Publications by authors named "Andrea H Bild"

37 Publications

Utility of Circulating Tumor DNA in Identifying Somatic Mutations and Tracking Tumor Evolution in Patients with Non-small Cell Lung Cancer.

Chest 2021 Apr 17. Epub 2021 Apr 17.

City of Hope Comprehensive Cancer Center, Duarte, CA 91010. Electronic address:

Background: The utility of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC).

Research Question: How does the utility of circulating tumor DNA (ctDNA) compare to that of solid tumor biopsy in non-small cell lung cancer (NSCLC) patients?

Methods: We retrospectively evaluated 370 adult NSCLC patients treated at the City of Hope between November 2015 and August 2019 to assess the utility of ctDNA in mutation identification, survival, concordance with matched tissue samples in thirty-two genes, and tumor evolution.

Results: A total of 1688 somatic mutations were detected in 473 ctDNA samples from 370 NSCLC patients. Of the 473 samples, 177 had at least one actionable mutation with currently available FDA-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rates [FDR] < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR = 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR] 0.56, 95% CI: 0.37-0.85, p = 0.006). Overall survival was shorter in BRAF (HR 2.35, 95% CI: 1.24-4.6, p = 0.009, PIK3CA (HR 2.77, 95% CI: 1.56-4.9, P< 0.001 and KRAS-positive patients (HR 2.32, 95% CI: 1.30-4.1, P= 0.004). Gene-level concordance was 93.8% while the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsies. Treatment response and tumor evolution over time were detected in repeated ctDNA samples.

Interpretation: Although ctDNA exhibited similar utility to tissue biopsies, more mutations in targetable genes were missed in tissue biopsies. Therefore, the evaluation of ctDNA in conjunction with tissue biopsies may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.
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http://dx.doi.org/10.1016/j.chest.2021.04.016DOI Listing
April 2021

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Brief Bioinform 2021 Mar 3. Epub 2021 Mar 3.

Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.
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http://dx.doi.org/10.1093/bib/bbab039DOI Listing
March 2021

Leveraging Single-Cell Approaches in Cancer Precision Medicine.

Trends Cancer 2021 Apr 6;7(4):359-372. Epub 2021 Feb 6.

Department of Medical Oncology and Therapeutics Research, City of Hope, Monrovia, CA 91016, USA.

Cancer precision medicine aims to improve patient outcomes by tailoring treatment to the unique genomic background of a tumor. However, efforts to develop prognostic and drug response biomarkers largely rely on bulk 'omic' data, which fails to capture intratumor heterogeneity (ITH) and deconvolve signals from normal versus tumor cells. These shortcomings in measuring clinically relevant features are being addressed with single-cell technologies, which provide a fine-resolution map of the genetic and phenotypic heterogeneity in tumors and their microenvironment, as well as an improved understanding of the patterns of subclonal tumor populations. Here we present recent advances in the application of single-cell technologies, towards gaining a deeper understanding of ITH and evolution, and potential applications in developing personalized therapeutic strategies.
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http://dx.doi.org/10.1016/j.trecan.2021.01.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969443PMC
April 2021

Topsentinol L Trisulfate, a Marine Natural Product That Targets Basal-like and Claudin-Low Breast Cancers.

Mar Drugs 2021 Jan 18;19(1). Epub 2021 Jan 18.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake, UT 84112, USA.

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.
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http://dx.doi.org/10.3390/md19010041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7831112PMC
January 2021

A `one-two punch' therapy strategy to target chemoresistance in estrogen receptor positive breast cancer.

Transl Oncol 2021 Jan 19;14(1):100946. Epub 2020 Nov 19.

Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, United States. Electronic address:

Cancer cell phenotypes evolve during a tumor's treatment. In some cases, tumor cells acquire cancer stem cell-like (CSL) traits such as resistance to chemotherapy and diminished differentiation; therefore, targeting these cells may be therapeutically beneficial. In this study we show that in progressive estrogen receptor positive (ER+) metastatic breast cancer tumors, resistant subclones that emerge following chemotherapy have increased CSL abundance. Further, in vitro organoid growth of ER+ patient cancer cells also shows that chemotherapy treatment leads to increased abundance of ALDH+/CD44+ CSL cells. Chemotherapy induced CSL abundance is blocked by treatment with a pan-HDAC inhibitor, belinostat. Belinostat treatment diminished both mammosphere formation and size following chemotherapy, indicating a decrease in progenitor CSL traits. HDAC inhibitors specific to class IIa (HDAC4, HDAC5) and IIb (HDAC6) were shown to primarily reverse the chemo-resistant CSL state. Single-cell RNA sequencing analysis with patient samples showed that HDAC targets and MYC signaling were promoted by chemotherapy and inhibited upon HDAC inhibitor treatment. In summary, HDAC inhibition can block chemotherapy-induced drug resistant phenotypes with 'one-two punch' strategy in refractory breast cancer cells.
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http://dx.doi.org/10.1016/j.tranon.2020.100946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689336PMC
January 2021

Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy.

