High level analysis of genomic data reveals complex predictors of response to trastuzumab (T) and chemotherapy for early stage breast cancer

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 544-544
Author(s):  
L. N. Harris ◽  
S. Carter ◽  
F. You ◽  
A. Eklund ◽  
S. Hilsenbeck ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Expression Patterns ◽  
Rna Extraction ◽  
Pathologic Complete Response ◽  
Gene Expression Signature ◽  
Response To Therapy ◽  
Snp Analysis ◽  
Predictors Of Response ◽  
High Level

544 Background: Trastuzumab (T) with chemotherapy has been shown to improve survival in breast cancer patients but de novo resistance is common. Identifying predictors of response to T in primary cancers may lead to an understanding of mechanisms of resistance. We investigated whether combined microarray datasets from patients with early breast cancer treated with preoperative T and chemotherapy could predict for response to therapy. Methods: Two cohorts of patients with HER2 3+/FISH+, stage II-III breast cancer were included in this analysis: trial 1- T and docetaxel (n=38), trial 2 -T and vinorelbine (n=48), both for 12 weeks. Frozen tissue core biopsies were available and successfully amplified in 41 patients (trial 1: 20, trial 2: 21 patients), with standard sample processing, RNA extraction, amplification and hybridization to Affymetrix U133 chips. Differential expression of genes and chromosomal regions, (defined as >10 genes in a given chromosomal cytoband), between patients with pathologic complete response (pCR) vs. those with residual invasive disease were examined. A measure of total functional aneuploidy (tFA) was calculated by summing net deviation in expression of all chromosomal regions and a gene expression signature of genomic instability (CIN) was derived by the identification of genes showing a high level of correlation with tFA . Results: By unsupervised hierarchical analysis, both datasets interdigitated suggesting no inherent bias. Gene expression patterns of individual genes showed weak associations with pCR. However, distinct statistically significant chromosomal regions, Chr2p23 Chr6q24 Chr7q33 Chr2p2 Chr12q21.31 Chr14q32.2 Chr1p34.2 Chr8q21.3, were associated with pCR to T therapy (p<0.005), and were confirmed in more than 50% samples by SNP analysis. In addition, resistant tumors showed higher levels of the CIN signature (p<0.005). Conclusions: We have shown that gene expression data can be merged and used for discovery predictive chromosomal regions associated T response. In addition, chromosomal instability was associated with T resistance. If validated, these distinct dysregulated chromosomal regions may serve as predictive markers of response to trastuzumab therapy. [Table: see text]

FOXA1 in breast cancer

2009 ◽  
Vol 11 ◽  
Author(s):  
Harikrishna Nakshatri ◽  
Sunil Badve
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Signature ◽  
Specific Gene ◽  
Breast Cancers ◽  
Transcription Factor Network ◽  
Histopathological Classification

Breast cancer is a heterogeneous disease and classification is important for clinical management. At least five subtypes can be identified based on unique gene expression patterns; this subtype classification is distinct from the histopathological classification. The transcription factor network(s) required for the specific gene expression signature in each of these subtypes is currently being elucidated. The transcription factor network composed of the oestrogen (estrogen) receptor α (ERα), FOXA1 and GATA3 may control the gene expression pattern in luminal subtype A breast cancers. Breast cancers that are dependent on this network correspond to well-differentiated and hormone-therapy-responsive tumours with good prognosis. In this review, we discuss the interplay between these transcription factors with a particular emphasis on FOXA1 structure and function, and its ability to control ERα function. Additionally, we discuss modulators of FOXA1 function, ERα–FOXA1–GATA3 downstream targets, and potential therapeutic agents that may increase differentiation through FOXA1.

Gene Expression Signature Predicting Pathologic Complete Response With Gemcitabine, Epirubicin, and Docetaxel in Primary Breast Cancer

2006 ◽  
Vol 24 (12) ◽  
pp. 1839-1845 ◽  
Author(s):  
Olaf Thuerigen ◽  
Andreas Schneeweiss ◽  
Grischa Toedt ◽  
Patrick Warnat ◽  
Meinhard Hahn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Primary Breast Cancer ◽  
Core Biopsy ◽  
Pathologic Complete Response ◽  
Gene Expression Signature ◽  
Complete Response ◽  
Support Vector ◽  
Her2 Overexpression ◽  
Expression Signature

