Oncogenic Gain of Function in Glioblastoma Is Linked to Mutant p53 Amyloid Oligomers.

Tumor-associated p53 mutations endow cells with malignant phenotypes, including chemoresistance. Amyloid-like oligomers of mutant p53 transform this tumor suppressor into an oncogene. However, the composition and distribution of mutant p53 oligomers are unknown and the mechanism involved in the conversion is sparse. Here, we report accumulation of a p53 mutant within amyloid-like p53 oligomers in glioblastoma-derived cells presenting a chemoresistant gain-of-function phenotype. Statistical analysis from fluorescence fluctuation spectroscopy, pressure-induced measurements, and thioflavin T kinetics demonstrates the distribution of oligomers larger than the active tetrameric form of p53 in the nuclei of living cells and the destabilization of native-drifted p53 species that become amyloid. Collectively, these results provide insights into the role of amyloid-like mutant p53 oligomers in the chemoresistance phenotype of malignant and invasive brain tumors and shed light on therapeutic options to avert cancer

The angular spectrum of the scattering coefficient map reveals subsurface colorectal cancer

Abstract Colorectal cancer diagnosis currently relies on histological detection of endoluminal neoplasia in biopsy specimens. However, clinical visual endoscopy provides no quantitative subsurface cancer information. In this ex vivo study of nine fresh human colon specimens, we report the first use of quantified subsurface scattering coefficient maps acquired by swept-source optical coherence tomography to reveal subsurface abnormities. We generate subsurface scattering coefficient maps with a novel wavelet-based-curve-fitting method that provides significantly improved accuracy. The angular spectra of scattering coefficient maps of normal tissues exhibit a spatial feature distinct from those of abnormal tissues. An angular spectrum index to quantify the differences between the normal and abnormal tissues is derived, and its strength in revealing subsurface cancer in ex vivo samples is statistically analyzed. The study demonstrates that the angular spectrum of the scattering coefficient map can effectively reveal subsurface colorectal cancer and potentially provide a fast and more accurate diagnosis

A Novel HDL-Mimetic Peptide HM-10/10 Protects RPE and Photoreceptors in Murine Models of Retinal Degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. The retinal pigment epithelium (RPE) is a critical site of pathology in AMD. Oxidative stress plays a key role in the development of AMD. We generated a chimeric high-density lipoprotein (HDL), mimetic peptide named HM-10/10, with anti-oxidant properties and investigated its potential for the treatment of retinal disease using cell culture and animal models of RPE and photoreceptor (PR) degeneration. Treatment with HM-10/10 peptide prevented human fetal RPE cell death caused by tert-Butyl hydroperoxide (tBH)-induced oxidative stress and sodium iodate (NaIO3), which causes RPE atrophy and is a model of geographic atrophy in mice. We also show that HM-10/10 peptide ameliorated photoreceptor cell death and significantly improved retinal function in a mouse model of N-methyl-N-nitrosourea (MNU)-induced PR degeneration. Our results demonstrate that HM-10/10 protects RPE and retina from oxidant injury and can serve as a potential therapeutic agent for the treatment of retinal degeneration

A golden age for working with public proteomics data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature 'omics' disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets

Ultrasound-guided Optical Techniques for Cancer Diagnosis: System and Algorithm Development

Worldwide, breast cancer is the most common cancer among women. In the United States alone, the American cancer society has estimated there will be 271,270 new breast cancer cases in 2019, and 42,260 lives will be lost to the disease. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) are regularly used for breast cancer diagnosis and therapy monitoring. However, they sometimes fail to diagnose breast cancer effectively. These shortcomings have motivated researchers to explore new modalities. One of these modalities, diffuse optical tomography (DOT), utilizes near-infrared (NIR) light to reveal the optical properties of tissue. NIR-based DOT images the contrast between a suspected lesion’s location and the background tissue, caused by the higher NIR absorption of the hemoglobin which characterizes tumors. The limitation of high light scattering inside tissue is minimized by using ultrasound image to find the tumor location. This thesis focuses on developing a compact, low-cost ultrasound guided diffuse optical tomography imaging system and on improving optical image reconstruction by extracting the tumor’s location and size from co-registered ultrasound images. Several electronic components have been redesigned and optimized to save space and cost and to improve the user experience. In terms of software and algorithm development, manual extraction of tumor information from ultrasound images has been replaced by using a semi-automated ultrasound image segmentation algorithm that reduces the optical image reconstruction time and operator dependency. This system and algorithm have been validated with phantom and clinical data and have demonstrated their efficacy. An ongoing clinical trial will continue to gather more patient data to improve the robustness of the imaging algorithm. Another part of this research focuses on ovarian cancer diagnosis. Ovarian cancer is the most deadly of all gynecological cancers, with a less than 50% five-year survival rate. This cancer can evolve without any noticeable symptom, which makes it difficult to diagnose in an early stage. Although ultrasound-guided photoacoustic tomography (PAT) has demonstrated potential for early detection of ovarian cancer, clinical studies have been very limited due to the lack of robust PAT systems. In this research, we have customized a commercial ultrasound system to obtain real-time co-registered PAT and US images. This system was validated with several phantom studies before use in a clinical trial. PAT and US raw data from 30 ovarian cancer patients was used to extract spectral and statistical features for training and testing classifiers for automatic diagnosis. For some challenging cases, the region of interest selection was improved by reconstructing co-registered Doppler images. This study will be continued in order to obtain quantitative tissue properties using US-guided PAT

Investigation of cellular microenvironments and heterogeneity with biodynamic imaging

Imaging of biological tissue in a relevant environment is critical to accurately assessing the effectiveness of chemotherapeutic agents in combatting cancer. Though many three-dimensional (3D) culture models exist, conventional in vitro assays continue to use two-dimensional (2D) cultures because of the difficulty in imaging through deep tissue. 3D tomographic imaging techniques exist and are being used in the development of 3D efficacy assays. However, most of these assays look at therapy endpoint (dead or living cancer cell count) and do not capture the dynamics of tissue response. Biodynamic imaging (BDI) is a 3D tomographic imaging and assay technique that uses the dynamics of scattered coherent light, or speckle, to measure dynamic response of tissue to perturbations. Dynamic measurements allow BDI to not only assess overall efficacy, but to also measure phenotypic changes in cancer tissue as it responds to therapy. Because BDI captures the phenotypic response of tissue, it naturally accounts for genetic and microenvironmental factors, and shows promise as an accurate predictor of in vivo chemotherapeutic response. This thesis presents the development of BDI into a predictive assay for assisting in chemotherapy selection. It shows how microenvironmental factors alter BDI response measurements. It reports how different BDI biomarkers can accurately assess sensitivity to platinum treatment in xenograpft models of ovarian cancer. Changes in sensitivity during metastasis are observed, and a method for addressing sample variability and heterogeneity is presented. A predictive model for chemotherapeutic selection is developed and applied retrospectively to primary esophageal cancer. Finally, a new imaging modality called tissue dynamic spectroscopic imaging (TDSI) is presented, which is capable of directly assessing spatial functional patterns in patient samples