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(7 of 14) was the TP53 tumor suppressor, and aberrations were observed in additional
tumor suppressor genes including CTNNA1, which was detected in 2 of 6 African-
American patients (who typically have more aggressive and treatment- resistant dis-
ease). Alterations were also seen in the ERBB4 gene, known to be involved in
mammary-gland maturation during pregnancy and lactation, but not previously
linked to mTNBC. RNA sequencing revealed consistent overexpression of the
FOXM1 gene, when tumor gene expression was compared to nonmalignant breast
samples. Using an outlier analysis of gene expression comparing one cancer to all
the others, the authors detected expression patterns unique to each patient’s tumor.
Integrative DNA/RNA analysis provided evidence for deregulation of mutated
genes. Finally, molecular alterations in several cancers supported targeted therapeu-
tic intervention on clinical trials with known inhibitors, particularly for alterations
in the RAS/RAF/MEK/ERK and PI3K/AKT/MTOR pathways. In conclusion,
whole genome and transcriptome profi ling of mTNBC have provided insights into
somatic events occurring in this diffi cult to treat cancer. These genomic data have
guided patients to investigational treatment trials and provide hypotheses for future
trials in this irremediable cancer. Genome sequencing will eventually become a
standard tool for oncologists, enabling them to tailor therapies to the unique genetic
profi les of each of their patients.
Trends and Future Prospects of Breast Cancer Research
Currently expression profi ling can uncover pathway regulation of gene expression
and defi ne molecular classes on the basis of integration of the total signals experi-
enced by the cancer cell. The future trends that will have a great impact on breast
cancer research are as follows:
- The data content will increase. Inclusion of miRNAs that are not well covered by
the existing array technologies would result in greater precision and
comprehensiveness. - The analytical systems will become more informative.
- Metadata sets will emerge that will markedly expand the ability to validate and
to model transcriptional networks of biological and clinical signifi cance. This is
already taking place with Oncomine and follows the success of other genomic
databases. In molecular epidemiology, whole-genome SNP databases with
linked clinical data are being made available to qualifi ed researchers for analysis
and data mining.
Primary breast cancers have been analyzed by genomic DNA copy number
arrays, DNA methylation, exome sequencing, mRNA arrays, miRNA sequencing
and reverse-phase protein arrays (Koboldt et al. 2012 ). Integration of information
across platforms provided key insights into previously defi ned gene expression sub-
types and demonstrated the existence of four main breast cancer classes when com-
bining data from fi ve platforms, each of which shows signifi cant molecular
heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3)
10 Personalized Therapy of Cancer