Andre Balla

Tissue refractive index as marker of disease

The gold standard in histopathology relies on manual investigation of stained tissue biopsies. A sensitive and quantitative method for in situ tissue specimen inspection is highly desirable, as it would allow early disease diagnosis and automatic screening. Here we demonstrate that quantitative phase imaging of entire unstained biopsies has the potential to fulfill this requirement. Our data indicates that the refractive index distribution of histopathology slides, which contains information about the molecular scale organization of tissue, reveals prostate tumors and breast calcifications. These optical maps report on subtle, nanoscale morphological properties of tissues and cells that cannot be recovered by common stains, including hematoxylin and eosin. We found that cancer progression significantly alters the tissue organization, as exhibited by consistently higher refractive index variance in prostate tumors versus normal regions. Furthermore, using the quantitative phase information, we obtained the spatially resolved scattering mean free path and anisotropy factor g for entire biopsies and demonstrated their direct correlation with tumor presence. In essence, our results show that the tissue refractive index reports on the nanoscale tissue architecture and, in principle, can be used as an intrinsic marker for cancer diagnosis.

Breast cancer diagnosis using spatial light interference microscopy

Abstract. The standard practice in histopathology of breast cancers is to examine a hematoxylin and eosin (H&E) stained tissue biopsy under a microscope to diagnose whether a lesion is benign or malignant. This determination is made based on a manual, qualitative inspection, making it subject to investigator bias and resulting in low throughput. Hence, a quantitative, label-free, and high-throughput diagnosis method is highly desirable. We present here preliminary results showing the potential of quantitative phase imaging for breast cancer screening and help with differential diagnosis. We generated phase maps of unstained breast tissue biopsies using spatial light interference microscopy (SLIM). As a first step toward quantitative diagnosis based on SLIM, we carried out a qualitative evaluation of our label-free images. These images were shown to two pathologists who classified each case as either benign or malignant. This diagnosis was then compared against the diagnosis of the two pathologists on corresponding H&E stained tissue images and the number of agreements were counted. The agreement between SLIM and H&E based diagnosis was 88% for the first pathologist and 87% for the second. Our results demonstrate the potential and promise of SLIM for quantitative, label-free, and high-throughput diagnosis.

Correlation-induced spectral changes in tissues

We report experimental evidence of correlation-induced spectral changes in biological tissues. The overall spectral shift in our transmission measurements is to the red and the mean wavelength of the original spectrum is up 10% larger. These results indicate that the spectral changes due to elastic scattering are significant and likely to hinder all spectroscopic measurements based on the inelastic (i.e., emission and absorption) interaction between light and tissues. Thus, simultaneous morphology and spectral measurements are required for accurate measurements spectroscopic information.

Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms

We report, for the first time, the use of Quantitative Phase Imaging (QPI) images to perform automatic prostate cancer diagnosis. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to predict outcomes of the testing samples. Current performance is reported on a dataset of more than 300 cores of various diagnosis results.

Sequential Testing Approach As an Efficient and Easier Alternative for the Validation of New Predictive Technologies in the Clinic

PURPOSE When clinicians contemplate the use of a new predictive technology in their practice, such as a nomogram, there is always a question of whether the new test is beneficial to their own clinical population. Unfortunately, traditional validation methods require a large number of subjects for validation testing and delay the decision-making process. We present an efficient and easy-to-use method based on the concept of sequential data analysis. PATIENTS AND METHODS We illustrate with an example determining the validity of a technology for predicting Gleason score upgrading from biopsy to postprostatectomy (the Chun nomogram) in a clinical population different from the one used to initially validate the technology. Clinical data required by the Chun nomogram were available from 201 patients from the Cooperative Prostate Cancer Tissue Resource. RESULTS Of 124 patients predicted by the Chun nomogram to have an upgrading event, 47 actually did. The positive predictive value (PPV) of the model was therefore 38% and significantly (P < .05) less than the value of 80% which we considered to be the smallest clinically useful PPV in this situation. Had the sequential methods introduced in this article been employed prospectively in this cohort, the same conclusion would have been reached using data from only the first 15 patients. CONCLUSION In-clinic validation of predictive technologies will help the clinician adopt truly beneficial technologies and avoid the adoption of technologies which provide no significant benefit to their local patient population. For this task, sequential methods offer clear advantages.

