Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative.

BACKGROUND: Selection of systemic therapy for primary breast cancer is currently based on clinical biomarkers along with stage. Novel genomic tests are continuously being introduced as more precise tools for guidance of therapy, although they are often developed for specific patient subgroups. The Sweden Cancerome Analysis Network - Breast (SCAN-B) initiative aims to include all patients with breast cancer for tumour genomic analysis, and to deliver molecular subtype and mutational data back to the treating physician. METHODS: An infrastructure for collection of blood and fresh tumour tissue from all patients newly diagnosed with breast cancer was set up in 2010, initially including seven hospitals within the southern Sweden regional catchment area, which has 1.8 million inhabitants. Inclusion of patients was implemented into routine clinical care, with collection of tumour tissue at local pathology departments for transport to the central laboratory, where routines for rapid sample processing, RNA sequencing and biomarker reporting were developed. RESULTS: More than 10 000 patients from nine hospitals have currently consented to inclusion in SCAN-B with high (90 per cent) inclusion rates from both university and secondary hospitals. Tumour samples and successful RNA sequencing are being obtained from more than 70 per cent of patients, showing excellent representation compared with the national quality registry as a truly population-based cohort. Molecular biomarker reports can be delivered to multidisciplinary conferences within 1 week. CONCLUSION: Population-based collection of fresh tumour tissue is feasible given a decisive joint effort between academia and collaborative healthcare groups, and with governmental support. An infrastructure for genomic analysis and prompt data output paves the way for novel systemic therapy for patients from all hospitals, irrespective of size and location.

Targeted sequencing of BRCA1 and BRCA2 across a large unselected breast cancer cohort suggests that one-third of mutations are somatic.

BACKGROUND: A mutation found in the BRCA1 or BRCA2 gene of a breast tumor could be either germline or somatically acquired. The prevalence of somatic BRCA1/2 mutations and the ratio between somatic and germline BRCA1/2 mutations in unselected breast cancer patients are currently unclear. PATIENTS AND METHODS: Paired normal and tumor DNA was analyzed for BRCA1/2 mutations by massively parallel sequencing in an unselected cohort of 273 breast cancer patients from south Sweden. RESULTS: Deleterious germline mutations in BRCA1 (n = 10) or BRCA2 (n = 10) were detected in 20 patients (7%). Deleterious somatic mutations in BRCA1 (n = 4) or BRCA2 (n = 5) were detected in 9 patients (3%). Accordingly, about 1 in 9 breast carcinomas (11%) in our cohort harbor a BRCA1/2 mutation. For each gene, the tumor phenotypes were very similar regardless of the mutation being germline or somatically acquired, whereas the tumor phenotypes differed significantly between wild-type and mutated cases. For age at diagnosis, the patients with somatic BRCA1/2 mutations resembled the wild-type patients (median age at diagnosis, germline BRCA1: 41.5 years; germline BRCA2: 49.5 years; somatic BRCA1/2: 65 years; wild-type BRCA1/2: 62.5 years). CONCLUSIONS: In a population without strong germline founder mutations, the likelihood of a BRCA1/2 mutation found in a breast carcinoma being somatic was ∼1/3 and germline 2/3. This may have implications for treatment and genetic counseling.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks.

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns.

To investigate the phenotype associated with estrogen receptor alpha (ER) expression in breast carcinoma, gene expression profiles of 58 node-negative breast carcinomas discordant for ER status were determined using DNA microarray technology. Using artificial neural networks as well as standard hierarchical clustering techniques, the tumors could be classified according to ER status, and a list of genes which discriminate tumors according to ER status was generated. The artificial neural networks could accurately predict ER status even when excluding top discriminator genes, including ER itself. By reference to the serial analysis of gene expression database, we found that only a small proportion of the 100 most important ER discriminator genes were also regulated by estradiol in MCF-7 cells. The results provide evidence that ER+ and ER- tumors display remarkably different gene-expression phenotypes not solely explained by differences in estrogen responsiveness.

Expression profiling in cancer using cDNA microarrays.

Currently there are over 1,000,000 human expressed sequence tag (EST) sequences available on the public database, representing perhaps 50-90% of all human genes. The cDNA microarray technique is a recently developed tool that exploits this wealth of information for the analysis of gene expression. In this method, DNA probes representing cDNA clones are arrayed onto a glass slide and interrogated with fluorescently labeled cDNA targets. The power of the technology is the ability to perform a genome-wide expression profile of thousands of genes in one experiment. In our review we describe the principles of the microarray technology as applied to cancer research, summarize the literature on its use so far, and speculate on the future application of this powerful technique.

cDNA microarrays detect activation of a myogenic transcription program by the PAX3-FKHR fusion oncogene.

Alveolar rhabdomyosarcoma is an aggressive pediatric cancer of striated muscle characterized in 60% of cases by a t(2;13)(q35;q14). This results in the fusion of PAX3, a developmental transcription factor required for limb myogenesis, with FKHR, a member of the forkhead family of transcription factors. The resultant PAX3-FKHR gene possesses transforming properties; however, the effects of this chimeric oncogene on gene expression are largely unknown. To investigate the actions of these transcription factors, both Pax3 and PAX3-FKHR were introduced into NIH 3T3 cells, and the resultant gene expression changes were analyzed with a murine cDNA microarray containing 2,225 elements. We found that PAX3-FKHR but not PAX3 activated a myogenic transcription program including the induction of transcription factors MyoD, Myogenin, Six1, and Slug as well as a battery of genes involved in several aspects of muscle function. Notable among this group were the growth factor gene Igf2 and its binding protein Igfbp5. Relevance of this model was suggested by verification that three of these genes (IGFBP5, HSIX1, and Slug) were also expressed in alveolar rhabdomyosarcoma cell lines. This study utilizes cDNA microarrays to elucidate the pattern of gene expression induced by an oncogenic transcription factor and demonstrates the profound myogenic properties of PAX3-FKHR in NIH 3T3 cells.