Stefania Tommasi

Landscape of somatic mutations in 560 breast cancer whole-genome sequences

We analysed whole genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. 93 protein-coding cancer genes carried likely driver mutations. Some non-coding regions exhibited high mutation frequencies but most have distinctive structural features probably causing elevated mutation rates and do not harbour driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed 12 base substitution and six rearrangement signatures. Three rearrangement signatures, characterised by tandem duplications or deletions, appear associated with defective homologous recombination based DNA repair: one with deficient BRCA1 function; another with deficient BRCA1 or BRCA2 function; the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operative, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Cytoskeleton and paclitaxel sensitivity in breast cancer : the role of β-tubulins

The antineoplastic effect of paclitaxel is mainly related to its ability to bind the β subunit of tubulin, thus preventing tubulin chain depolarization and inducing apoptosis. The relevance of the Class I β‐tubulin characteristics have also been confirmed in the clinical setting where mutations of paclitaxel‐binding site of β‐tubulin Class I have been related to paclitaxel resistance in non small cell lung and ovarian cancers. In the present study, we verified the hypothesis of a relationship between molecular alterations of β‐tubulin Class I and paclitaxel sensitivity in a panel of breast cell lines with different drug IC50. The Class I β‐tubulin gene cDNA has been sequenced detecting heterozygous missense mutations (exon 1 and 4) only in MCF‐7 and SK‐BR‐3 lines. Furthermore, the expression (at both mRNA and protein level) of the different isotypes have been analyzed demonstrating an association between low cell sensitivity to paclitaxel and Class III β‐tubulin expression increasing. Antisense oligonucleotide (ODN) experiments confirmed that the inhibition of Class III β‐tubulin could at least partially increase paclitaxel‐chemosensitivity. The hypothesis of a relationship between β‐tubulin tumor expression and paclitaxel clinical response has been finally verified in a series of 92 advanced breast cancer patients treated with a first line paclitaxel‐based chemotherapy. Thirty‐five percent (95% CI: 45–31) of patients with high Class III β‐tubulin expression showed a disease progression vs. only 7% of patients with low expression (35% vs. 7%, p < 0.002). Our study suggests that Class III β‐tubulin tumor expression could be considered a predictive biomarker of paclitaxel‐clinical resistance for breast cancer patients.

Nonrandom Distribution of Aberrant Promoter Methylation of Cancer-Related Genes in Sporadic Breast Tumors

Purpose: In an effort to additionally determine the global patterns of CpG island hypermethylation in sporadic breast cancer, we searched for aberrant promoter methylation at 10 gene loci in 54 primary breast cancer and 10 breast benign lesions. Experimental Design: Genomic DNA sodium bisulfate converted from benign and malignant tissues was used as template in methyl-specific PCR for BRCA1, p16, ESR1, GSTP1, TRβ1, RARβ2, HIC1, APC, CCND2, and CDH1 genes. Results: The majority of the breast cancer (85%) showed aberrant methylation in at least 1 of the loci tested with half of them displaying 3 or more methylated genes. The highest frequency of aberrant promoter methylation was found for HIC1 (48%) followed by ESR1 (46%), and CDH1 (39%). Similar methylation frequencies were detected for breast benign lesions with the exception of the CDH1 gene (P = 0.02). The analysis of methylation distribution indicates a statistically significant association between methylation of the ESR1 promoter, and methylation at CDH1, TRβ1, GSTP1, and CCND2 loci (P < 0.03). Methylated status of the BRCA1 promoter was inversely correlated with methylation at the RARβ2 locus (P < 0.03). Conclusions: Our results suggest a nonrandom distribution for promoter hypermethylation in sporadic breast cancer, with tumor subsets characterized by aberrant methylation of specific cancer-related genes. These breast cancer subgroups may represent separate biological entities with potential differences in sensitivity to therapy, occurrence of metastasis, and overall prognosis.

Cell kinetics and hormonal receptor status in inflammatory breast carcinoma. Comparison with locally advanced disease.