PSIP1/p75 promotes tumorigenicity in breast cancer cells by promoting the transcription of cell cycle genes

Breast cancer (BC) is a highly heterogeneous disease, both at the pathological and molecular level, and several chromatin-associated proteins play crucial roles in BC initiation and progression. Here, we demonstrate the role of PSIP1 (PC4 and SF2 interacting protein)/p75 (LEDGF) in BC progression. PSIP1/p75, previously identified as a chromatin-adaptor protein, is found to be upregulated in basal-like/triple negative breast cancer (TNBC) patient samples and cell lines. Immunohistochemistry in tissue arrays showed elevated levels of PSIP1 in metastatic invasive ductal carcinoma. Survival data analyses revealed that the levels of PSIP1 showed a negative association with TNBC patient survival. Depletion of PSIP1/p75 significantly reduced the tumorigenicity and metastatic properties of TNBC cell lines while its over-expression promoted tumorigenicity. Further, gene expression studies revealed that PSIP1 regulates the expression of genes controlling cell-cycle progression, cell migration and invasion. Finally, by interacting with RNA polymerase II, PSIP1/p75 facilitates the association of RNA pol II to the promoter of cell cycle genes and thereby regulates their transcription. Our findings demonstrate an important role of PSIP1/p75 in TNBC tumorigenicity by promoting the expression of genes that control the cell cycle and tumor metastasis.

Automatic glomerulus extraction in whole slide images towards computer aided diagnosis

Renal biopsies form the gold standard of diagnostic and prognostic assessments of renal transplants. With the addition of new quantitative strategies to supplement renal biopsy interpretation such as gene array and metabolomics, the capability to incorporate all quantitative measures for clinical interpretation will require multi-dimensional analyses. Currently, renal biopsies are analyzed manually; the quantitative features of pathology observed on the biopsies are limited to hand counts. Standardized, automated detection of pathology observed in a kidney transplant biopsy will enable the input of these digital images alongside other quantitative measures of new technologies, with potential gains in precision in patient care. We investigate a learning framework to detect pathological changes in biopsy image that addresses two main issues: the inadequate training set and the significant diversity of color and tissue shape on whole slide images. Two case studies, automatic detection of interstitial inflammation and tubular cast, are presented in this work. Afterwards, we propose a fully automated glomerulus extraction framework on micrograph of entire renal tissue, focusing on extracting Bowmans capsule, the supportive structure of glomeruli. Statistical approaches are also introduced to further improve the performance. Human expert annotations of interstitial inflammation and tubular casts in 10 H&E stained renal tissues of nonhuman primates and more than 100 glomeruli are used to demonstrate the superior performance of the proposed algorithm over existing solutions.

Automatic tissue segmentation of breast biopsies imaged by QPI

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel’s label and the histogram of these textons in that pixel’s neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel’s neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

Quantifying collagen orientation in breast tissue biopsies using SLIM (Conference Presentation)

