Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

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Correlation of HER2, p95HER2 and HER3 Expression and Treatment Outcome of Lapatinib plus Capecitabine in her2-Positive Metastatic Breast Cancer

PLoS ONE ◽

10.1371/journal.pone.0039943 ◽

2012 ◽

Vol 7 (7) ◽

pp. e39943 ◽

Cited By ~ 19

Author(s):

Sae-Won Han ◽

Yongjun Cha ◽

Agnes Paquet ◽

Weidong Huang ◽

Jodi Weidler ◽

...

Keyword(s):

Breast Cancer ◽

Treatment Outcome ◽

Metastatic Breast Cancer ◽

Metastatic Breast ◽

Her2 Positive ◽

Her3 Expression

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Uncommon Breast Malignancies: Presentation Pattern, Prognostic Issue and Treatment Outcome in an Italian Single Institution Experience

Tumori Journal ◽

10.1177/030089161309900107 ◽

2013 ◽

Vol 99 (1) ◽

pp. 39-44

Author(s):

Claudia Maria Regina Bareggi ◽

Dario Consonni ◽

Barbara Galassi ◽

Donatella Gambini ◽

Elisa Locatelli ◽

...

Keyword(s):

Breast Cancer ◽

Treatment Outcome ◽

Invasive Ductal Carcinoma ◽

Ductal Carcinoma ◽

Early Stage ◽

Medullary Carcinoma ◽

Mucinous Carcinoma ◽

Lymph Node Involvement ◽

Tubular Carcinoma ◽

Breast Malignancies

Aims and background Often neglected by large clinical trials, patients with uncommon breast malignancies have been rarely analyzed in large series. Patients and methods Of 2,052 patients diagnosed with breast cancer and followed in our Institution from January 1985 to December 2009, we retrospectively collected data on those with uncommon histotypes, with the aim of investigating their presentation characteristics and treatment outcome. Results Rare histotypes were identified in 146 patients (7.1% of our total breast cancer population), being classified as follows: tubular carcinoma in 75 (51.4%), mucinous carcinoma in 36 (24.7%), medullary carcinoma in 25 (17.1%) and papillary carcinoma in 10 patients (6.8%). Whereas age at diagnosis was not significantly different among the diverse diagnostic groups, patients with medullary and papillary subtypes had a higher rate of lymph node involvement, similar to that of invasive ductal carcinoma. Early stage diagnosis was frequent, except for medullary carcinoma. Overall, in comparison with our invasive ductal carcinoma patients, those with rare histotypes showed a significantly lower risk of recurrence, with a hazard ratio of 0.28 (95% CI, 0.12–0.62; P = 0.002). Conclusions According to our analysis, patients with uncommon breast malignancies are often diagnosed at an early stage, resulting in a good prognosis with standard treatment.

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