TRKA expression and NTRK1 gene copy number across solid tumours.

AIMS: Neurotrophic Tropomyosin Kinase Receptor 1 (NTRK1) gene encodes for the protein Tropomyosin-related kinase A (TRKA). Deregulated activity of TRKA has been shown to have oncogenic potential. We present here the results of an immunohistochemical (IHC) observational cohort study of TRKA expression together with gene copy number (GCN) assessment in various solid tumours. METHODS: Formalin-fixed, paraffin-embedded consecutive samples of different tumour types were tested for TRKA expression. Samples showing TRKA IHC staining in at least 10% of cells were analysed by fluorescence in situ hybridisation to assess NTRK1 gene rearrangements and/or individual GCN gain. All patients underwent this molecular assessment within the phase I ALKA-001 clinical trial. RESULTS: 1043 samples were tested and annotation for histology was available in 1023. Most of the samples were colorectal adenocarcinoma (CRC) (n=550, 52.7%) and lung adenocarcinoma (n=312, 29.9%). 24 samples (2.3%) were biliary tract carcinoma (BTC). Overall, 17 (1.6%) samples were characterised by TRKA IHC expression (four weak, eight moderate, five strong): 9/17 lung adenocarcinoma, 3/17 CRC, 3/17 BTC, 1/17 thyroid cancer and 1/17 cancer of unknown primary. Of these, 1/17 with strong TRKA IHC staining displayed NTRK1 gene rearrangement and 15/17 NTRK1 GCN gain by FISH. No correlation was found between intensity of TRKA IHC staining and number of copies of NTRK1. CONCLUSIONS: TRKA expression can be found in 1.6% of solid tumours and can be paralleled by NTRK1 gene rearrangements or mostly GCN gain. The prognostic and translational therapeutic impact of the latter remains to be established.

Molecular Profiling of Mammary Analog Secretory Carcinoma Revealed a Subset of Tumors Harboring a Novel ETV6-RET Translocation: Report of 10 Cases.

ETV6 gene abnormalities are well described in tumor pathology. Many fusion partners of ETV6 have been reported in a variety of epithelial, mesenchymal, and hematological malignancies. In salivary gland tumor pathology, however, the ETV6-NTRK3 translocation is specific for (mammary analog) secretory carcinoma, and has not been documented in any other salivary tumor type. The present study comprised a clinical, histologic, and molecular analysis of 10 cases of secretory carcinoma, with typical morphology and immunoprofile harboring a novel ETV6-RET translocation.

AngioMap is a novel image analysis algorithm for assessment of plasma cell distribution within bone marrow vascular niche.

The ability to characterize distribution of neoplastic hematopoietic cells and their progenitors in their native microenvironment is emerging as an important challenge and potential therapeutic target in many disease areas, including multiple myeloma. In multiple myeloma, bone marrow (BM) angiogenesis is typically increased and microvessel density is a known indicator of poor prognosis. However, the difficulty of consistently measuring 3D vessels from 2D cut sections has previously limited the study of spatial distribution of plasma cells (PC) and their interaction with BM microenvironment. The aim of the study is to report a novel method to study myeloma cells spatial distribution within their hematopoietic niche context using readily available tissue sections and standard histology approaches. We utilized a novel whole-tissue image analysis approach to identify vessels, and then applied computational grown regions extended out from each vessel at 15, 35, 55, 75, and 100 µm to identify the spatial distribution of PC on CD34/CD138 double-stained core biopsy slides. Percent PC to total cells (TC) was significantly higher at <15 µm distance compared with those at 16 to 35, 36 to 55, 56 to 75, and 76 to 100 µm distance (P=0.0001). Similarly, PC/TC at <35 µm region was significantly higher compared with 36 to 55 (P=0.0001), 56 to 75 (P≤0.0001), and 76 to 100 (P=0.0002) µm distances. The mean PC/TC differences in the spatial gradient of 36 to 55, 56 to 75, and 76 to 100 µm distance regions were not significant. Our findings suggest possible preferential advantage to neoplastic PC in the proximity of blood vessels compared with other hematopoietic marrow cells. We demonstrate the feasibility of analyzing the spatial distribution of PC, and possibly other hematopoietic/stem cells in their microenvironment, as characterized by the distance to vessels in BM using a novel image analysis approach.

Tissue pattern recognition error rates and tumor heterogeneity in gastric cancer.

