Dr. Aguirre is the recipient of the 1st Sperling Family Fellowship Program, 2017.
Colorectal cancer (CRC) is a major cause of cancer death worldwide. Defining the multicellular heterogeneity and the features of colon cancer cells and their associated stromal cells is critical to understanding the biology and clinical behavior of these tumors.
1. To develop a comprehensive CRC cell atlas to inform biologic discovery and molecular subtyping of the disease
2. to identify novel features of fibroblasts and immune cells that have clinico-pathologic importance and may be amenable to therapeutic targeting.
No reliable parameters exist that can consistently predict the tumor subtype and associated clinical course upon initial radiographic detection of a mass in the central nervous system. The analysis of large radiology data sets (radiomics) offers opportunities for non-invasive characterization and tracking of tumors.
Dr. Bi’s team applies artificial intelligence algorithms, such as deep learning, to images, to recognize complex patterns, with strong association to the biological characteristics of the tumor and clinical outcome. Initial results have demonstrated the association of tumor phenotype, based on its imaging appearance, with tumor grade and specific genetic features. Dr. Bi’s work is focusing on tracking and predicting tumor behavior over time and improve our ability to inform management decisions and patient counseling.
Dr. Cho is the recipient of the Sperling Family Fellowship 2018.
Advanced brain cancers, including glioblastoma, are extremely deadly and incurable. Even after undergoing surgery followed by chemotherapy, radiotherapy or both, patients life expectancy is limited because:
Dr. Cho has established a collaboration among Brigham and Women’s Hospital, the Broad Institute and Massachusetts Institute of Technology, aimed at developing a high-throughput platform that can be used to accurately model drug delivery across the blood brain barrier to reach the tumor cells. Additionally, she is also working to develop new therapeutic drugs which can recognize and kill tumor cells without harming healthy tissues.
In the era of widespread mammography, patients are frequently diagnosed with “high-risk lesion” (HRL) of the breast, a group of abnormalities that confer an increased risk of future breast cancer. The most common HRL is called Atypical Ductal Hyperplasia (ADH), usually treated with surgical excision to ensure that breast cancer is not present in the adjacent tissue. Each year nearly 80,000 women will undergo surgery unnecessarily due to the lack of diagnostic tools to precisely detect ADH.
Dr. King and her team utilize machine learning to identify the subset of patients diagnosed with ADH who are most likely to be upgraded to cancer. Their hypothesis is that machine learning algorithms applied to digitized mammographic and pathologic images, combined with clinical data, can provide a novel and much needed strategy to identify those women who require surgical excision and those that don’t sparing a large percentage of women an unnecessary operation
Cancer cells are driven by genetic alterations. A particular group of genetic alterations that may cause cancer are called gene fusions, and result from the rearrangement of chromosomal material, which brings together fragments of distant, previously unconnected genes, to create novel genetic sequences. Oncogenic gene fusions are abnormal and are generally present in every cancer cell in a given tumor, and only in the cancer cells in a given patient. For these reasons, gene fusions are extremely useful biomarkers to accurately classify tumors and constitute ideal therapeutic targets to selective attack the cancer cells. Gene fusions are very frequent in some tumor types, such as sarcoma, a type of cancer that is particularly difficult to diagnose and to treat. At present, the identification of sarcoma gene fusions in clinical practice remains challenging and requires complicated methods which are time-consuming and expensive.
Dr. Marino-Enriquez’s team apply the latest Next Generation Sequencing technologies to detect oncogenic gene fusion transcripts in patients’ tumor samples. The goal is to design and implement a reliable, cost-effective, and clinically useful gene fusion detection assay.
Lifestyle, diet, and other behavioral factors influence cancer initiation and progression. However, it is not well understood how those factors can prevent abnormal somatic mutations and enhance a DNA repair mechanism. Also, it is not clear who can most benefit from life-style changes after cancer diagnosis. Thus, cancer research at both genomic and population levels plays an essential role in progressing precision prevention and treatment. Currently, my studies focus on colorectal cancer utilizing large prospective cohort studies, the Nurses’ Health Study and Health Professionals Follow-up Study.
– To determine whether pre-diagnostic lifestyle and diet decrease driver gene mutations in colorectal carcinoma.
– To identify interactions of post-diagnostic lifestyle and diet with tumor somatic mutations in relation to patient survival.
More at: Nishihara’s Lab
We take the integrative molecular pathological epidemiology (MPE) approach to assess influences of various health related factors (drugs, diet, lifestyle, genetic variation, microbiota, environment, etc.) on molecular pathology and immune response in diseases, using colorectal cancer as a disease model. Our research can provide unique insights into how modifiable factors can prevent disease occurrence or progression, which can lead to personalized strategies for prevention and treatment. In addition, we actively develop new analytic framework and statistical models, to decipher complex multi-level databases. In the era of omics and big data science, the development of appropriate methods is critical to enhance rigor and reproducibility in science. All of our new statistical programs are publicly available and can be applied to any disease settings.
We integrate molecular pathology and population data science (epidemiology, statistics) to create a single unified field of molecular pathological epidemiology (MPE). The MPE paradigm and approach are powerful enough to not only generate new insights into disease pathogenesis but also provide new opportunities for pathologists, epidemiologists, and population data scientists in research, education, and medical and public health practices.
Utilizing the integrative MPE approach, we aim to understand influences of various modifiable factors and personal genomic variations on disease processes (including host immunity status) using colorectal cancer as a disease model. New insights from MPE research can help personalize prevention and treatment strategies toward our achievement of precision medicine.
We have been developing analytic framework and statistical models to decipher complex multi-level data in large population studies, including MPE data. Use of proper data analysis tools is essential in rigorous science that can support precision medicine. Our user-friendly programs are freely available and can be applied to any disease settings.
With its versatile nature, MPE can further expand to incorporate other biomedical disciplines (including genomics, immunology, microbiology, pharmacology, nutritional science, environmental science, social science, etc.), which can generate new scientific frontiers. As a major part of the NCI R35 Outstanding Investigator Award (OIA) research program (http://grantome.com/grant/NIH/R35-CA197735-01), we have developed various integrative concepts, including “causal inference – MPE integration”, “immuno-MPE”, and “pharmaco-MPE”.
Endometrial cancer is the most common gynecologic malignancy, with approximately 40,000 new cases and 7,400 deaths per year in the US. The diagnosis of EIN, the precursor lesion that gives rise to endometrial cancer, is challenging for pathologists but the diagnosis has major treatment implications because EIN is treated with hysterectomy while a benign diagnosis is not.
Computational tools have the potential to standardize diagnostic practice by reigning in this lack of agreement.
Dr. Tempany leads the The National Center for Image Guided Therapy (NCIGT), located at Brigham and Women’s Hospital and Harvard Medical School in Boston. NCIGT is a Biomedical Technology Resource Center funded by the National Institute of Biomedical Imaging and Bioengineering which serves as a national resource for all aspects of research into medical procedures enhanced by imaging, with the common goal of providing more effective patient care.
Dr. Tempany is particulary involved in the Prostate Project. There are two complex issues that drive the clinical need to change current paradigms for prostate cancer (PCa): The inability to predict aggressiveness of a given cancer, which in turn leads to over treatment, and the increasing evidence that disease progression in men with seemingly low-risk PCa is due to inadequate biopsy sampling. Technical solutions to address these challenges, and their validation in clinic, are lacking.