Gene Expression Profiling For Prediction Research Articles

Unique Gene Expression Profiles within South Africa Are Associated with Varied Chemotherapeutic Responses in Conventional Osteosarcoma.

We determined the predictive gene expression profiles associated with chemo-response in conventional osteosarcomas (COS) within South Africa. In 28 patients, we performed an RNA extraction, cDNA synthesis, and quantitative analysis using the RT-PCR 2-∆∆CT method to determine the fold change in gene expression alongside GAPDH (housekeeping gene). We observed a significant downregulation in the mRNA expression profiles of ABCB1-p-glycoprotein (p = 0.0007), ABCC3 (p = 0.002), ERCC1 (p = 0.007), p-53 (p = 0.007), and RFC1 (p = 0.003) in the COS patients compared to the healthy donors. Furthermore, ABCB1-p-glycoprotein (p = 0.008) and ABCC3 (p = 0.020) exhibited a significant downregulation in the COS tumour tissues when compared to the healthy donors. In our univariate logistic regression, the predictors of chemotherapeutic response comprised ERCC1 [restricted cubic spline (RCS) knot: OR -0.27; CI -0.504 to -0.032; p = 0.036]; osteoblastic subtype [OR -0.36; CI -0.652 to -0.092; p = 0.026); fibroblastic subtype [OR 0.91; CI 0.569 to 1.248; p < 0.001]; and mixed subtype [OR 0.53; CI 0.232 to 0.032; p = 0.032]. In our multivariable logistic regression, the significant predictors of chemotherapeutic response comprised age [RCS knot: OR -2.5; CI -3.616 to -1.378; p = 0.022]; ABCC3 [RCS knot: OR 0.67; CI 0.407 to 0.936, p = 0.016]; ERCC1 [RCS knot: OR 0.57; CI 0.235 to 0.901; p = 0.044]; RFC1 [RCS knot: OR -1.04; CI -1.592 to -0.487; p = 0.035]; chondroblastic subtype [OR -0.83; CI -1.106 to -0.520; p = 0.012]; and osteoblastic subtype [OR -1.28; CI -1.664 to -0.901; p = 0.007]. In this South African cohort, we observed the unique gene expression profiles of osteosarcoma tumourigenesis and chemotherapeutic responses. These may serve as prognostication and therapeutic targets. Larger-scale research is needed on the African continent.

Predictive Microbial Community and Functional Gene Expression Profiles in Pineapple Peel Fermentation Using 16S rRNA Gene Sequences

Pineapple peel (PP) is a by-product with the potential to be used as a raw material for functional beverages. Traditional PP fermentation has so far paid little attention to the microbial community and its role in the fermentation process. As a result, the current research looked into the microbial communities and their roles during PP fermentation. A metagenomic approach based on the 16S rRNA sequencing data was used to assess the microbial communities. Subsequent analysis was performed using PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) to analyze the microbial functions in the fermentation system. The microecology of the fermentation process in three samples was predominated by Firmicutes. Furthermore, the well-known probiotic genera Weissella, Lactobacillus, and Lactococcus were found to be predominating in the gumer, promic, and control samples, respectively. It was obvious that microenvironmental differences have an effect on the microbial composition of PP fermentation. Moreover, functional prediction revealed that carbohydrate metabolism was the most prevalent metabolic pathway during the fermentation process. Additionally, it was discovered that all of the bacteria found in the samples played significant roles in carbohydrate, amino acid, vitamin, and co-factor metabolism, which can be inferred to result in the production of beneficial metabolites.

