2023 Canary CREST Program Projects
Mentor: Christina Curtis, PhD, MSc
Type of Research: Dry lab
Possible Project:
Recently, we developed a mutation agnostic diagnostic method to detect clonal hematopoiesis of indeterminate potential (CHIP) using whole peripheral blood and fluctuating methylation clocks. We are interested in whether this same technique can be used as a prognostic indicator for the development of therapy-related myeloid neoplasms (tMN) in patients undergoing chemotherapy and/or radiation for a primary malignant disease prior to beginning the course of treatment. We will explore this through a combination of bioinformatics analysis of methylation data, mechanistic mathematical modeling, and statistical analysis to evaluate the usefulness of FMCs as an early detector of tMN risk.
Lab Profile: https://med.stanford.edu/curtislab.html
PI: Jeremy Dahl, PhD
Type of Research: Combination of wet and dry lab
Possible Project:
Ultrasound molecular imaging (UMI) is a promising tool that can provide noninvasive, non-ionizing, real-time, freehand breast cancer tumor assessment at the point of care. UMI uses targeted ultrasound contrast agents (UCAs) to differentiate between benign and malignant lesions and has the potential to reduce false positive rates and overdiagnosis. However, poor UMI image quality has led researchers to trade the benefits of real-time and freehand imaging for better image quality, resulting in longer exam times, higher UCA dosage, and potentially missed targets.
Our lab is focused on 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 used to formulate targeted-microbubbles. Areas of research include cancer biology, cell culture, protein conjugations, microbubble synthesis, and in vivo imaging studies.
Lab Profile: http://med.stanford.edu/ultrasound
Type of Research: Combination of wet and dry lab
Possible Project:
The general research in BAMM lab is focused on identifying the exosome derived biosignatures of various cancers, infectious diseases, and brain related diseases. We are exploring the exosomal markers of Alzheimer's, brain tumors, HIV, and COVID-19 diseases to bioengineer new diagnostic optical technologies. We do translational research by combining engineering, medicine, and biology know-how to address the important clinical challenges using spectroscopy, proteomics, and genomics techniques. Based on their interest, the intern will be involved in biology and/or engineering aspects of the project and learn the main exosome isolation, characterization, detection techniques and/or mass and Raman spectroscopy data collection and analysis techniques using machine learning. The intern is expected to provide intellectual contribution to their project of interest and will be mentored to learn basic research principles as well as dry and wet lab procedures.
Project #1: Estimation of the optical transmission matrix of a tissue by dynamic wavefront controlling using low-cost LCDs. Estimation of transmission matrices is the initial step to create a device that can collect optical data without suffering from scattering in complex tissue medium. In this project, the intern will use a projector's LCD to control the laser light's wavefront by changing its phase using computer-generated hologram (CGH) algorithms. The mentor will guide the intern in learning how to use the CGH algorithms in MATLAB and how to change the phase of the incoming beam dynamically. The results of this project will be used to support the "deep tissue Raman imaging by dynamic wavefront correction" project.
Project #2: Microrobotics and acoustics device development and their applications in medical research
Project #3: AI-ML-assisted computational biology for cancer research. The applicant needs to have strong demonstratable programming skils in Python and R languages as well as WebApp development.
Lab Profile: https://bammlabs.stanford.edu/people/dr-utkan-demirci
Type of Research: Combination of wet and dry lab
Possible Project:
Project #1: Automated sorting of circulating cancer cells using magnetic levitation and machine learning
Durmus Lab develops novel, label-free, and magnetic levitation-based microfluidic chips for the measurement of cancer cell densities. This unique feature of the cells, density, affects their levitation height under the magnetic field which can be detected and monitored in real time. Levitated cells are sorted at the end of the microfluidic chip into the two different outlets. In this way, targeted cancer cells can be separated from the sample. Herein, we will develop a cost-effective, label-free, highly sensitive lab-on-a-chip platform to autonomously adjust sorting parameters to increase throughput by visual feedback (i.e., purity calculation in real-time). Different chip designs, flow rates, and cancer cell types will be utilized to create a dataset that will be trained with different machine learning algorithms with 5-fold cross-validation. The results from the AI-based system will then be compared with user performance (i.e., manual adjustments during the separation) to report the advantage of this system in daily practice.
Project #2: Capturing and Subtyping of Exosomes from Plasma Using Microfluidic Magnetic Levitation
Exosomes, extracellular vesicles in the size of 30-150 nm, can carry molecules which are essential for cell-to-cell communication, including DNA, RNA, and metabolites. Tumor cells are thought to secrete exosomes into bodily fluids such as plasma to facilitate angiogenesis and metastasis, to make space for migration and proliferation while deactivating tumor suppressors. However, their small size makes the isolation and subtyping of exosomes extremely difficult using traditional methods. A simple method of isolating and subtyping exosomes will be beneficial to better understand disease progression and identify tumor-associated biomarkers.
In this project, we will develop a rapid, cost-effective, and straightforward method to capture and subtype exosomes from plasma. We will develop a multiplexed subtyping platform where the beads decorated with antibodies will act as mobile assay surfaces for each subtype when mixed with plasma. The beads of different subtypes will be sorted out at different outlets with the microfluidic magnetic levitation platform and subjected to downstream analysis for further characterization.
