Canary CREST Program Projects

PI: Zhen Cheng, PhD
Mentor:  Hao Chen, PhD

Type of Research: Combination of wet and dry lab

Project or lab description:

Students will work on the development of optical probes with long emission wavelengths for bioimaging purposes.

The overall objective of my laboratory is to develop novel molecular imaging probes and techniques for non-invasive detection of cancer and its metastasis at the earliest stage, so that cancer can be cured or transformed into a chronic, manageable disease. The techniques developed in my research will allow a close examination of the molecular, metabolic and physiological characteristics of cancers and their responses to therapy. In order to achieve this goal, my lab is aimed to identify novel cancer biomarkers with significant clinical relevance, develop new chemistry for probes preparation, and validate new strategies for probes high-throughput screening.

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


PIChristina Curtis, PhD
Mentor: Jose Seoane, PhD

Type of Research: Dry lab

Project or lab description:

Research in the Curtis lab is focused on the development and application of integrated computational and experimental approaches to improve the diagnosis, treatment, and earlier detection of cancer. To this end, we leverage genome-scale data derived from clinically annotated samples coupled with computational modeling, statistical inference, and iterative experimentation to characterize the evolutionary dynamics of tumor progression, delineate the genotype to phenotype map in cancer, and develop prognostic and predictive biomarkers (see for e.g. Curtis et al., Nature 2012; TCGA, Nature 2014; Sottoriva et al., Nature Genetics 2015; TCGA Nature 2017; Sun et al. Nature Genetics, 2017; Corces et al. Science 2018). The proposed project will build on our established analytic approaches to develop predictive/prognostic biomarkers.

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


PIJeremy Dahl, PhD
Mentor: TBD

Type of Research: Combination of wet and dry lab

Project or lab description:

Ultrasound molecular imaging is an emerging tool for cancer early detection.  In this project, we are developing a high-sensitivity, high-specificity ultrasound molecular imaging platform for the early detection of breast cancer.  This project aims to (1) develop high-sensitivity through the development of novel imaging techniques, including topics such as coherence beamforming and machine-learning-based beamformers and (2) develop high-specificity by creating monodisperse microbubbles targeted to the B7-H3 receptor expressed on the neovasculature of breast cancer.  We will work with the student to develop a manageable project in this area. Students with backgrounds in the following areas or interest in the following areas may be most suitable for this work:  electrical, biomedical, chemical, or protein engineering; ultrasound signal processing and image reconstruction; microbubble production and conjugation; in vivo experimentation in animal models of cancer.

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


PIJeremy Dahl, PhD
Mentor: Rakesh Bam, PhD

Type of Research: Combination of wet and dry lab

Project or lab description:

We are a translational molecular imaging lab and focus on basic/ pre-clinical research that have the potential for future clinical use. We engineer small peptide ligands that can bind to tumor blood vessel specific receptors, such as B7-H3. Peptide ligands can be conjugated to imaging contrast agents such as microbubbles for ultrasound tumor imaging or to therapeutic drugs for targeted drug delivery in mouse models of cancer. In the lab, the intern will learn about biology of cancer, cell culture, peptide-drug conjugations, toxicity assays and ultrasound imaging at basic levels. This project is very collaborative and we understand the importance of mentorship for a meaningful research. Upon completion of this summer project, you will also recognize the importance of soft skills such as communication, creativity, patience as well as managing research-related pressure to become a productive scientist.

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


PI: Utkan Demirci, PhD
Mentor: Fatih Inci, PhD

Type of Research: Wet lab

Project or lab description:

Multimode Nanoplasmonic Chips 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 and point-of-need settings. Hence, portable, rapid, robust, and inexpensive technologies are needed at clinical settings. In this project, we will develop multimode nanoplasmonic surfaces and integrate them with microfluidic chips for efficient detection of cancer cell biomarkers from bodily fluids. The chips will be modified by a layer-by-layer surface chemistry approach to decorate antibodies to capture protein biomarkers.

 

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


PI: Utkan Demirci, PhD
Mentor: Mehmet Ozen, PhD

Type of Research: Wet lab

Project or lab description:

Decoding EV-mediated cell cross-talk in tissue-on-chip platform.

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 cardiac niche with different cells, on an in-house developed tissue-on-chip platform to mimic in vivo conditions, applying static and dynamic culture conditions. Culture media from these cultures will be collected for EV isolation using ExoTIC, a chip that we have developed in our Lab. Then, we will profile EVs and their contents (in terms of RNAs). Results of this work will allow us to better understand interactions between regulatory and recipient cells through secreted vesicles in different conditions.

