The Shah Lab is an international computational cancer biology lab dedicated to dissecting fundamental properties of cancer evolution. The lab is led by Dr. Sohrab Shah and has on-site locations both at the Memorial Sloan Kettering Cancer Center in New York and at BC Cancer in Vancouver.

At the Shah Lab, we use high-resolution genomics to study human cancers, and couple these measurements with innovation in computational methods to infer cancer biology at genome and single-cell scales. An overview of our research can be found here and our publications are listed here.




Cynthia Ferguson

Research Projects and Operations Leader

Daniel Lai

Senior Bioinformatics Scientist


Selected Publications

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

Cell, 2018
Zhang, Allen W, McPherson, Andrew, Milne, Katy, Kroeger, David R, Hamilton, Phineas T, Miranda, Alex, Funnell, Tyler, Little, Nicole, de Souza, Camila P E, Laan, Sonya, LeDoux, Stacey, Cochrane, Dawn R, Lim, Jamie L P, Yang, Winnie, Roth, Andrew, Smith, Maia A, Ho, Julie, Tse, Kane, Zeng, Thomas, Shlafman, Inna, Mayo, Michael R, Moore, Richard, Failmezger, Henrik, Heindl, Andreas, Wang, Yi Kan, Bashashati, Ali, Grewal, Diljot S, Brown, Scott D, Lai, Daniel, Wan, Adrian N C, Nielsen, Cydney B, Huebner, Curtis, Tessier-Cloutier, Basile, Anglesio, Michael S, Bouchard-Côté, Alexandre, Yuan, Yinyin, Wasserman, Wyeth W, Gilks, C Blake, Karnezis, Anthony N, Aparicio, Samuel, McAlpine, Jessica N, Huntsman, David G, Holt, Robert A, Nelson, Brad H, Shah, Sohrab P

Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes.

Nature genetics, 2017
Wang, Yi Kan, Bashashati, Ali, Anglesio, Michael S, Cochrane, Dawn R, Grewal, Diljot S, Ha, Gavin, McPherson, Andrew, Horlings, Hugo M, Senz, Janine, Prentice, Leah M, Karnezis, Anthony N, Lai, Daniel, Aniba, Mohamed R, Zhang, Allen W, Shumansky, Karey, Siu, Celia, Wan, Adrian, McConechy, Melissa K, Li-Chang, Hector, Tone, Alicia, Provencher, Diane, de Ladurantaye, Manon, Fleury, Hubert, Okamoto, Aikou, Yanagida, Satoshi, Yanaihara, Nozomu, Saito, Misato, Mungall, Andrew J, Moore, Richard, Marra, Marco A, Gilks, C Blake, Mes-Masson, Anne-Marie, McAlpine, Jessica N, Aparicio, Samuel, Huntsman, David G, Shah, Sohrab P

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.

Nature, 2015
Eirew, Peter, Steif, Adi, Khattra, Jaswinder, Ha, Gavin, Yap, Damian, Farahani, Hossein, Gelmon, Karen, Chia, Stephen, Mar, Colin, Wan, Adrian, Laks, Emma, Biele, Justina, Shumansky, Karey, Rosner, Jamie, McPherson, Andrew, Nielsen, Cydney, Roth, Andrew J L, Lefebvre, Calvin, Bashashati, Ali, de Souza, Camila, Siu, Celia, Aniba, Radhouane, Brimhall, Jazmine, Oloumi, Arusha, Osako, Tomo, Bruna, Alejandra, Sandoval, Jose L, Algara, Teresa, Greenwood, Wendy, Leung, Kaston, Cheng, Hongwei, Xue, Hui, Wang, Yuzhuo, Lin, Dong, Mungall, Andrew J, Moore, Richard, Zhao, Yongjun, Lorette, Julie, Nguyen, Long, Huntsman, David, Eaves, Connie J, Hansen, Carl, Marra, Marco A, Caldas, Carlos, Shah, Sohrab P, Aparicio, Samuel


Selection and drug response

"Making predictions is hard, especially about the future" - Nils Bohr We have a keen interest in learning fitness trajectories from timeseries study of cancer populations within controlled interventions such as CRISPR or pharmacologic methods as a means to predict response to drugs. Using extensions of population genetics theory, we are interested in predicting how cell populations will respond in the presence of a perturbation. This is indeed ‘hard’ and entails the need to decipher stochastic drift, clonal interaction and positive selection.

Mutational signatures in DNA repair deficient cancers

We recently published a landmark study showing how the genomes of ovarian cancer histotypes reflect the DNA repair abnormalities they harbour. We are interested in how to optimize the computational discovery of genome-wide structural and point mutational signatures and how signatures can identify treatment opportunities for ovarian and breast cancers. This work is being carried out at bulk and single cell resolution. In addition, we are working in translation capacity to develop a robust genome-wide test to stratify ovarian cancers in the clinic.

Single cell genomics of cancer

The unit of evolutionary selection in cancer is the cell. Extraordinary progress in measurement technologies has made it possible to reliably and accurately sequence the genomes of individual cancer cells at scale. We have recently optimized biophysical techniques and hidden Markov model approaches to ascertain highly accurate copy number profiles of thousands of cancer cells. As such, studying the ‘population genetics’ of cancer cells is a tractable goal.

Cancer Evolution

Our lab is motivated by studying cancer through the lens of evolution. We are engaged in several studies that span both temporal and spatial multi-sample studies of our cancers of interest. Observing the dynamics of genomically-defined clones reflected in timeseries biopsies of patient tumours, patient-derived xenografts, or through spreading of clones across anatomical sites is a key area of interest for our lab.


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