2 Matching Annotations
  1. Jul 2018
    1. On 2014 Jan 25, Mones Abu-Asab commented:

      Have We Learned Anything from Tumor Heterogeneity?!

      Sullivan and Chandel wrote about an important topic in cancer biology namely the TCA aberrations and how cancer cells are able to circumvent these defects. However, the authors completely ignored the issue of heterogeneity in tumors and the challenges it poses when extrapolating from tissue culture model to in vivo tumors<sup>1-3</sup>. Additionally, the implications of heterogeneity for designing targeted therapies cannot be overemphasized since it has thus far proven to be the Achilles heel of targeted treatments of cancer<sup>1, 4</sup>. Zeroing in on mitochondrial metabolism for cancer therapy poses even bigger challenges than the targeting of cytoplasmic and nuclear metabolism. Intratumoral heterogeneity confers a selective advantage on a population of cells under many selective pressures targeting their proliferation and migration. Furthermore, the population of mitochondria within each cell is also heterogeneous—a phenomenon known as heteroplasmy that the cancer cell inherits by default. In cancer cells, there are not usually any normal mitochondria<sup>5</sup>. However, the damage of cancer mitochondria is also heterogeneous; each mitochondrion varies in the type of damage it harbors; this has thus far been confirmed by ultrastructural features. In this regard, mitochondria seem to be vulnerable organelles that vary in their sensitivity to insults; this fact extends beyond cancers to other pathologies as well such as age-related macular degeneration, bacterial septicemia, and adverse drug effects. Understanding the abnormal metabolic reactions within cancer cells is very important because it teaches us about the pathways diversity that can be generated in an allostatic environment; the new metabolic reactions confer the adaptive survival advantages upon cancer cells. Therefore, single-cell mitochondrial analyses from patients tumors are in order here if we want to reach an accurate understanding of cancer’s mitochondrial metabolism. Like other evolutionary events within any population, heterogeneity within a tumor arises by independent events within individual cells and their mitochondria. Thus, heterogeneities of tumor cells and their mitochondria permit resistance to treatment and survival in a persistently dynamic process. Fortunately, the tools for studying single cell models are here. Combining the microfluidic chip for single cell characterization<sup>6</sup> and Next Generation Sequencing (NGS), as well as other techniques, could supply us with needed data. If we are to target mitochondrial metabolism for cancer therapy, as Sullivan and Chandel have suggested, we need first to study the diversity of the mitochondrial population within a single cancer cell and determine the suitable universal targets that will not play “hide and seek” as the case has been with current targeted treatments<sup>1</sup>. Hopefully, this approach will bring us closer to applying a predictive paradigm of personalized medicine.

      References

      <sup>1</sup> Nathanson, D.A., et al., Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science, 2014. 343(6166): p. 72-6.

      <sup>2</sup> Gerlinger, M., et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med, 2012. 366(10): p. 883-92.

      <sup>3</sup> Horswell, S., N. Matthews, and C. Swanton, Cancer heterogeneity and "the struggle for existence": diagnostic and analytical challenges. Cancer Lett, 2013. 340(2): p. 220-6.

      <sup>4</sup> Abu-Asab, M.S., et al., Biomarkers in the age of omics: time for a systems biology approach. OMICS, 2011. 15(3): p. 105-12.

      <sup>5</sup> Davila, A.F. and P. Zamorano, Mitochondria and the evolutionary roots of cancer. Phys Biol, 2013. 10(2): p. 026008.

      <sup>6</sup> Sun, J., et al., A microfluidic platform for systems pathology: multiparameter single-cell signaling measurements of clinical brain tumor specimens. Cancer Res, 2010. 70(15): p. 6128-38.


      This comment, imported by Hypothesis from PubMed Commons, is licensed under CC BY.

  2. Feb 2018
    1. On 2014 Jan 25, Mones Abu-Asab commented:

      Have We Learned Anything from Tumor Heterogeneity?!

      Sullivan and Chandel wrote about an important topic in cancer biology namely the TCA aberrations and how cancer cells are able to circumvent these defects. However, the authors completely ignored the issue of heterogeneity in tumors and the challenges it poses when extrapolating from tissue culture model to in vivo tumors<sup>1-3</sup>. Additionally, the implications of heterogeneity for designing targeted therapies cannot be overemphasized since it has thus far proven to be the Achilles heel of targeted treatments of cancer<sup>1, 4</sup>. Zeroing in on mitochondrial metabolism for cancer therapy poses even bigger challenges than the targeting of cytoplasmic and nuclear metabolism. Intratumoral heterogeneity confers a selective advantage on a population of cells under many selective pressures targeting their proliferation and migration. Furthermore, the population of mitochondria within each cell is also heterogeneous—a phenomenon known as heteroplasmy that the cancer cell inherits by default. In cancer cells, there are not usually any normal mitochondria<sup>5</sup>. However, the damage of cancer mitochondria is also heterogeneous; each mitochondrion varies in the type of damage it harbors; this has thus far been confirmed by ultrastructural features. In this regard, mitochondria seem to be vulnerable organelles that vary in their sensitivity to insults; this fact extends beyond cancers to other pathologies as well such as age-related macular degeneration, bacterial septicemia, and adverse drug effects. Understanding the abnormal metabolic reactions within cancer cells is very important because it teaches us about the pathways diversity that can be generated in an allostatic environment; the new metabolic reactions confer the adaptive survival advantages upon cancer cells. Therefore, single-cell mitochondrial analyses from patients tumors are in order here if we want to reach an accurate understanding of cancer’s mitochondrial metabolism. Like other evolutionary events within any population, heterogeneity within a tumor arises by independent events within individual cells and their mitochondria. Thus, heterogeneities of tumor cells and their mitochondria permit resistance to treatment and survival in a persistently dynamic process. Fortunately, the tools for studying single cell models are here. Combining the microfluidic chip for single cell characterization<sup>6</sup> and Next Generation Sequencing (NGS), as well as other techniques, could supply us with needed data. If we are to target mitochondrial metabolism for cancer therapy, as Sullivan and Chandel have suggested, we need first to study the diversity of the mitochondrial population within a single cancer cell and determine the suitable universal targets that will not play “hide and seek” as the case has been with current targeted treatments<sup>1</sup>. Hopefully, this approach will bring us closer to applying a predictive paradigm of personalized medicine.

      References

      <sup>1</sup> Nathanson, D.A., et al., Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science, 2014. 343(6166): p. 72-6.

      <sup>2</sup> Gerlinger, M., et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med, 2012. 366(10): p. 883-92.

      <sup>3</sup> Horswell, S., N. Matthews, and C. Swanton, Cancer heterogeneity and "the struggle for existence": diagnostic and analytical challenges. Cancer Lett, 2013. 340(2): p. 220-6.

      <sup>4</sup> Abu-Asab, M.S., et al., Biomarkers in the age of omics: time for a systems biology approach. OMICS, 2011. 15(3): p. 105-12.

      <sup>5</sup> Davila, A.F. and P. Zamorano, Mitochondria and the evolutionary roots of cancer. Phys Biol, 2013. 10(2): p. 026008.

      <sup>6</sup> Sun, J., et al., A microfluidic platform for systems pathology: multiparameter single-cell signaling measurements of clinical brain tumor specimens. Cancer Res, 2010. 70(15): p. 6128-38.


      This comment, imported by Hypothesis from PubMed Commons, is licensed under CC BY.