Modeling anti-IL-6 therapy using breast cancer patient-derived xenografts

The pleiotropic cytokine IL-6 accelerates the progression of breast cancer in a variety of preclinical models through the activation of the STAT3 (signal transducer and activator of transcription 3) signaling pathway. However, the proportion of breast cancers sensitive to anti-IL-6 therapies is not known. This study evaluates the efficacy of anti-IL-6 therapies using breast cancer patient derived xenografts (PDXs). During the generation of our collection of PDXs, we showed that the successful engraftment of tumor tissue in immunodeficient mice correlates with bad prognosis. Four PDXs out of six were resistant to anti-IL-6 therapies and the expression of IL-6, its receptor or the levels of phospho-STAT3 (the active form of the signal transducer) did not correlate with sensitivity. Using cell cultures established from the PDXs as well as samples from in vivo treatments, we showed that only tumors in which the activation of STAT3 depends on IL-6 respond to the blocking antibodies. Our results indicate that only a fraction of breast tumors are responsive to anti-IL-6 therapies. In order to identify responsive tumors, a functional assay to determine the dependence of STAT3 activation on IL-6 should be performed

High HER2 protein levels correlate with increased survival in breast cancer patients treated with anti-HER2 therapy

Introduction: Current methods to determine HER2 (human epidermal growth factor receptor 2) status are affected by reproducibility issues and do not reliably predict benefit from anti-HER2 therapy. Quantitative measurement of HER2 may more accurately identify breast cancer (BC) patients who will respond to anti-HER2 treatments. Methods: Using selected reaction monitoring mass spectrometry (SRM-MS), we quantified HER2 protein levels in formalin-fixed, paraffin-embedded (FFPE) tissue samples that had been classified as HER2 0, 1+, 2+ or 3+ by immunohistochemistry (IHC). Receiver operator curve (ROC) analysis was conducted to obtain optimal HER2 protein expression thresholds predictive of HER2 status (by standard IHC or in situ hybridization [ISH]) and of survival benefit after anti-HER2 therapy. Results: Absolute HER2 amol/μg levels were significantly correlated with both HER2 IHC and amplification status by ISH (p 2200 amol/μg were significantly associated with longer disease-free survival (DFS) and overall survival (OS) in an adjuvant setting and with longer OS in a metastatic setting. Conclusion: Quantitative HER2 measurement by SRM-MS is superior to IHC and ISH in predicting outcome after treatment with anti-HER2 therapy

Modeling anti-IL-6 therapy using breast cancer patient-derived xenografts

The pleiotropic cytokine IL-6 accelerates the progression of breast cancer in a variety of preclinical models through the activation of the STAT3 (signal transducer and activator of transcription 3) signaling pathway. However, the proportion of breast cancers sensitive to anti-IL-6 therapies is not known. This study evaluates the efficacy of anti-IL-6 therapies using breast cancer patient derived xenografts (PDXs). During the generation of our collection of PDXs, we showed that the successful engraftment of tumor tissue in immunodeficient mice correlates with bad prognosis. Four PDXs out of six were resistant to anti-IL-6 therapies and the expression of IL-6, its receptor or the levels of phospho-STAT3 (the active form of the signal transducer) did not correlate with sensitivity. Using cell cultures established from the PDXs as well as samples from in vivo treatments, we showed that only tumors in which the activation of STAT3 depends on IL-6 respond to the blocking antibodies. Our results indicate that only a fraction of breast tumors are responsive to anti-IL-6 therapies. In order to identify responsive tumors, a functional assay to determine the dependence of STAT3 activation on IL-6 should be performed

GDF15 is an Eribulin Response Biomarker also required for Survival of DTP Breast Cancer Cells.

