The Cancer Genome Atlas Project (TCGA): Understanding Glioblastoma

TCGAIn 2003, Cold Spring Harbor Laboratory (CSHL) and researchers around the world celebrated the 50th Anniversary of the discovery of the structure of DNA by Jim Watson and Francis Crick.  I was a graduate student in the Watson School of Biological Science at CSHL, named after James Watson who was the chancellor of the CSHL, and in 2003, I participated in (and planned!) some of the 50th anniversary events. Coinciding with this celebration was a meeting about DNA that brought world-renowned scientists and Nobel Prize winners from around the world to CSHL to celebrate how much had been accomplished in 50 years (including sequencing the human genome) and to look to the future for what could be done next. That meeting was the first time I had heard about the Cancer Genome Atlas Project. At this point, the TCGA (as the project was affectionately called) was just a pipe dream – a proposal by the National Cancer Institute and the National Human Genome Research Institute (two institutes in the National Institutes of Health – the NIH).  The idea was to use DNA sequencing and other techniques to understand different types of cancer at the genome level. The goal was to see what changes are happening in these cancer cells that might be exploited to detect or treat these cancers.  I remember that there was a heated debate about whether or not this idea would work. I was actually firmly against it, but now with the luxury of hindsight, the scientific advances of the TCGA seem to be clearly worth the time and cost.

The first part of the TCGA started in 2006 as a pilot project to study glioblastoma multiforme, lung, and ovarian cancer. In 2009, the project was expanded, and in the end, the TCGA consortium studied over 33 cancer types (including 10 rare cancers).  All of the data that was made publically available so that any results could be used by any scientist to better understand these diseases. To accomplish this goal, the TCGA created a network of institutions to provide the tissue for over 11,000 tumor and normal samples (from biobanks including the one that I currently manage).  These samples were analyzed using techniques like Next Generation Sequencing and researchers used heavy-duty computing power to put all of the data together. So what did they find? This data has contributed to hundreds of publications, but the one I’m going to talk about today is the results from the analysis of the glioblastoma multiforme tumors.

Title: Comprehensive genomic characterization defines human glioblastoma genes and core pathways published in Nature in October 2008.

Authors: The Cancer Genome Atlas Network

gbmBackground: Glioblastoma is a fast-growing, high grade, malignant brain tumor​ that is the most common brain tumor found in adults.  The most common treatments are surgery​, radiation therapy​, and/or chemotherapy (temozolomide​). Researchers are also testing new treatments such as NovoTFF, but these have not yet been approved for regular use. However, even with these treatments the median survival for someone diagnosed with glioblastoma is only ~15 months.  At the time that this study was published, little was known about the genetic cause of glioblastoma – a small handful of mutations were known, but nothing comprehensive. Because of the poor prognosis and lack of understanding of this disease, the TGCA targeting it for a full molecular analysis.

Methods: The TCGA requested tissue samples from glioblastoma patients from biobanks around the country. They received 206 samples that were of good enough quality to use for these experiments.  143 of these also had matching blood samples.  Because the DNA changes in the tumor only happen in the tumor, the blood is a good source of normal, unchanged DNA to compare the tumor DNA to. To these samples, the study sites did a number of different analyses:

  • They looked at the number of copies of each piece of DNA. This is called DNA copy number, and copy number is often changed in tumor cells (see more about what changes in the number of chromosomes can do here)
  • They looked at gene expression.  The genes are what makes proteins, which do all of the stuff in your body.  If you have a mutation in a gene, it could change the protein so that it contributes to the development of cancer.
  • They also looked at DNA methylation.  Methylation is a mark that can be added to the DNA telling the cell to turn off that part of DNA.  If there is methylation on gene that normally stops a cell from growing like crazy, that methylation would turn that gene off and the cell could grow out of control.
  • In a subset of samples, they performed next generation sequencing to know the full sequence of the tumor genomes.

Results and Discussion: From all of this data, the researchers found  quite a bit.

  • Copy number results: There were many differences in copy number including deletions of genes important for slowing growth and duplications of genes the told the cell to grow more.
  • Gene expression results: Genes that are responsible for cell growth, like the gene EGFR, were expressed more in glioblastoma tumor cells.  This has proven to be an interesting result because there are drugs that inhibit EGFR.  These drugs are currently being tested in the clinic to see if this EGFR drug is a good treatment for patients with a glioblastoma that expresses a lot of EGFR.
  • Methylation results: They found a gene called MGMT that is responsible for fixing mutated DNA was highly methylated.  This mutation was actually beneficial to patients because it made them more sensitive to the most common chemotherapy, temozolomide.  However, this result also suggests that losing MGMT methylation may cause treatment resistance.
  • Sequencing results: From all of the sequencing they created over 97 million base pairs of data! They found mutations in over 200 human genes. From statistical analysis, seven genes had significant mutations including a gene called p53, which usually prevents damaged cells from growing, but when mutated the cell can more easily grow out of control
glioblastoma_pathways

This is the summary figure from this paper that shows the three main pathways changed in glioblastoma and the evidence they found to support these genes’ involvement. Each colored circle or rectangle represents a different gene. Blue means that the gene is deleted and red means that there is more of that gene in glioblastoma tumors.

Bringing all of this data together, scientists found three main pathways that lead to cancer in glioblastoma (see the image above for these pathways).  These pathways provide targets for treatment by targeting drugs to specific genes in these pathways. Scientists also identified a new glioblastoma subtype that has improved survival​. This is great for patients who find out that they have this subtype!  Changes in the methylation also show how patients could acquire resistance to chemotherapy. Although chemotherapy resistance is bad for the patient, understanding how it happens allows scientists to develop drugs to overcome the resistance based on these specific pathways.

Although this is where the story ended for this article, the TCGA data has been used for many more studies about glioblastoma.  For example, in 2010, TCGA data was used to identify four different subtypes of glioblastoma: Proneural, Neural, Classical, and Mesenchymal that have helped to tailor the type of treatments use for each group. For example proneural glioblastoma does not benefit from aggressive treatment, whereas other subtypes do. Other researchers are using the information about glioblastoma mutations to develop new treatments for the disease

To learn more about the Cancer Genome Atlas Project, check out this article “The Cancer Genome Atlas: an immeasurable source of knowledge” in the journal or watch this video about the clinical implications of the TCGA finding about glioblastoma

How do we know the genome sequence?

Imagine someone asked you to explain how a car works. Even if you knew nothing about cars, you could take the car apart piece by piece, inspect each piece in your hand and probably draw a pretty good diagram of how a car is put together.  You wouldn’t understand how it works, but you’d have a good start in trying to figure it out.

Now what if someone asked you to figure out how the genome works? You know it’s made of DNA, but it’s the ORDER of the nucleotides that helps to understand how the genome works (remember genes and proteins?). All the time in the news, you hear about a scientist or a doctor who looked at the sequence of the human genome and from that information could conclude possible causes of the disease or a way to target the treatment. DNA sequencing forms a cornerstone of personalized medicine, but how does this sequencing actually work? How do you take apart the genome like a car so you can start to understand how it works?

As a quick reminder – DNA is made out of four different nucleotides, A, T, G, and C, that are lined up in a specific order to make up the 3 billion nucleotides in the human genome.  DNA looks like a ladder where the rungs are made up of bases that stick to one another: A always sticking to T and G always sticking to C.  Since A always sticks to T and G always sticks to C, if you know the sequence that makes up one side of the ladder, you also know the sequence of the other side.

DNA_ladder

The first commonly used sequencing is called Sanger sequencing, named after Frederick Sanger who invented the method in 1977. Sanger sequencing takes advantage of this DNA ladder – this method breaks it in half and using glowing (fluorescent) nucleotides of different colors, this technique rebuilds the other side of the ladder one nucleotide at a time. A detector that can detect the different fluorescent colors creates an image of these colors that a program then “reads” to give the researcher the sequence of the nucleotides (see image below to see what this looks like).  These sequences are just long strings of As, Ts, Gs, and Cs that the researcher can analyze to better understand the sequence for their experiments.

sanger_sequencing

This was a revolutionary technique, and when the Human Genome Project started in 1990, Sanger Sequencing was the only technique available to scientists. However, this method can only sequence about 700 nucleotides at one time and even the most advanced machine in 2015 only runs 96 sequencing reactions at one time.  In 1990, using Sanger sequencing, scientists planned on running lots and lots of sequencing reaction at one time, and they expected this effort would take 15 years and cost $3 Billion. The first draft of the Human Genome was published in 2000 through a public effort and a parallel private effort by Celera Genomics that cost only $300 million and took only 3 years once they jumped into the ring at 2007 (why was it cheaper and fast, you ask? They developed a fast “shotgun” method and analysis techniques that sped up the process).

As you may imagine, for personalized medicine where sequencing a huge part of the genome may be necessary for every man, woman, and child, 3-15 years and $300M-$3B dollars per sequence is not feasible. Fortunately, the genome sequencing technology advanced in the 1990s to what’s called Next Generation Sequencing. There are a lot of different versions of the Next Gen Sequencing (often abbreviated as NGS), but basically all of them run thousands and thousands of sequencing reactions all at the same time. Instead of reading 700 nucleotides at one time in Sanger sequencing, NGS methods can read up to 3 billion bases in one experiments.

How does this work? Short DNA sequences are stuck to a slide and replicated over and over. This makes dots of the exact same sequence and thousands and thousands of these dots are created on one slide. Then, like Sanger sequencing, glowing nucleotides build the other side of the DNA ladder one nucleotide at a time. In this case though, the surface looks like a confetti of dots that have to be read by a sophisticated computer program to determine the millions of sequencing.

NGS

So what has this new technology allowed scientists to do? It has decreased the cost of sequencing a genome to around $1000. It has also allowed researchers to sequence large numbers of genomes to better understand the genetic differences between people, to better understand other species genomes (including the bacteria that colonize us or the viruses that infect us), and to help determineexomee the genetic changes in tumors to better detect and treat these diseases. Next Generation Sequencing allows doctors to actually use genome sequencing in the
clinic. A version of genome sequencing has been developed called “exome sequencing” that only sequences the genes.  Since genes only make up about 1-2% of the genome, NGS of the exome takes less time and money but provides lots of information about what some argue is the most important part of the genome – the part that encodes proteins.  Much of the promise of personalized medicine can be found through this revolutionary DNA sequencing technique – and with the cost getting lower and lower, there may be a day soon when you too will have your genome sequence as part of your medical record.


For more information about the history of Sequencing, check out this article “DNA Sequencing: From Bench to Bedside and Beyond” in the journal Nucleic Acids Research.

Here is an amusing short video about how Next Generation Sequencing works described by the most interesting pathologist in the world.

Book Club: The Immortal Life of Henrietta Lacks

The_Immortal_Life_Henrietta_Lacks

Thanks to Wikipedia for the image

In 2002, one of my first set of experiments in graduate school was treating the prostate cancer cell line (named DU145) with a chemotherapeutic drug and comparing how these cells responded to how HeLa cells responded to this chemotherapy. Little did I realize at the time that 51 years earlier, these cells were removed from a poor black woman named Henrietta Lacks without her even knowing. She subsequently died, but her cells have lived on for over 60 years being used by researchers around the world to better understand cancer. It’s estimated that over 60,000 research papers have used HeLa cells (I just searched the literature for “HeLa” and found over 83,000 results). HeLa cells helped to develop the polio vaccine (HeLa cells were easily infected by polio, and therefore ideal to test the vaccine).  In 2013, HeLa cells were the first cell line to have its genome fully sequenced (the genome of HeLa cells is a hot mess with more than 5 copies of some chromosomes – likely caused by the number of times that the cells have divided over the past 60 years).  In fact, HeLa cells are so popular and so widespread that they have been found to be contaminating a large percentage of the OTHER cell lines that researchers are using (for example, the bladder cancer cell line KU7 was found to exclusively be HeLa cells in one research lab).

With all of this activity surrounding HeLa cells, you may think that she is famous and her family has received recognition from her donation.  However, as so artfully described in Rebecca Skloot’s “The Immortal Life of Henrietta Lacks” these cells were taken and grown without her consent and her family had no idea that Henrietta was was “immortal” through her cells growing in las around the world. Skloot describes the moral and ethical issues surrounding how these cells were obtained while weaving a story about Henrietta Lacks and her family’s life and discovery of HeLa cell’s fascinating rise to prominence.  Although the story is interesting to a scientist and a biobanker, the book is definitely written in such a way that the public will completely understand the scientific significance.

The best week ever – Nobel Prize week!

nobelLast week was one of my favorite weeks of the year – Nobel Prize week. Some people wait for the Emmys or the Superbowl or Christmas.  I wait for the Nobels. To be fair, I care most about the science Nobels – Physics, Chemistry and Physiology or Medicine, though one cannot ignore the amazing accomplishments of the winners in Literature, Peace, and Economics. Every year, I try to guess who may win – though Thomson Reuters and others are far more scientific about their guesses than I am.  And each morning of Nobel Week, first thing I do is check the news on my phone to see who won, what for and whether or not I know them (this year – no).  Let’s talk about who won the science awards this year and what amazing discoveries they won for.

Physiology or Medicine. A lot of attention has been given to infectious diseases this year with the huge Ebola outbreak in western Africa.  Although tens of thousands of people were infected and died, other infectious diseases are even more widespread and affect millions of people a year. Malaria is a parasitic disease transmitted by mosquitoes that 3.4 billion people are at risk of contracting and that kills over 450,000 people per year. Parasitic worms are also rampant in the third world, can affect up to a third of the human population, and cause such diseases as river blindness.  This is the second most common cause of blindness by infection, with 17 million people infected and 0.8 million blinded by the disease.  The three winners of the Nobel for Physiology or Medicine this year discovered novel treatments for these parasitic diseases.  William C. Campbell and Satoshi Ōmura for roundworm parasites and Youyou Tu for malaria, saving hundreds of thousands of lives each year.

