Hideaki Mizuno

Identification of Genes Upregulated in ALK-Positive and EGFR/KRAS/ALK-Negative Lung Adenocarcinomas

Activation of the EGFR, KRAS, and ALK oncogenes defines 3 different pathways of molecular pathogenesis in lung adenocarcinoma. However, many tumors lack activation of any pathway (triple-negative lung adenocarcinomas) posing a challenge for prognosis and treatment. Here, we report an extensive genome-wide expression profiling of 226 primary human stage I-II lung adenocarcinomas that elucidates molecular characteristics of tumors that harbor ALK mutations or that lack EGFR, KRAS, and ALK mutations, that is, triple-negative adenocarcinomas. One hundred and seventy-four genes were selected as being upregulated specifically in 79 lung adenocarcinomas without EGFR and KRAS mutations. Unsupervised clustering using a 174-gene signature, including ALK itself, classified these 2 groups of tumors into ALK-positive cases and 2 distinct groups of triple-negative cases (groups A and B). Notably, group A triple-negative cases had a worse prognosis for relapse and death, compared with cases with EGFR, KRAS, or ALK mutations or group B triple-negative cases. In ALK-positive tumors, 30 genes, including ALK and GRIN2A, were commonly overexpressed, whereas in group A triple-negative cases, 9 genes were commonly overexpressed, including a candidate diagnostic/therapeutic target DEPDC1, that were determined to be critical for predicting a worse prognosis. Our findings are important because they provide a molecular basis of ALK-positive lung adenocarcinomas and triple-negative lung adenocarcinomas and further stratify more or less aggressive subgroups of triple-negative lung ADC, possibly helping identify patients who may gain the most benefit from adjuvant chemotherapy after surgical resection.

PrognoScan: a new database for meta-analysis of the prognostic value of genes

BackgroundIn cancer research, the association between a gene and clinical outcome suggests the underlying etiology of the disease and consequently can motivate further studies. The recent availability of published cancer microarray datasets with clinical annotation provides the opportunity for linking gene expression to prognosis. However, the data are not easy to access and analyze without an effective analysis platform.DescriptionTo take advantage of public resources in full, a database named PrognoScan has been developed. This is 1) a large collection of publicly available cancer microarray datasets with clinical annotation, as well as 2) a tool for assessing the biological relationship between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis that finds the optimal cutpoint in continuous gene expression measurement without prior biological knowledge or assumption and, as a result, enables systematic meta-analysis of multiple datasets.ConclusionPrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and would accelerate cancer research. The database is publicly accessible at http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html.

Molecular classification of prostate cancer using curated expression signatures

High Gleason score is currently the best prognostic indicator for poor prognosis in prostate cancer. However, a significant number of patients with low Gleason scores develop aggressive disease as well. In an effort to understand molecular signatures associated with poor outcome in prostate cancer, we analyzed a microarray dataset characterizing 281 prostate cancers from a Swedish watchful-waiting cohort. Patients were classified on the basis of their mRNA microarray signature profiles indicating embryonic stem cell expression patterns (stemness), inactivation of the tumor suppressors p53 and PTEN, activation of several oncogenic pathways, and the TMPRSS2–ERG fusion. Unsupervised clustering identified a subset of tumors manifesting stem-like signatures together with p53 and PTEN inactivation, which had very poor survival outcome, a second group with intermediate survival outcome, characterized by the TMPRSS2–ERG fusion, and three groups with benign outcome. The stratification was validated on a second independent dataset of 150 tumor and metastatic samples from a clinical cohort at Memorial Sloan–Kettering Cancer Center. This classification is independent of Gleason score and therefore provides useful unique molecular profiles for prostate cancer prognosis, helping to predict poor outcome in patients with low or average Gleason scores.

Inactivation of p53 in breast cancers correlates with stem cell transcriptional signatures

Breast cancer comprises a heterogeneous set of diseases distinguishable from one another by pathologic presentation and molecular signatures. However, each breast cancer subtype is also heterogeneous. Some of the heterogeneity may be attributable to genetic instability, but recent data emphasize that developmental plasticity may also contribute. The p53 tumor suppressor could constitute a nodal control point underlying both sources of heterogeneity because it is frequently inactivated during malignant progression and has recently been shown to function as a potent barrier preventing fully differentiated cells from reverting to pluripotent stem cells after expression of appropriate oncogenes. Using archival microarray datasets, we tested the hypothesis that a p53 mutation could allow cells within a tumor to acquire a stem cell-like state by looking for coordinate expression of stem cell identity genes. We show that breast and lung cancers with p53 mutations do exhibit stem cell-like transcriptional patterns. Such tumors were also depleted for differentiation genes regulated by the polycomb repressor complex 2. These data are consistent with a model in which loss of p53 function enables acquisition of stem cell properties, which are positively selected during tumor progression.

