https://doi.org/10.3988/jcn.2022.18.1.71
https://pubmed.ncbi.nlm.nih.gov/35021279
Abstract
BACKGROUND AND PURPOSE
A multifactorial antiepileptic mechanism underlies the ketogenic diet (KD), and one of the proposed mechanisms of action is that the KD inhibits the mammalian target of rapamycin (mTOR) pathway. To test this clinically, this study aimed to determine the efficacy of the KD in patients with pathologically confirmed focal cortical dysplasia (FCD) due to genetically identifiable mTOR pathway dysregulation.
METHODS
A cohort of patients with pathologically confirmed FCD after epilepsy surgery and who were screened for the presence of germline and somatic mutations related to the mTOR pathway in peripheral blood and resected brain tissue was constructed prospectively. A retrospective review of the efficacy of the prior KD in these patients was performed.
RESULTS
Twenty-five patients with pathologically confirmed FCD and who were screened for the presence of detectable somatic mTOR pathway mutations had received a sufficient KD. Twelve of these patients (48.0%) had germline or somatic detectable mTOR pathway mutations. A response was defined as a ≥50% reduction in seizure frequency. The efficacy of the KD after 3 months of dietary therapy was superior in patients with detectable mTOR pathway mutations than in patients without detectable mTOR pathway mutations, although the difference was not statistically significant (responder rates of 58.3% vs. 38.5%,p =0.434).
CONCLUSIONS
A greater proportion of patients with mTOR pathway responded to the KD, but there was no statistically significant difference in efficacy of the KD between patients with and without detectable mTOR pathway mutations. Further study is warranted due to the smallness of the sample and the limited number of mTOR pathway genes tested in this study.
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Open Access: True
Authors: Ara Ko - Nam Suk Sim - Han Som Choi - Donghwa Yang - Se Hee Kim - Joon Soo Lee - Dong Seok Kim - Jeong Ho Lee - Heung Dong Kim - Hoon-Chul Kang -
Additional links:
Can the Mitochondrial Metabolic Theory Explain Better the Origin and Management of Cancer than Can the Somatic Mutation Theory?
by Thomas N. Seyfried 1, and Christos Chinopoulos 2 1 Department of Biology, Boston College, Chestnut Hill, MA 02467, USA 2 Department of Medical Biochemistry, Semmelweis University, 1094 Budapest, Hungary * Author to whom correspondence should be addressed. Academic Editor: Daniel Oscar Cicero Metabolites 2021, 11(9), 572; https://doi.org/10.3390/metabo11090572 Received: 11 July 2021 / Revised: 19 August 2021 / Accepted: 20 August 2021 / Published: 25 August 2021 (This article belongs to the Special Issue Is Cancer a Metabolic Disease? The Answer of Metabolomics) Download PDF Browse Figures Review Reports Citation Export
Abstract
A theory that can best explain the facts of a phenomenon is more likely to advance knowledge than a theory that is less able to explain the facts. Cancer is generally considered a genetic disease based on the somatic mutation theory (SMT) where mutations in proto-oncogenes and tumor suppressor genes cause dysregulated cell growth. Evidence is reviewed showing that the mitochondrial metabolic theory (MMT) can better account for the hallmarks of cancer than can the SMT. Proliferating cancer cells cannot survive or grow without carbons and nitrogen for the synthesis of metabolites and ATP (Adenosine Triphosphate). Glucose carbons are essential for metabolite synthesis through the glycolysis and pentose phosphate pathways while glutamine nitrogen and carbons are essential for the synthesis of nitrogen-containing metabolites and ATP through the glutaminolysis pathway. Glutamine-dependent mitochondrial substrate level phosphorylation becomes essential for ATP synthesis in cancer cells that over-express the glycolytic pyruvate kinase M2 isoform (PKM2), that have deficient OxPhos, and that can grow in either hypoxia (0.1% oxygen) or in cyanide. The simultaneous targeting of glucose and glutamine, while elevating levels of non-fermentable ketone bodies, offers a simple and parsimonious therapeutic strategy for managing most cancers.
Keywords: mutations; IDH1; glycolysis; glutaminolysis; mitochondrial substrate level phosphorylation; ketogenic metabolic therapy; metastasis; oncogenes; chimpanzees; fermentation; respiration; evolution
https://www.mdpi.com/2218-1989/11/9/572/htm
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Hello,
I am currently reading a paper which uses both germline and somatic mutations to predict the drug responds of a certain cancer cell line.
For that they use multivariant linear regression.
However, right now I have a really hard time picturing how the training data looks like.
The variable they want to predict is the drug responds (1-AUC), which should be a 1D array with values between 0 and 1.
However, how does the input array look like? For a given cell line, is it also just a 1D array with a 1 if the mutation occurred at that position or a 0 if that mutation was not observed in the cell line?
Honestly it feels kind of weird to use this kind of binary input data (1-> mutated site, 0 -> site not mutated) to predict a continuous variable. Is there something I miss?
Any help is much appreciated!
Cheers.
I understand that the eukaryotic/germ-line mutation rate is higher because there are two replication events, as opposed to 1 in bacterial binary fission. However, I don't understand why somatic mutation rate is higher if there is also a single replication event in mitosis.
Thank you
Here's the slide from my lecture:
https://preview.redd.it/vwpcouuohfv61.png?width=629&format=png&auto=webp&s=33b0fc119b398c00eaff0f1eccebd8375dff332d
Kim, S., Baldassari, S., Sim, N.S., Chipaux, M., Dorfmüller, G., Kim, D.S., Chang, W.S., Taly, V., Lee, J.H. and Baulac, S. (2021), Detection of Brain Somatic Mutations in Cerebrospinal Fluid from Refractory Epilepsy Patients. Ann Neurol.
DOI: https://doi.org/10.1002/ana.26080
URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/ana.26080
Do mutations, that are useful for evolutionary purposes of an organism, always occur in their germ cells? and does mutations in somatic cells have any effect in evolution of an organism? sorry if the questions are silly but im curios about the answers thanks
Anyone know of any good R packages to extract mutational signatures in cancer tissue? I've been using MutationalPatterns but can't figure out how to apply it hierarchically to extract every last bit of signature.
https://preview.redd.it/wlmfvlqh8vf61.png?width=827&format=png&auto=webp&s=6e44255bf76db9bde602581ff7c9bd3bb7dd7773
https://preview.redd.it/aw63vaqh8vf61.png?width=653&format=png&auto=webp&s=1e5b2e48da49f7d9b37cdf20bd3f551f0a93b5cb