 

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


PI: Utkan Demirci, PhD
Mentor: Naside Gozde Durmus, PhD

Type of Research: Wet lab

Project or lab description:

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, label-free 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: http://bammlabs.stanford.edu/people/dr-utkan-demirci


PI: Utkan Demirci, PhD
Mentor: Tanchen Ren, PhD

Type of Research: Wet lab

Project or lab description:

Levitating Cells to Decode Rare Signatures from Blood  

The replication of the complex structure and three dimensional (3-D) interconnectivity of neurons in the brain is a great challenge. A few 3-D neuronal patterning approaches have been developed to mimic the cell distribution in the brain but none have demonstrated the relationship between 3-D neuron patterning and network connectivity.  We propose to shift the paradigm in experimental modeling by bioprinting 3-dimensional biomimetic tissues that mimic healthy and diseased brain circuits. We will develop a breakthrough bioprinting platform to create 3-dimensional neural tissues that emulate the directional connectivity, spatial architecture, multicellular composition, and circuit functionality of the healthy and diseased brain. These 3D brain biomimetic tissues will transform our ability to study molecular, cellular and circuitry aspects of human brain functions and their relationships to mental illnesses. It will open up new regenerative tissue engineering and clinical technologies, including personalized diagnosis, treatment, and drug discovery for brain disorders.  

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


PI: Alice Fan, PhD
Mentor: Christian Hoerner, PhD

Type of Research: Wet lab

Project or lab description:

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 intern will have the opportunity to experience both translational and clinical research.

 


PI: Sanjiv Sam Gambhir, MD, PhD
Mentor: Demir Akin, DVM, PhD

Type of Research: Combination of wet and dry lab

Project or lab description:

This project, MedDroid©, involves development of a software automaton for medical knowledge mining and truth extraction using Natural Language Processing, AI/ML and software crawler agents--the initial focus will be on cancer. On a daily basis thousands of medical/science articles are published in biomedical sciences and cancer research and  it is impossible for a "human" medical researcher to keep track of this continuous stream of published literature yet this capability is crucial for the scientist and physicians to be current on the contemporary research matters and the newly emerged ("trustable") information that may have drastic implications in how diseases are managed clinically.  Hence, the goal of this project is to create a single app platform with multiple autonomous software modules (agents) that have the ability to crawl and aggregate medical information, cluster it, and subject it to AI-based knowledge mining  to create associations and scores that can serve as inputs for the derivation of trustable medical truths/insights that can be  deposited in a continiually evolving/"learning" central warehose as well as "tweeted" to the researchers on a daily basis. This project is ideally suitable for interns with CS backgrounds who  want to learn about the current issues in cancer early detection research or improve/apply their coding/algorithmic skills in cancer literature mining. Fluency in a coding language, ideally Python, or a flavor of OOP, e.g. C, C++, Java is required on day 1. If needed, the mentor will train the intern in AI/ML, medical databases and medical knowledge mining etc. For further info please feel free to contact the mentor.


PI: Sanjiv Sam Gambhir, MD, PhD
Mentor: Edwin Chang, PhD

Type of Research: Combination of wet and dry lab

Project or lab description:

Glioblastoma (GBM) is an exceptionally aggressive and chief class of brain cancer.  GBM is not only extremely proliferative but is genomically and phenotypically 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 internship, the intern 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 intern 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 internship will involve the exploration of TTField therapy in combination with novel chemotherapies against GBM.

The internship will expose the individual to a number of techniques that are relevant and necessary for the fields of biomedical and molecular imaging research.  The intern 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 intern will familiarize herself/himself with equipment for the application of alternating electric fields on glioma-derived cell cultures.  If time permits, the intern will be shown fundamentals of preclinical model development.  Such investigations will also introduce the intern to established bioluminescence assays of cancer cell growth and development.  


Instructor: Ahmed El Kaffas, PhD (El Kaffas/Gambhir Lab)

Type of Research: Combination of wet and dry lab

Project or lab description:

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 Matlab 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)


Core Director: Frezghi Habte, PhD (Habte/Gambhir Lab)

Type of Research: Dry lab

Project or lab description:

Canary Center preclinical imaging facility is a shared service center formed to provide resources for in-vivo imaging in small animal models and biological assessment for early cancer detection and other preclinical and clinical translation research. 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.  

