Mutation-directed Neo-protein-protein Interactions in Cancer
Genetic mutations in cells are an important cause of cancer development, progression, and drug resistance. How to quickly and accurately translate massive tumor genomic information into safe and effective cancer targeted therapies in clinical practice is a scientific frontier today. At present, a large number of scientific studies have focused on how oncogenic mutations regulate the activity of proto-oncogene protein products themselves. However, whether amino acid residues formed by genetic mutations directly induce or link nascent protein interactions (neoPPIs), which in turn lead to new functions of protein networks unique to tumor cells, has not been fully elucidated.
Professor Fu Haian's team at Emory University published a paper entitled: “Systematic discovery of mutation-directed neo-protein-protein interactions in cancer” online in Cell.
In this study, they mapped the first draft neo-protein-protein interaction (neoPPI) network specific for common cancer gene mutations and found that the mutated amino acid groups could directly induce protein binding and form new tumor-specific interactions. Thus, it provides many new ideas for the study of carcinogenic mechanism of gene mutation, targeted drug discovery, and clinical translation.
Due to the rapidly changing protein interaction network in cells, how to precisely capture the changes in protein interactions resulting from single point mutations in cancer is fraught with challenges. Professor Fu Haian's laboratory has been working on the study of protein interaction networks in tumor cells for a long time. To solve the problem, the team first developed a new generation of automated protein interaction detection technology, named quantitative high-throughput differential screening platform (qHT-dS). The qHT-dS technique is based on the principle of bioluminescence resonance energy transfer generated by novel nanoluciferases to quantitatively detect direct interactions between proteins within living cells.
The unique qHT-dS technology combined with an automated robotic platform makes it possible for the system to efficiently detect protein interactions caused by cancer mutations. The team made full use of the qHT-dS automated platform and developed corresponding computer algorithms to complete hundreds of thousands of quantitative tests for protein-protein interactions over the past few years and obtained millions of protein interaction data, providing an important resource for elucidating the mechanism of cancer and developing treatments.
Using these valuable data, the team carefully explored whether single point mutations in small cancer genes alter intracellular protein interaction networks. Through a series of comprehensive means such as systems biology, bioinformatics, chemical biology, biophysics and biochemistry, the team confirmed that cancer mutations do change intracellular protein interactions, especially protein mutations create neo protein interactions. Co-first author Niu Qiankun introduced: A simple amino acid residue change can not only change the known function of the protein, such as improving enzyme activity or disrupting existing protein interactions, but also may produce new epitopes, that is, new interaction interfaces, inducing the production of new protein interactions, so that the mutant protein shows new functions different from the wild-type protein.
By examining the protein interactions of a large number of common cancer mutations, the team found that the neo protein interactions resulting from this mutation from 'none' to 'have' far exceeded the previously reported individual cases and were present in almost all common cancer mutations examined, showing a common phenomenon. Traditional textbooks suggest that the carcinogenic mechanism of cancer suppressor gene mutations is mainly due to the loss of the function of mutant proteins to inhibit carcinogenesis, like driving cars without brakes. However, neoPPI studies have shown that mutated oncostatin not only loses known interactions, but also can cause neo protein interactions, that is, switching interaction partners, thereby obtaining new protein functions and promoting tumor development.
These 'from scratch' cancer mutation-specific neo protein interactions can not only provide a new research perspective for understanding the inherent complex mechanism of cancer occurrence and development, but also provide effective clinical guidance for anticancer therapy. Take the BRAF-V600E mutation as an example. BRAF-V600E is an oncogene mutation commonly found in skin, rectal, and lung cancers. The V600E mutation leads to persistent activation of BRAF serine-threonine kinase, which activates the downstream mitogen-activated protein kinase (MAPK) signaling pathway, ultimately leading to uncontrolled proliferation of cancer cells. Numerous targeted inhibitors against BRAF-V600E have been widely used in clinical practice, such as vemurafenib. However, almost all cancer patients with BRAF-V600E mutations show primary or acquired drug resistance, which eventually leads to disease progression or relapse.
In the process of deeply mining big data on protein interactions, the team found that BRAF-V600E can induce the production of numerous mutation-specific de novo protein interactions, which affect changes in a variety of cancer-promoting signaling pathways. For example, BRAF-V600E not only attenuated known protein interactions with MEK1, but also produced mutation-specific nascent protein interactions with KEAP1.
Further molecular mechanism studies showed that BRAF-V600E/KEAP1 neoPPI activated the NRF2-reactive oxygen species signaling pathway dominated by KEAP1 protein, which resulted in the up-regulation of NRF2 downstream genes, such as elevated NAD (P) H quinone oxidoreductase-1 (NQO1) levels. In contrast, while inhibiting BRAF kinase activity, vemurafenib also disrupts the BRAF-V600E/KEAP1 neoPPI, leading to downregulation of NRF2-NQO1 signaling. In the meantime, the team discovered a series of derivatives of quinones, including Deoxynyboquinone (DNQ), by independent chemobiological means, with selective killing effects on cancer cells carrying BRAF-V600E. Interestingly, the anticancer effect of DNQ was dependent on BRAF-V600E/KEAP1 neoPPI-mediated upregulation of NRF2-NQO1 signaling.
Combined with a series of experimental data, the team developed a strategy of using DNQ followed by vemurafenib in order to produce more effective synergistic medication. The strategy to support 'first-come' sequential combination therapy with anticancer targeted drugs is currently obtained in in vitro cell models and needs to be further validated in in vivo animal models.
Represented by the BRAF-V600E/KEAP1 neoPPI, studies combining validation and transformation of molecular mechanisms unique to such tumors provide an effective idea for how to utilize a large amount of neoPPI data. The large number of neoPPIs and related signaling pathways identified in this study provides a rich resource and rare opportunity to explore potential novel anticancer drug targets.
'It is like a' butterfly effect 'in the cell—an initial small mutation that accidentally produces unpredictable changes in the overall protein interaction network, ultimately leading to cancer. The study helps understand the inextricable association in the process of 'butterfly effects' in this cancer cell and provides a new perspective to promote scientific progress from genome to clinical translation,' said Mo Xiulei, co-first author of the paper and assistant professor at Emory University.
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