Straver, M. E. et al. The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer. Breast Cancer Res. Treat. 119, 551-558

Breast Cancer Research and Treatment (Impact Factor: 3.94). 02/2010; 119(3):551-558. DOI: 10.1007/s10549-009-0333-1
Source: PubMed


The 70-gene signature (MammaPrint™) is a prognostic tool used to guide adjuvant treatment decisions. The aim of this study
was to assess its value to predict chemosensitivity in the neoadjuvant setting. We obtained the 70-gene profile of stage II–III
patients prior to neoadjuvant chemotherapy and classified the prognosis-signatures. Pathological complete remission (pCR)
was used to measure chemosensitivity. Among 167 patients, 144 (86%) were having a poor and 23 (14%) a good prognosis-signature.
None of the good prognosis-signature patients achieved a pCR (0/23), whereas 29/144 patients (20%) in the poor prognosis-signature
group did (P=0.015). All triple-negative tumors (n=38) had a poor prognosis-signature. Within the non triple-negative subgroup, the response of the primary tumor remained
associated with the classification of the prognosis-signature (P=0.023). A pCR is unlikely to be achieved in tumors that have a good prognosis-signature. Tumors with a poor prognosis-signature
are more sensitive to chemotherapy.

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    • "In such applications, most of these features are irrelevant with a relatively small sample size (tens or at most hundreds). These situations arise particularly in the bioinformatics field involving the analysis of gene expression and proteomic profiles for different purposes such as disease diagnosis, prognosis and treatment response prediction [72] [78]. The second challenge concerns the problem of processing simultaneously mixed-type and heterogeneous data (qualitative, quantitative, interval, etc.) which are present almost in all daily produced datasets (for instance most of the UCI repository datasets are of mixed type). "
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    ABSTRACT: The present paper describes a new feature weighting method based on a membership margin. Distinctive properties of the proposed method include its capability to process problems characterized by mixed-type data (quantitative, qualitative and interval) as well as a huge number of features. The key idea is to map simultaneously all the features of different types into a common space; the membership space. Once all features are represented in a homogeneous space, a feature weighting task can be performed in unified way. This weighting approach is integrated here within a fuzzy classifier through a fuzzy rule weighted concept in order to improve its performance. Each antecedent fuzzy set in the fuzzy if–then rule is weighted to characterize the importance of each proposition and therefore its corresponding feature. Weight estimation process is based on membership margin maximization to estimate a fuzzy weight of each feature in the membership space. Experiments on low and high dimensional real-world datasets demonstrate that the proposed approach can improve significantly the performance of the fuzzy rule-based as well as other state of the art classifiers and can even outperform classical feature weighting approaches. In particular, we show that this approach can yield meaningful results on two real-world applications for cancer prognosis and industrial process diagnosis.
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    • "Several studies have been performed that measure chemo-sensitivity by pCR in patients classified according to molecular subgroups by MammaPrint and BluePrint. The results are shown in Table 2.49,61,62,67 "
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    • "However, the high mortality from breast cancer has pushed researchers to seek for accurate cancer prognosis tools that help physicians to take the necessary treatment decisions that spare patients from side effects and thereby reduce its high medical costs. In the past decade microarray analysis has had a great interest in cancer management such as diagnosis (Ramaswamy et al., 2001), prognosis (Van't Veer et al., 2002), and treatment benefit prediction (Straver et al., 2009). "
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    ABSTRACT: Abstract Microarray profiling has recently generated the hope to gain new insights into breast cancer biology and thereby improve the performance of current prognostic tools. However, it also poses several serious challenges to classical data analysis techniques related to the characteristics of resulting data, mainly high dimensionality and low signal-to-noise ratio. Despite the tremendous research work performed to handle the first challenge in the feature selection framework, very little attention has been directed to address the second one. We propose in this article to address both issues simultaneously based on symbolic data analysis capabilities in order to derive more accurate genetic marker-based prognostic models. In particular, interval data representation is employed to model various uncertainties in microarray measurements. A recent feature selection algorithm that handles symbolic interval data is used then to derive a genetic signature. The predictive value of the derived signature is then assessed by following a rigorous experimental setup and compared with existing prognostic approaches in terms of predictive performance and estimated survival probability. It is shown that the derived signature (GenSym) performs significantly better than other prognostic models, including the 70-gene signature, St. Gallen, and National Institutes of Health criteria.
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