Zinaida Good is specializing in computational immunology, cancer immunotherapy, and lymphocyte development. She is a new Parker Institute for Cancer Immunotherapy Scholar who started her postdoctoral training with Crystal Mackall (CAR T cells) and Sylvia Plevritis (Cancer Systems Biology) at Stanford University in April 2018. During her Ph.D. studies in Computational & Systems Immunology at Stanford University, she trained with Garry Nolan and Sean Bendall. Her projects included: (1) predicting clinical outcomes for children with leukemia based on single-cell mass cytometry data; (2) guiding T-cell differentiation in the context of ex vivo expansion for cancer immunotherapy applications; and (3) comparing dimensionality reduction methods for single-cell data. Her background is in immunology (B.Sc. and M.Sc. from University of British Columbia in Vancouver, Canada) and oncology (she worked in Discovery Oncology at Genentech for 2 years). Her long-term interest is in the systems-level events required for a coordinated immune attack against cancer, as such insights can be applied to benefit patients by boosting the attack efficacy of endogenous or engineered immune cells.
Honors & Awards
Keystone Symposia Scholar, Keystone Symposia for Molecular and Cellular Biology (2018)
Parker Institute for Cancer Immunotherapy Scholar, Parker Institute for Cancer Immunotherapy (2017)
Member of the DARPA Shredder Challenge winning team “All Your Shreds Are Belong to Us”, Defense Advanced Research Projects Agency (2011)
4th prize in speed poster competition, ImmunoVancouver conference (2011)
2nd prize in the Life Sciences Institute junior poster competition, University of British Columbia (2009)
Graduate entrance scholarship, University of British Columbia (2008)
Boards, Advisory Committees, Professional Organizations
Member, Parker Institute for Cancer Immunotherapy (2018 - Present)
Member, American Association for Cancer Research (2016 - Present)
Member, International Society for the Advancement of Cytometry (2016 - 2018)
Member, International Society for Stem Cell Research (2015 - 2016)
Reviewer, PLoS One (2013 - 2014)
Member, Canadian Society for Immunology (2009 - 2012)
Member, American Association for the Advancement of Science (2009 - 2011)
Member, Student Biotechnology Network (2005 - 2011)
Doctor of Philosophy, Stanford University, IMMUN-PHD (2018)
Master of Science, University of British Columbia, Microbiology & Immunology (2012)
Bachelor of Science, University of British Columbia, Microbiology & Immunology (2008)
Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse.
2018; 24 (4): 474–83
Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.
View details for DOI 10.1038/nm.4505
View details for PubMedID 29505032
Automated mapping of phenotype space with single-cell data
2016; 13 (6): 493-?
Accurate identification of cell subsets in complex populations is key to discovering novelty in multidimensional single-cell experiments. We present X-shift (http://web.stanford.edu/~samusik/vortex/), an algorithm that processes data sets using fast k-nearest-neighbor estimation of cell event density and arranges populations by marker-based classification. X-shift enables automated cell-subset clustering and access to biological insights that 'prior knowledge' might prevent the researcher from discovering.
View details for DOI 10.1038/NMETH.3863
View details for Web of Science ID 000377480100015
View details for PubMedID 27183440
View details for PubMedCentralID PMC4896314
Lymph node-independent liver metastasis in a model of metastatic colorectal cancer
Deciphering metastatic routes is critically important as metastasis is a primary cause of cancer mortality. In colorectal cancer (CRC), it is unknown whether liver metastases derive from cancer cells that first colonize intestinal lymph nodes, or whether such metastases can form without prior lymph node involvement. A lack of relevant metastatic CRC models has precluded investigations into metastatic routes. Here we describe a metastatic CRC mouse model and show that liver metastases can manifest without a lymph node metastatic intermediary. Colorectal tumours transplanted onto the colonic mucosa invade and metastasize to specific target organs including the intestinal lymph nodes, liver and lungs. Importantly, this metastatic pattern differs from that observed following caecum implantation, which invariably involves peritoneal carcinomatosis. Anti-angiogenesis inhibits liver metastasis, yet anti-lymphangiogenesis does not impact liver metastasis despite abrogating lymph node metastasis. Our data demonstrate direct hematogenous spread as a dissemination route that contributes to CRC liver malignancy.
