Systematic drug screening reveals specific vulnerabilities and co-resistance patterns in endocrine-resistant breast cancer

Abstract Background The 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. Methods Using 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. Results We 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. Conclusion This 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

Systematic drug screening reveals specific vulnerabilities and co-resistance patterns in endocrine-resistant breast cancer

Background: The 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. Methods: Using 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. Results: We 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. Conclusion: This 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.Peer reviewe

Seven shades of tamoxifen resistance : Molecular mechanisms of drug resistance in breast cancer

Tamoxifen treatment of estrogen receptor-positive breast cancer reduces breast cancer mortality. However, resistance to tamoxifen develops frequently. A plethora of resistance mechanisms have been described but their biological importance, clinical significance, and possibilities for diagnostic or therapeutic intervention are poorly understood. Fusion genes, for example, have the potential as therapeutic targets or diagnostic tools as they are highly cancer-specific. In order to determine the mechanisms underlying endocrine therapy resistance and to identify new opportunities to defy resistance in breast cancer, we created seven tamoxifen-resistant breast cancer cell lines. We characterized the resistant cell lines by exome-sequencing to identify possible mutations or genomic rearrangements involved in drug resistance. RNA-sequencing was applied to shed light on the nature of fusion genes in the parental cell line as well as their contribution to acquired drug resistance. RNA-sequencing also exposed gene expression and pathway changes, which were followed up in detail in one of the resistant cell lines. In addition to drug sensitivity and resistance testing combined with high-content imaging, network analysis determined the drug response profiles. We further uncovered potential drug targets of tamoxifen resistance. This intensive molecular profiling revealed that each tamoxifen-resistant cell line developed its own resistance mechanism and acquired individual drug vulnerabilities. However, we were able to detect a common increased sensitivity towards an ERK1/2-inhibitor. On the other hand, we discovered co-resistance to paclitaxel, which is mostly driven by the slower growth rate of the resistant cells. Analysis of differentially expressed genes identified pathways which were associated with cell cycle, protein modification, and metabolism, especially with the cholesterol pathway. After further investigation we identified that the prevention of lysosomal membrane permeabilization was associated with drug resistance in the T-47D tamoxifen-resistant cell lines. Targeting this mechanism remains challenging. We further revealed the complex nature of fusion genes, which include the high prevalence of alternative splicing and the lack of recurrence across different breast cancer cell lines. Additionally, we identified fusion genes only present in the resistant cell lines. However, these were mainly cell clone-specific or recurrent read-through fusions. Exome-sequencing revealed no known or common mutations or copy number changes related to resistance development. With the diversity of fusion genes, mutations, copy number changes, differentially expressed genes, pathway changes, and drug responses in tamoxifen-resistant cells, it is safe to say that tamoxifen resistance cannot simply be explained by one common mechanism. Therefore, it is likely that countering the resistance will require different therapeutic approaches.Verschiedene Therapien, wie Operation, Bestrahlung, Chemo-, Targeted- und Hormontherapie, werden zur Behandlung von Brustkrebs eingesetzt. Die Überlebensraten haben sich aufgrund von neuen Therapien und verbesserter Diagnostik erhöht. Es können sich jedoch Arzneimittelresistenzen entwickeln, die oft zu einem Therapieversagen führen. Es wird angenommen, dass die Entwicklung von Arzneimittelresistenzen, die zu einem fortschreitenden Wachstum des Krebses führen, durch eine Vielzahl von Mechanismen verursacht wird. Diese Vielseitigkeit spiegelt höchstwahrscheinlich die molekularen Subtypen des Brustkrebses, sowie die spezifischen Eigenschaften einzelner Krebszellen wider. Ziel meiner Doktorarbeit war es daher, molekulare Resistenzmechanismen mit Hilfe von Tamoxifen-resistenten Zellen zu erforschen und mögliche alternative Therapieansätze zu identifizieren. Tamoxifen ist ein Arzneistoff der zur Behandlung von Östrogen-Rezeptor-positiven Brustkrebs eingesetzt wird. Intensives molekulares Profilieren zeigte, dass jede Tamoxifen-resistente Zelllinie ihren eigenen Resistenzmechanismus entwickelte. Verdeutlicht wurde dies durch die Vielfalt an Fusionsgenen, Mutationen und Chromosomveränderungen, sowie dem individuellen Wechsel der Geneexpression, der Signalwege und der Arzneimittelreaktionen in den einzelnen Tamoxifen-resistenten Zellen. Es ist daher wahrscheinlich, dass die Bekämpfung der Resistenz unterschiedliche therapeutische Ansätze erfordert

Association of tamoxifen resistance and lipid reprogramming in breast cancer

Background: Tamoxifen 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. Methods: In 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 Results: We 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. Conclusion: Altogether, our findings highlight a major role of LMP prevention in tamoxifen resistance, and suggest novel drug vulnerabilities associated with this phenotype.Peer reviewe

Reanalysis of RNA-Sequencing Data Reveals Several Additional Fusion Genes with Multiple Isoforms

<div><p>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.</p> </div

Identified and validated fusion gene candidates.

<p>13 fusion genes were detected from BT-474 and MCF-7 breast cancer cells. A minimum of two paired-end reads and two fusion junction spanning reads were a prerequisite for choosing a fusion gene candidate for further analysis. Copy number amplification, location on a genomic break point (at least one of the fusion partner genes in both cases) and <i>in-frame</i> prediction are indicated. Lower level copy number gains were not included in the analysis. “Previous characterization” -column summarizes the level of information available for the individual fusion transcripts prior to this study. Validation refers to verification at the transcript level by RT-PCR or Northern blotting (for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048745#pone.0048745-Futreal1" target="_blank">[20]</a>).</p

Transcript- and genomic level validation of the fusion genes.

<p>A. Validation of the fusion genes from BT-474 and MCF-7 on the cDNA level by RT-PCR. B. Genomic DNA sequence at the fusion gene break point of <i>THRA-AC090627.1</i> (top), <i>TOB1-SYNRG</i> (middle) and <i>MED1-ACSF2</i> (bottom). Chromosomal positions of the fusion break points are indicated by black arrows. Gene and transcript structures as well as nucleotide sequences at the break points are drawn in blue for 5′ and in red for 3′ partner genes. Gene structures above and below chromosome coordinates imply forward and reverse strand, respectively. Transcript structures of gene fusions are indicated below the gene structures and connected with gray lines. Genomic DNA sequence at the break point (indicated by asterisk) is shown below the transcript structures. Black color indicates nucleotides that match to both (<i>THRA-AC090627.1, MED1-ACSF2</i>) or neither partner genes (<i>TOB1-SYNRG</i>).</p

Genomic rearrangements underlying fusion gene formation.

<p>Circos plots illustrating chromosomal translocations in BT-474 (upper) and MCF-7 (lower). Chromosomes are drawn into scale around the rim of the circle and data are plotted on these coordinates. Intrachromosomal (red) and interchromosomal (blue) fusions are indicated by arcs. Copy number profiles are plotted in the inner circle. Amplifications are shown in red and deletions in blue. N denotes the number of fusion genes per cell line.</p

STAT3 activation in HER2-positive breast cancers: Analysis of data from a large prospective trial.

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.info:eu-repo/semantics/publishe