Re-interpretation of PAM50 gene expression as quantitative tumor dimensions shows utility for clinical trials: application to prognosis and response to paclitaxel in breast cancer

Abstract

Background We recently showed PAM50 gene expression data can be represented by five quantitative, orthogonal, multi-gene breast tumor traits. These novel tumor ‘dimensions’ were superior to categorical intrinsic subtypes for clustering in high-risk breast cancer pedigrees, indicating potential to represent underlying genetic susceptibilities and biological pathways. Here we explore the prognostic and predictive utility of these dimensions in a sub-study of GEICAM/9906, a Phase III randomized prospective clinical trial of paclitaxel in breast cancer. Methods Tumor dimensions, PC1–PC5, were calculated using pre-defined coefficients. Univariable and multivariable Cox proportional hazards (PH) models for disease-free survival (DFS) were used to identify associations between quantitative dimensions and prognosis or response to the addition of paclitaxel. Results were illustrated using Kaplan–Meier curves. Results Dimensions PC1 and PC5 were associated with DFS (Cox PH p = 6.7 10 −7 and p = 0.036), remaining significant after correction for standard clinical–pathological prognostic characteristics. Both dimensions were selected in the optimal multivariable model, together with nodal status and tumor size (Cox PH p = 1.4 10 −12 ). Interactions with treatment were identified for PC3 and PC4. Response to paclitaxel was restricted to tumors with low PC3 and PC4 (log-rank p = 0.0021). Women with tumors high for PC3 or PC4 showed no survival advantage. Conclusions Our proof-of-concept application of quantitative dimensions illustrated novel findings and clinical utility beyond standard clinical–pathological characteristics and categorical intrinsic subtypes for prognosis and predicting chemotherapy response. Consideration of expression data as quantitative tumor dimensions offers new potential to identify clinically important patient subsets in clinical trials and advance precision medicine. Electronic supplementary material The online version of this article (10.1007/s10549-018-05097-5) contains supplementary material, which is available to authorized users.

Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

“…Clinically-relevant stratification may be better represented using thresholds within a transcriptome framework. 23 The potential for increased power using spectra variables is illustrated by the discovery of novel associations between spectra and patient demographic risk groups with known differences in incidence (age, gender, race). Prior studies, using the UAMC 70-gene panel and a Ki67 proliferation index, were not able to identify gene expression differences in malignant cells from self-reported AA and white patients.…”

“…Clinically-relevant stratification may be better represented using thresholds within a transcriptome framework. 23…”

“…We previously used PCA to define a new framework and multi-gene expression variables for the PAM50, a targeted and standardized gene-panel for breast cancer. 1,23 With PCA we reduced the 50 genes to five dimensions using a population cohort of breast tumors. When the framework was implemented in an external dataset of tumors from high-risk breast cancer pedigrees, PCA variables as quantitative phenotypic outcomes proved superior to PAM50 subtypes for gene mapping.…”

“…1,25 Further, when implemented in a second external clinical trial dataset, PCA variables were able to predict response to paclitaxel. 24 Here, we extend the approach to the whole transcriptome.…”

<p>Hypermethylated <em>KLF9</em> Is An Independent Prognostic Factor For Favorable Outcome In Breast Cancer</p>

“…The human positive cofactor 4 ( PC4 ), initially identified as a transcriptional cofactor, exerted its oncogenic functions by directly binding to c-Myc promoters and inducing Warburg effect 14. The expression of PC4 has effects on sensitivity to paclitaxel of cancer cells and is associated with poor survival in patients with BC 15. Krüppel-like factors (KLFs) are DNA-binding transcription regulators that control essential cellular processes, such as proliferation, differentiation, migration, and maintenance of pluripotency 16–18.…”