ABSTRACT
An in-depth multi-omic molecular characterisation of poly(adenosine 5'-diphosphate [ADP]-ribose) polymerase (PARP) inhibitors revealed a distinct poly-pharmacology of niraparib (Zejula®) mediated by its interaction with lanosterol synthase (LSS), which is not observed with other PARP inhibitors. Niraparib, in a similar way to the LSS inhibitor Ro-48-8071, induced activation of the 24,25-epoxysterol shunt pathway, which is a regulatory signalling branch of the cholesterol biosynthesis pathway. Interestingly, the combination of a LSS inhibitor with a PARP inhibitor that does not bind to LSS, such as olaparib, had an additive effect on killing of cancer cells to levels comparable to Niraparib as single agent. In addition, the combination of PARP inhibitors and statins, inhibitors of HMGCR, an enzyme catalysing the rate-limiting step in the mevalonate pathway, had a synergistic effect on tumor cell killing in cell lines and patient-derived ovarian tumor organoids. These observations suggest that concomitant inhibition of cholesterol biosynthesis pathway and PARP activity might result in stronger efficacy of these inhibitors against tumor types highly dependent on cholesterol metabolism.
ABSTRACT
The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.
