Porphyrin overdrive rewires cancer cell metabolism

All cancer cells reprogram metabolism to support aberrant growth. Here, we report that cancer cells employ and depend on imbalanced and dynamic heme metabolic pathways, to accumulate heme intermediates, i.e., porphyrins. We coined this essential metabolic rewiring ‘porphyrin overdrive’ and determined that it is cancer-essential and cancer-specific. Among the major drivers are genes encoding mid-step enzymes governing the production of heme intermediates. CRISPR/Cas9 editing to engineer leukemia cell lines with impaired heme biosynthetic steps confirmed our whole genomic data analyses that porphyrin overdrive is linked to oncogenic states and cellular differentiation. While porphyrin overdrive is absent in differentiated cells or somatic stem cells, it is present in patient-derived tumor progenitor cells, demonstrated by single cell RNAseq, and in early embryogenesis. In conclusion, we identified a dependence of cancer cells on non-homeostatic heme metabolism, and we targeted this cancer metabolic vulnerability with a novel “bait-and-kill” strategy to eradicate malignant cells. Blast cells from AML patient donors with over 60% blast expansion in marrow biopsies were isolated according to standard tissue-banking protocols, washed and resuspended at 10^6 cells/ml. Cells were processed using the 10x Genomics Chromium controller and loaded according to standard protocol of the Chromium single cell 3’ library and v3 gel bead kit to capture 2,000 cells/Chromium Chip B. Single-cell capture, cell lysis, reverse transcription, and library preparation were performed per manufacturer instructions. Sequencing was performed on Illumina NextSeq 550 targeting150,000 reads/cell. The Cell Ranger Single-Cell software Suite (10x Genomics) was used for data processing, sample demultiplexing and gene expression quantification. t-Distributed stochastic neighbor embedding (t-SNE) and k-means clustering were used to reduce the data to a two-dimensional space and identify cell populations. Once the mean expression profiles of all cells were calculated, each cell was assigned to a subpopulation by the highest Spearman’s correlation.