Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology

High-throughput biological assays such as microarrays let us ask very detailed questions about how diseases operate, and promise to let us personalize therapy. Data processing, however, is often not described well enough to allow for exact reproduction of the results, leading to exercises in "forensic bioinformatics" where aspects of raw data and reported results are used to infer what methods must have been employed. Unfortunately, poor documentation can shift from an inconvenience to an active danger when it obscures not just methods but errors. In this report we examine several related papers purporting to use microarray-based signatures of drug sensitivity derived from cell lines to predict patient response. Patients in clinical trials are currently being allocated to treatment arms on the basis of these results. However, we show in five case studies that the results incorporate several simple errors that may be putting patients at risk. One theme that emerges is that the most common errors are simple (e.g., row or column offsets); conversely, it is our experience that the most simple errors are common. We then discuss steps we are taking to avoid such errors in our own investigations.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS291 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Protein and phosphoprotein levels in glioma and adenocarcinoma cell lines grown in normoxia and hypoxia in monolayer and three-dimensional cultures

<p>Abstract</p> <p>Background</p> <p>Three dimensional (3D) growths of cancer cells in vitro are more reflective of in situ cancer cell growth than growth in monolayer (2D). The present study is designed to determine changes in protein and phosphoprotein that reflect adaptation of tumor cells to 3D as compared to 2D. Since relative hypoxia is a common feature of most solid tumors, the present study also aims to look at the impact of transition from normoxia to hypoxia in these two growth conditions.</p> <p>Results</p> <p>Using reverse-phase protein arrays, we compared levels of 121 different phosphorylated and non-phosphorylated proteins in 5 glioma and 6 adenocarcinoma lines under conditions of 3D and monolayer culture in normoxia and hypoxia. A three-way analysis of variance showed levels of 82 antibodies differed between media (2D vs. 3D) and 49 differed between treatments (hypoxia vs. normoxia). Comparing 2D to 3D growth, 7 proteins were commonly (i.e., > 50% of tumors) elevated in 3D: FAK, AKT, Src, GSK3αβ, TSC2, p38, and NFκβp65. Conversely, 7 other proteins are commonly decreased: ATRIP, ATR, β-catenin, BCL-X, cyclin B1, Egr-1, and HIF-1α. Comparing normoxia to hypoxia, only NCKIPSD was commonly elevated in hypoxia; 6 proteins were decreased: cyclin B1, 4EBP1(Ser65), c-Myc, SMAD3(Ser423), S6(Ser235), and S6(Ser240). Hypoxia affected glioma cell lines differently from adenocarcinoma cell lines: 8 proteins were increased in gliomas (BAX, caspase 7, HIF-1α, c-JUN, MEK1, PARP 1 cleaved, Src, and VEGFR2) and none in adenocarcinomas.</p> <p>Conclusions</p> <p>We identified subsets of proteins with clearly concordant/discordant behavior between gliomas and adenocarcinomas. In general, monolayer to 3D culture differences are clearer than normoxia to hypoxia differences, with anti-apoptotic, cytoskeletal rearrangement and cell survival pathways emphasized in the former and mTOR pathway, transcription, cell-cycle arrest modulation, and increased cell motility in the latter.</p

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

BACKGROUND: Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed. RESULTS: We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages. CONCLUSIONS: The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling

Gene expression signature of estrogen receptor α status in breast cancer

BACKGROUND: Estrogens are known to regulate the proliferation of breast cancer cells and to modify their phenotypic properties. Identification of estrogen-regulated genes in human breast tumors is an essential step toward understanding the molecular mechanisms of estrogen action in cancer. To this end we generated and compared the Serial Analysis of Gene Expression (SAGE) profiles of 26 human breast carcinomas based on their estrogen receptor α (ER) status. Thus, producing a breast cancer SAGE database of almost 2.5 million tags, representing over 50,000 transcripts. RESULTS: We identified 520 transcripts differentially expressed between ERα-positive (+) and ERα-negative (-) primary breast tumors (Fold change ≥ 2; p < 0.05). Furthermore, we identified 220 high-affinity Estrogen Responsive Elements (EREs) distributed on the promoter regions of 163 out of the 473 up-modulated genes in ERα (+) breast tumors. In brief, we observed predominantly up-regulation of cell growth related genes, DNA binding and transcription factor activity related genes based on Gene Ontology (GO) biological functional annotation. GO terms over-representation analysis showed a statistically significant enrichment of various transcript families including: metal ion binding related transcripts (p = 0.011), calcium ion binding related transcripts (p = 0.033) and steroid hormone receptor activity related transcripts (p = 0.031). SAGE data associated with ERα status was compared with reported information from breast cancer DNA microarrays studies. A significant proportion of ERα associated gene expression changes was validated by this cross-platform comparison. However, our SAGE study also identified novel sets of genes as highly expressed in ERα (+) invasive breast tumors not previously reported. These observations were further validated in an independent set of human breast tumors by means of real time RT-PCR. CONCLUSION: The integration of the breast cancer comparative transcriptome analysis based on ERα status coupled to the genome-wide identification of high-affinity EREs and GO over-representation analysis, provide useful information for validation and discovery of signaling networks related to estrogen response in this malignancy

