KLHL5 knockdown increases cellular sensitivity to anticancer drugs

Abstract

KLHL family genes are noted for their involvement in the E3 ligase ubiquitination pathway through binding with Cullin-3 (CUL3) resulting in degradation of specific binding partners. KLHLs are thus intriguing genes for cancer as they can directly influence the degradation of therapeutically relevant cell cycle regulators such as Aurora Kinase, PLK1, or CDK1. However, most KLHL family members remain understudied within the literature. This study explores the relationship of expression of KLHL member, KLHL5, with the pharmacologic effect of anti-cancer drugs. KLHL5 knockdown decreased the proliferation and viability of cancer cells and sensitized cancer cells to numerous anti-cancer drugs. Drugs related to cell cycle including Akt/PI3K/mTOR inhibitors were especially sensitized by KLHL5 knockdown. The potential of KLHL5 as a prognostic or diagnostic cancer marker was compared to other KLHLs through a pan-cancer study of The Cancer Genome Atlas (TCGA) tumor groups. While KLHL5 expression shows marginal dysregulation in cancer, other KLHLs exhibit significant dysregulation in all cancer types, and exceptionally in renal carcinomas. This study advocates for further study of KLHLs as potential alternative therapeutic targets, since while KLHL5 is a novel gene impacting anticancer drug effects, others may have a similar impact on drug effect while having greater potential as diagnostic or prognostic markers.

Keywords: KLHL; KLHL5; cell cycle; combined therapy; synergistic effects.

Figure 1. KLHL5 knockdown decreases cancer proliferation or viability

(A) Confirmation of KLHL5 knockdown efficacy using qRT-PCR. (B) Relative viability of OVCAR-8 and SN12C cells 72 hours after shRNA knockdown of KLHL5. (C) Representative imagery of KLHL5 knockdown in OVCAR-8 cells over time. Puromycin was added at day 5 to ensure selection for cells with successful transfection (D) Viability of cells over time after KLHL5 knockdown compared to scramble control. Data represents the mean of three replicates. Asterisks mark p-values from a two-tailed t-test that are < 0.05.

Figure 2. KLHL5 knockdown sensitizes to anticancer drugs

(A) Viability change for paired library screening of anticancer compounds. The difference reflects the normalized observed viability minus the expected viability. Data represents the average distance between the means of two replicates. (B) Drug sensitivity shifts with KLHL5 knockdown by ratio and difference. Observations represented with a round symbol are distinguished for those ratios of greater 1.3 or less than 0.7 (common thresholds for synergistic or antagonistic relationships). Plus symbols are used for relationships where little change in sensitivity was observed. Color is used for drugs targetting cell cycle (red) or PI3K/Akt/mTOR pathways (blue). Data represents the mean of two replicates.

Figure 3. Dose reduction by KLHL5 knockdown

(A–B) GI50 values were calculated from serial dilution curves for several anticancer compounds. In this experiment, KLHL5 knockdown alone resulted in 78.7% ± 2.2% viability. Viabilities were normalized to 100% to account for the effect of knockdown-only on viability. Data represent means ± s.d. from three replicates. (A) The dose reduction of the GI50 is represented as a fold change. (B) The viability for the combination of knockdown and drug at the GI50 concentration observed with drug treatment only.

Figure 4. KLHL expression differences in cancer

Representation of RNAseq data for fifteen TCGA tumor types (A) Heatmap of expression differences between cancer and adjacent normal tissue for KLHLs in the TCGA data. Size of each block reflects AUC from ROC curves comparing cancer vs. normal. (B) Comparison of gene expression in cancer vs. normal for specific KLHLs in TCGA tumor types which demonstrated high AUC values from ROC analysis.

Figure 5. KLHL expression associated with prognostic differences

Patients with high and low expression of each gene were compared by survival within each TCGA tumor type. Kaplan–Meier survival curves and hazard ratios were calculated for each gene in each cancer separating the patient cohort into two groups at ten percentile increments. Shown are the strongest hazard ratios for any gene-cancer type pair where a significant survival difference p < 0.05 was observed.