Prof Crispin Miller - Computational Biology
The Computational Biology group is focused on using data-driven approaches from machine learning to develop a better understanding of the processes that underpin tumour growth and development. We are a highly interdisciplinary group that integrates computer science, mathematics, bench- and clinical science.
A major aspect of our work is the use of cancer ‘omics data generated by large-scale tumour sequencing projects. These datasets are large enough to use machine learning algorithms that seek to correlate patterns with phenotype. This is allowing us to explore aspects of tumour evolution, and to ask how the regulatory systems that control gene expression are perturbed in tumour cells.
Our group is particularly interested in the regulatory pathways that act downstream of transcription, including the processes that govern how alternative splicing is coordinated across different pathways. Other projects in the group focus on uncovering novel regulatory sequences within the genome, and in making use of comparative genomics to help interpret the genome rearrangements that occur in tumour cells.
Education and qualifications
2000: PhD, Bioinformatics, The University of Manchester
1996: BSc, Artificial Intelligence, The University of Manchester
2019-: Head of Bioinformatics, Cancer Research UK Beatson Institute
2012-2018: Senior Group Leader, Cancer Research UK Manchester Institute
2007-2012: Senior Staff Scientist, The Paterson Institute for Cancer Research, The University of Manchester
2002-2007: Junior Group Leader, The Paterson Institute for Cancer Research, The University of Manchester
2000-2002: MRC Fellowship, The University of Manchester
Edwards SC, Hedley A, Hoevenaar WHM, Glauner T, Wiesheu R, Kilbey A, Shaw R, Boufea K, Batada N, Blyth K, Miller C, Kirschner K, Coffelt SB. Single-cell analysis uncovers differential regulation of lung γδ T cell subsets by the co-inhibitory molecules, PD-1 and TIM-3. bioRxiv. 2021.
Tsim S, Alexander L, Kelly C, Shaw A, Hinsley S, Clark S, Evison M, Holme J, Cameron EJ, Sharma D, Wright A, Grundy S, Grieve D, Ionescu A, Breen DP, Paramasivam E, Psallidas I, Mukherjee D, Chetty M, Cox G, Hart-Thomas A, Naseer R, Edwards J, Daneshvar C, Panchal R, Munavvar M, Ostroff R, Alexander L, Hall H, Neilson M, Miller C, McCormick C, Thomson F, Chalmers AJ, Maskell NA, Blyth KG. Serum Proteomics and Plasma Fibulin-3 in Differentiation of Mesothelioma From Asbestos-Exposed Controls and Patients With Other Pleural Diseases. J Thorac Oncol. 2021; 16: 1705-1717
Humphrey S, Kerr A, Rattray M, Dive C, Miller CJ. A model of k-mer surprisal to quantify local sequence information content surrounding splice regions. PeerJ. 2020;8:e10063.
Lallo A, Gulati S, Schenk MW, Khandelwal G, Berglund UW, Pateras IS, Chester CPE, Pham TM, Kalderen C, Frese KK, Gorgoulis VG, Miller C, Blackhall F, Helleday T, Dive C. Ex Vivo Culture of Cells Derived From Circulating Tumour Cell Xenograft to support Small Cell Lung Cancer Research and Experimental Therapeutics Br J Pharmacol. 2019;176: 436-50.
Rothwell, DG, Ayub, M, Cook, N, Thistlethwaite, F, Carter, L, Dean, E, Smith, N, Villa, S, Dransfield, J, Clipson, A, White, D, Nessa, K, Ferdous, S, Howell, M, Gupta, A, Kilerci, B, Mohan, S, Frese, K, Gulati, S, Miller, C, Jordan, A, Eaton, H, Hickson, N, O’Brien, C, Graham, D, Kelly, C, Aruketty, S, Metcalf, R, Chiramel, J, Tinsley, N, Vickers, AJ, Kurup, R, Frost, H, Stevenson, J, Southam, S, Landers, D, Wallace, A, Marais, R, Hughes, AM, Brady, G, Dive, C, Krebs, MG. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study Nature Medicine 2019; 25: 738-43.
Bennett, L, Howell, M, Memon, D, Smowton, C, Zhou, C, Miller, C. Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma Scientific Reports 2018; 8: 14830.
Hudson, AM, Stephenson, NL, Li, C, Trotter, E, Fletcher, AJ, Katona, G, Bieniasz-Krzywiec, P, Howell, M, Wirth, C, Furney, S, Miller, CJ & Brognard, J. Truncation- and motif-based pan-cancer analysis reveals tumorsuppressing kinases Science Signaling 2018; 11: 526.
