Ki-67 as a controversial predictive and prognostic marker in breast cancer patients treated with neoadjuvant chemotherapy

BACKGROUND: Studies have partly demonstrated the clinical validity of Ki-67 as a predictive marker in the neoadjuvant setting, but the question of the best cut-off points as well as the importance of this marker as a prognostic factor in partial responder/non-responder groups remains uncertain. METHODS: One hundred twenty patients diagnosed with invasive breast cancer and treated with neoadjuvant chemotherapy (NAC) between 2002 and 2013 were retrospectively recruited to this study. The optimal cut-off value for Ki-67 labeling index (LI) to discriminate response to treatment was assessed by receiver operating characteristic (ROC) curve analysis. Kaplan-Meier curve estimation, log-rank test and cox regression analysis were carried out to reveal the association between Ki-67 categories and survival (DMFS = Distant metastases-free survival, OS = Overall survival). RESULTS: Twenty three out of 120 patients (19.2%) achieved pathologic complete remission (pCR), whereas partial remission (pPR) and no response (pNR) to neoadjuvant chemotherapy (NAC) was detected in 60.8% and 20.0%, respectively. The distribution of subtypes showed a significant difference in pathological response groups (p < 0.001). Most of the TNBC cases were represented in pCR group. The most relevant cut-off value for the Ki-67 distinguishing pCR from pNR cases was 20% (p = 0.002). No significant threshold for Ki-67 was found regarding DMFS (p = 0.208). Considering OS, the optimal cut-off point occurred at 15% Ki-67 (p = 0.006). The pPR group represented a significant Ki-67 threshold at 30% regarding OS (p = 0.001). Ki-67 and pPR subgroups were not significantly associated (p = 0.653). For prognosis prediction, Ki-67 at 30% cut-off value (p = 0.040) furthermore subtype (p = 0.037) as well as pathological response (p = 0.044) were suitable to separate patients into good and unfavorable prognosis cohorts regarding OS. However, in multivariate analyses, only Ki-67 at 30% threshold (p = 0.029), and subtype (p = 0.008) were independently linked to OS. CONCLUSIONS: NAC is more efficient in tumors with at least 20% Ki-67 LI. Both Ki-67 LI and subtype showed a significant association with pathological response. Ki-67 LI represented independent prognostic potential to OS in our neoadjuvant patient cohort, while pathological response did not. Additionally, our data also suggest that if a tumor is non-responder to NAC, increased Ki-67 is a poor prognostic marker

Splice Expression Variation Analysis (SEVA) for inter-tumor heterogeneity of gene isoform usage in cancer

Motivation:Current bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches. Results:We introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g. tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVA's performance against EBSeq, DiffSplice and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVA in identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery. These results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data. Availability and implementation:SEVA is implemented in the R/Bioconductor package GSReg. Contact:[email protected] or [email protected] or [email protected]. Supplementary information:Supplementary data are available at Bioinformatics online