Purpose Primary systemic therapy (PST) with gemcitabine (G), epirubicin (E), and docetaxel (Doc) has resulted in a pathologic complete response (pCR) in 26% of primary breast cancer patients. This study was aimed at the identification of a gene expression signature in diagnostic core biopsy tissue samples that predicts pCR. Patients and Methods Core biopsy samples from patients with operable primary breast cancer, T2-4N0-2M0, enrolled onto two phase I and II trials evaluating GEDoc (n = 48) and GE sequentially followed by Doc (GEsDoc; n = 52) as PST were snap frozen and subjected to RNA expression profiling. A signature predicting pCR was discovered in the training set (GEsDoc) applying a support vector machine algorithm, and performance of this classifier was validated on the independent test set (GEDoc) by receiver operator characteristics analysis. Results We identified a signature consisting of 512 genes, which was enriched in genes involved in transforming growth factor beta and RAS-mediated signaling pathways, that predicts pCR with a sensitivity of 78%, a specificity of 90%, and an overall accuracy of 88% (95% CI, 75% to 95%). Apart from our signature, only HER2 overexpression was an independent predictor of pCR in multivariate analysis. Conclusion In conclusion, our gene expression signature allows prediction of pCR to PST containing G, E, and Doc with unprecedented high overall accuracy and robustness.

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.

Prediction of Docetaxel Response in Human Breast Cancer by Gene Expression Profiling

2005 ◽  
Vol 23 (3) ◽  
pp. 422-431 ◽  
Author(s):  
Kyoko Iwao-Koizumi ◽  
Ryo Matoba ◽  
Noriko Ueno ◽  
Seung Jin Kim ◽  
Akiko Ando ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Side Effects ◽  
Gene Expression Profiling ◽  
Clinical Response ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Redox System ◽  
Response To Therapy

Purpose Docetaxel is one of the most effective anticancer drugs available in the treatment of breast cancer. Nearly half of the treated patients, however, do not respond to chemotherapy and suffer from side effects. The ability to reliably predict a patient's response based on tumor gene expression will improve therapeutic decision making and save patients from unnecessary side effects. Patients and Methods A total of 44 breast tumor tissues were sampled by biopsy before treatment with docetaxel, and the response to therapy was clinically evaluated by the degree of reduction in tumor size. Gene expression profiling of the biopsy samples was performed with 2,453 genes using a high-throughput reverse transcriptase polymerase chain reaction technique. Using genes differentially expressed between responders and nonresponders, a diagnostic system based on the weighted-voting algorithm was constructed. Results This system predicted the clinical response of 26 previously unanalyzed samples with over 80% accuracy, a level promising for clinical applications. Diagnostic profiles in nonresponders were characterized by elevated expression of genes controlling the cellular redox environment (ie, redox genes, such as thioredoxin, glutathione-S-transferase, and peroxiredoxin). Overexpression of these genes protected cultured mammary tumor cells from docetaxel-induced cell death, suggesting that enhancement of the redox system plays a major role in docetaxel resistance. Conclusion These results suggest that the clinical response to docetaxel can be predicted by gene expression patterns in biopsy samples. The results also suggest that one of the molecular mechanisms of the resistance is activation of a group of redox genes.

A gene expression signature of eventual brain metastases in patients with breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 1019-1019
Author(s):  
Y. Tham ◽  
C. Creighton ◽  
C. Gutierrez ◽  
C. K. Osborne ◽  
P. Brown ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Brain Metastases ◽  
Lung Metastases ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Breast Cancers ◽  
Primary Tumors ◽  
Er Positive ◽  
Her 2

1019 Background: The incidence of brain metastases (BM) from breast cancer may be increasing, in part due to more effective systemic therapy. A metastatic signature of bone or lung metastases has been identified in mice but not for BM in human. We hypothesized that gene expression patterns of primary breast cancers may provide a specific metastatic signature for eventual BM. Methods: Core biopsies from primary breast cancers of 11 patients with BM and 12 patients who have other non-brain metastases were identified. Double- stranded cDNA was synthesized using an oligo-dT primer containing a T7 RNA polymerase promoter, followed by in vitro transcription with biotinylated ribonucleotides. The labeled cRNA was hybridized to Affymetrix U133-A chips. Results: Of the patients with BM, 55% were ER negative/HER-2 positive while 36% were ER positive/HER-2 negative. Of the patients with non brain metastases, 42% were ER negative/HER-2 positive and 50% were ER positive/HER-2 negative, with. A differential pattern of gene expression was seen in primary tumors of patients with BM when compared with those who had non-brain metastases. Many more genes were found elevated in patients with BM over what would be expected by chance (after correcting for multiple testing). Tumors that developed BM had expression of genes related to the neurological development pathways such as fetal Alzheimer antigen, MAD, neuropilin 1, and others. Several kinase pathways were involved such as protein kinase C and casein kinase substrate in neurons 2, and A kinase (PRKA) anchor protein 13. These genes will be validated in an independent set of patients with BM and non-brain metastases using real time PCR. Conclusions: The identification of genes that may predict for future development of brain metastases has many implications in terms of screening or prophylactic treatment. This would also help identify potential targets for the treatment of brain metastases. No significant financial relationships to disclose.