Chemistry. This is by far my favorite award this year because it is directly related to how humans safeguard their DNA, but also why when this safeguard does work, that we get cancer.  Awarded to Tomas Lindahl (UK), Paul Modrich (USA), and Aziz Sancar (USA), this Nobel celebrates the discovery of the mechanism of DNA repair. I’ve discussed in this blog how UV and other environmental factors can cause mutations in DNA, and with too many mutations, people can develop cancer or other diseases.  However, the genome doesn’t mutate out of control because cell contain the machinery that is always working to fix any DNA damage using DNA repair mechanisms. It’s like a NASCAR race, where the car is always being monitored, wheels replaced, and minor problems fixed by the pit crew.  DNA repair is the genome’s pit crew and these three scientists figured out three different ways that the cells monitors and fixes the DNA depending on the type of damage that has occurred.

Physics. We all know I’m not a physicist, but I’ll try my best. The Physics Nobel was awarded to Takaaki Kajita of Japan and Arthur B. McDonald of Canada for discovering that neutrinos have mass.  You may remember from high school that atoms are made up of protons, neutrons and electrons. However, scientists now know that there are even tinier parts of an atom called subatomic particles that include the neutrino, fermions and bosons (and others). Other than photons, which are the particles of light, neutrinos are the most numerous subatomic particle in the entire cosmos, so understanding how they work is incredibly important.  These researchers found that the three different types of neutrinos can convert from one to the other. It was predicted by the Standard Model of Physics that these neutrinos wouldn’t have mass, but these scientists also proved that they did. Their studies help to better understand matter and the universe. My favorite reporting of this award was by NPR.

So until next year Nobel Prizes.  I will be waiting with baited breath!

 

What is Personalized Medicine?

bullseyeA few years ago I was asked to teach a course to adults at the ASU Osher School of Lifelong Learning about the Emerging Era of Personalized Medicine. This was exciting because it would give me the opportunity to help empower these adults to better understand their health, the science behind what make them sick, and what scientists and doctors are doing to cure them.  This was also a challenging course to develop because only a few years ago personalized medicine wasn’t the common buzzword like it is today. In fact, in early 2014, the Personalized Medicine Coalition contracted a research survey that found that 6 in 10 of people surveyed hadn’t heard of the term “personalized medicine” (see all results of the survey here). Despite the public being unaware of this huge advance, in the past few years, scientists and doctors continue to evolve this concept and medicine isn’t just “personalized” but now it can also be described as “precision,” predictive,” “individualized,” “stratified,” “evidence-based,” “genomic” and much, much more.

So what is new about this type of medicine?  Of course since the days of Hippocrates, doctors have provided care to patients that take their “personalized” needs in mind. Based on the patient’s symptoms and their experiences, the doctor provides treatment. But what if two patients have the same symptoms but different underlying diseases?  A fever and a headache could be the flu or malaria. Or two people could have the same disease, like breast cancer, but the underlying genetic changes are different so that the cancer should be treated differently for each patient.

The current concept of personalized/precision medicine uses each person’s individual traits (genetic, proteomic, metabolomic, all the -omics) and harnesses our molecular understanding of disease for the prevention, diagnosis, and treatment of disease.

personalized-med2The ultimate goal of personalized medicine is to improve patient health and disease outcomes. The graph above shows how better understanding the genetic and molecular causes of disease can improve health at all phases of disease progression.

  1. Knowing the risk factors that cause of disease (either environmental, like smoking, or genetic, like the BRCA gene mutation) can help to prevent disease before it starts by eliminating the risk factors or providing additional screening to catch the disease early.
  2. Biomarkers that detect disease before major symptoms can be used to treat the disease early, which usually has a better outcome than treating a disease that has progressed further (think stage 1 versus stage 4 metastatic cancer).
  3. Once a disease has been diagnosed, the molecular understanding of the disease can help determine what treatment the patient should receive (see below for an example).
  4. Biomarkers can also be used to predict whether the disease will progress slowly or quickly or whether or not a selected treatment is working.

For all aspects of personalized medicine, there lies the promise to make an enormous impact both on public health but also on decreasing the cost of healthcare.

breast_cancerLet’s use breast cancer as an example of how personalized medicine plays out in real life, right now. For breast cancer detection, breast self-exams and mammograms are typically used.  With personalized medicine, we now have an understanding of one of the genetic risk factors of breast cancer – mutations in the BRCA genes.  Patients at higher risk for developing breast cancer because of these mutations can be monitored more closely or preventative action can be taken. In the past, breast cancer treatment focused on treated with non-specific chemotherapy and surgery. Although both of these treatments are still of value, now doctors also test for the presence of certain breast cancer genes like Her2.  If Her2 is present in breast cancer cell, the drug Herceptin that specifically targets this Her2 gene can be used to specifically kill those cancer cells. If Her2 isn’t present, this drug isn’t effective, causes negative side effects and wastes time and money when a more effective treatment could be used.  Once breast cancer is diagnosed, a patient would be interested in knowing how quickly their cancer will progress. This used to be primarily based on the stage of the cancer, where stage 4 cancers have spread to other locations in the body so the prognosis isn’t great. Based on molecular markers, scientists have now created panels of biomarkers (Oncotype DX and MammaPrint) that predict breast cancer recurrence after treatment.

