Understanding the need for digital twins' data in patient advocacy and forecasting oncology

Digital twins are made of a real-world component where data is measured and a virtual component where those measurements are used to parameterize computational models. There is growing interest in applying digital twins-based approaches to optimize personalized treatment plans and improve health outcomes. The integration of artificial intelligence is critical in this process, as it enables the development of sophisticated disease models that can accurately predict patient response to therapeutic interventions. There is a unique and equally important application of AI to the real-world component of a digital twin when it is applied to medical interventions. The patient can only be treated once, and therefore, we must turn to the experience and outcomes of previously treated patients for validation and optimization of the computational predictions. The physical component of a digital twins instead must utilize a compilation of available data from previously treated cancer patients whose characteristics (genetics, tumor type, lifestyle, etc.) closely parallel those of a newly diagnosed cancer patient for the purpose of predicting outcomes, stratifying treatment options, predicting responses to treatment and/or adverse events. These tasks include the development of robust data collection methods, ensuring data availability, creating precise and dependable models, and establishing ethical guidelines for the use and sharing of data. To successfully implement digital twin technology in clinical care, it is crucial to gather data that accurately reflects the variety of diseases and the diversity of the population.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/estrogen receptor-positive (HER2+/ER+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of patients with HER2+/ER+ receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized 2 in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. To mimic ETR to aromatase inhibitors (AIs), we developed 2 long-term estrogen deprivation (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 subtyping, and genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of aggressive MM361 LTEDs identified mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and ferroptosis-associated antioxidant genes, including GPX4. Combining a GPX4 inhibitor with anti-HER2 agents induced significant cell death in both MM361 and BT474 LTEDs. The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Background

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities.

Methods

To mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells.

Results

Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs.

Conclusions

The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Understanding Calmodulin Variants Affecting Calcium-Dependent Inactivation of L-Type Calcium Channels through Whole-Cell Simulation of the Cardiac Ventricular Myocyte

Mutations in the calcium-sensing protein calmodulin (CaM) have been linked to two cardiac arrhythmia diseases, Long QT Syndrome 14 (LQT14) and Catecholaminergic Polymorphic Ventricular Tachycardia Type 4 (CPVT4), with varying degrees of severity. Functional characterization of the CaM mutants most strongly associated with LQT14 show a clear disruption of the calcium-dependent inactivation (CDI) of the L-Type calcium channel (LCC). CPVT4 mutants on the other hand are associated with changes in their affinity to the ryanodine receptor. In clinical studies, some variants have been associated with both CPVT4 and LQT15. This study uses simulations in a model for excitation-contraction coupling in the rat ventricular myocytes to understand how LQT14 variant might give the functional phenotype similar to CPVT4. Changing the CaM-dependent transition rate by a factor of 0.75 corresponding to the D96V variant and by a factor of 0.90 corresponding to the F142L or N98S variants, in a physiologically based stochastic model of the LCC prolonger, the action potential duration changed by a small amount in a cardiac myocyte but did not disrupt CICR at 1, 2, and 4 Hz. Under beta-adrenergic simulation abnormal excitation-contraction coupling was observed above 2 Hz pacing for the mutant CaM. The same conditions applied under beta-adrenergic stimulation led to the rapid onset of arrhythmia in the mutant CaM simulations. Simulations with the LQT14 mutations under the conditions of rapid pacing with beta-adrenergic stimulation drives the cardiac myocyte toward an arrhythmic state known as Ca2+ overload. These simulations provide a mechanistic link to a disease state for LQT14-associated mutations in CaM to yield a CPVT4 phenotype. The results show that small changes to the CaM-regulated inactivation of LCC promote arrhythmia and underscore the significance of CDI in proper heart function.

Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation

We are rapidly approaching a future in which cancer patient digital twins will reach their potential to predict cancer prevention, diagnosis, and treatment in individual patients. This will be realized based on advances in high performance computing, computational modeling, and an expanding repertoire of observational data across multiple scales and modalities. In 2020, the US National Cancer Institute, and the US Department of Energy, through a trans-disciplinary research community at the intersection of advanced computing and cancer research, initiated team science collaborative projects to explore the development and implementation of predictive Cancer Patient Digital Twins. Several diverse pilot projects were launched to provide key insights into important features of this emerging landscape and to determine the requirements for the development and adoption of cancer patient digital twins. Projects included exploring approaches to using a large cohort of digital twins to perform deep phenotyping and plan treatments at the individual level, prototyping self-learning digital twin platforms, using adaptive digital twin approaches to monitor treatment response and resistance, developing methods to integrate and fuse data and observations across multiple scales, and personalizing treatment based on cancer type. Collectively these efforts have yielded increased insights into the opportunities and challenges facing cancer patient digital twin approaches and helped define a path forward. Given the rapidly growing interest in patient digital twins, this manuscript provides a valuable early progress report of several CPDT pilot projects commenced in common, their overall aims, early progress, lessons learned and future directions that will increasingly involve the broader research community.

