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, the Steif Lab uses statistical machine learning approaches to derive biological insights from these high-dimensional datasets in the presence of noise and measurement bias. 

Location

echelon

The Steif Lab is located at Canada's Michael Smith Genome Sciences Centre, Echelon Innovation Centre. 


570 West 7th Avenue 
Vancouver, British Columbia 
V5Z 4S6 

Selected Publications

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.

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.

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.

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.

Open Positions

Research Programmer – Bioinformatics

Steif Lab

Canada’s Michael Smith Genome Sciences Centre (GSC)

Today’s Research. Tomorrow’s Medicine.

The GSC is a department of the BC Cancer Research Institute and a high-throughput genome sequencing facility. We are leaders in genomics, proteomics and bioinformatics in pursuit of novel treatment strategies for cancers and other diseases.

Among the world’s first genome centres to be established within a cancer clinic, for more than two decades our scientists and innovators have been designing and deploying cutting-edge technologies to benefit health and advance clinical research.

Among the GSC’s most significant accomplishments are the first publication to demonstrate the use of whole-genome sequencing to inform cancer treatment planning, the first published sequence of the SARS coronavirus genome and major contributions to the first physical map of the human genome as part of the Human Genome Project.

By joining the GSC you will become part of an exceptional and diverse team of scientists, clinicians, experts and professionals operating at the leading edge of clinical research. We look for people who share our core values—science, timeliness, respect—to join us on our mission to use genome science for the betterment of health and society.

Summary

Job Reference Number: RP_R0008_Steif Lab_2023_02_23

We are looking for an outstanding research programmer with experience developing and applying computational workflows for high-throughput sequencing data analysis to join Dr. Adi Steif’s cancer genomics research group in Vancouver, Canada.

The successful applicant will be part of an exceptional multidisciplinary team of bioinformatic scientists and trainees, working alongside collaborators in biomedical engineering and molecular biology at the University of British Columbia. By joining the Steif Lab, you will help advance genomic technology alongside highly motivated specialists in a creative and dynamic environment. You will have access to robust high performance computing infrastructure and unique and complex single cell sequencing datasets, all while living in one of the most beautiful, diverse and eclectic cities in the world.

We offer a competitive salary, excellent benefits and significant career development opportunities.

Responsibilities

Working in a multidisciplinary team, the research programmer will develop and maintain analytical workflows for high-throughput cancer genomics. This is a one-year term appointment, with potential for renewal. Responsibilities include:

  • Design and implement robust computational workflows
  • Thoroughly test and maintain high-quality, well-documented code
  • Regularly review the scientific literature to ensure analysis is in line with best practices
  • Contribute to data preprocessing and quality control
  • Develop data visualizations and contribute to biological analysis and interpretation
  • Regularly update Dr. Steif, group members and collaborators on the status of projects
  • Participate in and present research updates at lab meetings and team meetings
  • Contribute to scientific writing, including the preparation of research manuscripts and grant applications
  • May be involved in the management and supervision of trainees

Qualifications

  • A B.Sc. or M.Sc. in bioinformatics, computer science, statistics or related discipline
  • At least 3 years of experience in bioinformatics
  • Experience in genomics and high-throughput sequencing data analysis
  • Experience in cancer research is an asset
  • Programming fluency in Python or R
  • Experience working in a Unix/Linux computing environment
  • Critical thinking and attention to detail
  • Excellent verbal, written and interpersonal communication skills
  • Ability to work independently and in a collaborative interdisciplinary team
  • A strong work ethic, with demonstrated ability to manage time and prioritize to meet deadlines

Application

Applicants should submit: a CV; a one-page cover letter explaining how your research experience, interests and career goals align with the position; and the names and contact details of three references to bcgscjobs@bcgsc.ca, using Job Reference No: RP_R0008_Steif Lab_2023_02_23 in the subject line of your email.

While we value and review all applications, please note that due to the volume of submissions only shortlisted candidates will be contacted. This posting will remain online until the position is filled.

All qualified candidates are encouraged to apply; however, Canadian citizens and permanent residents will be given priority.

Important!

As per the current Public Health Order, full vaccination against COVID-19 is a condition of employment with PHSA as of October 26, 2021.

Please note all jobs at the GSC are based in Vancouver, British Columbia, Canada. Flexible work options are available for this position upon request and is subject to change in accordance with GSC’s operational needs and PHSA’s Flexible Work Options Policy.

We believe that equity, diversity and inclusivity are essential for the advancement of human knowledge and science.

We welcome all applicants and provide all employees with equal opportunity for advancement, regardless of race, colour, ancestry, place of origin, political belief, religion, marital status, family status, physical or mental disability, sex, sexual orientation, gender identity or expression, age, conviction of a criminal or summary conviction offence unrelated to their employment.

Apply Here
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