BackgroundTamoxifen treatment of estrogen receptor (ER)-positive breast cancer reduces mortality by 31%. However, over half of advanced ER-positive breast cancers are intrinsically resistant to tamoxifen and about 40% will acquire the resistance during the treatment.MethodsIn order to explore mechanisms underlying endocrine therapy resistance in breast cancer and to identify new therapeutic opportunities, we created tamoxifen-resistant breast cancer cell lines that represent the luminal A or the luminal B. Gene expression patterns revealed by RNA-sequencing in seven tamoxifen-resistant variants were compared with their isogenic parental cells. We further examined those transcriptomic alterations in a publicly available patient cohort.ResultsWe show that tamoxifen resistance cannot simply be explained by altered expression of individual genes, common mechanism across all resistant variants, or the appearance of new fusion genes. Instead, the resistant cell lines shared altered gene expression patterns associated with cell cycle, protein modification and metabolism, especially with the cholesterol pathway. In the tamoxifen-resistant T-47D cell variants we observed a striking increase of neutral lipids in lipid droplets as well as an accumulation of free cholesterol in the lysosomes. Tamoxifen-resistant cells were also less prone to lysosomal membrane permeabilization (LMP) and not vulnerable to compounds targeting the lipid metabolism. However, the cells were sensitive to disulfiram, LCS-1, and dasatinib.ConclusionAltogether, our findings highlight a major role of LMP prevention in tamoxifen resistance, and suggest novel drug vulnerabilities associated with this phenotype.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4757-z) contains supplementary material, which is available to authorized users.

RNA-sequencing and tailored bioinformatic methodologies have paved the way for identification of expressed fusion genes from the chaotic genomes of solid tumors. We have recently successfully exploited RNA-sequencing for the discovery of 24 novel fusion genes in breast cancer. Here, we demonstrate the importance of continuous optimization of the bioinformatic methodology for this purpose, and report the discovery and experimental validation of 13 additional fusion genes from the same samples. Integration of copy number profiling with the RNA-sequencing results revealed that the majority of the gene fusions were promoter-donating events that occurred at copy number transition points or involved high-level DNA-amplifications. Sequencing of genomic fusion break points confirmed that DNA-level rearrangements underlie selected fusion transcripts. Furthermore, a significant portion (>60%) of the fusion genes were alternatively spliced. This illustrates the importance of reanalyzing sequencing data as gene definitions change and bioinformatic methods improve, and highlights the previously unforeseen isoform diversity among fusion transcripts.

BackgroundThe estrogen receptor (ER) inhibitor tamoxifen reduces breast cancer mortality by 31 % and has served as the standard treatment for ER-positive breast cancers for decades. However, 50 % of advanced ER-positive cancers display de novo resistance to tamoxifen, and acquired resistance evolves in 40 % of patients who initially respond. Mechanisms underlying resistance development remain poorly understood and new therapeutic opportunities are urgently needed. Here, we report the generation and characterization of seven tamoxifen-resistant breast cancer cell lines from four parental strains.MethodsUsing high throughput drug sensitivity and resistance testing (DSRT) with 279 approved and investigational oncology drugs, exome-sequencing and network analysis, we for the first time, systematically determine the drug response profiles specific to tamoxifen resistance.ResultsWe discovered emerging vulnerabilities towards specific drugs, such as ERK1/2-, proteasome- and BCL-family inhibitors as the cells became tamoxifen-resistant. Co-resistance to other drugs such as the survivin inhibitor YM155 and the chemotherapeutic agent paclitaxel also occurred.ConclusionThis study indicates that multiple molecular mechanisms dictate endocrine resistance, resulting in unexpected vulnerabilities to initially ineffective drugs, as well as in emerging co-resistances. Thus, combatting drug-resistant tumors will require patient-tailored strategies in order to identify new drug vulnerabilities, and to understand the associated co-resistance patterns.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2452-5) contains supplementary material, which is available to authorized users.

