Cancer therapies often fail to cure patients because a proportion of tumor cells withstand the toxic effects of chemotherapy. How surviving cancer cells recover from sublethal drug-induced stress is not known, but given that cellular resources are finite, stress resolution may come at the expense of less essential systems. Here, we studied the global cellular events of stress buildup and resolution in the bone marrow cancer, multiple myeloma, after proteasome inhibition, a commonly used therapeutic approach. Using a temporal multiomics approach, we delineate the unexpectedly complex and protracted changes myeloma cells undergo during stress resolution and demonstrate that recovering cells are more vulnerable to specific insults than acutely stressed cells. Thus, the findings may provide avenues for optimizing cancer therapies.
Cancer cells can survive chemotherapy-induced stress, but how they recover from it is not known. Using a temporal multiomics approach, we delineate the global mechanisms of proteotoxic stress resolution in multiple myeloma cells recovering from proteasome inhibition. Our observations define layered and protracted programs for stress resolution that encompass extensive changes across the transcriptome, proteome, and metabolome.
Cellular recovery from proteasome inhibition involved protracted and dynamic changes of glucose and lipid metabolism and suppression of mitochondrial function. We demonstrate that recovering cells are more vulnerable to specific insults than acutely stressed cells and identify the general control nonderepressable 2 (GCN2)-driven cellular response to amino acid scarcity as a key recovery-associated vulnerability. Using a transcriptome analysis pipeline, we further show that GCN2 is also a stress-independent bona fide target in transcriptional signature-defined subsets of solid cancers that share molecular characteristics.
Thus, identifying cellular trade-offs tied to the resolution of chemotherapy-induced stress in tumor cells may reveal new therapeutic targets and routes for cancer therapy optimization.