About

Jimin Lee is a Professor in the Arts Division at UC Santa Cruz, affiliated with the Faculty and holding a regular faculty position. She holds a B.F.A. and an M.F.A. from the College of Fine Art at Seoul National University in Seoul, Korea, and has also been a Research Student in the Post Experience Program at Tokyo University of the Arts in Tokyo, Japan. Additionally, she earned an M.F.A. from the San Francisco Art Institute. Her expertise lies in print media, with a focus on digital and new media technology in print, Digital Hanji and Washi paper, print in the social sphere, and large format and laser-cut woodblock printing. She has exhibited her work in various international venues, including Reykjavik, Tokyo, Berlin, Yeoncheon, Seoul, and Chengdu. Her courses include ART 20G, 165, 168, 161C, 161J, and 280.

Research topics

• Biology
• Genetics
• Computational biology
• Evolutionary biology
• Statistics
• Mathematics

Selected publications

• Precision generalized phase I-II designs

  Biometrics · 2025-07-03

  articleOpen access

  A new family of precision Bayesian dose optimization designs, PGen I-II, based on early efficacy, early toxicity, and long-term time to treatment failure is proposed. A PGen I-II design refines a Gen I-II design by accounting for patient heterogeneity characterized by subgroups that may be defined by prognostic levels, disease subtypes, or biomarker categories. The design makes subgroup-specific decisions, which may be to drop an unacceptably toxic or inefficacious dose, randomize patients among acceptable doses, or identify a best dose in terms of treatment success defined in terms of time to failure over long-term follow-up. A piecewise exponential distribution for failure time is assumed, including subgroup-specific effects of dose, response, and toxicity. Latent variables are used to adaptively cluster subgroups found to have similar dose-outcome distributions, with the model simplified to borrow strength between subgroups in the same cluster. Guidelines and user-friendly computer software for implementing the design are provided. A simulation study is reported that shows the PGen I-II design is superior to similarly structured designs that either assume patient homogeneity or conduct separate trials within subgroups.

  PublisherOA PDFDOI

• Abstract 1708: CKD-702, a novel bispecific antibody, enhances anti-tumor effect of combination with EGFR TKIs in NSCLC preclinical model

  Cancer Research · 2025-04-21

  articleSenior author

  Abstract The development of drug resistance is a major cause of poor prognosis and therapeutic failure in NSCLC. The mutual synergism between cMET and EGFR is known to promote acquired drug resistance, which complicates effective treatment strategies. In NSCLC, cMET gene amplification is reported in 2-4% of previously untreated patients and in 5-20% of patients with EGFR mutations and acquired resistance to EGFR TKIs. This crosstalk between the cMET and EGFR pathways underscores the need for dual inhibition as a promising treatment for NSCLC patients, especially those with acquired cMET activation. cMET, a proto-oncogene encoding the RTK HGFR, plays a crucial role in cell proliferation, motility, and survival. Aberrant activation of cMET through gene amplification, mutation, or overexpression has been implicated in the development of various cancers, including NSCLC. EGFR, another critical RTK, is frequently mutated in NSCLC, leading to uncontrolled cell growth and survival. The interplay between cMET and EGFR signaling pathways facilitates the emergence of resistance to EGFR TKIs, a common occurrence in NSCLC patients undergoing treatment. This resistance often results from secondary mutations in the EGFR gene, such as T790M and C797S, or from activation of alternative signaling pathways, including cMET amplification. CKD-702 is a bispecific antibody specifically designed to target both cMET and EGFR. It is a tetravalent IgG1-like bispecific antibody, in which a single-chain variable fragment specific to EGFR is genetically fused to the C-terminus of a conventional IgG1 of cMET. This innovative design allows CKD-702 to bind with high affinity to the extracellular domains of both cMET and EGFR, effectively inhibiting their signaling by blocking ligand binding. Additionally, CKD-702 induces the internalization and degradation of both receptors, thereby further inhibiting their activity and downstream signaling pathways. This study aims to evaluate the efficacy of combination therapy with EGFR TKIs and CKD-702 for overcoming resistance in NSCLC, particularly in EGFR exon 20 insertion mutant models and triple mutant models, including the C797S mutation. The combination therapy demonstrates superior efficacy compared to monotherapy in both cellular and animal models, effectively inhibiting signaling pathways and tumor growth. These findings are promising for addressing the significant challenge posed by the development of resistance to EGFR TKIs in the treatment of NSCLC. A Phase I study of CKD-702, which began in 2020, is currently ongoing. Current clinical trials of CKD-702 monotherapy show promising results, with combination therapy being considered to address resistance mechanisms in NSCLC. Citation Format: Mi Jin Yoon, Soon-Ki Hong, Gun-Woo Park, Kyung-Woo Lee, Sunhong Kim, Young-Shin Kwak, Ju-Hee Lee. CKD-702, a novel bispecific antibody, enhances anti-tumor effect of combination with EGFR TKIs in NSCLC preclinical model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1708.