The JAK/STAT3 signaling pathway may be aberrantly activated and have various and conflicting roles in breast cancer. The current study explored prognostic implications of activated STAT3 in human epidermal growth factor receptor 2 (HER2)‐positive primary breast cancers in the context of a large prospective study (ALTTO). Activated STAT3 was determined by immunohistochemical analysis of STAT3 phosphorylation (Y705) performed on the primary tumors. This analysis evaluated whether patients with activated STAT3 had disease‐free survival (DFS) and overall survival (OS) different from patients without activated STAT3. A total of 5694 patients out of the 8381 patients enrolled in ALTTO were included in this analysis (67.9%), and 2634 of them (46%) had evidence of STAT3 activation (minimum tumor Allred score ≥2). The median follow‐up was 6.93 years (6.85‐6.97 years), at the end of which 1035 (18.18%) and 520 (9.13%) patients experienced DFS and OS events, respectively. Patients with STAT3 activation experienced improved DFS compared to those without it (multivariable hazard ratio [HR], 0.84; 95% confidence interval [CI] 0.74‐0.95; P = .006). There were no group differences in OS (multivariable HR, 0.92; 95% CI 0.78‐1.10; P = .37). This effect was limited to ER‐positive tumors. In conclusion, these findings support the role of STAT3 activation as a marker of favorable outcome in ER‐positive/HER2‐positive breast cancer patients.

12 Conclusion 32 Altogether, our findings highlight a major role of LMP prevention in tamoxifen resistance, and suggest 33 novel drug vulnerabilities associated with this phenotype. 34 Keywords 35

Most drug-testing approaches published so far focus on identifying a single drug that shows favorable response and is associated with a known cancer biomarker such as the drug Imatinib in BCR-ABL gene fusion positive cells. We developed and applied drug set enrichment analysis (DSEA) to find enriched patterns or statistically significant similarities (overlaps) between the drug responses of a test sample against a cohort of 182 previously screened cancer samples. The samples studied included established (ATCC) cancer cell lines, drug-resistant cancer cell models, ex-vivo patient cancer cells in primary cultures, including conditionally reprogrammed cancer cells from patients. DSEA is adopting Gene Set Enrichment Analysis statistics commonly used for gene expression analysis to high throughput drug testing data. Our drug screening (Pemovska et al., Cancer Discovery, 2013) was done with a panel of 306 established (FDA-approved) and emerging targeted cancer drugs such as tyrosine-kinase inhibitors (e.g. EGFR, PDGFR, BRAF, MET), S/T-type inhibitors, (e.g. MEK, Plk1, Akt, Aurora, Chk1), and inhibitors of other pathways (HDACs, Hh, BCL2, PI3K, PARP) and many others. The readout was based on viability of cells after a 72 hour culture. The DSEA approach is based on taking the top most sensitive drugs (above a defined sensitivity score cut-off) in an individual cancer sample and then identifying overlapping drug response profiles in previously screened reference samples. Our hypothesis is that the most sensitive drug sets in any given sample tend to show similar response profiles in a cohort of similar samples. We convey the correlations and drug set enrichment analysis results as dendrogram trees, plots and tables with enrichment and significance scores. Interestingly, our results show that clustering of drug sensitivity testing data does not place all cancer cell line samples within well-established subgroups based on biological features or histological origin. We find a similar tendency in ex vivo patient samples. Therefore, comprehensive drug response profiles seen may reveal novel biological data that reflect pharmacologically-relevant, phenotypic cancer cell states. DSEA could also provide novel means to subtype previously poorly characterized cancer samples based on their drug response profiles and thereby in the future facilitate the choice of therapies to patients whose cancers repond in an atypical way as compared to the expectations based on anatomical origin or genomic composition. Citation Format: John Patrick Mpindi, Dimitry Bychkov, Yadav Bhagwan, Disha Malani, Hirasawa Akira, Khalid Saeed, Susanne Hultsch, Sara Kangaspeska, Astrid Murumägi, Caroline A Heckman, Kimmo Porkka, Tero Aittokallio, Krister Wennerberg, Päivi Östling, Olli Kallioniemi. Drug set enrichment analysis : A computational approach to identify functional drug sets. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4184. doi:10.1158/1538-7445.AM2014-4184

