Xiaping He

Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer

IntroductionIn breast cancer, gene expression analyses have defined five tumor subtypes (luminal A, luminal B, HER2-enriched, basal-like and claudin-low), each of which has unique biologic and prognostic features. Here, we comprehensively characterize the recently identified claudin-low tumor subtype.MethodsThe clinical, pathological and biological features of claudin-low tumors were compared to the other tumor subtypes using an updated human tumor database and multiple independent data sets. These main features of claudin-low tumors were also evaluated in a panel of breast cancer cell lines and genetically engineered mouse models.ResultsClaudin-low tumors are characterized by the low to absent expression of luminal differentiation markers, high enrichment for epithelial-to-mesenchymal transition markers, immune response genes and cancer stem cell-like features. Clinically, the majority of claudin-low tumors are poor prognosis estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and epidermal growth factor receptor 2 (HER2)-negative (triple negative) invasive ductal carcinomas with a high frequency of metaplastic and medullary differentiation. They also have a response rate to standard preoperative chemotherapy that is intermediate between that of basal-like and luminal tumors. Interestingly, we show that a group of highly utilized breast cancer cell lines, and several genetically engineered mouse models, express the claudin-low phenotype. Finally, we confirm that a prognostically relevant differentiation hierarchy exists across all breast cancers in which the claudin-low subtype most closely resembles the mammary epithelial stem cell.ConclusionsThese results should help to improve our understanding of the biologic heterogeneity of breast cancer and provide tools for the further evaluation of the unique biology of claudin-low tumors and cell lines.

MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery

The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.

Estrogen-Regulated Genes Predict Survival in Hormone Receptor–Positive Breast Cancers

PURPOSE The prognosis of a patient with estrogen receptor (ER) and/or progesterone receptor (PR) -positive breast cancer can be highly variable. Therefore, we developed a gene expression-based outcome predictor for ER+ and/or PR+ (ie, luminal) breast cancer patients using biologic differences among these tumors. MATERIALS AND METHODS The ER+ MCF-7 breast cancer cell line was treated with 17beta-estradiol to identify estrogen-regulated genes. These genes were used to develop an outcome predictor on a training set of 65 luminal epithelial primary breast carcinomas. The outcome predictor was then validated on three independent published data sets. Results The estrogen-induced gene set identified in MCF-7 cells was used to hierarchically cluster a 65 tumor training set into two groups, which showed significant differences in survival (P = .0004). Supervised analyses identified 822 genes that optimally defined these two groups, with the poor-prognosis group IIE showing high expression of cell proliferation and antiapoptosis genes. The good prognosis group IE showed high expression of estrogen- and GATA3-regulated genes. Mean expression profiles (ie, centroids) created for each group were applied to ER+ and/or PR+ tumors from three published data sets. For all data sets, Kaplan-Meier survival analyses showed significant differences in relapse-free and overall survival between group IE and IIE tumors. Multivariate Cox analysis of the largest test data set showed that this predictor added significant prognostic information independent of standard clinical predictors and other gene expression-based predictors. CONCLUSION This study provides new biologic information concerning differences within hormone receptor-positive breast cancers and a means of predicting long-term outcomes in tamoxifen-treated patients.

Endocrine-Therapy-Resistant ESR1 Variants Revealed by Genomic Characterization of Breast-Cancer-Derived Xenografts

To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation.

EGFR associated expression profiles vary with breast tumor subtype

BackgroundThe epidermal growth factor receptor (EGFR/HER1) and its downstream signaling events are important for regulating cell growth and behavior in many epithelial tumors types. In breast cancer, the role of EGFR is complex and appears to vary relative to important clinical features including estrogen receptor (ER) status. To investigate EGFR-signaling using a genomics approach, several breast basal-like and luminal epithelial cell lines were examined for sensitivity to EGFR inhibitors. An EGFR-associated gene expression signature was identified in the basal-like SUM102 cell line and was used to classify a diverse set of sporadic breast tumors.ResultsIn vitro, breast basal-like cell lines were more sensitive to EGFR inhibitors compared to luminal cell lines. The basal-like tumor derived lines were also the most sensitive to carboplatin, which acted synergistically with cetuximab. An EGFR-associated signature was developed in vitro, evaluated on 241 primary breast tumors; three distinct clusters of genes were evident in vivo, two of which were predictive of poor patient outcomes. These EGFR-associated poor prognostic signatures were highly expressed in almost all basal-like tumors and many of the HER2+/ER- and Luminal B tumors.ConclusionThese results suggest that breast basal-like cell lines are sensitive to EGFR inhibitors and carboplatin, and this combination may also be synergistic. In vivo, the EGFR-signatures were of prognostic value, were associated with tumor subtype, and were uniquely associated with the high expression of distinct EGFR-RAS-MEK pathway genes.

Mutation of GATA3 in human breast tumors

GATA3 is an essential transcription factor that was first identified as a regulator of immune cell function. In recent microarray analyses of human breast tumors, both normal breast luminal epithelium and estrogen receptor (ESR1)-positive tumors showed high expression of GATA3. We sequenced genomic DNA from 111 breast tumors and three breast-tumor-derived cell lines and identified somatic mutations of GATA3 in five tumors and the MCF-7 cell line. These mutations cluster in the vicinity of the highly conserved second zinc-finger that is required for DNA binding. In addition to these five, we identified using cDNA sequencing a unique mis-splicing variant that caused a frameshift mutation. One of the somatic mutations we identified was identical to a germline GATA3 mutation reported in two kindreds with HDR syndrome/OMIM #146255, which is an autosomal dominant syndrome caused by the haplo-insufficiency of GATA3. The ectopic expression of GATA3 in human 293T cells caused the induction of 73 genes including six cytokeratins, and inhibited cell line doubling times. These data suggest that GATA3 is involved in growth control and the maintenance of the differentiated state in epithelial cells, and that GATA3 variants may contribute to tumorigenesis in ESR1-positive breast tumors.

The functional loss of the retinoblastoma tumour suppressor is a common event in basal-like and luminal B breast carcinomas.

IntroductionBreast cancers can be classified using whole genome expression into distinct subtypes that show differences in prognosis. One of these groups, the basal-like subtype, is poorly differentiated, highly metastatic, genomically unstable, and contains specific genetic alterations such as the loss of tumour protein 53 (TP53). The loss of the retinoblastoma tumour suppressor encoded by the RB1 locus is a well-characterised occurrence in many tumour types; however, its role in breast cancer is less clear with many reports demonstrating a loss of heterozygosity that does not correlate with a loss of RB1 protein expression.MethodsWe used gene expression analysis for tumour subtyping and polymorphic markers located at the RB1 locus to assess the frequency of loss of heterozygosity in 88 primary human breast carcinomas and their normal tissue genomic DNA samples.ResultsRB1 loss of heterozygosity was observed at an overall frequency of 39%, with a high frequency in basal-like (72%) and luminal B (62%) tumours. These tumours also concurrently showed low expression of RB1 mRNA. p16INK4a was highly expressed in basal-like tumours, presumably due to a previously reported feedback loop caused by RB1 loss. An RB1 loss of heterozygosity signature was developed and shown to be highly prognostic, and was potentially a predictive marker of response to neoadjuvant chemotherapy.ConclusionsThese results suggest that the functional loss of RB1 is common in basal-like tumours, which may play a key role in dictating their aggressive biology and unique therapeutic responses.

RNA expression analysis of formalin-fixed paraffin-embedded tumors

RNA expression analysis is an important tool in cancer research, but a limitation has been the requirement for high-quality RNA, generally derived from frozen samples. Such tumor sets are often small and lack clinical annotation, whereas formalin-fixed paraffin-embedded (FFPE) materials are abundant. Although RT-PCR-based methods from FFPE samples are finding clinical application, genome-wide microarray analysis has proven difficult. Here, we report expression profiling on RNA from 157 FFPE tumors. RNA was extracted from 2- to 8-year-old FFPE or frozen tumors of known and unknown histologies. Total RNA was analyzed, reverse-transcribed and used for the synthesis of labeled aRNA after two rounds of amplification. Labeled aRNA was hybridized to a 3′-based 22K spot oligonucleotide arrays, and compared to a labeled reference by two-color microarray analysis. After normalization, gene expression profiles were compared by unsupervised hierarchical clustering. Using this approach, at least 24% of unselected FFPE samples produced RNA of sufficient quality for microarray analysis. From our initial studies, we determined criteria based on spectrophotometric analyses and a novel TaqMan-based assay to predict which samples were of sufficient quality for microarray analysis before hybridization. These criteria were validated on an independent set of tumors with a 100% success rate (20 of 20). Unsupervised analysis of informative gene expression profiles distinguished tumor type and subtype, and identified tumor tissue of origin in three unclassified carcinomas. Although only a minority of FFPE blocks could be analyzed, we show that informative RNA expression analysis can be derived from selected FFPE samples.

Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low “Differentiation Scores,” or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior.

Gene expression patterns associated with p53 status in breast cancer

BackgroundBreast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53s independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function).MethodsThe p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors.ResultsEach cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data.ConclusionIn the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes.