Ultrasensitive Molecular Monitoring of Breast Cancer and Acute Myeloid Leukemia

Cancer is the common name to a group of biologically diverse malignant neoplastic diseases. Approximately 18 million people are diagnosed with cancer annually and 8.8 million patients die from it. Tumorigenesis and progression of cancer are driven by alterations in the cancer cell genome. These alterations lead to gain of oncogenic functions, loss of tumor suppressor functions, or may be chromosomal rearrangements without obvious function, and these alterations themselves can serve as tumor-specific biomarkers that may have diagnostic and clinical utility.In this thesis, we investigated oncogenic and tumor suppressive genes in breast cancers and leukemias, with a focus on the PTEN/PIK3CA pathway as well as minimally-invasive monitoring of cancer patients using “liquid biopsies.” We studied the underlying mechanism of PTEN protein loss in breast cancer, and showed how various types of tumor-specific mutations, including those in PIK3CA, can be used as biomarkers to monitor the dynamics of occult tumor burden, evaluate the degree of tumor content dissemination into the bloodstream with mammographic compression, and detect minimal residual disease in breast cancer and acute myeloid leukemia.In Paper I, we found that the frequent loss of PTEN protein in human breast cancer is not attributable to the overexpression of the E3 ubiquitin ligase NEDD4, and thus NEDD4 is unlikely to be a regulator of the oncogenic PI3K/PTEN signaling pathway. In Paper II, we showed that serial monitoring of tumor specific chromosomal rearrangements, identified with low coverage whole genome sequencing and then measured in blood samples by digital PCR (dPCR), is a highly sensitive and specific approach to detect occult breast cancer disease prior to the onset of symptoms and clinical detection. Detected plasma ctDNA level was a quantitative predictor of poor relapse-free and overall survival. In Paper III, we confirmed the general safety of mammography, using FDA approved CellSearch® and our ultrasensitive mutation detection dPCR technology IBSAFE, that mammographic compression of the breast with a breast tumor does not appear to lead to significant additional dissemination of CTCs and ctDNA into the bloodstream. In Paper IV, we showed that acute myeloid leukemia specific mutations can be serially monitored in follow-up bone marrow samples by IBSAFE, providing an insight in subclonal evolution of the leukemia and the status of minimal residual disease.These results and our mutation detection technology suggest they have high potential to be utilized in assessing treatment response, monitoring the disease course, detecting remnant tumor deposits with targetable mutations, and helping to speed the development of new drugs in in the future

Fine-Grained Car Detection for Visual Census Estimation

Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201