The RNA-Binding Protein HuR Posttranscriptionally Regulates the Protumorigenic Activator YAP1 in Pancreatic Ductal Adenocarcinoma

Yes-associated protein 1 (YAP1) is indispensable for the development of mutant KRAS-driven pancreatic ductal adenocarcinoma (PDAC). High YAP1 mRNA is a prognostic marker for worse overall survival in patient samples; however, the regulatory mechanisms that mediate its overexpression are not well understood. YAP1 genetic alterations are rare in PDAC, suggesting that its dysregulation is likely not due to genetic events. HuR is an RNA-binding protein whose inhibition impacts many cancer-associated pathways, including the "conserved YAP1 signature" as demonstrated by gene set enrichment analysis. Screening publicly available and internal ribonucleoprotein immunoprecipitation (RNP-IP) RNA sequencing (RNA-Seq) data sets, we discovered that YAP1 is a high-confidence target, which was validated in vitro with independent RNP-IPs and 3' untranslated region (UTR) binding assays. In accordance with our RNA sequencing analysis, transient inhibition (e.g., small interfering RNA [siRNA] and small-molecular inhibition) and CRISPR knockout of HuR significantly reduced expression of YAP1 and its transcriptional targets. We used these data to develop a HuR activity signature (HAS), in which high expression predicts significantly worse overall and disease-free survival in patient samples. Importantly, the signature strongly correlates with YAP1 mRNA expression. These findings highlight a novel mechanism of YAP1 regulation, which may explain how tumor cells maintain YAP1 mRNA expression at dynamic times during pancreatic tumorigenesis.

Abstract 1776: Loss of estrogen receptor alters drug responsiveness and supports a basal-like phenotype in endocrine-resistant HER2+/ER+ breast cancer

At least 50% of human epidermal growth factor 2 (HER2) enriched breast cancer (BC) is estrogen receptor positive (ER+). In clinical trials, patients with HER2+ BC expressing ER respond poorly to neoadjuvant anti-HER2 therapy versus those lacking ER expression. Additionally, combining anti-HER2 with endocrine therapy (ET), like aromatase inhibitor (AI), does not significantly improve pathological complete response in HER2+/ER+ patients. In advanced or de novo HER2+/ER+ metastatic BC (MBC), treatment with ET plus anti-HER2 yields the highest 5-year overall survival. Unfortunately, in the real-world setting outside of clinical trials, just 23% of patients receive this combination, and nearly 40% receive ET alone. The clinical challenge associated with treating advanced HER2+/ER+ BC demands a better understanding of ER signaling and downstream pathways in HER2-driven BC. Our goal is to identify alternative targeted therapies and/or tailor therapeutic combinatorial strategies to treat and/or delay progression of HER2+/ER+ BC. In this study, we established two long-term estrogen (E2) deprived (LTED) HER2+/ER+ cell line models from BT-474 (BT) and MDA-MB-361 (MM) to mimic endocrine therapy resistance (ETR) to AI, and characterized the response of these cell lines to anti-HER2 and other ETs. We also performed whole-genome sequencing (WGS) and single-cell RNA sequencing (scRNAseq) to identify differences between parental and derived ETR-LTEDs. The ER expression was retained in BT-LTEDs but lost in MM-LTEDs as compared to parental cells. Additionally, ER transcriptional activity was confirmed by increased expression of ER-target genes upon E2 stimulation only in BT-LTEDs. However, both LTED variants were less responsive to fulvestrant and showed upregulation of pro-survival AKT signaling. Focusing on the now ER- MM-LTED model, WGS and targeted duplex sequencing identified a significant increase in exonic missense mutations, notably C>T and C>A. Several of the mutated genes encode for transcription and chromatin regulatory factors. scRNAseq analysis showed that MM-LTEDs shift from a luminal- to more basal-like phenotype, with enrichment of genes that regulate immune response and cell motility. Thus, loss of ER expression in MM-LTEDs on the mRNA and protein levels may explain observed intrinsic resistance to fulvestrant and gain of the basal-like phenotype. In summary, we report that our ETR BT- and MM-LTED models capture distinct ER-associated phenotypes of HER2+/ER+ BC. Additional studies are necessary to understand the functional significance of these missense mutations on the development of drug resistance in HER2+/ER+ BC. Ongoing studies are testing pharmacological inhibition of key upregulated genes associated with the basal phenotype to potentially delay disease progression and provide better treatments for ETR HER2+/ER+ BC.