The anatomic pathology discipline is slowly moving toward a digital workflow, where pathologists will evaluate whole-slide images on a computer monitor rather than glass slides through a microscope. One of the driving factors in this workflow is computer-assisted scoring, which depends on appropriate selection of regions of interest. With advances in tissue pattern recognition techniques, a more precise region of the tissue can be evaluated, no longer bound by the pathologist's patience in manually outlining target tissue areas. Pathologists use entire tissues from which to determine a score in a region of interest when making manual immunohistochemistry assessments. Tissue pattern recognition theoretically offers this same advantage; however, error rates exist in any tissue pattern recognition program, and these error rates contribute to errors in the overall score. To provide a real-world example of tissue pattern recognition, 11 HER2-stained upper gastrointestinal malignancies with high heterogeneity were evaluated. HER2 scoring of gastric cancer was chosen due to its increasing importance in gastrointestinal disease. A method is introduced for quantifying the error rates of tissue pattern recognition. The trade-off between fully sampling tumor with a given tissue pattern recognition error rate versus randomly sampling a limited number of fields of view with higher target accuracy was modeled with a Monte-Carlo simulation. Under most scenarios, stereological methods of sampling-limited fields of view outperformed whole-slide tissue pattern recognition approaches for accurate immunohistochemistry analysis. The importance of educating pathologists in the use of statistical sampling is discussed, along with the emerging role of hybrid whole-tissue imaging and stereological approaches.

Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue.

Quantitative clinical measurement of heterogeneity in immunohistochemistry staining would be useful in evaluating patient therapeutic response and in identifying underlying issues in histopathology laboratory quality control. A heterogeneity scoring approach (HetMap) was designed to visualize a individual patient's immunohistochemistry heterogeneity in the context of a patient population. HER2 semiquantitative analysis was combined with ecology diversity statistics to evaluate cell-level heterogeneity (consistency of protein expression within neighboring cells in a tumor nest) and tumor-level heterogeneity (differences of protein expression across a tumor as represented by a tissue section). This approach was evaluated on HER2 immunohistochemistry-stained breast cancer samples using 200 specimens across two different laboratories with three pathologists per laboratory, each outlining regions of tumor for scoring by automatic cell-based image analysis. HetMap was evaluated using three different scoring schemes: HER2 scoring according to American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) guidelines, H-score, and a new continuous HER2 score (HER2(cont)). Two definitions of heterogeneity, cell-level and tumor-level, provided useful independent measures of heterogeneity. Cases where pathologists had disagreement over reads in the area of clinical importance (+1 and +2) had statistically significantly higher levels of tumor-level heterogeneity. Cell-level heterogeneity, reported either as an average or the maximum area of heterogeneity across a slide, had low levels of dependency on the pathologist choice of region, while tumor-level heterogeneity measurements had more dependence on the pathologist choice of regions. HetMap is a measure of heterogeneity, by which pathologists, oncologists, and drug development organizations can view cell-level and tumor-level heterogeneity for a patient for a given marker in the context of an entire patient cohort. Heterogeneity analysis can be used to identify tumors with differing degrees of heterogeneity, or to highlight slides that should be rechecked for QC issues. Tumor heterogeneity plays a significant role in disconcordant reads between pathologists.

Multiplexed measurement of proteins in tissue in a clinical environment.

There is an emerging need for more effective approaches to accurately quantitate protein expression in tissue samples. In many clinical studies and particularly in pharmaceutical clinical trials, access to adequate tissue samples is a major bottleneck, and thus techniques to measure protein expression in these valuable tissue specimens is important. This study will review current approaches in multiplexing of protein expression in tissue, and discusses new approaches using a novel image registration technique across multiple tissue sections.

The role and impact of quantitative discovery pathology.

The decision to advance an early-stage compound into formal preclinical testing depends on confidence in mechanism, efficacy and toxicity profiles. A substantial percentage of this confidence comes from histopathology interpretation, as the local tissue environment contains strong signals of both efficacy and toxicity. Accessing this tissue information is made difficult by biological variability across organs and tissues, an insufficient pool of pathology experts working in discovery, and the high subjectivity and individual isolation of microscope-based observations. This article describes how whole-slide imaging and quantitative analysis by trained pathologists are improving early-stage decision-making.

Digital pathology in drug discovery and development: multisite integration.

Digital pathology is an emerging technology that provides an image-based environment for managing and interpreting the information generated from a digitized glass slide, offering substantial improvements in pharmaceutical drug development across discovery, preclinical GLP pathology and oncology clinical trials. Digital pathology is transforming global pharmaceutical research by enabling data sharing to integrate dispersed pharma pathology labs around the world. This article reviews the stages of multisite digital pathology integration in large pharmaceutical companies, offering suggestions for success and highlighting challenges.