Development and validation of a nomogram incorporating gene expression profiling and clinical factors for accurate prediction of metastasis in patients with cutaneous melanoma following Mohs micrographic surgery

There is a need to improve prognostic accuracy for patients with cutaneous melanoma. A 31-gene expression profile (31-GEP) test uses the molecular biology of primary tumors to identify individual patient metastatic risk. Develop a nomogram incorporating 31-GEP with relevant clinical factors to improve prognostic accuracy. In an IRB-approved study, 1124 patients from 9 Mohs micrographic surgery centers were prospectively enrolled, treated with Mohs micrographic surgery, and underwent 31-GEP testing. Data from 684 of those patients with at least 1-year follow-up or a metastatic event were included in nomogram development to predict metastatic risk. Logistic regression modeling of 31-GEP results and T stage provided the simplest nomogram with the lowest Bayesian information criteria score. Validation in an archival cohort (n=901) demonstrated a significant linear correlation between observed and nomogram-predicted risk of metastasis. The resulting nomogram more accurately predicts the risk for cutaneous melanoma metastasis than T stage or 31-GEP alone. The patient population is representative of Mohs micrographic surgery centers. Sentinel lymph node biopsy was not performed for most patients and could not be used in the nomogram. Integration of 31-GEP and T stage can gain clinically useful prognostic information from data obtained noninvasively.

RNA-Seq analysis of seasonal and individual variation in blood transcriptomes of healthy managed bottlenose dolphins.

BackgroundThe blood transcriptome can reflect both systemic exposures and pathological changes in other organs of the body because immune cells recirculate through the blood, lymphoid tissues, and affected sites. In human and veterinary medicine, blood transcriptome analysis has been used successfully to identify markers of disease or pathological conditions, but can be confounded by large seasonal changes in expression. In comparison, the use of transcriptomic based analyses in wildlife has been limited. Here we report a longitudinal study of four managed bottlenose dolphins located in Waikoloa, Hawaii, serially sampled (approximately monthly) over the course of 1 year to establish baseline information on the content and variation of the dolphin blood transcriptome.ResultsIllumina based RNA-seq analyses were carried out using both the Ensembl dolphin genome and a de novo blood transcriptome as guides. Overall, the blood transcriptome encompassed a wide array of cellular functions and processes and was relatively stable within and between animals over the course of 1 year. Principal components analysis revealed moderate clustering by sex associated with the variation among global gene expression profiles (PC1, 22 % of variance). Limited seasonal change was observed, with < 2.5 % of genes differentially expressed between winter and summer months (FDR < 0.05). Among the differentially expressed genes, cosinor analysis identified seasonal rhythmicity for the observed changes in blood gene expression, consistent with studies in humans. While the proportion of seasonally variant genes in these dolphins is much smaller than that reported in humans, the majority of those identified in dolphins were also shown to vary with season in humans. Gene co-expression network analysis identified several gene modules with significant correlation to age, sex, or hematological parameters.ConclusionsThis longitudinal analysis of healthy managed dolphins establishes a preliminary baseline for blood transcriptome analysis in this species. Correlations with hematological parameters, distinct from muted seasonal effects, suggest that the otherwise relatively stable blood transcriptome may be a useful indicator of health and exposure. A robust database of gene expression in free-ranging and managed dolphins across seasons with known adverse health conditions or contaminant exposures will be needed to establish predictive gene expression profiles suitable for biomonitoring.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3020-8) contains supplementary material, which is available to authorized users.

Abstract ES5-1: Predictive gene expression profiles in breast cancer, where are we?

