Canary CREST Program Projects

PIJeremy Dahl, PhD

Type of Research: Combination of wet and dry lab

Possible Project:

Ultrasound molecular imaging is an emerging approach with large potential for improving the diagnostic accuracy and characterization of focal breast lesions. Molecular ultrasound imaging involves both traditional ultrasound imaging techniques with molecularly-targeted microbubbles that bind to specific biomarkers, such as proteins expressed on the endothelial cells of breast tumor neovasculature.  As the microbubbles bind to the tumor, they highly reflect the ultrasound waves to make the cancer easily visible in the ultrasound image.  Our lab is focused on on an end-to-end high-sensitivity high-specificity ultrasound imaging system involving both technological advancements in the detection of bound microbubbles under ultrasound as well as the development of novel peptide ligands that can be attached to microbubbles in order to bind the microbubble to specific receptors in the neovasculature of the tumor, such as B7-H3.  Under this project, students will have the opportunity to select from a wide variety of research topic areas of research involving ultrasound imaging and engineering, cancer biology, cell culture, peptide conjugations, microbubble synthesis, cell toxicity assays, and mouse models of breast cancer.  This project is very collaborative and involves many areas of discipline.  Students will require good communication, self-motivation, creativity, and patience and will learn some of the basic skills of research necessary to become a productive scientist.

 

Lab Profile: http://med.stanford.edu/ultrasound


PI: Utkan Demirci, PhD

Type of Research: Wet lab

Possible Projects:

Project #1: Decoding cancer cell-derived extracellular
                                                vesicle release triggered by shear stress.

Extracellular vesicles (EVs) are cargo packed nano- and micro-sized particles carrying information from cell-to-cell at different niches in the body. They have been isolated from all the body fluids as well as tissue biopsies for diagnostic and prognostic purposes. In this study, we will generate a microfluidic cell culture platform to profile EVs released by cancer cells at static and dynamic culture conditions. Culture media from these cultures will be collected for EV isolation. Then, we will profile EVs and their content. Results of this work will allow us to better understand interactions of cancer cells with their microenvironment through secreted vesicles.

Project #2: Cancer cell detection could provide an indicator for the minimally invasive cancer early diagnosis, tumor progression monitoring, and treatment evaluation. However, current standard approaches for CTCs isolation involve extensive sample preparation steps and/or require modified with surface micro/nanostructures to detect the extremely low abundance of cancer cells in blood sample. We propose to design self-propelled micro/nanomotors that can specific isolate cells with high efficiency from biological samples without extensive sample preparation process and exhibit powerful tool for the early diagnosis of cancer. We will develop a new bio-template-assisted approach for facile fabrication of multifunctional self-propelled platforms engineered with nano-topographic structures and surface chemistry to capture target cancer cells from whole blood with high efficiency. Meanwhile, the captured cells will be released on-demand responding to external stimuli because the redox-responsive molecules on our platform surface. We envision the “live template” strategy that employs living organisms with their own biofunctions is very promising in the design of diverse functional micro/nanomotors for desired biomedical applications.

Project #3: Nanoplasmonic Sensing for Cancer Biomarker Detection

Advances in micro- and nano-scale technologies have denoted broad applications in biosensing and biomedical engineering, as well as provided promising platforms for clinical applications such as diagnostics and identification of biological agents. However, bulky instrumentation, assay cost, and need for skilled personnel for clinical applications are still hindering their applicability at point-of-care diagnosis. Hence, portable, rapid, robust, and inexpensive technologies are needed at clinical settings. In this project, we will develop a cost-effective, label-free, highly sensitive nanoplasmonic platform and integrate them with microfluidic chips for efficient detection of cancer cell biomarkers from bodily fluids. Integrating two/three-dimensional (2D/3D) plasmonic structures into the microfluidic chips will open new fashion to the field of cancer biosensing. The chips will be modified by a layer-by-layer surface chemistry approach to decorate antibodies to capture surface protein biomarkers and their binding interaction.

Project #4: Malaria infects over 200 million annually in low-and-middle income countries, and is being exacerbated by antimalarial resistance problems. There is a significant unmet need for a field-accessible tool (inexpensive, rapid, operable without electricity, laboratory infrastructure, or specialized training) to detect and quantify Plasmodium falciparum parasites in blood, for sensitive, specific diagnosis as well as rapid drug response monitoring. We are developing an innovative magnetic levitation platform that precisely measures subtle differences in cell levitation heights as inherent biomarkers for infection in red blood cells. We have shown biophysical separation of ring-stage parasites, and recently conducted a pilot field study in Uganda with clinical samples. We have also observed that drug treatment causes measurable, physical changes in the parasites. We are developing this as a rapid test for antimalarial drug resistance, and aim to conduct more field tests with further developed prototypes. We hope that this accessible but quantitative platform will aid healthcare workers’ changing needs in the malaria field.

 

Lab Profile: http://bammlabs.stanford.edu/people/dr-utkan-demirci


PINaside Gozde Durmus, PhD

Type of Research: Wet lab

Possible Project:

Levitating Cells to Decode Rare Signatures from Blood

The human body comprises diverse cells and circulating markers including; circulating cells, cell-free DNA and exosomes. We have recently created a magnetic levitation platform, which uniquely enables ultra-precise density measurements, magnetic profiling, imaging, sorting and profiling of cells in seconds in real-time at single-cell resolution. In this project, we will combine our expertise in microfluidics and magnetics, in collaboration with experts in cancer biology, (i) to develop a novel, high throughput platform for label-free isolation of rare circulating tumor cells and exosomes from patient's blood, and (ii) to subsequently analyze the isolated cells for their molecular profiling. The expected outcome of this study is an innovative approach for high throughput, biomarker-free isolation of extremely rare signatures (i.e., 1-10 circulating tumor cells) from a large volume (~10 mL) of cancer patient's blood, followed by downstream molecular characterization of the isolated rare cells. By doing so, we will understand and investigate how these changes in physiological and pathological information can be detected at early stages of disease. This will broadly impact personalized and precision treatment of patients.

 

Lab Profile: https://gdurmus.people.stanford.edu/


Instructor: Ahmed El Kaffas, PhD (PI: Gambhir)

Type of Research: Combination of wet and dry lab

Possible Project:

We seek candidates interested in advanced image processing and quantification methods. This is an opportunity to be exposed to analysis, algorithm development and validation of medical imaging biomarkers, and to potentially work directly with clinicians and researchers in gathering data, in patients and preclinical models. Candidates will be working w/ 1-2 postdocs and a principle investigator (clinician/scientist) to explore image quantification methodologies in the context of imaging, and to carryout experimental work to validate these parameters. The candidate will first be exposed to a variety of activities in the lab and subsequently be given an opportunity to carryout a specific project from start to end with guidance. 

Previous Mat lab and/or Python experience, specifically in the context of image processing, is an asset. C/C++ would also be useful. The following list of other specific experiences are assets:

- Experience with bash scripting, and working with NIFTI data and associated toolboxes

- Ultrasound research and/or imaging (MRI/CT/Microscopy)

- Machine learning and statistical models

- Open CV or other programing languages for image processing

- Pre-clinical research in rodents and/or cells (with focus on cancer)


PI: Alice Fan, MD

Type of Research: Wet lab

Possible Project:

Dr. Fan's translational research focus in Urologic Oncology is developing biomarkers for early detection and early measurement of therapeutic response in patients with renal cell carcinoma. Her laboratory develops novel technologies to determine the mechanism of activity of novel and standard agents directed against metabolic and oncogenic signaling pathways, define biomarkers for diagnosis and therapeutic response in patients with cancer, and investigate the immune response to targeted therapeutics. Her clinical research includes enrolling patients on clinical trials testing new agents for kidney cancer. A CREST student will have the opportunity to experience both translational and clinical research.


PI: Sanjiv Sam Gambhir, MD, PhD

Type of Research: Wet lab

Possible Projects:

Glioblastoma (GBM) is an exceptionally aggressive yet common form of brain cancer. The disease is extremely proliferative and heterogeneous. The diversity of subgroups within a GBM makes therapy difficult and, consequently, there are few therapeutic and diagnostic tools that can be used to combat the disease. In this program, the student will participate in finding such new tools by screening promising drugs against glioblastoma cell cultures of human origin. Once we have identified the leaders, we will then validate them in preclinical models of human GBM. In addition to finding chemotherapeutic targets against GBM, the student will also explore the feasibility of combating GBM with a novel, physico-chemical therapy that recently received approval for clinical use from the Food and Drug Administration (FDA), namely, the application of exogenous, alternating fields or Tumor Treating Fields (TTFields). Part of the summer research program will involve the exploration of TTField therapy in combination with novel chemotherapies against GBM.