Basis for resistance to imatinib in 16 BCR-ABL mutants as determined using molecular dynamics.

Large-scale (approximately 36,000 atoms) long-time (30 ns each) molecular dynamics (MD) simulations on the complex of imatinib and 16 common mutants of the ABL tyrosine kinase domain have been performed to study the imatinib resistance mechanisms at the atomic level. MD simulations show that long time computational simulations could offer insight information that static models, simple homology modeling methods, or short-time simulations cannot provide for the BCR-ABL imatinib resistance problem. Three possible types of mutational effects from those mutants are found: the direct effect on the contact interaction with imatinib (e.g. some P-loop mutations), the effect on the conformation of a remote region contacting with imatinib (e.g. T315I), and the effect on interaction between two regions within the BCR-ABL domain (e.g. H396P). Insights of possible imatinib resistance mechanisms, not consistent with current consensus, are revealed from various analyses and our findings suggest that drugs with different binding modes may be necessary to overcome the drug resistance due to T315I and other mutations. The relevant patents are discussed.

Proteomics-based prediction of clinical response in acute myeloid leukemia.

OBJECTIVE: Response to chemotherapy is achieved in 60% to 70% of patients with acute myeloid leukemia. The ability to predict responders may help in stratifying patients and exploring different therapeutic approaches for nonresponders. Proteomics methods were used to search for predictive factors or combinations of factors. MATERIALS AND METHODS: Peripheral blood plasma samples from 41 patients with confirmed acute myeloid leukemia with intermediate or poor cytogenetics were obtained prior to induction therapy for proteomic analysis. For each plasma sample, four fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces and 12 surface-enhanced laser desorption/ionization spectra were generated. Peaks that correlated with response were identified, and decision trees incorporating these peaks along with various clinical and laboratory findings were constructed to predict response. RESULTS: Multiple decision trees were constructed. One peak, when combined with age, provided strong positive prediction of responders with 83% accuracy. A second tree, which combined one peak with both cytogenetics and the percent of monocytes in peripheral blood, detected responders with 95% accuracy. A third peak was adequate to predict responders in the intermediate cytogenetic group with 86% accuracy. CONCLUSIONS: Proteomic analysis should be further explored to define factors important in predicting clinical response in patients with acute myeloid leukemia.

Molecular basis explanation for imatinib resistance of BCR-ABL due to T315I and P-loop mutations from molecular dynamics simulations.

BACKGROUND: Computational simulations have become powerful tools for understanding detailed interactions in biologic systems. To the authors' knowledge to date, the mechanism of imatinib resistance in BCR-ABL has not been clarified at the atomic level, and computational studies are required. METHODS: Molecular dynamics (MD) simulations on the complex of imatinib with the wild-type, T315I mutant, and 10 other P-loop mutants of the tyrosine kinase BCR-ABL were performed to study the mechanism of imatinib resistance. RESULTS: Simulations suggested that imatinib resistance of T315I results mainly comes from the breakdown of interactions between imatinib and both E286 and M290, contradictory to what was believed previously, in that the missing hydrogen bonding is the main contribution. The current results also demonstrated that the unfavorable electrostatic interaction between P-loop and imatinib is the main reason for resistance for the P-loop mutations. Furthermore, in Y253H, protonation of the histidine at the epsilon position is essential for rendering this mutation resistant to imatinib. CONCLUSIONS: The current results indicated that large-scale simulations may offer insight and information that other simple modeling methods cannot provide regarding the problem of BCR-ABL imatinib resistance, especially in the case of conformational changes because of remote mutations. Imatinib resistance mechanisms that were not anticipated previously were revealed by analyzing the interactions between imatinib and individual residues based on simulation results. This results demonstrated that MD is a powerful way to verify and predict the clinical response or resistance to imatinib and to other potential drugs.

Development of a novel, accurate, automated, rapid, high-throughput technique suitable for population-based carrier screening for Fragile X syndrome.

