Dissecting T cell lineage relationships by cellular barcoding

T cells, as well as other cell types, are composed of phenotypically and functionally distinct subsets. However, for many of these populations it is unclear whether they develop from common or separate progenitors. To address such issues, we developed a novel approach, termed cellular barcoding, that allows the dissection of lineage relationships. We demonstrate that the labeling of cells with unique identifiers coupled to a microarray-based detection system can be used to analyze family relationships between the progeny of such cells. To exemplify the potential of this technique, we studied migration patterns of families of antigen-specific CD8+ T cells in vivo. We demonstrate that progeny of individual T cells rapidly seed independent lymph nodes and that antigen-specific CD8+ T cells present at different effector sites are largely derived from a common pool of precursors. These data show how locally primed T cells disperse and provide a technology for kinship analysis with wider utility

KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data

Background: Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe signal, its local genomic environment and the signal distribution across multiple samples. Results: Here we extend the approach to allow comparative analyses of two groups of samples and introduce the R implementation of these two approaches. The comparative module allows for a supervised analysis to be performed, to enable the identification of regions that are differentially aberrated between two user-defined classes. We analyzed data from a series of B- and T-cell lymphomas and were able to retrieve all positive control regions (VDJ regions) in addition to a number of new regions. A t-test employing segmented data, that we implemented, was also able to locate all the positive control regions and a number of new regions but these regions were highly fragmented. Conclusions: KC-SMARTR offers recurrent CNA and class specific CNA detection, at different genomic scales, in a single package without the need for additional segmentation. It is memory efficient and runs on a wide range of machines. Most importantly, it does not rely on data discretization and therefore maximally exploits the biological information in the aCGH data.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

Patient-Derived Organoid Models of Human Neuroendocrine Carcinoma

Gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) is a poorly understood disease with limited treatment options. A better understanding of this disease would greatly benefit from the availability of representative preclinical models. Here, we present the potential of tumor organoids, three-dimensional cultures of tumor cells, to model GEP-NEC. We established three GEP-NEC organoid lines, originating from the stomach and colon, and characterized them using DNA sequencing and immunohistochemistry. Organoids largely resembled the original tumor in expression of synaptophysin, chromogranin and Ki-67. Models derived from tumors containing both neuroendocrine and non-neuroendocrine components were at risk of overgrowth by non-neuroendocrine tumor cells. Organoids were derived from patients treated with cisplatin and everolimus and for the three patients studied, organoid chemosensitivity paralleled clinical response. We demonstrate the feasibility of establishing NEC organoid lines and their potential applications. Organoid culture has the potential to greatly extend the repertoire of preclinical models for GEP-NEC, supporting drug development for this difficult-to-treat tumor type

The T7-Primer Is a Source of Experimental Bias and Introduces Variability between Microarray Platforms

Eberwine(-like) amplification of mRNA adds distinct 6–10 bp nucleotide stretches to the 5′ end of amplified RNA transcripts. Analysis of over six thousand microarrays reveals that probes containing motifs complementary to these stretches are associated with aberrantly high signals up to a hundred fold the signal observed in unaffected probes. This is not observed when total RNA is used as target source. Different T7 primer sequences are used in different laboratories and platforms and consequently different T7 primer bias is observed in different datasets. This will hamper efforts to compare data sets across platforms

Hallmarks of Aromatase Inhibitor Drug Resistance Revealed by Epigenetic Profiling in Breast Cancer

Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor alpha (ER alpha), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ER alpha, H3K4me3, and H3K27me3 were enriched between patients with good or poor outcomes after aromatase inhibition. ER alpha and H3K27me3 patterns were validated in an additional independent set of breast cancer cases (n = 10). We coupled these patterns to array-based proximal gene expression and progression-free survival data derived from a further independent cohort of 72 aromatase inhibitor-treated patients. Through this approach, we determined that the ER alpha and H3K27me3 profiles predicted the treatment outcomes for first-line aromatase inhibitors. In contrast, the H3K4me3 pattern identified was not similarly informative. The classification potential of these genes was only partially preserved in a cohort of 101 patients who received first-line tamoxifen treatment, suggesting some treatment selectivity in patient classification. (C) 2013 AACR

XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data.

BACKGROUND: Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary. RESULTS: We developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations. CONCLUSIONS: XenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples

Fanconi anemia and homologous recombination gene variants are associated with functional DNA repair defects in vitro and poor outcome in patients with advanced head and neck squamous cell carcinoma

