Bra-wearing and Breast Cancer Risk: When research supports me.

If you follow enough media news, you might become overly discouraged: everything causes cancer.  The food you eat, the air your breathe, the environment you live in, the clothes you wear.  Everything.  Although some of these claims may be grounded in science, the media has a tendency to extend the truth.  For example, awhile back I read an Internet posting about how wearing bras may cause breast cancer.  I shook my head is disbelief and continued wearing a bra.  And then recently, I came across this journal article, Bra wearing not associated with breast cancer risk”, refuting the claim.  I was intrigued and slightly amused.

New trick for an old hat: Extranuclear Cyclin D1

Research that blows our mind.

In one of the most highly cited papers from July 2014, researchers from the departments of Cancer Biology and the Kimmel Cancer Center at Thomas Jefferson University in Philadelphia describe a new function in a new location with a novel interaction for the cell cycle protein cyclin D1.  

Such a discovery can shake your world just a little bit.  It’s like seeing your supervisor, the highly intelligent yet quiet and reserved enigma of a man, at your favourite coffee shop.  Wait, my boss leaves his office?  And he works here too, just like me?  Your mind is blown and getting coffee there is never the same.

And so it is with cyclin D1. 

Shifting our mindset back to the basics.

As this is a forum for learning and discovery, I wanted to stay away from any form of writing that might come across as a rant.  But perhaps you will permit me, this one time, to go off on a slight tangent.  Some of this article has appeared in Queen’s Health Science Journal, volume 13, 2013.

I was inspired but a recent editorial by Bob MacDonald, a well-known, well-liked Canadian science journalist who works for the Canadian Broadcast Corporation.  In his editorial (check it out here), he argues for increased funding for the basic sciences.  The trend across many countries involves funneling research dollars away from basic research to those projects most likely to have direct impact on its citizens and/or on economic growth.  Quite frankly, this trend is short-sited.  As MacDonald writes, “To focus only on applied sciences is to limit future possibilities”¹. 

Cancer Avatars for Personalized Medicine

They’re not blue and they’re not science fiction.  They are avatars, at least of the cancer kind.  From the hands of Dr. Sandeep Pingle, MD PhD from Moore’s Cancer Center in UC San Diego, CA comes computer representations of tumor cells.  Defined as an icon or figure representing a person, avatar is an appropriate term to describe the computer simulation modeling the genomic signatures of individual cancers and how they will respond to specific therapeutic interventions. 

In light of the recent discussions on how each tumor is unique and the need for personalized medicine, this newest discovery provides a tool for a comprehensive analysis of individual cancers.  Most importantly, it can be customized for each patient, allowing clinicians to treat patients selectively with drugs believed to have the greatest chance of success.

Thoughts so far . . .

Let’s recalibrate:

At the start of this blog, I had a clear goal: to share breaking cancer research in a manner that is easy to understand.  Who knew – it’s harder than it might seem!  The intricacies of cancer combined with the complicated technical jargon and methods make this task somewhat difficult, particularly if the goal is to break down the science into meaningful pieces of information without taking away from the science.

And so, I find that the beginnings of this blog has become more educational and perhaps more useful for entry-level students hoping to break into the cancer research field.  This information is still valuable for the general public and those merely curious about cancer and cancer research, but for them, it might take some slugging to get through the articles.

And so, I’m dividing this blog into two parts: one is basic informational posts about techniques or topics relevant to cancer, without focusing on newly published research; the other part will highlight new cancer research in a more newsy manner.  The former will be similar to what’s already been discussed, with more explanations and introductions to key players in the cancer field.  The latter section aims to highlight major findings without going into all the details.  My perspective on these topics can still be found in the “Where do we go from here” section.  You'll know by the title (Educational vs Research) where today's topic falls.

The combination of these two parts will hopefully give you, my readers, the information necessary to critical evaluate the science for yourself and to see the progress being made in the cancer research field.

