Predictive gene expression patterns of response to adriamycin and cyclophosphamide (AC) in human breast cancers

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 527-527
Author(s):  
A. Makris ◽  
C. Creighton ◽  
K. C. Osborne ◽  
S. Hilsenbeck ◽  
M. K. Harrison ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Molecular Profile ◽  
Gene Expression Patterns ◽  
Predictive Test ◽  
Qrt Pcr

527 Background: Adriamycin and cyclophosphamide (AC) and docetaxel (D) are widely used in the treatment of breast cancer. We conducted a prospective, randomized, multicenter trial to discover predictive markers of AC and D and hypothesized that gene expression profiles can select appropriate patients who may respond to AC, and that these patterns are different from our published docetaxel (D) profiles Methods: One hundred and twenty patients were randomized to either 4 cycles of AC (60/600 mg/m2) or D (100mg/m2) prior to surgery. Core biopsies from 60 patients were obtained before treatment with neoadjuvant AC. Pathologic responses were assessed after AC. Gene expression patterns were determined using Affymetrix U133A GeneChips. Differential genes for AC response were then validated by QRT-PCR in an independent cohort of 33 patients treated with AC. Results: The median age was 48 yrs (range 30–72), clinical response rates were 57% (34/60), and pathological complete response (pCR) or near pCR (npCR) was observed in 22% (12/60) in AC arm. Differential expression between sensitive and resistant tumors with a low false discovery rate (FDR 5–10%) was obtained. Of these 82 differentially expressed genes, pathways up-regulated in sensitive tumors included TOP2A, metabolism (LYZ), survival (CFLAR, CASP3), cell cycle (MKI67), cytokines and other inflammatory genes. This molecular portrait for AC was not predictive of docetaxel response. By QRT-PCR of 4 genes (LYZ, CFLAR, MKI67 and TOP2A) in the independent tumor set, LYZ was predictive of AC pathologic complete response. Additional genes will be validated in the second cohort. Conclusions: The molecular profile for AC is different from the docetaxel expression profile. This potential predictive test may allow selection of the most appropriate chemotherapy schedule for women with breast cancer. No significant financial relationships to disclose.

scholarly journals QRT-PCR-based DNA Homologous Recombination Associated 4-Gene Score Predicts Pathologic Complete Response to Platinum-Based Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

2021 ◽  
Author(s):  
Ke Zuo ◽  
Xiaoying Yuan ◽  
Xizi Liang ◽  
Xiangjie Sun ◽  
Shujin Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Training Set ◽  
Gene Score ◽  
Qrt Pcr ◽  
Validation Set ◽  
Platinum Agents

Abstract BackgroundCumulative evidences suggested the addition of platinum agents as neoadjuvant chemotherapy (NACT) could improve pathologic complete response (pCR) in triple-negative breast cancers (TNBC). Previous studies showed DNA homologous recombination deficiency (HRD) was a potential biomarker predicting pCR in ER-negative breast cancer. It would be helpful to personalize the use of platinum agents if a predictive biomarker for platinum sensitivity could be developed. Therefore, we tried to develop a HRD gene expression score to predict tumor sensitivity to platinum-based NACT in TNBC.MethodsA retrospective cohort of 127 TNBC patients from 2012 to 2017 was included in this study. All of them were diagnosed and received platinum-based NACT in Fudan University Shanghai Cancer Center. Clinical data and pathological data of the patients were collected and reviewed. By using quantitative reverse transcription-polymerase chain reaction (qRT-PCR), the expression level of eight HRD associated genes was analyzed from the formalin-fixed paraffin-embedded core needle biopsy samples which obtained before NACT. A random forest model was built to estimate the weight of each gene expression level and clinical-pathological factors. Samples were randomized into the training set and validation set with different splitting percentage from 50%:50% to 90%:10%. The training set was used to modulate parameters and select the best model using 5-fold cross validation. The performance of the final model was evaluated in the validation set. ResultsA 4-gene (BRCA1, XRCC5, PARP1, RAD51) expression signature scoring system was developed. TNBC with higher score had nearly quadruple likelihood to achieve pCR to platinum-based NACT compared with a lower score [odds ratio (OR)=3.878; P<0.001]. At the cut-off value of -2.644, the 4-gene score system showed high sensitivity in predicting pCR in breast (93.0%) and pCR in both breast/axilla (91.8%), while, at the cut-off value of -1.969, the 4-gene score showed high specificity for pCR in breast (85.7%) and pCR in both breast/axilla (80.8%). 4-gene score was positively correlated with Ki-67≥40% (P=0.002), but negatively correlated with positive lymph nodes counts (P=0.003). ConclusionThe qRT-PCR-based 4-gene score can be used as an effective predictor of pCR to platinum-based NACT in TNBC.

scholarly journals qRT-PCR-based DNA homologous recombination associated 4-gene score predicts pathologic complete response to platinum-based neoadjuvant chemotherapy in triple-negative breast cancer

2021 ◽  
Author(s):  
Ke Zuo ◽  
Xiaoying Yuan ◽  
Xizi Liang ◽  
Xiangjie Sun ◽  
Shujin Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Homologous Recombination ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Expression Level ◽  
Cancer Center ◽  
Gene Score ◽  
Qrt Pcr

