Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology.

BACKGROUND: Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context. PATIENTS AND METHODS: The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively. RESULTS: Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method. CONCLUSION: The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.

The need for precompetitive integrative bionetwork disease model building.

If you doubt that there are enormous gaps in the current drug discovery process, you should probably skip to the next article. Yet even while critiques rightly highlight inefficiencies or operational issues, they often miss a fundamental reality: until we better understand diseases as altered bionetworks and view diseases at an individual patient level, efforts to develop effective biomarkers and therapies will be inefficient at best.

Emerging approaches in molecular profiling affecting oncology drug discovery.

The purpose of this paper is to provide some perspectives on whether we are at a tipping point in understanding oncology and oncology drug discovery. It describes how model organisms have prepared us for more efficient drug discovery, lessons that are in use today. It provides examples of the emerging integration of biomarkers in patient care. It also details how over the next several years the processes of carrying out target identification and identifying responders to drugs will become more and more similar. In conclusion, a discussion is provided about who can do what to link the various components of this information-rich drug discovery process together.

A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets.

Application of statistical genetics approaches to variations in mRNA transcript abundances in segregating populations can be used to identify genes and pathways associated with common human diseases. The combination of this genetic information with gene expression and clinical trait data can also be used to identify subtypes of a disease and the genetic loci specific to each subtype. Here we highlight results from some of our recent work in this area and further explore the many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes, and in applying such methods to the validation of targets for complex traits in the drug discovery process.

Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer.

We describe a flexible system for gene expression profiling using arrays of tens of thousands of oligonucleotides synthesized in situ by an ink-jet printing method employing standard phosphoramidite chemistry. We have characterized the dependence of hybridization specificity and sensitivity on parameters including oligonucleotide length, hybridization stringency, sequence identity, sample abundance, and sample preparation method. We find that 60-mer oligonucleotides reliably detect transcript ratios at one copy per cell in complex biological samples, and that ink-jet arrays are compatible with several different sample amplification and labeling techniques. Furthermore, results using only a single carefully selected oligonucleotide per gene correlate closely with those obtained using complementary DNA (cDNA) arrays. Most of the genes for which measurements differ are members of gene families that can only be distinguished by oligonucleotides. Because different oligonucleotide sequences can be specified for each array, we anticipate that ink-jet oligonucleotide array technology will be useful in a wide variety of DNA microarray applications.

Functional discovery via a compendium of expression profiles.

Ascertaining the impact of uncharacterized perturbations on the cell is a fundamental problem in biology. Here, we describe how a single assay can be used to monitor hundreds of different cellular functions simultaneously. We constructed a reference database or "compendium" of expression profiles corresponding to 300 diverse mutations and chemical treatments in S. cerevisiae, and we show that the cellular pathways affected can be determined by pattern matching, even among very subtle profiles. The utility of this approach is validated by examining profiles caused by deletions of uncharacterized genes: we identify and experimentally confirm that eight uncharacterized open reading frames encode proteins required for sterol metabolism, cell wall function, mitochondrial respiration, or protein synthesis. We also show that the compendium can be used to characterize pharmacological perturbations by identifying a novel target of the commonly used drug dyclonine.

Widespread aneuploidy revealed by DNA microarray expression profiling.

Expression profiling using DNA microarrays holds great promise for a variety of research applications, including the systematic characterization of genes discovered by sequencing projects. To demonstrate the general usefulness of this approach, we recently obtained expression profiles for nearly 300 Saccharomyces cerevisiae deletion mutants. Approximately 8% of the mutants profiled exhibited chromosome-wide expression biases, leading to spurious correlations among profiles. Competitive hybridization of genomic DNA from the mutant strains and their isogenic parental wild-type strains showed they were aneuploid for whole chromosomes or chromosomal segments. Expression profile data published by several other laboratories also suggest the use of aneuploid strains. In five separate cases, the extra chromosome harboured a close homologue of the deleted gene; in two cases, a clear growth advantage for cells acquiring the extra chromosome was demonstrated. Our results have implications for interpreting whole-genome expression data, particularly from cells known to suffer genomic instability, such as malignant or immortalized cells.