Mutations in Fanconi Anemia or Homologous Recombination (FA/HR) genes can cause DNA repair defects and could therefore impact cancer treatment response and patient outcome. Their functional impact and clinical relevance in head and neck squamous cell carcinoma (HNSCC) is unknown. We therefore questioned whether functional FA/HR defects occurred in HNSCC and whether they are associated with FA/HR variants. We assayed a panel of 29 patient-derived HNSCC cell lines and found that a considerable fraction is hypersensitive to the crosslinker Mitomycin C and PARP inhibitors, a functional measure of FA/HR defects. DNA sequencing showed that these hypersensitivities are associated with the presence of bi-allelic rare germline and somatic FA/HR gene variants. We next questioned whether such variants are associated with prognosis and treatment response in HNSCC patients. DNA sequencing of 77 advanced stage HNSCC tumors revealed a 19% incidence of such variants. Importantly, these variants were associated with a poor prognosis (p = 0.027; HR = 2.6, 1.1–6.0) but favorable response to high cumulative cisplatin dose. We show how an integrated in vitro functional repair and genomic analysis can improve the prognostic value of genetic biomarkers. We conclude that repair defects are marked and frequent in HNSCC and are associated with clinical outcome.</p

Cross-species comparison of aCGH data from mouse and human BRCA1- and BRCA2-mutated breast cancers

Background: Genomic gains and losses are a result of genomic instability in many types of cancers. BRCA1- and BRCA2-mutated breast cancers are associated with increased amounts of chromosomal aberrations, presumably due their functions in genome repair. Some of these genomic aberrations may harbor genes whose absence or overexpression may give rise to cellular growth advantage. So far, it has not been easy to identify the driver genes underlying gains and losses. A powerful approach to identify these driver genes could be a cross-species comparison of array comparative genomic hybridization (aCGH) data from cognate mouse and human tumors. Orthologous regions of mouse and human tumors that are commonly gained or lost might represent essential genomic regions selected for gain or loss during tumor development. Methods: To identify genomic regions that are associated with BRCA1- and BRCA2-mutated breast cancers we compared aCGH data from 130 mouse Brca1?/?;p53?/?, Brca2?/?;p53?/? and p53?/? mammary tumor groups with 103 human BRCA1-mutated, BRCA2-mutated and non-hereditary breast cancers. Results: Our genome-wide cross-species analysis yielded a complete collection of loci and genes that are commonly gained or lost in mouse and human breast cancer. Principal common CNAs were the well known MYCassociated gain and RB1/INTS6-associated loss that occurred in all mouse and human tumor groups, and the AURKA-associated gain occurred in BRCA2-related tumors from both species. However, there were also important differences between tumor profiles of both species, such as the prominent gain on chromosome 10 in mouse Brca2?/?;p53?/? tumors and the PIK3CA associated 3q gain in human BRCA1-mutated tumors, which occurred in tumors from one species but not in tumors from the other species. This disparity in recurrent aberrations in mouse and human tumors might be due to differences in tumor cell type or genomic organization between both species. Conclusions: The selection of the oncogenome during mouse and human breast tumor development is markedly different, apart from the MYC gain and RB1-associated loss. These differences should be kept in mind when using mouse models for preclinical studies.MediamaticsElectrical Engineering, Mathematics and Computer Scienc

BRCA1-mutated and basal-like breast cancers have similar aCGH profiles and a high incidence of protein truncating TP53 mutations

<p>Abstract</p> <p>Background</p> <p>Basal-like breast cancers (BLBC) are aggressive breast cancers for which, so far, no targeted therapy is available because they typically lack expression of hormone receptors and HER2. Phenotypic features of BLBCs, such as clinical presentation and early age of onset, resemble those of breast tumors from <it>BRCA1</it>-mutation carriers. The genomic instability of <it>BRCA1</it>-mutated tumors can be effectively targeted with DNA-damaging agents and poly-(ADP-ribose) polymerase 1 (PARP1) inhibitors. Molecular similarities between BLBCs and <it>BRCA1</it>-mutated tumors may therefore provide predictive markers for therapeutic response of BLBCs.</p> <p>Methods</p> <p>There are several known molecular features characteristic for <it>BRCA1</it>-mutated breast tumors: 1) increased numbers of genomic aberrations, 2) a distinct pattern of genomic aberrations, 3) a high frequency of <it>TP53 </it>mutations and 4) a high incidence of complex, protein-truncating <it>TP53 </it>mutations. We compared the frequency of <it>TP53 </it>mutations and the pattern and amount of genomic aberrations between <it>BRCA1</it>-mutated breast tumors, BLBCs and luminal breast tumors by <it>TP53 </it>gene sequencing and array-based comparative genomics hybridization (aCGH) analysis.</p> <p>Results</p> <p>We found that the high incidence of protein truncating <it>TP53 </it>mutations and the pattern and amount of genomic aberrations specific for BRCA1-mutated breast tumors are also characteristic for BLBCs and different from luminal breast tumors.</p> <p>Conclusions</p> <p>Complex, protein truncating TP53 mutations in BRCA1-mutated tumors may be a direct consequence of genomic instability caused by BRCA1 loss, therefore, the presence of these types of TP53 mutations in sporadic BLBCs might be a hallmark of BRCAness and a potential biomarker for sensitivity to PARP inhibition. Also, our data suggest that a small subset of genomic regions may be used to identify BRCA1-like BLBCs. BLBCs share molecular features that were previously found to be specific for BRCA1-mutated breast tumors. These features might be useful for the identification of tumors with increased sensitivity to (high-dose or dose-dense) alkylating agents and PARP inhibitors.</p