Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution arraybased comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1%), breast cancer (72.8%), and melanoma (85.9%), where copy number alterations observed in >15% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNAassociated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.genome ͉ noncoding RNA ͉ comparative genomic hybridization

Mantle cell lymphoma (MCL) is an aggressive malignancy that is characterized by poor prognosis. Large-scale pharmacological profiling across more than 100 hematological cell line models identified a subset of MCL cell lines that are highly sensitive to the B cell receptor (BCR) signaling inhibitors ibrutinib and sotrastaurin. Sensitive MCL models exhibited chronic activation of the BCR-driven classical nuclear factor-κB (NF-κB) pathway, whereas insensitive cell lines displayed activation of the alternative NF-κB pathway. Transcriptome sequencing revealed genetic lesions in alternative NF-κB pathway signaling components in ibrutinib-insensitive cell lines, and sequencing of 165 samples from patients with MCL identified recurrent mutations in TRAF2 or BIRC3 in 15% of these individuals. Although they are associated with insensitivity to ibrutinib, lesions in the alternative NF-κB pathway conferred dependence on the protein kinase NIK (also called mitogen-activated protein 3 kinase 14 or MAP3K14) both in vitro and in vivo. Thus, NIK is a new therapeutic target for MCL treatment, particularly for lymphomas that are refractory to BCR pathway inhibitors. Our findings reveal a pattern of mutually exclusive activation of the BCR-NF-κB or NIK-NF-κB pathways in MCL and provide critical insights into patient stratification strategies for NF-κB pathway-targeted agents.

Introduction Genomic aberrations in the form of subchromosomal DNA copy number changes are a hallmark of epithelial cancers, including breast cancer. The goal of the present study was to analyze such aberrations in breast cancer at high resolution.

Regions of gain and loss of genomic DNA occur in many cancers and can drive the genesis and progression of disease. These copy number aberrations (CNAs) can be detected at high resolution by using microarray-based techniques. However, robust statistical approaches are needed to identify nonrandom gains and losses across multiple experiments/samples. We have developed a method called Significance Testing for Aberrant Copy number (STAC) to address this need. STAC utilizes two complementary statistics in combination with a novel search strategy. The significance of both statistics is assessed, and P-values are assigned to each location on the genome by using a multiple testing corrected permutation approach. We validate our method by using two published cancer data sets. STAC identifies genomic alterations known to be of clinical and biological significance and provides statistical support for 85% of previously reported regions. Moreover, STAC identifies numerous additional regions of significant gain/loss in these data that warrant further investigation. The P-values provided by STAC can be used to prioritize regions for follow-up study in an unbiased fashion. We conclude that STAC is a powerful tool for identifying nonrandom genomic amplifications and deletions across multiple experiments. A Java version of STAC is freely available for download at

Mutations in BRCA1 and BRCA2 account for a significant proportion of hereditary breast cancers. Earlier studies have shown that inherited and sporadic tumors progress along different somatic genetic pathways and that global gene expression profiles distinguish between these groups. To determine whether genomic profiles similarly discriminate among BRCA1, BRCA2, and sporadic tumors, we established DNA copy number profiles using comparative genomic hybridization to BAC-clone microarrays providing <1 Mb resolution. Tumor DNA was obtained from BRCA1 (n = 14) and BRCA2 (n = 12) mutation carriers, as well as sporadic cases (n = 26). Overall, BRCA1 tumors had a higher frequency of copy number alterations than sporadic breast cancers (P = 0.00078). In particular, frequent losses on 4p, 4q, and 5q in BRCA1 tumors and frequent gains on 7p and 17q24 in BRCA2 tumors distinguish these from sporadic tumors. Distinct amplicons at 3q27.1-q27.3 were identified in BRCA1 tumors and at 17q23.3-q24.2 in BRCA2 tumors. A homozygous deletion on 5q12.1 was found in a BRCA1 tumor. Using a set of 169 BAC clones that detect significantly (P < 0.001) different frequencies of copy number changes in inherited and sporadic tumors, these could be discriminated into separate groups using hierarchical clustering. By comparing DNA copy number and RNA expression for genes in these regions, several candidate genes affected by up-or down-regulation were identified. Moreover, using support vector machines, we correctly classified BRCA1 and BRCA2 tumors (P < 0.0000004 and 0.00005, respectively). Further validation may prove this tumor classifier to be useful for selecting familial breast cancer cases for further mutation screening, particularly, as these data can be obtained using archival tissue. (Cancer Res 2005; 65(17): 7612-21)

We used array-based comparative genomic hybridization (aCGH) to measure genomic copy number alterations (CNAs) in 42 neuroblastoma cell lines with known 1p36.3, 2p24 (MYCN), 11q23, and 17q23 allelic status. All cell lines showed CNAs, with an average of 22.0% of the genome of each sample showing evidence of gain (11.6%) or loss (10.4%). MYCN amplification was detected in 81% of cell lines, but other regions with high-level genomic amplification were observed only rarely. Gain of 17q material was present in 75% of the samples, and four discrete genomic regions at 17q23.2-17q25.3 were defined. Novel regions of gain were identified, including a 2.6-Mb subtelomeric region at 5p that includes the telomerase reverse transcriptase gene (TERT), which was found in 45% of the cell lines. Hemizygous deletions were noted at 1p36.23-1p36.32 and 11q23.3-11q25 in 60% and 36%, respectively, of the samples, with other frequent (>25%) regions of deletion localized to 1p32.1, 3p21.31-3p22.1, 5q35.2-5q35.3, 7q31.2, 7q34, 9q22.3-9q24.1, 10q26.11-10q26.12, 16q23.1-16q24.3, 18q21.32-18q23, and 20p11.21-20p11.23. A smallest region of overlap (SRO) for CNAs was mapped across all experiments and in each case was consistent with or refined the published data. A single cell line showed a homozygous deletion at 3p22.3, which was verified, and this location was refined by FISH and PCR. There was outstanding concordance of aCGH with PCR-based CNA detection methods. Several potential cooperating loci were identified, including deletion of 11q23-25, which was highly associated with both regional gain and loss at multiple chromosomal loci but was inversely correlated with the deletion of 1p36. Taking all of this together indicates that aCGH can accurately measure CNAs in the neuroblastoma genome and facilitate gene discovery efforts by high-throughput refinement of candidate loci.

Array-based comparative genomic hybridization (aCGH) is a recently developed tool for genome-wide determination of DNA copy number alterations. This technology has tremendous potential for disease-gene discovery in cancer and developmental disorders as well as numerous other applications. However, widespread utilization of a CGH has been limited by the lack of well characterized, high-resolution clone sets optimized for consistent performance in aCGH assays and specifically designed analytic software. We have assembled a set of approximately 4100 publicly available human bacterial artificial chromosome (BAC) clones evenly spaced at approximately 1-Mb resolution across the genome, which includes direct coverage of approximately 400 known cancer genes. This aCGH-optimized clone set was compiled from five existing sets, experimentally refined, and supplemented for higher resolution and enhancing mapping capabilities. This clone set is associated with a public online resource containing detailed clone mapping data, protocols for the construction and use of arrays, and a suite of analytical software tools designed specifically for aCGH analysis. These resources should greatly facilitate the use of aCGH in gene discovery.