The Canary CREST program will expose the individual to a number of techniques that are relevant and necessary for the fields of biomedical and molecular imaging research. The student will learn both adherent and 3-dimensional cell cultures for several glioblastoma lines. Standardized bioassays for cellular activity (cell counts, alamar blue and MTT proliferation assays, neurospheres size distribution, bioluminescence activity) will be introduced. The student will familiarize herself/himself with equipment for the application of alternating electric fields on glioma-derived cell cultures. If time permits, the student will be shown fundamentals of preclinical model development. Such investigations will also introduce the student to established bioluminescence assays of cancer cell growth and development. 

 

Lab Profile: http://med.stanford.edu/mips/research/mmil.html


PI: Olivier Gevaert, PhD

Type of Research: Dry lab

Possible Project:

My lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Additionally, we developed computational algorithms for the identification of driver genes using multi-omics data. Furthermore, we are working on multi-scale biomedical data fusion methods, bridging the molecular using omics data, cellular using pathology data and tissue using medical imaging data.

 

Lab Profile: http://med.stanford.edu/gevaertlab.html


Core Director: Frezghi Habte, PhD (PI: Gambhir)

Type of Research: Dry lab

Possible Project:

This project will be conducted under the Canary Center Preclinical Imaging Lab, which is a shared facility for in-vivo imaging in small animal models. Its mission is to provide centralized shared advanced state-of-the-art instrumentation, strategies, expertise and software tools to perform multimodality in vivo imaging and image quantification for various applications including in-vivo biological assessment, early cancer detection and other disease detection and treatment research including clinical translation. Biomedical imaging is a fast growing field and increasingly emerging as an essential basic tool for basic science research. Specifically, quantitative imaging is currently becoming a routine practice due to its superior advantage in characterizing biological processes quantitatively. However, due to the complexity of the imaging instrument and associated analysis tools, there is still significant variability and limitations, which makes the development of standardized methods difficult. With Increased image data, imaging is also becoming an important approach to extract and translate useful information from large multi-dimensional databases that may not be obtained from single study. Under this specific project, we will assess/evaluate existing tools and methods, and develop new imaging strategies and/or tools that improve the accuracy and efficiency of image quantification techniques. We will also assess new tools and/or methods for improved image data management and processing large image data sets. The task of the project will require an interest and basic knowledge of molecular biology, the physics of medical imaging, programming preferably in Mat lab, Python, C++ or other related image analysis tools and interest or experience in deep learning and/or 3D Printing. The task may also involve performing imaging experiments using mouse models and some basic wet-lab experiments.  

 

Preclinical Imaging Core Facility Profile: https://canarycenter.stanford.edu/core-facilities/preclinical-imaging.html


Instructor: Sharon Hori, PhD (PI: Gambhir)

Type of Research: Combination of wet and dry lab

Possible Project:

Most cancers can be more effectively treated if they are discovered early. The Hori Lab is improving the detection of early-stage cancers using secreted cancer-specific biomarkers, which are proteins or other molecules released from tumor cells into the blood. The purpose of this training opportunity is to develop innovative strategies to detect small, early-stage cancers from a routine blood sample. This involves learning how to design and conduct molecular and cellular biology experiments, utilize novel molecular imaging techniques, and integrate these experimental results with computational models to study the relationship between a growing tumor and the amount of biomarker it secretes. Cancers of the breast, ovary, lung, pancreas, and prostate will be the primary focus.

The applicant should be a highly-motivated undergraduate with a basic background in molecular/cellular biology and/or mathematics (single-variable calculus), and have a strong desire to learn and integrate biological and computational modeling techniques. Computer programming and mathematical modeling skills are preferred but not required. The student will have the opportunity to learn how to culture cancer cells, perform assays to assess cell viability and measure secreted biomarker levels, image live cancer cells using fluorescence and bioluminescence techniques, study cell properties using flow cytometry, develop basic mathematical/computational models for tumor growth and biomarker secretion, and/or use mathematical modeling and machine learning approaches to study or make predictions about cancer state. This summer research program will provide a unique opportunity to gain hands-on experience in biological and computational research, and is ideal for students interested in cancer research, molecular/cellular biology, medicine, computational and systems biology, computer science, biomedical engineering and related fields. Minimum 40 hr./week required.


