2025 Canary CREST Program Projects
Type of Research: Combination of wet and dry lab
Possible Project:
The Brooks laboratory focuses on benign and malignant diseases of the kidney and prostate with the goal of making discoveries that can aid in patient care. We use discovery-based platforms including genomics, transcriptomics, proteomics and glycoproteomics to identify disease-specific biomarkers that can be used in diagnosis, prognostication, therapy selection, and monitoring treatment efficacy. For biomarkers of particular interest, we have pursued mechanistic studies to understand their roles in disease origin and progression and are now developing therapies based on those discoveries. We have developed many resources, such as tissue microarrays with detailed clinical annotation, as well as blood, urine and tissue resources from clinical trials to test and validate candidate biomarkers. Many or our projects are transdisciplinary, taking advantage of the rich research environment at Stanford.
Lab profile: https://med.stanford.edu/brooks-lab.html
Mentor: Christina Curtis, PhD, MSc
Type of Research: Dry lab
Possible Project:
We are using advanced genomic and spatial profiling techniques to understand the patterns of metastasis and organotropism (i.e., preferential relapse of a cancer in specific organs). Colorectal cancer (CRC) is the second most common neoplasm and third most common cause of cancer-related death. 25% of patients diagnosed with CRC present with metastatic CRC and another 20% develop mCRC following treatment of primary CRC. The liver is the most common site of CRC spread, followed by lung. However, in rare instances (<4%) CRC foregoes its common route of metastasis and metastasizes to the brain. In this project, we aim to utilize multi-modal spatial profiling to develop biomarkers of organotropism in primary CRC.
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.
#1: This project is multidisciplinary, involving concepts in ultrasound image reconstruction, data/computer science, microbubble formulation, preclinical cancer models, cancer biomarkers, and protein engineering. In this project, we are developing a high-sensitivity, high-specificity, ultrasound molecular imaging system that involves real-time non-destructive imaging of targeted microbubbles. The student may be involved in technical tasks such as developing, programming, or executing large-scale physics simulations of ultrasound imaging of cancer-bound microbubbles, developing deep learning models to reconstruct ultrasound molecular images from raw ultrasound data, and perform imaging of preclinical models of breast or kidney cancer. Computer programming skills (e.g., Python, MATLAB, or julia) are a prerequisite for technical aspects to this project. Students may also engage in microbubble fabrication and design, involving the use of microfluidics systems, protein engineering, phantom fabrication, and other chemistry or life science laboratory skills.
#2: This project will develop a robotic guided microbubble-mediated drug delivery system based on ultrasound. The project involves technical developments in robotics and ultrasound, incorporated robotic feedback and control systems, ultrasound pulse sequence programming and image reconstruction, optimization problems, and preclinical cancer models. The student will focus on integrating feedback between the robotics system and the ultrasound system to guide a robotic arm to scan tumors and determine optimal placement of therapeutic ultrasound beams over the tumor. Computer programming skills (e.g., Python, MATLAB) are a prerequisite for technical aspects to this project.
#3: Screening for hepatocellular carcinoma (HCC) is typically achieved using ultrasound imaging due to its low cost and good ability to identify targets in soft tissue. However, suboptimal visualization of the liver can occur due to a number of factors, primarily related to the "body habitus" of the patient. For these difficult-to-image patients, large subcutaneous layers of adipose and connective tissue can distort the ultrasound image as well as produce acoustic noise that reveberates throughout the layers, making it difficult to distinguish noise from tissue in the ultrasound images. In this project, the student will develop deep-learning based filters to suppress acoustic noise due to reveberation to address the suboptimal image quality for HCC screening. These filters are applied to the channel data of ultrasound systems prior to image reconstruction. The goal of the filter will be to suppress the reverberation noise while preserving the fidelity of the channel signals to allow for image reconstruction processes. This project will involve signal processing, ultrasound imaging theory, deep learning, and ultrasound systems and the student will potentially work with human subjects data. Strong computer programming skills (e.g. python, MATLAB) are required.
