Different Pathological Complete Response Rates According to PAM50 Subtype in HER2+ Breast Cancer Patients Treated With Neoadjuvant Pertuzumab/Trastuzumab vs. Trastuzumab Plus Standard Chemotherapy: An Analysis of Real-World Data.

Background: Double blockade with pertuzumab and trastuzumab combined with chemotherapy is the standard neoadjuvant treatment for HER2-positive early breast cancer. Data derived from clinical trials indicates that the response rates differ among intrinsic subtypes of breast cancer. The aim of this study is to determine if these results are valid in real-world patients. Methods: A total of 259 patients treated in eight Spanish hospitals were included and divided into two cohorts: Cohort A (132 patients) received trastuzumab plus standard neoadjuvant chemotherapy (NAC), and Cohort B received pertuzumab and trastuzumab plus NAC (122 patients). Pathological complete response (pCR) was defined as the complete disappearance of invasive tumor cells. Assignment of the intrinsic subtype was realized using the research-based PAM50 signature. Results: There were more HER2-enriched tumors in Cohort A (70 vs. 56%) and more basal-like tumors in Cohort B (12 vs. 2%), with similar luminal cases in both cohorts (luminal A 12 vs. 14%; luminal B 14 vs. 18%). The overall pCR rate was 39% in Cohort A and 61% in Cohort B. Better pCR rates with pertuzumab plus trastuzumab than with trastuzumab alone were also observed in all intrinsic subtypes (luminal PAM50 41 vs. 11.4% and HER2-enriched subtype 73.5 vs. 50%) but not in basal-like tumors (53.3 vs. 50%). In multivariate analysis the only significant variables related to pCR in both luminal PAM50 and HER2-enriched subtypes were treatment with pertuzumab plus trastuzumab (Cohort B) and histological grade 3. Conclusions: With data obtained from patients treated in clinical practice, it has been possible to verify that the addition of pertuzumab to trastuzumab and neoadjuvant chemotherapy substantially increases the rate of pCR, especially in the HER2-enriched subtype but also in luminal subtypes, with no apparent benefit in basal-like tumors.

BLASSO: integration of biological knowledge into a regularized linear model.

BACKGROUND: In RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genes that is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. The discovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded at gene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genes included in each sample usually exists, thus obtaining very low overlapping rates between the genetic signatures proposed by different authors. In this sense, this paper proposes BLASSO, a simple and highly interpretable linear model with l1-regularization that incorporates prior biological knowledge to the prediction of breast cancer outcomes. Two different approaches to integrate biological knowledge in BLASSO, Gene-specific and Gene-disease, are proposed to test their predictive performance and biomarker stability on a public RNA-Seq gene expression dataset for breast cancer. The relevance of the genetic signature for the model is inspected by a functional analysis. RESULTS: BLASSO has been compared with a baseline LASSO model. Using 10-fold cross-validation with 100 repetitions for models' assessment, average AUC values of 0.7 and 0.69 were obtained for the Gene-specific and the Gene-disease approaches, respectively. These efficacy rates outperform the average AUC of 0.65 obtained with the LASSO. With respect to the stability of the genetic signatures found, BLASSO outperformed the baseline model in terms of the robustness index (RI). The Gene-specific approach gave RI of 0.15±0.03, compared to RI of 0.09±0.03 given by LASSO, thus being 66% times more robust. The functional analysis performed to the genetic signature obtained with the Gene-disease approach showed a significant presence of genes related with cancer, as well as one gene (IFNK) and one pseudogene (PCNAP1) which a priori had not been described to be related with cancer. CONCLUSIONS: BLASSO has been shown as a good choice both in terms of predictive efficacy and biomarker stability, when compared to other similar approaches. Further functional analyses of the genetic signatures obtained with BLASSO has not only revealed genes with important roles in cancer, but also genes that should play an unknown or collateral role in the studied disease.

Triple negative breast cancer subtypes and pathologic complete response rate to neoadjuvant chemotherapy.