Citation Format: Shaymaa Bahnassy, Hillary Stires, Lu Jin, Stanley Tam, Yasmin J. Dunn, Brendan F. Kohrn, Lawrence A. Loeb, Manasi Balachandran, Mircea Podar, Matthew D. McCoy, Robert A. Beckman, Rebecca B. Riggins. Loss of estrogen receptor alters drug responsiveness and supports a basal-like phenotype in endocrine-resistant HER2+/ER+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1776.

Machine learning-based prediction of drug and ligand binding in BCL-2 variants through molecular dynamics

Venetoclax is a BH3 (BCL-2 Homology 3) mimetic used to treat leukemia and lymphoma by inhibiting the anti-apoptotic BCL-2 protein thereby promoting apoptosis of cancerous cells. Acquired resistance to Venetoclax via specific variants in BCL-2 is a major problem for the successful treatment of cancer patients. Replica exchange molecular dynamics (REMD) simulations combined with machine learning were used to define the average structure of variants in aqueous solution to predict changes in drug and ligand binding in BCL-2 variants. The variant structures all show shifts in residue positions that occlude the binding groove, and these are the primary contributors to drug resistance. Correspondingly, we established a method that can predict the severity of a variant as measured by the inhibitory constant (Ki) of Venetoclax by measuring the structure deviations to the binding cleft. In addition, we also applied machine learning to the phi and psi angles of the amino acid backbone to the ensemble of conformations that demonstrated a generalizable method for drug resistant predictions of BCL-2 proteins that elucidates changes where detailed understanding of the structure-function relationship is less clear.

The FDA-Approved Anthelmintic Pyrvinium Pamoate Inhibits Pancreatic Cancer Cells in Nutrient-Depleted Conditions by Targeting the Mitochondria

Pancreatic ductal adenocarcinoma (PDAC) is a lethal aggressive cancer, in part due to elements of the microenvironment (hypoxia, hypoglycemia) that cause metabolic network alterations. The FDA-approved antihelminthic pyrvinium pamoate (PP) has previously been shown to cause PDAC cell death, although the mechanism has not been fully determined. We demonstrated that PP effectively inhibited PDAC cell viability with nanomolar IC50 values (9-93 nmol/L) against a panel of PDAC, patient-derived, and murine organoid cell lines. In vivo, we demonstrated that PP inhibited PDAC xenograft tumor growth with both intraperitoneal (IP; P < 0.0001) and oral administration (PO; P = 0.0023) of human-grade drug. Metabolomic and phosphoproteomic data identified that PP potently inhibited PDAC mitochondrial pathways including oxidative phosphorylation and fatty acid metabolism. As PP treatment reduced oxidative phosphorylation (P < 0.001), leading to an increase in glycolysis (P < 0.001), PP was 16.2-fold more effective in hypoglycemic conditions similar to those seen in PDAC tumors. RNA sequencing demonstrated that PP caused a decrease in mitochondrial RNA expression, an effect that was not observed with established mitochondrial inhibitors rotenone and oligomycin. Mechanistically, we determined that PP selectively bound mitochondrial G-quadruplexes and inhibited mitochondrial RNA transcription in a G-quadruplex-dependent manner. This subsequently led to a 90% reduction in mitochondrial encoded gene expression. We are preparing to evaluate the efficacy of PP in PDAC in an IRB-approved window-of-opportunity trial (IND:144822).

Cadherin 11 Promotes Immunosuppression and Extracellular Matrix Deposition to Support Growth of Pancreatic Tumors and Resistance to Gemcitabine in Mice

BACKGROUND & AIMS

Pancreatic ductal adenocarcinomas (PDACs) are characterized by fibrosis and an abundance of cancer-associated fibroblasts (CAFs). We investigated strategies to disrupt interactions among CAFs, the immune system, and cancer cells, focusing on adhesion molecule CDH11, which has been associated with other fibrotic disorders and is expressed by activated fibroblasts.

