Reflection

Over the course of this service project,we thought that using grid computing was a unique idea to help researchers find out more about cancer. In our project, we lent our computing power to find drugs to help deactivate proteins associated with neuroblastoma, a common form of childhood cancer. Grid computing is an intelligent idea that uses volunteer devices and deliver information to researchers to use. It was really amazing how we contributed to cancer research without actually physically conducting the research. It was impressive to think that running a program on a computer can help further research in cancer. Also, it was simple because we didn't have to actually do anything on the computer but use it and the grid computing program just uses energy from volunteer devices to power the research. It was a rewarding feeling to know that we are contributing research to cancer without actually taking time out of our lives.

In our interview with Dr. Sirridge, we thought he was amazing to talk to. He answered our questions to the fullest extent and seemed very knowledgeable in cancer. We could tell that he loved his job. He talked a lot about his patients and how they grew as individuals. Many of them got stronger as a result of fighting cancer. We learned so much more about cancer that we hadn't known before. He related cancer to evolution by telling us that cancer has evolved very rapidly. This makes cancer very difficult to treat. But, cancer screenings and cancer treatment have improved. He also enlightened us about childhood cancer and how children's immune systems are better at fighting cancer than adults. Dr. Sirridge was a very nice person to talk to and we enjoyed interviewing him.

Overall, we liked how this project related back to evolution as we read the article on intratumor heterogeneity. We thought it was fascinating applying Darwin's postulates to cancer tumors. Natural selection can act on cancer tumors and make some tumors more resistant to drugs. It was interesting that using phylogenetic trees can map back cancer mutations and allow scientists to track cancer genetic paths. This could lead to treatments to help eliminate cancer. In conclusion, contributing research to childhood cancer was a rewarding feeling and we felt very good to know that we contributed to a great cause and gained more knowledge as a result.

World Community Grid

To help contribute to the research in childhood cancer treatment, we lent our computing power to the World Community Grid. The project's goal is to find drugs to deactivate proteins that are associated with neuroblastoma, one of the most common forms of childhood cancer. These drugs could potentially make neuroblastoma more curable when combined with chemotherapy treatment. Researchers are trying to find a drug that can bind to the protein and inactivate it. They are using a program to see if the shape of the protein and the shape of the drug will fit together enough to disable the protein. The project is expected to be completed in two years.


Below are the project statistics:

Help Fight Childhood Cancer Project Statistics

Statistics Last Updated: 2/13/15 00:06:02 (UTC) [6 hour(s) ago]

	Totals:
	Run Time (y:d:h:m:s)	55874:259:17:42:17
	Points Generated	58,340,545,661
	Results Returned	73,581,917

	Averages:
	Run Time Per Calendar Day (y:d:h:m:s)	38:204:17:17:15
	Run Time Per Result (y:d:h:m:s)	0:000:06:39:07
	Points Per Hour of Run Time	119.19
	Points Per Calendar Day	40,262,626.41
	Points Per Result	792.87
	Results Per Calendar Day	50,781.17

We also decided to lend our computing power to help identify cancer markers. The research being done on cancer was mapping genetic markers to identify various types of cancers. Markers are tissue samples of unique chemical indicators, like DNA or protein. The pattern of markers determine if the individual is more likely to develop a certain type of cancer and what type of treatment is best for the individual. Mapping cancer markers can use tissues in the lungs, ovaries, prostate, pancreas and breast. Millions of data points are analyzed from tissues of healthy humans and cancerous patients. By analyzing this, researchers can analyze patterns of different cancers and test treatments.



Below are the project statistics:

Mapping Cancer Markers Project Statistics

Statistics Last Updated: 2/13/15 00:06:02 (UTC) [6 hour(s) ago]

	Totals:
	Run Time (y:d:h:m:s)	115546:055:08:19:15
	Points Generated	169,325,214,004
	Results Returned	231,844,109

	Averages:
	Run Time Per Calendar Day (y:d:h:m:s)	249:204:07:10:17
	Run Time Per Result (y:d:h:m:s)	0:000:04:21:57
	Points Per Hour of Run Time	167.29
	Points Per Calendar Day	365,713,205.19
	Points Per Result	730.34
	Results Per Calendar Day	500,743.22

Tumor Markers: Analyzing Intratumor Heterogeneity and Branched Evolution

1. a. Name ten other taxa that share some sequence identity with this Rattus gene?

Mus musculus (House mouse)

Peromyscus maniculatas (Deer Mouse)

Mesocricetus auratus (Golden Hamster)

Cricetulus griseus (Chinese Hamster)

Nannospalax galili (Blind Mole Rat)

Ceratotherium simum (White Rhinoceros)

Saimiri boliviensis (Black-capped squirrel monkey)

Equus caballus (Horse)

Rhinopithecus roxellana (Golden snub-nose monkey)

Homo sapiens (Humans)

b. What is Rattus? In an evolutionary sense, why study the mTOR gene in this animal?

The common name for Rattus  is rat. Rats are medium-size rodents with long tails. These animals prove useful in laboratory research when studying cancer. Their biological, genetic, and even some behavioral characteristics closely resemble Humans. As you can see previously the mTOR gene that is in Rattus closely resembles that of the Homo sapiens, the query coverage is 95% similar. 

It is also easy to replicate experiments done on rats. Cancer can be replicated in rats and studies. Researchers can breed rats to have certain genetic mutations to discover the affects. 