Global analysis of aberrant pre-mRNA splicing in glioblastoma using exon expression arrays

<p>Abstract</p> <p>Background</p> <p>Tumor-predominant splice isoforms were identified during comparative <it>in silico </it>sequence analysis of EST clones, suggesting that global aberrant alternative pre-mRNA splicing may be an epigenetic phenomenon in cancer. We used an exon expression array to perform an objective, genome-wide survey of glioma-specific splicing in 24 GBM and 12 nontumor brain samples. Validation studies were performed using RT-PCR on glioma cell lines, patient tumor and nontumor brain samples.</p> <p>Results</p> <p>In total, we confirmed 14 genes with glioma-specific splicing; seven were novel events identified by the exon expression array (<it>A2BP1, BCAS1, CACNA1G, CLTA, KCNC2, SNCB</it>, and <it>TPD52L2</it>). Our data indicate that large changes (> 5-fold) in alternative splicing are infrequent in gliomagenesis (< 3% of interrogated RefSeq entries). The lack of splicing changes may derive from the small number of splicing factors observed to be aberrantly expressed.</p> <p>Conclusion</p> <p>While we observed some tumor-specific alternative splicing, the number of genes showing exclusive tumor-specific isoforms was on the order of tens, rather than the hundreds suggested previously by <it>in silico </it>mining. Given the important role of alternative splicing in neural differentiation, there may be selective pressure to maintain a majority of splicing events in order to retain glial-like characteristics of the tumor cells.</p

BRCA2 inhibition enhances cisplatin-mediated alterations in tumor cell proliferation, metabolism, and metastasis

Tumor cells have unstable genomes relative to non-tumor cells. Decreased DNA integrity resulting from tumor cell instability is important in generating favorable therapeutic indices, and intact DNA repair mediates resistance to therapy. Targeting DNA repair to promote the action of anti-cancer agents is therefore an attractive therapeutic strategy. BRCA2 is involved in homologous recombination repair. BRCA2 defects increase cancer risk but, paradoxically, cancer patients with BRCA2 mutations have better survival rates. We queried TCGA data and found that BRCA2 alterations led to increased survival in patients with ovarian and endometrial cancer. We developed a BRCA2-targeting second-generation antisense oligonucleotide (ASO), which sensitized human lung, ovarian, and breast cancer cells to cisplatin by as much as 60%. BRCA2 ASO treatment overcame acquired cisplatin resistance in head and neck cancer cells, but induced minimal cisplatin sensitivity in non-tumor cells. BRCA2 ASO plus cisplatin reduced respiration as an early event preceding cell death, concurrent with increased glucose uptake without a difference in glycolysis. BRCA2 ASO and cisplatin decreased metastatic frequency invivo by 77%. These results implicate BRCA2 as a regulator of metastatic frequency and cellular metabolic response following cisplatin treatment. BRCA2 ASO, in combination with cisplatin, is a potential therapeutic anti-cancer agent

Methylation of the candidate biomarker TCF21 is very frequent across a spectrum of early-stage nonsmall cell lung cancers

The transcription factor TCF21 is involved in mesenchymal-to-epithelial differentiation and was shown to be aberrantly hypermethylated in lung and head and neck cancers. Because of its reported high frequency of hypermethylation in lung cancer, we sought to characterize the stages and types of non-small cell lung cancer (NSCLC) that are hypermethylated and to define the frequency of hypermethylation and associated “second hits”