Abstract PurposeCumulative evidences suggested the addition of platinum agents as neoadjuvant chemotherapy (NACT) could improve pathologic complete response (pCR) in triple-negative breast cancers (TNBC). We tried to develop a DNA homologous recombination (HR) associated gene expression score to predict tumor sensitivity to platinum-based NACT in TNBC.MethodsA retrospective cohort of 127 TNBC patients, who were diagnosed and received platinum-based NACT in Fudan University Shanghai Cancer Center from 2012 to 2017, was included in this study. By using quantitative reverse transcription-polymerase chain reaction (qRT-PCR), the expression level of eight HR associated genes was analyzed from the formalin-fixed paraffin-embedded core needle biopsy samples which obtained before NACT. A random forest model was built to estimate the weight of each gene expression level and clinical-pathological factors. The training set was used to modulate parameters and select the best model. The performance of the final model was evaluated in the validation set. ResultsA 4-gene (BRCA1, XRCC5, PARP1, RAD51) expression scoring system was developed. TNBC with higher score had nearly quadruple likelihood to achieve pCR to platinum-based NACT compared with a lower score [odds ratio (OR)=3.878; P<0.001]. At the cut-off value of -2.644, the 4-gene score system showed high sensitivity in predicting pCR in breast (93.0%) and pCR in both breast/axilla (91.8%), while, at the cut-off value of -1.969, the 4-gene score showed high specificity for pCR in breast (85.7%) and pCR in both breast/axilla (80.8%). ConclusionThe qRT-PCR-based 4-gene score has the potential to predict pCR to platinum-based NACT in TNBC.

Predicting features of breast cancer with gene expression patterns

2007 ◽  
Vol 108 (2) ◽  
pp. 191-201 ◽  
Author(s):  
Xuesong Lu ◽  
Xin Lu ◽  
Zhigang C. Wang ◽  
J. Dirk Iglehart ◽  
Xuegong Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns

scholarly journals 415. Airway Gene-Expression Classifiers for Respiratory Syncytial Virus (RSV) Disease Severity in Infants

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S210-S210
Author(s):  
Mary T Caserta ◽  
Lu Wang ◽  
Chin-Yi Chu ◽  
Christopher Slaunwhite ◽  
Jeanne Holden-Wiltse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Model Selection ◽  
Disease Severity ◽  
Severity Score ◽  
Cross Validation ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Multivariate Linear Regression Analysis ◽  
Linear Predictor

Abstract Background RSV infection is common in infants with a majority of those affected displaying mild clinical symptoms. However, a substantial number develop severe symptoms requiring hospitalization. We currently lack sensitive and specific predictors to identify a majority of those who develop severe disease. Methods High throughput RNA sequencing (RNAseq) of nasal epithelial cells defined airway gene expression patterns in RSV-infected subjects. Using multivariate linear regression analysis with AIC-based model selection, we built a sparse linear predictor of RSV disease severity, the Nasal Gene Severity Score-NGSS1. Using a similar statistical approach, we built an alternate predictor based upon genes displaying stable expression over time (NGSS2). We evaluated predictive performance of both models using leave-one-out cross-validation analyses. Results We defined comprehensive airway gene expression profiles from 106 full-tem previously healthy RSV-infected subjects with a range of RSV disease severity prospectively enrolled in the AsPIRES study. Nasal samples were obtained during acute infection (day 1–10 of illness; 106 samples), and convalescence (day 14–28 of illness; 69 samples). All subjects had a primary infection and were assigned a cumulative clinical illness severity score (GRSS) (Table 1). From the RNA seq data 41 genes were identified as the NGSS1 which is strongly correlated with disease severity (GRSS) in both the naive (ρ=0.935) and cross-validated analysis (ρ of 0.813). As a binary classifier (mild vs. severe), NGSS1 correctly classifies 89.6% of the subjects following cross-validation (Figure 1). Next, we evaluated genes that were stably expressed in both acute illness and convalescence samples in 54 subjects with data from both time points. Repeating the regression based step wise model selection identified 13 genes as NGSS2, which was significantly correlated with GRSS (ρ = 0.741). This model has slightly less, but comparable, prediction accuracy with a cross-validated correlation of 0.741 and cross-validated classification accuracy of 84.0% (Figure 2). Conclusion Airway gene expression patterns, obtained following a minimally-invasive nasal procedure, have potential utility as prognostic biomarkers for severe infant RSV infections. Disclosures All authors: No reported disclosures.

scholarly journals SIGN: similarity identification in gene expression

2019 ◽  
Vol 35 (22) ◽  
pp. 4830-4833 ◽  
Author(s):  
Seyed Ali Madani Tonekaboni ◽  
Venkata Satya Kumar Manem ◽  
Nehme El-Hachem ◽  
Benjamin Haibe-Kains
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Open Source ◽  
Supervised Learning ◽  
Cancer Patients ◽  
Biomedical Research ◽  
Expression Patterns ◽  
R Package ◽  
Gene Expression Patterns ◽  
Learning Schemes

Abstract Motivation High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes. Results We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples. We defined a new measure, the transcriptional similarity coefficient, which captures similarity of gene expression patterns, instead of quantifying overall activity, in biological pathways between the samples. To demonstrate the utility of SIGN in biomedical research, we establish that SIGN discriminates subtypes of breast tumors and patients with good or poor overall survival. SIGN outperforms the best models in DREAM challenge in predicting survival of breast cancer patients using the data from the Molecular Taxonomy of Breast Cancer International Consortium. In summary, SIGN can be used as a new tool for interrogating pathway activity and gene expression patterns in unsupervised and supervised learning schemes to improve prognostic risk estimation for cancer patients by the biomedical research community. Availability and implementation An open-source R package is available (https://cran.r-project.org/web/packages/SIGN/).

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