How the melanoma mutational landscape can be used to help research, patients, doctors and society

In my last blog post, I tried to convince you why heterogeneity (diversity) is important to consider in the case of melanoma. As individuals, we are all different. Therefore, as patients our disease progression as well as our response to treatment will also be different.

An example of how patients with melanoma differs, is in the genetic landscape. By this I mean the composition of mutations (gene changes) in the tumour cells leading up to development of the disease. Most melanoma tumours are full of mutations, mainly due to UV damage (protect yourself!). Most mutations won’t be important for the development of melanoma. We call such mutations “passenger mutations”. But mutations in a small number of specific genes will give the tumour cells an advantage to become more cancerous. We call such mutations “driver mutations”. These mutations could lead to for example faster growth, bypassing cell death signals or evading the immune system.

Figure 1. Genomic landscape of driver mutations in melanoma, adapted from Hodis et al.

Mutations in the BRAF gene is a good example of a key driver mutation. This leads to a hyperactive protein and massive growth of these tumour cells. Luckily, therapies have been made to target this hyperactive protein. But, only about half of melanoma patients have such BRAF mutations and these therapies will only work for these patients. Therefore, it is essential to understand the differences in the underlying genetic landscape for different melanoma patients. Then, scientists can research tailored and better drugs, and doctors can prescribe each patient the best treatment.



Recently, scientists have discovered not only specific mutations to be linked to a favourable prognosis, or better response to therapy. They linked number of mutations to response to immunotherapy and survival, making mutation load a possible new biomarker. Hugo et al., showed a correlation between high mutation load and better survival. Rizvi et al., and Van Allen et al., showed patients with a higher number of mutations generally responded better to immunotherapy (Pembrolizumab anti-PD-1 and Ipilimumab anti-CTLA-4 respectively).

One possible explanation to this, is that more mutations will lead to bigger differences between a tumour and a normal cell. This in turn gives the immune system more evidence to recognise and act against the cancer. With each mutation, the potential creation of a new neoantigen (a new target for the immune system it has not encountered before) increases. And with more neoantigens, the higher chances of the cancer being targeted by immune cells!

Figure 2 – Relationship between mutational load and survival (left) or response to immunotherapy, Ipilimumab (center) or response to immunotherapy, Pembrolizumab (right). Adapted from Hugo et al.,  Van Allen et al.,  Rizvi et al.

As today’s scientists, we are fortunate to live in the era of technology. New tools are continuously being developed, genetic sequencing is becoming cheaper, and collaborations and data sharing makes it possible to gather the large patient cohorts necessary for these types of analyses (more about this in a future blog post). With all of these resources, we can find out how the mutational landscape makes every patient unique. And this in turn will help us understand how different melanomas develop, find new targets for therapy, and reduce harm and cost for both patients and society.


Written by Sofia Chen, ESR07



Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251-63 (2012)

Hugo, W. et al. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell 165, 35–44 (2016).

Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).

Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).