Richard A. Moffitt

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

Molecular Mapping of Tumor Heterogeneity on Clinical Tissue Specimens with Multiplexed Quantum Dots

Tumor heterogeneity is one of the most important and challenging problems not only in studying the mechanisms of cancer development but also in developing therapeutics to eradicate cancer cells. Here we report the use of multiplexed quantum dots (QDs) and wavelength-resolved spectral imaging for molecular mapping of tumor heterogeneity on human prostate cancer tissue specimens. By using a panel of just four protein biomarkers (E-cadherin, high-molecular-weight cytokeratin, p63, and alpha-methylacyl CoA racemase), we show that structurally distinct prostate glands and single cancer cells can be detected and characterized within the complex microenvironments of radical prostatectomy and needle biopsy tissue specimens. The results reveal extensive tumor heterogeneity at the molecular, cellular, and architectural levels, allowing direct visualization of human prostate glands undergoing structural transitions from a double layer of basal and luminal cells to a single layer of malignant cells. For clinical diagnostic applications, multiplexed QD mapping provides correlated molecular and morphological information that is not available from traditional tissue staining and molecular profiling methods.

k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.

In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463 320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors.

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Recent advances in biomarker discovery, biocomputing and nanotechnology have raised new opportunities in the emerging fields of personalized medicine (in which disease detection, diagnosis and therapy are tailored to each individuals molecular profile) and predictive medicine (in which genetic and molecular information is used to predict disease development, progression and clinical outcome). Here, we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, in addition to the prospects for and challenges involved in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

Automatic batch-invariant color segmentation of histological cancer images

We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts—i.e., pathologistsߞoften leads to the best color segmentation or “ground truth” regardless of stain color variations in different batches. However, such guidance limits the objectivity, reproducibility and speed of diagnostic systems. Our system uses knowledge from pre-segmented reference images to normalize and classify pixels in patient images. The system then refines the segmentation by re-classifying pixels in the original color space. We test our system on four batches of H&E stained images and, in comparison to a system with no normalization (39% average accuracy), we obtain an average segmentation accuracy of 85%.

Circulating Tumor Cells as a Biomarker of Response to Treatment in Patient-Derived Xenograft Mouse Models of Pancreatic Adenocarcinoma

Circulating tumor cells (CTCs) are cells shed from solid tumors into circulation and have been shown to be prognostic in the setting of metastatic disease. These cells are obtained through a routine blood draw and may serve as an easily accessible marker for monitoring treatment effectiveness. Because of the rapid progression of pancreatic ductal adenocarcinoma (PDAC), early insight into treatment effectiveness may allow for necessary and timely changes in treatment regimens. The objective of this study was to evaluate CTC burden as a biomarker of response to treatment with a oral phosphatidylinositol-3-kinase inhibitor, BKM120, in patient-derived xenograft (PDX) mouse models of PDAC. PDX mice were randomized to receive vehicle or BKM120 treatment for 28 days and CTCs were enumerated from whole blood before and after treatment using a microfluidic chip that selected for EpCAM (epithelial cell adhesion molecule) positive cells. This microfluidic device allowed for the release of captured CTCs and enumeration of these cells via their electrical impedance signatures. Median CTC counts significantly decreased in the BKM120 group from pre- to post-treatment (26.61 to 2.21 CTCs/250 µL, p = 0.0207) while no significant change was observed in the vehicle group (23.26 to 11.89 CTCs/250 µL, p = 0.8081). This reduction in CTC burden in the treatment group correlated with tumor growth inhibition indicating CTC burden is a promising biomarker of response to treatment in preclinical models. Mutant enriched sequencing of isolated CTCs confirmed that they harbored KRAS G12V mutations, identical to the matched tumors. In the long-term, PDX mice are a useful preclinical model for furthering our understanding of CTCs. Clinically, mutational analysis of CTCs and serial monitoring of CTC burden may be used as a minimally invasive approach to predict and monitor treatment response to guide therapeutic regimens.

