Difference between revisions of "Research Topics"

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'''Research Topics'''<div>
 
'''Research Topics'''<div>
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Spatial omic imaging and machine learning are rapidly transforming the nature of biology research, providing rich avenues for discovery at the nexus of AI and mechanistic interpretation. My lab uses computational, mathematical, and high-throughput data generation approaches to study how cancer ecosystems function, evolve, and respond to therapeutic treatment. We study problems in cancer sequence and image analysis across a wide spectrum of cancer types, with particular expertise in breast cancer and patient-derived xenografts. These projects involve collaborations with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and a number of outside groups.
  
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Advances in sequencing have radically transformed the scale and nature of genetic studies. These have made it possible to analyze genomic changes across species, individuals, and single cells as mutations accrue and are subject to selection. Diverse phenotypic datasets have also grown rapidly, not only for sequencing-based assays such as gene expression and protein-nucleic acid interactions, but also other types including clinical and drug-screening investigations. My lab develops computational and mathematical approaches to understand how genomes function and evolve in order to make these findings clinically relevant. We use techniques from a variety of disciplines, including data science, evolutionary modeling, and biophysics. We are currently focused on two major areas: 1) Computational Approaches for Cancer Genomics, and 2) Gene Regulation. Our projects involve collaborations with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and multiple outside groups.
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For the most up-to-date information of the lab's work, see the Publications page.
  
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'''Computational Approaches for Cancer Genomics'''<div>
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'''Computational Tissue Image Analysis'''<div>
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[[File:Jie_crc_schematic.jpg|800px]]
  
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Our lab focuses on understanding cancer using patient-derived xenografts, a model system in which human tumors are engrafted and studied in NSG mice. JAX has developed >400 such models from cancer types including breast, lung, bladder, and others, and these are a [http://tumor.informatics.jax.org/mtbwi/pdxSearch.do community wide resource]. Our lab is involved studies using these models to understand the genetic drivers of cancer and drug resistance, with a  focus on tumor heterogeneity and evolution. Within JAX we work closely with other groups studying xenografts, including the Bult (JAX-MG), Liu (JAX-GM), and Lee (JAX-GM) labs. These projects include studies to identify drivers of drug susceptibility in triple negative breast cancers (Menghi et al 2016) and to determine intratumoral evolution in response to chemotherapy.<br>
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Cancer treatment response is affected by tumor cell heterogeneity, immune cells, and other microenvironmental features, analogous to factors important in macroscopic ecosystems. Our lab uses advanced computational image analysis to reveal the spatial processes in tumors. We have developed convolutional neural networks that can be applied to H&E images to accurately distinguish tumor regions, identify cancer subtypes, and classify mutation status. Critically, our work has shown that images from diverse cancer types can be combined to enhance discovery from large image sets. The lab analyzes many types of imaging data, including spatial transcriptomics (Visium, Xenium, VisiumHD, merFISH), spatial proteomics (CODEX, imaging mass cytometry), 3D confocal microscopy, spatial long-read sequencing, and H&E/IHC across tumors, organoids, and xenografts. Our studies integrate discussions among computational biologists, cancer biologists, oncologists and pathologists.  
  
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[[File: Image:Pdx.png | 400 px]]
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Our lab co-leads the Data Analysis Core for the KAPP-Sen U54 that is part of the NIH Cellular Senescence Network (SenNet). In this project we perform spatial analysis of H&E, spatial transcriptomics, and spatial proteomics datasets for kidney, adipose, pancreas, and placental tissues, among others.  <div>
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[[File:Cancer_ecology.png|500px]]
  
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As of September 2017, our lab leads the NCI [https://www.pdxnetwork.org/ PDXNet] Data Commons and Coordination Center together with our colleagues at Seven Bridges Genomics. In this project, we are coordinating the analysis of novel xenograft studies across multiple centers around the United States in order to advance the development of clinical trials. Through this and other projects (Bais et al 2017), our lab has been one of the leaders in cloud computing approaches for cancer genomics analysis. <div>
 
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The lab also studies evolutionary and ecological processes in a variety of other cancer systems. Recent projects have included studies of selective pressures in intratumoral evolution across thousands of cancer samples (Noorbakhsh et al 2017) and investigations into immune and stromal introgression across cancer types (Chae et al 2018).
 