Abstract Successful sequencing of the entire human genome offered an enormous potential for improving human health, especially in oncology. Nearly 20 years after successful completion of the human genome project, the Cancer Genome Atlas (TCGA) has catalogued the most common mutations in most cancers, including breast. Further, the technical advances that facilitate the ability to fully analyze both germline and somatic DNA and RNA has resulted in rapid (&amp;lt;4 weeks) and relatively inexpensive (&amp;lt;$3000) assays to emerge. Indeed, the NCI-sponsored Lung MAP and Cancer MATCH, and the ASCO-sponsored TAPUR, trials are now opening in the U.S., testing whether knowledge of somatic genetic changes improves patient outcomes. However, arguably, the most clinically useful product of the Human Genome Project to date has been the emergence of multiparameter gene expression assays that can be used to direct cancer care, particularly in breast cancer. The 21-gene assay was the first of these to be shown to have sufficiently high analytical validity and clinical utility to be used to identify patients with node negative, ER positive early stage breast cancer who, assuming adequate adjuvant endocrine therapy, could be spared the toxicity of adjuvant chemotherapy based on prognosis alone. Subsequently, several other assays have now been reported to be equally satisfactory for this same use context. The Oxford Overview has suggested that adjuvant chemotherapy reduces the odds of distant recurrence and death by approximately 1/3, regardless of initial prognosis. However, many retrospective correlative studies over the last 4 decades have suggested that chemotherapy may have differing relative activities within distinct biological, or more recently designated “intrinsic,” subtypes, based mostly on expression of estrogen receptor and proliferation genes. Preliminary, prospective retrospective studies by the NSABP in B20 and SWOG in S8814 have, indeed, suggested that patients with ER positive breast cancer with low recurrence scores, as determined by the 21-gene assay, not only have better prognosis than those with intermediate or higher RS, but that chemotherapy may simply not work in this category. However, these studies were both confounded by a number of biases. Therefore, before potentially life-saving chemotherapy is with-held from node positive breast cancer patients based on prediction alone, a prospective clinical trial, SWOG S1007 (the RxPonder Trial), was initiated. The primary objective of the RxPonder trial is to determine if women with node positive, ER positive breast cancer with RS &amp;lt;25 may not benefit from adjuvant chemotherapy even if their prognosis is worse than those with node negative disease. This trial, which is now fully accrued, will not only tell us if we can spare yet another group of women from receiving unnecessary or inactive chemotherapy, it also offers the promise of identification of targets that could be further interrogated with newer biological treatments in the future. Citation Format: Hayes DF. Predictive gene expression profiles in breast cancer, where are we?. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr ES5-1.

Indications for prognostic gene expression profiling in early breast cancer.

Breast cancer is a heterogeneous disease. While breast cancer mortality has dropped substantially over the past three decades due to early detection and adjuvant systemic therapy (AST), the risk of recurrence is highly dependent upon numerous factors including tumor size, involvement of regional lymph nodes, histologic grade, expression of hormone receptors (estrogen and progesterone), and human epidermal growth factor receptor 2 (HER2) amplification. We use these factors to determine which early breast cancer (EBC) patients should be treated with AST, including endocrine therapy (ET), chemotherapy, and HER2-directed treatments. While these factors aid in this determination, it remains challenging to identify those patients unlikely to benefit from adjuvant chemotherapy, resulting in over-treatment of patients. Given this dilemma, there has been great interest in the development of prognostic and predictive gene expression profiles. The most extensively studied profile, the 21-gene recurrence score (Oncotype Dx®), estimates 10-year risk of breast cancer recurrence in patients with estrogen receptor (ER)-positive, HER2-negative, node-negative EBC and is likely predictive of chemotherapy benefit. This assay has established analytic validity, clinical validity, and clinical utility for this patient group and, therefore, is indicated in this patient population to help inform decisions regarding administration of adjuvant chemotherapy. Several other assays may have utility in this clinical context or perhaps to identify patients who do not require extended adjuvant ET. These assays include the following: PAM 50 Risk of Recurrence (ROR) Score (Prosigna™), Breast Cancer Index, and EndoPredict®.

Abstract OT3-2-02: MINT I: Multi-Institutional Neo-adjuvant Therapy, MammaPrint Project I