METHODS

We compared levels of CDH11 messenger RNA in human pancreatitis and pancreatic cancer tissues and cells with normal pancreas, and measured levels of CDH11 protein in human and mouse pancreatic lesions and normal tissues. We crossed p48-Cre;LSL-Kras^G12D/+;LSL-Trp53^R172H/+ (KPC) mice with CDH11-knockout mice and measured survival times of offspring. Pancreata were collected and analyzed by histology, immunohistochemistry, and (single-cell) RNA sequencing; RNA and proteins were identified by imaging mass cytometry. Some mice were given injections of PD1 antibody or gemcitabine and survival was monitored. Pancreatic cancer cells from KPC mice were subcutaneously injected into Cdh11^+/+ and Cdh11^-/- mice and tumor growth was monitored. Pancreatic cancer cells (mT3) from KPC mice (C57BL/6), were subcutaneously injected into Cdh11^+/+ (C57BL/6J) mice and mice were given injections of antibody against CDH11, gemcitabine, or small molecule inhibitor of CDH11 (SD133) and tumor growth was monitored.

RESULTS

Levels of CDH11 messenger RNA and protein were significantly higher in CAFs than in pancreatic cancer epithelial cells, human or mouse pancreatic cancer cell lines, or immune cells. KPC/Cdh11^+/- and KPC/Cdh11^-/- mice survived significantly longer than KPC/Cdh11^+/+ mice. Markers of stromal activation entirely surrounded pancreatic intraepithelial neoplasias in KPC/Cdh11^+/+ mice and incompletely in KPC/Cdh11^+/- and KPC/Cdh11^-/- mice, whose lesions also contained fewer FOXP3⁺ cells in the tumor center. Compared with pancreatic tumors in KPC/Cdh11^+/+ mice, tumors of KPC/Cdh11^+/- mice had increased markers of antigen processing and presentation; more lymphocytes and associated cytokines; decreased extracellular matrix components; and reductions in markers and cytokines associated with immunosuppression. Administration of the PD1 antibody did not prolong survival of KPC mice with 0, 1, or 2 alleles of Cdh11. Gemcitabine extended survival of KPC/Cdh11^+/- and KPC/Cdh11^-/- mice only or reduced subcutaneous tumor growth in mT3 engrafted Cdh11^+/+ mice when given in combination with the CDH11 antibody. A small molecule inhibitor of CDH11 reduced growth of pre-established mT3 subcutaneous tumors only if T and B cells were present in mice.

CONCLUSIONS

Knockout or inhibition of CDH11, which is expressed by CAFs in the pancreatic tumor stroma, reduces growth of pancreatic tumors, increases their response to gemcitabine, and significantly extends survival of mice. CDH11 promotes immunosuppression and extracellular matrix deposition, and might be developed as a therapeutic target for pancreatic cancer.

Predicting Genetic Variation Severity Using Machine Learning to Interpret Molecular Simulations

Distinct missense mutations in a specific gene have been associated with different diseases as well as differing severity of a disease. Current computational methods predict the potential pathogenicity of a missense variant but fail to differentiate between separate disease or severity phenotypes. We have developed a method to overcome this limitation by applying machine learning to features extracted from molecular dynamics simulations, creating a way to predict the effect of novel genetic variants in causing a disease, drug resistance, or another specific trait. As an example, we have applied this novel approach to variants in calmodulin associated with two distinct arrhythmias as well as two different neurodegenerative diseases caused by variants in amyloid-β peptide. The new method successfully predicts the specific disease caused by a gene variant and ranks its severity with more accuracy than existing methods. We call this method molecular dynamics phenotype prediction model.

SNP2SIM: a modular workflow for standardizing molecular simulation and functional analysis of protein variants

Background

Molecular simulations are used to provide insight into protein structure and dynamics, and have the potential to provide important context when predicting the impact of sequence variation on protein function. In addition to understanding molecular mechanisms and interactions on the atomic scale, translational applications of those approaches include drug screening, development of novel molecular therapies, and targeted treatment planning. Supporting the continued development of these applications, we have developed the SNP2SIM workflow that generates reproducible molecular dynamics and molecular docking simulations for downstream functional variant analysis. The Python workflow utilizes molecular dynamics software (NAMD (Phillips et al., J Comput Chem 26(16):1781-802, 2005), VMD (Humphrey et al., J Mol Graph 14(1):33-8, 27-8, 1996)) to generate variant specific scaffolds for simulated small molecule docking (AutoDock Vina (Trott and Olson, J Comput Chem 31(2):455-61, 2010)).