A signature-based method for indexing cell cycle phase distribution from microarray profiles

BackgroundThe cell cycle machinery interprets oncogenic signals and reflects the biology of cancers. To date, various methods for cell cycle phase estimation such as mitotic index, S phase fraction, and immunohistochemistry have provided valuable information on cancers (e.g. proliferation rate). However, those methods rely on one or few measurements and the scope of the information is limited. There is a need for more systematic cell cycle analysis methods.ResultsWe developed a signature-based method for indexing cell cycle phase distribution from microarray profiles under consideration of cycling and non-cycling cells. A cell cycle signature masterset, composed of genes which express preferentially in cycling cells and in a cell cycle-regulated manner, was created to index the proportion of cycling cells in the sample. Cell cycle signature subsets, composed of genes whose expressions peak at specific stages of the cell cycle, were also created to index the proportion of cells in the corresponding stages. The method was validated using cell cycle datasets and quiescence-induced cell datasets. Analyses of a mouse tumor model dataset and human breast cancer datasets revealed variations in the proportion of cycling cells. When the influence of non-cycling cells was taken into account, buried cell cycle phase distributions were depicted that were oncogenic-event specific in the mouse tumor model dataset and were associated with patients prognosis in the human breast cancer datasets.ConclusionThe signature-based cell cycle analysis method presented in this report, would potentially be of value for cancer characterization and diagnostics.

ERK Signal Suppression and Sensitivity to CH5183284/Debio 1347, a Selective FGFR Inhibitor

Drugs that target specific gene alterations have proven beneficial in the treatment of cancer. Because cancer cells have multiple resistance mechanisms, it is important to understand the downstream pathways of the target genes and monitor the pharmacodynamic markers associated with therapeutic efficacy. We performed a transcriptome analysis to characterize the response of various cancer cell lines to a selective fibroblast growth factor receptor (FGFR) inhibitor (CH5183284/Debio 1347), a mitogen-activated protein kinase kinase (MEK) inhibitor, or a phosphoinositide 3-kinase (PI3K) inhibitor. FGFR and MEK inhibition produced similar expression patterns, and the extracellular signal–regulated kinase (ERK) gene signature was altered in several FGFR inhibitor–sensitive cell lines. Consistent with these findings, CH5183284/Debio 1347 suppressed phospho-ERK in every tested FGFR inhibitor–sensitive cell line. Because the mitogen-activated protein kinase (MAPK) pathway functions downstream of FGFR, we searched for a pharmacodynamic marker of FGFR inhibitor efficacy in a collection of cell lines with the ERK signature and identified dual-specificity phosphatase 6 (DUSP6) as a candidate marker. Although a MEK inhibitor suppressed the MAPK pathway, most FGFR inhibitor–sensitive cell lines are insensitive to MEK inhibitors and we found potent feedback activation of several pathways via FGFR. We therefore suggest that FGFR inhibitors exert their effect by suppressing ERK signaling without feedback activation. In addition, DUSP6 may be a pharmacodynamic marker of FGFR inhibitor efficacy in FGFR-addicted cancers. Mol Cancer Ther; 14(12); 2831–9. ©2015 AACR.

Fine-scale detection of population-specific linkage disequilibrium using haplotype entropy in the human genome

BackgroundThe creation of a coherent genomic map of recent selection is one of the greatest challenges towards a better understanding of human evolution and the identification of functional genetic variants. Several methods have been proposed to detect linkage disequilibrium (LD), which is indicative of natural selection, from genome-wide profiles of common genetic variations but are designed for large regions.ResultsTo find population-specific LD within small regions, we have devised an entropy-based method that utilizes differences in haplotype frequency between populations. The method has the advantages of incorporating multilocus association, conciliation with low allele frequencies, and independence from allele polarity, which are ideal for short haplotype analysis. The comparison of HapMap SNPs data from African and Caucasian populations with a median resolution size of ~23 kb gave us novel candidates as well as known selection targets. Enrichment analysis for the yielded genes showed associations with diverse diseases such as cardiovascular, immunological, neurological, and skeletal and muscular diseases. A possible scenario for a selective force is discussed. In addition, we have developed a web interface (ENIGMA, available at http://gibk21.bse.kyutech.ac.jp/ENIGMA/index.html), which allows researchers to query their regions of interest for population-specific LD.ConclusionThe haplotype entropy method is powerful for detecting population-specific LD embedded in short regions and should contribute to further studies aiming to decipher the evolutionary histories of modern humans.

The PFA-AMeX method achieves a good balance between the morphology of tissues and the quality of RNA content in DNA microarray analysis with laser-capture microdissection samples

Recently, large-scale gene expression profiling is often performed using RNA extracted from unfixed frozen or formalin-fixed paraffin embedded (FFPE) samples. However, both types of samples have drawbacks in terms of the morphological preservation and RNA quality. In the present study, we investigated 30 human prostate tissues using the PFA-AMeX method (fixation using paraformaldehyde (PFA) followed by embedding in paraffin by AMeX) with a DNA microarray combined with laser-capture microdissection. Morphologically, in contrast to the case of atypical adenomatous hyperplasia, loss of basal cells in prostate adenocarcinomas was as obvious in PFA-AMeX samples as in FFPE samples. As for quality, the loss of rRNA peaks 18S and 28S on the capillary electropherograms from both FFPE and PFA-AMeX samples showed that the RNA was degraded equally during processing. However, qRT-PCR with 3’ and 5’ primer sets designed against human beta-actin revealed that, although RNA degradation occurred in both methods, it occurred more mildly in the PFA-AMeX samples. In conclusion, the PFA-AMeX method is good with respect to morphology and RNA quality, which makes it a promising tool for DNA microarrays combined with laser-capture microdissection, and if the appropriate RNA quality criteria are used, the capture of credible GeneChip data is well over 80% efficient, at least in human prostate specimens.

