A Deep Dive into Targeted Therapy

              If you take a cursory glance at the headlines regarding cancer research today, you'll see the word immunotherapy an inordinate number of times. It's the hottest story in cancer research right now. But is it the only one? The answer is no. There's another field that holds just as much promise and is being developed just as rigorously, but its impact is overshadowed by the hype surrounding immunotherapy. This month, we'll set aside the hype and focus our light on the fascinating landscape of targeted therapy - an equally capable and yet often overlooked field of cutting edge cancer research.

A Moving Target

              What exactly is the target in targeted therapy? Put simply, the target refers to critical molecules or enzymes that cancer cells rely on to survive. The idea behind targeted therapy is relatively simple to grasp: if we can eliminate the critical molecules that cancer relies on, we can eliminate cancer itself. But like many things in biology and in life, this is much easier said than done.

              Technically, targeted therapy is a form chemotherapy, which is a systemic treatment for cancer using oral or IV medication. But it's different than what most people think when they think of conventional chemotherapy. We often associate chemo with its unmistakable side effects: severe nausea, repetitive vomiting, generalized hair loss, etc. But this actually refers to a class of chemo known as cytotoxic chemotherapy. In general, these drugs kill rapidly growing cells in a non-specific way. Great, since the very definition of cancer is uncontrolled cell growth. But not so great when these drugs kill rapidly growing cells that are non-cancerous - like the ones lining your bone marrow, GI-tract, and hair follicles. 

              Conventional chemotherapy has been around since the mid-1950s, and while we've seen refinements to cytotoxic drugs, their side effects and lasting damage to the body leave much to be desired. Targeted therapy seeks to eliminate these side effects. We want to move away from damaging healthy cells and hone our therapeutic strikes on cancer cells exclusively. To do this, we need to understand how cancer cells are different from healthy cells on a whole new level. The molecular level to be precise. And the arena in which these differences are most apparent is in the cell cycle. Let's have ourselves a little look.

The Cell Cycle Explained

              To understand how most chemotherapies work, you need to understand the cell cycle. This is the natural life cycle of our cells, and it results in DNA replication and cell division.

              The cycle is broken into four major phases: G1 phase, S phase, G2 phase, and Mitosis (that annoying thing you had to memorize back in AP Bio). Let's break it down.

Cell Cycle.png

              G1 Phase - This is the initial growth phase of a cell that has committed itself to replication and division. During G1, metabolic activity within the cell surges. The protein supply chain ramps up by increasing DNA transcription (conversion of DNA to RNA) and translation (conversion of RNA to proteins). These proteins contribute to increase in metabolic activity. Cell organelles like the mitochondria and ribosomes are duplicated over and over so that there are enough for two independent cells. And most obviously, the cell significantly grows in size to accommodate for these extra components.

              S phase - This is the phase during which the entire DNA is replicated within the cell. Protein and organelle manufacturing slows down so that the blueprint (i.e. the DNA) can be copied with high fidelity. Remember biology 101: copying errors in DNA replication are mutations, and while most mutations can go unnoticed, the most severe ones can lead to horrible diseases, like cancer. So needless to say, this phase is pretty important and the cell knows it.

              G2 Phase - This is the second growth phase of the cell, and it immediately precedes cell division. As such, much of this phase is dedicated to preparing for division by another round protein manufacturing, spindle formation, and DNA quality control. Once the cell is prepped and it's ready for division, we move into Mitosis.

              Mitosis - This is final step of the cell cycle where division actually takes place. It also happens to be the one that most biology students are very familiar with. Remember the sub-divisions of Mitosis? Prophase, Metaphase, Anaphase, Telophase? My college professor had a great acronym to help us remember these sub-divisions: PMAT or Pour Me Another Tequila. 


              Okay so now that we're experts in the cell cycle, how do we relate it back to cancer? Well, in between some of these phases are critical checkpoints that are vital to healthy cell division. In particular, the G1/S checkpoint and the G2/M checkpoint are the most significant.

              G1/S Checkpoint - Before proceeding to S phase for DNA replication, the cell needs to ensure that it is truly ready for the task. If there's insufficient growth during G1, the cell enters a quiescent stage known as G0 and replication is put off for another time. The G1/S checkpoint is also significant because once it's passed, the cell is irreversibly committed to division or death.

              G2/M Checkpoint - This is the final check before the cell enters mitosis and actually divides. A critically important step in the G2/M checkpoint is the proofreading of replicated DNA to ensure no major errors or mutations have occurred. If the cell detects any irregularity in the DNA, it will steer away from mitosis and begin the process of apoptosis, otherwise known as programmed cell death. It's a built in protection to prevent abhorrent cells from dividing - a defensive harakiri, if you will.

              If these checkpoints are not obeyed and the cell flies through them during the cycle, cancer is a very likely and common outcome. The only reason that cells actually stop for these checkpoints is because specific regulatory molecules force them to. Once such molecule, probably the most studied in the history of cancer research, is the one and only p53.

               p53 is a central figure throughout the cell cycle. Its responsibilities include stopping cells at their appropriate checkpoints, activating DNA repair pathways if it detects damage, initiating apoptosis if necessary, and much more. It's rightly been referred by many as the guardian of the genome.

              In so many cancers, we find that the genes encoding p53 have been mutated. For example, we know that around 62% of head and neck cancers harbor a mutation in p53. That's a huge percentage! It speaks to the importance of this molecule as a tumor suppressor. 

              So how can we leverage our knowledge of these mutations to develop targeted therapies? It begins by understanding that cancer cells behave in predictable ways. We just have to know where to look.

Hello Cancer, This is an Intervention

              Imagine you're a cancer cell. You have a p53 mutation, so as you speed through the cell cycle, you don't care about stopping for checkpoints. While this may serve to your advantage at first, it invariably leads to your own destruction. 


              Because even cancer cells need to stop and make sure their internal processes are running correctly. If you keep blowing past the checkpoints and accumulate more mutations in other vital genes, you won't survive through many more cycles. So what do you do? You rely more heavily on other molecules similar to p53 to protect your mutated version of DNA. You do this by upregulating - i.e. producing more - molecules such as pRb, WEE1, or VEGF to name a few. 

              And herein lies the beauty of targeted therapy. We can target these molecules that cancer cells rely on to survive, and we can do this with such precision that we spare healthy cells in the process. So in an ideal world, targeted therapy promises to eliminate cancer by exposing and exploiting its unique dependencies. 

To Infinity and Beyond

              To be clear once again, this is much easier said than done. Each tumor has its own genomic profile and therefore its own pattern of molecular expression. Even cells found within the same tumor have different mutations and proteins driving their growth. So even if you're able to target one tumor successfully, that doesn't guarantee that you'll be able to target the next one in the same way.

              So where does this leave us?

              Right now, thousands upon thousands of genes, proteins, and drugs are being investigated for their respective roles in the cell cycle. And this research is being translated into tangible results. The FDA approved the first targeted therapy, tamoxifen, for HER2+ breast cancer back in 1977. Since then, our understanding of cancer genome profiles, tumor biomarkers, and molecular expression has evolved to the point that we now have hundreds of targeted therapies on the market. And there are literally thousands of clinical trials going on right now to advance the efficacy of these targeted drugs. 

              This is great news, obviously, but it raises a concern in my mind. Given the complexity of the cell cycle and the sheer number of potential targets, we are generating an overwhelming library of data and information. So much so that even the best clinicians are hard pressed to keep up with the latest developments. Paradoxically, this means that cancer patients who would be great candidates for new drugs may miss out because there are simply too many options for doctors to keep track.

              Because of this problem, I predict that a couple of novel solutions will rise. I believe medicine will soon deploy machine learning and artificial intelligence to connect patients to appropriate therapies. Imagine feeding a biopsy sample to an "intelligent" diagnostic device that can detect the entire mutational landscape of the specimen. From there, the unique set of proteins and molecules that the cancer relies on would be identified, and an algorithm could work out which drug or drug combinations would be most efficacious for that patient. 

              Of course, this still doesn't solve the problem of the genetic and proteomic heterogeneity that we observe even within the same tumors. In other words, a biopsy from one area of the tumor may spit out different results from a biopsy taken from another area of the tumor. To solve this, we'd truly need a revolutionary solution.

              I can imagine a day that this is solved by synthetic nanorobotics. Swarms of tiny, intelligent machinery floating through our circulatory system would constantly survey our body for cancer. If one detects an abnormality, it reports the finding to the cloud and generates a report for the best course of action. These nanobots could even be loaded with the drug of choice and deposit it in exactly the right place. Heck, we may not even needs drugs at that point - the bots may be able to fix or eliminate the cancer cells themselves.

              While it is fun to envision such a future, these examples still remain under the purview of science fiction. But perhaps not for long. In any case, one thing remains clear: medical scientists must continue to generate this data if we ever hope to make that future a reality. A reality that is free of cancer. That's the one I want to live in.