On consolidated predictive model of the natural history of breast cancer considering primary tumour and distant metastases growth
We propose a new mathematical growth model of primary tumor and primary metastases which may help to improve predicting accuracy of breast cancer process using an original mathematical model referred to CoM-IV and corresponding software. The CoM-IV model and predictive software: a) detect different growth periods of primary tumor and primary metastases; b) make forecast of patient survival; c) have higher average prediction accuracy than the other tools; d) can improve forecasts on survival of BC and facilitate optimisation of diagnostic tests. The CoM-IV enables us, for the first time, to predict the whole natural history of primary tumor and primary metastases growth on each stage (pT1, pT2, pT3, pT4) considering only on primary tumor sizes. Summarising: CoM-IV a) describes correctly primary tumor and primary distant metastases growth of IV (T1-4N0-3M1) stage with (N1-3) or without regional metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and manifestation of primary metastases.
Homogeneous and isotropic with respect to horizontal variables random fields are useful for study of geophysical (in particular, meteorological) functions of spatial-temporal variables. The following horizontal scale (30 — 3000 km), which is induced by the spatial scale of the observing grid for the Earth’s atmosphere and by the power of modern computers for solutions of the system of hydrothermodynamics equations, which included water phase transformations etc, is important for the weather forecast problems.
The correlation functions (CFs) of the random fields may be applied for the following goals:
1) For the optimal interpolation of the meteorological information from the points of observation into the points of a regular finite-difference grid, as well as (for the checking of some observations by other ones) into another point of the observation.
2) For the models’ testing, if a climate model simulates adequately not only mean fields, but the fields of the relative dispersions and CFs, too, then we should consider the climate model as a certain one.
The CFs are evaluated by the global checked archive of meteorological observations by meteorological sounds. A special regularization procedure provides the strong positive definiteness of the CFs. The areas in the Earth atmosphere, where the isotropy hypothesis is essentially not fulfilled, were localized by a special algorithm.
Let us consider an algorithm, which can construct atmospheric fronts that separate so named homogeneous synoptic atmospheric volumes. Then we can evaluate separately CFs for the ensemble of the pairs of points, which are in a unite volume and CFs for the ensemble of the pairs of points, which are in a various volumes. We can see the difference between the different CFs. The difference will be more for a better algorithm. So, we obtain a quality criterion for such algorithms. The statistical approach given possibility to optimize the algorithm with respect to a lot of numerical parameters. The optimal algorithm was exploited in the operative regime in Hydrometeorological Center of Russia. The similar algorithms of numerical construction of boundaries between homogeneous volumes by a discrete set of observations can be realized for various physical media.
Quantification and normalization of RT-qPCR data critically depends on the expression of so called reference genes. Our goal was to develop a strategy for the selection of reference genes that utilizes microarray data analysis and combines known approaches for gene stability evaluation and to select a set of appropriate reference genes for research and clinical analysis of breast samples with different receptor and cancer status using this strategy.
A preliminary search of reference genes was based on high-throughput analysis of microarray datasets. The final selection and validation of the candidate genes were based on the RT-qPCR data analysis using several known methods for expression stability evaluation: comparative ∆Ct method, geNorm, NormFinder and Haller equivalence test.
A set of five reference genes was identified: ACTB, RPS23, HUWE1, EEF1A1 and SF3A1. The initial selection was based on the analysis of publically available well-annotated microarray datasets containing different breast cancers and normal breast epithelium from breast cancer patients and epithelium from cancer-free patients. The final selection and validation were performed using RT-qPCR data from 39 breast cancer biopsy samples. Three genes from the final set were identified by the means of microarray analysis and were novel in the context of breast cancer assay. We showed that the selected set of reference genes is more stable in comparison not only with individual genes, but also with a system of reference genes used in commercial OncotypeDX test.
A selection of reference genes for RT-qPCR can be efficiently performed by combining a preliminary search based on the high-throughput analysis of microarray datasets and final selection and validation based on the analysis of RT-qPCR data with a simultaneous examination of different expression stability measures. The identified set of reference genes proved to be less variable and thus potentially more efficient for research and clinical analysis of breast samples comparing to individual genes and the set of reference genes used in OncotypeDX assay.
PRIMARY THERAPY OF EARLY BREAST CANCER
Evidence, Controversies, Consensus
The search for novel parameters to predict the risk of relapse in breast cancer was conducted. Significant correlation between the risk of relapse and α-2A adrenergic receptor (ADRA2A) expression was revealed using public microarray datasets. This relationship was confirmed by validation on independent microarray dataset. It was found that when assessing the risk of BC relapse, the accuracy of prediction based solely on the expression of ADRA2A gene is close to that made using OncotypeDX and MammaPrint test systems. In this case, addition of only one or two supplemental prognostic markers (for instance, expression of SQLE gene or SQLE andDSCC1genes) to ADRA2A ensures the accuracy of prediction not inferior to reliability of these test systems.
Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2–5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.