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Spellman, Paul T.

Publications and source records attributed to Spellman, Paul T..

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Erratum to: Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer

In the version of this article that was published on PubMed [1] the author’s name “Mark A. Dane” was formatted incorrectly in the XML mark up and therefore appeared incorrectly on PubMed. In the XML mark up, the middle initial “A” was added as a Particle when it should have been included as a Given Name. Due to this error, the author’s name was incorrectly formatted in PubMed as “A Dane M” and not as “Dane MA”. The author’s name is correct on the BioMed Central website. The author's name in the XML of the original article [1] has been updated accordingly.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Molecular predictors of therapeutic response to specific anti-cancer agents

Herein is described the use of a collection of 50 breast cancer cell lines to match responses to 77 conventional and experimental therapeutic agents with transcriptional, proteomic and genomic subtypes found in primary tumors. Almost all compounds produced strong differential responses across the cell lines produced responses that were associated with transcriptional and proteomic subtypes and produced responses that were associated with recurrent genome copy number abnormalities. These associations can now be incorporated into clinical trials that test subtype markers and clinical responses simultaneously.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer

Background: High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors. Methods: We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA). Results: High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to i nhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup. Conclusions: We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer

We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2 + breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKT i ) GSK690693 and GSK2141795 in a panel of 22 HER2 + breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKT i were synergistic in HER2 /PIK3CA mut cell lines but not in HER2 + /PIK3CA wt cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKT i or lapatinib + AKT i to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2 + /PIK3CA mut cells compared to HER2 + /PIK3CA wt cells and that lapatinib + AKT i reduced p-S6RP levels to those achieved in HER2 + /PIK3CA wt cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA mut cells following lapatinib + AKT i treatment. Responses of HER2 + SKBR3 cells transfected with lentiviruses carrying control or PIK3CA mut sequences were similar to those observed in HER2 + /PIK3CA mut cell lines but not in HER2 + /PIK3CA wt cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKT i . This combination does not confer substantial benefit beyond lapatinib in HER2 + /PIK3CA wt cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Modeling precision treatment of breast cancer

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

A robust prognostic signature for hormone-positive node-negative breast cancer

Background: Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs). Methods: We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates. Results: Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients. Conclusions: RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

ATM Suppresses SATB1-Induced Malignant Progression in Breast Epithelial Cells

SATB1 drives metastasis when expressed in breast tumor cells by radically reprogramming gene expression. Here, we show that SATB1 also has an oncogenic activity to transform certain non-malignant breast epithelial cell lines. We studied the non-malignant MCF10A cell line, which is used widely in the literature. We obtained aliquots from two different sources (here we refer to them as MCF10A-1 and MCF10A-2), but found them to be surprisingly dissimilar in their responses to oncogenic activity of SATB1. Ectopic expression of SATB1 in MCF10A-1 induced tumor-like morphology in three dimensional cultures, led to tumor formation in immunocompromised mice, and when injected into tail veins, led to lung metastasis. The number of metastases correlated positively with the level of SATB1 expression. In contrast, SATB1 expression in MCF10A-2 did not lead to any of these outcomes. Yet DNA copy-number analysis revealed that MCF10A-1 is indistinguishable genetically from MCF10A-2. However, gene expression profiling analysis revealed that these cell lines have significantly divergent signatures for the expression of genes involved in oncogenesis, including cell cycle regulation and signal transduction. Above all, the early DNA damage-response kinase, ATM, was greatly reduced in MCF10A-1 cells compared to MCF10A-2 cells. We found the reason for reduction to be phenotypic drift due to long-term cultivation of MCF10A. ATM knockdown in MCF10A-2 and two other non-malignant breast epithelial cell lines, 184A1 and 184B4, enabled SATB1 to induce malignant phenotypes similar to that observed for MCF10A-1. These data indicate a novel role for ATM as a suppressor of SATB1-induced malignancy in breast epithelial cells, but also raise a cautionary note that phenotypic drift could lead to dramatically different functional outcomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Cross-platform pathway-based analysis identifies markers of response to the PARP inhibitor olaparib

Poly(ADP-ribose) polymerase (PARP) is an enzyme involved in DNA repair. PARP inhibitors can act as chemosensitizers, or operate on the principle of synthetic lethality when used as single agent. Clinical trials have shown drugs in this class to be promising for BRCA mutation carriers. We postulated that inability to demonstrate response in non-BRCA carriers in which BRCA is inactivated by other mechanisms or with deficiency in homologous recombination for DNA repair is due to lack of molecular markers that define a responding subpopulation. We identified candidate markers for this purpose for olaparib (AstraZeneca) by measuring inhibitory effects of nine concentrations of olaparib in 22 breast cancer cell lines and identifying features in transcriptional and genome copy number profiles that were significantly correlated with response. We emphasized in this discovery process genes involved in DNA repair. We found that the cell lines that were sensitive to olaparib had a significant lower copy number of BRCA1 compared to the resistant cell lines (p value 0.012). In addition, we discovered seven genes from DNA repair pathways whose transcriptional levels were associated with response. These included five genes (BRCA1, MRE11A, NBS1, TDG, and XPA) whose transcript levels were associated with resistance and two genes (CHEK2 and MK2) whose transcript levels were associated with sensitivity. We developed an algorithm to predict response using the seven-gene transcription levels and applied it to 1,846 invasive breast cancer samples from 8 U133A/plus 2 (Affymetrix) data sets and found that 8–21 % of patients would be predicted to be responsive to olaparib. A similar response frequency was predicted in 536 samples analyzed on an Agilent platform. Importantly, tumors predicted to respond were enriched in basal subtype tumors. Our studies support clinical evaluation of the utility of our seven-gene signature as a predictor of response to olaparib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology

Background: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for path way and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma

The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets. Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3'-untranslated region (with less frequency observed for coding sequence and 5'-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anticorrelated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability. This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Unifying Gene Expression Measures from Multiple Platforms Using Factor Analysis

In the Cancer Genome Atlas (TCGA) project, gene expression of the same set of samples is measured multiple times on different microarray platforms. There are two main advantages to combining these measurements. First, we have the opportunity to obtain a more precise and accurate estimate of expression levels than using the individual platforms alone. Second, the combined measure simplifies downstream analysis by eliminating the need to work with three sets of expression measures and to consolidate results from the three platforms. We propose to use factor analysis (FA) to obtain a unified gene expression measure (UE) from multiple platforms. The UE is a weighted average of the three platforms, and is shown to perform well in terms of accuracy and precision. In addition, the FA model produces parameter estimates that allow the assessment of the model fit. The R code is provided in File S2. Gene-level FA measurements for the TCGA data sets are available from http://tcga-data.nci.nih.gov/docs/publications/unified_expression/.

59 BASIC BIOLOGICAL SCIENCES↗