Drug tolerant persister (DTP) cells enter into a reversible slow-cycling state after drug treatment. We performed proteomic characterization of the breast cancer (BC) DTP cell secretome after eribulin treatment. We showed that the growth differentiation factor 15 (GDF15) is a protein significantly over-secreted upon eribulin treatment. The biomarker potential of GDF15 was confirmed in 3D-cell culture models using BC cells lines and PDXs, as well as in a TNBC in vivo model. We also found that GDF15 is required for survival of DTP cells. Direct participation of GDF15 and its receptor GFRAL in eribulin-induction of DTPs was established by the enhanced cell killing of DTPs by eribulin seen under GDF15 and GFRAL loss of function assays. Finally, we showed that combination therapy of eribulin plus an anti-GDF15 antibody kills BC-DTP cells. Our results suggest that targeting GDF15 may help eradicate DTP cells and block the onset of acquired resistance

The Second Generation Antibody-Drug Conjugate SYD985 Overcomes Resistances to T-DM1

Trastuzumab-emtansine (T-DM1) is an antibody-drug conjugate (ADC) approved for the treatment of HER2 (human epidermal growth factor receptor 2)-positive breast cancer. T-DM1 consists of trastuzumab covalently linked to the cytotoxic maytansinoid DM1 via a non-cleavable linker. Despite its efficacy, primary or acquired resistance frequently develops, particularly in advanced stages of the disease. Second generation ADCs targeting HER2 are meant to supersede T-DM1 by using a cleavable linker and a more potent payload with a different mechanism of action. To determine the effect of one of these novel ADCs, SYD985, on tumors resistant to T-DM1, we developed several patient-derived models of resistance to T-DM1. Characterization of these models showed that previously described mechanisms-HER2 downmodulation, impairment of lysosomal function and upregulation of drug efflux pumps-account for the resistances observed, arguing that mechanisms of resistance to T-DM1 are limited, and most of them have already been described. Importantly, SYD985 was effective in these models, showing that the resistance to first generation ADCs can be overcome with an improved design

High HER2 protein levels correlate with increased survival in breast cancer patients treated with anti-HER2 therapy

Introduction: Current methods to determine HER2 (human epidermal growth factor receptor 2) status are affected by reproducibility issues and do not reliably predict benefit from anti-HER2 therapy. Quantitative measurement of HER2 may more accurately identify breast cancer (BC) patients who will respond to anti-HER2 treatments. Methods: Using selected reaction monitoring mass spectrometry (SRM-MS), we quantified HER2 protein levels in formalin-fixed, paraffin-embedded (FFPE) tissue samples that had been classified as HER2 0, 1+, 2+ or 3+ by immunohistochemistry (IHC). Receiver operator curve (ROC) analysis was conducted to obtain optimal HER2 protein expression thresholds predictive of HER2 status (by standard IHC or in situ hybridization [ISH]) and of survival benefit after anti-HER2 therapy. Results: Absolute HER2 amol/μg levels were significantly correlated with both HER2 IHC and amplification status by ISH (p 2200 amol/μg were significantly associated with longer disease-free survival (DFS) and overall survival (OS) in an adjuvant setting and with longer OS in a metastatic setting. Conclusion: Quantitative HER2 measurement by SRM-MS is superior to IHC and ISH in predicting outcome after treatment with anti-HER2 therapy

Patterns of HER2 Gene Amplification and Response to Anti-HER2 Therapies

<div><p>A chromosomal region that includes the gene encoding HER2, a receptor tyrosine kinase (RTK), is amplified in 20% of breast cancers. Although these tumors tend to respond to drugs directed against HER2, they frequently become resistant and resume their malignant progression. Gene amplification in double minutes (DMs), which are extrachromosomal entities whose number can be dynamically regulated, has been suggested to facilitate the acquisition of resistance to therapies targeting RTKs. Here we show that ~30% of HER2-positive tumors show amplification in DMs. However, these tumors respond to trastuzumab in a similar fashion than those with amplification of the HER2 gene within chromosomes. Furthermore, in different models of resistance to anti-HER2 therapies, the number of DMs containing HER2 is maintained, even when the acquisition of resistance is concomitant with loss of HER2 protein expression. Thus, both clinical and preclinical data show that, despite expectations, loss of HER2 protein expression due to loss of DMs containing HER2 is not a likely mechanism of resistance to anti-HER2 therapies.</p></div

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