Abstract Background: Women with locally advanced breast cancer (LABC) are often treated with neo-adjuvant chemotherapy to reduce the size of the tumor prior to surgery, to enable breast conserving surgery and to observe the clinical effect of therapy in real time. Studies have shown that the 25–27% of individuals who have a pathologic complete response (pCR) to neoadjuvant therapy have a survival advantage of 80% in 5 years, which is double the expected survival of the remaining patients without pCR. If patients who are likely to show a pCR could be identified prior to initiation of therapy, it would enable more informed treatment decisions – patients likely to respond would be served well by current neoadjuvant chemotherapy protocols, while those unlikely to respond may be better suited to innovative new strategies for drug discovery [von Minckwitz et al. JCO 2006]. Genomic assays, which are widely used to provide prognostic and predictive information in early breast cancer, have the potential to provide information on the likelihood of a patient with LABC responding to neo-adjuvant therapy [Glück et al. BRCRT2013]. Trial design: MINT I is a prospective study designed to test the ability of molecular profiling, as well as traditional pathologic and clinical prognostic factors, to predict response to neo-adjuvant chemotherapy in patients with LABC. MammaPrint risk profile, BluePrint molecular subtyping profile, TargetPrint estrogen receptor (ER), progesterone receptor (PR) and HER2 single gene readout, and TheraPrint Research Gene Panel will be analyzed on a fresh tumor specimen using the whole genome array. Patients will receive neo-adjuvant chemotherapy pre-specified in the protocol. Response will be measured centrally. pCR is defined as the absence of invasive carcinoma in both the breast and axilla at microscopic examination of the resection specimen, regardless of the presence of carcinoma in situ. Eligibility: The study will include women ≥18 years with histologically-proven invasive breast cancer T2 (≥3.5cm)-T4, N0M0 or T2-T4N1M0, adequate bone marrow reserves and normal renal and hepatic function who signed an IRB approved informed consent. Objectives: The objectives of the study are to: 1. Determine the predictive power of MammaPrint and BluePrint for sensitivity to neo-adjuvant chemotherapy as measured by pCR. 2. Compare TargetPrint ER, PR and HER2 with local and centralized IHC and/or CISH/FISH assessment. 3. Identify correlations between TheraPrint and response to neo-adjuvant chemotherapy. 4. Identify and/or validate predictive gene expression profiles of clinical response or resistance to neo-adjuvant chemotherapy. 5. Compare BluePrint with IHC-based subtype classification. Statistical methods: Standard statistical tests such as the Pearson Chi-square test will be used to characterize and evaluate the relationship between chemoresponsiveness and gene expression patterns. Accrual: A total of 226 eligible patients will be enrolled from multiple institutions. To date (June 06, 2014), 103 patients have been enrolled. Citation Format: Charles E Cox, Peter Blumencranz, Ruben Saez, Robert Wesolowski, William Dooley, Lisette Stork-Sloots, Femke de Snoo, Sarah Untch, Eli Avisar. MINT I: Multi-Institutional Neo-adjuvant Therapy, MammaPrint Project I [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr OT3-2-02.

Abstract OT1-2-01: MINT I: Multi-institutional neo-adjuvant therapy, MammaPrint project I

Abstract Background: Women with locally advanced breast cancer (LABC) are often treated with neo-adjuvant chemotherapy to reduce the size of the tumor prior to surgery, to enable breast conserving surgery and to observe the clinical effect of therapy in real time. Studies have shown that the 25–27% of individuals who have a pathologic complete response (pCR) to neoadjuvant therapy have a survival advantage of 80% in 5 years, which is double the expected survival of the remaining patients without pCR. If patients who are likely to show a pCR could be identified prior to initiation of therapy, it would enable more informed treatment decisions – patients likely to respond would be served well by current neoadjuvant chemotherapy protocols, while those unlikely to respond may be better suited to innovative new strategies for drug discovery [von Minckwitz et al. JCO 2006]. Genomic assays, which are widely used to provide prognostic and predictive information in early breast cancer, have the potential to provide information on the likelihood of a patient with LABC responding to neo-adjuvant therapy [Glück et al. ASCO 2012]. Trial design: MINT I is a prospective study designed to test the ability of molecular profiling, as well as traditional pathologic and clinical prognostic factors, to predict response to neo-adjuvant chemotherapy in patients with LABC. MammaPrint risk profile, BluePrint molecular subtyping profile, TargetPrint estrogen receptor (ER), progesterone receptor (PR) and HER2 single gene readout, and TheraPrint Research Gene Panel will be analyzed on a fresh tumor specimen using the whole genome array. Patients will receive neo-adjuvant chemotherapy pre-specified in the protocol. Response will be measured centrally. pCR is defined as the absence of invasive carcinoma in both the breast and axilla at microscopic examination of the resection specimen, regardless of the presence of carcinoma in situ. Eligibility: The study will include women ≥18 years with histologically-proven invasive breast cancer T2 (≥3.5cm)-T4, N0M0 or T2-T4N1M0, adequate bone marrow reserves and normal renal and hepatic function who signed an IRB approved informed consent. Objectives: The objectives of the study are to: 1. Determine the predictive power of MammaPrint and BluePrint for sensitivity to neo-adjuvant chemotherapy as measured by pCR. 2. Compare TargetPrint ER, PR and HER2 with local and centralized IHC and/or CISH/FISH assessment. 3. Identify correlations between TheraPrint and response to neo-adjuvant chemotherapy. 4. Identify and/or validate predictive gene expression profiles of clinical response or resistance to neo-adjuvant chemotherapy. 5. Compare BluePrint with IHC-based subtype classification. Statistical methods: Standard statistical tests such as the Pearson Chi-square test will be used to characterize and evaluate the relationship between chemoresponsiveness and gene expression patterns. Accrual: A total of 226 eligible patients will be enrolled from multiple institutions. To date (June 06, 2013), 57 patients have been enrolled. Clinical trial registry number: NCT01501487. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr OT1-2-01.