This new direction is a challenge: a challenge for me to write in a clear, concise, and truthful manner; and a challenge to my readers to keep reading.  More than that, ask questions.  What do you want to know about?  Is a term or concept not understood? Do you disagree with my perspective?  With that, we can be assured we are in this endeavor together.

The Beauty of Cancer

Most people would not describe cancer as beautiful or elegant, particularly those of us that have survived the disease.  Even more, battling cancer may affect your sense of beauty, may alter your own body image. 

Jacqueline Firkin, associate professor in theater and film at the University of British Columbia wanted to give women something beautiful, something that opened the discussion surrounding cancer, beauty, and body image, something women could identify with.  She created 10 ball gowns fashioned after microscopic images of cancer cells and cellular processes hijacked in cancer.  The results are beautiful.

Next Generation Sequencing in the Laboratory

With next generation sequencing (NGS) comes the power to change medicine, to personalize patient treatment, to improve patient outcomes.  But before we become overly optimistic, recall what we learned in our last discussion – that use of NGS in the clinic possesses some limiting factors, one of which is our incomplete understanding of cancer.  For personalized medicine to be effective, cancer scientists need to have an in-depth understanding of the biology behind cancer.  For those of us toiling away in the research field, utilizing NGS has become a new tool for deciphering intricate cancer networks.

In today’s discussion, we’ll uncover how a group of researchers based in Portland Oregon used NGS to assess the RNA levels or transcript levels of genes controlled by the tumor-promoting oncogene c-MYC in a breast cancer cell line (for an explanation of the relationship between RNA and DNA, look here).  In this report, the use of RNAseq (the NGS technology used to looked at RNA levels) composes only a small, though critical, fraction of the overall study.  It also represents the utility of NGS technology in basic research.

Personalized Medicine: Next Generation Sequencing in the Clinic

Now that we’ve established the basics behind next generation sequencing (NGS), we can more fully delve into its implications.  If you missed the last post, check it out here.  In the current post we’ll uncover if and how NGS can be used in the clinic.

The need to sequence DNA faster and cheaper stems partly from our desire to impact patient diseases.  The future of cancer therapy lies in our ability to target pathways (and the proteins involved) that are altered in cancer cells.  But as we’ve shown, each cancer is unique and the genes involved in that process can vary between patients and even within that same patient.  In a recent publication, using next generation sequencing techniques, the authors analyzed a series of patients with pancreatic cancer and assessed whether looking at them as a group versus looking at their mutations as individuals was more advantageous when deciding treatment options.  They compared pathways altered in individuals to those that were significantly altered across the group and found little overlap suggesting that grouping patients may not provide the most valuable information when deciding treatment options¹. Therefore, personalized medicine, or individual gene expression profiling is critical to the acquisition of clinically significant information.  And the ability to do this lies in next-generation sequencing.

Reading Genomes: A brief history of next-generation sequencing.

We’ve talked a lot about DNA, genes, and the identification of specific mutations in specific genes related to cancer progression.  But how is this done?  What is the methodology?  Why should you care? What are the practical applications for research? For patients? What are the limitations? 

The technology we are talking about is called “next-generation sequencing” and it is used to decipher the genomes of entire organisms or, for our purposes, the genome of cancers.  In fact, the genetic differences between cancer types or those responsible for intratumor heterogeneity, as we previously discussed (see Mutational Landscape of Cancer, Intratumor Heterogeneity), were identified through the use of next-generation sequencing.  In this three-part discussion, we will first learn about the methodology involved in this latest technology and in the following discussion we will delve into its implications for both researchers and patients.

Imagine your genome or your cancer genome are like books, composed of a series of letters, our DNA.  There are four letters called nucleotides in our genomic alphabet (A, T, C and G) and like letters, the number of nucleotides and the sequence of these letters create specific words or genes.  Like words organized into sentences and paragraphs, the series of genes are organized onto chromosomes.  Our entire genomic book is composed of 23 chromosomes.  Sequencing the entire genome of individuals or that of specific cancers is akin to reading that book.  And the longer the book, the more difficult it can be.  Additionally, spelling mistakes, or mutations can further complicate the reading.