These personalized medicine-based tests and drugs are incredible. However, this is a field that both holds considerable promise and requires lots of work to be done.  For every incredible targeted therapy developed, there are patients that are still waiting for the treatment for their disease or the genetic variant of their disease.  In future posts, I’ll talk a lot about both the promise and the pitfalls of personalized medicine.

If you want to learn more about personalized medicine, check out this YouTube video with a cartoon comparing treatment with and without the concept of personalized medicine.

Journal Club: The Microbiome Autism Connection

As I’ve mentioned in other posts, scientists have to read about and understand the current scientific literature. Lots of the time this is done alone, at your desk in the office or the lab, for hours and hours so that you can really understand whatever topic it is that you’re studying. But one of my favorite ways to share scientific papers is through a weekly meeting with the whole lab called a Journal Club. Although my husband laughs about this kind of nerdy science “club” (akin to his amusement about scientific societies), it’s a great way to discuss a particular topic and dive deep into a discussion about how the researchers got their results and came to their conclusions. This is the first of many Journal Clubs where we will do an abbreviated version of what we would discuss in a typical journal club in the lab. 

Paper TItle:  Reduced Incidence of Prevotella and Other Fermenters in Intestinal Microflora of Autistic Children

Authors: Dae-Wook Kang , Jin Gyoon Park ,Zehra Esra Ilhan, Garrick Wallstrom, Joshua LaBaer, James B. Adams, Rosa Krajmalnik-Brown
Full disclosure, Dr. LaBaer is the Director of the center I previously worked in at the Biodesign Institute at ASU. Drs. Park and Wallstrom worked in offices down the hall from me and Dr. Krajmalnik-Brown was in another center at the Biodesign Institute.

Journal: PLOS One (PLOS stands for “Public Library of Science”). In case you want to read the whole article, it can be downloaded (for free) here

Background/Introduction: Before this paper was published, scientists knew that many children with autism also had gastrointestinal (GI) issues suggesting that there may be a connection between the two. There have been some studies looking at antibiotic treatment (which could change the gut microbiome) before 3 years of age and how this might be connected with autism.  There have also been studies connecting the gut microbiome and the brain. So there was evidence that the gut microbiome and autism may be related in some way.

When this paper was written, scientists also knew about the microbiome and how changes in the bacteria (all 1,000,000,000,000,000 of them) that are in the gut are found in patients with many different diseases – from C. diff infection to obesity to depression.

Goal of this paper: Look closely at the changes in the gut microbiome of children with autism to better understand how these two might be related.

Methods/what did they do?: Bacteria in fecal samples is considered representative of bacteria in the gut microbiome. Therefore, the researchers collected fecal samples from children both with (20 children) and without (20 children) autism.  The samples from patients without autism were used as a “control” to compare to the autistic samples. The researchers also asked the children (or their parents) questions to help determine the level of GI issues, the severity of their autism, and their environmental factors like their diet. The researchers isolated bacterial DNA from each of these fecal samples and then sequenced the DNA to determine what types of bacteria are in the gut.

autism microbiome diversity

A figure from the paper comparing the phylogenetic diversity (PD) of the bacteria in autistic versus non-autistic children. you can see that the red boxes (for autistic children) are lower than the blue boxes (for non-autistic children) indicating a lower microbiome diversity.

Results: Through sequence analysis and other statistical methods, the authors found that children who did not have autism have a more diverse microbiome compared to autistic children.  If there is higher diversity, it means that the gut contains more different types of bacteria, and lower diversity means a smaller variety of bacteria in the gut. They also found that in the autistic patients with a greater diversity in their microbiome, their autism was generally less severe. They also did not find any correlation between age, gender or diet with these microbiome changes.

The scientists also looked at what specific genus and species of bacteria were more represented in non-autistic versus autistic children. Specifically the bacteria from genus Veillonellacaea, Provetella, and Coprococcus  are less abundant in autistic children.

Discussion/Significance: What does this all mean?  The researchers did find a correlation between decreased gut microbiome diversity and autism. It should be clarified that just because GI problems are often found in autistic children and the severity of the GI issues correlates with the severity of autism, this does not necessary mean that GI issues cause autism or vice versa.  That still needs to be determined. Also because the diversity of bacteria in autistic children is low, it is not clear if this is a cause of autism or an effect of a child having autism.  However, this paper does provide a “stepping stone” to better understand what is happening in the gut of autistic children and may help define a target for diagnosing autism (by looking at the decreased diversity in the gut as a diagnostic test) or treatment (perhaps through fecal transplant).