View details for DOI 10.1038/ncomms4530
View details for Web of Science ID 000334302800003
View details for PubMedID 24667486
Biomarkers of Residual Disease, Disseminated Tumor Cells, and Metastases in the MMTV-PyMT Breast Cancer Model
2013; 8 (3)
Cancer metastases arise in part from disseminated tumor cells originating from the primary tumor and from residual disease persisting after therapy. The identification of biomarkers on micro-metastases, disseminated tumors, and residual disease may yield novel tools for early detection and treatment of these disease states prior to their development into metastases and recurrent tumors. Here we describe the molecular profiling of disseminated tumor cells in lungs, lung metastases, and residual tumor cells in the MMTV-PyMT breast cancer model. MMTV-PyMT mice were bred with actin-GFP mice, and focal hyperplastic lesions from pubertal MMTV-PyMT;actin-GFP mice were orthotopically transplanted into FVB/n mice to track single tumor foci. Tumor-bearing mice were treated with TAC chemotherapy (docetaxel, doxorubicin, cyclophosphamide), and residual and relapsed tumor cells were sorted and profiled by mRNA microarray analysis. Data analysis revealed enrichment of the Jak/Stat pathway, Notch pathway, and epigenetic regulators in residual tumors. Stat1 was significantly up-regulated in a DNA-damage-resistant population of residual tumor cells, and a pre-existing Stat1 sub-population was identified in untreated tumors. Tumor cells from adenomas, carcinomas, lung disseminated tumor cells, and lung metastases were also sorted from MMTV-PyMT transplant mice and profiled by mRNA microarray. Whereas disseminated tumors cells appeared similar to carcinoma cells at the mRNA level, lung metastases were genotypically very different from disseminated cells and primary tumors. Lung metastases were enriched for a number of chromatin-modifying genes and stem cell-associated genes. Histone analysis of H3K4 and H3K9 suggested that lung metastases had been reprogrammed during malignant progression. These data identify novel biomarkers of residual tumor cells and disseminated tumor cells and implicate pathways that may mediate metastasis formation and tumor relapse after therapy.
View details for DOI 10.1371/journal.pone.0058183
View details for Web of Science ID 000318679900052
View details for PubMedID 23520493
View details for PubMedCentralID PMC3592916
Heterotrimeric G(i)/G(o) proteins modulate endothelial TLR signaling independent of the MyD88-dependent pathway
AMERICAN JOURNAL OF PHYSIOLOGY-HEART AND CIRCULATORY PHYSIOLOGY
2011; 301 (6): H2246-H2253
The innate immune recognition of bacterial lipopolysaccharide (LPS) is mediated by Toll-like receptor 4 (TLR4) and results in activation of proinflammatory signaling including NF-κB and MAPK pathways. Heterotrimeric G proteins have been previously implicated in LPS signaling in macrophages and monocytes. In the present study, we show that pertussis toxin sensitive heterotrimeric G proteins (Gα(i/o)) are involved in the activation of MAPK and Akt downstream of TLR2, TLR3, and TLR4 in endothelial cells. Gα(i/o) are also required for full activation of interferon signaling downstream of TLR3 and TLR4 but are not required for the activation of NF-κB. We find that Gα(i/o)-mediated activation of the MAPK is independent of the canonical MyD88, interleukin-1 receptor-associated kinase, and tumor necrosis factor receptor-associated factor 6 signaling cascade in LPS-stimulated cells. Taken together, the data presented here suggest that heterotrimeric G proteins are widely involved in TLR pathways along a signaling cascade that is distinct from MyD88-TRAF6.
View details for DOI 10.1152/ajpheart.01194.2010
View details for Web of Science ID 000298325200009
View details for PubMedID 21949112
Understanding the Mechanism of Virus Removal by Q Sepharose Fast Flow Chromatography During the Purification of CHO-Cell Derived Biotherapeutics
BIOTECHNOLOGY AND BIOENGINEERING
2009; 104 (2): 371-380
During production of therapeutic monoclonal antibodies (mAbs) in mammalian cell culture, it is important to ensure that viral impurities and potential viral contaminants will be removed during downstream purification. Anion exchange chromatography provides a high degree of virus removal from mAb feedstocks, but the mechanism by which this is achieved has not been characterized. In this work, we have investigated the binding of three viruses to Q sepharose fast flow (QSFF) resin to determine the degree to which electrostatic interactions are responsible for viral clearance by this process. We first used a chromatofocusing technique to determine the isoelectric points of the viruses and established that they are negatively charged under standard QSFF conditions. We then determined that virus removal by this chromatography resin is strongly disrupted by the presence of high salt concentrations or by the absence of the positively charged Q ligand, indicating that binding of the virus to the resin is primarily due to electrostatic forces, and that any non-electrostatic interactions which may be present are not sufficient to provide virus removal. Finally, we determined the binding profile of a virus in a QSFF column after a viral clearance process. These data indicate that virus particles generally behave similarly to proteins, but they also illustrate the high degree of performance necessary to achieve several logs of virus reduction. Overall, this mechanistic understanding of an important viral clearance process provides the foundation for the development of science-based process validation strategies to ensure viral safety of biotechnology products.
View details for DOI 10.1002/bit.22416
View details for Web of Science ID 000269846900015
View details for PubMedID 19575414