chip artifact CORRECTion (caCORRECT): A Bioinformatics System for Quality Assurance of Genomics and Proteomics Array Data

Quality assurance of high throughput “-omics” data is a major concern for biomedical discovery and translational medicine, and is considered a top priority in bioinformatics and systems biology. Here, we report a web-based bioinformatics tool called caCORRECT for chip artifact detection, analysis, and CORRECTion, which removes systematic artifactual noises that are commonly observed in microarray gene expression data. Despite the development of major databases such as GEO arrayExpress, caArray, and the SMD to manage and distribute microarray data to the public, reproducibility has been questioned in many cases, including high-profile papers and datasets. Based on both archived and synthetic data, we have designed the caCORRECT to have several advanced features: (1) to uncover significant, correctable artifacts that affect reproducibility of experiments; (2) to improve the integrity and quality of public archives by removing artifacts; (3) to provide a universal quality score to aid users in their selection of suitable microarray data; and (4) to improve the true-positive rate of biomarker selection verified by test data. These features are expected to improve the reproducibility of Microarray study. caCORRECT is freely available at: http://caCORRECT.bme.gatech.edu.

Local iontophoretic administration of cytotoxic therapies to solid tumors

Local administration of cytotoxic drugs using iontophoresis results in drug accumulation and therapeutic efficacy in mouse models of pancreatic and breast cancer and favorable PK in a large animal model. Electric field drives drugs into tumors Maintaining a high local concentration of anticancer drug may be key to killing tumors, but sometimes, therapeutics need an extra “push” to fully penetrate cancer tissues. Byrne and colleagues created a new implantable device that relies on iontophoresis—or, the flow of charged molecules in an electric field—to drive drugs into tumors. In doing so, the device, lodged in the tumor, enables local delivery of cytotoxic therapies. The authors tested their iontophoretic devices in mouse models of human pancreatic and breast cancers, using the standard drugs gemcitabine and cisplatin. The device enhanced the therapeutic efficacy of the drugs, slowing tumor growth in all animals and prolonging survival in the breast cancer models, especially when in combination with radiotherapy. In dogs, the device showed favorable pharmacokinetic profiles, indicating that, if implanted in humans, drugs would be retained primarily at the site of the tumor rather than traveling throughout the body, damaging healthy tissues. By maintaining high local drug concentrations and low systemic exposure, the iontophoretic device could improve long-term patient outcomes compared with intravenous injection of cytotoxic therapies. Currently, there are iontophoretic catheters (for bladder) and pumps (for arteries) being tested in patients, thus paving the way for this device to move into human solid tumors. Parenteral and oral routes have been the traditional methods of administering cytotoxic agents to cancer patients. Unfortunately, the maximum potential effect of these cytotoxic agents has been limited because of systemic toxicity and poor tumor perfusion. In an attempt to improve the efficacy of cytotoxic agents while mitigating their side effects, we have developed modalities for the localized iontophoretic delivery of cytotoxic agents. These iontophoretic devices were designed to be implanted proximal to the tumor with external control of power and drug flow. Three distinct orthotopic mouse models of cancer and a canine model were evaluated for device efficacy and toxicity. Orthotopic patient-derived pancreatic cancer xenografts treated biweekly with gemcitabine via the device for 7 weeks experienced a mean log2 fold change in tumor volume of –0.8 compared to a mean log2 fold change in tumor volume of 1.1 for intravenous (IV) gemcitabine, 3.0 for IV saline, and 2.6 for device saline groups. The weekly coadministration of systemic cisplatin therapy and transdermal device cisplatin therapy significantly increased tumor growth inhibition and doubled the survival in two aggressive orthotopic models of breast cancer. The addition of radiotherapy to this treatment further extended survival. Device delivery of gemcitabine in dogs resulted in more than 7-fold difference in local drug concentrations and 25-fold lower systemic drug levels than the IV treatment. Overall, these devices have potential paradigm shifting implications for the treatment of pancreatic, breast, and other solid tumors.

Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysis

The differential diagnosis of clear cell, papillary, and chromophobe renal cell carcinoma is clinically important, because these tumor subtypes are associated with different pathobiology and clinical behavior. For cases in which histopathology is equivocal, immunohistochemistry and quantitative reverse transcriptase-polymerase chain reaction can assist in the differential diagnosis by measuring expression of subtype-specific biomarkers. Several renal tumor biomarkers have been discovered in expression microarray studies. However, due to heterogeneity of gene and protein expression, additional biomarkers are needed for reliable diagnostic classification. We developed novel bioinformatics systems to identify candidate renal tumor biomarkers from the microarray profiles of 45 clear cell, 16 papillary, and 10 chromophobe renal cell carcinomas; the microarray data was derived from 2 independent published studies. The ArrayWiki biocomputing system merged the microarray data sets into a single file, so gene expression could be analyzed from a larger number of tumors. The caCORRECT system removed non-random sources of error from the microarray data, and the omniBioMarker system analyzed data with several gene-ranking algorithms to identify algorithms effective at recognizing previously described renal tumor biomarkers. We predicted these algorithms would also be effective at identifying unknown biomarkers that could be verified by independent methods. We selected 6 novel candidate biomarkers from the omniBioMarker analysis and verified their differential expression in formalin-fixed paraffin-embedded tissues by quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry. The candidate biomarkers were carbonic anhydrase IX, ceruloplasmin, schwannomin-interacting protein 1, E74-like factor 3, cytochrome c oxidase subunit 5a, and acetyl-CoA acetyltransferase 1. Quantitative reverse transcriptase-polymerase chain reaction was performed on 17 clear cell, 13 papillary and 7 chromophobe renal cell carcinoma. Carbonic anhydrase IX and ceruloplasmin were overexpressed in clear cell renal cell carcinoma; schwannomin-interacting protein 1 and E74-like factor 3 were overexpressed in papillary renal cell carcinoma; and cytochrome c oxidase subunit 5a and acetyl-CoA acetyltransferase 1 were overexpressed in chromophobe renal cell carcinoma. Immunohistochemistry was performed on tissue microarrays containing 66 clear cell, 16 papillary, and 12 chromophobe renal cell carcinomas. Cytoplasmic carbonic anhydrase IX staining was significantly associated with clear cell renal cell carcinoma. Strong cytoplasmic schwannomin-interacting protein 1 and cytochrome c oxidase subunit 5a staining were significantly more frequent in papillary and chromophobe renal cell carcinoma, respectively. In summary, we developed a novel process for identifying candidate renal tumor biomarkers from microarray data, and verifying differential expression in independent assays. The tumor biomarkers have potential utility as a multiplex expression panel for classifying renal cell carcinoma with equivocal histology. Biomarker expression assays are increasingly important for renal cell carcinoma diagnosis, as needle core biopsies become more common and different therapies for tumor subtypes continue to be developed.

Computer Aided Histopathological Classification of Cancer Subtypes

In this paper we present the results of our effort to develop a computer aided diagnosis system for pathological imaging data using renal cell carcinoma as a case study. Traditionally, cancer diagnosis is performed by an expert pathologist studying biopsy tissue under a microscope. Due to the complex nature of the task and the heterogeneity of patient tissue, these methods are not only time consuming but also suffer from subjective variability. To improve the repeatability and accuracy of the diagnosis process, a computational diagnosis system is proposed here. In this paper we report that with our novel knowledge-based methodology, we are able to achieve high level of classification accuracy (98%) when trying to classify 64 images (n=64) using a simple Bayesian classifier based on 8 extracted features and complete-leave-one-out cross-validation. This methodology is implemented in MATLAB and is expected to aid pathologists in the clinical setting to diagnose renal cell carcinoma as well as other types of cancer.