The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no ...reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way.
A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability.
This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN).
Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
According to the present view, metastasis marks the end in a sequence of genomic changes underlying the progression of an epithelial cell to a lethal cancer. Here, we aimed to find out at what stage ...of tumor development transformed cells leave the primary tumor and whether a defined genotype corresponds to metastatic disease. To this end, we isolated single disseminated cancer cells from bone marrow of breast cancer patients and performed single-cell comparative genomic hybridization. We analyzed disseminated tumor cells from patients after curative resection of the primary tumor (stage M0), as presumptive progenitors of manifest metastasis, and from patients with manifest metastasis (stage M1). Their genomic data were compared with those from microdissected areas of matched primary tumors. Disseminated cells from M0-stage patients displayed significantly fewer chromosomal aberrations than primary tumors or cells from M1-stage patients (P < 0.008 and P < 0.0001, respectively), and their aberrations appeared to be randomly generated. In contrast, primary tumors and M1 cells harbored different and characteristic chromosomal imbalances. Moreover, applying machine-learning methods for the classification of the genotypes, we could correctly identify the presence or absence of metastatic disease in a patient on the basis of a single-cell genome. We suggest that in breast cancer, tumor cells may disseminate in a far less progressed genomic state than previously thought, and that they acquire genomic aberrations typical of metastatic cells thereafter. Thus, our data challenge the widely held view that the precursors of metastasis are derived from the most advanced clone within the primary tumor.
The outcome of prostate cancer is highly unpredictable. To assess the dynamics of systemic disease and to identify patients at high risk for early relapse we followed the fate of disseminated tumor ...cells in bone marrow for up to 10 years and genetically analyzed such cells isolated at various stages of disease.
Nine hundred bone marrow aspirates from 384 patients were stained using the monoclonal antibody A45-B/B3 directed against cytokeratins 8, 18, and 19. Log-rank statistics and Cox regression analysis were applied to determine the prognostic impact of positive cells detected before surgery (244 patients) and postoperatively (214 patients). Samples from primary tumors (n = 55) and single disseminated tumor cells (n = 100) were analyzed by comparative genomic hybridization.
Detection of cytokeratin-positive cells before surgery was the strongest independent risk factor for metastasis within 48 months (P < .001; relative risk RR, 5.5; 95% CI, 2.4 to 12.9). In contrast, cytokeratin-positive cells detected 6 months to 10 years after radical prostatectomy were consistently present in bone marrow with a prevalence of approximately 20% but had no influence on disease outcome. Characteristic genotypes of cytokeratin-positive cells were selected at manifestation of metastasis.
Cytokeratin-positive cells in the bone marrow of prostate cancer patients are only prognostically relevant when detected before surgery. Because we could not identify significant genetic differences between pre- and postoperatively isolated tumor cells before manifestation of metastasis, we postulate the existence of perioperative stimuli that activate disseminated tumor cells. Patients with cytokeratin-positive cells in bone marrow before surgery may therefore benefit from adjuvant therapies.
Abstract Molecular profiling of rare cancer cells derived from liquid biopsy holds promise to monitor systemic cancer progression and guide personalized therapy interventions. However, this ...necessitates reliable and comprehensive profiling of samples at the single cell level and requires whole genome amplification (WGA), which adds amplification errors and bias to common sequencing errors. Therefore, we established a single-cell mutation calling workflow that can identify clinically actionable somatic mutations in rare single cell DNA with high sensitivity and accuracy. Our workflow is an end-to-end solution which can take FASTQ reads from any Panel Sequencing (PanelSeq) data and gives out single nucleotide variations (SNVs), insertion-deletions (InDels), and copy number variations (CNVs) in single cells alongside with mutation specific information from ClinVar, OncoKB, pathogenicity scores, conservations scores as well as known association to drug response and resistance. We incorporated a Bayesian Neural Network (NN) based Classifier algorithm that assigns a single statistical confidence score to the mutations, enabling prioritization of the mutations in a robust manner. On the training dataset including MDA-MB-453, BT474, BT549 and ZR-75.1 cell lines, the classifier achieved 91% sensitivity and 96% specificity (AUC=0.98), while on the independent test collective including HCC1395/HCC1395BL cell lines it achieved 84% sensitivity and 96% specificity (AUC=0.90). We applied the workflow to patient samples suffering from advanced metastatic Triple Negative Breast Cancer (TNBC) assayed with in-house single-cell Integrated Mutation Profiling of Actionable Cancer Targets (scIMPACT) and single-cell Whole Exome Sequencing (scWES). Using our method, we detected clinically actionable SNVs with pathogenic relevance in primary tumor, metastatic specimens, circulating tumor cells (CTCs) from blood and disseminated cancer cells (DCC) from pleural effusions, as well as the matched CTC-derived in vivo models (CDX). Notably, functional in vitro drug screens performed on CDX models uncovered susceptibility to approved drug treatments conferred by mutations detected in the samples. In summary, we introduce a novel mutation calling workflow facilitating detection of SNVs, Indels and CNVs in rare single cells with high sensitivity and precision. Confident scores assigned by the NN classifier enables identification of functionally actionable mutations. The workflow was executed in GDPR compliant fashion and follows all data protection standards imposed by the European laws. Accordingly, the method is well suited for molecular profiling, therapeutic target selection and longitudinal monitoring of liquid biopsy specimens in diagnostic environments. Citation Format: Adithi Ravikumar Varadarajan, Thomas Ragg, Jonas Grote, Vadim Dechand, Steffi Treitschke, Clara Chaiban, Christoph Klein, Zbigniew Czyz, Jens Warfsmann. A targeted mutation calling workflow - end to end - from FASTQ to clinical report for single cells abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2283.