Ongoing research projects of the facility focus on the development of tools and methods that advance biomedical imaging and image quantitation. Biomedical imaging is a fast growing field and increasingly emerging as an essentially basic tool for the application of preclinical and clinical basic science research. Specifically, quantitative imaging is currently becoming a routine practice due to its superior advantage in characterizing biological processes more objectively compared to a simple qualitative analysis. Due to the complexity of the imaging instrument and associated analysis tools, there is still significant variability and challenges in quantitative imaging, 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 exiting overall image analysis tools and methods, and develop new imaging strategies and/or analysis tools that improve the accuracy and efficiency of image quantification techniques. We will also assess tools for image data management and methods for 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 Matlab, C++ or other related image analysis tools and interest or experience in 3D printing and/or deep learning. The task may also involve imaging experiment 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 (Hori/Gambhir Lab)

Type of Research: Combination of wet and dry lab

Project or lab description:

Most cancers can be more effectively treated if they are discovered early. Dr. Sharon Hori is studying the detectability of early-stage cancers using secreted cancer-specific biomarkers - proteins or molecules released from cancer cells into the blood. The purpose of this internship 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 modeling techniques to study the relationship between a growing tumor and the amount of biomarker it secretes. Cancers of the breast, ovary, lung, and pancreas will be the primary focus.

The internship 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 intern will have the opportunity to learn how to culture cancer cells, treat cancer cells with chemotherapeutic drugs, perform assays to assess cell viability and measure secreted biomarker levels, image 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 approaches to study or make predictions about cancer state. This summer internship will provide a unique opportunity to gain hands-on experience in biological and computational research, and is ideal for students interested in molecular/cellular biology, cancer research, medicine, computational and systems biology, biomedical engineering and related fields. Minimum 40 hr/week required.


Mentor:  Johannes Reiter, PhD (Reiter/Gambhir Lab)

Type of Research: Dry lab

Project or lab description:

Cancer is an evolutionary process that spans multiple decades and manifests in an uncontrolled growth of abnormal cells. 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 clonal sweeps in the tumor. These recurrently mutated driver genes form an important component of cancer precision medicine. Nevertheless, in a tumor with billions of cells virtually any point mutation is expected to be present in some cells. Hence, at a genetic level, not only is every cancer type different, but also every tumor of the same type and every cell of the same tumor are different. This enormous heterogeneity poses a major barrier to drug development and long-term disease control but also represents a unique opportunity to study the evolutionary principles that govern cancer initiation and progression. We aim to advance diagnosis, prognosis, and treatment of tumors by exploiting these mechanistic principles.

During the internship, students will learn about designing mathematical models of cancer evolution and about developing computational methods to analyze DNA sequencing data. 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).


PI: Siddhartha Jaiswal, MD, PhD

Mentor: TBD

Type of Research: Combination of wet and dry lab

Project or lab description:

Somatic mutations that are prevalent in the aging population cause clonal hematopoiesis, a disorder that increases the risk of blood cancer, cardiovascular disease, and overall mortality. Understanding the biology of these mutations and how they contribute to the development of cancer and other age-related diseases is the current focus of work in our lab.

 

Lab Profile: https://www.jaiswallab.org


PI: Olivier Gevaert, PhD
Mentor:  TBD

Type of Research: Dry lab

Project or lab description:

Projects are available modeling multi-scale data to study complex diseases. This includes multi-omics data fusion, pathology and radiology image analysis using machine learning and linking all these data in a multi-scale model. All work is focused on prediction of diagnosis, prognosis and treatment response.

 

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


PI: Parag Mallick, PhD

Mentor: TBD

Type of Research: Combination or wet and dry lab

Project or lab description:

The Mallick lab focuses on translating multi-omic discovery into precision diagnostics. We use integrative, multi-omic approaches to model the processes that govern proteome dynamics and then use those models to discover cancer biomarkers and mechanisms.

 

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


PI: Sharon Pitteri, PhD

Mentor: TBD

Type of Research: Wet lab

Project or lab description:

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 1-3 highly motivated undergraduate students looking for an internship to work on a summer research project focused on cancer early detection. The internship 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: Sylvia Plevritis, PhD

Mentor: TBD

Type of Laboratory Research: Combination of wet and dry lab

The Plevritis Lab is interested in the cancer microenvironment, focusing on understanding the impact and of cell-cell interactions in tumor growth, progression, metastasis, and therapy resistance. As a translational bench and computational lab, utilizing traditional molecular biology methods in conjunction with algorithmic modeling and imaging techniques. In particular, we use a single-cell approach to characterize and form hypotheses about cancer as a multifaceted system. Currently, we seek to optimize a new highly multiplexed single-cell imaging technology, CODEX, to interrogate the microenvironment of non-small cell lung cancer, especially the tumor stroma.

