Canary CREST Program Projects

Mentor: Edwin Chang, PhD (PI: Heike Daldrup-Link)

Type of Research: Combination of wet and dry lab

Possible Project:

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.  Another component in our research concerns modeling of TTFields's effects on cancer cell membranes and how this impacts the aforementioned combination therapies.   

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.  For the modeling component, the candidate will be required to comb the scientific literature to find and assess relevant models.   Data analysis and Data validation will be required.

Lab Profile: http://daldrup-link-lab.stanford.edu/


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 use of molecularly-targeted microbubbles that bind to biomarkers on the endothelial cells of tumor neovasculature. 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


PI: Utkan Demirci, PhD

Type of Research: Combination of wet and dry lab

Possible Project:

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.

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


PINaside Gozde Durmus, PhD

Type of Research: Combination of wet and dry 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

Type of Research: Combination of wet and dry lab

We are developing state-of-the-art ultrasound-based imaging, quantification and processing methods with AI (radiomics, deep learning, machine learning), as well as conventional image processing methods. An important question motivating our research is: what clinically relevant tissue properties can we derive from raw ultrasound signals and associated tissue acoustic properties?

We are seek candidates interested in medical AI, 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 neural network development, image quantification methodologies in the context of imaging, and to carryout experimental work to potentially validate these parameters (depending on the state of in-person research). The candidate will first be exposed to a variety of activities in the group and subsequently be given an opportunity to carryout a specific project from start to end with guidance, with the hope of generating a publication or abstract.

Previous Python/Matlab experience, specifically in the context of image processing or AI (tensoflow, keras, pytorch, jupyter notebooks, ITK), is an asset. C/C++ and/or mathematical modelling would also be useful. If wet lab work is permitted, then previous cell culture or animal research work would also be an asses. The following list of other specific experiences are also useful to have:

- Machine learning/deep learning and statistical models
- Bash scripting, and working with NIFTI data and associated toolboxes
- Ultrasound research and/or imaging (MRI/CT/Microscopy)
- Open CV or other programming languages for image processing
- Pre-clinical research in rodents and/or cells (with focus on cancer)


PI: Olivier Gevaert, PhD

Type of Research: Dry lab

Possible Project:

Vast amounts of biomedical data are now routinely available for cancer patients ranging from sequencing of tissues to liquid biopsies. In addition, new computational tools for quantitatively analyzing radiographic images are now available. Multi-scale data is now available for complex diseases at molecular, cellular and tissue scale to establish a more comprehensive view of key biological processes. Intra and inter individual heterogeneities are often quoted as the main challenge for studying cancer. These heterogeneities exist at all scales, from microscopic to macroscopic. We develop multi-scale modeling approach to counter heterogeneity and uncover potentially untapped synergies between different data modalities by integrating information across spatial scales. Multi-scale modeling involves linking information from molecules, cells, tissues, and organs all the way to the organism and the population. We propose to use high dimensional molecular data with tissue scale image data to develop a statistical multi-scale modeling approach in the context of multi-modal & multi-scale modeling. Such radiogenomic modeling can contribute toward predicting diagnosis and treatment by revealing synergies and previously unappreciated relationships. Multi-scale modeling also can contribute to a more fundamental understanding of cancer development and can reveal novel insights in how data at different scales are linked to each other.

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


Core Director: Frezghi Habte, PhD (PI: Heike Daldrup-Link)

Type of Research: Combination of wet and dry lab

Possible Project:

This project will be conducted under the MIPS/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 and evaluate existing tools and methods, and develop new imaging strategies and tools that improve the accuracy and efficiency of image quantification techniques. We will also assess new tools and 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 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  

Type of Research: Wet and/or 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.


Instructor: Arut Natarajan, PhD  

Type of Research: Wet and/or Dry lab

Possible Project:

Protein engineering of oncology drugs for cancer diagnosis, imaging and immunotherapy

Engineered proteins shown effective targeting of cancer cells or immune cells by binding on to cell surface receptors. Protein engineering is allowed to modify the protein formats for tuning the target binding properties e.g., enhance the protein binding affinity. Currently, we are screening, and selection of small proteins from a library (10^8) to target a novel immune checkpoint receptor. This receptor is expressed on T cells or NK cells. Best protein binder will be selected and tested in culture and pre-clinical models for imaging and immunotherapy (References: 1-3).

References:

1: Natarajan A, et al. Novel Engineered Small Protein for Positron Emission Tomography Imaging of Human Programmed Death Ligand-1: Validation in Mouse Models and Human Cancer Tissues. Clin Cancer Res. 2019 Mar 15;25(6):1774-1785. PMID: 30373750.

2: Natarajan A, et al. FN3 Protein Conjugates for Cancer Diagnosis and

Imaging Studies. Methods Mol Biol. 2019; 2033:301-313. PMID: 31332762.

3: Ramakrishnan S, Natarajan A, et al. Engineering of a novel subnanomolar affinity fibronectin III domain binder targeting human programmed death-ligand 1. Protein Eng Des Sel. 2019 Dec 31;32(5):231-240. PMID: 31612217.


PI: Ramasamy Paulmurugan, PhD

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


PI: Sharon Pitteri, PhD

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:  Johannes Reiter, PhD

Type of Research: Dry lab

Possible Project:

The Translational Cancer Evolution Laboratory focuses on the stochastic evolutionary dynamics of cancer with the goal to improve the detection and treatment of tumors. We develop computational and statistical methods to learn from large-scale biological data sets and design mathematical models to predict and explain clinical observations on a mechanistic level. For example, we have developed mathematical models to explain why liquid biopsies exhibit a low sensitivity to detect single actionable mutations of tumors smaller than 2 cm or why a single biopsy is typically sufficient to capture the essential information for initial therapeutic decision-making.

During the CREST program, students will learn about how to analyze big data, how to develop computational and statistical methods to learn from these data, and how to design mathematical models of cancer evolution. 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: In which communities should we screen for cancers? Which of the detected tumors require active treatment, which can be monitored, and which are unlikely to progress in the lifetime of a patient? Which tumors have a high metastatic potential? Which tumors have already metastasized and need more aggressive treatment? Students will be free to come up with their own ideas for a project around these questions. Students need 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: 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 7 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: Combination of wet and dry lab

Possible Project:

Dr. Stoyanova’s research program focuses on understanding fundamental molecular mechanisms underlying the development of epithelial cancers and their potential as biomarkers and therapeutic targets. A major focus of her research is in prostate cancer as well as breast and neuroendocrine cancers. The ultimate goals of her laboratory are to: 1) improve the stratification of indolent from aggressive prostate cancer and 2) guide the development of novel and effective therapeutic strategies for metastatic cancers. The research in Stoyanova lab has led to the discovery of new mechanisms underlying the development of aggressive prostate cancer and biomarkers for significant disease as well as new therapies for prostate cancer.

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