Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity

Abstract

SUMMARY The target profiles of many drugs are established early in their development and are not systematically revisited at the time of FDA approval. Thus, it is often unclear whether therapeutics with the same nominal targets but different chemical structures are functionally equivalent. In this paper we use five different phenotypic and biochemical assays to compare approved inhibitors of cyclin-dependent kinases 4/6 – collectively regarded as breakthroughs in the treatment of hormone receptor-positive breast cancer. We find that transcriptional, proteomic, and phenotypic changes induced by palbociclib, ribociclib, and abemaciclib differ significantly; abemaciclib in particular has advantageous activities partially overlapping those of alvocidib, an older polyselective CDK inhibitor. In cells and mice, abemaciclib inhibits kinases other than CDK4/6 including CDK2/Cyclin A/E – implicated in resistance to CDK4/6 inhibition – and CDK1/Cyclin B. The multi-faceted experimental and computational approaches described here therefore uncover under-appreciated differences in CDK4/6 inhibitor activities with potential importance in treating human patients.

Health-related quality of life (HRQoL) in MONARCH 2: Abemaciclib plus fulvestrant in women with HR+, HER2- advanced breast cancer (ABC) who progressed on endocrine therapy.

“…Compared with other CDK4 and 6 inhibitors, abemaciclib is 14 times more potent against cyclin D1-CDK4 than cyclin D3-CDK6 in enzymatic assays [1,4,6] and exhibits greater selectivity for CDK4 compared with CDK6 [3]. Additionally, abemaciclib has recently demonstrated advantageous activities compared with other CDK4 and 6 inhibitors, including inhibiting other kinases, CDK1-cyclin B and CDK2-cyclin A/E, implicated in CDK4 and 6-inhibition resistance [7].…”

Drug GRADE: an integrated analysis of population growth and cell death reveals drug-specific and cancer subtype-specific response profiles

“…Furthermore, our comparisons of RV and FV values over time, rather than across doses, revealed that Abemaciclib induces death only following a prolonged period of growth arrest (Supplemental Figure 4). A discontinuous biphasic responses for Abemaciclib was also recently reported to occur due to its off-target activity against CDK2 (Hafner et al, 2019).…”

“…We evaluated drug responses in U2OS cells using the STACK assay, a quantitative live cell microscopy assay that measures both live and dead cells and has equal sensitivity in quantifying RV and FV (Forcina et al, 2017). We began by investigating RV and FV responses to two drugs: camptothecin, a topoisomerase I inhibitor and potent apoptotic agent, and palbociclib, CDK4/6 inhibitor that primarily induces growth arrest without inducing any cell death (Hafner et al, 2019). As expected, camptothecin induced high levels of cell death, whereas palbociclib strongly inhibited growth of the population without To characterize the relationship between RV and FV responses, we profiled each drug using an eight-point half-log dose titration.…”

“…Precise evaluation of a cell's response to drug is a critical step in pre-clinical drug development. Failures in this process have contributed to issues with irreproducibility of phenotypes across experimental platforms, spurious associations in precision medicine, and misannotated mechanisms of drug action (Bruno et al, 2017;Chopra et al, 2019;Hafner et al, 2019;Haibe-Kains et al, 2013). Recent studies continue to reveal that we generally do not know how drugs function, even for drugs that are well-studied and precisely engineered (Lin et al, 2019).…”

“…It was unclear if drug GRADEs are stable features of a given drug, or if these would also vary for a given drug across genotypes or cancer subtypes. To explore this question, we analyzed a publicly available dataset collected by the LINCS consortium, which contained 34 drugs tested across 35 breast cancer cell lines, with the data collected in a manner that would allow both GR and FV calculations (Hafner et al, 2019). For essentially all drugs, we found striking differences in drug GRADE across the cell lines (Supplemental Figure 9).…”