Immunohistochemical (IHC) assays performed on formalin-fixed paraffin-embedded (FFPE) tissue sections traditionally have been semi-quantified by pathologist visual scoring of staining. IHC is useful for validating biomarkers discovered through genomics methods as large clinical repositories of FFPE specimens support the construction of tissue microarrays (TMAs) for high throughput studies. Due to the ubiquitous availability of IHC techniques in clinical laboratories, validated IHC biomarkers may be translated readily into clinical use. However, the method of pathologist semi-quantification is costly, inherently subjective, and produces ordinal rather than continuous variable data. Computer-aided analysis of digitized whole slide images may overcome these limitations. Using TMAs representing 215 ovarian serous carcinoma specimens stained for S100A1, we assessed the degree to which data obtained using computer-aided methods correlated with data obtained by pathologist visual scoring. To evaluate computer-aided image classification, IHC staining within pathologist annotated and software-classified areas of carcinoma were compared for each case. Two metrics for IHC staining were used: the percentage of carcinoma with S100A1 staining (%Pos), and the product of the staining intensity (optical density [OD] of staining) multiplied by the percentage of carcinoma with S100A1 staining (OD*%Pos). A comparison of the IHC staining data obtained from manual annotations and software-derived annotations showed strong agreement, indicating that software efficiently classifies carcinomatous areas within IHC slide images. Comparisons of IHC intensity data derived using pixel analysis software versus pathologist visual scoring demonstrated high Spearman correlations of 0.88 for %Pos (p < 0.0001) and 0.90 for OD*%Pos (p < 0.0001). This study demonstrated that computer-aided methods to classify image areas of interest (e.g., carcinomatous areas of tissue specimens) and quantify IHC staining intensity within those areas can produce highly similar data to visual evaluation by a pathologist.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1649068103671302

INTRODUCTION Neuropathologic assessment is the current “gold standard” for evaluating the Alzheimer’s disease (AD), but there is no consensus on methods used. METHODS Fifteen unstained slides (eight brain regions) from each of the fourteen cases were prepared and distributed to ten different National Institute on Aging AD Centers for application of usual staining and evaluation following recently revised guidelines for AD neuropathologic change. RESULTS Current practice used in the AD Centers program achieved robustly excellent agreement for the severity score for AD neuropathologic change (average weighted κ =.88, 95% CI: 0.77 – 0.95), and good to excellent agreement for the three supporting scores. Some improvement was observed with consensus evaluation but not with central staining of slides. Evaluation of glass slides and digitally-prepared whole slide images was comparable. CONCLUSION AD neuropathologic evaluation as performed across AD Centers yields data that have high agreement with potential modifications for modest improvements.

Sarcomas differ from carcinomas in their mesenchymal origin. Therapeutic advancements have come slowly so alternative drugs and models are urgently needed. These studies report a new drug for sarcomas that simultaneously targets both tumor and tumor neovasculature. eBAT is a bispecific angiotoxin consisting of truncated, deimmunized Pseudomonas exotoxin fused to epidermal growth factor (EGF) and the amino terminal fragment (ATF) of urokinase. Here, we study the drug in an in vivo “ontarget” companion dog trial since eBAT effectively kills canine hemangiosarcoma (HSA) and human sarcoma cells in vitro. We reasoned the model has value due to the common occurrence of spontaneous sarcomas in dogs and a limited lifespan allowing for rapid accrual and data collection. Splenectomized dogs with minimal residual disease were given one cycle of eBAT followed by adjuvant doxorubicin in an adaptive dose-finding, phase I–II study of 23 dogs with spontaneous, stage I–II, splenic HSA. eBAT improved 6-month survival from <40% in a comparison population to ~70% in dogs treated at a biologically active dose (50 μg/kg). Six dogs were long-term survivors, living >450 days. eBAT abated expected toxicity associated with EGFR-targeting, a finding supported by mouse studies. Urokinase plasminogen activator receptor (uPAR) and EGFR are targets for human sarcomas, so thorough evaluation is crucial for validation of the dog model. Thus, we validated these markers for human sarcoma targeting in the study of 212 human and 97 canine sarcoma samples. Our results support further translation of eBAT for human patients with sarcomas and perhaps other EGFR-expressing malignancies.

Alzheimer’s disease neuropathologic change (ADNC) is defined by progressive accumulation of β-amyloid plaques and hyperphosphorylated tau (pTau) neurofibrillary tangles across diverse regions of brain. Non-demented individuals who reach advanced age without significant ADNC are considered to be resistant to AD, while those burdened with ADNC are considered to be resilient. Understanding mechanisms underlying ADNC resistance and resilience may provide important clues to treating and/or preventing AD associated dementia. ADNC criteria for resistance and resilience are not well-defined, so we developed stringent pathologic cutoffs for non-demented subjects to eliminate cases of borderline pathology. We identified 14 resistant (85+ years old, non-demented, Braak stage ≤ III, CERAD absent) and 7 resilient (non-demented, Braak stage VI, CERAD frequent) individuals out of 684 autopsies from the Adult Changes in Thought study, a long-standing community-based cohort. We matched each resistant or resilient subject to a subject with dementia and severe ADNC (Braak stage VI, CERAD frequent) by age, sex, year of death, and post-mortem interval. We expanded the neuropathologic evaluation to include quantitative approaches to assess neuropathology and found that resilient participants had lower neocortical pTau burden despite fulfilling criteria for Braak stage VI. Moreover, limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) was robustly associated with clinical dementia and was more prevalent in cases with high pTau burden, supporting the notion that resilience to ADNC may depend, in part, on resistance to pTDP-43 pathology. To probe for interactions between tau and TDP-43, we developed a C. elegans model of combined human (h) Tau and TDP-43 proteotoxicity, which exhibited a severe degenerative phenotype most compatible with a synergistic, rather than simply additive, interaction between hTau and hTDP-43 neurodegeneration. Pathways that underlie this synergy may present novel therapeutic targets for the prevention and treatment of AD.

Purpose To develop multiparametric magnetic resonance imaging (mpMRI) models for generating a quantitative, user-independent, voxel-wise composite biomarker score (CBS) for prostate cancer (PCa) detection utilizing co-registered correlative histopathology and comparing CBS-based detection performance against single quantitative MRI (qMR) parameters. Materials and Methods After providing informed consent to participate in an IRB approved protocol, patients with an initial diagnosis of PCa electing surgery as definitive therapy were imaged preoperatively with an mpMRI protocol from which quantitative MR parameters (qMR) were calculated. Per patient, all voxels in the prostate from an MRI imaging slice were classified as cancer or non-cancer based on deformably mapped histologically determined regions of disease to assess individual qMR parameters. Predictive models were developed using more than one qMR to generate CBS maps for cancer detection. Model development and evaluations of individual qMR and CBS were performed separately for the peripheral zone (PZ) alone and the whole gland (WG). Model accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and confidence intervals were calculated using the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for our multiparametric model and the single best performing qMR both at the individual level and in aggregate. Results T2TSE, ADC, Ktrans, kep and AUGC90 were significantly different between cancer and non-cancer voxels (p < 0.001 in all cases), with ADC exhibiting the best classification accuracy (AUC 0.82 for PZ and 0.74 for WG). A 4 parameter PZ-Model (AUC =0.85; p = 0.010 vs. ADC alone) and a 4 parameter WG-Model (AUC = 0.77; p = 0.043 vs. ADC alone) had the best performance of the multiparametric models considered. Based on individual-level analysis, we observed a statistically significant improvement in the AUC in 82% (23/28) and 71% (24/34) of patients when using the PZ and WG models, respectively, compared to ADC alone. Model-based CBS maps for cancer detection demonstrate improved visualization of cancer location and extent. Conclusions Co-registered correlative histopathology data was used as the ground truth for development of quantitative mpMRI models yielding voxel-wise CBS which outperform single qMR parameters for PCa detection especially when assessed at the individual level.

