Artificial neural network techniques to investigate potential interactions between biomarkers

High-throughput technologies in biomedical sciences, including gene microarrays, supposed to revolutionise the post-genomic era, have barely met the great expectations they inspired to the biomedical community at first. Current efforts are still focused toward improving the technology, its reproducibility and accuracy. In the meantime, computational techniques for the analysis of the data from these technologies have achieved great progresses and show encouraging results. New approaches have been developed to extract relevant information out from these results. However, important work needs to be further conducted in order to extract even more meaningful and relevant information. These techniques offer great possibilities to explore the overall dynamic held within a living organism. The potential information contained in their output can reveal important leads at deciphering the interconnection, interaction or regulation influences that can exist between several molecules. In front of an increasing interest of the scientific community toward the exploration of these dynamics, some groups have started to develop solutions based on different technologies to extract these information related to interactions. Here we present an Artificial Neural Network-based methodology for the study of interactions in gene transcriptomic data. This will be applied and validated in a breast cancer context

DACH1: its role as a classifier of long term good prognosis in luminal breast cancer

Background: Oestrogen receptor (ER) positive (luminal) tumours account for the largest proportion of females with breast cancer. Theirs is a heterogeneous disease presenting clinical challenges in managing their treatment. Three main biological luminal groups have been identified but clinically these can be distilled into two prognostic groups in which Luminal A are accorded good prognosis and Luminal B correlate with poor prognosis. Further biomarkers are needed to attain classification consensus. Machine learning approaches like Artificial Neural Networks (ANNs) have been used for classification and identification of biomarkers in breast cancer using high throughput data. In this study, we have used an artificial neural network (ANN) approach to identify DACH1 as a candidate luminal marker and its role in predicting clinical outcome in breast cancer is assessed. Materials and methods: A reiterative ANN approach incorporating a network inferencing algorithm was used to identify ER- associated biomarkers in a publically available cDNA microarray dataset. DACH1 was identified in having a strong influence on ER associated markers and a positive association with ER. Its clinical relevance in predicting breast cancer specific survival was investigated by statistically assessing protein expression levels after immunohistochemistry in a series of unselected breast cancers, formatted as a tissue microarray. Results: Strong nuclear DACH1 staining is more prevalent in tubular and lobular breast cancer. Its expression correlated with ER-alpha positive tumours expressing PgR, epithelial cytokeratins (CK)18/19 and 'luminal-like' markers of good prognosis including FOXA1 and RERG (p , 0.05). DACH1 is increased in patients showing longer cancer specific survival and disease free interval and reduced metastasis formation (p , 0.001). Nuclear DACH1 showed a negative association with markers of aggressive growth and poor prognosis. Conclusion: Nuclear DACH1 expression appears to be a Luminal A biomarker predictive of good prognosis, but is not independent of clinical stage, tumour size, NPI status or systemic therapy

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesized that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables, can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). NPI+ was then used to predict outcome in the different molecular classes with.Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological breast cancer class provides improved patient outcome stratification superior to the traditional NPI. This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

RNASeq analysis reveals biological processes governing the clinical behaviour of endometrioid and serous endometrial cancers

BACKGROUND: Endometrial carcinoma comprises a group of tumors with distinct histologic and molecular features, and clinical behavior. Here we sought to define the biological processes that govern the clinical behavior of endometrial cancers. METHODS: Sixteen prototype genes representative of different biological processes that would likely play a role in endometrial and other hormone-driven cancers were defined. RNA-sequencing gene expression data from 323 endometrial cancers from The Cancer Genome Atlas were used to determine the transcription module of each prototype gene. The expression of prototype genes and modules and their association with outcome was assessed in univariate and multivariate survival analyses. The association of MSH6 expression with outcome was validated in an independent cohort of 243 primary endometrial cancers using immunohistochemistry. RESULTS: We observed that the clinical behavior of endometrial carcinomas as a group was associated with hormone receptor signaling, PI3K pathway signaling and DNA mismatch repair processes. When analyzed separately, in endometrioid carcinomas, hormone receptor, PI3K and DNA mismatch repair modules were significantly associated with outcome in univariate analysis, whereas the clinical behavior of serous cancers was likely governed by apoptosis and Wnt signaling. Multivariate survival analysis revealed that MSH6 expression was associated with outcome of endometrial cancer patients independently from traditional prognostic clinicopathologic parameters, which was confirmed in an independent cohort at the protein level. CONCLUSION: Endometrioid and serous endometrial cancers are underpinned by distinct molecular pathways. MSH6 expression levels may be associated with outcome in endometrial cancers as a group

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Background: Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. Methods: In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. Results: Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. Conclusion: This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

Bacterial natural product biosynthetic domain composition in soil correlates with changes in latitude on a continent-wide scale

Although bacterial bioactive metabolites have been one of the most prolific sources of lead structures for the development of small-molecule therapeutics, very little is known about the environmental factors associated with changes in secondary metabolism across natural environments. Large-scale sequencing of environmental microbiomes has the potential to shed light on the richness of bacterial biosynthetic diversity hidden in the environment, how it varies from one environment to the next, and what environmental factors correlate with changes in biosynthetic diversity. In this study, the sequencing of PCR amplicons generated using primers targeting either ketosynthase domains from polyketide biosynthesis or adenylation domains from nonribosomal peptide biosynthesis was used to assess biosynthetic domain composition and richness in soils collected across the Australian continent. Using environmental variables collected at each soil site, we looked for environmental factors that correlated with either high overall domain richness or changes in the domain composition. Among the environmental variables we measured, changes in biosynthetic domain composition correlate most closely with changes in latitude and to a lesser extent changes in pH. Although it is unclear at this time the exact mix of factors that may drive the relationship between biosynthetic domain composition and latitude, from a practical perspective the identification of a latitudinal basis for differences in soil metagenome biosynthetic domain compositions should help guide future natural product discovery efforts.Christophe Lemetrea, Jeffrey Manikoa, Zachary Charlop-Powersa, Ben Sparrow, Andrew J. Lowec and Sean F. Brad