Metabolic memory underlying minimal residual disease in breast cancer

Abstract

Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their nonproliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence.

Multi-omics insights into host-viral response and pathogenesis in Crimean-Congo hemorrhagic fever viruses for novel therapeutic target

“…Reporter metabolites ( Patil and Nielsen, 2005 ) were identified through R package PIANO (nperm = 500, geneset stat = reporter).The human reference genome-scale metabolic model obtained from metabolic atlas ( Robinson et al, 2020 ) was used to generate the metabolite-gene sets. Metabolites with adjusted p-values < 0.1 were chosen as significantly regulated ( Radic Shechter et al, 2021 ). Metabolic subsystems associated with significant reporter metabolites were extracted from reference metabolic model using in-house Perl scripts.…”

Unravelling metabolic cross‐feeding in a yeast–bacteria community using 13C‐based proteomics

“…The dried metabolite extracts from the bacteria‐conditioned media were derivatized to (MeOx)TMS‐derivatives through reaction with 100 μl of 20 mg/ml methoxyamine hydrochloride (Alfa Aesar, A19188) solution in pyridine (Sigma‐Aldrich, 437611) for 90 min at 40°C, followed by reaction with 200 μl N‐methyl‐trimethylsilyl‐trifluoroacetamide (MSTFA; Alfa Aesar, A13141) for 12 h at room temperature (Kanani & Klapa, 2007 ; Kanani et al , 2008 ). GC–MS analysis was performed as previously described (Radic Shechter et al , 2021 ). Briefly, a Shimadzu TQ8040 GC‐(triple quadrupole) MS system (Shimadzu Corp.) equipped with a 30 mÅ ~ 0.25 mmÅ ~ 0.25 μm ZB‐50 capillary column (7HG‐G004‐11; Phenomenex) was used.…”

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

“…4b). In the second, Shechter and colleagues 45 combined transcriptomics, MS-based lipidomics and metabolomics, and DNA methylomics measurements to better understand the mechanisms involved with tumor relapse from treatment-resistant cells (minimal residual disease, or MRD). By combining transcriptomic and metabolic data and flux modeling, not only were metabolic alterations in the MRD organoids revealed, these MRD organoids held a metabolic memory of the prior tumor state.…”