Proc Natl Acad Sci U S A 2020 07 22;117(27):16072-16082. Epub 2020 Jun 22.

Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA 91010;

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients' tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.
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http://dx.doi.org/10.1073/pnas.1918937117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355015PMC
July 2020

Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model.

Cancer Cell Int 2020 17;20:253. Epub 2020 Jun 17.

Department of Medical Oncology and Therapeutics Research, Beckman Research Institute, City of Hope National Medical Center, 1218 S Fifth Ave, Monrovia, CA 91016 USA.

Background: CDK4/6 inhibitors such as ribociclib are becoming widely used targeted therapies in hormone-receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer. However, cancers can advance due to drug resistance, a problem in which tumor heterogeneity and evolution are key features.

Methods: Ribociclib-resistant HR+/HER2- CAMA-1 breast cancer cells were generated through long-term ribociclib treatment. Characterization of sensitive and resistant cells were performed using RNA sequencing and whole exome sequencing. Lentiviral labeling with different fluorescent proteins enabled us to track the proliferation of sensitive and resistant cells under different treatments in a heterogeneous, 3D spheroid coculture system using imaging microscopy and flow cytometry.

Results: Transcriptional profiling of sensitive and resistant cells revealed the downregulation of the G2/M checkpoint in the resistant cells. Exploiting this acquired vulnerability; resistant cells exhibited collateral sensitivity for the Wee-1 inhibitor, adavosertib (AZD1775). The combination of ribociclib and adavosertib achieved additional antiproliferative effect exclusively in the cocultures compared to monocultures, while decreasing the selection for resistant cells.

Conclusions: Our results suggest that optimal antiproliferative effects in heterogeneous cancers can be achieved via an integrative therapeutic approach targeting sensitive and resistant cancer cell populations within a tumor, respectively.
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http://dx.doi.org/10.1186/s12935-020-01337-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301982PMC
June 2020

Opportunities for improving cancer treatment using systems biology.

Curr Opin Syst Biol 2019 Oct 27;17:41-50. Epub 2019 Nov 27.

Department of Medical Oncology, Division of Molecular Pharmacology, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA.

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.
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http://dx.doi.org/10.1016/j.coisb.2019.10.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282416PMC
October 2019

Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes.

BMC Cancer 2019 Sep 5;19(1):881. Epub 2019 Sep 5.

Department of Medical Oncology and Therapeutics Research, City of Hope, 1218 S Fifth Ave, Monrovia, CA, 91016, USA.

Background: Gene expression profiling of rare cancers has proven challenging due to limited access to patient materials and requirement of intact, non-degraded RNA for next-generation sequencing. We customized a gene expression panel compatible with degraded RNA from formalin-fixed, paraffin-embedded (FFPE) patient cancer samples and investigated its utility in pathway activity profiling in patients with metaplastic breast cancer (MpBC).

Methods: Activity of various biological pathways was profiled in samples from nineteen patients with MpBC and 8 patients with invasive ductal carcinoma with triple negative breast cancer (TNBC) phenotype using a custom gene expression-based assay of 345 genes.

Results: MpBC samples of mesenchymal (chondroid and/or osteoid) histology demonstrated increased SNAI1 and BCL2L11 pathway activity compared to samples with non-mesenchymal histology. Additionally, late cornified envelope and keratinization genes were downregulated in MpBC compared to TNBC, and epithelial-to-mesenchymal transition (EMT) and collagen genes were upregulated in MpBC. Patients with high activity of an invasiveness gene expression signature, as well as high expression of the mesenchymal marker and extracellular matrix glycoprotein gene SPARC, experienced worse outcomes than those with low invasiveness activity and low SPARC expression.

Conclusions: This study demonstrates the utility of gene expression profiling of metaplastic breast cancer FFPE samples with a custom counts-based assay. Gene expression patterns identified by this assay suggest that, although often histologically triple negative, patients with MpBC have distinct pathway activation compared to patients with invasive ductal TNBC. Incorporation of targeted therapies may lead to improved outcome for MpBC patients, especially in those patients expressing increased activity of invasiveness pathways.
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http://dx.doi.org/10.1186/s12885-019-6052-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6727561PMC
September 2019

Publisher Correction: Combating subclonal evolution of resistant cancer phenotypes.

Nat Commun 2018 02 5;9(1):572. Epub 2018 Feb 5.

Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, 30 South 2000 East, Salt Lake City, UT, 84112, USA.

The originally published version of this Article contained an error in Figure 4. In panel a, grey boxes surrounding the subclones associated with patients #2 and #4 obscured adjacent portions of the heatmap. This error has now been corrected in both the PDF and HTML versions of the Article.
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http://dx.doi.org/10.1038/s41467-017-02383-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799176PMC
February 2018

Combating subclonal evolution of resistant cancer phenotypes.