Results

SNP2SIM is composed of three independent modules that can be used sequentially to generate the variant scaffolds of missense protein variants from the wildtype protein structure. The workflow first generates the mutant structure and configuration files required to execute molecular dynamics simulations of solvated protein variant structures. The resulting trajectories are clustered based on the structural diversity of residues involved in ligand binding to produce one or more variant scaffolds of the protein structure. Finally, these unique structural conformations are bound to small molecule ligand libraries to predict variant induced changes to drug binding relative to the wildtype protein structure.

Conclusions

SNP2SIM provides a platform to apply molecular simulation based functional analysis of sequence variation in the protein targets of small molecule therapies. In addition to simplifying the simulation of variant specific drug interactions, the workflow enables large scale computational mutagenesis by controlling the parameterization of molecular simulations across multiple users or distributed computing infrastructures. This enables the parallelization of the computationally intensive molecular simulations to be aggregated for downstream functional analysis, and facilitates comparing various simulation options, such as the specific residues used to define structural variant clusters. The Python scripts that implement the SNP2SIM workflow are available (SNP2SIM Repository. https://github.com/mccoymd/SNP2SIM, Accessed 2019 February ), and individual SNP2SIM modules are available as apps on the Seven Bridges Cancer Genomics Cloud (Lau et al., Cancer Res 77(21):e3-e6, 2017; Cancer Genomics Cloud [www.cancergenomicscloud.org; Accessed 2018 November]).

A Computational Approach for Prioritizing Selection of Therapies Targeting Drug Resistant Variation in Anaplastic Lymphoma Kinase

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase implicated as a driver of a number of cancer types, and activates cellular pathways involved in cell proliferation and differentiation. Tyrosine kinase inhibitors (TKIs) are a small molecule therapeutic that blocks ALK function, but tumor evolution leads to the rapid emergence of drug resistant somatic variation and necessitates selection of a new treatment strategy. Computational simulations of protein:drug interactions were used to investigate the impact of seven drug resistant mutations on binding to eleven TKIs approved, or under investigation, for treatment of ALK positive cancers. The results show variant specific disruptions to TKI molecular interactions, and demonstrate the potential to aid prioritization of therapeutic interventions. Validation remains a challenge due to the complex dependence of biomolecular interactions on the local biophysical environment, but improvements to the underlying structural model and continued curation efforts will improve the clinical utility of computational predictions.

Curation of the Pancreatic Ductal Adenocarcinoma Subset of the Cancer Genome Atlas Is Essential for Accurate Conclusions about Survival-Related Molecular Mechanisms

Purpose: Publicly available databases, for example, The Cancer Genome Atlas (TCGA), containing clinical and molecular data from many patients are useful in validating the contribution of particular genes to disease mechanisms and in forming novel hypotheses relating to clinical outcomes.Experimental Design: The impact of key drivers of cancer progression can be assessed by segregating a patient cohort by certain molecular features and constructing survival plots using the associated clinical data. However, conclusions drawn from this straightforward analysis are highly dependent on the quality and source of tissue samples, as demonstrated through the pancreatic ductal adenocarcinoma (PDAC) subset of TCGA.Results: Analyses of the PDAC-TCGA database, which contains mainly resectable cancer samples from patients in stage IIB, reveal a difference from widely known historic median and 5-year survival rates of PDAC. A similar discrepancy was observed in lung, stomach, and liver cancer subsets of TCGA. The whole transcriptome expression patterns of PDAC-TCGA revealed a cluster of samples derived from neuroendocrine tumors, which have a distinctive biology and better disease prognosis than PDAC. Furthermore, PDAC-TCGA contains numerous pseudo-normal samples, as well as those that arose from tumors not classified as PDAC.Conclusions: Inclusion of misclassified samples in the bioinformatic analyses distorts the association of molecular biomarkers with clinical outcomes, altering multiple published conclusions used to support and motivate experimental research. Hence, the stringent scrutiny of type and origin of samples included in the bioinformatic analyses by researchers, databases, and web-tool developers is of crucial importance for generating accurate conclusions. Clin Cancer Res; 24(16); 3813-9. ©2018 AACR.

