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Hello everyone. I am JiYoon Bu. I am graduate student of Nano sentuating systems laboratory
in Bio and brain engineering department. Today’s lecture will be about the metabolism of cancer
cell. Contents are as followed. First, I will introduce
you why targeting cancer cell became important in cancer therapies.
Left figure shows five-year survival of major cancer cites and right figure shows breast
cancer survival rates. As you can see, in most cases cancer survival rates has been
increased recently. It appears to be improved. But if we look into details, unsolved problems
still exist. First, for the sites having difficulties in
early diagnosis of cancer, such as lung or pancreatic, survival rates are still low.
As you can see in the graphs, survival rates remained lower than 10% for past 30 years.
Next, for the cancers over stage 4, survival rates decrease dramatically. Therefore current
therapeutic drugs are not appropriate therapy for targeting universality of cancer nature.
Before introducing new candidate for targeting cancer, let’s see how cancer therapies have
evolved. It was found that the mutations in gene which control normal cell proliferation
can lead to cancer. The mutations that contribute to the development of cancer affect three
general class of gene. First are oncogenes. Oncogenes are genes whose presence in certain
forms and over-activity can stimulate the development of cancer. In many cases, oncogenes
arise from the mutation of proto-oncogenes. Therefore, oncogenes resemble proto-oncogenes
in that they code for the production of proteins involved in growth control. However, oncogenes
code for an altered version (or excessive quantities) of these growth-control proteins,
thereby disrupting a cell's growth-signaling pathway.
However, tumor suppressor genes are normal genes whose absence can lead to cancer. Since
tumor suppressor genes code for proteins that slow down cell growth and division, the loss
of such proteins allows a cell to grow and divide in an uncontrolled fashion.
Now for DNA repair genes, they code for proteins whose normal function is to correct errors
that arise when cells duplicate their DNA prior to cell division. Mutations in DNA repair
genes can lead to a failure in DNA repair, which in turn allows subsequent mutations
in tumor suppressor genes and proto-oncogenes to accumulate.
Therefore, previous cancer therapies focused on repairing these genes. However, it was
found that oncogenomics cannot provide answer for cancer therapy. After human genome project
was completed, many researchers looked into cancer genome. Overall, 510 tumours from 507
patients were subjected to whole exome sequencing, identifying more than 30000 somatic mutations
among 177 cancer genes. This means that empiric probability is 2 to the 500 which suggest
that it is almost impossible to identify the role of individual gene. Also, even among
tumours arising from the same tissue, they exhibit heterogeneous alterations. Different
accessibility to various nutrients, changes in signaling mechanisms, and genetic alteration
all influence heterogeneity of cancer. So it can be concluded that previous inductive
methods are not appropriate for cancer therapy. More general and universal therapeutic methods
are required for the cancer. This figure illustrates some of the many approaches
employed in developing therapeutics targeted to the known and emerging hallmarks of cancer.
In the earlier version of this illustrate, only 8 approaches are introduced. However,
2 of different approaches, which are in red box, are added. These approaches are targeting
universality of cancer nature and are based on the deductive method.
Most of scientific research is based on inductive method. In inductive method, a causing factor
must prove the result. In contrast, in deductive method, a causing factor explains the result.
If we apply it to cancer therapy, previous somatic mutation targeted therapy which seeks
and fixes abnormal gene expression is based on inductive method. However, since all the
cancer cells have different metabolism compared to normal cells, targeting cell metabolism
can be a good example of deductive method. Before going into cancer, to help your understanding
I will give one good example of how deductive methods are used in biopharmaceuticals. Sulfur
mustard is well used in chemical warfare. Autopsy of dead body found dramatic decrease
of white blood cells. They applied analogues to Hodgkin’s disease and other lymphomas.
Now let’s get back to the topic and see how deductive method can be applied in general
cancer therapy. Cancer cells have altered metabolism which is called Warburg effect
or aerobic glycolysis. By controlling metabolism it will be possible to specifically target
cancer cells. First I will briefly introduce you what Warburg
effect is. tumour, cancer cells tend to convert most of glucose to lactate, no matter how
much oxygen is available. This process is called aerobic glycolysis or Warburg effect.
In this process only about 10% of ATP is generated compared to oxidative phosphorylation. Aerobic
glycolysis is less efficient than oxidative phosphorylation for generating ATP because
for proliferating cells, it is required to generate not only ATP but also various biomasses.
Targeting cell metabolism is beneficial because of three reasons. First, metabolism may influence
cancer initiation and progression. Second, targeting metabolism could improve existing
approaches. Finally, metabolism is a proven target of successful therapies.
For targeting cancer cell metabolisms, such compounds have the potential to limit macromolecular
synthesis needed for cell growth. Alternatively there are compounds that can limit pathways
that are important for supplying nutrients to the cell and impair bioenergetics. Therefore,
approaches for targeting cancer cell metabolism include these five in black box.
Now I will give some examples of targeting cancer metabolism.
Tumors contain oxygenated and hypoxic regions, so the tumor cell population is heterogeneous.
Cancer cells use glucose under hypoxia & lactate under normoxia. Inhibiting MCT1 with α-cyano-4-hydroxycinnamate
(CHC) or siRNA in these cells induced a switch from lactate-fueled respiration to glycolysis.
This retarded tumor growth, as the hypoxic/glycolytic tumor cells died from glucose starvation,
and rendered the remaining cells sensitive to irradiation. As MCT1 was found to be expressed
by an array of primary human tumors, it was suggested that inhibition has clinical antitumor
potential. Many researchers focused on finding metabolic
enzyme that is critical in proliferation and other metabolic mechanisms in cancer cells.
Pink circles are metabolic enzymes that are currently being considered as therapeutic
targets for cancer. Another important discovery was found from
diabetic patients. It has been found that cancer incidence and mortality have decreased
in diabetic patients with breast cancer receiving metformin and neoadjuvant chemotherapy. With
this fact, it has been proved that metformin induces AMPK that inhibits cell proliferation.
Now I will introduce you one more interesting discovery and end this part. Novartis announced
that Rapamycin failed in the phase 3 clinical trial. The main reason appears to be autophagy
which supports the growth of the late stage cancers by supplying biosysnthetic intermediates
through recycling organelles under harsh conditions. Most cancer drugs trigger autophagy in cancer
that benefits late stage cancer for survival. Therefore, various experiments were conducted
and the relationship between autophagy inhibition and anti-cancer activity was proved. However,
clinical trials are not reported yet. Some key issues for targeting cancer cell
metabolism is as followed. There are challenges of directly targeting metabolic pathways because
all cells rely on the same metabolic pathways to generate ATP. Also, metabolic flux in cancer
cells is not well understood. In addition, genetic events define an ideal set of possible
targets for cancer therapy, but unfortunately many of the gene products are transcription
factors or signaling molecules that rely on protein-to-protein interaction and present
challenges to drug development. This is the end of my presentation. I recommend
you to read summary part by yourself. Thank you for your attention.