Marit Krohn

Identifying microRNAs regulating B7-H3 in breast cancer: the clinical impact of microRNA-29c.

Background:B7-H3, an immunoregulatory protein, is overexpressed in several cancers and is often associated with metastasis and poor prognosis. Here, our aim was to identify microRNAs (miRNAs) regulating B7-H3 and assess their potential prognostic implications in breast cancer.Methods:MicroRNAs targeting B7-H3 were identified by transfecting two breast cancer cell lines with a library of 810 miRNA mimics and quantifying changes of B7-H3 protein levels using protein lysate microarrays. For validations we used western immunoblotting and 3′-UTR luciferase assays. Clinical significance of the miRNAs was assayed by analysing whether their expression levels correlated with outcome in two cohorts of breast cancer patients (142 and 81 patients).Results:We identified nearly 50 miRNAs that downregulated B7-H3 protein levels. Western immunoblotting validated the impact of the 20 most effective miRNAs. Thirteen miRNAs (miR-214, miR-363*, miR-326, miR-940, miR-29c, miR-665, miR-34b*, miR-708, miR-601, miR-124a, miR-380-5p, miR-885-3p, and miR-593) targeted B7-H3 directly by binding to its 3′-UTR region. Finally, high expression of miR-29c was associated with a significant reduced risk of dying from breast cancer in both cohorts.Conclusions:We identified miRNAs efficiently downregulating B7-H3 expression. The expression of miR-29c correlated with survival in breast cancer patients, suggesting a tumour suppressive role for this miRNA.

Proteomic Characterization of Breast Cancer Xenografts Identifies Early and Late Bevacizumab-Induced Responses and Predicts Effective Drug Combinations

Purpose: Neoangiogenesis is an important feature in tumor growth and progression, and combining chemotherapy and antiangiogenic drugs have shown clinical efficacy. However, as treatment-induced resistance often develops, our goal was to identify pathways indicating response and/or evolving resistance to treatment and inhibit these pathways to optimize the treatment strategies. Experimental Design: To identify markers of response and/or resistance, reverse-phase protein array (RPPA) was used to characterize treatment-induced changes in a bevacizumab-responsive and a nonresponsive human breast cancer xenograft. Results were combined with bioinformatic modeling to predict druggable targets for optimization of the treatment. Results: RPPA analysis showed that both tumor models responded to bevacizumab with an early (day 3) upregulation of growth factor receptors and downstream signaling pathways, with persistent mTOR signaling until the end of the in vivo experiment. Adding doxorubicin to bevacizumab showed significant and superior growth inhibition of basal-like tumors, whereas no additive effect was seen in the luminal-like model. The combination treatment corresponded to a continuous late attenuation of mTOR signaling in the basal-like model, whereas the inhibition was temporary in the luminal-like model. Integrating the bevacizumab-induced dynamic changes in protein levels with bioinformatic modeling predicted inhibition of phosphoinositide 3-kinase (PI3K) pathway to increase the efficacy of bevacizumab monotherapy. In vivo experiments combining bevacizumab and the PI3K/mTOR inhibitor BEZ235 confirmed their significant and additive growth-inhibitory effect in the basal-like model. Conclusions: Treatment with bevacizumab caused compensatory upregulation of several signaling pathways. Targeting such pathways increased the efficacy of antiangiogenic therapy. Clin Cancer Res; 20(2); 404–12. ©2013 AACR.

The G1-S checkpoint in fission yeast is not a general DNA damage checkpoint

Inhibitory mechanisms called checkpoints regulate progression of the cell cycle in the presence of DNA damage or when a previous cell-cycle event is not finished. In fission yeast exposed to ultraviolet light the G1-S transition is regulated by a novel checkpoint that depends on the Gcn2 kinase. The molecular mechanisms involved in checkpoint induction and maintenance are not known. Here we characterise the checkpoint further by exposing the cells to a variety of DNA-damaging agents. Exposure to methyl methane sulphonate and hydrogen peroxide induce phosphorylation of eIF2α, a known Gcn2 target, and an arrest in G1 phase. By contrast, exposure to psoralen plus long-wavelength ultraviolet light, inducing DNA adducts and crosslinks, or to ionizing radiation induce neither eIF2α phosphorylation nor a cell-cycle delay. We conclude that the G1-S checkpoint is not a general DNA-damage checkpoint, in contrast to the one operating at the G2-M transition. The tight correlation between eIF2α phosphorylation and the presence of a G1-phase delay suggests that eIF2α phosphorylation is required for checkpoint induction. The implications for checkpoint signalling are discussed.

Copy Number Gain of hsa-miR-569 at 3q26.2 Leads to Loss of TP53INP1 and Aggressiveness of Epithelial Cancers

Small noncoding miRNAs represent underexplored targets of genomic aberrations and emerging therapeutic targets. The 3q26.2 amplicon is among the most frequent genomic aberrations in multiple cancer lineages including ovarian and breast cancers. We demonstrate that hsa-miR-569 (hereafter designated as miR569), which is overexpressed in a subset of ovarian and breast cancers, at least in part due to the 3q26.2 amplicon, alters cell survival and proliferation. Downregulation of TP53INP1 expression by miR569 is required for the effects of miR569 on survival and proliferation. Targeting miR569 sensitizes ovarian and breast cancer cells overexpressing miR569 to cisplatin by increasing cell death both in vitro and in vivo. Thus targeting miR569 could potentially benefit patients with the 3q26.2 amplicon and subsequent miR569 elevation.

Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes

BackgroundThe heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level.MethodsThe study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data.ResultsOur result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity.ConclusionsThree naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs.

Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer

BackgroundThe role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets.MethodsProtein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs.ResultsAn interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line.ConclusionsA comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.

Induction of a G1-S checkpoint in fission yeast

Entry into S phase is carefully regulated and, in most organisms, under the control of a G1-S checkpoint. We have previously described a G1-S checkpoint in fission yeast that delays formation of the prereplicative complex at chromosomal replication origins after exposure to UV light (UVC). This checkpoint absolutely depends on the Gcn2 kinase. Here, we explore the signal for activation of the Gcn2-dependent G1-S checkpoint in fission yeast. If some form of DNA damage can activate the checkpoint, deficient DNA repair should affect the length of the checkpoint-induced delay. We find that the cell-cycle delay differs in repair-deficient mutants from that in wild-type cells. However, the duration of the delay depends not only on the repair capacity of the cells, but also on the nature of the repair deficiency. First, the delay is abolished in cells that are deficient in the early steps of repair. Second, the delay is prolonged in repair mutants that fail to complete repair after the incision stage. We conclude that the G1-S delay depends on damage to the DNA and that the activating signal derives not from the initial DNA damage, but from a repair intermediate(s). Surprisingly, we find that activation of Gcn2 does not depend on the processing of DNA damage and that activated Gcn2 alone is not sufficient to delay entry into S phase in UVC-irradiated cells. Thus, the G1-S delay depends on at least two different inputs.

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

BackgroundBreast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.MethodsTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering.ResultsBased on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.ConclusionsThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

The longitudinal transcriptional response to neoadjuvant chemotherapy with and without bevacizumab in breast cancer

Purpose: Chemotherapy-induced alterations to gene expression are due to transcriptional reprogramming of tumor cells or subclonal adaptations to treatment. The effect on whole-transcriptome mRNA expression was investigated in a randomized phase II clinical trial to assess the effect of neoadjuvant chemotherapy with the addition of bevacizumab. Experimental Design: Tumor biopsies and whole-transcriptome mRNA profiles were obtained at three fixed time points with 66 patients in each arm. Altogether, 358 specimens from 132 patients were available, representing the transcriptional state before treatment start, at 12 weeks and after treatment (25 weeks). Pathologic complete response (pCR) in breast and axillary nodes was the primary endpoint. Results: pCR was observed in 15 patients (23%) receiving bevacizumab and chemotherapy and 8 patients (12%) receiving only chemotherapy. In the estrogen receptor–positive patients, 11 of 54 (20%) treated with bevacizumab and chemotherapy achieved pCR, while only 3 of 57 (5%) treated with chemotherapy reached pCR. In patients with estrogen receptor–positive tumors treated with combination therapy, an elevated immune activity was associated with good response. Proliferation was reduced after treatment in both treatment arms and most pronounced in the combination therapy arm, where the reduction in proliferation accelerated during treatment. Transcriptional alterations during therapy were subtype specific, and the effect of adding bevacizumab was most evident for luminal-B tumors. Conclusions: Clinical response and gene expression response differed between patients receiving combination therapy and chemotherapy alone. The results may guide identification of patients likely to benefit from antiangiogenic therapy. Clin Cancer Res; 23(16); 4662–70. ©2017 AACR.