Nat Commun 2017 11 1;8(1):1231. Epub 2017 Nov 1.

Department of Pharmacology and Toxicology, College of Pharmacy, University of Utah, 30 South 2000 East, Salt Lake City, UT, 84112, USA.

Metastatic breast cancer remains challenging to treat, and most patients ultimately progress on therapy. This acquired drug resistance is largely due to drug-refractory sub-populations (subclones) within heterogeneous tumors. Here, we track the genetic and phenotypic subclonal evolution of four breast cancers through years of treatment to better understand how breast cancers become drug-resistant. Recurrently appearing post-chemotherapy mutations are rare. However, bulk and single-cell RNA sequencing reveal acquisition of malignant phenotypes after treatment, including enhanced mesenchymal and growth factor signaling, which may promote drug resistance, and decreased antigen presentation and TNF-α signaling, which may enable immune system avoidance. Some of these phenotypes pre-exist in pre-treatment subclones that become dominant after chemotherapy, indicating selection for resistance phenotypes. Post-chemotherapy cancer cells are effectively treated with drugs targeting acquired phenotypes. These findings highlight cancer's ability to evolve phenotypically and suggest a phenotype-targeted treatment strategy that adapts to cancer as it evolves.
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http://dx.doi.org/10.1038/s41467-017-01174-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5666005PMC
November 2017

Mechanisms and clinical implications of tumor heterogeneity and convergence on recurrent phenotypes.

J Mol Med (Berl) 2017 11 4;95(11):1167-1178. Epub 2017 Sep 4.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.

Tumor heterogeneity has been identified at various -omic levels. The tumor genome, transcriptome, proteome, and phenome can vary widely across cells in patient tumors and are influenced by tumor cell interactions with heterogeneous physical conditions and cellular components of the tumor microenvironment. Here, we explore the concept that while variation exists at multiple -omic levels, changes at each of these levels converge on the same pathways and lead to convergent phenotypes in tumors that can provide common drug targets. These phenotypes include cellular growth and proliferation, sustained oncogenic signaling, and immune avoidance, among others. Tumor heterogeneity complicates treatment of patient cancers as it leads to varied response to therapies. Identification of convergent cellular phenotypes arising in patient cancers and targeted therapies that reverse them has the potential to transform the way clinicians treat these cancers and to improve patient outcome.
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http://dx.doi.org/10.1007/s00109-017-1587-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6296823PMC
November 2017

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes.

Genome Med 2017 04 26;9(1):40. Epub 2017 Apr 26.

Department of Pharmacology and Toxicology, University of Utah, 30 S 2000 E, Salt Lake City, UT, 84108, USA.

Background: The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

Methods: Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

Results: Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

Conclusions: Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.
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http://dx.doi.org/10.1186/s13073-017-0429-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406893PMC
April 2017

Window-of-Opportunity Study of Valproic Acid in Breast Cancer Testing a Gene Expression Biomarker.

JCO Precis Oncol 2017 7;1. Epub 2017 Apr 7.

, , , , , , , , and , University of Utah; , , , , and , Huntsman Cancer Institute, Salt Lake City; , Brigham Young University, Provo, UT; , University of Arizona, Tucson, AZ; and , Advanced Imaging Research Center, Portland, OR.

Purpose: The anticancer activity of valproic acid (VPA) is attributed to the inhibition of histone deacetylase. We previously published the genomically derived sensitivity signature for VPA (GDSS-VPA), a gene expression biomarker that predicts breast cancer sensitivity to VPA in vitro and in vivo. We conducted a window-of-opportunity study that examined the tolerability of VPA and the ability of the GDSS-VPA to predict biologic changes in breast tumors after treatment with VPA.

Patients And Methods: Eligible women had untreated breast cancer with breast tumors larger than 1.5 cm. After a biopsy, women were given VPA for 7 to 12 days, increasing from 30 mg/kg/d orally divided into two doses per day to a maximum of 50 mg/kg/d. After VPA treatment, serum VPA level was measured and then breast surgery or biopsy was performed. Tumor proliferation was assessed by using Ki-67 immunohistochemistry. Histone acetylation of peripheral blood mononuclear cells was assessed by Western blot. Dynamic contrast-enhanced magnetic resonance imaging scans were performed before and after VPA treatment.

Results: Thirty women were evaluable. The median age was 54 years (range, 31-73 years). Fifty-two percent of women tolerated VPA at 50 mg/kg/d, but 10% missed more than two doses as a result of adverse events. Grade 3 adverse events included vomiting and diarrhea (one patient) and fatigue (one patient). The end serum VPA level correlated with a change in histone acetylation of peripheral blood mononuclear cells (ρ = 0.451; = .024). Fifty percent of women (three of six) with triple-negative breast cancer had a Ki-67 reduction of at least 10% compared with 17% of other women. Women whose tumors had higher GDSS-VPA were more likely to have a Ki-67 decrease of at least 10% (area under the curve, 0.66).

