Somatic tumor mutations detected by targeted next generation sequencing in minute amounts of serum-derived cell-free DNA

The use of blood-circulating cell-free DNA (cfDNA) as 'liquid-biopsy' is explored worldwide, with hopes for its potential in providing prognostic or predictive information in cancer treatment. In exploring cfDNA, valuable repositories are biobanks containing material collected over time, however these retrospective cohorts have restrictive resources. In this study, we aimed to detect tumor-specific mutations in only minute amounts of serum-derived cfDNA by using a targeted next generation sequencing (NGS) approach. In a retrospective cohort of ten metastatic breast cancer patients, we profiled DNA from primary tumor tissue (frozen), tumor-adjacent normal tissue (formalin-fixed paraffin embedded), and three consecutive serum samples (frozen). Our presented workflow includes comparisons with matched normal DNA or in silico reference DNA to discriminate germline from somatic variants, validation of variants through the detection in at least two DNA samples of an individual, and the use of public databases on variants. By our workflow, we were able to detect a total of four variants traceable as circulating tumor DNA (ctDNA) in the sera of three of the ten patients

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

Ovarian cancer cell line panel (OCCP): Clinical importance of in vitro morphological subtypes

Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological a

TP53 Mutations in Serum Circulating Cell-Free Tumor DNA As Longitudinal Biomarker for High-Grade Serous Ovarian Cancer

The aim of this study was to determine an optimal workflow to detect TP53 mutations in baseline and longitudinal serum cell free DNA (cfDNA) from high-grade serous ovarian carcinomas (HGSOC) patients and to define whether TP53 mutations are suitable as biomarker for disease. TP53 was investigated in tissue and archived serum from 20 HGSOC patients by a next-generation sequencing (NGS) workflow alone or combined with digital PCR (dPCR). AmpliSeq™-focused NGS panels and customized dPCR assays were used for tissue DNA and longitudinal cfDNAs, and Oncomine NGS panel with molecular barcoding was used for baseline cfDNAs. TP53 missense mutations were observed in 17 tissue specimens and in baseline cfDNA for 4/8 patients by AmpliSeq, 6/9 patients by Oncomine, and 4/6 patients by dPCR. Mutations in cfDNA were detected in 4/6 patients with residual disease and 3/4 patients with disease progression within six months, compared to 5/11 patients with no residual disease and 6/13 patients with progression after six months. Finally, mutations were detected at progression in 5/6 patients, but not during chemotherapy. NGS with molecular barcoding and dPCR were most optimal workflows to detect TP53 mutations in baseline and longitudinal serum cfDNA, respectively. TP53 mutations were undetectable in cfDNA during treatment but re-appeared at disease progression, illustrating its promise as a biomarker for disease monitoring

Cell-free DNA mutations as biomarkers in breast cancer patients receiving tamoxifen

The aim was to identify mutations in serum cell-free DNA (cfDNA) associated with disease progression on tamoxifen treatment in metastatic breast cancer (MBC). Sera available at start of therapy, during therapy and at disease progression were selected from 10 estrogen receptor (ER)-positive breast cancer patients. DNA from primary tumor and normal tissue and cfDNA from minute amounts of sera were analyzed by targeted next generation sequencing (NGS) of 45 genes (1,242 exons). At disease progression, stop-gain single nucleotide variants (SNVs) for CREBBP (1 patient) and SMAD4 (1 patient) and non-synonymous SNVs for AKAP9 (1 patient), PIK3CA (2 patients) and TP53 (2 patients) were found. Mutations in CREBBP and SMAD4 have only been occasionally reported in breast cancer. All mutations, except for AKAP9, were also present in the primary tumor but not detected in all blood specimens preceding progression. More sensitive detection by deeper re-sequencing and digital PCR confirmed the occurrence of circulating tumor DNA (ctDNA) and these biomarkers in blood specimens

Ovarian Cancer Cell Line Panel (OCCP): Clinical Importance of In Vitro Morphological Subtypes

Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological and molecular ovarian cancer subtypes is critical to enable reliable preclinical testing. There are approximately 100 publicly available ovarian cancer cell lines but their cellular and molecular characteristics are largely undescribed. We have characterized 39 ovarian cancer cell lines under uniform conditions for growth characteristics, mRNA/microRNA expression, exon sequencing, drug response for clinically-relevant therapeutics and collated all available information on the original clinical features and site of origin. We tested for statistical associations between the cellular and molecular features of the lines and clinical features. Of the 39 ovarian cancer cell lines, 14 were assigned as high-grade serous, four serous-type, one low-grade serous and 20 non-serous type. Three morphological subtypes: Epithelial (n = 21), Round (n = 7) and Spindle (n = 12) were identified that showed distinct biological and molecular characteristics, including overexpression of cell movement and migration-associated genes in the Spindle subtype. Comparison with the original clinical data showed association of the spindle-like tumours with metastasis, advanced stage, suboptimal debulking and poor prognosis. In addition, the expression profiles of Spindle, Round and Epithelial morphologies clustered with the previously described C1-stromal, C5-mesenchymal and C4 ovarian subtype expression profiles respectively. Comprehensive profiling of 39 ovarian cancer cell lines under controlled, uniform conditions demonstrates clinically relevant cellular and genomic characteristics. This data provides a rational basis for selecting models to develop specific treatment approaches for histological and molecular subtypes of ovarian cancer

The prognostic and predictive value of ESR1 fusion gene transcripts in primary breast cancer

Background: In breast cancer (BC), recurrent fusion genes of estrogen receptor alpha (ESR1) and AKAP12, ARMT1 and CCDC170 have been reported. In these gene fusions the ligand binding domain of ESR1 has been replaced by the transactivation domain of the fusion partner constitutively activating the receptor. As a result, these gene fusions can drive tumor growth hormone independently as been shown in preclinical models, but the clinical value of these fusions have not been reported. Here, we studied the prognostic and predictive value of different frequently reported ESR1 fusion transcripts in primary BC. Methods: We evaluated 732 patients with primary BC (131 ESR1-negative and 601 ESR1-positive cases), including two ER-positive BC patient cohorts: one cohort of 322 patients with advanced disease who received first-line endocrine therapy (ET) (predictive cohort), and a second cohort of 279 patients with lymph node negative disease (LNN) who received no adjuvant systemic treatment (prognostic cohort). Fusion gene transcript levels were measured by reverse transcriptase quantitative PCR. The presence of the different fusion transcripts was associated, in uni- and multivariable Cox regression analysis taking along current clinico-pathological characteristics, to progression free survival (PFS) during first-line endocrine therapy in the predictive cohort, and disease- free survival (DFS) and overall survival (OS) in the prognostic cohort. Results: The ESR1-CCDC170 fusion transcript was present in 27.6% of the ESR1-positive BC subjects and in 2.3% of the ESR1-negative cases. In the predictive cohort, none of the fusion transcripts were associated with response to first-line ET. In the prognostic cohort, the median DFS and OS were respectively 37 and 93 months for patients with an ESR1-CCDC170 exon 8 gene fusion transcript and respectively 91 and 212 months for patients without this fusion transcript. In a multivariable analysis, this ESR1-CCDC170 fusion transcript was an independent prognostic factor for DFS (HR) (95% confidence interval (CI): 1.8 (1.2–2.8), P = 0.005) and OS (HR (95% CI: 1.7 (1.1–2.7), P = 0.023). Conclusions: Our study shows that in primary BC only ESR1-CCDC170 exon 8 gene fusion transcript carries prognostic value. None of the ESR1 fusion transcripts, which are considered to have constitutive ER activity, was predictive for outcome in BC with advanced disease treated with endocrine treatment

Ovarian cancer cell line panel (OCCP): Clinical importance of in vitro morphological subtypes

Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological a

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