Recent studies suggest that intrinsic breast cancer subtypes may differ in their responsiveness to specific chemotherapy regimens. We examined this hypothesis on NCIC.CTG MA.5, a clinical trial randomizing premenopausal women with node-positive breast cancer to adjuvant CMF (cyclophosphamide-methotrexate-fluorouracil) versus CEF (cyclophosphamide-epirubicin-fluorouracil) chemotherapy.
Intrinsic subtype was determined for 476 tumors using the quantitative reverse transcriptase PCR PAM50 gene expression test. Luminal A, luminal B, HER2-enriched (HER2-E), and basal-like subtypes were correlated with relapse-free survival (RFS) and overall survival (OS), estimated using Kaplan-Meier plots and log-rank testing. Multivariable Cox regression analyses determined significance of interaction between treatment and intrinsic subtypes.
Intrinsic subtypes were associated with RFS (P = 0005) and OS (P < 0.0001) on the combined cohort. The HER2-E showed the greatest benefit from CEF versus CMF, with absolute 5-year RFS and OS differences exceeding 20%, whereas there was a less than 2% difference for non-HER2-E tumors (interaction test P = 0.03 for RFS and 0.03 for OS). Within clinically defined Her2+ tumors, 79% (72 of 91) were classified as the HER2-E subtype by gene expression and this subset was strongly associated with better response to CEF versus CMF (62% vs. 22%, P = 0.0006). There was no significant difference in benefit between CEF and CMF in basal-like tumors [n = 94; HR, 1.1; 95% confidence interval (CI), 0.6−.1 for RFS and HR, 1.3; 95% CI, 0.7−2.5 for OS].
HER2-E strongly predicted anthracycline sensitivity. The chemotherapy-sensitive basal- like tumors showed no added benefit for CEF over CMF, suggesting that nonanthracycline regimens may be adequate in this subtype although further investigation is required.
Gene expression profiling classifies breast cancer into intrinsic subtypes based on the biology of the underlying disease pathways. We have used material from a prospective randomized trial of tamoxifen versus placebo in premenopausal women with primary breast cancer (NCIC CTG MA.12) to evaluate the prognostic and predictive significance of intrinsic subtypes identified by both the PAM50 gene set and by immunohistochemistry.
Total RNA from 398 of 672 (59%) patients was available for intrinsic subtyping with a quantitative reverse transcriptase PCR (qRT-PCR) 50-gene predictor (PAM50) for luminal A, luminal B, HER-2–enriched, and basal-like subtypes. A tissue microarray was also constructed from 492 of 672 (73%) of the study population to assess a panel of six immunohistochemical IHC antibodies to define the same intrinsic subtypes.
Classification into intrinsic subtypes by the PAM50 assay was prognostic for both disease-free survival (DFS; P = 0.0003) and overall survival (OS; P = 0.0002), whereas classification by the IHC panel was not. Luminal subtype by PAM50 was predictive of tamoxifen benefit [DFS: HR, 0.52; 95% confidence interval (CI), 0.32–0.86 vs. HR, 0.80; 95% CI, 0.50–1.29 for nonluminal subtypes], although the interaction test was not significant (P = 0.24), whereas neither subtyping by central immunohistochemistry nor by local estrogen receptor (ER) or progesterone receptor (PR) status were predictive. Risk of relapse (ROR) modeling with the PAM50 assay produced a continuous risk score in both node-negative and node-positive disease.
In the MA.12 study, intrinsic subtype classification by qRT-PCR with the PAM50 assay was superior to IHC profiling for both prognosis and prediction of benefit from adjuvant tamoxifen.
Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.
Human lung adenocarcinomas with activating mutations in EGFR (epidermal growth factor receptor) often respond to treatment with EGFRtyrosine kinase inhibitors(TKIs),butthe magnitude of tumour regression is variable and transient1,2. This heterogeneity in treatment response could result from genetic modifiers that regulate the degree to which tumour cells are dependent on mutant EGFR. Through a pooled RNA interference screen, we show that knockdown of FAS and several components of the NF-κB pathway specifically enhanced cell death induced by the EGFR TKI erlotinib in EGFR-mutant lung cancer cells. Activation of NF-κB through overexpression of c-FLIP or IKK (also known as CFLAR and IKBKB, respectively), or silencing of IκB (also known as NFKBIA), rescued EGFR-mutant lung cancer cells from EGFR TKI treatment. Genetic or pharmacologic inhibition of NF-κB enhanced erlotinib-induced apoptosis in erlotinib-sensitive and erlotinib-resistant EGFR-mutant lung cancer models. Increased expression of the NF-κB inhibitor IκB predicted for improved response and survival in EGFR-mutant lung cancer patients treated with EGFR TKI. These data identify NF-κB as a potential companion drug target, together with EGFR, in EGFR-mutant lung cancers and provide insight into the mechanisms by which tumour cells escape from oncogene dependence.
In breast cancer, the basal-like subtype has high levels of genomic instability relative to other breast cancer subtypes with many basal-like-specific regions of aberration. There is evidence that this genomic instability extends to smaller scale genomic aberrations, as shown by a previously described micro-deletion event in the PTEN gene in the Basal-like SUM149 breast cancer cell line.
We sought to identify if small regions of genomic DNA copy number changes exist by using a high density, gene-centric Comparative Genomic Hybridizations (CGH) array on cell lines and primary tumors. A custom tiling array for CGH (244,000 probes, 200 bp tiling resolution) was created to identify small regions of genomic change, which was focused on previously identified basal-like-specific, and general cancer genes. Tumor genomic DNA from 94 patients and 2 breast cancer cell lines was labeled and hybridized to these arrays. Aberrations were called using SWITCHdna and the smallest 25% of SWITCHdna-defined genomic segments were called micro-aberrations (<64 contiguous probes, ∼ 15 kb).
Our data showed that primary tumor breast cancer genomes frequently contained many small-scale copy number gains and losses, termed micro-aberrations, most of which are undetectable using typical-density genome-wide aCGH arrays. The basal-like subtype exhibited the highest incidence of these events. These micro-aberrations sometimes altered expression of the involved gene. We confirmed the presence of the PTEN micro-amplification in SUM149 and by mRNA-seq showed that this resulted in loss of expression of all exons downstream of this event. Micro-aberrations disproportionately affected the 5′ regions of the affected genes, including the promoter region, and high frequency of micro-aberrations was associated with poor survival.
Using a high-probe-density, gene-centric aCGH microarray, we present evidence of small-scale genomic aberrations that can contribute to gene inactivation. These events may contribute to tumor formation through mechanisms not detected using conventional DNA copy number analyses.
Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results.
Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy.
ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.
Next-generation sequencing; Quality score; Recalibration; Bioinformatics; Bioconductor
Preoperative aromatase inhibitor (AI) treatment promotes breast-conserving surgery (BCS) for estrogen receptor (ER) –positive breast cancer. To study this treatment option, responses to three AIs were compared in a randomized phase II neoadjuvant trial designed to select agents for phase III investigations.
Patients and Methods
Three hundred seventy-seven postmenopausal women with clinical stage II to III ER-positive (Allred score 6-8) breast cancer were randomly assigned to receive neoadjuvant exemestane, letrozole, or anastrozole. The primary end point was clinical response. Secondary end points included BCS, Ki67 proliferation marker changes, the Preoperative Endocrine Prognostic Index (PEPI), and PAM50-based intrinsic subtype analysis.
On the basis of clinical response rates, letrozole and anastrozole were selected for further investigation; however, no other differences in surgical outcome, PEPI score, or Ki67 suppression were detected. The BCS rate for mastectomy-only patients at presentation was 51%. PAM50 analysis identified AI-unresponsive nonluminal subtypes (human epidermal growth factor receptor 2 enriched or basal-like) in 3.3% of patients. Clinical response and surgical outcomes were similar in luminal A (LumA) versus luminal B tumors; however, a PEPI of 0 (best prognostic group) was highest in the LumA subset (27.1% v 10.7%; P = .004).
Neoadjuvant AI treatment markedly improved surgical outcomes. Ki67 and PEPI data demonstrated that the three agents tested are biologically equivalent and therefore likely to have similar adjuvant activities. LumA tumors were more likely to have favorable biomarker characteristics after treatment; however, occasional paradoxical increases in Ki67 (12% of tumors with > 5% increase after therapy) suggest treatment-resistant cells, present in some LumA tumors, can be detected by post-treatment profiling.
There is significant need to identify novel prostate cancer drug targets because current hormone therapies eventually fail, leading to a drug-resistant and fatal disease termed castration-resistant prostate cancer. To functionally identify genes that, when silenced, decrease prostate cancer cell proliferation or induce cell death in combination with antiandrogens, we employed an RNA interference-based short hairpin RNA barcode screen in LNCaP human prostate cancer cells. We identified and validated four candidate genes (AKT1, PSMC1, STRADA, and TTK) that impaired growth when silenced in androgen receptor positive prostate cancer cells and enhanced the antiproliferative effects of antiandrogens. Inhibition of AKT with a pharmacologic inhibitor also induced apoptosis when combined with antiandrogens, consistent with recent evidence for PI3K and AR pathway crosstalk in prostate cancer cells. Recovery of hairpins targeting a known prostate cancer pathway validates the utility of shRNA library screening in prostate cancer as a broad strategy to identify new candidate drug targets.
Recurrent head and neck squamous cell carcinoma (HNSCC) remains a difficult cancer to treat. Here, we describe a patient with HNSCC who had complete response to methotrexate (MTX) after progressing on multiple cytotoxic agents, cetuximab, and AMG-479 (monoclonal antibody against insulin-like growth factor-1 receptor [IGF-1R]).
The clinical information was collected by a retrospective medical record review under an Institutional Review Board–approved protocol. From 4 tumors and 2 normal mucosal epithelia, global gene expression, and IGF-1R and dihydrofolate reductase (DHFR) protein levels were determined.
Effective target inhibition in the tumor was confirmed by the decreased protein levels of total and phospho-IGF-1R after treatment with AMG-479. Decreased level of DHFR and conversion of a gene expression profile associated with cetuximab-resistance to cetuximab-sensitivity were also observed.
This suggests that the combination of AMG- 479 and MTX or cetuximab may be a promising therapeutic approach in refractory HNSCC.
IGF-1R inhibitor; cetuximab; head and neck squamous cell carcinoma; AMG-479; methotrexate
Breast cancer is a heterogeneous disease with known expression-defined tumor subtypes. DNA copy number studies have suggested that tumors within gene expression subtypes share similar DNA Copy number aberrations (CNA) and that CNA can be used to further sub-divide expression classes. To gain further insights into the etiologies of the intrinsic subtypes, we classified tumors according to gene expression subtype and next identified subtype-associated CNA using a novel method called SWITCHdna, using a training set of 180 tumors and a validation set of 359 tumors. Fisher’s exact tests, Chi-square approximations, and Wilcoxon rank-sum tests were performed to evaluate differences in CNA by subtype. To assess the functional significance of loss of a specific chromosomal region, individual genes were knocked down by shRNA and drug sensitivity, and DNA repair foci assays performed. Most tumor subtypes exhibited specific CNA. The Basal-like subtype was the most distinct with common losses of the regions containing RB1, BRCA1, INPP4B, and the greatest overall genomic instability. One Basal-like subtype-associated CNA was loss of 5q11–35, which contains at least three genes important for BRCA1-dependent DNA repair (RAD17, RAD50, and RAP80); these genes were predominantly lost as a pair, or all three simultaneously. Loss of two or three of these genes was associated with significantly increased genomic instability and poor patient survival. RNAi knockdown of RAD17, or RAD17/RAD50, in immortalized human mammary epithelial cell lines caused increased sensitivity to a PARP inhibitor and carboplatin, and inhibited BRCA1 foci formation in response to DNA damage. These data suggest a possible genetic cause for genomic instability in Basal-like breast cancers and a biological rationale for the use of DNA repair inhibitor related therapeutics in this breast cancer subtype.
Electronic supplementary material
The online version of this article (doi:10.1007/s10549-011-1846-y) contains supplementary material, which is available to authorized users.
Basal-like breast cancer; Genome instability; BRCA1 pathway; Copy number aberration; Molecular subtypes; Array CGH
To compare clinical, immunohistochemical and gene expression models of prognosis applicable to formalin-fixed, paraffin-embedded blocks in a large series of estrogen receptor positive breast cancers, from patients uniformly treated with adjuvant tamoxifen.
qRT-PCR assays for 50 genes identifying intrinsic breast cancer subtypes were completed on 786 specimens linked to clinical (median followup 11.7 years) and immunohistochemical (ER, PR, HER2, Ki67) data. Performance of predefined intrinsic subtype and Risk-Of-Relapse scores was assessed using multivariable Cox models and Kaplan-Meier analysis. Harrell’s C index was used to compare fixed models trained in independent data sets, including proliferation signatures.
Despite clinical ER positivity, 10% of cases were assigned to non-Luminal subtypes. qRT-PCR signatures for proliferation genes gave more prognostic information than clinical assays for hormone receptors or Ki67. In Cox models incorporating standard prognostic variables, hazard ratios for breast cancer disease specific survival over the first 5 years of followup, relative to the most common Luminal A subtype, are 1.99 (95% CI: 1.09–3.64) for Luminal B, 3.65 (1.64–8.16) for HER2-enriched and 17.71 (1.71–183.33) for the basal like subtype. For node-negative disease, PAM50 qRT-PCR based risk assignment weighted for tumor size and proliferation identifies a group with >95% 10 yr survival without chemotherapy. In node positive disease, PAM50-based prognostic models were also superior.
The PAM50 gene expression test for intrinsic biological subtype can be applied to large series of formalin-fixed paraffin-embedded breast cancers, and gives more prognostic information than clinical factors and immunohistochemistry using standard cutpoints.
Globally, gastric cancer is the second most common cause of cancer-related death, with the majority of the health burden borne by economically less-developed countries.
Here, we report a genetic characterization of 50 gastric adenocarcinoma samples, using affymetrix SNP arrays and Illumina mRNA expression arrays as well as Illumina sequencing of the coding regions of 384 genes belonging to various pathways known to be altered in other cancers.
Genetic alterations were observed in the WNT, Hedgehog, cell cycle, DNA damage and epithelial-to-mesenchymal-transition pathways.
The data suggests targeted therapies approved or in clinical development for gastric carcinoma would be of benefit to ~22% of the patients studied. In addition, the novel mutations detected here, are likely to influence clinical response and suggest new targets for drug discovery.
Genomic tests are available to predict breast cancer recurrence and to guide clinical decision making. These predictors provide recurrence risk scores along with a measure of uncertainty, usually a confidence interval. The confidence interval conveys random error and not systematic bias. Standard tumor sampling methods make this problematic, as it is common to have a substantial proportion (typically 30-50%) of a tumor sample comprised of histologically benign tissue. This "normal" tissue could represent a source of non-random error or systematic bias in genomic classification.
To assess the performance characteristics of genomic classification to systematic error from normal contamination, we collected 55 tumor samples and paired tumor-adjacent normal tissue. Using genomic signatures from the tumor and paired normal, we evaluated how increasing normal contamination altered recurrence risk scores for various genomic predictors.
Simulations of normal tissue contamination caused misclassification of tumors in all predictors evaluated, but different breast cancer predictors showed different types of vulnerability to normal tissue bias. While two predictors had unpredictable direction of bias (either higher or lower risk of relapse resulted from normal contamination), one signature showed predictable direction of normal tissue effects. Due to this predictable direction of effect, this signature (the PAM50) was adjusted for normal tissue contamination and these corrections improved sensitivity and negative predictive value. For all three assays quality control standards and/or appropriate bias adjustment strategies can be used to improve assay reliability.
Normal tissue sampled concurrently with tumor is an important source of bias in breast genomic predictors. All genomic predictors show some sensitivity to normal tissue contamination and ideal strategies for mitigating this bias vary depending upon the particular genes and computational methods used in the predictor.
biomarker validation; genomic assays; breast cancer; normal tissue; bias
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.
Electronic supplementary material
The online version of this article (doi:10.1007/s10549-011-1619-7) contains supplementary material, which is available to authorized users.
Breast cancer; Gene expression; Intrinsic subtype; Metastasis; Microarray
To determine whether plasma estradiol (E2) levels are related to gene expression in estrogen receptor (ER)–positive breast cancers in postmenopausal women.
Materials and Methods
Genome-wide RNA profiles were obtained from pretreatment core-cut tumor biopsies from 104 postmenopausal patients with primary ER-positive breast cancer treated with neoadjuvant anastrozole. Pretreatment plasma E2 levels were determined by highly sensitive radioimmunoassay. Genes were identified for which expression was correlated with pretreatment plasma E2 levels. Validation was performed in an independent set of 73 ER-positive breast cancers.
The expression of many known estrogen-responsive genes and gene sets was highly significantly associated with plasma E2 levels (eg, TFF1/pS2, GREB1, PDZK1 and PGR; P < .005). Plasma E2 explained 27% of the average expression of these four average estrogen-responsive genes (ie, AvERG; r = 0.51; P < .0001), and a standardized mean of plasma E2 levels and ER transcript levels explained 37% (r, 0.61). These observations were validated in an independent set of 73 ER-positive tumors. Exploratory analysis suggested that addition of the nuclear coregulators in a multivariable analysis with ER and E2 levels might additionally improve the relationship with the AvERG. Plasma E2 and the standardized mean of E2 and ER were both significantly correlated with 2-week Ki67, a surrogate marker of clinical outcome (r = −0.179; P = .05; and r = −0.389; P = .0005, respectively).
Plasma E2 levels are significantly associated with gene expression of ER-positive breast cancers and should be considered in future genomic studies of ER-positive breast cancer. The AvERG is a new experimental tool for the study of putative estrogenic stimuli of breast cancer.
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.
Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.
We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.
Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.
The diagnosis and management of drug-induced liver injury (DILI) is hindered by the limited utility of traditional clinical chemistries. It has recently been shown that hepatotoxicants can produce compound-specific changes in the peripheral blood (PB) transcriptome in rodents, suggesting the blood transcriptome might provide new biomarkers of DILI. To investigate in humans, we used DNA microarrays as well as serum metabolomic methods to characterize changes in the transcriptome and metabolome in serial PB samples obtained from 6 healthy adults treated with a 4 g bolus dose of acetaminophen (APAP) and from 3 receiving placebo. Treatment did not cause liver injury as assessed by traditional liver chemistries. However, 48 hours after exposure, treated subjects showed marked down-regulation of genes involved in oxidative phosphorylation/mitochondrial function that was not observed in the placebos (p <1.66E-19). The magnitude of down-regulation was positively correlated with the percent of APAP converted to the reactive metabolite NAPQI (r = 0.739; p=0.058). In addition, unbiased analysis of the serum metabolome revealed an increase in serum lactate from 24 to 72 hours post dosing in the treated subjects alone (p<0.005). Similar PB transcriptome changes were observed in human overdose patients and rats receiving toxic doses.
The single 4 gm APAP dose produced a transcriptome signature in PB cells characterized by down regulation of oxidative phosphorylation genes accompanied by increased serum lactate. Similar gene expression changes were observed in rats and several patients after consuming hepatotoxic doses of APAP. The timing of the changes and the correlation with NAPQI production are consistent with mechanisms known to underlie APAP hepatoxicity. These studies support the further exploration of the blood transcriptome for biomarkers of DILI.
hepatotoxicity; microarray; biomarker; surrogate; mitochondria
By virtue of their accumulated genetic alterations, tumor cells may acquire vulnerabilities that create opportunities for therapeutic intervention. We have devised a massively parallel strategy for screening short hairpin RNA (shRNA) collections for stable loss-of-function phenotypes. We assayed from 6000 to 20,000 shRNAs simultaneously to identify genes important for the proliferation and survival of five cell lines derived from human mammary tissue. Lethal shRNAs common to these cell lines targeted many known cell-cycle regulatory networks. Cell line–specific sensitivities to suppression of protein complexes and biological pathways also emerged, and these could be validated by RNA interference (RNAi) and pharmacologically. These studies establish a practical platform for genome-scale screening of complex phenotypes in mammalian cells and demonstrate that RNAi can be used to expose genotype-specific sensitivities.
The transcription factor c-Myb has been well characterized as an oncogene in several human tumor types, and its expression in the hematopoietic stem/progenitor cell population is essential for proper hematopoiesis. However, the role of c-Myb in mammopoeisis and breast tumorigenesis is poorly understood, despite its high expression in the majority of breast cancer cases (60–80%).
We find that c-Myb high expression in human breast tumors correlates with the luminal/ER+ phenotype and a good prognosis. Stable RNAi knock-down of endogenous c-Myb in the MCF7 luminal breast tumor cell line increased tumorigenesis both in vitro and in vivo, suggesting a possible tumor suppressor role in luminal breast cancer. We created a mammary-derived c-Myb expression signature, comprised of both direct and indirect c-Myb target genes, and found it to be highly correlated with a published mature luminal mammary cell signature and least correlated with a mammary stem/progenitor lineage gene signature.
These data describe, for the first time, a possible tumor suppressor role for the c-Myb proto-oncogene in breast cancer that has implications for the understanding of luminal tumorigenesis and for guiding treatment.
In 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.
The 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.
Claudin-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.
These 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.
Several phosphoinositide-3-kinase (PI3K) catalytic subunit inhibitors are currently in clinical trial. We therefore sought to examine relationships between pharmacological inhibition and somatic mutations in PI3K catalytic subunits in ER+ breast cancer, where these mutations are particularly common. RNA interference (RNAi) was used to determine the effect of selective inhibition of PI3K catalytic subunits, p110α and p110β, in ER+ breast cancer cells harboring either mutation (PIK3CA) or gene amplification (PIK3CB). p110α RNAi inhibited growth and promoted apoptosis in all tested ER+ breast cancer cells under estrogen deprived-conditions, whereas p110β RNAi only affected cells harboring PIK3CB amplification. Moreover, dual p110α/p110β inhibition potentiated these effects. In addition, treatment with the clinical grade PI3K catalytic subunit inhibitor BEZ235 also promoted apoptosis in ER+ breast cancer cells. Importantly, estradiol suppressed apoptosis induced by both gene knockdowns and by BEZ235 treatment. Our results suggest that PI3K inhibitors should target both p110α and p110β catalytic subunits, whether wild-type or mutant, and be combined with endocrine therapy for maximal efficacy when treating ER+ breast cancer.
breast cancer; estrogen receptor; PI3 kinase; endocrine therapy; synthetic lethality
The goal of this study was to understand gene expression signatures of hepatocellular carcinoma (HCC) recurrence in subjects with hepatitis C virus (HCV) infection. Recurrence-free survival (RFS) following curative resection of HCC in subjects with HCV is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized patient management. Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS.
The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGFβ1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses.
Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression predictor may hold important insights into the pathobiology of HCC recurrence and de novo tumor formation in cirrhotic patients.