| 1. |
- Chapman, Lesley M, et al.
(författare)
-
A crowdsourced set of curated structural variants for the human genome
- 2020
-
Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-7358. ; 16:6
-
Tidskriftsartikel (refereegranskat)abstract
- A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is more challenging. In this study, we manually curated 1235 SVs, which can ultimately be used to evaluate SV callers or train machine learning models. We developed a crowdsourcing app - SVCurator - to help GIAB curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy. SVCurator displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. 'Expert' curators were 93% concordant with each other, and 37 of the 61 curators had at least 78% concordance with a set of 'expert' curators. The curators were least concordant for complex SVs and SVs that had inaccurate breakpoints or size predictions. After filtering events with low concordance among curators, we produced high confidence labels for 935 events. The SVCurator crowdsourced labels were 94.5% concordant with the heuristic-based draft benchmark SV callset from GIAB. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.
|
|
| 2. |
- Brueffer, Christian, et al.
(författare)
-
Biopython Project Update 2016
- 2016
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The Biopython Project is a long-running distributed collaborative effort, supported by the Open Bioinformatics Foundation, which develops a freely available Python library for biological computation.We present here details of the latest Biopython release - version 1.66. New features include: extended Bio.KEGG and Bio.Graphics modules to support drawing KEGG pathways with transparency; extended “abi” Bio.SeqIO parser to decode almost all documented fields used by ABIF instruments; a QCPSuperimposer module using the Quaternion Characteristic Polynomial algorithm for superimposing structures to Bio.PDB; and an extended Bio.Entrez module to implement the NCBI Entrez Citation Matching function and to support NCBI XML files with XSD schemas. Additionally we fixed miscellaneous bugs, enhanced our test suite and continued our efforts to abide by the PEP8 coding style guidelines.We are currently preparing a new release – version 1.67 – that will deprecate the ability to compare SeqRecord objects with “==”, which sometimes lead to surprising results. In addition it will feature a new experimental Bio.phenotype module for working with Phenotype Microarray data; updates to Bio.Data toinclude NCBI genetic code table 25, covering Candidate Division SR1 and Gracilibacteria; an update to Bio.Restriction to include the REBASE May 2016 restriction enzyme list; updates to BioSQL to use foreign keys with SQLite3 databases; as well as corrections to the Bio.Entrez module and the MMCIF structure parser.Our website has been migrated from MediaWiki to GitHub Pages and is now under version control. The continuous integration process on GitHub has been enhanced by including external services like Landscape, Quantified Code and Codecov to perform quality review, test coverage analysis and generation of quality metrics.Finally, our range of Docker containers has been greatly enhanced. In addition to a basic container that includes Python 2 and 3 with Biopython and all its dependencies, as well as a BioSQL container, we now also provide two versions of Jupyter notebook containers: a basic one, and a version including the Biopython tutorial as notebooks.
|
|
| 3. |
- Brueffer, Christian, et al.
(författare)
-
Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative
- 2018
-
Ingår i: JCO Precision Oncology. - 2473-4284. ; 2, s. 1-18
-
Tidskriftsartikel (refereegranskat)abstract
- PurposeIn early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification.MethodsIn total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses.ResultsPathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34).ConclusionClassification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.
|
|
| 4. |
- Brueffer, Christian
(författare)
-
RNA Sequencing for Molecular Diagnostics in Breast Cancer
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Breast cancer is the most common type of cancer in women and, in Sweden, is the most deadly second only to lung cancer. While treatment and diagnostic options have improved in the past decades and short- to mid-term survival is good, long-term survival is much poorer. On the other hand, many women are likely cured by surgery and radiotherapy alone, but receive unnecessary adjuvant treatment leading to undesirable health-related and economic side-effects. Reliably differentiating high-risk from low-risk patients to provide optimal treatment remains a challenge.The Sweden Cancerome Analysis Network–Breast (SCAN-B) project was initiated in 2009 and aims to improve breast cancer outcomes by developing new diagnostics and treatment-predictive tests. Within SCAN-B, tumor material and blood are being biobanked and the transcriptomes of many thousands of breast tumors are being analyzed using RNA sequencing (RNA-seq). The resulting sample collection and dataset provide an unprecedented resource for research, and the information therein may harbor ways to improve prognosis and to predict tumor susceptibility or resistance to therapies.In the four original studies included in this thesis we explored the use of RNA-seq as a diagnostic tool within breast cancer. In study I we described the SCAN-B processes and protocols, and analyzed early data to show the feasibility of using RNA-seq as a diagnostic platform. We showed that the patient population enrolled in SCAN-B largely reflects the characteristics of the total breast cancer patient population and benchmarked RNA-seq against prior techniques. In study II we diagnosed problems in commonly used RNA-seq alignment software and described the development of a software tool to correct the problems and improve data usability. Study III focused on diagnostics for determining the status of the important breast cancer biomarkers ER, PgR, HER2, Ki67, and Nottingham histological grade. We assessed the reproducibility of histopathology in measuring these biomarkers, and developed new ways of predicting their status using RNA-seq-based gene expression. We showed that expression-based biomarkers add value to histopathology by improving prognostic possibilities. In study IV we focused on the prospects of using RNA-seq to detect mutations. We developed a new computational method to profile mutations and used it to describe the mutational landscape of thousands of patient tumors and its impact on patient survival. In particular, we identified mutations in a subset of patients that are known to confer resistance to standard treatments.The hope is that, together, the diagnostic results made possible by the studies herein may one day enable oncologists to adapt treatment plans accordingly and improve patient quality of life and outcomes
|
|
| 5. |
- Brueffer, Christian, et al.