The biological and prognostic role of hormone receptor status and proliferative activity have been studied in two series of patients affected by inflammatory breast carcinoma (IBC, 28 patients) and locally advanced breast cancer (LABC, 50 patients). Estrogen receptor (ER) and progesterone receptor (PgR) were measured by dextran‐coated charcoal (DCC) method whereas proliferative activity was measured by 3H‐thymidine autoradiographic labeling index (LI). The percentages of ER+ and PgR+ cases resulted lower in IBC than in LABC (ER+, 44% versus 64%; PgR+, 30% versus 51%, respectively), pertaining to both premenopausal and postmenopausal women. Inflammatory breast carcinoma showed a higher median LI value than LABC (3.5% versus 1.6%; P = 0.006). Regarding clinical aspects, time to progression (TTP) in IBC patients was not affected by hormone receptor status (19 evaluable patients) or by LI (17 evaluable patients); PgR+ status and low LI resulted important for individualizing women with a longer median overall survival (OS). Inflammatory breast carcinoma has been verified to be a heterogeneous biological entity for which hormone receptors and cell kinetics could be useful in identifying patients with different prognoses and therefore candidates for a personalized therapy.

Molecular Pathways and Related Target Therapies in Liver Carcinoma

Hepatocellular carcinoma (HCC) is a frequent neoplasia which still misses a therapeutical gold standard. Recently, new acquisitions in cancerogenesis process evidenced the genetic and epigenetic alterations of genes involved in the different metabolic pathways of liver cancer suggesting that antibodies, small molecules, demethylating agents, etc. specifically acting against molecular target can be utilized alone or in combination in clinical practice. The main altered targets are: cell membrane receptors, in particular tyrosine kinase receptors, factors involved in cell signalling, specifically Wnt/beta-catenin, Ras/Raf/MEK/ERK and PI3K/Akt/mTOR pathways, proteins linked to cell cycle regulation pathway (i.e. p53, p16/INK4, cyclin/cdk complex) or in invasiveness (EMT, TGFbeta) and proteins involved in DNA metabolism. Genetic or epigenetic changes in these molecules have been used in preclinical settings and, some of them also in clinical trials of phase II and III. This scenario opens new avenues for the prevention and the treatment of HCC. In the present review the main metabolic pathways and molecular alterations have been described together with recent advances in molecular and gene therapy.

Aging impacts transcriptomes but not genomes of hormone-dependent breast cancers

IntroductionAge is one of the most important risk factors for human malignancies, including breast cancer; in addition, age at diagnosis has been shown to be an independent indicator of breast cancer prognosis. Except for inherited forms of breast cancer, however, there is little genetic or epigenetic understanding of the biological basis linking aging with sporadic breast cancer incidence and its clinical behavior.MethodsDNA and RNA samples from matched estrogen receptor (ER)-positive sporadic breast cancers diagnosed in either younger (age ≤ 45 years) or older (age ≥ 70 years) Caucasian women were analyzed by array comparative genomic hybridization and by expression microarrays. Array comparative genomic hybridization data were analyzed using hierarchical clustering and supervised age cohort comparisons. Expression microarray data were analyzed using hierarchical clustering and gene set enrichment analysis; differential gene expression was also determined by conditional permutation, and an age signature was derived using prediction analysis of microarrays.ResultsHierarchical clustering of genome-wide copy-number changes in 71 ER-positive DNA samples (27 younger women, 44 older women) demonstrated two age-independent genotypes; one with few genomic changes other than 1q gain/16q loss, and another with amplifications and low-level gains/losses. Age cohort comparisons showed no significant differences in total or site-specific genomic breaks and amplicon frequencies. Hierarchical clustering of 5.1 K genes variably expressed in 101 ER-positive RNA samples (53 younger women, 48 older women) identified six transcriptome subtypes with an apparent age bias (P < 0.05). Samples with higher expression of a poor outcome-associated proliferation signature were predominantly (65%) younger cases. Supervised analysis identified cancer-associated genes differentially expressed between the cohorts; with younger cases expressing more cell cycle genes and more than threefold higher levels of the growth factor amphiregulin (AREG), and with older cases expressing higher levels of four different homeobox (HOX) genes in addition to ER (ESR1). An age signature validated against two other independent breast cancer datasets proved to have >80% accuracy in discerning younger from older ER-positive breast cancer cases with characteristic differences in AREG and ESR1 expression.ConclusionThese findings suggest that epigenetic transcriptome changes, more than genotypic variation, account for age-associated differences in sporadic breast cancer incidence and prognosis.

