2022 Canary CREST Program Projects

Mentor: Howard Chang, MD, PhD

Type of Research: Combination of wet and dry lab

Possible Project:

Circular RNAs are a type of RNA molecule resistant to degradation and may be promising for gene therapy. This project will optimize the in vivo properties of circular RNAs with the goal of creating a new class of therapeutics for genetic diseases.

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


PIJeremy 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.

This REU project focuses on improving ultrasound molecular imaging for early cancer detection by developing a deep learning model to reconstruct UMI signals from noisy raw ultrasound data. The student will help to program and execute large-scale physics simulations of ultrasound imaging of cancer-bound UCAs. These simulations will then be used to implement and train a spatiotemporal deep learning model for breast cancer detection from raw data. Computer programming skills (e.g., Python, MATLAB, or julia) are a prerequisite for this project. Students will gain hands-on experience working with raw medical imaging data, Linux-based compute clusters, data analysis techniques, and deep learning algorithms.

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:

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.

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


PINaside Gozde Durmus, PhD

Type of Research: Wet lab

Possible Project:

Our research has led to the first demonstration of magnetic levitation of single cells with broad applications in biology and medicine (https://www.pnas.org/content/112/28/E3661). This is an entirely new method for single cell research and created a new field of investigation of biological systems, setting a new dimension that has yet to be explored. The project will be related to developing novel, label-free, magnetic levitation based liquid biopsy tools for the measurement of extremely rare circulating tumor cells (CTCs), cell clusters and exosomes and to study the biology of rare cell populations and understand their role in metastasis.

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


Instructor: Ahmed El Kaffas, PhD

Type of Research: Dry lab

Our lab develops ultrasound-based tissue characterization and stimulation methods for managing disease at bedside. This includes development of ultrasound contrast/molecular imaging methods, new quantitative approaches that employ machine learning to mine raw ultrasound signals, as well as studying mechanobiology induced by ultrasound-based physical forces.

For this project, we will work on development of software tools for ultrasound data analysis and tissue characterization, as well as machine learning components. Several image processing techniques will developed in Python and used to extract quantitative biomarkers from ultrasound data; candidates will also learn to develop machine learning approaches for analyzing ultrasound data in pre-clinical and clinical applications. If in person work is possible, microbubble fabrication and wet lab work will also be carried out and the candidate may be able to partake in this work.

Skills required:
- Python (and some Matlab)
- Basic experience with Tensorflow or Pytorch or SciKit

Skills that will be learned:
- ITK/VTK and MevisLab
- Ultrasound imaging and physics/eng
- Python and UI development
- Tensorflow
- Working with raw ultrasound signals and 3D data sets
- Contrast agent fabrication (maybe) and some ultrasound experiments


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


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.


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: Tanya Stoyanova, PhD

Type of Research: Combination of wet and dry lab

Possible Project:

Stoyanova lab develops new early cancer detection methods and therapeutic strategies for late stage cancers. The current research focus is on protein-based biomarkers for early cancer detection as well as development of new small molecule inhibitors and antibody-based therapies for prostate and other epithelial cancers. The ultimate goals of the laboratory are to improve the early diagnosis and prognosis of clinically significant cancers and guide the development of novel and effective therapeutic strategies for metastatic prostate and other epithelial cancers.

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