PI: Sanjay Malhotra, PhD, FRSC

Type of Research: Combination or wet and dry lab

Possible Project:

Project-1: Our lab majorly focused on synthetic/medicinal chemistry and chemical biology, and routinely develops target-specific small molecules to treat various cancers. In this project, we are develop a PIM1 inhibitor to treat triple-negative breast cancer. Using bio/chemo-informatics approach, we have designed a small molecule library, and screened them using the PIM1 inhibition assay. The potential 'hit compounds' are being optimized further. The student will learn some in vitro assays and get a demonstration of in vivo experiments conducted during this project.

Project-2: With the drug development focus our lab is developing immunomodulators to target the tumor microenvironment. The specific idea behind this project is to modulate the therapy-resistant tumor microenvironments to increase the therapeutic regime. We have designed a library of immunomodulators and screened them on different cancer models. The student will learn in vitro assays and how of in vivo experiments are conducted for drug development process.

 

Lab Profile: http://med.stanford.edu/sanjaymalhotralab/


PI: Ramasamy Paulmurugan, PhD

Type of Laboratory Research: Wet lab

Possible Project:

CELLULAR PATHWAY IMAGING LABORATORY (CPIL)

The main focus of our laboratory is to develop in vivomolecular imaging assays for studying cellular signal transduction networks and to develop promising new therapeutics for treating various cancers (Breast cancer, Hepatocellular carcinoma, and Glioma) which are aggressive and drug resistant phenotypes. We adopt various strategies to achieve our goals.  

1. Imaging Epigenetic changes in cells and in vivo in animal models

We are studying the epigenetic changes, such as histone methylations and protein sumoylations that control cellular functions at different stages of pathological conditions, using various imaging techniques. Histone methylation marks are altered when pathogenesis occurs in many diseases including cancer. Therefore, histone methylation marks are considered a promising therapeutic target for developing drugs. However, simple and sensitive tools that monitor these changes are not currently available. We develop molecular imaging biosensors for real-time monitoring of histone methylation associated protein-protein interactions (H3K9me/chromodomain and H3K27me/chromodomain) in cells and small animal models. Imaging sensors for various important methylation marks are currently under evaluations. These imaging sensors are essential tools for studying changes in histone methylation during disease development process, and for screening small molecule drugs that can be used to modulate histone methylation patterns in cancer cells as a preventive or therapeutic mechanism in oncogenesis. 

2. Developing genetically encoded Molecular sensors for Imaging Protein-Protein Interactions and Protein folding

We extensively use the split-reporter protein complementation systems (Firefly, Renilla, Gaussia, GFP, and mRFP) we previously developed in our laboratory for designing molecular imaging biosensors that monitor protein-protein interactions and protein folding in cells. We designed genetically encoded molecular imaging biosensors for studying histone methylation, p53-protein folding, p53-sumoylation, ligand-induced changes in estrogen receptor folding, NRF2-Keap1 interactions, and major cellular pathways of NFκB, and NQO1 mediated apoptotic and survival signaling  mechanisms in cells. We are currently adopting these sensors for high throughput screening drugs that can be used for cancer therapy.

3. Imaging BPA induced changes in Estrogen Receptor signaling and the associated oncogenesis in transgenic animals

Estrogen receptors (ER) are the major cell growth and development regulators, and its dysregulation is implicated with breast, ovarian, and endometrial cancers. We developed firefly luciferase reporter complementation sensor for imaging ligand-induced conformational changes in ERα. We also developed a transgenic mouse model expressing this complementation sensor. We used this transgenic mouse model to study the Bisphenol A (xenoestogen) induced changes in ER-signaling and oncogenesis. This transgenic mouse model can also be used for screening novel ligands to treat tamoxifen-resistant breast cancer sub-types. Currently we are using this complementation sensors and transgenic mouse to extend our studies to assess the role of ER-β in pathogenesis of breast cancer. We also focus on estrogen independent molecular mechanisms involved in the development, progression, and invasiveness of breast cancers that are negative for ERaexpression.

4. Screening Small Molecules modulating NRF2 and NFkB signaling pathways for developing combination therapy to treat cancers

We are also exploring the role of Nrf2-pathway for acquired chemoresistance in cancer therapy. Overexpression or activation of NRF2 protein in cancer cells can results  in developing chemo- and/or radioresistance. This necessitates understanding of Nrf2-regulation, and identification of Nrf2 activators/inhibitors sensitizing cancer cells to improve chemotherapy. We developed complementation sensor to study NRF2-Keap1 interactions and ARE- activation to study the activation of antioxidant responsive genes and for screening small molecule drugs that can modulate NRF2 pathway. NFkB signaling pathway is another major signaling network regulating immune response to various infections, inflammation and in oncogenesis. Dysregulation of NFkB proteins are implicated in major diseases including cancer. We developed imaging sensors to monitor the activation of NFkB genes in response various stress signaling. We are currently applying these imaging sensors to screen small molecules that simultaneously modulate both Nrf2 and NFkB signaling to improve cancer therapy.

5. Targeting microRNAs for cancer therapy (Breast cancer, Hepato-cellular carcinoma, and Glioblastoma)

Small molecule chemotherapeutic agents lead non-targeted cytotoxicity when used for cancer treatment. Further, targeted delivery of therapeutic agents minimize the optimum concentrations of drugs needed to treat cancer. We develop biocompatible polymer based nanoparticles for delivering small molecule drugs like tamoxifen, Gemcitabine, antisense-microRNAs, and therapeutic DNAs for cancer therapy. MicroRNAs play critical role in the molecular mechanisms responsible for cancer development and drug resistance. We investigate the possible association of microRNAs with breast cancer development and tamoxifen resistance in particular. Additionally, we study the therapeutic utility of PLGA-loaded sense- and antisense- microRNAs to curtail the metastasis of breast cancer.

6. Developing translational cancer nanotheranostics for the treatment of cancers (Breast cancer, Hepato-cellular carcinoma, and Brain Cancer).  

With rapid advances in nanomedicine, cell derived lipid vesicles (CDLV) and cell derived lipid vesicles functionalized nanoparticles (CDLVs-NPs) have emerged as promising nanocarriers for several biomedical applications, including cancer theranostics delivery and imaging. Our lab has significant research experience in the preparation, characterization and preclinical investigation of CDLVs and CDLV-NPs for various biomedical applications. We also exploring the various bioengineering methods to enhance the multifunctionality of CDLVs and CDLV-NPs for cancer therapy. In addition to this, our lab also investigating the possibilities to merge the synthetic nano vesicles and CDLVs as robust nanocarrier for different biomedical applications. Furthermore, we are also investigating the liposomes and non-lamellar lipid nanocarriers such as cubosomes and hexosomes, and lipid polymer hybrids as a potent nanocarrier for targeted cancer therapy and imaging applications.

 

Lab Profile: https://med.stanford.edu/mips/research/cpil.html


PI: Sharon Pitteri, PhD

Type of Research: Wet lab

Possible Project:

The Pitteri Laboratory is focused on the discovery and validation of proteins and other types of molecules in the blood that can be used as indicators of risk, diagnosis, progression, and recurrence of cancer. We specialize in molecular analysis of clinical and biological samples to detect cancer and understand biology. We utilize state-of-the art technologies including liquid chromatography and mass spectrometry to identify, quantify, and characterize proteins and other molecules of interest.

We are looking for highly motivated undergraduate students looking for an opportunity to work on a summer research project focused on cancer early detection. The Canary CREST program is well-suited for students with interests in chemistry, biochemistry, biology, applied physical science, and/or medical research. You will gain hands-on experience with biochemistry and analytical chemistry techniques, and data analysis. Possible projects include analysis of clinical samples and/or cancer cell lines. A positive attitude, willingness to learn and contribute, and meticulous attention to details are a must. 

 

Lab Profile: http://med.stanford.edu/pitterilab/lab-members.html


Mentor:  Johannes Reiter, PhD

Type of Research: Dry lab

Possible Project:

Cancer is an evolutionary process that spans multiple decades. Tumor initiation and progression is driven by the sequential acquisition of mutations in driver genes which increase the net reproductive rate of cells and lead to an uncontrolled growth of these abnormal cells. During the CREST program, students will learn about designing mathematical models of cancer evolution and about developing computational methods to analyze and interpret large-scale DNA sequencing datasets. We analyze liquid biopsy data and develop machine learning based classifiers to investigate the potential and the limitations of various biomarkers such as ctDNA (circulating tumor DNA) for cancer early detection. We use reconstructed cancer phylogenies from multi-region sequencing data to learn about the evolutionary history of a tumor and use that knowledge to predict a cancer’s future evolutionary trajectory. Based on this evolutionary approach, we tackle various questions: Which tumors will require continuous monitoring and which will less likely progress in the lifetime of a patient? Which tumors will relapse after surgery and require adjuvant therapy? Which tumors have a high metastatic potential? Which tumors have already metastasized and need more aggressive treatment?