The prospective MammaPrint MINT (Multi-Institutional Neo-adjuvant Therapy) study.

TPS11122 Background: Patients with locally advanced breast cancer (LABC) are often treated with neo-adjuvant chemotherapy to reduce the size of the tumor before definitive surgery. Complete pathologic Response (pCR) predicts better long term outcome. Genomics assays that measure specific gene expression patterns in a patient's primary tumor have become important prognostic and predictive tools for early breast cancer. This study is designed to test the ability of molecular profiling, as well as traditional pathologic and clinical prognostic factors to predict responsiveness to neo-adjuvant chemotherapy in patients with LABC. Methods: Women ≥ 18 yrs with histologically-proven invasive breast cancer T2(≥3.5cm)-T4,N0M0 or T2-T4N1M0, with measurable disease, adequate bone marrow reserves and normal renal and hepatic function who signed informed consent are enrolled. Axillary lymph nodes will be staged according to protocol. MammaPrint risk profile, BluePrint molecular subtyping profile, TargetPrint ER, PR and HER2 single gene readout, and the 56-gene TheraPrint Research Gene Panel will be analysed using the whole genome expression array. Patients will receive neo-adjuvant chemotherapy treatment according to protocol. Response will be measured by centrally assessed Residual Cancer Burden (RCB). Objectives are: (1) To determine the predictive power of MammaPrint and BluePrint for sensitivity to neo-adjuvant chemotherapy as measured by pCR. (2) To identify and/or validate predictive gene expression profiles of clinical response or resistance to neo-adjuvant chemotherapy. (3) To compare TargetPrint ER, PR and HER2 with local and centralized IHC and/or CISH/FISH assessment. (4) To identify correlations between TheraPrint and response to neo-adjuvant chemotherapy. (5) To compare BluePrint molecular subtype with IHC-based subtype classification. To achieve a difference of 20% in chemotherapy sensitivity for patients stratified by MammaPrint, a total of 226 samples is needed (significance level 0.05 and power of 0.90). So far 45 patients have been enrolled from multiple institutions. Clinical trial information: NCT01501487.

Abstract 861: Analysis of predictive genes in triple negative breast cancer in response to a gamma-secretase inhibitor.