What has been done since? This paper was published in 2013.  So what has changed since the paper was published?  Do we know whether or not changes in the gut microbiome cause autism or not?  Unfortunately, this is still unclear.  However, if these microbiome changes are a cause of the neurological changes in autism, then one would want to do a clinical trial to test what happens to autism symptoms when the microbiome has been altered.  This could be done in a number ways including diet modulations, prebiotics, probiotics, synbiotics, postbiotics, antibiotics, fecal transplantation, and activated charcoal.  Researchers have started this process by holding a meeting that included patients and their families to figure out how this type of trial could be designed (for more details, check out this journal article).

For more information about the microbiome/autism connection, check out Autism Speaks. To read more Journal Clubs, visit the archives here.

Can your body contain DNA that isn’t yours? Yes! Chimerism

There was a House episode (Season 3 Episode 2 Cane an Abel) about a young boy who was convinced that he was being probed by aliens.  Though a series of very House-like (aka “unrealistic”) medical twists and turns, House’s team finds cells clumped together in different parts of this boy’s body, including his brain, that are functioning abnormally and causing his various systems.  From sequencing the DNA from those cells, House’s team finds that those cells have different DNA than the boy’s.  In the fictional world of House, the doctors were able to quickly create a probe for the foreign DNA, find all the cells that were different and remove them (House does brain surgery!!) also removing the symptoms including the alien hallucinations. This episode is so wildly out of the realm of current medical ability and practice – so much so that I participated in a “Science Fiction TV Dinner”  all about the science and “science” found in this episode. You can watch highlight from this discussion, which included Dr. Kenneth Ramos from University of Arizona Center for Precision Medicine, below or listen to the whole podcast here.

Science Fiction TV Dinner: House M.D. from Science & the Imagination on Vimeo.

However, what I found most interesting about this episode was the fact that the boy had TWO DIFFERENT genomes in his body. Can this actually happen in real life?  If so, how??

chimera

“Chimera d’arezzo, fi, 04” by I, Sailko. Licensed under CC BY-SA 3.0 via Wikimedia Commons

The answer is yes, and it’s called a chimera or chimerism. In mythology, a chimera is a terrifying hybrid animal that’s a lion, with a goat head coming out of it’s back and a tail with a snakes head on the end. It may be clear that I’m a scientist since I’m sitting here wondering whether all three heads eat and if so, do they all connect to the same or different stomachs. But I digress…  In genetics, a chimera is an organism composed of two distinct sets of cells with two different sets of DNA. As you may remember from other discussions on this blog, DNA is the genetic material that provides the blueprint to make an organism.  DNA is also what is passed along to offspring, so that a child has 50% of the DNA from one parent and 50% of the DNA from the other (more about hereditary here). This is also why paternity tests work – if the child has 0% of DNA from the father, it’s clear that he isn’t the father,

So how in the world can a person have two different sets of DNA?  Especially since all people start off as one fertilized egg with one set of DNA instructions?  There are a few ways:

  • Organ transplants or stem cell transplants.  That organ or cells (more about stem cell transplants here) come from another person who has entirely different DNA from the transplant recipient.  So when a person gets an organ transplant, they become a chimera.  This is also true, at least temporarily, for people who have blood transfusions.
  • Are you a mother or a person who a mother gave birth to?  In that case, you might also be a chimera.  This is called fetomaternal microchimerism.  Mothers usually have a few cells identical to their children that stay in their body long term. This is often caused by immune cells being transferred back and forth between the mother and the placenta during development.   Even 22% of adults were found to still have blood cells from their mother! These cells are really difficult to find – in part because there usually isn’t any reason to look for them – but easier if the mother has a boy because then the chimeric cells contain a Y chromosome whereas the mother’s cells do not.
  • In vitro fertilization (IVF) also has the possibility of resulting in chimeric adults.  Since IVF often implants multiple fertilized eggs into the mother, there is an increased possibility of two fertilized eggs fusing – resulting in one developing human developing into an adult with two sets of DNA.
chimericperson

Illustration of a chimeric person from and awesome article about chimeras from the EMBO journal

Let’s talk about this last option a bit more because this is what House and Co. blamed as the cause of the child’s chimerism, but also because this is really interesting medically and socially.  There have been several noteworthy stories about  women who, for different reasons, were found to be chimeras.  Lydia Fairchild was looking for child support after a divorce, but when DNA tests to prove paternity were requested, it was found that the father was the father, but Lydia wasn’t the mother.  After numerous traumatic events, including being accusing of trying to commit fraud to obtain benefits and having the birth of her third child observed (to prove that she was that child’s mother, even though genetically it didn’t appear that the child was), Lydia was lucky.  Her lawyer heard about Karen Keegan, a women in need of a kidney transplant several years earlier, but when testing her three sons for compatibility, the tests indicated that only one was hers.  Only by looking at other cells in her body were doctors able to determine that her body contained two sets of DNA – one set was passed on to two of her boys and the other set was passed on the other.  Fortunately for Lydia, this discovery prompted DNA testing of members of Lydia’s extended family as well as other parts of Lydia’s body.  This testing showed that Lydia’s cervical cells matched her childrens’ and she was able to obtain child support. The media LOVED this. Just an example of the titles for news articles about Lydia and Karen:

However, besides the obvious media hype, chimerism has practical and legal implications. For example, in 2005, a cyclist Tyler Hamilton was charged with blood doping because they found another person’s blood mixed with his own.  He blamed this on chimerism (New York Times article here) where he had chimeric blood cells that had different DNA than the rest of his body.  He lost his case 2-1, but it brings up an interesting idea.  If everyone knows about chimerism – either through the popular media or TV shows like House – then this could lead to the “reverse CSI effect” where the jury is so aware of the possibility of chimerism that they discard all mismatched DNA evidence blaming it on chimerism.

Besides how strange this all seems, what’s even stranger is that more people are probably chimeras that scientists even realize. The only reason we know that Karen Keegan and Lydia Fairchild are chimeras is because scientists were forced to look at them more closely.  For the rest of us, we likely won’t need a transplant or have alien-probing hallucinations that induce scientists to look at many different parts in our body to see if the DNA is different.  And if more of us are chimeras, what does that mean? This is still something scientists will have to figure out.

Want to hear more about this amazing phenonemon? Check out  or this great article from the EMBO journalNPR’s RadioLab story Mix and Match featuring Karen Keegan’s story.

Book Club – A Short History of Nearly Everything

short

Thanks to Amazon for the image

A Short History of Nearly Everything by Bill Bryson is a brilliant book. Bill Bryson is known for his travel writing and humorous writing style, but it this book he focuses his talents on explaining science. He starts at the beginning looking at the advent of our universe to understanding atoms and quarks to delving into our planet to the beginnings of life itself.  In particular, he has a chapter called “Cells” that provides one of the best descriptions of cell biology written for the public that I have ever read.  A few chapters later in “The Stuff of Life” he describes DNA and genetics in an equally accessible way.  This is one of the few popular science books that I would unreservedly suggest to anyone from ages 15 to 115.

The book won numerous, well-deserved awards including the 2004  Aventis Prize for best general science book and the 2005 EU Descartes Prize for science communication.  Please feel free to continue the conversation once you read the book by commenting below or by Asking me a Question.

For more Book Club books, click here.

How do you find a biomarker? A needle in the haystack.

biomarker_useBiomarkers are biological substances that can be measured to indicate some state of disease.  They can be used to detect a disease early, diagnose a disease, track the progression of the disease, predict how quickly a disease will progress, determine what the best treatment is for the disease, or monitor whether or not a treatment is working. Biomarkers have the potential to do so much, and identifying biomarkers for different steps in the health/disease continuum would help doctors to provide each individual with targeted, precision healthcare.  Biomarkers have the potential to save billions of healthcare dollars by helping prevent disease, by treating disease early (when it’s usually less expensive to treat), or by targeting treatments and avoid giving a treatment that won’t be effective.

spotthedifferencesWith all this potential, you would expect doctors to be using data from biomarkers to guide every single healthcare decision – but this isn’t the case quite yet.  First scientists have to find these biomarkers – a process often referred to as biomarker discovery.  I like to compare finding a biomaker to those “spot the differences” games where you have to look at two images and circle what is different in one picture compared to the other.  This is exactly what scientists do when finding a biomarker, except instead of comparing pictures, they are comparing patients.  And it’s not an easy game of “spot the differences” it’s complicated: the pictures are small and there are tons of details.

Let’s imagine a scenario that a scientist might face when wanting to find a biomarker for the early detection of pancreatic cancer.   Cancer is caused by mutations in the DNA, so you decide to look for DNA mutations as your biomarker for pancreatic cancer. So how do you “spot the differences” to find DNA biomarkers for pancreatic cancer?  First, you will need patient samples – maybe tissue or blood samples from a biobank that already has samples from patients with pancreatic cancer.  If samples aren’t already available, you will have to initiate a study partnering with doctors to collect samples from pancreatic cancer patients for you.  You will also need the second “picture” to compare the pancreatic cancer “picture” to.  This second picture will be samples from people who don’t have pancreatic cancer (scientists usually call this group the “control” group).  Then you have to “look” at the two groups’ DNA so you can find those differences.  This “looking” is often done by some genomics method like sequencing the DNA. This is where a lot of the complication comes in because if you look at all of the DNA, you will be comparing 3 billion individual nucleotides (the A, T, G, and Cs we’ve discussed in earlier posts) from each patient to each of the controls.  Even if you just look at the DNA that makes proteins, you’re still comparing 30 million nucleotides per patient.  And you can’t just compare one patient to one control!  Each of us is genetically different by ~1%, so you need to compare many patients to many controls to make sure that you find DNA that is involved in the disease and not just the ~1% that is already different between individuals.  But wait, we’re not done yet!  The biomarkers that you identify have to be validated – or double checked – to make sure that these differences just weren’t found by mistake.  And before biomarkers can be used in the clinic, they need to be approved by the Food and Drug Administration (FDA)

biomarker_discovery

From http://www.pfizer.ie/personalized_med.cfm


Whew… that was a lot of work! And so many people were involved: lead scientists who directed the project and got the money to fund it, researchers who do most of the work, computer people who are experts at crunching all of the data, and maybe even engineers to help run the equipment. Finding the biomarker needle in the biological haystack is difficult and takes time, money, and lots of people.  This is one of the reasons why there are only 20 FDA approved biomarkers for cancer (data from 2014).  But just because it’s difficult, doesn’t mean it’s impossible.  Furthermore, this effort is necessary to improve healthcare and decrease healthcare costs in the future.  It just might take a bit more time than we’d all like.

If you want to read more about the challenges and some of the solutions to biomarker discovery in cancer, take a look at this scientific article.  Or read about some successes from right in our backyard at Arizona State University on identifying biomarkers for the early detection of ovarian cancer and breast cancer.

 

What are all the “-omes” in science?

If you’re into yoga, you may be very familiar with “OM” or if you’re an electrician with “ohms”, but in science, we use “-ome” in a very different way.  To start off, let’s give a silly example: the studentome (which I’m fairly sure does not actually exist).  This would be the study of all “students” in a certain place.  Maybe we’re interested in all of the students in a particular high school or college, and they can be categorized and better understood by looking at the distribution of their ages, their heights, their grade level, their clothing style, etc.  The studentome would be different in an inner city school compared to a private Catholic school, and understanding these differences could help to improve or change aspects of the studentome in a certain place. The study of the studentome, would be called studentomics.

So what does this “-ome” mean? In Greek: “-ome” means “all” or “complete”. So whenever scientists put “-ome” at the end of a word, they are talking about all of something (like with the “studentome” all of the students), and in biology this usually is referring to all of something in an organism or a particular cell type.  It’s not clear when scientists started using words ending in “-ome”, though the word biome was coined in the early 1900s.  The modern usage likely started in the early 1990s as technologies, like computers and DNA sequencing, allowed scientists to study all of something in an organism with more ease.  A derivative of the “-ome” is “-omics”, which is the study of all of something in an organism or a cell.  This terminology is so common, there is even a wiki dedicated to “-omes” and “-omics” here.

Let’s explore some of the common “-omes” (you can find a more comprehensive list of scientific “-omes” here).

  • Genome: The most famous “ome” is the genome. The genome refers to all of the DNA in an organism.  In humans, this includes all 23 pairs of chromosomes (number 1-22 and the two sex chromosome, XX if you are female and XY if you are male). Scientists study the genome to understand the genetic blueprint of DNA because DNA codes for proteins, which are the functional machines that do everything in a cell.
  • Transcriptome: For DNA to make a protein, the DNA needs to be “transcribed” in RNA first (read more details about this process here).  All of the RNA in a cell is called a transcriptome.  Scientists study the transcriptome because not all DNA is “turned on” to make proteins in every cell.  This helps explain why certain cells look different (skin cells look different than eye cells) and have a different function (skin cells provide a barrier from the environment and specialized eye cells allow you to see).
  • Proteome: And this brings us to the proteome, which is all of the proteins in a cell or organism.  Since proteins are what’s actually doing stuff in a cells, by understanding what proteins are present in certain cells, scientists are able to better understand how those cells function. And in the case when there are problems, for example in cancer cells, it can help understand why there is a problem and possible ways to fix it.

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  • Interactome: Proteins interact with one another in a variety of ways.  The interactome maps all of these interactions.  The interactome is also different in different cell types because the proteins expressed are different in different cell types, so there are many interactomes
  • Metabolome: Even though proteins are the machinery in a cell, there are lots of other small molecules and chemicals called metabolites.  For example, glucose is a metabolite that is broken down to produce energy.  All of the metabolites in an organism are called the Metabolome.

And there are hundreds more of these “-omes”!  This “omeome” (originally and jokingly coined here) has even seeped into more popular culture.  For example, the Facebookome, described as “The totality of facebook social network connections and nodes information such as people’s names, relationships, and multimedia contents.”  Although it may seem to be an unnecessary wordy trend, these “-omes” and “-omics” are necessary for scientists to better understand health and disease.