  
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'''RNA-level Gene Regulation'''<div>
 
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Our group has been studying mechanisms of post-transcriptional gene regulation, with focuses on regulation of translation, protein-RNA binding, and splicing. For example, in collaboration with Prof. Susan Ackerman (JAX-MG) we have identified and characterized a mutation in a tRNA as a driver for neurodegeneration and shown that this phenotype is mediated by specific translational pausing at the codons complementary to the tRNA anticodon (Ishimura et al 2014). To our knowledge, this is the first tRNA mutation found to have a phenotypic consequence in a mammal. Another current interest is how proteins interact with RNAs to achieve specific binding. In this area, we have previously developed approaches to clarify functional elements in RNA based on a combination of functional genomic, structural, and modeling approaches (Zarringhalam et al 2012). More broadly, we have been studying the functions and neutral evolutionary behavior of synonymous sites in coding sequences for more than a decade (Chuang and Li 2004; Chin et al 2005). We have shown for example that coding sequences are replete with binding sites for microRNAs, as well as other types of functional sequences such as exonic splicing enhancers. Such sites exhibit a strong selective pressure on the synonymous sites of coding regions (Kural et al 2009; Ding et al 2012; Ritter et al 2012).
 
  
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[[File:400px-Types of regulation.png]]
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'''Cancer Evolution and Patient-Derived Xenografts'''<div>
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Our lab is a leader in the field of patient-derived xenografts, a model system in which human tumors are engrafted and studied in NSG mice. Xenografts play a critical role in cancer research, as they are often used in therapeutic testing just prior to clinical trials. JAX has developed >300 such models from cancer types including breast, lung, bladder, and others, and these are a [http://tumor.informatics.jax.org/mtbwi/pdxSearch.do community wide resource]. Since 2017, our lab co-leads the Data Coordination Center for the NCI [https://www.pdxnetwork.org/ PDXNet], a multi-institute consortium supported by the NCI Cancer Moonshot Initiative. We are partnering with institutes around the country including the Huntsman Cancer Institute, Baylor College of Medicine, MD Anderson, Washington University, the Wistar Institute, NCI's Frederick National Laboratory, and Seven Bridges Genomics to jointly study cancer using xenografts. Our lab uses these to understand the genetic drivers of cancer and drug resistance, with a focus on computational modeling of intratumoral evolution (Woo, Giordano et al 2021, Kim et al 2018, Noorbakhsh and Chuang 2017) and also the robustness of xenografts for preclinical testing therapeutic agents (Evard et al 2020). For more information see [http://pdxnetwork.org the PDXNetwork site].
  
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'''Other Interests'''<div>
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[[File:400px-Pdx.png]]
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One of our major prior interests has been to characterize the functions of highly conserved noncoding sequences. Just as morphological features shared among species (e.g. all vertebrates have a spine) are likely to be important to those species, DNA sequences shared among species are likely to be functional. Our lab has collaborated with the Guo lab at UCSF to study conserved noncoding elements (CNEs) via a variety of methods involving computational analysis of sequence, expression, and epigenomic data, as well as experiments testing the enhancer activity of sequences in zebrafish embryos. CNEs are abundant in vertebrate genomes, e.g. at a threshold of at least 50 bp and at least 50% sequence identity, there are 73187 strand-specific CNEs conserved between zebrafish and human. We have characterized the relative importance of cis- and trans- regulatory evolution on the functional behavior of enhancers (Ritter et al 2010) and also developed tools to organize the functions of CNEs (cneviewer.zebrafishcne.org, Persampieri et al 2008).
 
  
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[[File:Aggregate_hzm.jpg]]
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Within JAX we work closely with a number of groups studying cancer with xenografts and clinical samples, including the Liu, Bult, Lee, Palucka, Robson, and Anczukow-Camarda labs, on projects such as triple negative breast cancer (Menghi et al 2016), gastric cancer (Cho et al 2018), and immune cell activity (Yu et al, 2021).  
  