Tamoxifen, as a standard treatment of estrogen receptor (ER)-positive breast cancer, reduces breast cancer mortality by 31%. In 50% of advanced ER-positive cancers, however, de novo resistance exists and in 40% of patients with initial response acquired resistance frequently evolves. In order to explore mechanisms underlying endocrine therapy resistance in breast cancer and to identify new opportunities to treat patients, we created seven tamoxifen-resistant breast cancer cell line models that represent the luminal A subtype (MCF-7, T-47D, ZR-75-1), which expresses ER, and the luminal B subtype (BT-474), which additionally expresses the HER2 oncoprotein. We then performed drug sensitivity and resistance testing (DSRT), exome-sequencing and network analysis on all these cancer cell lines to determine the molecular and drug response profiles specific to tamoxifen resistance. As the cells became tamoxifen-resistant, we observed increasing sensitivity towards drugs like ERK1/2-, proteasome- and BCL-family inhibitors, but each of the isogenic cell line pairs had its distinct genomic and drug response profile. We then studied the molecular profiles of the 7 drug-resistant variants by RNA-sequencing in comparison to their 4 isogenic parental cells. We could not detect any common significantly differentially expressed genes (more than 2 fold change) across all the cell lines. However, the cell lines could be grouped into two different categories: The ones with low amount of differentially expressed genes, < 800 genes in the BT-474 and ZR-75-1 cell line pairs (with a 1,2% overlap), and the ones with high amount of differentially expressed genes, >1800 genes in T-47D and MCF-7 (with 7,3% overlap). Further analysis with the Ingenuity pathway analysis tool revealed an involvement of “Fatty Acid Activation” as well as “Stearate Biosynthesis I” in this group. Additionally, we discovered that genes involved in iron metabolism (TFRC, IREB2 and FTL) or iron-regulated genes like CP and NDRG1 are deregulated. These genes and pathways thereby provide avenues to identify new drug vulnerabilities for the tamoxifen resistant cancer cells, which we are now investigating at gene and protein level e.g. by image-based phenotyping. In summary, by combining drug testing data with the RNA-sequencing results, we hope to provide a number of potential drugs as well as matching biomarkers for planning clinical trials for patients with tamoxifen-resistant breast cancer. Citation Format: Susanne Hultsch, Sara Kangaspeska, Matti Kankainen, Vilja Pietiäinen, Olli Kallioniemi. Systematic drug testing and RNA-sequencing of tamoxifen resistant breast cancer cell lines. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2935.

Background: MammaPrintÒ (MP) is a 70-gene based assay that stratifies early-stage breast cancer patients into low and high-risk of relapse. BlueprintÒ (BP) is a 80-gene based assay that stratifies breast cancer patients in 3 molecular subtypes (Basal, Luminal and HER2). Previously, we showed that the MP genes reflect the six hallmarks of cancer (HoC) as defined by Hanahan and Weinberg (Tian et al 2011). Later, these were extended to ten HoC (Hanahan et al. 2011). In this study we annotated the MP 70-and BP 80-genes with respect to the ten HoC. In addition, further stratification of the MP risk results identified ultra low-and high-risk subgroups with specific prognostic (Delahaye et al, 2017, Esserman et al, 2017 and predictive outcomes (Wolf et al, 2017). To gain more insight into their biological significance we related gene expression profiles of the ultra low/high MP subgroups to the ten HoC per BP subtype.Methods: To associate the MP and BP genes to the hallmarks of cancer we used the Cancer Hallmarks Analytics Tool (CHAT). For expression analysis, we selected fulltranscriptome data from 600 FFPE samples that were archived at Agendia. MP subgroups (Ultra high (UH) vs High risk (HR) and Ultra Low (UL) vs Low risk (LR)) from each BP subtype were compared to further understand the biological characteristics by use of Limma and subsequent pathway analysis with GSEA.Results: MP and BP gene functions reflected all ten HoC. Majority of MP and BP genes were associated to sustaining proliferative signaling, followed by genome instability and mutation. Based on the gene expression profiles, UL and UH subgroups were enriched, in opposite directions, in pathways reflecting proliferative and metastatic features. Additionally, the UH subgroup was enriched in evading growth suppressors, genome instability, mutation and enabling replicative immortality pathways. Notably, the UH HER2 subgroup was enriched in several immune signaling pathways. Conclusions:In this study we show that also the extended 10 HoC reflect the MP and BP test. Importantly, our results highlight underlying biological processes of extreme risk MP samples, which might guide relevant treatment decisions as it pertains to the broad spectrum of early breast cancers.Legal entity responsible for the study: Agendia.