Melissa Ko earned an S.B. in biology from MIT and a PhD in cancer biology from Stanford University. Her research aims to develop novel computational pipelines to make sense of the deluge of single-cell high-dimensional data collected by biologists. Using visualizations and modeling, Melissa reveals mechanisms of drug resistance in cancer and identifies more effective treatment combinations. During her graduate career, Melissa received support from the National Science Foundation (NSF) Graduate Research Fellowship, Stanford’s Diversifying Academia, Recruiting Excellence (DARE) Fellowship, and the National Cancer Institute F99/K00 Fellowship. Melissa has taught computational and cancer biology workshops at Stanford University as well as general biology at Foothill College. As a Thinking Matters Fellow, Melissa teaches THINK 3: Breaking Codes, Finding Patterns, THINK 61: Living with Viruses, and THINK 23: The Cancer Problem: Causes, Treatments, and Prevention.
In addition, Melissa has been involved with numerous educational outreach programs including the Splash program at MIT, Stanford, and Berkeley. Through these programs and related efforts, she has taught middle school and high school students in a variety of subjects, from cancer biology to personal finance to poetry. Melissa is dedicated to improving the experience of underrepresented students in all STEM disciplines. She served as a mentor and program leader for numerous Stanford Bioscience programs including SSRP and ADVANCE. Through prior work with Stanford’s Vice Provost for Teaching and Learning, Melissa has also developed diversity and inclusion content for instructors of in-person, online, and hybrid format classes.
Outside of work, Melissa enjoys cooking, playing video games, reading poetry, and going on walks in the park.
Lecturer, Stanford Introductory Studies - Thinking Matters
Honors & Awards
Diversifying Academia, Recruiting Excellence (DARE) Fellowship, Stanford University (2016-2018)
F99/K00 Predoctoral to Postdoctoral Fellow Transition Award, National Cancer Institute (2016-2018)
Graduate Research Fellowship, National Science Foundation (2013-2016)
SB, Massachusetts Institute of Technology, Biology (2012)
PhD, Stanford University, Cancer Biology (2018)
- Our Genome
THINK 68 (Win)
- The Cancer Problem: Causes, Treatments, and Prevention
THINK 23 (Spr)
Prior Year Courses
- Breaking Codes, Finding Patterns
THINK 3 (Aut)
- Living with Viruses
THINK 61 (Win)
- The Cancer Problem: Causes, Treatments, and Prevention
THINK 23 (Spr)
- Breaking Codes, Finding Patterns
Undergraduate Biology Education Research Gordon Research Conference: A Meeting Report.
CBE life sciences education
2020; 19 (2): mr1
The 2019 Undergraduate Biology Education Research Gordon Research Conference (UBER GRC), titled "Achieving Widespread Improvement in Undergraduate Education," brought together a diverse group of researchers and practitioners working to identify, promote, and understand widespread adoption of evidence-based teaching, learning, and success strategies in undergraduate biology. Graduate students and postdocs had the additional opportunity to present and discuss research during a Gordon Research Seminar (GRS) that preceded the GRC. This report provides a broad overview of the UBER GRC and GRS and highlights major themes that cut across invited talks, poster presentations, and informal discussions. Such themes include the importance of working in teams at multiple levels to achieve instructional improvement, the potential to use big data and analytics to inform instructional change, the need to customize change initiatives, and the importance of psychosocial supports in improving undergraduate student well-being and academic success. The report also discusses the future of the UBER GRC as an established meeting and describes aspects of the conference that make it unique, both in terms of facilitating dissemination of research and providing a welcoming environment for conferees.
View details for DOI 10.1187/cbe.19-09-0188
View details for PubMedID 32357093
Deep profiling of apoptotic pathways with mass cytometry identifies a synergistic drug combination for killing myeloma cells.
Cell death and differentiation
Multiple myeloma is an incurable and fatal cancer of immunoglobulin-secreting plasma cells. Most conventional therapies aim to induce apoptosis in myeloma cells but resistance to these drugs often arises and drives relapse. In this study, we sought to identify the best adjunct targets to kill myeloma cells resistant to conventional therapies using deep profiling by mass cytometry (CyTOF). We validated probes to simultaneously detect 26 regulators of cell death, mitosis, cell signaling, and cancer-related pathways at the single-cell level following treatment of myeloma cells with dexamethasone or bortezomib. Time-resolved visualization algorithms and machine learning random forest models (RFMs) delineated putative cell death trajectories and a hierarchy of parameters that specified myeloma cell survival versus apoptosis following treatment. Among these parameters, increased amounts of phosphorylated cAMP response element-binding protein (CREB) and the pro-survival protein, MCL-1, were defining features of cells surviving drug treatment. Importantly, the RFM prediction that the combination of an MCL-1 inhibitor with dexamethasone would elicit potent, synergistic killing of myeloma cells was validated in other cell lines, in vivo preclinical models and primary myeloma samples from patients. Furthermore, CyTOF analysis of patient bone marrow cells clearly identified myeloma cells and their key cell survival features. This study demonstrates the utility of CyTOF profiling at the single-cell level to identify clinically relevant drug combinations and tracking of patient responses for future clinical trials.
View details for DOI 10.1038/s41418-020-0498-z
View details for PubMedID 31988495
FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets.
High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.
View details for DOI 10.1038/s41596-019-0246-3
View details for PubMedID 31932774
Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line.
2020; 11 (1): 2345
The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.
View details for DOI 10.1038/s41467-020-15956-9
View details for PubMedID 32393797
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
2019; 10: 2674
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
View details for DOI 10.1038/s41467-019-09799-2
View details for Web of Science ID 000471758500010
View details for PubMedID 31209238
View details for PubMedCentralID PMC6572829
CDX2 is an amplified lineage-survival oncogene in colorectal cancer
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2012; 109 (46): E3196-E3205
The mutational activation of oncogenes drives cancer development and progression. Classic oncogenes, such as MYC and RAS, are active across many different cancer types. In contrast, "lineage-survival" oncogenes represent a distinct and emerging class typically comprising transcriptional regulators of a specific cell lineage that, when deregulated, support the proliferation and survival of cancers derived from that lineage. Here, in a large collection of colorectal cancer cell lines and tumors, we identify recurrent amplification of chromosome 13, an alteration highly restricted to colorectal-derived cancers. A minimal region of amplification on 13q12.2 pinpoints caudal type homeobox transcription factor 2 (CDX2), a regulator of normal intestinal lineage development and differentiation, as a target of the amplification. In contrast to its described role as a colorectal tumor suppressor, CDX2 when amplified is required for the proliferation and survival of colorectal cancer cells. Further, transcriptional profiling, binding-site analysis, and functional studies link CDX2 to Wnt/β-catenin signaling, itself a key oncogenic pathway in colorectal cancer. These data characterize CDX2 as a lineage-survival oncogene deregulated in colorectal cancer. Our findings challenge a prevailing view that CDX2 is a tumor suppressor in colorectal cancer and uncover an additional piece in the multistep model of colorectal tumorigenesis.
View details for DOI 10.1073/pnas.1206004109
View details for PubMedID 23112155