A genomic approach to predict synergistic combinations for breast cancer treatment

Abstract

We leverage genomic and biochemical data to identify synergistic drug regimens for breast cancer. In order to study the mechanism of the histone deacetylase (HDAC) inhibitors valproic acid (VPA) and suberoylanilide hydroxamic acid (SAHA) in breast cancer, we generated and validated genomic profiles of drug response using a series of breast cancer cell lines sensitive to each drug. These genomic profiles were then used to model drug response in human breast tumors and show significant correlation between VPA and SAHA response profiles in multiple breast tumor datasets, highlighting their similar mechanism of action. The genes deregulated by VPA and SAHA converge on the cell cycle pathway (Bayes Factor 5.21, and 5.94, respectively, p-value 10−8.6 and 10−9, respectively). In particular, VPA and SAHA upregulate key cyclin-dependent kinase (CDK) inhibitors. In two independent datasets, cancer cells treated with CDK inhibitors have similar gene expression profile changes to the cellular response to HDAC inhibitors. Together, these results led us to hypothesize that VPA and SAHA may interact synergistically with CDK inhibitors such as PD-033299. Experiments show that HDAC and CDK inhibitors have statistically significant synergy in both breast cancer cell lines and primary 3-dimensional cultures of cells from pleural effusions of patients. Therefore, synergistic relationships between HDAC and CDK inhibitors may provide an effective combinatorial regimen for breast cancer. Importantly, these studies provide an example of how genomic analysis of drug response profiles can be used to design rational drug combinations for cancer treatment.

GENVISAGE: Rapid Identification of Discriminative and Explainable Feature Pairs for Genomic Analysis

“…For the LINCS example (Figure 8(b)), positive objects mostly 571 have elevated expression for the two reported genes (GLRX and NME7) compared to 572 the negative objects. The direction of this differential gene expression for both genes is 573 consistent with literature for vorinostat experiments [32], [33]. These above two 574 examples illustrate how visualization of significant feature pairs can be a useful way to 575 explain the separability of object sets and understand the data.…”

“…One example is (GLRX, NME7) that is 531 especially good for separating vorinostat experiments from all others. Not only are both 532 of these genes known to have increased mRNA expression in response to 533 vorinostat [32], [33], but the two genes are annotated by STRING to both be in 534 database pathways of nucleotide biosynthesis, co-express with each other in other model 535 organisms, and mentioned together often in literature abstracts. Later, in Section 3.4, 536 we will demonstrate that the positive objects and negative objects are visually 537 separated under this feature pair, as in Figure 8.…”

Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition

“…This builds on our previous work with gene expression-based signatures, which were derived using the same approach, and which showed that drug response signatures predict whether a tumor or cell line will be sensitive or resistant to a drug [49,51,52]. The rapamycin response signature contained 200 probe sets and represented 175 unique genes (See Table S4 in Additional file 5).…”

Window-of-Opportunity Study of Valproic Acid in Breast Cancer Testing a Gene Expression Biomarker

“…We previously published that inhibition of HDAC downregulates the MYC pathway in vitro. 3 In the tumor samples before and after VPA treatment in the VAST trial, we examined the gene expression patterns using gene set omic analysis (GSOA) and generally applicable gene set enrichment (GAGE). 34 , 35 By using either the C6 data sets ( P = .022 by GSOA; P = 4.47 × 10 −7 by GAGE) or the Hallmark pathway gene sets ( P = .001 by GSOA; P = 4.42 × 10 −61 by GAGE) from MSigDB, 36 genes regulated by MYC were downregulated in the post-treatment samples compared with the pretreatment samples.…”

“…Histone deacetylase (HDAC) inhibitors have shown promise in breast cancer in vitro, although this promise has not yet translated to clinical benefit. HDAC inhibitors have multiple cellular effects, including increasing the expression of tumor suppression genes, 2 increasing the expression of cell cycle regulators, 3 increasing the expression of mediators of apoptosis, 4 - 7 decreasing proteasome-mediated degradation of tumor suppressor genes, 8 decreasing oncoprotein levels via loss of hsp90-mediated protection, 9 - 11 decreasing mitotic stability, 12 and decreasing angiogenesis. 13 , 14 HDAC inhibitors potentiate the apoptotic effect of anthracyclines on breast cancer cell lines.…”