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Our lab has been interested in a variety of issues in molecular evolution related to the balance of functional and neutral pressures in genomes. For example, one puzzle is why mutation rates are uniform in some species, such as the sensu stricto yeasts, while rates vary by location in other species, such as mouse and human. We have found that all mammalian species have regional mutation biases, typically on a scale of several megabases. In contrast, all yeasts have uniform mutation rates, with the exception of the Candida clade (Fox et al 2008; Chuang and Li 2004; Chuang and Li 2007; Chin, Chuang, and Li 2005). In species where the mutation rate is non-uniform, we are interested in questions such as what structural or sequence features affect mutation rates, and whether gene locations have evolved to make use of mutational heterogeneity.
 
  
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Other model organisms with which we have expertise are the malaria parasite Plasmodium falciparum and the yeast S. cerevisiae. A central mystery of the malaria genome is how transcription is regulated. We have observed that there is far less intergenic sequence apparently under purifying selection in malaria than in yeast genomes, suggesting that transcription regulation is simpler in malaria (Imamura, Persampieri and Chuang, 2007). We have also applied comparative techniques to identify functional sites in the promoters of the Saccharomyces genus of yeasts, to estimate the complexity of gene regulation and the types of genes likely to be under the strictest regulation (Chin, Chuang, and Li 2005).
 
  
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Previously the lab also has worked on the analysis of high-throughput lipidomic data. We developed tools to analyze which aspects of lipid content are important to cancer phenotypes (Kiebish et al 2008). This work is closely tied to evaluating the Warburg theory of cancer, as described in [https://www.bc.edu/bc-web/schools/mcas/departments/biology.html this report]. We have also developed both equilibrum and dynamic models to explain the distributions of lipids found in normal and cancerous tissues (Kiebish et al 2010).
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'''Prior Interests'''<div>
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[[File:500px-Simulation.jpg]]
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The lab has covered a range of topics prior to the current emphasis on cancer image analysis. One interest has been mammalian gene regulation at the RNA level, which stems from our longtime experience in computational analysis of selection pressures on RNA (Chuang and Li 2004, Chin et al 2005, Kural et al 2009, Dotu et al 2018). For example, in collaboration with Prof. Susan Ackerman (UCSD) our team performed the computational analysis for the first study showing a tRNA mutation as the driver for a mammalian phenotype (Ishimura et al 2014). We continue to study translational dysregulation mechanisms, particularly in the context of neurodegeneration (Kapur et al 2020, Terrey et al 2020). Much of the lab's research has grown out of early interests in molecular evolution and statistical physics. In some of our earlier work, we have characterized the relative importance of cis- and trans- regulatory evolution on enhancers (Ritter et al 2010; Persampieri et al 2008). We have also studied the evolution of mutational processes across species and cancers. This has included research into why mutation rates are uniform in some species, such as the sensu stricto yeasts, while rates vary by location in other species, such as mouse and human (Fox et al 2008; Chuang and Li 2004; Chuang and Li 2007; Chin, Chuang, and Li 2005). Biophysics interests have included the dynamics of translocation of a polymer through a nanopore (Chuang et al, Phys Rev E 2001) and the thermodynamic stability of protein folds (Chuang et al, Phys Rev Lett 2001).

Latest revision as of 08:57, 13 November 2024

Research Topics

Spatial omic imaging and machine learning are rapidly transforming the nature of biology research, providing rich avenues for discovery at the nexus of AI and mechanistic interpretation. My lab uses computational, mathematical, and high-throughput data generation approaches to study how cancer ecosystems function, evolve, and respond to therapeutic treatment. We study problems in cancer sequence and image analysis across a wide spectrum of cancer types, with particular expertise in breast cancer and patient-derived xenografts. These projects involve collaborations with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and a number of outside groups.

For the most up-to-date information of the lab's work, see the Publications page.