 

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


PI: Geoffrey Sonn, MD

Mentor: Geoffrey Sonn, MD

Type of Laboratory Research: Dry lab

Project or lab description:

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.


PI: Ramasamy Paulmurugan, PhD

Mentor:  Ramasamy Paulmurugan, PhD

Type of Laboratory Research: Wet lab

Project or lab description:

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: H. Tom Soh, PhD
Mentor: Alex Yoshikawa

Type of Research: Combination of wet and dry lab

Project or lab description:

Sugar polymers, known as glycans, are attached to proteins and lipids through a process known as glycosylation. Changes in the expression of glycans are key hallmarks of many human diseases, such as cancer. However glycans remain difficult to study and target therapeutically, largely because of the lack of affinity reagents that can specifically recognize and bind glycan structures. In this project we will be creating a class of affinity reagents known as XNA aptamers that can bind glycans with great specificity. XNA aptamers are nucleic acid based probes which contain unnatural chemical modifications and can bind to targets, such as proteins and small molecules. The XNA aptamers created in this project will have use as cancer therapeutics and as biosensors to detect specific disease states.

 

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


PI: Tanya Stoyanova, PhD
Mentor: Mentor

Type of Research: Wet lab

Project or lab description:

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: Shan Xiang Wang, PhD
Mentor: TBD

Type of Research: Combination of wet and dry lab

Project or lab description:

Prof. Wang and his group are engaged in the research of magnetic nanotechnologies and information storage in general, including magnetic biochips, in vitro diagnostics, cell sorting, magnetic nanoparticles, nano-patterning, spin electronic materials and sensors, magnetic inductive heads, as well as magnetic integrated inductors and transformers. He uses modern thin-film growth techniques, lithography, and nanofabrication to engineer new electromagnetic materials and devices and to study their behavior at nanoscale and at very high frequencies.  His group is investigating magnetic nanoparticles, high saturation soft magnetic materials, giant magnetoresistance spin valves, magnetic tunnel junctions, and spin electronic materials, with applications in cancer nanotechnology, in vitro diagnostics, spin-based information processing, efficient energy conversion and storage, and extremely high-density magnetic recording. His group conducts research in the Geballe Laboratory for Advanced Materials (GLAM), Stanford Nanofabrication Facility (SNF) and Stanford Nano Shared Facilities (SNSF), Center for Cancer Nanotechnology Excellence (CCNE) hosted at Stanford, and Stanford Cancer Institute.  The Center for Magnetic Nanotechnology (formerly CRISM) he directs has close ties with the Information Storage Industry and co-sponsors The Magnetic Recording Conference (TMRC).

 

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


PI: Joy Wu, MD, PhD
Mentor: TBD

Type of Research: Combination of wet and dry lab

Project or lab description:

Dr. Joy Wu is a board-certified endocrinologist with over 12 years' experience who specializes in treating women and men with osteoporosis and other bone and mineral diseases. She has a special interest in optimizing skeletal health for those at risk of bone loss from glucocorticoid treatment, cancer therapies, or organ transplant. The Joy Wu Lab is focused on the role of bone-forming osteoblasts in the bone marrow hematopoietic and malignant niche. They have demonstrated that parathyroid hormone (PTH) decreases breast cancer metastases to bone, and are now examining the early molecular events that underlie this protective effect of PTH. We have been manipulating PTH receptor signaling genetically and pharmacologically in mice with breast cancer, and use bioluminescence imaging and flow cytometry to follow the initial stages of breast cancer spread to bone. 

 

Lab Profile: http://med.stanford.edu/joywulab/home.html


PI: Jiangbin Ye, PhD
Mentor: Yang Li, PhD

Type of Research: Wet lab

Project or lab description:

Our research interest is to investigate the causes and consequences of the abnormal metabolic phenotypes of cancer cells, with the prospect that therapeutic approaches might be developed to target these metabolic pathways to improve cancer treatment. The goal of this project is to investigate how mitochondrial metabolites function as signaling molecular to regulate histone modifications and gene expression, and cancer cell differentiation.  Through the training, the student will be able to understand the basic concept of metabolomics and cancer metabolism, and practice LC-MS based flux analysis.

 

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