Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

Introduction: As part of the MicroArray Quality Control (MAQC)-II project, this analysis examines how the choice of univariate feature-selection methods and classification algorithms may influence the performance of genomic predictors under varying degrees of prediction difficulty represented by three clinically relevant endpoints. Methods: We used gene-expression data from 230 breast cancers (grouped into training and independent validation sets), and we examined 40 predictors (five univariate feature-selection methods combined with eight different classifiers) for each of the three endpoints. Their classification performance was estimated on the training set by using two different resampling methods and compared with the accuracy observed in the independent validation set. Results: A ranking of the three classification problems was obtained, and the performance of 120 models was estimated and assessed on an independent validation set. The bootstrapping estimates were closer to the validation performance than were the cross-validation estimates. The required sample size for each endpoint was estimated, and both gene-level and pathway-level analyses were performed on the obtained models. Conclusions: We showed that genomic predictor accuracy is determined largely by an interplay between sample size and classification difficulty. Variations on univariate feature-selection methods and choice of classification algorithm have only a modest impact on predictor performance, and several statistically equally good predictors can be developed for any given classification problem

Can shear-wave elastography predict response to neoadjuvant chemotherapy in women with invasive breast cancer?

BACKGROUND: Response of invasive breast cancer to neoadjuvant chemotherapy (NAC) is variable, and prediction of response is imperfect. We aimed to ascertain whether tissue stiffness in breast cancers, as assessed by shear-wave elastography (SWE) before treatment, is associated with response. METHODS: We retrospectively compared pre-treatment tumour mean tissue stiffness, with post-treatment Residual Cancer Burden (RCB) scores and its components in 40 women with breast cancer treated by NAC using Pearson's correlation coefficient (CC), a general linear model and multiple linear regression. Subgroup analysis was carried out for luminal, HER2-positive and basal immuno-histochemical subtypes. RESULTS: Statistically significant correlations were shown between stiffness and RCB scores and between stiffness and percentage tumour cellularity. The correlation between stiffness and percentage cellularity was strongest (CC 0.35 (P<0.0001) compared with CC 0.23 (P=0.004) for the RCB score). The results of a general linear model show that cellularity and RCB score maintain independent relationships with stiffness. By multiple linear regression, only cellularity maintained a significant relationship with stiffness. CONCLUSION: Pre-treatment tumour stiffness measured by SWE, has a statistically significant relationship with pathological response of invasive breast cancer to NAC

Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers

INTRODUCTION: Because of its high rate of metastasis, inflammatory breast cancer (IBC) has a poor prognosis compared with non-inflammatory types of breast cancer (non-IBC). In a recent study, Lehmann and colleagues identified seven subtypes of triple-negative breast cancer (TNBC). We hypothesized that the distribution of TNBC subtypes differs between TN-IBC and TN-non-IBC. We determined the subtypes and compared clinical outcomes by subtype in TN-IBC and TN-non-IBC patients. METHODS: We determined TNBC subtypes in a TNBC cohort from the World IBC Consortium for which IBC status was known (39 cases of TN-IBC; 49 cases of TN-non-IBC). We then determined the associations between TNBC subtypes and IBC status and compared clinical outcomes between TNBC subtypes. RESULTS: We found the seven subtypes exist in both TN-IBC and TN-non-IBC. We found no association between TNBC subtype and IBC status (P = 0.47). TNBC subtype did not predict recurrence-free survival. IBC status was not a significant predictor of recurrence-free or overall survival in the TNBC cohort. CONCLUSIONS: Our data show that, like TN-non-IBC, TN-IBC is a heterogeneous disease. Although clinical characteristics differ significantly between IBC and non-IBC, no unique IBC-specific TNBC subtypes were identified by mRNA gene-expression profiles of the tumor. Studies are needed to identify the subtle molecular or microenvironmental differences that contribute to the differing clinical behaviors between TN-IBC and TN-non-IBC