Breast cancer is a major public health problem worldwide, being the most common type of cancer among women according to the World Health Organization (WHO). The WHO has further stressed the importance of an early determination of the disease course through prognostic markers. Recent studies have shown that the alignment of collagen fibers in tumor adjacent stroma correlate with poorer health outcomes in patients. Such studies have typically been carried out using Second-Harmonic Generation (SHG) microscopy. SHG images are very useful for quantifying collagen fiber orientation due their specificity to non-centrosymmetric structures in tissue, leading to high contrast in collagen rich areas. However, the imaging throughput in SHG microscopy is limited by its point scanning geometry. In this work, we show that SLIM, a wide-field high-throughput QPI technique, can be used to obtain the same information on collagen fiber orientation as is obtainable through SHG microscopy. We imaged a tissue microarray containing both benign and malignant cores using both SHG microscopy and SLIM. The cellular (non-collagenous) structures in the SLIM images were next segmented out using an algorithm developed in-house. Using the previously published Fourier Transform Second Harmonic Generation (FT-SHG) tool, the fiber orientations in SHG and segmented SLIM images were then quantified. The resulting histograms of fiber orientation angles showed that both SHG and SLIM generate similar measurements of collagen fiber orientation. The SLIM modality, however, can generate these results at much higher throughput due to its wide-field, whole-slide scanning capabilities.

Abstract 119: Vitamin K2 targets castration-resistant prostate cancer VCaP cells by reactive oxygen species mediated apoptotic cell death

Prostate Cancer (PCa) is the second most common cancer in western countries especially in US population, in which castration-resistant prostate cancer (CRPC) is the major cause for patient mortality. Current treatment options available for CRPC are not efficient and have undesirable side effects. Hence there is an urgent need to develop non-toxic and effective treatment strategies for CRPC. Vitamin K2 (VK2), a natural menaquinone has several medicinal values including anti-cancer activity and anti-osteoporosis effect. The aim of this study is to evaluate the therapeutic effects of Vitamin K2 (VK2) and its anti-cancer mechanism against CRPC. In this study, we have used VCaP cell line (ATCC) which is established from a patient with hormone refractory prostate cancer. VCaP cells were treated with various concentrations of VK2 to evaluate its effects on cell viability by MTT assay, anchorage independent growth by soft agar assay, cellular senescence by beta-galactosidase staining assay and cancer cell migration by wound healing assay. We have also assessed the VK2-induced production of intracellular reactive oxygen species (ROS) using DCF (2′,7′-dichlorofluorescein) probe based fluorescence assay. VK2 induced apoptosis was detected by Annexin-V FITC and TUNEL assays. Western blot analysis was utilized to uncover the anti-proliferative and anti-metastatic mechanisms of VK2 against CRPC. Our results showed that VK2 significantly inhibits the proliferation of VCaP cells in a dose dependent manner at 48 hrs treatment in vitro. MTT data also showed that anti-proliferative effects of VK2 were significantly abrogated in the presence of anti-oxidant N-acetyl cysteine (NAC) and caspase inhibitor Z-VAD-FMK suggesting that ROS and caspase activation as the underlying anti-cancer mechanisms of VK2 in CRPC cells. In addition, VK2 reduced the migration potential of VCaP cells in wound healing assay and inhibited anchorage independent growth of these cells when compared to untreated cells. Annexin-V and TUNEL assays confirmed that VK2 induces apoptosis in VCaP cells. Our results also suggested that the VK2 has the ability to enforce growth arrest in CRPC cells by activating cellular senescence. Western blot analysis revealed that VK2 downregulated the expression of BiP, survivin, MMP-2, and PCNA while activating PARP-1, p21 and DNA damage response marker, phospho-H2AX in VCaP cells. Furthermore, VCaP cells treated with VK2 resulted in the activation of Caspase-3 and-7 apoptotic mediators. These results correlated with translocation of Bax and Cytochrome C to cytoplasm following VK2 treatment in VCaP cells as determined by confocal immunofluorescence analysis. In conclusion our study suggests that VK2 might be an effective anti-proliferative and anti-metastatic agent for CRPC by specifically targeting key anti-apoptotic, cell cycle progression and metastasis promoting signaling molecules. Citation Format: Subramanyam Dasari, Maarten C. Bosland, Andre Kajdacsy- Balla, Gnanasekar Munirathinam. Vitamin K2 targets castration-resistant prostate cancer VCaP cells by reactive oxygen species mediated apoptotic cell death [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 119. doi:10.1158/1538-7445.AM2017-119