As we move along this formidable rollercoaster of cancer biology and treatment, the view from the peaks is thrilling, and what we’re experiencing is three-fold: complexity, challenges and hope.
With immuno-oncology (IO) therapy, we are not only attempting to kill cancer cells, we are also trying to simultaneously train our immune systems to defend our bodies in case the cancer returns. There is an indication that PD1/PD-L1 checkpoint inhibitors may work for multiple tumor types.1 This gives us hope.
Although the explosion of IO therapies and drugs is mind-boggling, we’re quickly realizing that cancer biology is more sophisticated than anticipated. We are learning that each patient has a unique cancer blueprint and the variability between tumors is astonishing; one tumor may not be generalizable to another. Patients present with multiple factors which may be independent, sometimes in total isolation; or interdependent, making it difficult to determine their true cancer signature.
In addition, we’re still struggling to interrogate tumor biology and how to develop combination therapies without proper biomarkers. Can we find an effective IO-IO or chemotherapy-IO combination? Or do we need to look at a completely different biologic? And how do we deal with the added complexities of combination therapies? As a field, we’re still digesting how to get there.
There’s a simple way of looking at this. Before IO emerged, we used biomarkers to find tumor cells. By developing a targeted therapy to find a particular biomarker on a tumor, we could exploit the tumor’s weakness and kill it.
With IO, we can potentially kill a cell indirectly, by reactivating the host immune system (and we don’t actually know what the interaction is between the host immune system and tumor cell). So, we unleash the immune system and hope that it recognizes the tumor. Recent developments of mutation burden (the number of mutations) and microsatellite instability (high instability means predisposition to mutations) have shed some light on this interaction and these may provide us with new types of biomarkers.
Challenges: the PD-L1 conundrum
It’s ironic that the only biomarker that currently works is not specific. It’s more like a fever. We know that there’s a tussle going on between the immune system and the invader, but we don’t know where the fight is or who’s fighting. Though antibodies against the PD-1/PD-L1 pathway have had success in treating certain types of cancers, these types of drugs can allow the immune system to attack other organs in the body, which can lead to serious side effects.1 PD-L1 gives us an enrichment – not precision – it gives us a higher chance of the probability of reactivating the immune system. Fortunately, antibodies against the PD-1/PD-L1 pathway have shown promise in patients with various cancer types, however this response has only been shown for a subset of patients.2 We need to therefore improve our understanding of resistance and investigate mechanisms other than PD-L1.
IO is changing how we think about therapy
The overall paradigm of cancer therapy has evolved significantly. Urgency reverberates throughout the oncology community, and as a physician, I’ve been witness to life and death and the speed of decisions. Oncology has to advance, simply because people are dying.
IO is undoubtedly transforming our drug discovery infrastructure. It’s changing how we design clinical trials, how we collaborate and how we handle data. We are moving away from traditional approaches and attitudes to immuno-oncology studies. This paradigm shift has resulted in many clinical trials implementing accelerated study designs for the treatment of cancer.3 Through combination therapies, our competitors are now becoming our partners as we may need access to a medicine that another group has or is developing. Additionally, if a drug is already on the market and we have to buy it, costs can become astronomical.
And finally, we need to use real-world data to fill in the gaps. We need to understand long-term super responders; Recent findings estimate that the population of cancer survivors will increase to 20.3 million by 2026, however clinical trials only tend to follow patients for a defined period of time.4 For patients with rare diseases, trials are difficult to conduct and we need real-world data to support our studies, both for referencing perspective as a control to confirm efficacy (i.e., a synthetic control arm) and for long-term treatment effects.
What sets Merck KGaA, Darmstadt, Germany, apart?
We have extensive experience in the IO space, which we’ve never left. We’re in it for the long-term, and the fact that we’ve developed two in-house IO therapies demonstrates our dedication and priorities to this field. I also believe it’s in our approach to partnering; we’re mindful of our ability to develop large platforms and we can only do this when everyone is equally responsible. So, we take a leap of faith and partner where possible, 50/50, because we believe in our assets and in our patients. We’re optimistic, which is a good thing.
1 American Cancer Society. Immune checkpoint inhibitors to treat cancer. 2018; Retrieved from https://www.cancer.org/treatment/treatments-and-side-effects/treatment-types/immunotherapy/immune-checkpoint-inhibitors.html.
2 Zou W et al. Science Translational Medicine 2016. 8(328): doi: 10.1126/scitranslmed.aad7118.
3 Tang J et al. Annals of Oncology 2017. 29(1):doi:10.1093/annonc/mdx755.
4 American Cancer Society 2016-2017. Cancer treatment & survivorship facts & figures. Retrieved from https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/cancer-treatment-and-survivorship-facts-and-figures/cancer-treatment-and-survivorship-facts-and-figures-2016-2017.pdf.