Our ability to read through genomes, to sequence the genes and the DNA that makes up those genes is based on our understanding of the structure of DNA.  Thanks to Watson and Crick, we know that DNA is configured as an anti-parallel double helix.  Essentially this means DNA consists of two strands that twist together in a head-to-toe fashion.  Think of it like a zipper, each half binds, connects, to the other half to make a tightly closed structure.  The most critical aspect of this structure lies in the fact that the two DNA strands are complementary.  Through experimentation, we know that each of the 4 nucleotides binds each other with precise specificity: A only binds T and C only binds G.  Thus, if we know the sequence of one strand, we can easily deduce the sequence of the second.

Sanger Sequencing: the Gold Standard

For the last 35 years, the gold standard for sequencing has been Sanger sequencing also referred to as the chain termination method.  Although next-generation sequencing (NGS) has taken this knowledge to the next level, allowing for analysis of large genomes in a quick and accurate manner, Sanger sequencing is still widely utilized for its simplicity and cost-effectiveness.  To understand how NGS has improved our ability to read DNA sequences, we need to understand the methodology behind Sanger sequencing.

This method utilizes our knowledge of how DNA is replicated in normal cellular processes.  It involves unzipping template DNA (the DNA that needs to be sequenced) to create single stranded DNA and mixing it with a short single stranded complementary DNA strand, called a primer, that specifically binds to the template DNA.  The reaction is started by the addition of an enzyme called a polymerase and a mixture of labeled nucleotides (the letters of our DNA).  The DNA polymerase is the powerhouse behind DNA replication, adding complementary nucleotides (ddNTPs) one by one.  In sequencing reactions, these four nucleotides bases (A,T,C,G) have been altered in two ways: they are labeled (by fluorescence) for identification in downstream reactions, and they are modified so that elongation terminates upon their addition.  By halting the elongation with one of these labeled ddNTPs, the length of the fragment can be utilized for interrogating the base identity of the terminating base.  Future reactions include capillary or gel electrophoresis which essentially separates sequences by length and then identifies the terminating base (See Figure 1). 

Figure 1: Sanger Sequencing¹.

The success story of Sanger sequencing belongs to the Human Genome Project.  Sequencing the human genome required the cooperation of multiple international research institutions and the injection of billions of dollars by governments and private corporations.  After more than a decade, an understanding of what the human genome looks like was generated.²  Such knowledge yields power for understanding the genetic basis behind all diseases from cancer to neurological disorders.  It also helps answer some basic biological questions: why do we taste bitter foods? Why do we see colour? The implications for this technology are huge. However, limitations including speed, scalability, and resolution or accuracy prompted the development of technologies that could sequence larger sequences more quickly and more accurately, and therefore answer more genetic questions.

Next generation sequencing: Illumina

Next generation sequencing is the all-encompassing term for these new methodologies that aim for high-throughput analysis of large sequences such as entire chromosomes or even entire genomes.  Several biotech companies developed NGS methods that differ in their template preparation, method of sequencing and types of analysis.  For an example, let’s highlight the method used by Illumina.

Figure 2: Illumina Next Generation sequencing³.

The first step in this process involves fragmenting the DNA to be sequenced into small fragments. These fragments attach to adaptors which are essentially primers.  This solid surface substrate is propriety for this technology.  The next step involves amplifying these DNA fragments in a manner referred to as bridge amplification.  With the same polymerase used in Sangar sequencing, complementary DNA strands are created.  With this technology, up to 1000 identical fragments can be generated.  This amplifaction uses another propriety element: the incorporation of 4 nucleotides each labeled with a different dye.  Like Sanger sequencing, these modified nucleotides also terminate the reaction after addition to the DNA fragment.  In this way, after laser excitation, the emitted fluorescent signal is captured and the subsequent sequencing reactions can proceed.  These DNA clusters are sequenced, one base at a time and then aligned to a reference sample.

The advantages of NGS compared to Sanger sequencing are clear.  Whereas Sanger sequencing sequences one region at a time, next generation sequencing can sequence multiple fragments simultaneously, speeding up the process tremendously.  Additionally, Sanger sequencing generally only sequences a specific region a limited number of times.  This “read-depth” is dramatically improved with NGS technology whose techniques allow for deep sequencing.  This increases the accuracy of the seqeunce.  With the increased speed and accuracy, the ability to sequence large genomes is now feasible in a short amount of time. 

Once a sequence has been determined it can be mapped to the latest human whole-genome reference using computational algorithms.   The technology to accurately map samples and identify mutations has also dramatically improved in our technological age.  When it comes to using this technology for the identification of mutations in cancers, additional factors need to be considered including germline mutations (what the patient carries independent of the cancer) as well as single nucleotide polymorphisms or small difference in coding DNA.  The interpretation of this data requires intensive knowledge of genetics. 

Stay tuned for the implications of this technology!  We’ll be discussing the role of NGS in the cancer clinic as well as in the cancer research lab.

Today’s uncovered cancer morsel: The advancement of technology directly impacts cancer research and cancer patients.

References

     1.     http://www.eisenlab.org/FunFly/?page_id=24

     2.     International Human Genome Sequencing Consortium.  Initial sequencing and analysis of the human genome.  Nature.   
           2001. 409: 860-922.

     3.     http://res.illumina.com/documents/products/techspotlights/techspotlight_sequencing.pdf

     4.     Michael Metzker.  Sequencing technologies – the next generation.  Nature Reviews: Genetics.  2010. 11: 31-46

Of Mice and Men: Do men really cause stress?

When you walk in the door, Fluffy (your dog, cat, or pet of choice) jumps up, greets you, voices their approval.  He can sense you; she can smell you; he loves you.  What you may not realize is that when Fluffy senses the men in your house, she might become a little stressed out.

You may have heard about this research article published in Nature Methods recently.  I saw this story on the news: men induce stress in laboratory mice.¹  I laughed, and then I needed to learn more.  So let us take a brief departure from the seriousness that cancer evokes and discuss how and why men cause stress in mice.

The Science:

This study comes out of Montreal and the laboratory of Jeffrey Mogil.  As a pain geneticist, he studies the factors that determine sensitivity to pain.  His lab staff started to notice and anecdotally reported how their presence might affect the behavior of the laboratory mice.  Could this be true? Or just a researcher’s too-critical eye?  The only way to know for sure was to design some experiments to answer the question: do animals respond differently in response to pain when exposed to male and female researchers?

Cancer Commonalities: The Hallmarks of Cancer Part 3

Welcome to the final discussion on the defining characteristics of cancer.  As we’ve discussed in parts 1 and 2 (Part1, Part2) cancer cells share several fundamental traits which are outlined in the figure below. 

Figure 1: Hallmarks of Cancer

In this final discussion, we take a step back:

What allows cancer cells to acquire these traits?  What allows cancer cells to tip the balance on all these processes towards tumorigenesis?  The answer begins with the following two enabling hallmarks. 

Cancer Commonalities: The Hallmarks of Cancer Part 2

Welcome to part 2 of our 3-part discussion on cancer commonalities!  In this series we are defining the features shared by most cancer cells as outlined in Hanahan and Weinberg’s review article.  It’s a great introduction to what defines cancer.  Let’s first recap our last discussion:  In part 1 we learned about the first 3 hallmarks of cancer: sustained proliferative signaling, evading growth suppressors, and resisting cell death.  Although distinct, these traits integrate several key signaling pathways including the Ras-MAPK pathway, the PI3K-AKT axis, and the p53 tumor suppressor pathway.  Their intricate interactions imply that alterations in one pathway can affect multiple pathways and therefore, multiple cancer traits. 

Figure 1: Hallmarks of Cancer

In today’s discussion, we will introduce the 3 remaining classical characteristics shared by cancer cells.