Expression of hormone receptors, cathepsin D, and HER-2/neu oncoprotein in normal colon and colonic disease

BACKGROUND

Chronic ulcerative colitis and familial adenomatous polyposis are associated with an increased risk of colorectal carcinoma. Currently, there are no reliable methods to assess carcinoma risk.

METHODS

Several prognostic factors known to be useful in breast carcinoma were determined in 102 specimens of colonic mucosa from 38 patients: 22 specimens from "normal," non-neoplastic colon, 49 from chronic ulcerative colitis, 10 from Crohn's colitis, 14 from familial adenomatous polyposis, four from mucosa adjacent to carcinoma, and three from colon carcinoma. Expression of estrogen receptor, progestin receptor, epidermal growth factor receptor, HER-2/neu (c-erb B-2) oncoprotein, and cathepsin D were determined.

RESULTS

Epidermal growth factor receptor expression was higher in chronic ulcerative colitis, Crohn's colitis, familial adenomatous polyposis, and colon carcinoma and varied with location within the colon for chronic ulcerative colitis, Crohn's colitis, and familial adenomatous polyposis. Epidermal growth factor receptor expression in mucosa adjacent to carcinoma was similar to that in "normal" colon.

CONCLUSION

Further analyses are needed to determine which parameters are related to and possibly predictive of increased carcinoma risk.

Thermography: a technique for subclinical diagnosis of osteoarthritis

Thermographic and radiographic evaluations of the tarsus (hock) were done on 20 Standardbred racehorses before and after exercise at three consecutive 6-week intervals. All horses were from the same stable and given the same care and training under identical schedules and conditions. Normal thermographic patterns were established before and after exercise. These patterns corresponded to the underlying tarsal vasculature. Postexercise thermal patterns indicated a warming trend, and the increases were uniform. Abnormal thermal patterns were more localized and did not conform to the normal underlying vascular distribution. Five of the 20 horses trained successfully and competed professionally, with 1 of the 5 showing abnormal thermographic changes. Four horses were too slow to race, and all of these had abnormal thermographic changes of their tarsi. The medial aspect of the right tarsus was more commonly involved than the lateral in these horses. Only one horse was clinically lame and exhibited thermal increases, as well as radiographic changes in the right tarsus. The remaining horses were removed from training due to miscellaneous causes. It was believed that the four horses that failed to make minimum track times (for racing) suffered discomfort in their tarsi sufficient to impair performance. This problem was attributed to early subclinical inflammatory changes within the joints. Though difficult to recognize radiographically, these early changes were conspicuous as abnormal thermographic patterns typical of inflammation.

Thermographic diagnosis of Horner's syndrome in the horse

Lateral and frontal thermographic patterns of the head of normal horses before and after exercise were characterized to aid the diagnosis of diseases of the head. Surgical induction of Horner's syndrome was done in four horses by isolation and transection of the vagosympathetic trunk. One clinical case and the surgically induced cases of Horner's syndrome were evaluated clinically. Thermographic findings of the clinical case were similar to the experimental cases.

Thermography in the diagnosis of inflammatory processes in the horse

To evaluate the use of thermography in equine medicine, a three-phase study was conducted. In the first phase, six horses were examined thermographically, before and after exercise, to determine a normal thermal pattern. In the second phase, nine horses with acute and chronic inflammatory processes were examined thermographically. In the third phase, thermography was used to evaluate the effectiveness of anti-inflammatory drugs on chemically induced inflammatory reactions. All normal horses tested had similar infrared emission patterns. There was a high degree of symmetry between right and left and between front (dorsal) to rear (palmar, plantar) in the legs distal to the carpus and the tarsus. The warmer areas of the thermogram tended to follow major vascular structures. The coronary band was the warmest area of the leg. Heat increase due to exercise did not substantially alter the normal thermographic pattern. Use of thermography in clinical cases successfully detected a subluxation of the third lumbar vertebra, a subsolar abscess, alveolar periostitis and abscess, laminitis, serous arthritis of the femoropatellar joint, and tendonitis. Thermography was effective in quantitative and qualitative evaluation of anti-inflammatory compounds in the treatment of chemically induced inflammation.