Lab Profile: https://gdurmus.people.stanford.edu/
PI: Alice Fan, MD
Type of Research: Wet lab and/or clinical trials
Possible Project:
Dr. Fan's translational research focus is in Urologic Oncology. Her laboratory is harnessing the platelet transcriptome and blood autoantibody signatures for early detection of 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 imaging and therapeutic approaches for kidney cancer and prostate cancer. A CREST student will have the opportunity to experience both translational and clinical research.
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
Instructor: Sharon Hori, PhD
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, mathematical modeling, and machine learning skills are preferred but not required. The student mayl 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.
Type of Laboratory Research: Combination of wet and dry lab
Possible Projects:
1. Imaging epigenetic changes in cells
Epigenetic changes, such as histone methylations and protein sumoylations control various cellular functions at different stages of developments and in pathological conditions. Since these epigenetic changes are considered early event in cellular pathogenesis, we develop various imaging techniques to monitor these events in vitro in cells and in living animals. Specifically, 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. 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. Genetically encoded molecular biosensors for imaging protein function
We developed split-reporter protein complementation systems (Firefly, Renilla, and Gaussia luciferases, GFP, and mRFP) in our laboratory for designing molecular imaging biosensors that can monitor protein-protein interactions, protein folding, and posttranslational protein modifications in cells. We are currently designing sensors based on split reporters for studying histone methylation, p53-protein folding, p53-sumoylation, ligand-induced changes in estrogen receptor folding, NRF2-Keap1 interactions, and NFκB, and NQO1 mediated apoptotic and survival signaling in cells. We are currently adopting these sensors for high throughput screening of drugs that can be used for cancer therapy.
3. Imaging the role of xenoestrogen on Estrogen Receptor signaling and oncogenesis
Estrogen receptors (ERa and ERb) are the major cell growth and development regulators of reproductive organs, and their expressions are dysregulated in cancers of reproductive organs. We developed firefly luciferase reporter complementation sensor for imaging ligand-induced conformational changes in ERα to study xenoestrogen Bisphenol A (BPA) induced changes in ER-signaling and oncogenesis in a transgenic mouse model. Currently, we are using this complementation sensors and transgenic mouse to extend our studies to assess the role of ER-β in the 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 ER expression.
4. Targeting microRNAs for cancer therapy
Cytotoxic chemotherapy is a commonly used treatment method for cancer therapy. Chemotherapy is non-specific and can generate toxicity to normal cells when used at higher doses. Hence, we developed microRNA mediated presensitization strategy to improve chemotherapy by reducing the doses. We develop biocompatible polymer-based nanoparticles for delivering small molecule drugs like tamoxifen, Gemcitabine, antisense-microRNAs, and therapeutic DNAs for cancer therapy. We also 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.
5. Developing translational cancer nanotheranostics for the treatment of cancers
With the rapid advances in nanomedicine, cell derived lipid vesicles (CDLV) and CDLV functionalized nanoparticles (CDLVs-NPs) have emerged as promising nanocarriers for biomedical applications, including cancer therapy 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 are also exploring the various bioengineering methods to enhance the multifunctionality of CDLVs and CDLV-NPs for cancer therapy.
Lab Profile: https://med.stanford.edu/mips/research/cpil.html
Type of Research: Combination of wet and dry 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
PI: Jiangbin Ye, PhD
Type of Research: Wet lab
Possible Project:
Our current research interest focuses on how cancer cells reprogram metabolic pathways to remodel epigenetic landscape, and how to target these metabolic vulnerabilities with effective metabolic therapy. We use LS-MS-based metabolomics approaches to analyze metabolic alternations and flux changes in cancer. Previously, we discovered that hypoxia represses retinoic acid (RA)-induced neuroblastoma cell differentiation by reducing cellular acetyl-CoA levels, leading to reduction of global histone acetylation and chromatin accessibility. Importantly, acetate or glycerol triacetate (GTA) supplementation restored chromatin accessibility to reactivate differentiation markers expression and neuron differentiation program under hypoxia (Cell Death & Disease, 2020). We also discovered that in breast cancer cells, serine starvation also decreases cellular acetyl-CoA levels, leading to reduction of global histone acetylation and silencing of estrogen receptor (ER) signaling, which can also be restored by acetate or GTA supplementation (Li, et al., BioRxiv, 2021). Together, these data uncovered that the key of the Warburg effect is reducing pyruvate flux toward acetyl-CoA generation to decrease histone acetylation and turn off linage-specific gene expression.
Unlike histone modifications, which are more transient and dynamic, DNA methylation is more stable and has long lasting effect on gene expression and cell fate control. Dysregulated DNA methylation is associated with poor prognosis in cancer patients, promoting tumorigenesis and therapeutic resistance. Recently we discovered that mitochondrial uncoupling effectively increased NAD+/NADH ratio and α-KG/2-HG ratio, which promotes global DNA demethylation, neuroblastoma cell differentiation and N-Myc downregulation. These results suggest that mitochondrial uncoupling is an effective metabolic and epigenetic intervention that remodels the tumor epigenome for better prognosis (Cancer Research, 2022).
The intern will work with one our postdocs to 1) investigate how metabolic stress in tumor microenvironment, such as hypoxia, reprograms tumor cell metabolism and epigenetic landscape to alter gene expression and inhibit cell differentiation, and how to reverse this reprograming and reactivate differentiation with metabolic therapy. 2) perform untargeted metabolomics analysis using LC-MS on patient plasma samples to identify metabolites as biomarkers for cancer early detection.
Lab Profile: http://med.stanford.edu/yelab.html