RB2, enables melanoma cells to shift the functional response via both IL-12-mediated and molecular crowding-based IL12RB2 homodimerization.

cancer biology↗

bioRxiv2018

CCN4 shifts melanoma cells from a fragile proliferative state to a resilient metastatic state

While deregulated intracellular signaling initiates melanoma, intercellular crosstalk within the tumor microenvironment, often coordinated by soluble factors, is essential for melanoma progression and metastasis. One such secreted matricellular protein, cellular communication network factor 4 (CCN4), stimulates metastasis in other malignancies. Here, we report that CCN4 expression is associated progressively with reduced overall survival in patients with primary melanomas. To reveal the roles of CCN4 in melanoma progression, we used mouse melanoma models and knocked out Ccn4 using a homology-directed repair CRISPR/CAS9 system to generate pools of Ccn4-knockout cells. In vitro assays supported previous findings using clones generated using a double nickase-based CRISPR/CAS9 system that CCN4 promoted an epithelial - mesenchymal-like transition in melanoma cells and stimulated invasion and metastasis. We also found that, while Ccn4 knockout enhanced cell growth in optimal 2D culture conditions, the knockout suppressed certain cell survival signaling pathways and rendered cells less resistant to stress conditions. Tumor cell growth assays at sub-optimal conditions in vitro, quantitative analysis of tumor growth assays in vivo, and transcriptomics analysis of human melanoma cell lines suggested that CCN4 repressed cell growth and simultaneously enhanced cell survival. The collective role of CCN4 suggests a potential therapeutic target for limiting metastatic invasion in melanoma and a biomarker for metastatic potential.

cancer biology↗

bioRxiv2018

WNT1 Inducible Signaling Pathway Protein 1 (WISP1) Stimulates Melanoma Cell Invasion and Metastasis by Promoting Epithelial - Mesenchymal Transition (EMT)

Besides intrinsic changes, malignant cells release soluble signals to reshape their microenvironment. Among the signaling factors is WNT1 inducible signaling pathway protein 1 (WISP1), a secreted matricellular protein that is elevated in a variety of cancers including melanoma and is associated with reduced overall survival of patients diagnosed with primary melanoma. In this work, we found that WISP1 knockout both increased cell proliferation and repressed wound healing, migration and invasion of mouse and human melanoma cells in an ensemble of in vitro assays. In vivo metastasis assays showed that WISP1 knockout repressed tumor metastasis in both C57BL/6Ncrl and NOD-scid IL2Rgammanull (NSG) mice with B16F10 and YUMM1.7 melanoma cells. Mechanistically, B16F10 cells that invaded in a transwell assay possessed a gene expression signature similar to Epithelial - Mesenchymal Transition (EMT), including coincident repression of E-cadherin and induction of fibronectin and N-cadherin. Upon WISP1 knockout, these EMT signature genes went in opposite directions in both mouse and human cell lines and were rescued by media containing WISP1 or recombinant WISP1 protein. In vivo, metastasis repression by WISP1 knockout was reversed by the reintroduction of either WISP1 or SNAI1. A set of EMT gene activation and inhibition experiments using recombinant WISP1 or kinase inhibitors in B16F10 and YUMM1.7 cells suggested that WISP1 activates Akt and MAP kinase signaling pathways to shift melanoma cells from a proliferative to invasive phenotype. Collectively, the results supported a model that WISP1 within the tumor microenvironment stimulates melanoma invasion and metastasis by promoting an EMT-like process.

cancer biology↗

bioRxiv2017

Quantifying Spontaneous Metastasis In A Syngeneic Mouse Melanoma Model Using Real Time PCR

Modeling metastasis in vivo with animals is a priority for both revealing mechanisms of tumor dissemination and developing therapeutic methods. While conventional intravenous injection of tumor cells provides an efficient and consistent system for studying tumor cell extravasation and colonization, studying spontaneous metastasis derived from orthotopic tumor sites has the advantage of modeling more aspects of the metastatic cascade, but is challenging as it is difficult to detect small numbers of metastatic cells. In this work, we developed an approach for quantifying spontaneous metastasis in the syngeneic mouse B16 system using real time PCR. We first transduced B16 cells with lentivirus expressing firefly luciferase Luc2 gene for bioluminescence imaging. Next, we developed a real time quantitative PCR (qPCR) method for the detection of luciferase-expressing, metastatic tumor cells in mouse lungs and other organs. To illustrate the approach, we quantified lung metastasis in both spontaneous and experimental scenarios using B16F0 and B16F10 cells in C57BL/6Ncrl and NOD-Scid Gamma (NSG) mice. We tracked B16 melanoma metastasis with both bioluminescence imaging and qPCR, which were found to be self-consistent. Using this assay, we can quantitatively detect one Luc2 positive tumor cells out of 104 tissue cells, which corresponds to a metastatic burden of 1.8x104 metastatic cells per whole mouse lung. More importantly, the qPCR method was at least a factor of 10 more sensitive in detecting metastatic cell dissemination and should be combined with bioluminescence imaging as a high-resolution, end-point method for final metastatic cell quantitation. Given the rapid growth of primary tumors in many mouse models, assays with improved sensitivity can provide better insight into biological mechanisms that underpin tumor metastasis.

cancer biology↗