Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.

Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study ( difference in patients) for learning discriminative low dimensional representations.

Robust Collaborative Clustering of Subjects and Radiomic Features for Cancer Prognosis.

Feature dimensionality reduction plays an important role in radiomic studies with a large number of features. However, conventional radiomic approaches may suffer from noise, and feature dimensionality reduction techniques are not equipped to utilize latent supervision information of patient data under study, such as differences in patients, to learn discriminative low dimensional representations. To achieve robustness to noise and feature dimensionality reduction with improved discriminative power, we develop a robust collaborative clustering method to simultaneously cluster patients and radiomic features into distinct groups respectively under adaptive sparse regularization.

DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGING DATA BASED SURVIVAL ANALYSIS OF RECTAL CANCER.

Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations. To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model.

COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS.

Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning method to simultaneously stratify cancer patients into distinct risk groups based on their radiomic features and learn low-dimensional representations of the radiomic features for robust prediction of their clinical outcomes. Based on nonnegative matrix tri-factorization techniques, the proposed method applies collaborative clustering to radiomic features of cancer patients to obtain clusters of both the patients and their radiomic features so that patients with distinct imaging patterns are stratified into different risk groups and highly correlated radiomic features are grouped in the same radiomic feature clusters.

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer.

Purpose: Convolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy.

Cholangiocarcinoma and Gallbladder Cases: An Expert Panel Case-Based Discussion.

Cholangiocarcinoma and gallbladder malignancies are aggressive gastrointestinal malignancies with management dependent on resectability, comorbidities, and location. A multidisciplinary discussion with medical oncologists, radiation oncologists, and surgeons is necessary to determine the optimal treatment approach for each patient. Surgical resection offers the best chance for a long-term cure.

Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.

Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of different patients are of different sizes.

Proton beam reirradiation for locally recurrent pancreatic adenocarcinoma.

Background: Local recurrence following definitive treatment for pancreatic adenocarcinoma is common and can be associated with significant morbidity and mortality. Retreatment options for these patients are limited. Proton beam reirradiation (PRT) may limit dose and toxicity to previously irradiated normal tissues in patients without evidence of metastatic disease.

Management of elderly patients with early-stage medically inoperable endometrial cancer: Systematic review and National Cancer Database analysis.

Purpose: To evaluate the effectiveness of radiation therapy among elderly patients who are deemed medically inoperable.

Methods And Materials: We searched PubMed to identify studies from the past 25 years that reported outcomes of medically inoperable endometrial cancer patients treated with radiation alone. The National Cancer Database (NCDB) was queried to identify patients 65 years and older with Stage I-II medically inoperable endometrial cancer.

Contribution of CXCL12 secretion to invasion of breast cancer cells.

Introduction: Neu (HER2/ErbB2) is overexpressed in 25% to 30% of human breast cancer, correlating with a poor prognosis. Researchers in previous studies who used the mouse mammary tumor virus Neu-transgenic mouse model (MMTV-Neu) demonstrated that the Neu-YB line had increased production of CXCL12 and increased metastasis, whereas the Neu-YD line had decreased metastasis. In this study, we examined the role of increased production of CXCL12 in tumor cell invasion and malignancy.

Tumor-stroma: In vivo assays and intravital imaging to study cell migration and metastasis.

The development of metastatic disease is often correlated with poor patient outcome in a variety of different cancers. The metastatic cascade is a complex, multistep process that involves the growth of the primary tumor and angiogenesis, invasion into the local environment, intravasation into the vasculature, tumor cell survival in the circulation, extravasation from the vasculature and sustained growth at secondary organ sites to form metastases. Although in vitro assays of single cell types can provide information regarding cell autonomous mechanisms contributing to metastasis, the in vivo microenvironment entails a network of interactions between cells which is also important.

A functional in vivo screen for regulators of tumor progression identifies HOXB2 as a regulator of tumor growth in breast cancer.

Microarray profiling in breast cancer patients has identified genes correlated with prognosis whose functions are unknown. The purpose of this study was to develop an in vivo assay for functionally screening regulators of tumor progression using a mouse model. Transductant shRNA cell lines were made in the MDA-MB-231 breast cancer line.

ERBB1 and ERBB2 have distinct functions in tumor cell invasion and intravasation.

Purpose: The epidermal growth factor receptor (ERBB1) and related family member HER-2/neu (ERBB2) are often overexpressed in aggressive breast cancers and their overexpression is correlated with poor prognosis. Clinical studies using ERBB inhibitors have focused on tumor growth effects, but ERBBs can contribute to malignancy independent of their effects on tumor growth. Our studies were designed to evaluate the effect of ERBB inhibition on tumor cell motility and intravasation in vivo using clinically relevant small-molecule inhibitors.

Identification of cellular proteins that maintain retroviral epigenetic silencing: evidence for an antiviral response.

Integrated retroviral DNA is subject to epigenetic gene silencing, resulting in loss of expression of viral genes as well as reporter or therapeutic genes transduced by retroviral vectors. Possible mediators of such silencing include the histone deacetylase (HDAC) family of cellular proteins. We previously isolated HeLa cell populations that harbored silent avian sarcoma virus-based green fluorescent protein (GFP) vectors that could be reactivated by treatment with HDAC inhibitors.

High-frequency epigenetic repression and silencing of retroviruses can be antagonized by histone deacetylase inhibitors and transcriptional activators, but uniform reactivation in cell clones is restricted by additional mechanisms.

Integrated retroviral DNA is subject to epigenetic gene silencing, but the viral and host cell properties that influence initiation, maintenance, and reactivation are not fully understood. Here we describe rapid and high-frequency epigenetic repression and silencing of integrated avian sarcoma virus (ASV)-based vector DNAs in human HeLa cells. Initial studies utilized a vector carrying the strong human cytomegalovirus (hCMV) immediate-early (IE) promoter to drive expression of a green fluorescent protein (GFP) reporter gene, and cells were sorted into two populations based on GFP expression [GFP(+) and GFP(-)].

The cloning and characterization of the histone acetyltransferase human homolog Dmel\TIP60 in Drosophila melanogaster: Dmel\TIP60 is essential for multicellular development.

Chromatin packaging directly influences gene programming as it permits only certain portions of the genome to be activated in any given developmental stage, cell, and tissue type. Histone acetyltransferases (HATs) are a key class of chromatin regulatory proteins that mediate such developmental chromatin control; however, their specific roles during multicellular development remain unclear. Here, we report the first isolation and developmental characterization of a Drosophila HAT gene (Dmel\TIP60) that is the homolog of the human HAT gene TIP60.

Human immunodeficiency virus type 1 DNA nuclear import and integration are mitosis independent in cycling cells.

An essential step in human immunodeficiency virus type 1 (HIV-1) replication is the movement of the viral preintegration complex from the cytoplasm into the nucleus. The pathway(s) and timing for HIV-1 DNA nuclear entry in cycling cells have not been established. Here, we show that if cycling cells are infected before S phase, viral DNA can be integrated prior to passage of the host DNA replication fork through the integration site, as indicated by stable inheritance in both daughter cells.

Transduction of interphase cells by avian sarcoma virus.

It has been generally believed that oncoretroviruses are dependent on mitosis for efficient nuclear entry of viral DNA. We previously identified a nuclear localization signal in the integrase protein of an oncoretrovirus, avian sarcoma virus (ASV), suggesting an active import mechanism for the integrase-DNA complex (G. Kukolj, R.