Dr. Adi Steif’s research is focused on developing and applying computational methods for high-throughput genomics, with a particular interest in characterizing disease mechanisms and evolution in the context of cancer. New measurement technologies are enabling large-scale genetic, transcriptomic and epigenetic profiling of tissues at the single cell level. Working alongside experimental collaborators, Dr. Steif uses statistical machine learning approaches to derive biological insights from these high-dimensional datasets in the presence of noise and measurement bias. Her past research contributions focused on copy number inference and clonal evolution in breast cancer, and characterizing changes in normal mammary tissue in the context of ageing and inherited cancer susceptibility.

Dr. Steif was previously a Junior Research Fellow at Trinity College, University of Cambridge and a member of the inaugural class of Schmidt Science Fellows. She completed her postdoctoral research at the Cancer Research UK Cambridge Institute and European Bioinformatics Institute with Dr. John Marioni. Prior to this, she obtained her Ph.D. at UBC and BC Cancer under the supervision of Dr. Sohrab Shah and Dr. Sam Aparicio.

  • B.Sc. Honours Integrated Sciences, University of British Columbia (mathematics and biology)
  • Ph.D. Genome Science and Technology, University of British Columbia

Selected Publications

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
Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution, underpinning important emergent features such as drug resistance and metastasis. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

Scalable whole-genome single-cell library preparation without preamplification.

Nature methods, 2017
Zahn, Hans, Steif, Adi, Laks, Emma, Eirew, Peter, VanInsberghe, Michael, Shah, Sohrab P, Aparicio, Samuel, Hansen, Carl L
Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated 'bulk-equivalent' genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.

ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data.

Genome biology, 2017
Salehi, Sohrab, Steif, Adi, Roth, Andrew, Aparicio, Samuel, Bouchard-Côté, Alexandre, Shah, Sohrab P
Next-generation sequencing (NGS) of bulk tumour tissue can identify constituent cell populations in cancers and measure their abundance. This requires computational deconvolution of allelic counts from somatic mutations, which may be incapable of fully resolving the underlying population structure. Single cell sequencing (SCS) is a more direct method, although its replacement of NGS is impeded by technical noise and sampling limitations. We propose ddClone, which analytically integrates NGS and SCS data, leveraging their complementary attributes through joint statistical inference. We show on real and simulated datasets that ddClone produces more accurate results than can be achieved by either method alone.

clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers.

Genome biology, 2019
Campbell, Kieran R, Steif, Adi, Laks, Emma, Zahn, Hans, Lai, Daniel, McPherson, Andrew, Farahani, Hossein, Kabeer, Farhia, O'Flanagan, Ciara, Biele, Justina, Brimhall, Jazmine, Wang, Beixi, Walters, Pascale, , , Bouchard-Côté, Alexandre, Aparicio, Samuel, Shah, Sohrab P
Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.

Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.

Cell, 2019
Laks, Emma, McPherson, Andrew, Zahn, Hans, Lai, Daniel, Steif, Adi, Brimhall, Jazmine, Biele, Justina, Wang, Beixi, Masud, Tehmina, Ting, Jerome, Grewal, Diljot, Nielsen, Cydney, Leung, Samantha, Bojilova, Viktoria, Smith, Maia, Golovko, Oleg, Poon, Steven, Eirew, Peter, Kabeer, Farhia, Ruiz de Algara, Teresa, Lee, So Ra, Taghiyar, M Jafar, Huebner, Curtis, Ngo, Jessica, Chan, Tim, Vatrt-Watts, Spencer, Walters, Pascale, Abrar, Nafis, Chan, Sophia, Wiens, Matt, Martin, Lauren, Scott, R Wilder, Underhill, T Michael, Chavez, Elizabeth, Steidl, Christian, Da Costa, Daniel, Ma, Yussanne, Coope, Robin J N, Corbett, Richard, Pleasance, Stephen, Moore, Richard, Mungall, Andrew J, Mar, Colin, Cafferty, Fergus, Gelmon, Karen, Chia, Stephen, , , Marra, Marco A, Hansen, Carl, Shah, Sohrab P, Aparicio, Samuel
Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.
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