Kim CS, Mohan S, Ayub M, Rothwell DG, Dive C, Brady G, Miller C. In silico error correction improves cfDNA mutation calling Bioinformatics 2018; 35: 2380–2385
Lallo, A, Frese, KK, Morrow, C, Szczepaniak Sloane, R, Gulati, S, Schenk, MW, Trapani, F, Simms, N, Galvin, M, Brown, S, Hodgkinson, CL, Priest, L, Hughes, AM, Lai, Z, Cadogan, EB, Khandelwal, G, Simpson, KL, Miller, C, Blackhall, FH, O'Connor, MJ & Dive, C. The combination of the PARP inhibitor olaparib and the Wee1 inhibitor AZD1775 as a new therapeutic option for small cell lung cancer Clinical cancer research 2018; 24: 5153-64.
Torres-ayuso, P, Sahoo, S, Ashton, G, An, E, Simms, N, Galvin, M, Leong, HS, Frese, KK, Simpson, K, Cook, N, Hughes, A, Miller, CJ, Marais, R, Dive, C, Krebs, MG & Brognard, J. Signaling pathway screening platforms are an efficient approach to identify therapeutic targets in cancers that lack known driver mutations: a case report for a cancer of unknown primary origin Genomic Medicine 2018; 3: 15.
Carter, L, Rothwell, D, Mesquita, B, Smowton, C, Leong, HS, Fernandez-Gutierrez, F, Li, Y, Burt, DJ, Antonello, J, Morrow, CJ, Hodgkinson, C, Morris, K, Priest, L, Carter, M, Miller, C, Hughes, A, Blackhall, F, Dive, C & Brady, G. Molecular analysis of circulating tumor cells identifies distinct copy-number profiles in patients with chemosensitive and chemorefractory small-cell lung cancer Nature medicine 2017; 23: 114-9
Chiu, A, Ayub, M, Dive, C, Brady, G & Miller, C. twoddpcr: an R/Bioconductor package and Shiny app for Droplet Digital PCR analysis Bioinformatics 2017; 33: 2743-5.
Khandelwal, G, Girotti, MR, Smowton, C, Taylor, S, Wirth, C, Dynowski, M, Frese, KK, Brady, G, Dive, C, Marais, R & Miller, C. Next-generation sequencing analysis and algorithms for PDX and CDX models Molecular Cancer Research 2017; 15: 1012-6.
Smowton, C, Balla, A, Antoniades, D, Miller, C, Pallis, G, Dikaiakos, MD & Xing, W. A cost-effective approach to improving performance of big genomic data analyses in clouds Future Generation Computer
Systems 2017; 67: 368-81.
Williamson, SC, Metcalf, RL, Trapani, F, Mohan, S, Antonello, J, Abbott, B, Leong, HS, Chester, C, Simms, N, Polanski, R , Nonaka, D, Priest, L, Fusi, A, Carlsson, F, Carlsson, A, Hendrix, MJC, Seftor, REB, Seftor, EA,
Rothwell, DG, Hughes, A , Hicks, J, Miller, C, Kuhn, P, Brady, G, Simpson, KL, Blackhall, FH & Dive, C. Vasculogenic mimicry in small cell lung cancer Nature communications 2016; 7: 13322.
Carter, LR, Rothwell, DG, Leong, H, Li, Y, Burt, DJ, Antonello, J, Hodgkinson, C, Morris, K, Franklin, L, Miller, CJ, Blackhall, F, Dive, C & Brady, G. Investigating chemoresistance in small cell lung cancer through the molecular profiling of single circulating tumour cells Cancer Research 2016; 76: 3155.
Tape, CJ, Ling, S, Dimitriadi, M, McMahon, KM, Worboys, JD, Leong, HS, Norrie, IC, Miller, CJ, Poulogiannis, G, Lauffenburger, DA & Jørgensen, C. Oncogenic KRAS Regulates Tumor Cell Signaling via Stromal Reciprocation. Cell 2016; 165: 910-20.
Marusiak, A, Stephenson, N, Baik, H, Trotter, E, Li, Y, Blyth, K, Mason, S, Chapman, P, Puto, LA, Read, JA, Brassington, C, Pollard, HK, Phillips, C, Green, I, Overman, R, Collier, M, Testoni, E, Miller, C, Hunter, T, Sansom, OJ & Brognard, J. Recurrent MLK4 Loss-of-Function Mutations Suppress JNK Signaling to Promote Colon Tumorigenesis Cancer Res. 2016; 76: 724-35.
Draper, J, Sroczynska, P, Tsoulaki, O, Leong, HS, Fadlullah, MZH, Miller, C, Kouskoff, V & Lacaud, G. RUNX1B Expression Is Highly Heterogeneous and Distinguishes Megakaryocytic and Erythroid Lineage Fate in Adult Mouse Hematopoiesis. PLoS Genetics 2016; 12: e1005814.
Memon, D, Dawson, K, Smowton, C, Xing, W, Dive, C & Miller, C. Hypoxia-driven splicing into noncoding isoforms regulates the DNA damage response Genomic Medicine 2016; 1: 16020
Tamara J Luck