Abstract Introduction: Liquid biopsy enables minimally invasive molecular profiling of systemic cancer for diagnosis, therapy selection and longitudinal monitoring of minimal residual disease. ...Cell-based liquid biopsy methods are particularly well suited to examine the extent of genetic heterogeneity and clonal diversity among metastatic cancer cells, which are believed to be the major determinants of therapeutic failure. However, isolation and molecular profiling of circulating tumor cells (CTCs) from body fluids is challenging due to methodological constrains. Methods: To overcome these challenges, we have developed a workflow for detection, isolation, and mutational profiling of CTCs. The workflow utilizes the FDA-approved CellSearch System® and a novel method for single-cell Integrated Mutation Profiling of Actionable Cancer Targets (scIMPACT). Our workflow was optimized for reliable detection of copy number variations (CNVs) and single nucleotide variations (SNVs) in single CTCs. The method was then applied to 31 CTCs alongside with 15 matched germline samples. Results: After implementation of a customized Bayesian neural network based algorithm, accounting for biases introduced during amplification of single-cell DNA and prioritizing the detected mutations, we achieved a sensitivity of 91% and a specificity of 96%, AUC = 0.98. The CTC analysis revealed presence of CNVs affecting known oncogenes and tumor suppressor genes (including MYC, CCND1, FGF4, TP53 and RB1) as well as SNVs classified as pathogenic (e.g., ARID1A Q775*). Notably, in the examined specimens most of the chromosomal aberrations (51-82%) were clonal across all cells of a given patient. In contrast, most of the detected somatic SNVs (67-96%) were sub-clonal and thus present only in a subset of CTCs. Conclusion: In summary, the novel scIMPACT workflow enabled reliable and accurate genomic profiling of patient-derived CTCs in late-stage breast cancer patients. Our proof-of-concept study revealed evidence of genetic diversity of SNVs among the examined CTCs, which might have been acquired late in progression but relevant for therapy escape in the studied cases. Further analyses are still needed for assessing the generalizability of our finding as well as for deeper understanding of the molecular basis orchestrating the establishment and maintenance of heterogeneous genotypes of CTCs in breast cancer. Citation Format: Zbigniew T. Czyż, Clara Chaiban, Adithi Ravikumar Varadarajan, Cäcilia Köstler, Vadim Dechand, Jonas Grote, Thomas Ragg, Bernhard Polzer, Jens Warfsmann, Christoph A. Klein. Genomic profiling with the novel single-cell IMPACT assay reveals evidence of genetic heterogeneity among CTCs in late-stage breast cancer patients abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3692.