Lab Profile: http://med.stanford.edu/ultrasound
PI: Kara Davis, DO
Type of Research: Combination of wet and dry lab
Possible Project:
The Davis Lab is working to understand what types of acute lymphoblastic leukemia cells are resistant to modern therapies and why they are resistant. These studies seek to then target novel vulnerabilities to improve outcomes for kids with acute lymphoblastic leukemia. Work with us to gain experience in single-cell high-dimensional analysis, bioinformatics and molecular biology studies in patient samples and disease models.
Lab Profile: https://kldavislab.org/
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.
Lab Profile: https://bammlab.stanford.edu/
Type of Research: Combination of wet and dry lab
Possible Project:
The DeSimone laboratory's research efforts are focused on developing innovative, interdisciplinary solutions to complex problems centered around advanced polymer 3D fabrication methods. In Chemical Engineering and Materials Science, the lab is pursuing new capabilities in digital 3D printing, as well as the synthesis of new polymers for use in advanced additive technologies. In Translational Medicine, research is focused on exploiting 3D digital fabrication tools to engineer new vaccine platforms, enhanced drug delivery approaches, and improved medical devices for numerous conditions, with a current major focus in pediatrics. Complementing these research areas, the DeSimone group has a third focus in Entrepreneurship, Digital Transformation, and Manufacturing.
Lab Profile: https://desimonegroup.stanford.edu/
Type of Research: Combination of wet and dry lab
Possible Project:
The Dhanasekaran Laboratory is dedicated to understanding the immune biology of liver cancer and identifying new ways to improve treatment outcomes for patients. Our research focuses on using patient-derived models, including 3D tumoroids, to study how tumors interact with the immune system. By combining these models with cutting-edge spatial omics technologies, we aim to uncover how tumor cells and immune cells communicate, evolve, and influence disease progression.
We are looking for highly motivated undergraduate students interested in joining our lab to work on an exciting summer or long-term research project. This opportunity is perfect for students with interests in cancer biology, immunology, and advanced analytical techniques. You will gain hands-on experience with organoid models, spatial omics data analysis, and state-of-the-art imaging and molecular profiling methods. A strong desire to learn, contribute, and pay close attention to detail is essential. This is a chance to make a meaningful impact in translational cancer research!
Lab Profile: https://med.stanford.edu/dhanasekaran-lab.html
Type of Research: Combination of wet and dry lab
Possible Project:
Capturing and Subtyping of Exosomes from Plasma Using Microfluidic Magnetic Levitation: Exosomes, extracellular vesicles in the size of 30-150 nm, can carry molecules that 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, and 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/
Type of Research: Combination of wet and dry Lab
Possible Project:
The The MYC oncogene contributes to the pathogenesis of over 70% of human cancers, making it the most common activated oncogene in human cancer. Through conditional transgenic mouse models, the Felsher lab showed that inactivation of MYC results in rapid and sustained cancer regression in multiple types of cancer including HCC. We have shown that MYC regulates many hallmarks of cancer. The mechanism of tumor regression is associated with both changes in cancer cell intrinsic pathways (e.g. proliferative arrest, senescence and apoptosis) and host changes including restoration of cancer immune surveillance.
This project focuses on how disruption of The 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) pathway inhibit tumor initiation and progression of HCC driven by MYC under Metabolic dysfunction-associated steatohepatitis (MASH) conditions.
We will perform multiscale analysis with various powerful techniques. We will use LS-MS to measure the metabolites in mouse and human blood samples and Desorption Electro-Spray Ionization Mass Spectrometry Imaging (DESI-MSI) to map a global change of glycolysis, glutaminolysis and FAS intermediates in tumors with a high special resolution. We will measure immune cell populations using novel techniques such as Mass Cytometry.
Lab Profile: https://med.stanford.edu/felsherlab.html
Type of Research: Dry lab
Possible Project:
The Gevaert lab focuses on biomedical data fusion of complex diseases with a particular focus on oncology and cardiovascular diases. We develop novel machine learning approaches that digest multi-omics, multi-modal or multi-scale data. Previously we pioneered data fusion work using Bayesian and kernel methods studying breast and ovarian cancer. Subsequent work concerned the development of methods for multi-omics data fusion. This resulted in the development of MethylMix, to identify differentially methylated genes, and AMARETTO, a computational method to integrate DNA methylation, copy number and gene expression data to identify cancer modules. Additionally, my lab focuses on linking molecular data with cellular and tissue-level phenotypes. This led to key contributions in the field of imaging genomics/radiogenomics involving work in lung cancer and brain tumors. Our work in imaging genomics is focused on developing a framework for non-invasive personalized medicine. In summary, my lab has an interdisciplinary focus on developing novel algorithms for multi-scale biomedical data fusion.
