Transcriptional Shift Identifies a Set of Genes Driving Breast Cancer Chemoresistance

Background Distant recurrences after antineoplastic treatment remain a serious problem for breast cancer clinical management, which threats patients’ life. Systemic therapy is administered to eradicate cancer cells from the organism, both at the site of the primary tumor and at any other potential location. Despite this intervention, a significant proportion of breast cancer patients relapse even many years after their primary tumor has been successfully treated according to current clinical standards, evidencing the existence of a chemoresistant cell subpopulation originating from the primary tumor.Methods/Findings To identify key molecules and signaling pathways which drive breast cancer chemoresistance we performed gene expression analysis before and after anthracycline and taxane-based chemotherapy and compared the results between different histopathological response groups (good-, mid- and bad-response), established according to the Miller & Payne grading system. Two cohorts of 33 and 73 breast cancer patients receiving neoadjuvant chemotherapy were recruited for whole-genome expression analysis and validation assay, respectively. Identified genes were subjected to a bioinformatic analysis in order to ascertain the molecular function of the proteins they encode and the signaling in which they participate. High throughput technologies identified 65 gene sequences which were over-expressed in all groups (P ≤ 0·05 Bonferroni test). Notably we found that, after chemotherapy, a significant proportion of these genes were over-expressed in the good responders group, making their tumors indistinguishable from those of the bad responders in their expression profile (P ≤ 0.05 Benjamini-Hochgerg`s method).Conclusions These data identify a set of key molecular pathways selectively up-regulated in post-chemotherapy cancer cells, which may become appropriate targets for the development of future directed therapies against breast cancer.Thanks are due to the Consejería de Economia, Innovación y Ciencia (CEIC) from the Junta de Andalucía and Fondo Europeo de Desarrollo Regional (FEDER)/Fondo de Cohesión Europeo (FSE) to financial support through the Programa Operativo FEDER/FSE de Andalucía 2007-2013 and the research project CTS-5350. The authors also acknowledge financial support by the PN de I+D+i 2006-2009/ISCIII/Ministerio de Sanidad, Servicios Sociales e Igualdad (Spain) and Fondo Europeo de Desarrollo Regional (FEDER) from the European Union, through the research project PI06/90388

Population demographics and pre-chemotherapy clinical characteristics.

<p>Results are presented as n (%) of 33 patients for the whole-genome expression analysis cohort and as n (%) of 73 patients for the validation assay cohort.</p><p>Abbreviations: AJCC, American Joint Committee on Cancer; BR, bad response group; GR, good response group; Her2G, Her2-positive group; MRH, mid-response high group; MRL, mid-response low group; NA, not applicable.</p

Functional annotation and network analysis of the Chemoresitance dataset by IPA software.

<p>A) List of predicted inhibited and activated functions according the Chemoresistance dataset. B) Summary of the IPA network analysis of the Chemoresistance dataset C) Gene-expression network resulting from merging overlapping Networks 2, 3 and 4 according to IPA network analysis.</p

Genes differentially over-expressed after chemotherapy within the GR group –GR (Post-CT vs Pre-CT) comparison- and differentially repressed before chemotherapy in the GR group with respect the BR group – Pre-CT (GR vs BR) comparison.

<p>RQ _{GR (Post-CT vs Pre-CT)} describes the magnitude of change of each target gene after chemotherapy with respect its expression before chemotherapy for GR group and RQ _{Pre-CT (GR vs BR)} describes the magnitude of change of each target gene in the GR group with respect the BR group before chemotherapy. BR, bad response group; GR, good response group; Post-CT, after chemotherapy; Pre-CT, before chemotherapy; RQ, relative quantity.</p

Main results from Post-CT vs Pre-CT comparisons in the validation assay.

<p>A) Log₁₀ fold change in mRNA abundance of each differentially expressed gene after chemotherapy considering all experimental groups together (Post-CT vs Pre-CT) and each experimental group individually –GR (Post-CT vs Pre-CT), Her2G (Post-CT vs Pre-CT), MRH (Post-CT vs Pre-CT) and MRL (Post-CT vs Pre-CT)-. B) Venn diagram outlining differentially expressed genes after chemotherapy in each pathological response group with respect differentially expressed genes after chemotherapy considering all experimental groups. C) Venn diagram outlining differentially expressed genes after chemotherapy in the four pathological response groups. D) Log₁₀ fold change in mRNA abundance of genes differentially expressed after chemotherapy considering all experimental groups together (Post-CT vs Pre-CT) and GR –GR (Post-CT vs Pre-CT)-.</p

Diagram of pre-chemotherapy and post-chemotherapy comparisons and involvement of mid-response groups.

<p>A) Log₁₀ fold change in mRNA abundance of genes differentially expressed in common for Pre-CT (GR vs BR) and Pre-CT (GR vs MRL) comparisons. B) Venn diagram outlining differentially expressed genes in common between Pre-CT (GR vs BR) and Pre-CT (GR vs MRL) comparisons. C) Summary of pre-CT and post-CT comparisons.</p

Chemoresistance gene set.

<p>A) Log₁₀ fold change in mRNA abundance of genes differentially expressed before chemotherapy when comparing GR and BR groups. B) Log₁₀ fold change in mRNA abundance of genes differentially expressed for GR (Post-CT vs Pre-CT) comparison and Pre-CT (GR vs BR) comparison C) Venn diagram outlining differentially expressed genes in GR (Post-CT vs Pre-CT) and Pre-CT (GR vs BR) comparisons.</p

Experimental design and main results from genome-wide expression analysis.

<p>A) Experimental design of the discovery assay. B) Genes differentially over-expressed after chemotherapy C) Gene Ontology (GO) terms over-represented by the genes differentially over-expressed after chemotherapy at a significance level of <i>P</i><0.05. Circular representation must be read clockwise and legend must be read from left to right and top to bottom. Numbers within the figure correspond to the number of genes classified in each GO category.</p

Gene-expression regulatory networks of breast cancer chemoresistance.

<p>Pathway analysis by IPA software based on the Chemoresistance dataset and gene lists related to A) Chemoresistance, B) Survival, C) ECM invasion and remodeling, and D) Migration created using the Ingenuity Knowledge Base. The relations between the genes were inferred from the relationships known in the scientific literature using data-mining Ingenuity software. Each node represents a gene; red color denotes over-expressed genes; green color denotes down-expressed genes. The colors intensity appears according to the related expression level by fold change. Connections indicate direct regulatory interactions. Arrows are colored differently to ease the identification of the genes involved in over-represented Canonical Pathways and Biomarkers according to Ingenuity Knowledge Base.</p