Conclusion: VPA was well tolerated and there was a significant correlation between serum VPA levels and histone acetylation. VPA treatment caused a decrease in proliferation of breast tumors. The genomic biomarker correlated with decreased proliferation. Inhibition of histone deacetylase is a valid strategy for drug development in triple-negative breast cancer using gene expression biomarkers.
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http://dx.doi.org/10.1200/PO.16.00011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446454PMC
April 2017

The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers.

Semin Cell Dev Biol 2016 10 20;58:108-17. Epub 2016 Jun 20.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA; Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA. Electronic address:

The rise in genomic knowledge over the past decade has revealed the molecular etiology of many diseases, and has identified intricate signaling network activity in human cancers. Genomics provides the opportunity to determine genome structure and capture the activity of thousands of molecular events concurrently, which is important for deciphering highly complex genetic diseases such as cancer. In this review, we focus on genomic efforts directed towards one of cancer's most frequently mutated networks, the RAS pathway. Genomic tools such as gene expression signatures and assessment of mutations across the RAS network enable the capture of RAS signaling complexity. Due to this high level of interaction and cross-talk within the network, efforts to target RAS signaling in the clinic have generally failed, and we currently lack the ability to directly inhibit the RAS protein with high efficacy. We propose that the use of gene expression data can identify effective treatments that broadly inhibit the RAS network as this approach measures pathway activity independent of mutation status or any single mechanism of activation. Here, we review the genomic studies that map the complexity of the RAS network in cancer, and that show how genomic measurements of RAS pathway activation can identify effective RAS inhibition strategies. We also address the challenges and future directions for treating RAS-driven tumors. In summary, genomic assessment of RAS signaling provides a level of complexity necessary to accurately map the network that matches the intricacy of RAS pathway interactions in cancer.
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http://dx.doi.org/10.1016/j.semcdb.2016.06.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5951171PMC
October 2016

Integrative analyses reveal signaling pathways underlying familial breast cancer susceptibility.

Mol Syst Biol 2016 Mar 10;12(3):860. Epub 2016 Mar 10.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA Department of Oncological Sciences, University of Utah, Salt Lake City, UT, USA

The signaling events that drive familial breast cancer (FBC) risk remain poorly understood. While the majority of genomic studies have focused on genetic risk variants, known risk variants account for at most 30% of FBC cases. Considering that multiple genes may influence FBC risk, we hypothesized that a pathway-based strategy examining different data types from multiple tissues could elucidate the biological basis for FBC. In this study, we performed integrated analyses of gene expression and exome-sequencing data from peripheral blood mononuclear cells and showed that cell adhesion pathways are significantly and consistently dysregulated in women who develop FBC. The dysregulation of cell adhesion pathways in high-risk women was also identified by pathway-based profiling applied to normal breast tissue data from two independent cohorts. The results of our genomic analyses were validated in normal primary mammary epithelial cells from high-risk and control women, using cell-based functional assays, drug-response assays, fluorescence microscopy, and Western blotting assays. Both genomic and cell-based experiments indicate that cell-cell and cell-extracellular matrix adhesion processes seem to be disrupted in non-malignant cells of women at high risk for FBC and suggest a potential role for these processes in FBC development.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812528PMC
http://dx.doi.org/10.15252/msb.20156506DOI Listing
March 2016

Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility.

BMC Med Genomics 2015 Nov 4;8:72. Epub 2015 Nov 4.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.

Background: Women with a family history of breast cancer face considerable uncertainty about whether to pursue standard screening, intensive screening, or prophylactic surgery. Accurate and individualized risk-estimation approaches may help these women make more informed decisions. Although highly penetrant genetic variants have been associated with familial breast cancer (FBC) risk, many individuals do not carry these variants, and many carriers never develop breast cancer. Common risk variants have a relatively modest effect on risk and show limited potential for predicting FBC development. As an alternative, we hypothesized that additional genomic data types, such as gene-expression levels, which can reflect genetic and epigenetic variation, could contribute to classifying a person's risk status. Specifically, we aimed to identify common patterns in gene-expression levels across individuals who develop FBC.

Methods: We profiled peripheral blood mononuclear cells from women with a family history of breast cancer (with or without a germline BRCA1/2 variant) and from controls. We used the support vector machines algorithm to differentiate between patients who developed FBC and those who did not. Our study used two independent datasets, a training set of 124 women from Utah (USA) and an external validation (test) set from Ontario (Canada) of 73 women (197 total). We controlled for expression variation associated with clinical, demographic, and treatment variables as well as lymphocyte markers.