scholarly journals Clinical and genomic assessment of PD-L1 SP142 expression in triple-negative breast cancer

2021 ◽  
Author(s):  
Sung Gwe Ahn ◽  
Seon-Kyu Kim ◽  
Jonathan H. Shepherd ◽  
Yoon Jin Cha ◽  
Soong June Bae ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Checkpoint Inhibitors ◽  
Pathologic Complete Response ◽  
Gene Expression Signature ◽  
Complete Response ◽  
Gene Signature ◽  
Expression Signature

Abstract Purpose The SP142 PD-L1 assay is a companion diagnostic for atezolizumab in metastatic triple-negative breast cancer (TNBC). We strove to understand the biological, genomic, and clinical characteristics associated with SP142 PD-L1 positivity in TNBC patients. Methods Using 149 TNBC formalin-fixed paraffin-embedded tumor samples, tissue microarray (TMA) and gene expression microarrays were performed in parallel. The VENTANA SP142 assay was used to identify PD-L1 expression from TMA slides. We next generated a gene signature reflective of SP142 status and evaluated signature distribution according to TNBCtype and PAM50 subtypes. A SP142 gene expression signature was identified and was biologically and clinically evaluated on the TNBCs of TCGA, other cohorts, and on other malignancies treated with immune checkpoint inhibitors (ICI). Results Using SP142, 28.9% of samples were PD-L1 protein positive. The SP142 PD-L1-positive TNBC had higher CD8+ T cell percentage, stromal tumor-infiltrating lymphocyte levels, and higher rate of the immunomodulatory TNBCtype compared to PD-L1-negative samples. The recurrence-free survival was prolonged in PD-L1-positive TNBC. The SP142-guided gene expression signature consisted of 94 immune-related genes. The SP142 signature was associated with a higher pathologic complete response rate and better survival in multiple TNBC cohorts. In the TNBC of TCGA, this signature was correlated with lymphocyte-infiltrating signature scores, but not with tumor mutational burden or total neoantigen count. In other malignancies treated with ICIs, the SP142 genomic signature was associated with improved response and survival. Conclusions We provide multi-faceted evidence that SP142 PDL1-positive TNBC have immuno-genomic features characterized as highly lymphocyte-infiltrated and a relatively favorable survival.

scholarly journals The impact of RNA extraction method on accurate RNA sequencing from formalin-fixed paraffin-embedded tissues

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Michal Marczyk ◽  
Chunxiao Fu ◽  
Rosanna Lau ◽  
Lili Du ◽  
Alexander J. Trevarton ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Rna Sequencing ◽  
Rna Extraction ◽  
Formalin Fixed Paraffin ◽  
Ffpe Samples ◽  
Formalin Fixed Paraffin Embedded ◽  
The Impact ◽  
Formalin Fixed

Abstract Background Utilization of RNA sequencing methods to measure gene expression from archival formalin-fixed paraffin-embedded (FFPE) tumor samples in translational research and clinical trials requires reliable interpretation of the impact of pre-analytical variables on the data obtained, particularly the methods used to preserve samples and to purify RNA. Methods Matched tissue samples from 12 breast cancers were fresh frozen (FF) and preserved in RNAlater or fixed in formalin and processed as FFPE tissue. Total RNA was extracted and purified from FF samples using the Qiagen RNeasy kit, and in duplicate from FFPE tissue sections using three different kits (Norgen, Qiagen and Roche). All RNA samples underwent whole transcriptome RNA sequencing (wtRNAseq) and targeted RNA sequencing for 31 transcripts included in a signature of sensitivity to endocrine therapy. We assessed the effect of RNA extraction kit on the reliability of gene expression levels using linear mixed-effects model analysis, concordance correlation coefficient (CCC) and differential analysis. All protein-coding genes in the wtRNAseq and three gene expression signatures for breast cancer were assessed for concordance. Results Despite variable quality of the RNA extracted from FFPE samples by different kits, all had similar concordance of overall gene expression from wtRNAseq between matched FF and FFPE samples (median CCC 0.63–0.66) and between technical replicates (median expression difference 0.13–0.22). More than half of genes were differentially expressed between FF and FFPE, but with low fold change (median |LFC| 0.31–0.34). Two out of three breast cancer signatures studied were highly robust in all samples using any kit, whereas the third signature was similarly discordant irrespective of the kit used. The targeted RNAseq assay was concordant between FFPE and FF samples using any of the kits (CCC 0.91–0.96). Conclusions The selection of kit to purify RNA from FFPE did not influence the overall quality of results from wtRNAseq, thus variable reproducibility of gene signatures probably relates to the reliability of individual gene selected and possibly to the algorithm. Targeted RNAseq showed promising performance for clinical deployment of quantitative assays in breast cancer from FFPE samples, although numerical scores were not identical to those from wtRNAseq and would require calibration.

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