Abstract 1662: Understanding the sensitivity of cancer cells to myristoylation inhibitors for oncology applications
Bibliographic record
Abstract
Abstract PCLX-001 is a first-in-kind drug that targets a protein modification essential for membrane anchorage and signalling, and has been under human clinical trial evaluation for the treatment of lymphoma and solid malignancies for over a year. In human, over 600 proteoforms are modified with the fatty acid myristate by two N-myristoyltransferases: NMT1 and NMT2. These include many proto-oncogenic proteins (e.g. Src-family kinases and c-Abl) and metabolic regulators (e.g. AMPK β subunit). Thus, we validated the potential of NMTs as oncology targets on 300 cancer cell lines and uncovered its potential in hematological cancers. The multiple substrates of NMTs complicated the elucidation of a clear mechanism of action of the pan-NMT inhibitor PCLX-001. To circumvent this, we evaluated the individual roles of NMT1 and NMT2 in tumours as well as their relative contributions, including those of their substrates, to PCLX-001 sensitivity. Analysis of TCGA and CCLE databases revealed that NMT2 expression levels vary drastically (7 log2(TPM+1)) while those of NMT1 vary slightly (3 log2(TPM+1)). Hematological cancers comprised the vast majority of the lowest NMT2 expressing cells but lower NMT2 levels were also observed in solid tumour cell lines (e.g. breast & ovary). NMT2 levels were also lower in cancer versus normal corresponding tissues. We confirmed that NMT2 expression is epigenetically repressed at a CpG island found in the 5´end of the NMT2 gene thereby explaining the reduction in NMT2 expression. Since most hematological cancer cells are NMT2-deficient, we think that by targeting the remaining NMT1, PCLX-001 selectively kills these cells in a manner reminiscent of synthetic lethality, thereby sparing normal human cells with two NMTs. Therefore, identifying substrates specific to NMT1 in NMT2-deficient cancer cells became of the utmost importance to understand which NMT1 protein substrates were critical for survival. Using differential proteomics in the CRISPR/Cas9 NMT1 KO HAP1 near-haploid cell line derived from the chronic myelogenous leukemia versus WT HAP1 cells, we surprisingly found that few signaling proteins were downregulated, rather, 36 mitochondrial protein levels were decreased and 18 of these belonged to respiratory complex I. Myristoylated mitochondrial protein NDUFAF4, a major complex I assembly factor, was the most downregulated and PCLX-001 reduced its levels leading to decreased complex I activity. This seemingly ordinary observation could have transformational implications for cancer treatment since oxidative phosphorylation is essential for both cancer stem cell survival and metastasis. In addition, gene set enrichment analyses performed on RNASeq data obtained from 1200 cell lines treated with myristoylation inhibitors allowed us to derive an NMT inhibition sensitivity gene set that could be used to identify patients which would most benefit from NMT inhibition therapy. Citation Format: Erwan Beauchamp, Chistopher Cromwell, Eman Moussa, Aishwarya Iyer, Megan Yap, Rony Pain, Jay Gamma, Olivier Julien, Basil Hubbard, Luc Berthiaume. Understanding the sensitivity of cancer cells to myristoylation inhibitors for oncology applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1662.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".