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Multi-center retrospective evaluation of a RNA expression classifier to predict pathological complete response to neoadjuvant chemotherapy in breast cancer biopsies

ABSTRACT: We developed a test to predict which patients will achieve pCR to NAC, and which will have residual disease (RD). A retrospective collection of formalin-fixed, paraffin-embedded (FFPE) pretreatment biopsies from 222 multi-institutional breast cancer patients treated with NAC, including 90 TNBC patients, were processed using standard procedures. MNovel machine learning algorithms and statistical cross-validation were used to develop predictive classifiers based on RNA-seq differential gene expression analysis of patient samples. Two mRNA classifiers of 18 genes and 15 genes were sequentially applied to the total cohort, clustering patients into three distinct classes. The test accurately identified 83.75% of pCR and 86.62% of RD patients in the total population, and 92.10% of pCR and 80.77% of RD patients in the TNBC subset. The TNBC RD patients were subdivided by our classifiers, with one class showing significantly higher levels of Ki-67 expression and having significantly poorer survival rates than the other classes. Stratification of patients may allow identification of TNBC patients with the worst prognosis prior to NAC, allowing for personalized treatments with the potential to improve patient outcomes.

ORGANISM(S): Homo sapiens

PROVIDER: GSE163882 | GEO | 2021/03/01

REPOSITORIES: GEO

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Project description:PURPOSE: Non-small cell lung cancers (NSCLC) comprise multiple distinct biological groups with different prognoses. For example, patients with epithelial-like (EL) tumors have a better prognosis and exhibit greater sensitivity to inhibitors of the epidermal growth factor receptor (EGFR) pathway than patients with mesenchymal-like (ML) tumors. Here we test the hypothesis that EL NSCLCs can be distinguished from ML NSCLCs on the basis of global DNA methylation patterns. EXPERIMENTAL DESIGN: To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. RESULTS: We show that patterns of methylation divide NSCLCs into EL and ML subsets as defined by gene expression and that these signatures are similarly correlated in NSCLC cell lines and tumors. We identify multiple DMRs, including ERBB2 and ZEB2, whose methylation status is strongly associated with an epithelial phenotype in NSCLC cell lines, surgically resected tumors, and formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. CONCLUSIONS: Our data demonstrate that patterns of DNA methylation can divide NSCLCs into two phenotypically distinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used for predictive biomarker discovery and development. To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy.

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Multi-center retrospective evaluation of a RNA expression classifier to predict pathological complete response to neoadjuvant chemotherapy in breast cancer biopsies

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