BackgroundPrognostic multibiomarker signatures in prostate cancer (PCa) may improve patient management and provide a bridge for developing novel therapeutics and imaging methods. Our objective was to evaluate the association between expression of 33 candidate protein biomarkers and time to biochemical failure (BF) after prostatectomy.MethodsPCa tissue microarrays were constructed representing 160 patients for whom clinicopathologic features and follow-up data after surgery were available. Immunohistochemistry for each of 33 proteins was quantified using automated digital pathology techniques. Relationships between clinicopathologic features, staining intensity, and time to BF were assessed. Predictive modeling using multiple imputed datasets was performed to identify the top biomarker candidates.ResultsIn univariate analyses, lymph node positivity, surgical margin positivity, non-localized tumor, age at prostatectomy, and biomarkers CCND1, HMMR, IGF1, MKI67, SIAH2, and SMAD4 in malignant epithelium were significantly associated with time to BF. HMMR, IGF1, and SMAD4 remained significantly associated with BF after adjusting for clinicopathologic features while additional associations were observed for HOXC6 and MAP4K4 following adjustment. In multibiomarker predictive models, 3 proteins including HMMR, SIAH2, and SMAD4 were consistently represented among the top 2, 3, 4, and 5 most predictive biomarkers, and a signature comprised of these proteins best predicted BF at 3 and 5 years.ConclusionsThis study provides rationale for investigation of HMMR, HOXC6, IGF1, MAP4K4, SIAH2, and SMAD4 as biomarkers of PCa aggressiveness in larger cohorts.

Background The clinical course of prostate cancer (PCa) measured by biochemical failure (BF) after prostatectomy remains unpredictable in many patients, particularly in intermediate Gleason score (GS) 7 tumors, suggesting that identification of molecular mechanisms associated with aggressive PCa biology may be exploited for improved prognostication or therapy. Hyaluronan (HA) is a high molecular weight polyanionic carbohydrate produced by synthases (HAS1-3) and fragmented by oxidative/nitrosative stress and hyaluronidases (HYAL1-4, SPAM1) common in PCa microenvironments. HA and HA fragments interact with receptors CD44 and HMMR resulting in increased tumor aggressiveness in experimental PCa models. We evaluated the association of HA-related molecules with BF after prostatectomy in GS7 tumors. Methods Tissue microarrays were constructed from a 96-patient cohort. HA histochemistry and HAS2, HYAL1, CD44, CD44v6, and HMMR immunohistochemistry were quantified using digital pathology techniques. Results HA in tumor-associated stroma and HMMR in malignant epithelium were significantly and marginally significantly associated with time to BF in univariate analysis, respectively. After adjusting for clinicopathologic features, both HA in tumor-associated stroma and HMMR in malignant epithelium were significantly associated with time to BF. Although not significantly associated with BF, HAS2 and HYAL1 positively correlated with HMMR in malignant epithelium. Cell culture assays demonstrated that HMMR bound native and fragmented HA, promoted HA uptake, and was required for a pro-migratory response to fragmented HA. Conclusions HA and HMMR are factors associated with time to BF in GS7 tumors, suggesting that increased HA synthesis and fragmentation within the tumor microenvironment stimulates aggressive PCa behavior through HA-HMMR signaling.

Molecular classification of diseases based on multigene expression signatures is increasingly used for diagnosis, prognosis, and prediction of response to therapy. Immunohistochemistry (IHC) is an optimal method for validating expression signatures obtained using high-throughput genomics techniques since IHC allows a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections. Additionally, validated IHC assays may be readily implemented as clinical tests since IHC is performed on routinely processed clinical tissue samples. However, methods have not been available for automated n-gene expression profiling at the protein level using IHC data. We have developed methods to compute expression level maps (signature maps) of multiple genes from IHC data digitized on a commercial whole slide imaging system. Areas of cancer for these expression level maps are defined by a pathologist on adjacent, co-registered H&E slides, allowing assessment of IHC statistics and heterogeneity within the diseased tissue. This novel way of representing multiple IHC assays as signature maps will allow the development of n-gene expression profiling databases in three dimensions throughout virtual whole organ reconstructions.