Increased MAPK1/3 Phosphorylation in Luminal Breast Cancer Related with PIK3CA Hotspot Mutations and Prognosis

INTRODUCTION: While mutations in PIK3CA are most frequently (45%) detected in luminal breast cancer, downstream PI3K/AKT/mTOR pathway activation is predominantly observed in the basal subtype. The aim was to identify proteins activated in PIK3CA mutated luminal breast cancer and the clinical relevance of such a protein in breast cancer patients. MATERIALS AND METHODS: Expression levels of 171 signaling pathway (phospho-)proteins established by The Cancer Genome Atlas (TCGA) using reverse phase protein arrays (RPPA) were in silico examined in 361 breast cancers for their relation with PIK3CA status. MAPK1/3 phosphorylation was evaluated with immunohistochemistry on tissue microarrays (TMA) containing 721 primary breast cancer core biopsies to explore the relationship with metastasis-free survival. RESULTS: In silico analyses revealed increased phosphorylation of MAPK1/3, p38 and YAP, and decreased expression of p70S6K and 4E–BP1 in PIK3CA mutated compared to wild-type luminal breast cancer. Augmented MAPK1/3 phosphorylation was most significant, i.e. in luminal A for both PIK3CA exon 9 and 20 mutations and in luminal B for exon 9 mutations. In 290 adjuvant systemic therapy naïve lymph node negative (LNN) breast cancer patients with luminal cancer, high MAPK phosphorylation in nuclei (HR = 0.49; 95% CI, 0.25–0.95; P =.036) and in tumor cells (HR = 0.37; 95% CI, 0.18–0.79; P =.010) was related with favorable metastasis-free survival in multivariate analyses including traditional prognostic factors. CONCLUSION: Enhanced MAPK1/3 phosphorylation in luminal breast cancer is related to PIK3CA exon-specific mutations and correlated with favorable prognosis especially when located in the nuclei of tumor cells

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels

Abstract Next generation sequencing of cell-free DNA (cfDNA) is a promising method for treatment monitoring and therapy selection in metastatic breast cancer (MBC). However, distinguishing tumor-specific variants from sequencing artefacts and germline variation with low false discovery rate is challenging when using large targeted sequencing panels covering many tumor suppressor genes. To address this, we built a machine learning model to remove false positive variant calls and augmented it with additional filters to ensure selection of tumor-derived variants. We used cfDNA of 70 MBC patients profiled with both the small targeted Oncomine breast panel (Thermofisher) and the much larger Qiaseq Human Breast Cancer Panel (Qiagen). The model was trained on the panels’ common regions using Oncomine hotspot mutations as ground truth. Applied to Qiaseq data, it achieved 35% sensitivity and 36% precision, outperforming basic filtering. For 20 patients we used germline DNA to filter for somatic variants and obtained 245 variants in total, while our model found seven variants, of which six were also detected using the germline strategy. In ten tumor-free individuals, our method detected in total one (potentially germline) variant, in contrast to 521 variants detected without our model. These results indicate that our model largely detects somatic variants

MiRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs

Introduction: Breast cancer is a genetically and phenotypically complex disease. To understand the role of miRNAs in this molecular complexity, we performed miRNA expression analysis in a cohort of molecularly well-characterized human breast cancer cell lines to identify miRNAs associated with the most common molecular subtypes and the most frequent genetic aberrations. Methods: Using a microarray carrying LNA™ modified oligonucleotide capture probes), expression levels of 725 human miRNAs were measured in 51 breast cancer cell lines. Differential miRNA expression was explored by unsupervised cluster analysis and was then associated with the molecular subtypes and genetic aberrations commonly present in breast cancer. Results: Unsupervised cluster analysis using the most variably expressed miRNAs divided the 51 breast cancer cell lines into a major and a minor cluster predominantly mirroring the luminal and basal intrinsic subdivision of breast cancer cell lines. One hundred and thirteen miRNAs were differentially expressed between these two main clusters. Forty miRNAs were differentially expressed between basal-like and normal-like/claudin-low cell lines. Within the luminal-group, 39 miRNAs were associated with ERBB2 overexpression and 24 with E-cadherin gene mutations, which are frequent in this subtype of breast cancer cell lines. In contrast, 31 miRNAs were associated with E-cadherin promoter hypermethylation, which, contrary to E-cadherin mutation, is exclusively observed in breast cancer cell lines that are not of luminal origin. Thirty miRNAs were associated with p16INK4 status while only a fe

miRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs

INTRODUCTION: Breast cancer is a genetically and phenotypically complex disease. To understand the role of miRNAs in this molecular complexity, we performed miRNA expression analysis in a cohort of molecularly well-characterized human breast cancer cell lines to identify miRNAs associated with the most common molecular subtypes and the most frequent genetic aberrations. METHODS: Using a microarray carrying LNA™ modified oligonucleotide capture probes), expression levels of 725 human miRNAs were measured in 51 breast cancer cell lines. Differential miRNA expression was explored by unsupervised cluster analysis and was then associated with the molecular subtypes and genetic aberrations commonly present in breast cancer. RESULTS: Unsupervised cluster analysis using the most variably expressed miRNAs divided the 51 breast cancer cell lines into a major and a minor cluster predominantly mirroring the luminal and basal intrinsic subdivision of breast cancer cell lines. One hundred and thirteen miRNAs were differentially expressed between these two main clusters. Forty miRNAs were differentially expressed between basal-like and normal-like/claudin-low cell lines. Within the luminal-group, 39 miRNAs were associated with ERBB2 overexpression and 24 with E-cadherin gene mutations, which are frequent in this subtype of breast cancer cell lines. In contrast, 31 miRNAs were associated with E-cadherin promoter hypermethylation, which, contrary to E-cadherin mutation, is exclusively observed in breast cancer cell lines that are not of luminal origin. Thirty miRNAs were associated with p16(INK4 )status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status. Twelve miRNAs were associated with DNA copy number variation of the respective locus. CONCLUSION: Luminal-basal and epithelial-mesenchymal associated miRNAs determine the subdivision of miRNA transcriptome of breast cancer cell lines. Specific sets of miRNAs were associated with ERBB2 overexpression, p16(INK4a )or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations. Additionally, miRNAs, which are located in a genomic region showing recurrent genetic aberrations, may themselves play a driver role in breast carcinogenesis or contribute to a driver gene in their vicinity. In short, our study provides detailed molecular miRNA portraits of breast cancer cell lines, which can be exploited for functional studies of clinically important miRNAs

Increased MAPK1/3 Phosphorylation in Luminal Breast Cancer Related with PIK3CA Hotspot Mutations and Prognosis

INTRODUCTION: While mutations in PIK3CA are most frequently (45%) detected in luminal breast cancer, downstream PI3K/AKT/mTOR pathway activation is predominantly observed in the basal subtype. The aim was to identify proteins activated in PIK3CA mutated luminal breast cancer and the clinical relevance of such a protein in breast cancer patients. MATERIALS AND METHODS: Expression levels of 171 signaling pathway (phospho-)proteins established by The Cancer Genome Atlas (TCGA) using reverse phase protein arrays (RPPA) were in silico examined in 361 breast cancers for their relation with PIK3CA status. MAPK1/3 phosphorylation was evaluated with immunohistochemistry on tissue microarrays (TMA) containing 721 primary breast cancer core biopsies to explore the relationship with metastasis-free survival. RESULTS: In silico analyses revealed increased phosphorylation of MAPK1/3, p38 and YAP, and decreased expression of p70S6K and 4E–BP1 in PIK3CA mutated compared to wild-type luminal breast cancer. Augmented MAPK1/3 phosphorylation was most significant, i.e. in luminal A for both PIK3CA exon 9 and 20 mutations and in luminal B for exon 9 mutations. In 290 adjuvant systemic therapy naïve lymph node negative (LNN) breast cancer patients with luminal cancer, high MAPK phosphorylation in nuclei (HR = 0.49; 95% CI, 0.25–0.95; P =.036) and in tumor cells (HR = 0.37; 95% CI, 0.18–0.79; P =.010) was related with favorable metastasis-free survival in multivariate analyses including traditional prognostic factors. CONCLUSION: Enhanced MAPK1/3 phosphorylation in luminal breast cancer is related to PIK3CA exon-specific mutations and correlated with favorable prognosis especially when located in the nuclei of tumor cells

High‐throughput isolation of circulating tumor DNA: a comparison of automated platforms