  PublisherDOI

• Sparse Bayesian Group Factor Model for Feature Interactions in Multiple Count Tables Data

  Journal of the American Statistical Association · 2025-01-13

  articleOpen accessSenior author

  Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with log-normal mixture kernels for count vectors. A group factor model is used to model the covariance matrix of the mixing kernel that describes microorganism interaction. We construct a Dirichlet-Horseshoe (Dir-HS) shrinkage prior and use it as a joint prior for factor loading vectors. Joint sparsity induced by a Dir-HS prior greatly improves the performance in high-dimensional applications. We further model the effects of covariates on microbial abundances using regression. The semiparametric model flexibly accommodates large variability in observed counts and excess zero counts and provides a basis for robust estimation of the interaction and covariate effects. We evaluate Sp-BGFM using simulation studies and real data analysis, comparing it to popular alternatives. Our results highlight the necessity of joint sparsity induced by the Dir-HS prior, and the benefits of a flexible DP model for baseline abundances.

  PublisherOA PDFDOI

• Bayesian Safety and Futility Monitoring in Phase II Trials Using One Utility‐Based Rule

  Statistics in Medicine · 2024-11-05 · 3 citations

  articleOpen access1st authorCorresponding

  For phase II clinical trials that determine the acceptability of an experimental treatment based on ordinal toxicity and ordinal response, most monitoring methods require each ordinal outcome to be dichotomized using a selected cut-point. This allows two early stopping rules to be constructed that compare marginal probabilities of toxicity and response to respective upper and lower limits. Important problems with this approach are loss of information due to dichotomization, dependence of treatment acceptability decisions on precisely how each ordinal variable is dichotomized, and ignoring association between the two outcomes. To address these problems, we propose a new Bayesian method, which we call U-Bayes, that exploits elicited numerical utilities of the joint ordinal outcomes to construct one early stopping rule that compares the mean utility to a lower limit. U-Bayes avoids the problems noted above by using the entire joint distribution of the ordinal outcomes, and not dichotomizing the outcomes. A step-by-step algorithm is provided for constructing a U-Bayes rule based on elicited utilities and elicited limits on marginal outcome probabilities. A simulation study shows that U-Bayes greatly improves the probability of determining treatment acceptability compared to conventional designs that use two monitoring rules based on marginal probabilities.

  PublisherOA PDFDOI

• Reviewer #3 (Public Review): Artificial selection for microbial collective composition can succeed or fail depending on the initial and target values

  2024-05-29

  peer-reviewOpen access1st authorCorresponding

  Microbial collectives, capable of functions beyond the reach of individual populations, can be enhanced through artificial selection. However, this process presents unique challenges. Here, we explore the 'waterfall' phenomenon, a metaphor describing how the success in achieving a desired genotype or species composition in microbial collectives can depend on both the target characteristics and initial conditions. We focus on collectives comprising fast-growing (F) and slow-growing (S) types, aiming to achieve specific S frequencies. Through simulations and analytical calculations, we show that intermediate target S frequencies might be elusive, akin to maintaining a raft's position within a waterfall, rather than above or below it. This challenge arises because intra-collective selection, favoring F during growth, is the strongest at intermediate S frequencies, which can overpower counteracting inter-collective selection effects. Achieving low target S frequencies is consistently possible as expected, but high target S frequencies require an initially high S frequency — similar to a raft that can descend but not ascend a waterfall. The range of attainable target frequencies is significantly influenced by the initial population size of the collectives, while the number of collectives under selection plays a less critical role. In scenarios involving more than two types, the evolutionary trajectory must navigate entirely away from the metaphorical 'waterfall drop.' Our findings illustrate that the strength of intra-collective evolution is frequency-dependent, with implications in experimental planning.

  PublisherDOI

• Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction

  Nature Biotechnology · 2024-06-11 · 9 citations

  articleOpen access

  Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

  PublisherOA PDFDOI

• Bayesian Dose-Finding in Two Treatment Cycles based on Efficacy and Toxicity

  2024-10-07

  book-chapterSenior author

  Practicing physicians routinely use DTRs to make multi-cycle decisions for their patients. Most clinical trial designs ignore the actual DTRs being used, and instead only evaluate the treatments given initially, as if each patient's clinical outcomes were due to the first treatment alone. A common example in oncology arises when salvage therapy is given after disease progression following an initial front-line treatment. While the two-stage DTR is (front-line, salvage), with the possible outcomes in each stage determined by the times to progression, death, or administrative censoring, most designs and data analyses evaluate the front-line treatments and ignore salvage therapy. A flawed rationale for this dysfunctional convention is that effects of different salvage therapies on patients' times from progression to death will somehow “average out.” A detailed description of this pervasive practice, and its consequences, is given in Chapter 12 of Thall (2020). This problem also is very common in early-phase dose-finding trial designs, nearly all of which are myopic in that they focus on the dose or dose combination given initially while ignoring treatments and doses given in cycles of therapy after the first. Consequently, the nominally “optimal” dose chosen by such a design actually pertains only to the first cycle of therapy. The design described in this chapter was motivated by the desire to provide an alternative to this convention.

  PublisherDOI

• Reviewer #1 (Public Review): Artificial selection for microbial collective composition can succeed or fail depending on the initial and target values

  2024-05-29

  peer-reviewOpen access1st authorCorresponding

  Microbial collectives, capable of functions beyond the reach of individual populations, can be enhanced through artificial selection. However, this process presents unique challenges. Here, we explore the 'waterfall' phenomenon, a metaphor describing how the success in achieving a desired genotype or species composition in microbial collectives can depend on both the target characteristics and initial conditions. We focus on collectives comprising fast-growing (F) and slow-growing (S) types, aiming to achieve specific S frequencies. Through simulations and analytical calculations, we show that intermediate target S frequencies might be elusive, akin to maintaining a raft's position within a waterfall, rather than above or below it. This challenge arises because intra-collective selection, favoring F during growth, is the strongest at intermediate S frequencies, which can overpower counteracting inter-collective selection effects. Achieving low target S frequencies is consistently possible as expected, but high target S frequencies require an initially high S frequency — similar to a raft that can descend but not ascend a waterfall. The range of attainable target frequencies is significantly influenced by the initial population size of the collectives, while the number of collectives under selection plays a less critical role. In scenarios involving more than two types, the evolutionary trajectory must navigate entirely away from the metaphorical 'waterfall drop.' Our findings illustrate that the strength of intra-collective evolution is frequency-dependent, with implications in experimental planning.

  PublisherDOI

• A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data

  Journal of the Royal Statistical Society Series C (Applied Statistics) · 2023-04-25

  articleOpen access

  A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.

  PublisherOA PDFDOI

• Bayesian modeling of interaction between features in sparse multivariate count data with application to microbiome study

  The Annals of Applied Statistics · 2023-09-01 · 2 citations

  articleOpen accessSenior author

  Many statistical methods have been developed for the analysis of microbial community profiles, but due to the complexity of typical microbiome measurements, inference of interactions between microbial features remains challenging. We develop a Bayesian zero-inflated rounded log-normal kernel method to model interaction between microbial features in a community using multivariate count data in the presence of covariates and excess zeros. The model carefully constructs the interaction structure by imposing joint sparsity on the covariance matrix of the kernel and obtains a reliable estimate of the structure with a small sample size. The model also includes zero inflation to account for excess zeros observed in data and infers differential abundance of microbial features associated with covariates through log-linear regression. We provide simulation studies and real data analysis examples to demonstrate the developed model. Comparison of the model to a simpler model and popular alternatives in simulation studies shows that, in addition to an added and important insight on the feature interaction, it yields superior parameter estimates and model fit in various settings.

  PublisherOA PDFDOI

Recent grants

• Nonparametric Bayesian Methods for Joint Analysis of Recurrent Events and Survival Time

  NSF · $125k · 2020–2024

• Novel Bayesian Inference for Microbial Community Studies Using High-Throughput Sequencing Data

  NSF · $313k · 2017–2022

• 02 Research Animal Support Facility-Houston/Smithville

  NIH · $92.3M · 1996–2026

Frequent coauthors

• Geoff Macintyre

  Spanish National Cancer Research Centre

  79 shared

• Thomas J. Mitchell

  Wellcome Sanger Institute

  73 shared

• Rory Johnson

  University Hospital of Bern

  71 shared

• Florian Markowetz

  University of Cambridge

  71 shared

• Quaid Morris

  67 shared

• Roland Eils

  67 shared

• Marcin Imieliński

  66 shared

• Matthias Schlesner

  University of Augsburg

  63 shared