(författare)
-
The mutational landscape of the SCAN‐B real‐world primary breast cancer transcriptome
- 2020
-
Ingår i: EMBO Molecular Medicine. - : EMBO. - 1757-4684 .- 1757-4676. ; 12:10
-
Tidskriftsartikel (refereegranskat)abstract
- Breast cancer is a disease of genomic alterations, of which the panorama of somatic mutations and how these relate to subtypes and therapy response is incompletely understood. Within SCAN‐B (ClinicalTrials.gov: NCT02306096), a prospective study elucidating the transcriptomic profiles for thousands of breast cancers, we developed a RNA‐seq pipeline for detection of SNVs/indels and profiled a real‐world cohort of 3,217 breast tumors. We describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population‐based cohort and relate it to patient survival. We demonstrate that RNA‐seq can be used to call mutations in genes such as PIK3CA, TP53, and ERBB2, as well as the status of molecular pathways and mutational burden, and identify potentially druggable mutations in 86.8% of tumors. To make this rich dataset available for the research community, we developed an open source web application, the SCAN‐B MutationExplorer (http://oncogenomics.bmc.lu.se/MutationExplorer). These results add another dimension to the use of RNA‐seq as a clinical tool, where both gene expression‐ and mutation‐based biomarkers can be interrogated in real‐time within 1 week of tumor sampling.
|
|
| 6. |
- Chapman, Lesley M, et al.
(författare)
-
SVCurator: A Crowdsourcing app to visualize evidence of structural variants for the human genome
- 2019
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- A high quality benchmark for small variants encompassing 88 to 90% of the reference genome has been developed for seven Genome in a Bottle (GIAB) reference samples. However a reliable benchmark for large indels and structural variants (SVs) is yet to be defined. In this study, we manually curated 1235 SVs which can ultimately be used to evaluate SV callers ortrain machine learning models. We developed a crowdsourcing app - SVCurator - to help curators manually review large indels and SVs within the human genome, and report their genotype and size accuracy.SVCurator is a Python Flask-based web platform that displays images from short, long, and linked read sequencing data from the GIAB Ashkenazi Jewish Trio son [NIST RM 8391/HG002]. We asked curators to assign labels describing SV type (deletion or insertion), size accuracy, and genotype for 1235 putative insertions and deletions sampled from different size bins between 20 and 892,149 bp. The crowdsourced results were highly concordant with 37 out ofthe 61 curators having at least 78% concordance with a set of ‘expert’ curators, where there was 93% concordance amongst ‘expert’ curators. This produced high confidence labels for 935 events. When compared to the heuristic-based draft benchmark SV callset from GIAB, the SVCurator crowdsourced labels were 94.5% concordant with the benchmark set. We found that curators can successfully evaluate putative SVs when given evidence from multiple sequencing technologies.
|
|
| 7. |
- Dahlgren, Malin, et al.