Lab Profile: http://med.stanford.edu/gevaertlab.html
PI: Ted Graves, PhD
Type of Research: Combination of wet and dry Lab
Possible Project:
The Graves Laboratory, known as the Imaging Radiobiology Laboratory, is focused on development and application of molecular imaging techniques towards understanding radiation and cancer biology and improving treatment of human disease. Using modalities including positron emission tomography (PET), computed tomography (CT), fluorescence imaging, bioluminescence imaging, and small animal conformal radiotherapy, we are investigating the molecular and physiologic factors that determine tumor response to therapy. We are a multi-disciplinary group with expertise in engineering, biology, chemistry, medicine, and computer science.
The current primary focus of the laboratory is to understand how tumors and normal tissues respond to irradiation at the molecular, cellular, and tissue levels. At present we are keenly interested in the migration of tumor and immune cells in response to radiation. This process has important ramifications for the control of human disease by radiotherapy. Because trafficking of small numbers of cells may have great importance on tumor control and recurrence, study of this phenomenon requires methods capable of sensitively and specifically detecting and quantifying these few cells in vivo. To this end we have developed a variety of imaging- and FACS-based approaches towards monitoring specific cell populations, as well as techniques for delivering clinically-relevant conformal radiotherapy to small animals in order to probe the spatiotemporal relationships between cell trafficking and irradiation.
Lab Profile: https://med.stanford.edu/graveslab.html
PI: 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 internship is to learn 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 prostate, lung, breast, pancreas and colon will be the primary focus.
The applicant should be highly motivated 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 may 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 make predictions about cancer presence and aggressiveness. 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, anatomy/physiology, computational and systems biology, computer science, biomedical engineering and related fields. Minimum 40 hr/week required.
Type of Research: Combination of wet and dry lab
Possible Project:
Our research group focuses on developing diagnostics for early detection and improved management of bladder cancer. About 75% of bladder cancers are non-muscle invasive on initial diagnosis and are primarily managed with transurethral resection and intravesical therapies. The risk of recurrent non-muscle invasive bladder cancer varies greatly based on disease characteristics. With recurrences rates of 15–61% at 1 year and 31–78% by 5 years, surveillance cystoscopy as frequent as every three months after initial treatment is necessary. While cystoscopy is the current gold standard for bladder cancer surveillance, it is invasive and imperfect.
This is a multidisciplinary project involves analysis of longitudinal data from bladder cancer patients including cystoscopy video image reconstruction and urinary cancer biomarkers. We are developing tools to better define risk of recurrence, the timing of surveillance cystoscopies, and detection of disease by enhanced imaging. The student may be involved with technical tasks such as developing deep learning-based algorithms to reconstruct 3D bladder models from cystoscopy videos to evaluate changes in the bladder mucosa over time. Students may also be involved with development of prognostic urine-based biomarkers to identify bladder cancer recurrence and progression. Integrating imaging data and biomarker analysis over the course of treatment has to potential to significantly improve bladder cancer risk stratification.
Lab profile: https://med.stanford.edu/content/sm/liaolab.html/
Type of Research: Combination of wet and dry lab
Possible Project:
The Mallick Lab is focused on understanding the biophysical and biochemical processes that drive the kinetics of circulating protein biomarkers that might be used to detect cancers in their earliest and most treatable stages. As part of this, we will be examining the relationship between the tumor and circulating proteomes over time and as a function of tumors growing.
Type of Laboratory Research: Combination of wet and dry lab
Possible Projects:
Cellular Pathway Imaging Laboratory (CPIL):
Sensor imaging of Cellular Signaling Networks
Our research group mainly works on developing novel imaging assays for studying cellular signal transduction networks in cancer and other cellular diseases. Specifically, we apply our extensive experience in molecular biology to develop in vivo imaging assays for monitoring basic cellular processes in living animals. Our targets include, post-translational modifications of proteins, such as methylation, phosphorylation, sumoylation, and acetylation. We use split-reporter protein complementation systems based on luciferases, fluorescent proteins, and thymidine kinase for constructing our sensors. We applied these sensors to image tumor microenvironment, immune cytokine signaling, and cancer therapy monitoring. Other areas where we are currently applying these sensors include studying protein-protein interactions involved in estrogen receptor signaling, Nrf2-mediated antioxidant signaling in chemoresistance, p53-sumoylation mediated chemotherapy responses and NFkB mediated cytokine signaling in cancer, and the signaling mechanisms associated with APP and Tau proteins in Alzheimer’s disease.
MicroRNA Based Therapies in Cancer
In cancer therapy, we are establishing microRNA-based reprogramming approaches to sensitize cancer cells by eliminating drug-resistance. We are adopting this approach for treating breast cancer, hepatocellular carcinoma, and glioma to clinically used chemotherapies. We mainly target oncogenic and tumor suppressor microRNAs (miR-21, miR-10b, miR-122, and miR-100) which are dysregulated in cancers to improve chemotherapy. We use PLGA-PEG nanoparticles for loading the miRNAs, and ultrasound-microbubble (US-MB) triggered drug delivery strategy for locoregional enhancement of microRNAs in the tumor to improve cancer therapy. We evaluate miRNA delivery strategies in small animal models (mice and rats) and optimize US parameters (cavitation, PRF, mechanical energy, and delivery efficiency) in large animal models (pigs and dogs) to address clinical translational feasibilities of this approach to human. We have shown tremendous progress in this area of research with number of publications in high impact journals. We recently identified five sense and antisense miRNAs (miR-203, miR-218, antimiR-10b, antimiR-19b, and antimiR-21) through a rigorous analysis of miRNA expression data available in TCGA (GDC) and GEO using a biological basis-driven workflow, where these microRNAs target multiple hallmarks of cancer to improve chemo- and immunotherapies in cancer.
Synthetic Biology
In synthetic biology, we developed an application of high-pressure microfluidic system in the reconstruction of biomolecules derived from cells (proteins and lipids) along with synthetic sources (phospholipids, polymers, and surfactants) to develop self-assembled nano- and micro-structures that mimic biological membranes for drug delivery applications. As part of this process, we developed biomimetic microbubbles (biMBs) and nanobubbles (biNBs) using tumor cell derived exosomes (TDEs) for cancer immunotherapy applications. In this study, we applied this novel technology to exploit the natural accumulation of biMBs in the immune organs such as lymph nodes (LNs), lungs, and spleen for activating immune system while TDE -targeted biNBs accumulation in the tumor to achieve enhanced cancer immunotherapy.
Vaccine for cancer and infectious diseases
In addition, we also work on developing novel intranasal vaccine for cancer, Covid-19, and other respiratory diseases. As part of this project, we develop novel polymer-based adjuvant system for delivering DNA, mRNA, peptide, and protein vaccines for cancer and other viral diseases.
Specific Research Topics of the lab:
- Developing multiplex-imaging assays to simultaneously measure histone methylations in various lysine marks of histone proteins (H3K9, H3K27, H3K36, H3K79, and H4K20).
- Developing FDA approved polymer nanoparticles to co-deliver therapeutic sense- and antisense- microRNAs for cancer therapy.
- Studying estrogen receptor (ER) α and β cross-talk in breast cancer using sensor systems.
- Nrf2-Keap1 antioxidant mechanism in drug resistance and chemotherapy in cancers.
- Studying the stemness of cancer cells and cancer stem cells in cancer and targeting Wnt-Beta catenin and NFkB-Nrf2 signaling to improve cancer chemotherapy.
- Covid-19 intranasal vaccine for controlling different variants of SARS-CoV2.
- Ultrasound-Microbubble mediated targeted delivery of therapeutics (MicroRNAs and Biomimetic immunotherapies) in 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: https://med.stanford.edu/pitterilab.html