Abstract Liquid biopsy provides opportunities to guide therapy decisions using biomarkers such as circulating tumor cells (CTCs) even years after the primary tumor resection. For this, novel methods ...permitting the genomic profiling of CTCs are needed to characterize systemic cancer spread and identify clinically targetable genomic alterations. Hence, we developed an assay based on the FDA-approved MSK-IMPACT® assay to analyze the genomes of single-cells. We present a series of benchmarking experiments using well-characterized cell lines to demonstrate the performance of our new Single-Cell Integrated Mutation Profiling of Actionable Cancer Targets (scIMPACT) assay.The scIMPACT assay was developed using Ampli1® WGA products of single-cell and pool samples. The workflow consists of an optimized library preparation protocol followed by targeted sequencing with the scIMPACT panel. A bioinformatics mutation calling workflow facilitates the detection of single nucleotide variations (SNVs), indels, and copy number variations (CNVs). To account for WGA associated bias, we developed an in-house proprietary machine learning based classifier algorithm. Two sample collectives were prepared and used for training and validating the workflow. The first collective, including genomic libraries of MDA-MB-453, BT474, BT549 and ZR-75.1 samples, was used for training of the mutation detection workflow. The second collective, consisting of HCC1395/HCC1395BL cell lines representing matched tumor and normal cells, was used to validate the performance of the assay. The scIMPACT assay identified somatic mutations in all WGA products as predicted from bulk gDNA samples and published literature. Notably, evidence of genetic heterogeneity was reported at the single cell level. Pertaining to the SNV detection, the training collective displayed 91% sensitivity and 96% specificity (AUC=0.98) while the validation collective demonstrated similar performance with 84% sensitivity and 96% specificity (AUC=0.90) proving the applicability of our workflow across different datasets. Furthermore, CNV analysis showed high concordance between matching gDNA and WGA samples. Our workflow enables mutational profiling with high specificity and sensitivity and accurate CNV detection in single cells. The scIMPACT assay has been successfully adapted to analyze samples at the single-cell level. Therefore, our method can be applied for the genomic profiling of patient-derived CTCs. Citation Format: Clara Chaiban, Adithi Ravikumar Varadarajan, Jonas Grote, Thomas Ragg, Isabell Blochberger, Vadim Dechand, Jens Warfsmann, Christoph A. Klein, Zbigniew T. Czyż. Genomic profiling of single cancer cells using the novel single-cell integrated mutational profiling of actionable cancer targets (scIMPACT) assay abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3694.
Only few selected cancer cells drive tumor progression and are responsible for therapy resistance. Their specific genomic characteristics, however, are largely unknown because high-resolution genome ...analysis is currently limited to DNA pooled from many cells. Here, we describe a protocol for array comparative genomic hybridization (array CGH), which enables the detection of DNA copy number changes in single cells. Combining a PCR-based whole genome amplification method with arrays of highly purified BAC clones we could accurately determine known chromosomal changes such as trisomy 21 in single leukocytes as well as complex genomic imbalances of single cell line cells. In single T47D cells aberrant regions as small as 1-2 Mb were identified in most cases when compared to non-amplified DNA from 10⁶ cells. Most importantly, in single micrometastatic cancer cells isolated from bone marrow of breast cancer patients, we retrieved and confirmed amplifications as small as 4.4 and 5 Mb. Thus, high-resolution genome analysis of single metastatic precursor cells is now possible and may be used for the identification of novel therapy target genes.
We view the problem of estimating the defect content of a document after an inspection as a machine learning problem: The goal is to learn from empirical data the relationship between certain ...observable features of an inspection (such as the total number of different defects detected) and the number of defects actually contained in the document. We show that some features can carry significant nonlinear information about the defect content. Therefore, we use a nonlinear regression technique, neural networks, to solve the learning problem. To select the best among all neural networks trained on a given data set, one usually reserves part of the data set for later cross-validation; in contrast, we use a technique which leaves the full data set for training. This is an advantage when the data set is small. We validate our approach on a known empirical inspection data set. For that benchmark, our novel approach clearly outperforms both linear regression and the current standard methods in software engineering for estimating the defect content, such as capture-recapture. The validation also shows that our machine learning approach can be successful even when the empirical inspection data set is small.
In this paper I want to argue that the combination of evolutionary algorithms and neural networks can be fruitful in several ways. When estimating a functional relationship on the basis of empirical ...data we face three basic problems. Firstly, we have to deal with noisy and finite-sized data sets which is usually done be regularization techniques, for example Bayesian learning. Secondly, for many applications we need to encode the problem by features and have to decide which and how many of them to use. Bearing in mind the empty space phenomenon, it is often an advantage to select few features and estimate a nonlinear function in a low-dimensional space. Thirdly, if we have trained several networks, we are left with the problem of model selection. These problems can be tackled by integrating several stochastic methods into an evolutionary search algorithm. The search can be designed such that it explores the parameter space to find regions corresponding to networks with a high posterior probability o
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Every newspaper publisher has to solve the problem of printing a large number of copies and distributing them to the retail traders trying to keep the return quote as low as possible. To solve this ...task he needs to estimate as accurately as possible the sales rates for each retail trader. In this paper, we want to show how a prediction system for many thousands of retail traders can be built based on the prediction of the individual sales rates. This prediction is based on a neural network approach. We use a Bayesian learning algorithm to regularize the networks automatically. Furthermore, a top down search based on mutual information is used to optimize the input structure of the networks. The neural network approach reduces the return quota significantly. We conclude with the observation that several data sets are hard to predict and give reasons for that behaviour.
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