Differential toxicities of anticancer agents among DNA repair and checkpoint mutants of Saccharomyces cerevisiae.

Most cytotoxic anticancer agents damage DNA directly, interfere with DNA metabolism or chromosome segregation, and are particularly toxic in dividing cells. Although a considerable amount of information on the mechanisms of action of these agents is available, the molecular bases for selective tumor cell killing by chemotherapy are largely unknown. Many genetic alterations found in sporadic and hereditary cancers affect functions in DNA repair and cell cycle control and result in sensitivity to DNA damaging agents. We have therefore set out to determine the effects of these cancer mutations on sensitivity or resistance to various chemotherapeutic agents. Because most of the affected genes are well conserved among eukaryotes, we have carried out a comprehensive analysis of a panel of isogenic yeast strains, each defective in a particular DNA repair or cell cycle checkpoint function, for sensitivity to the Food and Drug Administration-approved cytotoxic anticancer agents. Widely different toxicity profiles were observed for 23 agents and X-rays, indicating that the type of DNA repair and cell cycle checkpoint mutations in individual tumors could strongly influence the outcome of a particular chemotherapeutic regimen.

Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles.

Genome-wide transcript profiling was used to monitor signal transduction during yeast pheromone response. Genetic manipulations allowed analysis of changes in gene expression underlying pheromone signaling, cell cycle control, and polarized morphogenesis. A two-dimensional hierarchical clustered matrix, covering 383 of the most highly regulated genes, was constructed from 46 diverse experimental conditions. Diagnostic subsets of coexpressed genes reflected signaling activity, cross talk, and overlap of multiple mitogen-activated protein kinase (MAPK) pathways. Analysis of the profiles specified by two different MAPKs-Fus3p and Kss1p-revealed functional overlap of the filamentous growth and mating responses. Global transcript analysis reflects biological responses associated with the activation and perturbation of signal transduction pathways.

Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis.

The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

Genetic selection of peptide inhibitors of biological pathways.

Genetic selections were used to find peptides that inhibit biological pathways in budding yeast. The peptides were presented inside cells as peptamers, surface loops on a highly expressed and biologically inert carrier protein, a catalytically inactive derivative of staphylococcal nuclease. Peptamers that inhibited the pheromone signaling pathway, transcriptional silencing, and the spindle checkpoint were isolated. Putative targets for the inhibitors were identified by a combination of two-hybrid analysis and genetic dissection of the target pathways. This analysis identified Ydr517w as a component of the spindle checkpoint and reinforced earlier indications that Ste50 has both positive and negative roles in pheromone signaling. Analysis of transcript arrays showed that the peptamers were highly specific in their effects, which suggests that they may be useful reagents in organisms that lack sophisticated genetics as well as for identifying components of existing biological pathways that are potential targets for drug discovery.

High-throughput assay for G2 checkpoint inhibitors and identification of the structurally novel compound isogranulatimide.

Treatment of cancer cells lacking p53 function with G2 checkpoint inhibitors sensitizes them to the toxic effects of DNA damage and has been proposed as a strategy for cancer therapy. However, few inhibitors are known, and they have been found serendipitously. We report the development of a G2 checkpoint inhibition assay that is suitable for high-throughput screening and its application to a screen of 1300 natural extracts. We present the isolation of a new G2 checkpoint inhibitor, the structurally novel compound isogranulatimide. In combination with gamma-irradiation, isogranulatimide selectively kills MCF-7 cells lacking p53 function.

Drug target validation and identification of secondary drug target effects using DNA microarrays.

We describe here a method for drug target validation and identification of secondary drug target effects based on genome-wide gene expression patterns. The method is demonstrated by several experiments, including treatment of yeast mutant strains defective in calcineurin, immunophilins or other genes with the immunosuppressants cyclosporin A or FK506. Presence or absence of the characteristic drug 'signature' pattern of altered gene expression in drug-treated cells with a mutation in the gene encoding a putative target established whether that target was required to generate the drug signature. Drug dependent effects were seen in 'targetless' cells, showing that FK506 affects additional pathways independent of calcineurin and the immunophilins. The described method permits the direct confirmation of drug targets and recognition of drug-dependent changes in gene expression that are modulated through pathways distinct from the drug's intended target. Such a method may prove useful in improving the efficiency of drug development programs.

Functional analysis of human MLH1 mutations in Saccharomyces cerevisiae.

Hereditary non-polyposis colorectal cancer (HNPCC; OMIM 120435-6) is a cancer-susceptibility syndrome linked to inherited defects in human mismatch repair (MMR) genes. Germline missense human MLH1 (hMLH1) mutations are frequently detected in HNPCC (ref. 3), making functional characterization of mutations in hMLH1 critical to the development of genetic testing for HNPCC. Here, we describe a new method for detecting mutations in hMLH1 using a dominant mutator effect of hMLH1 cDNA expressed in Saccharomyces cerevisiae. The majority of hMLH1 missense mutations identified in HNPCC patients abolish the dominant mutator effect. Furthermore, PCR amplification of hMLH1 cDNA from mRNA from a HNPCC patient, followed by in vivo recombination into a gap expression vector, allowed detection of a heterozygous loss-of-function missense mutation in hMLH1 using this method. This functional assay offers a simple method for detecting and evaluating pathogenic mutations in hMLH1.

Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues.

The U.S. National Cancer Institute (NCI) conducts a drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs of origin. Since 1990, approximately 63,000 compounds have been tested, and their patterns of activity profiled. Recently, we analyzed the antitumor activity patterns of 112 ellipticine analogues using a hierarchical clustering algorithm. Dramatic coherence between molecular structures and activity patterns was observed qualitatively from the cluster tree. In the present study, we further investigate the quantitative structure-activity relationships (QSAR) of these compounds, in particular with respect to the influence of p53-status and the CNS cell selectivity of the activity patterns. Independent variables (i.e., chemical structural descriptors of the ellipticine analogues) were calculated from the Cerius2 molecular modeling package. Important structural descriptors, including partial atomic charges on the ellipticine ring-forming atoms, were identified by the recently developed genetic function approximation (GFA) method. For our data set, the GFA method gave better correlation and cross-validation results (R2 and CVR2 were usually approximately 0.3 higher) than did classical stepwise linear regression. A procedure for improving the performance of GFA is proposed, and the relative advantages and disadvantages of using GFA for QSAR studies are discussed.

Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity.

The United States National Cancer Institute conducts an anticancer drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs. To date, approximately 62,000 compounds have been tested in the program, and a large amount of information on their activity patterns has been accumulated. For the current study, anticancer activity patterns of 112 ellipticine analogs were analyzed with the use of a hierarchical clustering algorithm. A dramatic coherence between molecular structures and their activity patterns could be seen from the cluster tree: the first subgroup (compounds 1-66) consisted principally of normal ellipticines, whereas the second subgroup (compounds 67-112) consisted principally of N2-alkyl-substituted ellipticiniums. Almost all apparent discrepancies in this clustering were explainable on the basis of chemical transformation to active forms under cell culture conditions. Correlations of activity with p53 status and selective activity against cells of central nervous system origin made this data set of special interest to us. The ellipticiniums, but not the ellipticines, were more potent on average against p53 mutant cells than against p53 wild-type ones (i.e., they seemed to be "p53-inverse") in this short term assay. This study strongly supports the hypothesis that "fingerprint" patterns of activity in the National Cancer Institute in vitro cell screening program encode incisive information on the mechanisms of action and other biological behaviors of tested compounds. Insights gained by mining the activity patterns could contribute to our understanding of anticancer drugs and the molecular pharmacology of cancer.