Triple negative breast cancer (TNBC) is a heterogeneous disease with distinct molecular subtypes that differentially respond to chemotherapy and targeted agents. The purpose of this study is to explore the clinical relevance of Lehmann TNBC subtypes by identifying any differences in response to neoadjuvant chemotherapy among them. We determined Lehmann subtypes by gene expression profiling in paraffined pre-treatment tumor biopsies from 125 TNBC patients treated with neoadjuvant anthracyclines and/or taxanes +/- carboplatin. We explored the clinicopathological characteristics of Lehmann subtypes and their association with the pathologic complete response (pCR) to different treatments. The global pCR rate was 37%, and it was unevenly distributed within Lehmann's subtypes. Basal-like 1 (BL1) tumors exhibited the highest pCR to carboplatin containing regimens (80% vs 23%, p=0.027) and were the most proliferative (Ki-67>50% of 88.2% vs. 63.7%, p=0.02). Luminal-androgen receptor (LAR) patients achieved the lowest pCR to all treatments (14.3% vs 42.7%, p=0.045 when excluding mesenchymal stem-like (MSL) samples) and were the group with the lowest proliferation (Ki-67≤50% of 71% vs 27%, p=0.002). In our cohort, only tumors with LAR phenotype presented non-basal-like intrinsic subtypes (HER2-enriched and luminal A). TNBC patients present tumors with a high genetic diversity ranging from highly proliferative tumors, likely responsive to platinum-based therapies, to a subset of chemoresistant tumors with low proliferation and luminal characteristics.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Pattern of recurrence of early breast cancer is different according to intrinsic subtype and proliferation index.

INTRODUCTION: Recurrence risk in breast cancer varies throughout the follow-up time. We examined if these changes are related to the level of expression of the proliferation pathway and intrinsic subtypes. METHODS: Expression of estrogen and progesterone receptor, Ki-67, human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR) and cytokeratin 5/6 (CK 5/6) was performed on tissue-microarrays constructed from a large and uniformly managed series of early breast cancer patients (N = 1,249). Subtype definitions by four biomarkers were as follows: luminal A (ER + and/or PR+, HER2−, Ki-67 <14), luminal B (ER + and/or PR+, HER2−, Ki-67 ≥14), HER2-enriched (any ER, any PR, HER2+, any Ki-67), triple-negative (ER−, PR−, HER2−, any Ki-67). Subtype definitions by six biomarkers were as follows: luminal A (ER + and/or PR+, HER2−, Ki-67 <14, any CK 5/6, any EGFR), luminal B (ER + and/or PR+, HER2−, Ki-67 ≥14, any CK 5/6, any EGFR), HER2-enriched (ER−, PR−, HER2+, any Ki-67, any CK 5/6, any EGFR), Luminal-HER2 (ER + and/or PR+, HER2+, any Ki-67, any CK 5/6, any EGFR), Basal-like (ER−, PR−, HER2−, any Ki-67, CK5/6+ and/or EGFR+), triple-negative nonbasal (ER−, PR−, HER2−, any Ki-67, CK 5/6−, EGFR−). Each four- or six-marker defined intrinsic subtype was divided in two groups, with Ki-67 <14% or with Ki-67 ≥14%. Recurrence hazard rate function was determined for each intrinsic subtype as a whole and according to Ki-67 value. RESULTS: Luminal A displayed a slow risk increase, reaching its maximum after three years and then remained steady. Luminal B presented most of its relapses during the first five years. HER2-enriched tumors show a peak of recurrence nearly twenty months post-surgery, with a greater risk in Ki-67 ≥14%. However a second peak occurred at 72 months but the risk magnitude was greater in Ki-67 <14%. Triple negative tumors with low proliferation rate display a smooth risk curve, but with Ki-67 ≥14% show sharp peak at nearly 18 months. CONCLUSIONS: Each intrinsic subtype has a particular pattern of relapses over time which change depending on the level of activation of the proliferation pathway assessed by Ki-67. These findings could have clinical implications both on adjuvant treatment trial design and on the recommendations concerning the surveillance of patients.