(författare)
-
Preexisting Somatic Mutations of Estrogen Receptor Alpha (ESR1) in Early-Stage Primary Breast Cancer
- 2021
-
Ingår i: JNCI Cancer Spectrum. - : Oxford University Press (OUP). - 2515-5091. ; 5:2
-
Tidskriftsartikel (refereegranskat)abstract
- More than three-quarters of primary breast cancers are positive for estrogen receptor alpha (ER; encoded by the gene ESR1), the most important factor for directing anti-estrogenic endocrine therapy (ET). Recently, mutations in ESR1 were identified as acquired mechanisms of resistance to ET, found in 12% to 55% of metastatic breast cancers treated previously with ET. We analyzed 3217 population-based invasive primary (nonmetastatic) breast cancers (within the SCAN-B study, ClinicalTrials.gov NCT02306096), sampled from initial diagnosis prior to any treatment, for the presence of ESR1 mutations using RNA sequencing. Mutations were verified by droplet digital polymerase chain reaction on tumor and normal DNA. Patient outcomes were analyzed using Kaplan-Meier estimation and a series of 2-factor Cox regression multivariable analyses. We identified ESR1 resistance mutations in 30 tumors (0.9%), of which 29 were ER positive (1.1%). In ET-treated disease, presence of ESR1 mutation was associated with poor relapse-free survival and overall survival (2-sided log-rank test P < .001 and P = .008, respectively), with hazard ratios of 3.00 (95% confidence interval = 1.56 to 5.88) and 2.51 (95% confidence interval = 1.24 to 5.07), respectively, which remained statistically significant when adjusted for other prognostic factors. These population-based results indicate that ESR1 mutations at diagnosis of primary breast cancer occur in about 1% of women and identify for the first time in the adjuvant setting that such preexisting mutations are associated to eventual resistance to standard hormone therapy. If replicated, tumor ESR1 screening should be considered in ER-positive primary breast cancer, and for patients with mutated disease, ER degraders such as fulvestrant or other therapeutic options may be considered as more appropriate.
|
|
| 8. |
- Dalal, Hina, et al.
(författare)
-
Clinical associations of ESR2 (estrogen receptor beta) expression across thousands of primary breast tumors
- 2022
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12, s. 1-12
-
Tidskriftsartikel (refereegranskat)abstract
- Estrogen receptor alpha (ERα, encoded by ESR1) is a well-characterized transcription factor expressed in more than 75% of breast tumors and is the key biomarker to direct endocrine therapies. On the other hand, much less is known about estrogen receptor beta (ERβ, encoded by ESR2) and its importance in cancer. Previous studies had some disagreement, however most reports suggested a more favorable prognosis for patients with high ESR2 expression. To add further clarity to ESR2 in breast cancer, we interrogated a large population-based cohort of primary breast tumors (n = 3207) from the SCAN-B study. RNA-seq shows ESR2 is expressed at low levels overall with a slight inverse correlation to ESR1 expression (Spearman R = -0.18, p = 2.2e-16), and highest ESR2 expression in the basal- and normal-like PAM50 subtypes. ESR2-high tumors had favorable overall survival (p = 0.006), particularly in subgroups receiving endocrine therapy (p = 0.03) and in triple-negative breast cancer (p = 0.01). These results were generally robust in multivariable analyses accounting for patient age, tumor size, node status, and grade. Gene modules consistent with immune response were associated to ESR2-high tumors. Taken together, our results indicate that ESR2 is generally expressed at low levels in breast cancer but associated with improved overall survival and may be related to immune response modulation.
|
|
| 9. |
|
|
| 10. |
- Dobilas, Arturas, et al.
(författare)
-
Preoperative ctDNA Levels Are Associated With Poor Overall Survival in Patients With Ovarian Cancer
- 2023
-
Ingår i: Cancer Genomics & Proteomics. - 1790-6245. ; 20:6 suppl, s. 763-770
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND/AIM: Circulating tumor DNA (ctDNA), which is shed from cancer cells into the bloodstream, offers a potential minimally invasive approach for cancer diagnosis and monitoring. This research aimed to assess the preoperative ctDNA levels in ovarian tumors patients' plasma and establish correlations with clinicopathological parameters and patient prognosis.PATIENTS AND METHODS: Tumor DNA was extracted from ovarian tumor tissue from 41 patients. Targeted sequencing using a panel of 127 genes recurrently mutated in cancer was performed to identify candidate somatic mutations in the tumor DNA. SAGAsafe digital PCR (dPCR) assays targeting the candidate mutations were used to measure ctDNA levels in patient plasma samples, obtained prior to surgery, to evaluate ctDNA levels in terms of mutant copy number/ml and variant allele frequency.RESULTS: Somatic mutations were found in 24 tumor samples, 17 of which were from ovarian cancer patients. The most frequently mutated gene was TP53. Preoperative plasma ctDNA levels were detected in 14 of the 24 patients. With higher stage, plasma ctDNA mutant concentration increased (p for trend <0.001). The overall survival of cancer patients with more than 10 ctDNA mutant copies/ml in plasma was significantly worse (p=0.008).CONCLUSION: Pre-operative ctDNA measurement in ovarian cancer patients' plasma holds promise as a predictive biomarker for tumor staging and prognosis.
|
|