Computational Tissue Image Analysis

Jie crc schematic.jpg

Cancer treatment response is affected by tumor cell heterogeneity, immune cells, and other microenvironmental features, analogous to factors important in macroscopic ecosystems. Our lab uses advanced computational image analysis to reveal the spatial processes in tumors. We have developed convolutional neural networks that can be applied to H&E images to accurately distinguish tumor regions, identify cancer subtypes, and classify mutation status. Critically, our work has shown that images from diverse cancer types can be combined to enhance discovery from large image sets. The lab analyzes many types of imaging data, including spatial transcriptomics (Visium, Xenium, VisiumHD, merFISH), spatial proteomics (CODEX, imaging mass cytometry), 3D confocal microscopy, spatial long-read sequencing, and H&E/IHC across tumors, organoids, and xenografts. Our studies integrate discussions among computational biologists, cancer biologists, oncologists and pathologists.

Our lab co-leads the Data Analysis Core for the KAPP-Sen U54 that is part of the NIH Cellular Senescence Network (SenNet). In this project we perform spatial analysis of H&E, spatial transcriptomics, and spatial proteomics datasets for kidney, adipose, pancreas, and placental tissues, among others.

Cancer ecology.png


Cancer Evolution and Patient-Derived Xenografts

Our lab is a leader in the field of patient-derived xenografts, a model system in which human tumors are engrafted and studied in NSG mice. Xenografts play a critical role in cancer research, as they are often used in therapeutic testing just prior to clinical trials. JAX has developed >300 such models from cancer types including breast, lung, bladder, and others, and these are a community wide resource. Since 2017, our lab co-leads the Data Coordination Center for the NCI PDXNet, a multi-institute consortium supported by the NCI Cancer Moonshot Initiative. We are partnering with institutes around the country including the Huntsman Cancer Institute, Baylor College of Medicine, MD Anderson, Washington University, the Wistar Institute, NCI's Frederick National Laboratory, and Seven Bridges Genomics to jointly study cancer using xenografts. Our lab uses these to understand the genetic drivers of cancer and drug resistance, with a focus on computational modeling of intratumoral evolution (Woo, Giordano et al 2021, Kim et al 2018, Noorbakhsh and Chuang 2017) and also the robustness of xenografts for preclinical testing therapeutic agents (Evard et al 2020). For more information see the PDXNetwork site.

400px-Pdx.png

Within JAX we work closely with a number of groups studying cancer with xenografts and clinical samples, including the Liu, Bult, Lee, Palucka, Robson, and Anczukow-Camarda labs, on projects such as triple negative breast cancer (Menghi et al 2016), gastric cancer (Cho et al 2018), and immune cell activity (Yu et al, 2021).


Prior Interests
The lab has covered a range of topics prior to the current emphasis on cancer image analysis. One interest has been mammalian gene regulation at the RNA level, which stems from our longtime experience in computational analysis of selection pressures on RNA (Chuang and Li 2004, Chin et al 2005, Kural et al 2009, Dotu et al 2018). For example, in collaboration with Prof. Susan Ackerman (UCSD) our team performed the computational analysis for the first study showing a tRNA mutation as the driver for a mammalian phenotype (Ishimura et al 2014). We continue to study translational dysregulation mechanisms, particularly in the context of neurodegeneration (Kapur et al 2020, Terrey et al 2020). Much of the lab's research has grown out of early interests in molecular evolution and statistical physics. In some of our earlier work, we have characterized the relative importance of cis- and trans- regulatory evolution on enhancers (Ritter et al 2010; Persampieri et al 2008). We have also studied the evolution of mutational processes across species and cancers. This has included research into why mutation rates are uniform in some species, such as the sensu stricto yeasts, while rates vary by location in other species, such as mouse and human (Fox et al 2008; Chuang and Li 2004; Chuang and Li 2007; Chin, Chuang, and Li 2005). Biophysics interests have included the dynamics of translocation of a polymer through a nanopore (Chuang et al, Phys Rev E 2001) and the thermodynamic stability of protein folds (Chuang et al, Phys Rev Lett 2001).
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