The emerging interest in circulating tumor DNA (ctDNA) analyses for clinical trials has necessitated the development of a high‐throughput method for fast, reproducible, and efficient isolation of ctDNA. Currently, the majority of ctDNA studies use the manual QIAamp (QA) platform to isolate DNA from blood. The purpose of this study was to compare two competing automated DNA isolation platforms [Maxwell (MX) and QIAsymphony (QS)] to the current ‘gold standard’ QA to facilitate high‐throughput processing of samples in prospective trials. We obtained blood samples from healthy blood donors and metastatic cancer patients for plasma isolation. Total cell‐free DNA (cfDNA) quantity was assessed by TERT quantitative PCR. Recovery efficiency was investigated by quantitative PCR analysis of spiked‐in synthetic plant DNA. In addition, a β‐actin fragmentation assay was performed to determine the amount of contamination by genomic DNA from lysed leukocytes. ctDNA quality was assessed by digital PCR for somatic variant detection. cfDNA quantity and recovery efficiency were lowest using the MX platform, whereas QA and QS showed a comparable performance. All platforms preferentially isolated small (136 bp) DNA fragments over large (420 and 2000 bp) DNA fragments. Detection of the number variant and wild‐type molecules was most comparable between QA and QS. However, there was no significant difference in variant allele frequency comparing QS and MX to QA. In summary, we show that the QS platform has comparable performance to QA, the ‘gold standard’, and outperformed the MX platform depending on the readout used. We conclude that the QS can replace the more laborious QA platform, especially when high‐throughput cfDNA isolation is needed

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels

Next generation sequencing of cell-free DNA (cfDNA) is a promising method for treatment monitoring and therapy selection in metastatic breast cancer (MBC). However, distinguishing tumor-specific variants from sequencing artefacts and germline variation with low false discovery rate is challenging when using large targeted sequencing panels covering many tumor suppressor genes. To address this, we built a machine learning model to remove false positive variant calls and augmented it with additional filters to ensure selection of tumor-derived variants. We used cfDNA of 70 MBC patients profiled with both the small targeted Oncomine breast panel (Thermofisher) and the much larger Qiaseq Human Breast Cancer Panel (Qiagen). The model was trained on the panels’ common regions using Oncomine hotspot mutations as ground truth. Applied to Qiaseq data, it achieved 35% sensitivity and 36% precision, outperforming basic filtering. For 20 patients we used germline DNA to filter for somatic variants and obtained 245 variants in total, while our model found seven variants, of which six were also detected using the germline strategy. In ten tumor-free individuals, our method detected in total one (potentially germline) variant, in contrast to 521 variants detected without our model. These results indicate that our model largely detects somatic variants.Pattern Recognition and Bioinformatic

Detection of circulating tumour DNA after neoadjuvant chemoradiotherapy in patients with locally advanced oesophageal cancer

Active surveillance instead of standard surgery after neoadjuvant chemoradiotherapy (nCRT) has been proposed for patients with oesophageal cancer. Circulating tumour DNA (ctDNA) may be used to facilitate selection of patients for surgery. We show that detection of ctDNA after nCRT seems highly suggestive of major residual disease. Tumour biopsies and blood samples were taken before, and 6 and 12 weeks after, nCRT. Biopsies were analysed with regular targeted next-generation sequencing (NGS). Circulating cell-free DNA (cfDNA) was analysed using targeted NGS with unique molecular identifiers and digital polymerase chain reaction. cfDNA mutations matching pre-treatment biopsy mutations confirmed the presence of ctDNA. In total, 31 patients were included, of whom 24 had a biopsy mutation that was potentially detectable in cfDNA (77%). Pre-treatment ctDNA was detected in nine of 24 patients (38%), four of whom had incurable disease progression before surgery. Pre-treatment ctDNA detection had a sensitivity of 47% (95% CI 24–71) (8/17), specificity of 85% (95% CI 42–99) (6/7), positive predictive value (PPV) of 89% (95% CI 51–99) (8/9), and negative predictive value (NPV) of 40% (95% CI 17–67) (6/15) for detecting major residual disease (>10% residue in the resection specimen or progression before surgery). After nCRT, ctDNA was detected in three patients, two of whom had disease progression. Post-nCRT ctDNA detection had a sensitivity of 21% (95% CI 6–51) (3/14), specificity of 100% (95% CI 56–100) (7/7), PPV of 100% (95% CI 31–100) (3/3), and NPV of 39% (95% CI 18–64) (7/18) for detecting major residual disease. The addition of ctDNA to the current set of diagnostics did not lead to more patients being clinically identified with residual disease. These results indicate that pre-treatment and post-nCRT ctDNA detection may be useful in identifying patients at high risk of disease progression. The addition of ctDNA analysis to the current set of diagnostic modalities may not improve detection of residual disease after nCRT