The project will require a mathematical/statistical background and computer programming skills (e.g., Python, R, MATLAB, Mathematica). For previous successful projects, visit our lab website. 

 

Lab Profile: https://reiterlab.stanford.edu/


PI: Mirabela Rusu, PhD

 

Type of Research: Dry lab

Possible Project:

Despite technical advancements in the diagnosis and treatment of prostate cancer, it remains the most common and the second most deadly cancer in men in US [1]. MRI plays an essential role in the diagnosis of prostate cancer, yet the radiologists’ interpretation of the MRI images has a relatively high sensitivity, 0.85-0.95, yet a reduced specificity, 0.71-0.75, to detect prostate cancer. There is a need to develop advanced machine learning methods to accurately detect cancer on MRI. This project will focus on developing deep learning methods to distinguish benign conditions (non-cancer) from cancer on multi-parametric MRI. 

 

Lab Profile: http://med.stanford.edu/rusulab.html


PI: H. Tom Soh, PhD

Type of Research: Wet lab

Possible Project:

Our lab is focused on generating continuous, real-time biosensors that allow us to better diagnose and manage disease, as well as probe fundamental biological questions. One of the key challenges in developing real-time sensors is engineering molecular recognition elements that can both bind specifically to the target of interest and produce a measurable signal upon binding. To this end, our lab has previously employed structure-switching aptamers, nucleic acid probes that produce large conformational changes upon target binding. This project will focus on developing alternative molecular switches for generating the binding signal. These reagents will be integrated with sensor platforms within our lab to achieve high temporal resolution of important biomarkers for better understanding disease states. During the summer program, students will learn about biosensor development from both the molecular and the device perspective. Ideal candidates will have a background or a keen interest in biomedical, chemical, or molecular engineering.

 

Lab Profile: http://sohlab.stanford.edu    


PI: Geoffrey Sonn, MD

Type of Laboratory Research: Dry lab

Possible Project:

We are interested in the use of artificial intelligence for interpretation of medical images. In particular, my group uses deep convolutional neural networks for prostate cancer detection using multiparametric MRI. I am a practicing urologic oncologist with a specialty in prostate cancer and kidney cancer. This research is motivated by a desire to improve the lives of men at risk for prostate cancer by improving diagnosis. Our competitive advantage in this project is a large database of patients with labeled images that we have accumulated over the last 5 years, and strong collaborations with radiology and computer science. This is an ideal project for students with an interest in medicine and ideally some background in engineering and/or computer science.

In this project, you will gain hands-on experience in working with clinical and medical imaging data, while developing and validating deep learning algorithms to improve interpretation of MRI. The overarching goal is to rapidly translate these research advances into clinical care.  Students working with my group should have some background in math, statistics and/or computer programming. However, the most important attributes that we are looking for are a willingness to learn and work hard in a collaborative environment.

 

Lab Profile: https://med.stanford.edu/ucil.html


PI: Tanya Stoyanova, PhD

Type of Research: Wet lab

Possible Project:

We focus on understanding molecular mechanisms underlying cancer development. We are particularly interested in signaling cascades initiated by cell surface receptors and their use as biomarkers for stratification of indolent from aggressive prostate cancer and therapeutic targets for the advanced disease.

 

Lab Profile: http://med.stanford.edu/stoyanovalab


PI: Jiangbin Ye, PhD

Type of Research: Wet lab

Possible Project: Remodeling epigenetic landscape in cancer to enhance differentiation therapy

Cell differentiation is the process that a stem/progenitor cell becomes specialized cells. When differentiation is dysregulated, cancer cells can be formed. The goal of differentiation therapy is to induce terminal differentiation of tumor cells, converting them back to normal cells. However, tumor cells often develop resistance and relapse. Our preliminary data demonstrated epigenetic reprograming through histone and DNA modifications is the cause of resistance. Using neuroblastoma as a model, we have successfully developed multiple metabolic intervention strategies that can restore the epigenetic landscape and differentiation in neuroblastoma cells, thus overcome the resistance to differentiation therapy. Now we propose to apply these metabolic intervention strategies in a variety of cancer types including breast, prostate, lung etc.

 

Lab Profile: http://med.stanford.edu/yelab/lab-members.html