Cancer Commonalities: The Hallmarks of Cancer Part 1

Previously we learned how cancer could be different and unique – both between cancer types and even within the same tumor.  And although these convolutions make treatment of this disease a challenging task, cancer also shares some overarching principles which help guide us in the identification of therapeutic targets.  Today we will highlight these so-called hallmarks of cancer.  As a side note, this discussion also serves as a good primer on cancer.

In the review article The Hallmarks of Cancer (originally published in 2001, but updated in 2011), two cancer research experts, Douglas Hanahan and Robert Weinberg comprised the characteristics that underpin cancer and then summarized the research to support their ideas.  These hallmarks, the features that almost all cancer cells acquire through the course of tumor progression, have become so fundamental to the way scientists think about cancer that they can actually drive the direction of our research.  Obviously this article is required reading for anyone interested in cancer research.  

Each is unique: Intratumor heterogeneity

In the last post, we considered how cancer is more than just one disease.  We learned, for example, that kidney cancer does not necessarily carry the same mutations as breast cancer.  And yet, despite these variations, certain genes were mutated across several cancer types and occurred early in the progression of the disease.  We called these mutations driver mutations.  In this week’s paper this idea will be reiterated in the context of a new concept: intratumor heterogeneity: that each tumor and regions within the same tumor are genetically distinct.  The article that prompts this discussion was published in the New England Journal of Medicine in 2012 (http://www.nejm.org/doi/full/10.1056/NEJMoa1113205).

First: a brief primer on intratumor heterogeneity.   Over time, cells acquire genetic mutations through division errors or external sources that allow them to divide uncontrollably and operate unchecked.  When these cells start to accumulate, we detect a mass or tumor.  Intratumor heterogeneity stems from the hypothesis that each cancer cell has the capability to acquire different mutations due to its own genetic instability.  As these cells expand and migrate, different tumor cell regions with distinct genetic profiles exist.  Additionally, cells that metastasize or migrate to different regions of the body can also evolve into different genetic subpopulations.  This type of progression is also referred to as clonal evolution. 

Cancer: One word, many diseases

Out in the real world it’s common to hear people talk about cancer as one disease.  But in the scientific community, cancer is not one disease; it is many.  Each cancer type, each patient is unique.  It’s part of the inherent complexity of cancer.  And so cancer scientists spend their time characterizing different tumor types to determine their intrinsic similarities and differences.  These similarities and differences can – hopefully – be exploited to tailor treatments for each specific disease.    A recent publication in Nature, as part of a larger initiative to understand the cancer genome, focuses on these similarities and differences by outlining the mutational landscape across 12 major cancer types (Find article here) (1). 

The methods are relatively straightforward.  The researchers took 3281 tumors representing 12 different cancer types including breast, two different types of lung cancer, colon, kidney, bladder, ovarian cancers, as well as acute myelogenous leukemia (AML) among others, extracted and purified the DNA, and then subsequently sequenced each tumor DNA sample.  Since we already know the sequence of the human genome, they compared these tumor samples to matched normal tissue to identify regions, or specific genes, where alterations such as mutations or small insertions or deletions, occurred.   Using powerful software and statistical methods, they investigated the mutational frequency for each cancer as well as identified common and/or unique genes that were mutated.

As expected, these 12 major cancer types differ genetically.  Importantly, they also share some similarities. 

Similarities:

The principal result generated from this study was the identification of 127 significantly mutated genes involved in a broad array of cellular functions including some of the obvious functions such as DNA repair, cell cycle control, and regulation of known key signaling transduction pathways including the MAPK pathway and TGFb pathways as well as less understood roles such as metabolism, histone modification and proteolysis.  Two things become apparent from this list of genes and their functions: 1) The list is small.  Of the ~25000 genes in the human genome, only 127 genes make the list of significantly mutated genes in different cancer types.  What makes these genes primary targets for mutagenesis?  I’m not sure we know the answer to that question. 2) Although the list of mutated genes is small, the functions they perform are broad and cover most of the processes necessary for maintaining homeostasis, highlighting the importance of maintaining cellular homeostasis for disease avoidance.  Dysregulation of these cellular functions leads to tumor progression no matter the cancer type.