Abstract Defined by the lack of estrogen and progesterone receptors and amplification of human epidermal growth factor receptor-2 (HER-2), triple negative breast cancer (TNBC) is a devastating disease with no approved, targeted therapy to date. Characterized by aggressive behavior which includes high rates of metastasis and very poor prognosis, TNBC is presently treated with DNA-damaging agents such as cisplatin or carboplatin. The overall survival outcomes for women with TNBC is dismal and consequently TNBC research requires identification of biomarkers that specifically mediate signaling pathways that lead to disease progression and metastasis. Notch receptors which are necessary for self-renewal and survival of breast cancer stem cells are excellent candidate biomarkers to investigate in various subtypes of TNBC. Based on the critical need to identify predictive biomarkers, we hypothesized that inhibition of the Notch pathway via γ-secretase inhibition (GSI) will provide an effective blockade of TNBC growth in specific subtypes where predictive Notch gene expression profiles are significantly up or downregulated. A panel of three TNBC cell lines were used in this study: MDA-MB-231 (mesenchymal stem-like), MDA-MB-468 (basal-like 1), and BT549 (luminal). Using real-time PCR, Notch pathway-associated genes were measured which include Notch-1 and Notch-4 receptors, Notch target genes (i.e. Deltex-1, Hes-1, Hes-5, and Hey-1), and genes such as IL-8 and MMP-9 which are associated with aggressive tumor behavior. Both anchorage-dependent and anchorage-independent growth assays were measured in response to MRK-003 GSI, carboplatin, and the combination. We have identified for the first time a TNBC line (MDA-MB-468) that expressed the highest level of Hes-1, a Notch gene target, and is the most sensitive to single agent MRK-003 GSI treatment. We also identified that this cell line with the highest sensitivity to MRK-003 GSI upregulates Notch-4 and IL-8 in response to the GSI to a greater extent than either of the two other TNBC lines. Furthermore, the addition of carboplatin at its IC50 concentration significantly increased the sensitivity of MRK-003 GSI in otherwise insensitive TNBC lines. These results suggest for the first time that: 1) Hes-1 could potentially be a valuable predictive biomarker of Notch activity in TBNC; 2) induction of Notch-4 and/or IL-8 could predict response to MRK-003 GSI; and 3) carboplatin might provide a means to increase sensitivity to MRK-003 GSI in resistant TNBC. Citation Format: Roma Olsauskas-Kuprys, Clodia Osipo. Analysis of predictive genes in triple negative breast cancer in response to a gamma-secretase inhibitor. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 861. doi:10.1158/1538-7445.AM2013-861

Abstract OT3-4-02: MINT I: Multi-Institutional Neo-adjuvant Therapy, MammaPrint Project I

Abstract Background: Women with locally advanced breast cancer (LABC) are often treated with neo-adjuvant chemotherapy to reduce the size of the tumor prior to surgery, to enable breast conserving surgery and to observe the clinical effect of therapy in real time. Studies have shown that the 25–27% of individuals who have a pathologic complete response (pCR) to neoadjuvant therapy have a survival advantage of 80% in 5 years, which is double the expected survival of the remaining patients without pCR. If patients who are likely to show a pCR could be identified prior to initiation of therapy, it would enable more informed treatment decisions – patients likely to respond would be served well by current neoadjuvant chemotherapy protocols, while those unlikely to respond may be better suited to innovative new strategies for drug discovery [von Minckwitz et al. JCO 2006]. Genomics assays, which are widely used to provide prognostic and predictive information in early breast cancer, have the potential to provide information on the likelihood of a patient with LABC responding to neo-adjuvant therapy [Glück et al. ASCO 2012]. Trial design: MINT I is a prospective study designed to test the ability of molecular profiling, as well as traditional pathologic and clinical prognostic factors, to predict responsiveness to neo-adjuvant chemotherapy in patients with LABC. MammaPrint risk profile, BluePrint molecular subtyping profile, TargetPrint estrogen receptor (ER), progesterone receptor (PR) and HER2 single gene readout, and the 56-gene TheraPrint Research Gene Panel will be analyzed on a fresh tumor specimen using the whole genome array. Patients will receive neo-adjuvant chemotherapy pre-specified in the protocol. Response will be measured centrally. pCR is defined as the absence of invasive carcinoma in both the breast and axilla at microscopic examination of the resection specimen, regardless of the presence of carcinoma in situ. Eligibility: The study will include women ≥18 years with histologically-proven invasive breast cancer T2 (≥3.5cm)-T4, N0M0 or T2-T4N1M0, adequate bone marrow reserves and normal renal and hepatic function who signed an IRB approved informed consent. Objectives: The objectives of the study are to: 1. Determine the predictive power of MammaPrint and BluePrint for sensitivity to neo-adjuvant chemotherapy as measured by pCR.2. Compare TargetPrint ER, PR and HER2 with local and centralized IHC and/or CISH/FISH assessment.3. Identify correlations between TheraPrint and response to neo-adjuvant chemotherapy.4. Identify and/or validate predictive gene expression profiles of clinical response or resistance to neo-adjuvant chemotherapy.5. Compare BluePrint with IHC-based subtype classification. Statistical methods: Standard statistical tests such as the Pearson Chi-square test will be used to characterize and evaluate the relationship between chemoresponsiveness and gene expression patterns. Accrual: A total of 226 eligible patients will be enrolled from multiple institutions. To date (June 06, 2012), 31 patients have been enrolled. Clinical trial registry number: NCT01501487. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr OT3-4-02.

