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. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized 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 operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Technological advances have led to the introduction of next-generation sequencing (NGS) platforms in cancer investigation. NGS allows massive parallel sequencing that affords maximal tumor genomic assessment. NGS approaches are different, and concern DNA and RNA analysis. DNA sequencing includes whole-genome, whole-exome, and targeted sequencing, which focuses on a selection of genes of interest for a specific disease. RNA sequencing facilitates the detection of alternative gene-spliced transcripts, posttranscriptional modifications, gene fusion, mutations/single-nucleotide polymorphisms, small and long noncoding RNAs, and changes in gene expression. Most applications are in the cancer research field, but lately NGS technology has been revolutionizing cancer molecular diagnostics, due to the many advantages it offers compared to traditional methods. There is greater knowledge on solid cancer diagnostics, and recent interest has been shown also in the field of hematologic cancer. In this review, we report the latest data on NGS diagnostic/predictive clinical applications in solid and hematologic cancers. Moreover, since the amount of NGS data produced is very large and their interpretation is very complex, we briefly discuss two bioinformatic aspects, variant-calling accuracy and copy-number variation detection, which are gaining a lot of importance in cancer-diagnostic assessment.

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.

Background: Several studies looked for tumor biomarkers predictive for paclitaxel sensitivity but till now no reliable biomarkers are available. The aim of this study was to verify the potential predictive value of β-tubulin III and IV, vascular endothelial growth factor-receptor (VEGFR-1), HER2/neu and microvessel density (mVD), in a group of 70 patients with advanced breast cancer (ABC) treated with paclitaxel-based chemotherapy. Patients and methods: Immunohistochemical analysis (ICA) of HER2, VEGFR-1, mVd, β-tubulin III and β-tubulin IV expression were performed in a series of 72 advanced breast cancer. Furthermore apoptotic fraction with TUNEL analysis was evaluated. Results: β–tubulin III ICA expression was predictive of progression after chemotherapy. In fact only 2% of the patients with low β-tubulin III expression progressed after paclitaxel chemotherapy vs 38% of those with high β-tubulin III tumor expression (P=0.000; by χ²). This evidence was confirmed by a logistic regression analysis (OR 28.789; 95% CI 3.212–258,072; P=0.004). There was not a significant association between other biomarkers' characteristics and clinical response to chemotherapy. A Cox multivariate analysis, with overall survival as a dependent variable, showed that only HER2 expression was independently associated (OR 2.39; 95% CI 1.09–5.23; P=0.03) with overall survival. Conclusions: We suggest that class III β-tubulin immunohistochemical expression analysis could be a potentially relevant tumor biomarker for paclitaxel resistance in advanced breast cancer patients.

NGS technology represents a powerful alternative to the standard Sanger sequencing in the context of clinical setting. The proprietary software that are generally used for variant calling often depend on preset parameters that may not fit in a satisfactory manner for different genes. GATK, which is widely used in the academic world, is rich in parameters for variant calling. However the self-adjusting parameter calibration of GATK requires data from a large number of exomes. When these are not available, which is the standard condition of a diagnostic laboratory, the parameters must be set by the operator (hard filtering). The aim of the present paper was to set up a procedure to assess the best parameters to be used in the hard filtering of GATK. This was pursued by using classification trees on true and false variants from simulated sequences of a real dataset data. We simulated two datasets, with different coverages, including all the sequence alterations identified in a real dataset according to their observed frequencies. Simulated sequences were aligned with standard protocols and then regression trees were built up to identify the most reliable parameters and cutoff values to discriminate true and false variant calls. Moreover, we analyzed flanking sequences of region presenting a high rate of false positive calls observing that such sequences present a low complexity make up. Our results showed that GATK hard filtering parameter values can be tailored through a simulation study based-on the DNA region of interest to ameliorate the accuracy of the variant calling.

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.