Abstract 1813: Bevacizumab potentiates the proteomic response to neoadjuvant chemotherapy in breast cancer patients: Rppa exploration of consecutive tumor samples in the NeoAva randomized phase II trial

Antiangiogenic therapy using bevacizumab has proven effective for a number of cancers; however, in breast cancer there is an unmet need to identify patients that benefit from such treatment. Sampling of tumor biopsies before and during treatment, as well as at the time of surgery enables the assessment of response at multiple molecular levels. At the proteomic level reverse phase protein analysis (RPPA) support expression of numerous cancer associated proteins simultaneously, which can further be used to unravel molecular mechanisms associated with clinical response to bevacizumab treatment. In this phase II clinical trial, patients with HER2 negative primary tumors of ≥25 mm were treated with neoadjuvant chemotherapy (4 x FEC100 + 12 weeks of taxane-based therapy) and randomized (1:1) to receive bevacizumab or not. Mammography, ultrasound and MR imaging were used for response evaluation, in addition to final pathology assessment. Tumor responses were evaluable in 132 patients; of which 66 received bevacizumab. Ratio of the tumor size at final pathology assessment, and at inclusion was calculated to obtain a continuous scale of response reflecting the percentage of tumor shrinkage in response to therapy. Tumor biopsies were removed before start of treatment, at week 12 at the start of taxane-based tharapy and at the time of surgery. Lysates from each sample was analyzed on reverse phase protein arrays (RPPA) for expression levels of 210 proteins of which 54 were phospho-specific. The addition of bevacizumab to the chemotherapy do not alter proteomic response from week 0 to 25 to such extent that this patient group cluster naturally together. While the proteomic response from week 0 to 12 in both treatment arms had an overall similar profile regarding up- and down-regulated proteins, the combination treatment (FEC100 + bevacizumab) induced substantially more effect on the regulation of each protein. This suggests that bevacizumab treatment have the capability to potentiate the effects of the anthracyclin based chemotherapy from week 0 to 12. Conversely, from week 12-25 (taxane-based therapy + bevacizumab) this effect was lost or even reversed, possibly due to a de-vascularized and less accessible tumor. An exception to this observation was a few phospho-proteins that do seem to have sustained stronger regulation over the whole treatment period. We are in the process of analyzing in more detail the impact of phosphorylation and thus protein activation states on treatment response. Deciphering molecular response and activity regulation at the proteomic level is a promising approach and may reveal novel knowledge with potential important clinical relevance. Citation Format: Mads H. Haugen, Ole Christian Lingjaerde, Marit Krohn, Wei Zhao, Evita M. Lindholm, Laxmi Silwal-Pandit, Elin Borgen, Oystein Garred, Anne Fangberget, Marit M. Holmen, Ellen Schlichting, Helle K. Skjerven, Steinar Lundgren, Erik Wist, Bjorn Naume, Gunhild M. Maelandsmo, Yiling Lu, Anne-Lise Boerresen-Dale, Gordon B. Mills, Olav Engebraaten. Bevacizumab potentiates the proteomic response to neoadjuvant chemotherapy in breast cancer patients: Rppa exploration of consecutive tumor samples in the NeoAva randomized phase II trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1813. doi:10.1158/1538-7445.AM2017-1813