Gene expression profiling for prediction of tissue of origin to direct site-specific therapy in patients with CUP: Treatment outcomes by assay predictive probability.

e21023 Background: Tumor profiling as a diagnostic technique in CUP depends on the detection of similarities in gene expression between the cancer and the tissue of origin. Assay-directed site-specific therapy may be most effective in tumors with high-confidence molecular predictions, since a) the prediction may be more often correct due to greater similarity between the gene expression profile of the unknown tumor to the assay reference tumor database, and b) the tumor would more likely share biologic characteristics/treatment sensitivity with cancers of known origin. Methods: In a prospective trial performed by the Sarah Cannon Research Institute (SCRI), a 92-gene RT-PCR assay (CancerTYPE ID; bioTheranostics, Inc.) was performed on tumor biopsies from previously untreated CUP pts. Pts were then assigned standard site-specific treatment directed by the assay prediction. Treatment responses were evaluated based on the strength of the assay predictions; probabilities ≥80% were arbitrarily regarded as high confidence results. Results: Between 10/08 and 12/11, 224 pts (26 predicted tumor types) received treatment. Median overall survival (OS) was longer in 113 pts (50%) with high confidence predictions: 11.7 months (mos) vs 9.8 mos; p=.03. 120 pts (59%) had assay predictions of responsive tumor types, known to derive substantial benefit from standard site-specific therapy. Treatment responses, irrespective of predictive probability, were better in these pts vs those with less responsive tumor types (median OS 12.8 vs 7.4 mos; p=.027). Further stratification of high vs lower confidence assay predictions resulted in 4 groups with different OS (p=.03) (Table). Conclusions: Site-specific therapy for CUP pts directed by results of the 92-gene assay was more effective when the probability of the assay prediction was higher, particularly in pts with assay diagnoses of more responsive tumor types. [Table: see text]

Prediction of Breast Cancer Metastasis by Gene Expression Profiles: A Comparison of Metagenes and Single Genes

BackgroundThe popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features.MethodsIn this study we compared the performance of either metagene-or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.ResultsMG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.ConclusionPrediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Evaluation of a 30-Gene Paclitaxel, Fluorouracil, Doxorubicin, and Cyclophosphamide Chemotherapy Response Predictor in a Multicenter Randomized Trial in Breast Cancer

We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FACx6 preoperative chemotherapy. We also did an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Two hundred and seventy-three patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n = 138), or FAC × 6 (n = 135) neoadjuvant chemotherapy. All patients underwent a pretreatment fine-needle aspiration biopsy of the tumor for gene expression profiling and treatment response prediction. The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (P < 0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% [95% confidence interval (95% CI), 21-56%], the negative predictive value was 88% (95% CI, 77-95%), and the area under the receiver operating characteristic curve (AUC) was 0.711. In the FAC arm, the PPV was 9% (95% CI, 1-29%) and the AUC was 0.584. This suggests that the genomic predictor may have regimen specificity. Its performance was similar to a clinical variable-based predictor nomogram. Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next-generation predictive markers will need to be developed separately for different molecular subsets of breast cancers.

Gene Expression Profiling for Survival Prediction in Pediatric Rhabdomyosarcomas: A Report From the Children's Oncology Group

We investigated whether tumors from diagnostic biopsies of primary rhabdomyosarcoma (RMS) contain relevant prognostic information in the form of gene expression signatures that can be used to model and predict outcome of patients. A 22,000-probe set microarray was used to evaluate 120 RMS specimens and correlate gene expression patterns to survival. Multivariate gene expression models or metagenes were developed using cross-validated Cox regression proportional hazards modeling and were evaluated using Kaplan-Meier analysis. A 34-metagene, based on expression patterns of 34 genes, was highly predictive of outcome. It was not highly correlated with individual clinical risk factors such as patient age, stage, tumor size, or histology. However, it was correlated with a risk classification used by the Children's Oncology Group and the biologic subsets of alveolar histology tumors. These data support further evaluation of RMS metagenes to discriminate patients with good prognosis from those with poor prognosis, with the potential to direct risk-adapted therapy.

Gene Expression Profiling for Survival Prediction in Pediatric Rhabdomyosarcomas: A Report From the Children's Oncology Group

Gene Expression Profiling for Survival Prediction in Pediatric Rhabdomyosarcomas: A Report From the Children's Oncology Group

A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility

BackgroundMicroarray diagnostics of tumour samples is based on measurement of prognostic and/or predictive gene expression profiles. Typically, diagnostic profiles have been developed using bulk tumour samples with a sufficient amount of tumour cells (usually >50%). Consequentially, a diagnostic results depends on the minimal percentage of tumour cells within a sample. Currently, tumour cell percentage is assessed by conventional histopathological review. However, even for experienced pathologists, such scoring remains subjective and time consuming and can lead to ambiguous results.MethodsIn this study we investigated whether we could use transcriptional activity of a specific set of genes instead of histopathological review to identify samples with sufficient tumour cell content. Genome-wide gene expression measurements were used to develop a transcriptional gene profile that could accurately assess a sample's tumour cell percentage.ResultsSupervised analysis across 165 breast tumour samples resulted in the identification of a set of 13 genes which expression correlated with presence of tumour cells. The developed gene profile showed a high performance (AUC 0.92) for identification of samples that are suitable for microarray diagnostics. Validation on 238 additional breast tumour samples indicated a robust performance for correct classification with an overall accuracy of 91 percent and a kappa score of 0.63 (95%CI 0.47–0.73).ConclusionThe developed 13-gene profile provides an objective tool for assessment whether a breast cancer sample contains sufficient tumour cells for microarray diagnostics. It will improve the efficiency and throughput for diagnostic gene expression profiling as it no longer requires histopathological analysis for initial tumour percentage scoring. Such profile will also be very use useful for assessment of tumour cell percentage in biopsies where conventional histopathology is difficult, such as fine needle aspirates.

Gene expression profiles and breast cancer metastasis: a genetic perspective

The majority of cancer mortality is attributed to metastasis, which is the spread of tumor cells to a secondary site. Several studies have demonstrated that the genetic background on which a tumor arises has a major effect on both metastatic efficiency and on predictive gene expression profiles. These observations suggest that there is variability in metastasis frequency between individuals and that some individuals could be more prone to secondary tumor formation and development than others. Thus, genetic background might have important clinical implications in metastasis detection, management and prevention.

Role of gene-expression profiling in chronic myeloid leukemia

Gene-expression profiling enables disease classification and risk stratification, and provides important insights into possible pathogenetic mechanisms. The clinical management and prognosis of chronic myeloid leukemia (CML) has substantially changed after the introduction of targeted therapies, such as imatinib and the second-generation tyrosine kinase inhibitors nilotinib and dasatinib. Although exact characterization of CML pathogenetics has been performed by showing the causal pathogenetic relevance of the reciprocal translocation between chromosomes 9 and 22 for CML development, the disease still exhibits a marked clinical and biological heterogenicity. Thus, prognostic scores for a more exact disease classification, as well as for prediction of response to tyrosine kinase inhibitor therapy are warranted, especially because scores established within the interferon era have lost their prognostic value when applied to patients treated with imatinib. Gene-expression profiling has been proven to represent a powerful tool for early identification of nonresponders to cancer therapy. Several profiling studies in CML have been reported thus far. However, the available data are inconsistent, which is mainly due to technical reasons, such as the use of various different chips, different biostatistical algorithms for data analysis and, most importantly, the use of various different cellular sources (i.e., mononuclear cells from peripheral blood, whole bone marrow cells or selected bone marrow-derived stem/progenitor cells). This review will summarize the recent literature on gene-expression profiling for CML classification and response prediction.

Non-Overlapping and Non–Cell-Type–Specific Gene Expression Signatures Predict Lung Cancer Survival

Gene expression profiling for outcome prediction of non-small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC. Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases. Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways. Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.