Results: Our multigene biomarker provided accurate, individual-level estimates of FBC occurrence for the Utah cohort (AUC = 0.76 [0.67-84]) . Even at their lower confidence bounds, these accuracy estimates meet or exceed estimates from alternative approaches. Our Ontario cohort resulted in similarly high levels of accuracy (AUC = 0.73 [0.59-0.86]), thus providing external validation of our findings. Individuals deemed to have "high" risk by our model would have an estimated 2.4 times greater odds of developing familial breast cancer than individuals deemed to have "low" risk.

Conclusions: Together, these findings suggest that gene-expression levels in peripheral blood cells reflect genomic variation associated with breast cancer risk and that such data have potential to be used as a non-invasive biomarker for familial breast cancer risk.
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http://dx.doi.org/10.1186/s12920-015-0145-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634735PMC
November 2015

Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results.

Bioinformatics 2015 Nov 24;31(22):3666-72. Epub 2015 Jul 24.

Department of Biology, Brigham Young University, Provo, UT 84604, USA.

Motivation: The Cancer Genome Atlas (TCGA) RNA-Sequencing data are used widely for research. TCGA provides 'Level 3' data, which have been processed using a pipeline specific to that resource. However, we have found using experimentally derived data that this pipeline produces gene-expression values that vary considerably across biological replicates. In addition, some RNA-Sequencing analysis tools require integer-based read counts, which are not provided with the Level 3 data. As an alternative, we have reprocessed the data for 9264 tumor and 741 normal samples across 24 cancer types using the Rsubread package. We have also collated corresponding clinical data for these samples. We provide these data as a community resource.

Results: We compared TCGA samples processed using either pipeline and found that the Rsubread pipeline produced fewer zero-expression genes and more consistent expression levels across replicate samples than the TCGA pipeline. Additionally, we used a genomic-signature approach to estimate HER2 (ERBB2) activation status for 662 breast-tumor samples and found that the Rsubread data resulted in stronger predictions of HER2 pathway activity. Finally, we used data from both pipelines to classify 575 lung cancer samples based on histological type. This analysis identified various non-coding RNA that may influence lung-cancer histology.

Availability And Implementation: The RNA-Sequencing and clinical data can be downloaded from Gene Expression Omnibus (accession number GSE62944). Scripts and code that were used to process and analyze the data are available from https://github.com/srp33/TCGA_RNASeq_Clinical.

Contact: stephen_piccolo@byu.edu or andreab@genetics.utah.edu

Supplementary Information: Supplementary material is available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv377DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804769PMC
November 2015

Inferring pathway dysregulation in cancers from multiple types of omic data.

Genome Med 2015 26;7(1):61. Epub 2015 Jun 26.

Department of Oncological Sciences, University of Utah, Salt Lake City, UT USA ; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT USA.

Although in some cases individual genomic aberrations may drive disease development in isolation, a complex interplay among multiple aberrations is common. Accordingly, we developed Gene Set Omic Analysis (GSOA), a bioinformatics tool that can evaluate multiple types and combinations of omic data at the pathway level. GSOA uses machine learning to identify dysregulated pathways and improves upon other methods because of its ability to decipher complex, multigene patterns. We compare GSOA to alternative methods and demonstrate its ability to identify pathways known to play a role in various cancer phenotypes. Software implementing the GSOA method is freely available from https://bitbucket.org/srp33/gsoa.
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http://dx.doi.org/10.1186/s13073-015-0189-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4499940PMC
July 2015

ASSIGN: context-specific genomic profiling of multiple heterogeneous biological pathways.

Bioinformatics 2015 Jun 22;31(11):1745-53. Epub 2015 Jan 22.

Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118 USA, Department of Biomedical Informatics and Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112 USA.

Motivation: Although gene-expression signature-based biomarkers are often developed for clinical diagnosis, many promising signatures fail to replicate during validation. One major challenge is that biological samples used to generate and validate the signature are often from heterogeneous biological contexts-controlled or in vitro samples may be used to generate the signature, but patient samples may be used for validation. In addition, systematic technical biases from multiple genome-profiling platforms often mask true biological variation. Addressing such challenges will enable us to better elucidate disease mechanisms and provide improved guidance for personalized therapeutics.

Results: Here, we present a pathway profiling toolkit, Adaptive Signature Selection and InteGratioN (ASSIGN), which enables robust and context-specific pathway analyses by efficiently capturing pathway activity in heterogeneous sets of samples and across profiling technologies. The ASSIGN framework is based on a flexible Bayesian factor analysis approach that allows for simultaneous profiling of multiple correlated pathways and for the adaptation of pathway signatures into specific disease. We demonstrate the robustness and versatility of ASSIGN in estimating pathway activity in simulated data, cell lines perturbed pathways and in primary tissues samples including The Cancer Genome Atlas breast carcinoma samples and liver samples exposed to genotoxic carcinogens.

Availability And Implementation: Software for our approach is available for download at: http://www.bioconductor.org/packages/release/bioc/html/ASSIGN.html and https://github.com/wevanjohnson/ASSIGN.
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http://dx.doi.org/10.1093/bioinformatics/btv031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443674PMC
June 2015

Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer.

Mol Oncol 2014 Oct 20;8(7):1339-54. Epub 2014 May 20.

Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA. Electronic address:

Better approaches are needed to evaluate a single patient's drug response at the genomic level. Targeted therapy for signaling pathways in cancer has met limited success in part due to the exceedingly interwoven nature of the pathways. In particular, the highly complex RAS network has been challenging to target. Effectively targeting the pathway requires development of techniques that measure global network activity to account for pathway complexity. For this purpose, we used a gene-expression-based biomarker for RAS network activity in non-small cell lung cancer (NSCLC) cells, and screened for drugs whose efficacy was significantly highly correlated to RAS network activity. Results identified EGFR and MEK co-inhibition as the most effective treatment for RAS-active NSCLC amongst a panel of over 360 compounds and fractions. RAS activity was identified in both RAS-mutant and wild-type lines, indicating broad characterization of RAS signaling inclusive of multiple mechanisms of RAS activity, and not solely based on mutation status. Mechanistic studies demonstrated that co-inhibition of EGFR and MEK induced apoptosis and blocked both EGFR-RAS-RAF-MEK-ERK and EGFR-PI3K-AKT-RPS6 nodes simultaneously in RAS-active, but not RAS-inactive NSCLC. These results provide a comprehensive strategy to personalize treatment of NSCLC based on RAS network dysregulation and provide proof-of-concept of a genomic approach to classify and target complex signaling networks.
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http://dx.doi.org/10.1016/j.molonc.2014.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450766PMC
October 2014

Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition.

Breast Cancer Res 2014 Apr 7;16(2):R36. Epub 2014 Apr 7.

Introduction: Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies. Phosphatidylinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathways are frequently activated in TNBC patient tumors at the genome, gene expression and protein levels, and mTOR inhibitors have been shown to inhibit growth in TNBC cell lines. We describe a panel of patient-derived xenografts representing multiple TNBC subtypes and use them to test preclinical drug efficacy of two mTOR inhibitors, sirolimus (rapamycin) and temsirolimus (CCI-779).

Methods: We generated a panel of seven patient-derived orthotopic xenografts from six primary TNBC tumors and one metastasis. Patient tumors and corresponding xenografts were compared by histology, immunohistochemistry, array comparative genomic hybridization (aCGH) and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) sequencing; TNBC subtypes were determined. Using a previously published logistic regression approach, we generated a rapamycin response signature from Connectivity Map gene expression data and used it to predict rapamycin sensitivity in 1,401 human breast cancers of different intrinsic subtypes, prompting in vivo testing of mTOR inhibitors and doxorubicin in our TNBC xenografts.

Results: Patient-derived xenografts recapitulated histology, biomarker expression and global genomic features of patient tumors. Two primary tumors had PIK3CA coding mutations, and five of six primary tumors showed flanking intron single nucleotide polymorphisms (SNPs) with conservation of sequence variations between primary tumors and xenografts, even on subsequent xenograft passages. Gene expression profiling showed that our models represent at least four of six TNBC subtypes. The rapamycin response signature predicted sensitivity for 94% of basal-like breast cancers in a large dataset. Drug testing of mTOR inhibitors in our xenografts showed 77 to 99% growth inhibition, significantly more than doxorubicin; protein phosphorylation studies indicated constitutive activation of the mTOR pathway that decreased with treatment. However, no tumor was completely eradicated.

Conclusions: A panel of patient-derived xenograft models covering a spectrum of TNBC subtypes was generated that histologically and genomically matched original patient tumors. Consistent with in silico predictions, mTOR inhibitor testing in our TNBC xenografts showed significant tumor growth inhibition in all, suggesting that mTOR inhibitors can be effective in TNBC, but will require use with additional therapies, warranting investigation of optimal drug combinations.
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http://dx.doi.org/10.1186/bcr3640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053092PMC
April 2014

A field guide to genomics research.

PLoS Biol 2014 Jan 7;12(1):e1001744. Epub 2014 Jan 7.

Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah, United States of America ; Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, United States of America.

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http://dx.doi.org/10.1371/journal.pbio.1001744DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3883637PMC
January 2014

Multiplatform single-sample estimates of transcriptional activation.

Proc Natl Acad Sci U S A 2013 Oct 15;110(44):17778-83. Epub 2013 Oct 15.

Departments of Pharmacology and Toxicology, and Oncological Sciences, The University of Utah, Salt Lake City, UT 84112.

Over the past two decades, many biotechnology platforms have been developed for high-throughput gene expression profiling. However, because each platform is subject to technology-specific biases and produces distinct raw-data distributions, researchers have experienced difficulty in integrating data across platforms. Data integration is crucial to data-generating consortiums, researchers transitioning to newer profiling technologies, and individuals seeking to aggregate data across experiments. We address this need with our Universal exPression Code (UPC) approach, which corrects for platform-specific background noise using models that account for the genomic base composition and length of target regions; this approach also uses a mixture model to estimate whether a gene is active in a particular profiling sample. The latter produces standardized UPC values on a zero-to-one scale, so that they can be interpreted consistently, irrespective of profiling technology, thus enabling downstream analysis pipelines to be developed in a platform-agnostic manner. The UPC method can be applied to one- and two-channel expression microarrays and to next-generation sequencing data (RNA sequencing). Furthermore, UPCs are derived using information from within a given sample only--no ancillary samples are required at processing time. Thus, UPCs are suitable for personalized-medicine workflows where samples must be processed individually rather than in batches. In a variety of analyses and comparisons, UPCs perform comparably to other methods designed specifically for microarrays or RNA sequencing in most settings. Software for calculating UPCs is freely available at www.bioconductor.org/packages/release/bioc/html/SCAN.UPC.html.
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http://dx.doi.org/10.1073/pnas.1305823110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3816418PMC
October 2013

Genomic pathway analysis reveals that EZH2 and HDAC4 represent mutually exclusive epigenetic pathways across human cancers.

BMC Med Genomics 2013 Sep 30;6:35. Epub 2013 Sep 30.

Huntsman Cancer Institute, 2000 Circle of Hope, Salt Lake City, UT 84112, USA.

Background: Alterations in epigenetic marks, including methylation or acetylation, are common in human cancers. For many epigenetic pathways, however, direct measures of activity are unknown, making their role in various cancers difficult to assess. Gene expression signatures facilitate the examination of patterns of epigenetic pathway activation across and within human cancer types allowing better understanding of the relationships between these pathways.

Methods: We used Bayesian regression to generate gene expression signatures from normal epithelial cells before and after epigenetic pathway activation. Signatures were applied to datasets from TCGA, GEO, CaArray, ArrayExpress, and the cancer cell line encyclopedia. For TCGA data, signature results were correlated with copy number variation and DNA methylation changes. GSEA was used to identify biologic pathways related to the signatures.

Results: We developed and validated signatures reflecting downstream effects of enhancer of zeste homolog 2(EZH2), histone deacetylase(HDAC) 1, HDAC4, sirtuin 1(SIRT1), and DNA methyltransferase 2(DNMT2). By applying these signatures to data from cancer cell lines and tumors in large public repositories, we identify those cancers that have the highest and lowest activation of each of these pathways. Highest EZH2 activation is seen in neuroblastoma, hepatocellular carcinoma, small cell lung cancer, and melanoma, while highest HDAC activity is seen in pharyngeal cancer, kidney cancer, and pancreatic cancer. Across all datasets studied, activation of both EZH2 and HDAC4 is significantly underrepresented. Using breast cancer and glioblastoma as examples to examine intrinsic subtypes of particular cancers, EZH2 activation was highest in luminal breast cancers and proneural glioblastomas, while HDAC4 activation was highest in basal breast cancer and mesenchymal glioblastoma. EZH2 and HDAC4 activation are associated with particular chromosome abnormalities: EZH2 activation with aberrations in genes from the TGF and phosphatidylinositol pathways and HDAC4 activation with aberrations in inflammatory and chemokine related genes.

Conclusion: Gene expression patterns can reveal the activation level of epigenetic pathways. Epigenetic pathways define biologically relevant subsets of human cancers. EZH2 activation and HDAC4 activation correlate with growth factor signaling and inflammation, respectively, and represent two distinct states for cancer cells. This understanding may allow us to identify targetable drivers in these cancer subsets.
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http://dx.doi.org/10.1186/1755-8794-6-35DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850967PMC
September 2013

A dynamic bronchial airway gene expression signature of chronic obstructive pulmonary disease and lung function impairment.

Am J Respir Crit Care Med 2013 May;187(9):933-42

Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA.

Rationale: Molecular phenotyping of chronic obstructive pulmonary disease (COPD) has been impeded in part by the difficulty in obtaining lung tissue samples from individuals with impaired lung function.

Objectives: We sought to determine whether COPD-associated processes are reflected in gene expression profiles of bronchial airway epithelial cells obtained by bronchoscopy.

Methods: Gene expression profiling of bronchial brushings obtained from 238 current and former smokers with and without COPD was performed using Affymetrix Human Gene 1.0 ST Arrays.

Measurements And Main Results: We identified 98 genes whose expression levels were associated with COPD status, FEV1% predicted, and FEV1/FVC. In silico analysis identified activating transcription factor 4 (ATF4) as a potential transcriptional regulator of genes with COPD-associated airway expression, and ATF4 overexpression in airway epithelial cells in vitro recapitulates COPD-associated gene expression changes. Genes with COPD-associated expression in the bronchial airway epithelium had similarly altered expression profiles in prior studies performed on small-airway epithelium and lung parenchyma, suggesting that transcriptomic alterations in the bronchial airway epithelium reflect molecular events found at more distal sites of disease activity. Many of the airway COPD-associated gene expression changes revert toward baseline after therapy with the inhaled corticosteroid fluticasone in independent cohorts.

Conclusions: Our findings demonstrate a molecular field of injury throughout the bronchial airway of active and former smokers with COPD that may be driven in part by ATF4 and is modifiable with therapy. Bronchial airway epithelium may ultimately serve as a relatively accessible tissue in which to measure biomarkers of disease activity for guiding clinical management of COPD.
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http://dx.doi.org/10.1164/rccm.201208-1449OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3707363PMC
May 2013

SIRT1 pathway dysregulation in the smoke-exposed airway epithelium and lung tumor tissue.

Cancer Res 2012 Nov 17;72(22):5702-11. Epub 2012 Sep 17.

Section of Computational Biomedicine, Department of Medicine, Boston University Medical Center; Bioinformatics Program, Boston University, Boston, Massachusetts, USA.

Cigarette smoke produces a molecular field of injury in epithelial cells lining the respiratory tract. However, the specific signaling pathways that are altered in the airway of smokers and the signaling processes responsible for the transition from smoking-induced airway damage to lung cancer remain unknown. In this study, we use a genomic approach to study the signaling processes associated with tobacco smoke exposure and lung cancer. First, we developed and validated pathway-specific gene expression signatures in bronchial airway epithelium that reflect activation of signaling pathways relevant to tobacco exposure, including ATM, BCL2, GPX1, NOS2, IKBKB, and SIRT1. Using these profiles and four independent gene expression datasets, we found that SIRT1 activity is significantly upregulated in cytologically normal bronchial airway epithelial cells from active smokers compared with nonsmokers. In contrast, this activity is strikingly downregulated in non-small cell lung cancer. This pattern of signaling modulation was unique to SIRT1, and downregulation of SIRT1 activity is confined to tumors from smokers. Decreased activity of SIRT1 was validated using genomic analyses of mouse models of lung cancer and biochemical testing of SIRT1 activity in patient lung tumors. Together, our findings indicate a role of SIRT1 in response to smoke and a potential role in repressing lung cancer. Furthermore, our findings suggest that the airway gene expression signatures derived in this study can provide novel insights into signaling pathways altered in the "field of injury" induced by tobacco smoke and thus may impact strategies for prevention of tobacco-related lung cancer.
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http://dx.doi.org/10.1158/0008-5472.CAN-12-1043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053174PMC
November 2012

A single-sample microarray normalization method to facilitate personalized-medicine workflows.

Genomics 2012 Dec 19;100(6):337-44. Epub 2012 Aug 19.

Department of Pharmacology and Toxicology, University of Utah, 201 Presidents Circle, Salt Lake City, UT 84112, USA.

Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.
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http://dx.doi.org/10.1016/j.ygeno.2012.08.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3508193PMC
December 2012

Major differences among chemopreventive organoselenocompounds in the sustained elevation of cytoprotective genes.

J Biochem Mol Toxicol 2012 Sep 16;26(9):344-53. Epub 2012 Jul 16.

Department of Pharmacology and Toxicology, University of Utah College of Pharmacy, Salt Lake City, UT 84112, USA.

Cytoprotective enzyme elevation through the nuclear erythroid 2 p45-related factor 2 (Nrf2)-Kelch-like ECH-associated protein 1/antioxidant response element pathway has been promulgated for cancer prevention. This study compares the redox insult and sustained cytoprotective enzyme elevation by organoselenocompounds and sulforaphane (SF) in lung cells. SF elicited a rise in reactive oxygen species (ROS) and drop in glutathione (GSH) at 2 h; nuclear accumulation of Nrf2 at 4 h; and a GSH rebound and elevation in NAD(P)H quinone oxidoreductase (NQO1), thioredoxin reductase (TR1), and glutamate-cysteine ligase (GCL) at 24 h. Selenocystine (SECY) elicited a similar 24 h response, despite lesser earlier time-point changes. 2-Cyclohexylselenazolidine-4-carboxylic acid effects were similar to SECY's but with a larger Nrf2 change and the largest 24 h increase in GSH, GCL, TR1, and NQO1 of any compound investigated. Selenomethionine elicited a similar acute rise in ROS, but lesser depletion of GSH, no 4 h increase in nuclear Nrf2, only minor 24 h elevations in TR1 and NQO1, and a GCL elevation insufficient to elevate GSH.
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http://dx.doi.org/10.1002/jbt.21427DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4551423PMC
September 2012

A pharmacogenomic method for individualized prediction of drug sensitivity.

Mol Syst Biol 2011 Jul 19;7:513. Epub 2011 Jul 19.

Division of Internal Medicine, Department of Oncology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.
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http://dx.doi.org/10.1038/msb.2011.47DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3159972PMC
July 2011