Resistance to taxanes in triple negative breast cancer associates with the dynamics of a CD49f+ tumor initiating population

Taxanes are a mainstay of treatment for breast cancer, but resistance often develops followed by metastatic disease and mortality. Aiming to reveal the mechanisms underlying taxane resistance, we used breast cancer patient-derived orthoxenografts (PDX). Mimicking clinical behavior, triple-negative breast tumors (TNBCs) from PDX models were more sensitive to docetaxel than luminal tumors, but they progressively acquired resistance upon continuous drug administration. Mechanistically, we found that a CD49f+ chemoresistant population with tumor-initiating ability is present in sensitive tumors and expands during the acquisition of drug resistance. In the absence of the drug, the resistant CD49f+ population shrinks and taxane sensitivity is restored. We describe a transcriptional signature of resistance, predictive of recurrent disease after chemotherapy in TNBC. Together, these findings identify a CD49f+ population enriched in tumor-initiating ability and chemoresistance properties and evidence a drug holiday effect on the acquired resistance to docetaxel in triple-negative breast cancer

Neonatal Fc receptor expression in macrophages is indispensable for IgG homeostasis

The maintenance of the homeostasis of immunoglobulin G (IgG) represents a fundamental aspect of humoral immunity that has direct relevance to the successful delivery of antibody-based therapeutics. The ubiquitously expressed neonatal Fc receptor (FcRn) salvages IgG from cellular degradation following pinocytic uptake into cells, conferring prolonged in vivo persistence on IgG. However, the cellular sites of FcRn function are poorly defined. Pinocytic uptake is a prerequisite for FcRn-mediated IgG salvage, prompting us to investigate the consequences of IgG uptake and catabolism by macrophages, which represent both abundant and highly pinocytic cells in the body. Site-specific deletion of FcRn to generate mice harboring FcRn-deficient macrophages results in IgG hypercatabolism and ~threefold reductions in serum IgG levels, whereas these effects were not observed in mice that lack functional FcRn in B cells and dendritic cells. Consistent with the degradative activity of FcRn-deficient macrophages, depletion of these cells in FcRn-deficient mice leads to increased persistence and serum levels of IgG. These studies demonstrate a pivotal role for FcRn-mediated salvage in compensating for the high pinocytic and degradative activities of macrophages to maintain IgG homeostasis

Resistance to taxanes in triple negative breast cancer associates with the dynamics of a CD49f+ tumor initiating population

Taxanes are a mainstay of treatment for breast cancer, but resistance often develops followed by metastatic disease and mortality. Aiming to reveal the mechanisms underlying taxane resistance, we used breast cancer patient-derived orthoxenografts (PDX). Mimicking clinical behavior, triple-negative breast tumors (TNBCs) from PDX models were more sensitive to docetaxel than luminal tumors, but they progressively acquired resistance upon continuous drug administration. Mechanistically, we found that a CD49f+ chemoresistant population with tumor-initiating ability is present in sensitive tumors and expands during the acquisition of drug resistance. In the absence of the drug, the resistant CD49f+ population shrinks and taxane sensitivity is restored. We describe a transcriptional signature of resistance, predictive of recurrent disease after chemotherapy in TNBC. Together, these findings identify a CD49f+ population enriched in tumor-initiating ability and chemoresistance properties and evidence a drug holiday effect on the acquired resistance to docetaxel in triple-negative breast cancer

Resistance to taxanes in triple negative breast cancer associates with the dynamics of a CD49f+ tumor initiating population

Taxanes are a mainstay of treatment for breast cancer, but resistance often develops followed by metastatic disease and mortality. Aiming to reveal the mechanisms underlying taxane resistance, we used breast cancer patient-derived orthoxenografts (PDX). Mimicking clinical behavior, triple-negative breast tumors (TNBCs) from PDX models were more sensitive to docetaxel than luminal tumors, but they progressively acquired resistance upon continuous drug administration. Mechanistically, we found that a CD49f+ chemoresistant population with tumor-initiating ability is present in sensitive tumors and expands during the acquisition of drug resistance. In the absence of the drug, the resistant CD49f+ population shrinks and taxane sensitivity is restored. We describe a transcriptional signature of resistance, predictive of recurrent disease after chemotherapy in TNBC. Together, these findings identify a CD49f+ population enriched in tumor-initiating ability and chemoresistance properties and evidence a drug holiday effect on the acquired resistance to docetaxel in triple-negative breast cancer

Resistance to taxanes in triple negative breast cancer associates with the dynamics of a CD49f+ tumor initiating population

Taxanes are a mainstay of treatment for breast cancer, but resistance often develops followed by metastatic disease and mortality. Aiming to reveal the mechanisms underlying taxane resistance, we used breast cancer patient-derived orthoxenografts (PDX). Mimicking clinical behavior, triple-negative breast tumors (TNBCs) from PDX models were more sensitive to docetaxel than luminal tumors, but they progressively acquired resistance upon continuous drug administration. Mechanistically, we found that a CD49f+ chemoresistant population with tumor-initiating ability is present in sensitive tumors and expands during the acquisition of drug resistance. In the absence of the drug, the resistant CD49f+ population shrinks and taxane sensitivity is restored. We describe a transcriptional signature of resistance, predictive of recurrent disease after chemotherapy in TNBC. Together, these findings identify a CD49f+ population enriched in tumor-initiating ability and chemoresistance properties and evidence a drug holiday effect on the acquired resistance to docetaxel in triple-negative breast cancer

Cancer network activity associated with therapeutic response and synergism

Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations

Cancer network activity associated with therapeutic response and synergism

Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations