Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.     

Whole-genome reconstruction and mutational signatures in gastric cancer

Background

Gastric cancer is the second highest cause of global cancer mortality. To explore the complete repertoire of somatic alterations in gastric cancer, we combined massively parallel short read and DNA paired-end tag sequencing to present the first whole-genome analysis of two gastric adenocarcinomas, one with chromosomal instability and the other with microsatellite instability.

Results

Integrative analysis and de novo assemblies revealed the architecture of a wild-type KRAS amplification, a common driver event in gastric cancer. We discovered three distinct mutational signatures in gastric cancer - against a genome-wide backdrop of oxidative and microsatellite instability-related mutational signatures, we identified the first exome-specific mutational signature. Further characterization of the impact of these signatures by combining sequencing data from 40 complete gastric cancer exomes and targeted screening of an additional 94 independent gastric tumors uncovered ACVR2A, RPL22 and LMAN1 as recurrently mutated genes in microsatellite instability-positive gastric cancer and PAPPA as a recurrently mutated gene in TP53 wild-type gastric cancer.

Conclusions

These results highlight how whole-genome cancer sequencing can uncover information relevant to tissue-specific carcinogenesis that would otherwise be missed from exome-sequencing data.     

Absence of evidence for epidermal growth factor receptor and human homolog of the Kirsten rat sarcoma-2 virus oncogene mutations in breast cancer

Aims: The epidermal growth factor receptor (EGFR) is an available target of effective anti-EGFR therapy for human breast cancer. KRAS, the human homolog of the Kirsten rat sarcoma-2 virus oncogene, encodes a main downstream signaling molecule in the EGFR pathway. The aim of this study was to assess the presence of EGFR and KRAS gene mutations in breast cancer. Materials and methods: EGFR and KRAS gene mutations were investigated in formalin-fixed, paraffin-embedded tissues from 143 Chinese female patients with breast cancer by means of real-time quantitative polymerase chain reaction (RT-PCR). Results: Based on RT-PCR, 2/143 (1.4%) samples and 1/143 (0.7%) had EGFR and KRAS gene mutations, respectively. Overall, none of the cases was identified with mutations of both of these two genes. Conclusions: In this study, both EGFR and KRAS mutations were present rarely in this cohort of samples with breast cancer. This suggested that mutation analyses for EGFR and KRAS are not useful as screening tests for sensitivity to anti-EGFR therapy for breast carcinomas.     

Analysis of PALB2 Gene in BRCA1/BRCA2 Negative Spanish Hereditary Breast/Ovarian Cancer Families with Pancreatic Cancer Cases

Background

The PALB2 gene, also known as FANCN, forms a bond and co-localizes with BRCA2 in DNA repair. Germline mutations in PALB2 have been identified in approximately 1% of familial breast cancer and 3–4% of familial pancreatic cancer. The goal of this study was to determine the prevalence of PALB2 mutations in a population of BRCA1/BRCA2 negative breast cancer patients selected from either a personal or family history of pancreatic cancer.

Methods

132 non-BRCA1/BRCA2 breast/ovarian cancer families with at least one pancreatic cancer case were included in the study. PALB2 mutational analysis was performed by direct sequencing of all coding exons and intron/exon boundaries, as well as multiplex ligation-dependent probe amplification.

Results

Two PALB2 truncating mutations, the c.1653T>A (p.Tyr551Stop) previously reported, and c.3362del (p.Gly1121ValfsX3) which is a novel frameshift mutation, were identified. Moreover, several PALB2 variants were detected; some of them were predicted as pathological by bioinformatic analysis. Considering truncating mutations, the prevalence rate of our population of BRCA1/2-negative breast cancer patients with pancreatic cancer is 1.5%.

Conclusions

The prevalence rate of PALB2 mutations in non-BRCA1/BRCA2 breast/ovarian cancer families, selected from either a personal or family pancreatic cancer history, is similar to that previously described for unselected breast/ovarian cancer families. Future research directed towards identifying other gene(s) involved in the development of breast/pancreatic cancer families is required.     

miR-10b-5p-mediated upregulation of PIEZO1 predicts poor prognosis and links to purine metabolism in breast cancer

BackgroundIncreasing evidence has reported the critical roles of PIEZO1 in organism. However, the knowledge of PIEZO1 in human cancers is still inadequate.MethodsIn silico analyses and experimental validation were performed to analyze PIEZO1's expression, prognostic values and potential upstream/downstream mechanism in breast cancer.ResultsPIEZO1 was significantly overexpressed in human breast cancer cell lines and tissue samples. PIEZO1 expression was statistically positively associated with malignant progression and short survival of breast cancer. miR-10b-5p downregulation was partially responsible for PIEZO1 upregulation in breast cancer. Further exploration revealed that PIEZO1 might exert its function by regulating purine metabolism (especially GUK1, POLD1 and APRT) in breast cancer.ConclusionsCollectively, the current study elucidated an important role of PIEZO1 in breast cancer and providing key clues for identifying PIEZO1 as a therapeutic target and prognostic biomarker in breast cancer.     

Concurrent Gene Signatures for Han Chinese Breast Cancers

The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes.We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer.     

Deciphering Signatures of Mutational Processes Operative in Human Cancer

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Highlights? Theoretical model describing mutational processes operative in cancer genomes ? Computational framework for deciphering signatures of mutational processes ? Extensive evaluation of the computational framework with simulated data ? Application to mutational catalogs of breast cancer genomes and exomes     

Breast tumor cells with PI3K mutation or HER2 amplification are selectively addicted to Akt signaling

Background

Dysregulated PI3K/Akt signaling occurs commonly in breast cancers and is due to HER2 amplification, PI3K mutation or PTEN inactivation. The objective of this study was to determine the role of Akt activation in breast cancer as a function of mechanism of activation and whether inhibition of Akt signaling is a feasible approach to therapy.

Methodology/Principal Findings

A selective allosteric inhibitor of Akt kinase was used to interrogate a panel of breast cancer cell lines characterized for genetic lesions that activate PI3K/Akt signaling: HER2 amplification or PI3K or PTEN mutations in order to determine the biochemical and biologic consequences of inhibition of this pathway. A variety of molecular techniques and tissue culture and in vivo xenograft models revealed that tumors with mutational activation of Akt signaling were selectively dependent on the pathway. In sensitive cells, pathway inhibition resulted in D-cyclin loss, G1 arrest and induction of apoptosis, whereas cells without pathway activation were unaffected. Most importantly, the drug effectively inhibited Akt kinase and its downstream effectors in vivo and caused complete suppression of the growth of breast cancer xenografts with PI3K mutation or HER2 amplification, including models of the latter selected for resistance to Herceptin. Furthermore, chronic administration of the drug was well-tolerated, causing only transient hyperglycemia without gross toxicity to the host despite the pleiotropic normal functions of Akt.

Conclusions/Significance

These data demonstrate that breast cancers with PI3K mutation or HER2 amplification are selectively dependent on Akt signaling, and that effective inhibition of Akt in tumors is feasible and effective in vivo. These findings suggest that direct inhibition of Akt may represent a therapeutic strategy for breast and other cancers that are addicted to the pathway including tumors with resistant to Herceptin.     

Combining gene signatures improves prediction of breast cancer survival

Background

Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.

Principal Findings

To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.

Conclusion

Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.     

Fully Automated Fluorescent in situ Hybridization (FISH) Staining and Digital Analysis of HER2 in Breast Cancer: A Validation Study

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.     

Household Income Is Associated with the p53 Mutation Frequency in Human Breast Tumors

Background

A study from Scotland reported that the p53 mutation frequency in breast tumors is associated with socio-economic deprivation.

Methods

We analyzed the association of the tumor p53 mutational status with tumor characteristics, education, and self-reported annual household income (HI) among 173 breast cancer patients from the greater Baltimore area, United States.

Results

p53 mutational frequency was significantly associated with HI. Patients with < $15,000 HI had the highest p53 mutation frequency (21%), followed by the income group between $15,000 and $60,000 (18%), while those above $60,000 HI had the fewest mutations (5%). When dichotomized at $60,000, 26 out of 135 patients in the low income category had acquired a p53 mutation, while only 2 out of 38 with a high income carried a mutation (P < 0.05). In the adjusted logistic regression analysis with 3 income categories (trend test), the association between HI and p53 mutational status was independent of tumor characteristics, age, race/ethnicity, tobacco smoking and body mass. Further analyses revealed that HI may impact the p53 mutational frequency preferentially in patients who develop an estrogen receptor (ER)-negative disease. Within this group, 42% of the low income patients (< $15,000 HI) carried a mutation, followed by the middle income group (21%), while those above $60,000 HI did not carry mutations (P _trend < 0.05).

Conclusions:

HI is associated with the p53 mutational frequency in patients who develop an ER-negative disease. Furthermore, high income patients may acquire fewer p53 mutations than other patients, suggesting that lifetime exposures associated with socio-economic status may impact breast cancer biology.     

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets.     

DNA repair,recombination, and damage signaling

DNA must be accurately copied and propagated from one cell division to the next, and from one generation to the next. To ensure the faithful transmission of the genome, a plethora of distinct as well as overlapping DNA repair and recombination pathways have evolved. These pathways repair a large variety of lesions, including alterations to single nucleotides and DNA single and double-strand breaks, that are generated as a consequence of normal cellular function or by external DNA damaging agents. In addition to the proteins that mediate DNA repair, checkpoint pathways have also evolved to monitor the genome and coordinate the action of various repair pathways. Checkpoints facilitate repair by mediating a transient cell cycle arrest, or through initiation of cell suicide if DNA damage has overwhelmed repair capacity. In this chapter, we describe the attributes of Caenorhabditis elegans that facilitate analyses of DNA repair, recombination, and checkpoint signaling in the context of a whole animal. We review the current knowledge of C. elegans DNA repair, recombination, and DNA damage response pathways, and their role during development, growth, and in the germ line. We also discuss how the analysis of mutational signatures in C. elegans is helping to inform cancer mutational signatures in humans.     

A pharmacogenomic method for individualized prediction of drug sensitivity

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (M erging genomic and pharmacologic A nalyses for T herapy CH oice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof‐of‐principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta‐analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three‐dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.     

Insulin Receptor Substrate Adaptor Proteins Mediate Prognostic Gene Expression Profiles in Breast Cancer

Therapies targeting the type I insulin-like growth factor receptor (IGF-1R) have not been developed with predictive biomarkers to identify tumors with receptor activation. We have previously shown that the insulin receptor substrate (IRS) adaptor proteins are necessary for linking IGF1R to downstream signaling pathways and the malignant phenotype in breast cancer cells. The purpose of this study was to identify gene expression profiles downstream of IGF1R and its two adaptor proteins. IRS-null breast cancer cells (T47D-YA) were engineered to express IRS-1 or IRS-2 alone and their ability to mediate IGF ligand-induced proliferation, motility, and gene expression determined. Global gene expression signatures reflecting IRS adaptor specific and primary vs. secondary ligand response were derived (Early IRS-1, Late IRS-1, Early IRS-2 and Late IRS-2) and functional pathway analysis examined. IRS isoforms mediated distinct gene expression profiles, functional pathways, and breast cancer subtype association. For example, IRS-1/2-induced TGFb2 expression and blockade of TGFb2 abrogated IGF-induced cell migration. In addition, the prognostic value of IRS proteins was significant in the luminal B breast tumor subtype. Univariate and multivariate analyses confirmed that IRS adaptor signatures correlated with poor outcome as measured by recurrence-free and overall survival. Thus, IRS adaptor protein expression is required for IGF ligand responses in breast cancer cells. IRS-specific gene signatures represent accurate surrogates of IGF activity and could predict response to anti-IGF therapy in breast cancer.     

Comparative Oncogenomics Implicates the Neurofibromin 1 Gene (NF1) as a Breast Cancer Driver

Identifying genomic alterations driving breast cancer is complicated by tumor diversity and genetic heterogeneity. Relevant mouse models are powerful for untangling this problem because such heterogeneity can be controlled. Inbred Chaos3 mice exhibit high levels of genomic instability leading to mammary tumors that have tumor gene expression profiles closely resembling mature human mammary luminal cell signatures. We genomically characterized mammary adenocarcinomas from these mice to identify cancer-causing genomic events that overlap common alterations in human breast cancer. Chaos3 tumors underwent recurrent copy number alterations (CNAs), particularly deletion of the RAS inhibitor Neurofibromin 1 (Nf1) in nearly all cases. These overlap with human CNAs including NF1, which is deleted or mutated in 27.7% of all breast carcinomas. Chaos3 mammary tumor cells exhibit RAS hyperactivation and increased sensitivity to RAS pathway inhibitors. These results indicate that spontaneous NF1 loss can drive breast cancer. This should be informative for treatment of the significant fraction of patients whose tumors bear NF1 mutations.     

Novel generating protective single nucleotide polymorphism barcode for breast cancer using particle swarm optimization

Background: High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging. Methods: In this study, we propose an odds ratio-based binary particle swarm optimization (OR-BPSO) method to evaluate the risk of breast cancer. Results: BPSO provides the combinational SNPs with their corresponding genotype, called SNP barcodes, with the maximal difference of occurrence between the control and breast cancer groups. A specific SNP barcode with an optimized fitness value was identified among seven SNP combinations within the space of one minute. The identified SNP barcodes with the best performance between control and breast cancer groups were found to be control-dominant, suggesting that these SNP barcodes may prove protective against breast cancer. After statistical analysis, these control-dominant SNP barcodes were processed for odds ratio analysis for quantitative measurement with regard to the risk of breast cancer. Conclusion: This study proposes an effective high-speed method to analyze the SNP–SNP interactions for breast cancer association study.