PURPOSE: To develop a high-throughput, automated, accurate method suitable for population-based carrier detection of fragile X syndrome. METHODS: We developed a new method called capillary Southern analysis that allows automated high-throughput screening for expanded fragile X mental retardation 1 (FMR1) alleles. Initially samples are analyzed by a multiplex polymerase chain reaction that contains an internal control to establish gender. All females heterozygous for two normal alleles are reported as normal without further analysis. All females homozygous at the FMR1 locus (24% of all analysis) are then analyzed by capillary Southern analysis. Theoretically this method can detect expansion as high as 2000 CGG repeats, although in our series the largest nonmosaic FMR1 present was 950 CGG repeats. After assay development, we performed capillary Southern analysis on 995 female and 557 male samples submitted for fragile X syndrome testing by polymerase chain reaction and Southern blot. RESULTS: The polymerase chain reaction/capillary Southern analysis technique identified 100% of six female premutation carriers, seven full mutation carrier females, one premutation male, and five affected males. There was only one discrepancy between analysis by polymerase chain reaction/Southern blot and analysis by polymerase chain reaction/capillary Southern analysis. A single female sample appeared to be heterozygous for a premutation allele by polymerase chain reaction/capillary Southern analysis but was negative by Southern blot. It is possible this patient is a mosaic for the premutation allele, but because samples were deidentified, we were unable to determine whether this was a true false positive. CONCLUSION: We have developed an automated, high-throughput technique capable of detecting carriers of fragile X syndrome with 100% sensitivity and at least 99.5% specificity. This should allow population-based carrier detection for the most commonly inherited form of mental retardation.

Multiple property tolerance analysis for the evaluation of missense mutations.

Computational prediction of the impact of a mutation on protein function is still not accurate enough for clinical diagnostics without additional human expert analysis. Sequence alignment-based methods have been extensively used but their results highly depend on the quality of the input alignments and the choice of sequences. Incorporating the structural information with alignments improves prediction accuracy. Here, we present a conservation of amino acid properties method for mutation prediction, Multiple Properties Tolerance Analysis (MuTA), and a new strategy, MuTA/S, to incorporate the solvent accessible surface (SAS) property into MuTA. Instead of combining multiple features by machine learning or mathematical methods, an intuitive strategy is used to divide the residues of a protein into different groups, and in each group the properties used is adjusted.The results for LacI, lysozyme, and HIV protease show that MuTA performs as well as the widely used SIFT algorithm while MuTA/S outperforms SIFT and MuTA by 2%-25% in terms of prediction accuracy. By incorporating the SAS term alone, the alignment dependency of overall prediction accuracy is significantly reduced. MuTA/S also defines a new way to incorporate any structural features and knowledge and may lead to more accurate predictions.

Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia.

BACKGROUND: Response in adult acute lymphoblastic leukemia (ALL) can be achieved in a majority of patients. However, unlike pediatric ALL, recurrence is common in adult ALL, and the ability to predict at an early stage which patients are most likely to experience recurrence may help in devising new therapeutic approaches to prevent recurrence. METHODS: Peripheral blood plasma from 57 patients with confirmed ALL was obtained before induction therapy for proteomic analysis. Follow-up continued for a median period of 71 weeks. For each plasma sample, 4 fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces, and 12 surface-enhanced laser desorption/ionization (SELDI) spectra were generated. Peaks that correlated with recurrence were identified and decision trees were constructed and evaluated, using only 2 peaks per predictive tree. RESULTS: The best decision trees provided strong positive prediction of recurrence, with correct predictions 84% to 92% of the time, whereas negative prediction of patients who did not experience recurrence was less robust, with 62% to 74% accuracy. Prediction of recurrence was independent of cytogenetics, bone marrow blast count, lactate dehydrogenase, beta-2-microglobulin, or surface markers. Positive prediction of L3 morphological classification was achieved in 80% of test cases. CONCLUSIONS: Peripheral blood plasma is adequate to predict clinical behavior in ALL patients irrespective of the percentage of bone marrow blasts. Proteomic analysis of plasma offers a useful approach for profiling patients with ALL.

Challenges of target/compound data integration from disease to chemistry: a case study of dihydrofolate reductase inhibitors.

Despite the improvements in informatics associated with initiatives in the structure-based design and genomics fields, no straight-forward links are available between a given disease class and drug chemistry. This involves effective linking of disease to protein targets, and then mapping these targets to drug chemistry. In practice, protein-ligand structural analyses and high-throughput screening experiments generate the links between targets implicated in disease and chemical leads. Additionally, large volumes of relevant data are also being produced by high-throughput X-ray crystallography and in-silico docking initiatives. Each of these efforts takes a distinctly different approach to how data is managed and mined, resulting in difficulties in sharing data across each area. This review discusses the diverse approaches taken to data management in these areas, and the challenges associated with the construction of a data warehouse that meets all of the needs of each data type. Using the current work available for dihydrofolate reductase inhibitors, we demonstrate the challenges and opportunities associated with data mining from disease to drug chemistry.