So because rats are genetically similar and are easy to manipulate in a laboratory setting, they are therefore useful organisms to aid in studying the mTOR gene.

c. What does wild type mTOR gene do in these animals? Why is it conserved across so many disparate species?

mTOR is responsible for coding a protein known as the mechanistic target of rapamycin or mammalian target of rapamycin. It is a serine/threonine protein kinase. The wild type mTORgene will code for proteins that function to regulate cell growth and cell proliferation as well as cell motility and cell survival. It also has a key role in protein synthesis and transcription. It is known as the mammalian target of rapamycin because it is conserved across so many mammalian species. It is conserved because it is a mammalian binding protein. That is, it is an important protein across all mammal species and functions in a similar way in each species.

2. Apply Darwin’s postulates to tumor adaptation in drug-resistant clones.

Darwin’s first postulate states that there is variation among individuals of the same species. In the New England Journal of Medicine, intratumor heterogeneity was examined. It is known that intratumor heterogeneity can cause tumors to evolve and adapt to their environment even with drug-resistant clones. So, there is variation in types of tumor. This variation causes tumors to resist drugs. The tumor samples were collected from primary renal carcinomas and metastatic sites. These tumor samples were characterized through the use of immunohistochemical analysis, profiling of messenger RNA, and mutation analysis. The tumor samples also underwent DNA sequencing, exome sequencing, and chromosome analysis. Variation in tumors creates difficulty in researchers to isolate single tumors to create biopsy samples. This leads to difficulty in creating drug-resistant medication. Other evidence for tumor heterogeneity comes from creation of phylogenetic trees of tumor regions. One branch evolved into clones in the metastatic site while other diversified into primary tumor regions.

Darwin’s second postulate states that some variations are hereditary. This means that some variations can be passed down from generation to generation. Results showed that some tumors mutated and underwent branched evolutionary tumor growth. Mutations were seen in tumor suppressing genes SETD2, PTEN, and KDM5C. These genes inactivate mutations with a single tumor suggesting convergent evolution. Gene expression also varied among tumors. Allelic composition was also tested and it was observed that intratumor heterogeneity existed. So, some variations in tumors are passed down to generations. These variations cause tumors to adapt to medications and resist it.

Darwin’s third postulate is that in every generation there are more offspring produced than can survive. In the case of intratumor heterogeneity, some tumor suppressor genes like SETD2, PTEN, and KDM5C prevent tumors from forming. Therefore, only tumors that haven’t been inactivated survive.

Darwin’s fourth postulate is that natural selection operates on populations. In this study, the mutations that were detected in the tumors showed clonal evolution of the tumors. Phylogenetic trees were created to analyze evolutions of tumor regions. Branching evolution of clones at metastatic sites were shown as well as diversified branches in primary tumor regions. This suggests convergent evolution. Other mutations like SETD2 harboring three mutations of missense, R4 (which is a tumor region) carrying a splice-site mutation, and 2-bp frameshift deletion also in R4 support convergent evolution. Convergent evolution in tumors means that tumors become more and more similar. Since SETD2 trimethylates H3K36, tumors become more similar to each other. These tumors are clones that are resistant to particular drugs. Natural selection acts on these mutations to pick out which tumors survive and which don’t.

3. The authors assert that intratumor heterogeneity will influence medical decisions and personalized treatments. Why then, might it be important for an oncologist to understand evolution?

“Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection."

When a cancer cell accumulates mutations, it can continuously give the cell a growth advantage over other cells. The cell will occupy the tumor until a stronger cell takes over.  At each stage, cancer cells go through selective pressure that drive their own evolution.

Cancers can also speed reproduction of the cells. They do this by obtaining mutations in genes, which normally fix DNA damage. When cancer cells shut down the cell repair system by shutting down DNA repair, this creates mutations that cannot be fixed. These mutations create more mutations that make the cancer evolve and resist medical drugs. This makes it difficult for oncologists to design medicine or treatments to treat cancer because cancer keeps evolving and mutating into something different. This fuels the evolution of cancer. 

Oncologists should know and understand evolution because knowing the genetic path that a particular cancer followed could one day help them better treat individual patients. When oncologists know how a particular cancer has evolved, they can determine how it will evolve later in the future and analyze patterns. By determining the genetic defects responsible for a specific cancer, physicians might be able to select the therapy that will be most successful at eliminating that cancer.  In addition, cancer-causing genes that are identified can be of use to develop specific therapies to help eliminate cancer. This is done while keeping healthy cells unharmed (Evolution Of Cancer).

Link Evolution of Cancer: http://www.hhmi.org/biointeractive/evolution-cancer

4. Consider Figures 2C and 4B, explain how phylogenetics can contribute to the understanding of tumor heterogeneity and to the generation of better tumor markers.

Phylogenetics allows scientists to construct a tree that shows which mutations are shared among tumor regions and which mutations are unique to each region that branches off. By mapping this, it shows which regions are the most “evolved” from the common “ancestor”, or tumor region that holds all the mutations shared amongst all the tumor regions. This allows scientists to mark the tumors into regions by the type of mutations they hold and which they share with other regions. This allows for the visualization of which tumor regions are the most heterogeneous from other regions.

Link to the article: http://www.nejm.org/doi/full/10.1056/NEJMoa1113205#t=articleBackground