Project description:Although the association between inflammation and cancer development has been recognized, how inflammation affects the outcomes of immunotherapy and chemotherapy hasn’t yet been well evaluated. In this study, we found that CKLF-like MARVEL transmembrane domain-containing member 4 (CMTM4) was highly expressed in multiple human and murine cancers. Loss of CMTM4 significantly reduced tumor growth and impaired NFB, mTOR, PI3K/Akt pathway activation. Interestingly, we found that CMTM4 can regulate epidermal growth factor (EGF) signaling post-translationally by promoting EGFR recycling and preventing its degradation through Rab proteins. Consequentially, CMTM4 knockout promoted response sensitivity of human tumor cells to EGFR inhibitors. Importantly, CMTM4 knockout tumors stimulated with EGF significantly decreased their production of inflammatory cytokines including G-CSF, leading to decreased recruitment of polymorphonuclear myeloid-derived suppressor cells and thus, a less suppressive tumor-immune-environment. Therapeutically, siRNA-liposome targeting CMTM4 reduced tumor growth in vivo and prolonged animal survival. Furthermore, CMTM4 knockout enhance immune checkpoint blockade or chemotherapy to reduce tumor growth. These data suggest that CMTM4 represents a novel target to inhibit tumor inflammation and improve immune response and drug sensitivity.
Project description:Tumor cell lines and drug-resistant counterparts. These data support the publication Gyorffy et al, Oncogene 2005 (July), Prediction of doxorubicin sensitivity in breast tumors based on gene expression profiles of drug-resistant cell lines correlates with patient survival. We contrasted the expression profiles of 13 different human tumor cell lines of gastric (EPG85-257), pancreatic (EPP85-181), colon (HT29) and breast (MCF7 and MDA-MB-231) origin and their counterparts resistant to the topoisomerase inhibitors daunorubicin, doxorubicin or mitoxantrone. We interrogated cDNA arrays with 43 000 cDNA clones ( approximately 30 000 unique genes) to study the expression pattern of these cell lines. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Using regression correlation
Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies
Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies
Project description:Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data – on more than 80 million single cells from 4,000 conditions – were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.