Of these 127 SMGs, several of them can be found across multiple cancers. The most commonly mutated gene, not surprisingly, is TP53, occurring in 42% of samples.  We’ve known about TP53 for many years now and the vital role it plays as a tumor suppressor by functioning as a transcription factor to control cell proliferation and apoptosis.   Next to TP53 is PIK3CA, which is mutated in greater than 10% of samples.  This gene codes for a subunit of the P13K protein which is essential for maintaining cell survival through induction of cell growth and inhibition of cell death.  The fact that these genes, among other, become mutated in many cancers suggests that these proteins are vital for maintaining cellular homeostasis.  Can they serve as therapeutic targets?

           

Differences

Although some overarching themes emerge that characterize cancer as a whole, the specific details defining each cancer are unique.  For example, the mutation frequency varies between tumor types, with AML having the fewest mutations and lung squamous cell carcinoma having the highest.  Not only does the mutation frequency differ, but the types of mutations are also different between cancer types.  Together, this suggests that factors other than age could contribute to the development of certain cancers.  For example, a certain type of mutation called a C>A transversion is most common in lung cancer, and not surprisingly, this type of mutation can be caused by cigarette smoke. 

           

More specifically, different tumors have different mutation signatures.  In other words, each cancer has different genes and their encoded proteins altered.  This is not a new concept, but this study takes this idea a step further by identifying driver mutations in different tumor types.  Driver mutations are identified by their early appearance in the progression of cancer and in their ability to provide the cancer cell with a selective advantage for continued growth.  By looking at the variant allele fraction (or the fraction of alleles that carry a mutation, VAF), the researchers assumed that genes with high VAF in specific cancers could be considered driver mutations.  The tumor suppressor TP53 has a high VAF across tumor types suggesting mutations in this gene are universal for driving cancer progression.  However, other genes differ in different tumor types.  In breast cancer, AKT1, CBFB, MAP2K4, ARID1A, FOXA1, and PIKCA carry mutations, whereas PIK3CA, PIK3R1, PTEN, FOXA2, ARIK1A are mutated in endometrial cancer. 

Furthermore, even within each tumor type, specific clusters can be separated out based on genetic signatures.  Therefore, not only do cancer differ, but variability also exists within that cancer type.  Thus, even breast cancer is more than one disease!

Where do we go from here?

I would not characterize this publication as one that identified novel concepts in cancer research.  We already knew that cancer is complex and differs by type.  However, by systematically analyzing the mutational landscape across 12 different cancer types, this study identified key genes and pathways involved in tumor progression.

At a scientific level, it is clear where this study should go.  We need to look at the list of 127 significantly mutated genes and assess their ability to act as therapeutic targets for cancer treatments.  Some of these can already be effectively targeted in cancer treatments including EGFR (Cetuximab, Lapatinab, for example) (2).  Moreover, the identification of rational drug combinations, targeting two of the key pathways identified in this study, may help to avoid drug resistance and disease recurrence.

At a more personal level, you could consider the implications of knowing your gene signature.  If you knew you carried a “driver mutation”, what would you do?  With companies, particularly in the United States, (23andMe) (3) offering to sequence your genome, the ability to do this is a realistic possibility albeit at a high financial cost.  In certain instances, for example in hereditary breast and ovarian cancer, knowing that you carry a BRCA1/2 mutation could cause you to undergo a radical mastectomy or hysterectomy.  Carrying a BRCA1 mutation prompted Angelina Jolie to undergo such a procedure.  What would you do? 

   

Uncovered cancer morse for today: cancer is complex: it is not merely one disease but a group of related yet distinct diseases.

References:

1. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333-339. doi: 10.1038/nature12634; 10.1038/nature12634.

2. Camp ER, Summy J, Bauer TW, Liu W, Gallick GE, Ellis LM. Molecular mechanisms of resistance to therapies targeting the epidermal growth factor receptor. Clin Cancer Res. 2005;11(1):397-405.

3. https://www.23andme.com/. Updated 2013.