Abstract 123: Mechanism of oncogenic signal activation by the novel fusion kinase FGFR3-BAIAP2L1

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PAnnRecent cancer genome profiling studies have identified many novel genetic alterations, including rearrangements of genes encoding fibroblast growth factor receptor (FGFR) family members. However, most fusion genes are not functionally well characterized, and the oncogenicity of some fusions as well as their potential sensitivity to targeted therapy are still unclear. In a previous study, we investigated a recently discovered gene fusion between FGFR3 and BAI1-associated protein 2-like 1 (BAIAP2L1). The FGFR3-BAIAP2L1 fusion gene was identified in 4 bladder cancer patients and 2 lung cancer patients via screens involving PCR and a break-apart fluorescence in situ hybridization assay. The functional analysis of FGFR3-BAIAP2L1 transfectant in Rat-2 fibroblast cells (Rat-2_F3-B) indicated that FGFR3-BAIAP2L1 forms a dimer via the Bin-Amphiphysin-Rvs (BAR) BAIAP2L1 dimerization domain that constitutively activates its FGFR3 kinase activity. CH5183284/Debio 1347*, a selective FGFR inhibitor, effectively inhibits growth of Rat-2_F3-B both in vitro and in vivo, indicating that the FGFR3 kinase activity is critical for tumorigenic activity of this fusion. These results indicate a potential application of FGFR inhibitors to treat FGFR3-BAIAP2L1fusion gene positive patients.nnIn this study, we further elucidate the mechanism of tumorigenic potential of FGFR3-BAIAP2L1A by a comprehensive gene expression analysis of 4 cell lines (Rat-2_mock, Rat-2_FGFR3, Rat-2_F3-B, and Rat-2_BAIAP2L1) using RNA sequencing. We identified 143 up-regulated and 67 down-regulated genes specifically engaged by FGFR3-BAIAP2L1. Our gene signature analysis with this gene set revealed that FGFR3-BAIAP2L1 activates growth signals, such as the mitogen-activated protein kinase pathway, and inhibits tumor-suppressive signals, such as the p53, RB1, and CDKN2A pathways. Analysis by Western blot in xenograft tissues confirmed the activation and inactivation status of those pathways. These data suggest that a concurrent regulation of an oncogenic pathway and a tumor-suppressive pathway results in the tumorigenic potential of FGFR3-BAIAP2L1.nn*CH5183284/Debio 1347 was discovered by Chugai Pharmaceutical Co., Ltd. and is being developed by Debiopharm International S.A. under an exclusive worldwide license.nnCitation Format: Yoshito Nakanishi, Nukinori Akiyama, Toshiyuki Tsukaguchi, Toshihiko Fujii, Yasuko Satoh, Hideaki Mizuno, Nobuya Ishii, Masahiro Aoki. Mechanism of oncogenic signal activation by the novel fusion kinase FGFR3-BAIAP2L1. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 123. doi:10.1158/1538-7445.AM2015-123

Abstract A41: Pre-clinical investigation of predictive biomarkers for drug candidates using a molecularly profiled large cancer cell line panel.

To deliver investigational drugs to appropriate patients from the early clinical development stage, it would be ideal to identify biomarker candidates at the preclinical stage. To investigate prediction biomarkers in preclinical models, a large cell panel consisting of over 500 cell lines from various tumor types was molecularly profiled. To find their gene expression profiles, gene copy number variations, single nucleotide variations and gene fusions, we conducted a comprehensive analysis with GeneChip, CGH array, exon sequencing and RNA sequencing by collaborative efforts between Chugai Pharmaceutical and F. Hoffmann-La Roche in the CELLO/CACTEL program (CELl Line profiling in Oncology/Chugai ACcumulative Tumor EncycLopedia). We further integrated the multidimensional profiling data of in-house cell lines together with those of tumor tissues in the public domain into our CELLO/CACTEL database by vocabulary controlling among databases. Using 300 cell lines in a molecularly profiled cell panel, we investigated the drug sensitivity profiles of representative investigational drug candidates, and identified potentially novel predictive features for sensitivity to these drugs. In summary, the CELLO/CACTEL database provides an effective platform for exploring biomarkers at the preclinical stage.nnCitation Information: Mol Cancer Ther 2013;12(11 Suppl):A41.nnCitation Format: Satoshi Aida, Hideaki Mizuno, Hiroshi Sakamoto, Yoshito Nakanishi, Hironori Mutoh, James Cai, Helmut Burtscher, Masahiro Aoki, Yuko Aoki, Nobuya Ishii. Pre-clinical investigation of predictive biomarkers for drug candidates using a molecularly profiled large cancer cell line panel. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A41.