cancer evolution

birkbak group

We apply computational approaches to study cancer evolution from a translational perspective.

Our mission is to understand cancer evolution at the molecular level, and to build tools and develop methods that use this information to improve patient treatment.

Nicolai Birkbak has a background in cancer biology, biomarker development, translational cancer research and cancer evolution and heterogeneity based on research undertaken at Technical University of Denmark (PhD and postdoc), Dana-Farber Cancer Institute (postdoc), and University College London & the Francis Crick Institute (senior postdoc).

Nicolai Juul Birkbak at Google Scholar

Cancer evolution

Key steps in carcinogenesis, © Nicolai Birkbak

An essential question in cancer research today and a focus of our research is understanding the key steps in carcinogenesis: how cells develop from a normal state to malignant cancer through benign, invasive and metastatic disease.

Over recent years, exponential drop in Next Generation Sequencing costs coupled with significant investment in cancer research has led to the creation of large cancer cohorts with extensively characterized tumor samples. This effort has improved our understanding of cancer as a molecular disease, but a focus on driver events has so far not led to a breakthrough in patient therapy, and patient survival has not significantly benefited.

While the dominant focus in the community remains tumor-centric, it has become increasingly clear that non-cancer cells play an active role in carcinogenesis and in the development of metastatic disease.

As part of our exploration of the drivers of cancer metastasis, we have started to investigate how patient health, demographics, and immune competency may play an active role in both suppressing and promoting cancer development and cancer metastasis.

Host factors for carcinogenesis, © Nicolai Birkbak

Metastatic dissemination and effect of treatment

Our lab utilizes cancer NGS data and computational tools to mine the developmental history on individual cancers, and to determine clonality of events.

In this manner, we aim to describe the order of carcinogenic events and to infer potential drivers of metastatic dissemination. This allows us to construct evolutionary trajectories for individual cancer types, potentially informing about likely changes malignant cells may be biased towards when subjected to anti-cancer therapy.

This opens the door to therapeutic approaches where treatment may be directed towards likely cancer clones not yet observed in a given sample.

Metastatic dissmination, © Christensen et al, Cancer Research, 2022.

Tracking cancer in vivo

To utilize our improved understanding of cancer therapeutically, properly characterizing and tracking cancer evolution in real time is vital.

While a tissue biopsy remains the most informative, it cannot be performed at high frequency due to costs and discomfort. Non-invasive technologies such as liquid biopsies and radiomics analysis of medical imaging data are comparatively cheap and can be performed at high frequency.

We utilize both tumor-informed approaches such as cancer-type specific ctDNA assays, and tumor agnostic approaches such as whole-genome ctDNA and T-cell receptor sequencing to characterize cancer biology, residual disease, patient-specific immune-capacity and treatment response in real-time, providing the treating clinician with crucial information about how to intervene, and when.

Liquid biopsies are very useful for tracking cancer in vivo and make treatment decisions, ©Nicolai Birkbak


Defining cGAS-STING activity in cancer

Original publication: Classifying cGAS-STING activity links chromosomal instability with immunotherapy response in metastatic bladder cancer

GENIUS: Multiomics data analysis based on spatial transformation

Original publication:GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data

Group leader

Nicolai Birkbak

Assoc. Professor, PhD, MSc, Group leader


Asbjørn Kjær

PhD student, MSc

Johanne Ahrenfeldt

Postdoc, MSc, PhD

Laura Andersen

PhD student, MSc

Naja Lange

Graduate student

Nicolai Birkbak

Assoc. Professor, PhD, MSc, Group leader

Randi Istrup Juul

Postdoc, PhD, MSc

Sofie Poder Jørgensen

Graduate student



We are very excited to be part of the UK-based TRACERx (Tracking Cancer Evolution Through Therapy) consortium,, led by Professor Charles Swanton at the Francis Crick Institute, London, UK.
Here we particularly contribute to the analysis of ctDNA-based phylogenetic tracking of cancer evolution during therapy.

Selected Publications

GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data. Sokač M, Kjær A, Dyrskjøt L, Haibe-Kains B, Aerts HJWL, Birkbak NJ (2023) eLife. 2023. Sep 5:12:RP87133. doi: 10.7554/eLife.87133. PMID37669321. Pubmed

Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Abbosh C, Frankell AM, Harrison T, Kisistok J, Garnett A, Johnson L, Veeriah S, Moreau M, Chesh A, Chaunzwa TL, Weiss J, Schroeder MR, Ward S, Grigoriadis K, Shahpurwalla A, Litchfield K, Puttick C, Biswas D, Karasaki T, Black JRM, Martínez-Ruiz C, Bakir MA, Pich O, Watkins TBK, Lim EL, Huebner A, Moore DA, Godin-Heymann N, L'Hernault A, Bye H, Odell A, Roberts P, Gomes F, Patel AJ, Manzano E, Hiley CT, Carey N, Riley J, Cook DE, Hodgson D, Stetson D, Barrett JC, Kortlever RM, Evan GI, Hackshaw A, Daber RD, Shaw JA, Aerts HJWL, Licon A, Stahl J, Jamal-Hanjani M; TRACERx Consortium; Birkbak NJ$, McGranahan N, Swanton C. Nature. 2023 Apr;616(7957):553-562. doi: 10.1038/s41586-023-05776-4. PMID: 37055640. Pubmed

Body composition and lung cancer-associated cachexia in TRACERx. Al-Sawaf O, Weiss J, Skrzypski M, Lam JM, Karasaki T, Zambrana F, Kidd AC, Frankell AM, Watkins TBK, Martínez-Ruiz C, Puttick C, Black JRM, Huebner A, Bakir MA, Sokač M, Collins S, Veeriah S, Magno N, Naceur-Lombardelli C, Prymas P, Toncheva A, Ward S, Jayanth N, Salgado R, Bridge CP, Christiani DC, Mak RH, Bay C, Rosenthal M, Sattar N, Welsh P, Liu Y, Perrimon N, Popuri K, Beg MF, McGranahan N, Hackshaw A, Breen DM, O'Rahilly S, Birkbak NJ$, Aerts HJWL$; TRACERx Consortium; Jamal-Hanjani M$, Swanton C$. Nature Medicine. 2023 Apr;29(4):846-858. doi: 10.1038/s41591-023-02232-8. PMID: 37045997. Pubmed

Treatment represents a key driver of metastatic cancer evolution. Christensen DS, Ahrenfeldt J, Sokač M, Kisistók J, Thomsen MK, Maretty L, McGranahan N, Birkbak NJ.
Cancer Res. 2022 Jun 22:canres.CAN-22-0562-E.2022. doi: 10.1158/0008-5472.CAN-22-0562. Online ahead of print. PubMed

Cancer Genome Evolutionary Trajectories in Metastasis. Birkbak NJ, McGranahan N. Cancer Cell. 2020 Jan 13;37(1):8-19. doi: 10.1016/j.ccell.2019.12.004. Review. PubMed

Artificial intelligence in cancer imaging: Clinical challenges and applications. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL.CA. Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5. Review. PubMed

Tracking the Evolution of Non-Small-Cell Lung Cancer Jamal-Hanjani M, Wilson GA, McGranahan N, Birkbak NJ, Watkins TBK, Veeriah S, Shafi S, Johnson DH, Mitter R, Rosenthal R, Salm M, Horswell S, Escudero M, Matthews N, Rowan A, Chambers T, Moore DA, Turajlic S, Xu H, Lee SM, Forster MD, Ahmad T, Hiley CT, Abbosh C, Falzon M, Borg E, Marafioti T, Lawrence D, Hayward M, Kolvekar S, Panagiotopoulos N, Janes SM, Thakrar R, Ahmed A, Blackhall F, Summers Y, Shah R, Joseph L, Quinn AM, Crosbie PA, Naidu B, Middleton G, Langman G, Trotter S, Nicolson M, Remmen H, Kerr K, Chetty M, Gomersall L, Fennell DA, Nakas A, Rathinam S, Anand G, Khan S, Russell P, Ezhil V, Ismail B, Irvin-Sellers M, Prakash V, Lester JF, Kornaszewska M, Attanoos R, Adams H, Davies H, Dentro S, Taniere P, O'Sullivan B, Lowe HL, Hartley JA, Iles N, Bell H, Ngai Y, Shaw JA, Herrero J, Szallasi Z, Schwarz RF, Stewart A, Quezada SA, Le Quesne J, Van Loo P, Dive C, Hackshaw A, Swanton C; TRACERx Consortium. N Engl J Med. 2017 Jun 1;376(22):2109-2121. doi: 10.1056/NEJMoa1616288. Epub 2017 Apr 26. PMID: 28445112. PubMed


Ditte Siggaard Christensen
Deciphering the evolution of metastatic cancer
Judit Kisistók
Exploring the biology and clinical utility of circulating tumor DNA
Janne Engestoft
Identifying Copy Number Signatures by Latent Dirichlet Allocation
Laura Andersen
Characterizing the role of tumor growth dynamics and proliferation in ctDNA release
Asbjørn Kjær
Establishing a pipeline for analysis of T-cell receptor sequencing data and characterizing the impact of the T-cell repertoire on the clinical outcome of bladder cancer
Mateo Sokač
Artificial Intelligence in Medicine: Using machine learning to improve patient stratification for precision medicine
Mateo Sokač
Utilization of Artificial Intelligence Towards Precision Medicine: Interplay Between Chromosomal Instability and Immune System
part A / MSc


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