Cancer Genome Evolution

The cancer genome contains within it an archaeological record about its past, with each genomic event representing a scar from the mutational processes that have been active during a tumour’s life history.

The advent of next-generation sequencing, coupled with its exponential cost decrease, has led to the sequencing of exomes and entire cancer genomes at a large-scale. Indeed, consortia such as TCGA (the Cancer Genome Atlas) and ICGC (the International Cancer Genome Consortium) have made thousands of sequenced cancer genomes and exomes publicly available.

To date, this data has been used to reveal many of the key genomic events involved in cancer, distinguishing key driver and passenger mutations (Lawrence et al., 2014), as well as shedding light on some of the key mutational processes shaping cancer evolution (Alexandrov et al., 2013). However, we have learned less than initially anticipated from these sequencing projects, and targeted therapies that have focused on established driver events have not appreciably improved cancer patients’ overall survival times.

Tumour heterogeneity

A primary obstacle to success of personalized medicine is intra-tumour heterogeneity (McGranahan and Swanton (2015)). Recent work from us and others have demonstrated not only that the genomic landscape of each tumour is unique but also even different regions of the same tumour can be vastly divergent (Burrell et al, 2013). This intra-tumour heterogeneity is likely a key cause of drug resistance and therapeutic failure. 

While intra-tumour heterogeneity may confound treatment success, it can also serve to illuminate the evolutionary history of tumours (Greaves, 2015; McGranahan and Swanton, 2015).

Bioinformatics

We utilize state-of-the-art bioinformatics and evolutionary methods to decipher cancer genome evolution and to explore patterns of tumour growth and development across cancers, focussing on lung cancer. Ultimately, we aim to gain a deeper understanding of the rules that govern tumour evolution and anti-tumour immunity to improve patient treatment.

Computational tools

We recently developed a novel computational tool (TcellExTRECT) to enable exploration of T cell content in DNA sequencing data.

Another example of a bioinformatics tool we have recently developed to understand immune escape, is LOHHLA: Loss Of Heterozygosity in Human Leukocyte Antigen.

Select publications

1. Bentham R, Litchfield K, Watkins TBK, Lim EL ... McGranahan N#. Using DNA sequencing data to quantify T cell fraction and therapy response. Nature. 2021 Sep;597(7877):555-560. Epub 2021 Sep 8.
2. Black JRM, McGranahan N. Genetic and non-genetic clonal diversity in cancer evolution. Nat Rev Cancer. 2021 Jun;21(6): 379-392.
3. Hernando B, Dietzen M, Parra G ... McGranahan N, Martinez-Cadenas C. The effect of age on theacquisition and selection of cancer driver mutations in sun-exposed normal skin. Ann Oncol. 2021 Mar;32(3) :412-421.
4. Litchfield K, Reading JL, Puttick C ... McGranahan N#, Swanton C. Meta-analysis of tumor- and T cell-intrinsic mechanisms ofsensitization to checkpoint inhibition. Cell. 2021 Feb 4;184(3): 596-614.e14.
5. Schenk A, López S, Kschischo M, McGranahan N. Germline ancestry influences the evolutionary disease course in lung adenocarcinomas. Evol Appl. 2020 Apr 17;13(7): 1550-1557.
6. Watkins TBK, Lim EL, Petkovic M ... Mourikis TP, Dietzen M ... McGranahan N#, Swanton C. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature. 2020 Nov;587(7832):126-132. # co-corresponding author.
7. Pennycuick A, Teixeira VH, AbdulJabbar K ... McGranahan N, Janes SM. Immune Surveillance in Clinical Regression of Preinvasive Squamous Cell Lung Cancer. Cancer Discov. 2020 Oct;10(10):1489-1499.
8. López S, Lim EL, Horswell S, Haase K, Huebner A, Dietzen M, Mourikis TP, Watkins TBK ... Swanton C, McGranahan N. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat Genet. 2020 Mar;52(3):283-293.
9. Birkbak NJ, McGranahan N. Cancer Genome Evolutionary Trajectories in Metastasis. Cancer Cell. 2020 Jan 13;37(1): 8-19.
10. Rosenthal R, Cadieux EL, Salgado R ... McGranahan N, Swanton C; TRACERx consortium. Neoantigen-directed immune escape in lung cancer evolution. Nature. 2019 Mar;567(7749): 479-485.
11. Messaoudene M, Mourikis TP, Michels J ... McGranahan N, André F, Zitvogel L. T cell Bispecific Antibodies in Node-Positive Breast Cancer: Novel Therapeutic Avenue for MHC class I Loss Variants. Ann Oncol. 2019 Mar 29.
12. McGranahan N*, Rosenthal R, Hiley CT, et al, Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution.Cell. 2017.
13. Jamal-Hanjani M, Wilson GA, McGranahan N, et al (2017). Tracking the Evolution of Non-Small-Cell Lung Cancer. New England Journal of Medicine. 376(22), 2109-2121.
14. McGranahan N, Furness AJS, Rosenthal R, et al (2016). Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade, Science
15. McGranahan N, Favero F, de Bruin EC, et al (2015). Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci Transl Med, 7(283).
16. de Bruin EC, McGranahan N, Mitter R, et al (2014). Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science, 346 (6206), 251-256.
17. Dewhurst, SM, McGranahan N, Burrell, RA, et al (2014). Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov, 4(2), 175-185.
18. Murugaesu N, Wilson GA, Birkbak NJ, Watkins T, McGranahan N, et al (2015). Tracking the genomic evolution of esophageal adenocarcinoma through neoadjuvant chemotherapy. Cancer Discov, 5(8): 821-31.
19. Favero F, McGranahan N, Salm M, et al (2015). Glioblastoma adaptation traced through decline of an IDH1 clonal driver and macro-evolution of a double-minute chromosome. Ann Oncol, 26(5), 880-887.
20. Gerlinger M, Horswell S, Larkin J ... McGranahan N (2014). Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet, 46(3), 225-233.

Group members

• Carlos Martinez Ruiz, Postdoc
• Maria Litovchenko, Postdoc
• Robert Bentham, Senior Postdoc
• Ariana Huebner, PhD student
• Kerstin Thol, PhD student
• Piotr Pawlik, PhD student
• James Black, Clinical PhD student
• Michelle Leung, PhD student

Alumni

• Michelle Dietzen, BioNtech
• Saioa Lopez, Data & Research Analyst at Wellcome Trust
• Thanos Mourikis, Investigator in Oncology Data Science at Novartis

Collaborators

• Professor Charles Swanton
• Dr Sergio Quezada
• Professor Sohrab Shah