BRCA1/BRCA2 rearrangements and CHEK2 common mutations are infrequent in Italian male breast cancer cases

Male breast cancer (MBC) is a rare and poorly known disease. Germ-line mutations of BRCA2 and, to lesser extent, BRCA1 genes are the highest risk factors associated with MBC. Interestingly, BRCA2 germ-line rearrangements have been described in high-risk breast/ovarian cancer families which included at least one MBC case. Germ-line mutations of CHEK2 gene have been also implicated in inherited MBC predisposition. The CHEK2 1100delC mutation has been shown to increase the risk of breast cancer in men lacking BRCA1/BRCA2 mutations. Intriguingly, two other CHEK2 mutations (IVS2+1G>A and I157T) and a CHEK2 large genomic deletion (del9-10) have been associated with an elevated risk for prostate cancer. Here, we investigated the contribution of BRCA1, BRCA2 and CHEK2 alterations to MBC predisposition in Italy by analysing a large series of MBC cases, unselected for breast cancer family history and all negative for BRCA1/BRCA2 germ-line mutations. A total of 102 unrelated Italian MBC cases were screened for deletions/duplications of BRCA1, BRCA2 and CHEK2 by multiplex ligation-dependent probe amplification. No BRCA1, BRCA2 and CHEK2 genomic rearrangements, including the CHEK2 del9-10, were found in the series analysed. Furthermore, none of the MBC cases and 263 male population controls, also included in this study, carried the CHEK2 1100delC, IVS2+1G>A and I157T common mutations. Overall, our data suggest that screening of BRCA1/2 rearrangements is not advantageous in MBC cases not belonging to high-risk breast cancer families and that common CHEK2 mutations play an irrelevant role in MBC predisposition in Italy.

Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression

BackgroundDNA microarray data are used to identify genes which could be considered prognostic markers. However, due to the limited sample size of each study, the signatures are unstable in terms of the composing genes and may be limited in terms of performances. It is therefore of great interest to integrate different studies, thus increasing sample size.ResultsIn the past, several studies explored the issue of microarray data merging, but the arrival of new techniques and a focus on SVM based classification needed further investigation. We used distant metastasis prediction based on SVM attribute selection and classification to three breast cancer data sets.ConclusionsThe results showed that breast cancer classification does not benefit from data merging, confirming the results found by other studies with different techniques.

Unclassified variants in BRCA genes: guidelines for interpretation

In the last few years, several studies have focused on the interpretation of unclassified variants (UVs) of BRCA1 and BRCA2 genes. Analysis of UVs through a unique approach is not sufficient to understand their role in the development of tumors. Thus, it is clear that assembling results from different sources (genetic and epidemiological data, histopathological features, and in vitro and in silico analyses) represents a powerful way to classify such variants. Building reliable integrated models for UV classification requires the joining of many working groups to collaborative consortia, allowing data exchange and improvements of methods. This will lead to improvement in the predictivity of gene testing in BRCA1 and BRCA2 and, consequently, to an increase in the number of families that can be correctly classified as linked or unlinked to these genes, allowing more accurate genetic counseling and clinical management.

Molecular and in silico analysis of BRCA1 and BRCA2 variants.

Germline mutations of high penetrant BRCA1 and BRCA2 genes have been associated to hereditary breast cancer risk, while polymorphic variants of the two genes still have an unknown role in breast pathogenesis. The aim of our study was to characterize BRCA1 and BRCA2 genes polymorphic variants in familial breast cancer. 110 patients affected by familial breast and/or ovarian cancer have been consecutively enrolled according to family history and BRCA mutation risk. All of them have been screened for BRCA1 and BRCA2 pathogenetic mutations, SNPs and intronic variants. In silico analysis have been also performed using different computational methods to individualize genetic variations that can alter the two genes expression and function. BRCA1 resulted mutated in 14% while BRCA2 in 3% of cases, while 80% of patients presented at least one polymorphism. A neural network splicing prediction model individualized one BRCA1 and one BRCA2 intronic variants able to determine alternative splicing. Furthermore, Q356R BRCA1 and N289H BRCA2 appear to show a possible harmful role also due to their location in functional regions of the two genes. However, in silico data are not always consistent with biological evidences. In conclusion, SNPs profile provides a basis for DNA-based cancer risk classification and help to define the gene alterations that could influence biochemistry activity protein or could modify drug sensitivity.