bims-pimaco Biomed News
on PI3K and MAPK signalling in colorectal cancer
Issue of 2021‒01‒31
four papers selected by
Lucas B. Zeiger
Beatson Institute for Cancer Research

  1. Dis Model Mech. 2021 Jan 28. pii: dmm.048298. [Epub ahead of print]
      Activating PIK3CA mutations are known "drivers" of human cancer and developmental overgrowth syndromes. We recently demonstrated that the "hotspot" PIK3CA H1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In the present study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFβ signalling to the stemness phenotype of homozygous PIK3CA H1047R cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling strength, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFβ signalling. A significant transcriptomic signature of TGFβ pathway activation in heterozygous PIK3CA H1047R was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in homozygous PIK3CA H1047R iPSCs was reversed by pharmacological inhibition of NODAL/TGFβ signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in human pluripotent stem cells and directly links strong PI3K activation to developmental NODAL/TGFβ signalling. This work illustrates the importance of allele dosage and expression when artificial systems are used to model human genetic disease caused by activating PIK3CA mutations.
    Keywords:  PI3K; PIK3CA; Pluripotent stem cells; Stemness
  2. Cell. 2021 Jan 18. pii: S0092-8674(20)31694-9. [Epub ahead of print]
      Ras GTPase-activating protein-binding proteins 1 and 2 (G3BP1 and G3BP2, respectively) are widely recognized as core components of stress granules (SGs). We report that G3BPs reside at the cytoplasmic surface of lysosomes. They act in a non-redundant manner to anchor the tuberous sclerosis complex (TSC) protein complex to lysosomes and suppress activation of the metabolic master regulator mechanistic target of rapamycin complex 1 (mTORC1) by amino acids and insulin. Like the TSC complex, G3BP1 deficiency elicits phenotypes related to mTORC1 hyperactivity. In the context of tumors, low G3BP1 levels enhance mTORC1-driven breast cancer cell motility and correlate with adverse outcomes in patients. Furthermore, G3bp1 inhibition in zebrafish disturbs neuronal development and function, leading to white matter heterotopia and neuronal hyperactivity. Thus, G3BPs are not only core components of SGs but also a key element of lysosomal TSC-mTORC1 signaling.
    Keywords:  G3BP1; G3BP2; TSC complex; cancer; lysosome; mTORC1; metabolism; neuronal function; stress granule
  3. Aging (Albany NY). 2021 Jan 20. 12
      Emerging evidence shows that type II protein arginine methyltransferase 5 (PRMT5) serves as an oncoprotein and plays a critical role in many types of human cancer. However, the precise role and function of PRMT5 in human colorectal cancer (CRC) growth and epithelial-mesenchymal transition (EMT) are still unclear, and the related molecular mechanism and signaling axis remains largely obscure. Here, we show that PRMT5 is highly expressed in CRC cell lines and tissues. Using PRMT5 stable depletion cell lines and specific inhibitor, we discover that down-regulation of PRMT5 by shRNA or inhibition of PRMT5 activity by specific inhibitor GSK591 markedly suppresses CRC cell proliferation and cell cycle progression, which is closely associated with PRMT5 enzyme activity. Moreover, PRMT5 regulates CRC cell growth and cycle progression via activation of Akt, but not through ERK1/2, PTEN, and mTOR signaling pathway. Further study shows that PRMT5 controls EMT of CRC cells by activation of EGFR/Akt/GSK3β signaling cascades. Collectively, our results reveal that PRMT5 promotes CRC cell proliferation, cell cycle progression, and EMT via regulation of EGFR/Akt/GSK3β signaling cascades. Most importantly, our findings also suggest that PRMT5 may be a potential therapeutic target for the treatment of human colorectal cancer.
    Keywords:  Akt; EGFR; EMT; GSK3β; PRMT5
  4. PLoS Comput Biol. 2021 Jan 28. 17(1): e1007900
      The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signaling pathways, facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles. We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviors identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model's ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets. This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis.