Progression from Prediabetes to Diabetes in a Diverse U.S. Population: A Machine Learning Model

Objective: To date, there are no widely implemented machine learning (ML) models that predict progression from prediabetes to diabetes. Addressing this knowledge gap would aid in identifying at-risk patients within this heterogeneous population who may benefit from targeted treatment and management in order to preserve glucose metabolism and prevent adverse outcomes. The objective of this study was to utilize readily available laboratory data to train and test the performance of ML-based predictive risk models for progression from prediabetes to diabetes. Methods: The study population was composed of laboratory information services data procured from a large U.S. outpatient laboratory network. The retrospective dataset was composed of 15,029 adults over a 5-year period with initial hemoglobin A1C (A1C) values between 5.0% and 6.4%. ML models were developed using random forest survival methods. The ground truth outcome was progression to A1C values indicative of diabetes (i.e., ≥6.5%) within 5 years. Results: The prediabetes risk classifier model accurately predicted A1C ≥6.5% within 5 years and achieved an area under the receiver-operator characteristic curve of 0.87. The most important predictors of progression from prediabetes to diabetes were initial A1C, initial serum glucose, A1C slope, serum glucose slope, initial HDL, HDL slope, age, and sex. Conclusions: Leveraging readily obtainable laboratory data, our ML risk classifier accurately predicts elevation in A1C associated with progression from prediabetes to diabetes. Although prospective studies are warranted, the results support the clinical utility of the model to improve timely recognition, risk stratification, and optimal management for patients with prediabetes.

MANIFEST: Pelabresib in Combination With Ruxolitinib for Janus Kinase Inhibitor Treatment-Naïve Myelofibrosis

PURPOSE

Standard therapy for myelofibrosis comprises Janus kinase inhibitors (JAKis), yet spleen response rates of 30%-40%, high discontinuation rates, and a lack of disease modification highlight an unmet need. Pelabresib (CPI-0610) is an investigational, selective oral bromodomain and extraterminal domain inhibitor (BETi).

METHODS

MANIFEST (ClinicalTrails.gov identifier: NCT02158858), a global, open-label, nonrandomized, multicohort, phase II study, includes a cohort of JAKi-naïve patients with myelofibrosis treated with pelabresib and ruxolitinib. The primary end point is a spleen volume reduction of ≥ 35% (SVR35) at 24 weeks.

RESULTS

Eighty-four patients received ≥ 1 dose of pelabresib and ruxolitinib. The median age was 68 (range, 37-85) years; 24% of patients were intermediate-1 risk, 61% were intermediate-2 risk, and 16% were high risk as per the Dynamic International Prognostic Scoring System; 66% (55 of 84) of patients had a hemoglobin level of < 10 g/dL at baseline. At 24 weeks, 68% (57 of 84) achieved SVR35, and 56% (46 of 82) achieved a total symptom score reduction of ≥ 50% (TSS50). Additional benefits at week 24 included 36% (29 of 84) of patients with improved hemoglobin levels (mean, 1.3 g/dL; median, 0.8 g/dL), 28% (16 of 57) with ≥ 1 grade improvement in fibrosis, and 29.5% (13 of 44) with > 25% reduction in JAK2V617F-mutant allele fraction, which was associated with SVR35 response (P = .018, Fisher's exact test). At 48 weeks, 60% (47 of 79) of patients had SVR35 response. Grade 3 or 4 toxicities seen in ≥ 10% patients were thrombocytopenia (12%) and anemia (35%), leading to treatment discontinuation in three patients. 95% (80 of 84) of the study participants continued combination therapy beyond 24 weeks.

CONCLUSION

The rational combination of the BETi pelabresib and ruxolitinib in JAKi-naïve patients with myelofibrosis was well tolerated and showed durable improvements in spleen and symptom burden, with associated biomarker findings of potential disease-modifying activity.

A Phase Ib Trial of AVID200, a TGFβ 1/3 Trap, in Patients with Myelofibrosis

PURPOSE

Myelofibrosis (MF) is a clonal myeloproliferative neoplasm characterized by systemic symptoms, cytopenias, organomegaly, and bone marrow fibrosis. JAK2 inhibitors afford symptom and spleen burden reduction but do not alter the disease course and frequently lead to thrombocytopenia. TGFβ, a pleiotropic cytokine elaborated by the MF clone, negatively regulates normal hematopoiesis, downregulates antitumor immunity, and promotes bone marrow fibrosis. Our group previously showed that AVID200, a potent and selective TGFβ 1/3 trap, reduced TGFβ1-induced proliferation of human mesenchymal stromal cells, phosphorylation of SMAD2, and collagen expression. Moreover, treatment of MF mononuclear cells with AVID200 led to increased numbers of progenitor cells (PC) with wild-type JAK2 rather than JAK2V617F.

PATIENTS AND METHODS

We conducted an investigator-initiated, multicenter, phase Ib trial of AVID200 monotherapy in 21 patients with advanced MF.

RESULTS

No dose-limiting toxicity was identified at the three dose levels tested, and grade 3/4 anemia and thrombocytopenia occurred in 28.6% and 19.0% of treated patients, respectively. After six cycles of therapy, two patients attained a clinical benefit by IWG-MRT criteria. Spleen and symptom benefits were observed across treatment cycles. Unlike other MF-directed therapies, increases in platelet counts were noted in 81% of treated patients with three patients achieving normalization. Treatment with AVID200 resulted in potent suppression of plasma TGFβ1 levels and pSMAD2 in MF cells.

CONCLUSIONS

AVID200 is a well-tolerated, rational, therapeutic agent for the treatment of patients with MF and should be evaluated further in patients with thrombocytopenic MF in combination with agents that target aberrant MF intracellular signaling pathways.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model

Rationale & Objective

Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression.

Study Design

Retrospective observational study.

Setting & Participants

The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2.

Predictors

Patient demographic and laboratory characteristics.

Outcomes

Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years.

Analytical Approach

Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline.

Results

The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex.

Limitations

The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model.

Conclusions

Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression.

Plain-Language Summary

Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

CXCL8/CXCR2 signaling mediates bone marrow fibrosis and is a therapeutic target in myelofibrosis

Proinflammatory signaling is a hallmark feature of human cancer, including in myeloproliferative neoplasms (MPNs), most notably myelofibrosis (MF). Dysregulated inflammatory signaling contributes to fibrotic progression in MF; however, the individual cytokine mediators elicited by malignant MPN cells to promote collagen-producing fibrosis and disease evolution are yet to be fully elucidated. Previously, we identified a critical role for combined constitutive JAK/STAT and aberrant NF-κB proinflammatory signaling in MF development. Using single-cell transcriptional and cytokine-secretion studies of primary cells from patients with MF and the human MPLW515L (hMPLW515L) murine model of MF, we extend our previous work and delineate the role of CXCL8/CXCR2 signaling in MF pathogenesis and bone marrow fibrosis progression. Hematopoietic stem/progenitor cells from patients with MF are enriched for a CXCL8/CXCR2 gene signature and display enhanced proliferation and fitness in response to an exogenous CXCL8 ligand in vitro. Genetic deletion of Cxcr2 in the hMPLW515L-adoptive transfer model abrogates fibrosis and extends overall survival, and pharmacologic inhibition of the CXCR1/2 pathway improves hematologic parameters, attenuates bone marrow fibrosis, and synergizes with JAK inhibitor therapy. Our mechanistic insights provide a rationale for therapeutic targeting of the CXCL8/CXCR2 pathway among patients with MF.

Deep learning applications in visual data for benign and malignant hematological conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have been previously explored. Similarly, DL research and applications within hematology are rapidly emerging, yet still largely within its infancy. Given the exponential rise of DL research for hematological conditions, it is essential for the practicing hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles as well as a systematic review of published investigations within malignant and non-malignant hematological conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade

BACKGROUND

Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience.

METHODS

Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus.

FINDINGS

Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology.

INTERPRETATION

This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation.

FUNDING

No specific funding was provided for this study.

MPN-375 BET Inhibitor Pelabresib (CPI-0610) Combined With Ruxolitinib in Patients With Myelofibrosis - JAK Inhibitor-Naïe or With Suboptimal Response to Ruxolitinib - Preliminary Data From the MANIFEST Study

CONTEXT

Myelofibrosis is characterized by splenomegaly, symptoms, cytopenias, and bone marrow (BM) fibrosis. Pelabresib is an investigational, oral, small-molecule BET inhibitor designed to selectively inhibit the BD1 and BD2 bromodomains of BET proteins.

OBJECTIVE

Evaluation of pelabresib combined with ruxolitinib in patients with myelofibrosis.

DESIGN

In Arm 3 of the Phase 2 MANIFEST study (NCT02158858), JAKi-naïve myelofibrosis patients are treated with pelabresib combined with ruxolitinib. In Arm 2, myelofibrosis patients with suboptimal response to ruxolitinib are treated with pelabresib as 'add-on' to ruxolitinib (Arm 2A: transfusion-dependent [TD]; Arm 2B: non-TD). The primary endpoints are ≥35% spleen volume reduction (SVR35) at Week 24 for Arms 3 and 2B and TD to transfusion independence (TI) in Arm 2A. The key secondary endpoint is ≥50% total symptom score reduction (TSS50) at Week 24; in Arm 2A, SVR35 is an additional key secondary endpoint. BM biopsies to assess BM fibrosis and safety data are also evaluated.

RESULTS

As of September 2021, at Week 24 in Arm 3 (N=84), 68% (57/84) of patients achieved SVR35 (median change: -50%), and 56% (46/82) of patients achieved TSS50 (median change: -59%). At Week 24 in Arm 2 (N=86), 20% (16/81; 17% in Arm 2A and 26% in Arm 2B) of patients achieved SVR35 (median change: -18%), and 37% (30/81) of patients achieved TSS50 (median change: -47%). In Arm 2A, the TD to TI rate was 16% (6/38). At Week 24, BM fibrosis improvement ≥ 1 grade was achieved in 28% (16/57) and 26% (12/47) of patients in Arms 3 and 2, respectively. Hematologic treatment-emergent adverse events (TEAEs) included thrombocytopenia, in 52% (≥Grade 3: 12%) and 52% (≥Grade 3: 33%) of patients, and anemia, in 42% (≥Grade 3: 35%) and 27% (≥Grade 3: 19%) of patients in Arms 3 and 2, respectively. Low-grade gastrointestinal TEAEs and respiratory infections were observed but rarely resulted in discontinuation.

CONCLUSIONS

In ruxolitinib treatment-naïve and previously treated patients with myelofibrosis, pelabresib combined with ruxolitinib resulted in splenic and symptom responses and BM fibrosis improvement and was generally well tolerated. The Phase 3 MANIFEST-2 study is evaluating pelabresib combined with ruxolitinib in JAKi treatment-naïve patients with myelofibrosis (NCT04603495).

Artificial Intelligence Enhances Diagnostic Flow Cytometry Workflow in the Detection of Minimal Residual Disease of Chronic Lymphocytic Leukemia

Flow cytometric (FC) immunophenotyping is critical but time-consuming in diagnosing minimal residual disease (MRD). We evaluated whether human-in-the-loop artificial intelligence (AI) could improve the efficiency of clinical laboratories in detecting MRD in chronic lymphocytic leukemia (CLL). We developed deep neural networks (DNN) that were trained on a 10-color CLL MRD panel from treated CLL patients, including DNN trained on the full cohort of 202 patients (F-DNN) and DNN trained on 138 patients with low-event cases (MRD < 1000 events) (L-DNN). A hybrid DNN approach was utilized, with F-DNN and L-DNN applied sequentially to cases. “Ground truth” classification of CLL MRD was confirmed by expert analysis. The hybrid DNN approach demonstrated an overall accuracy of 97.1% (95% CI: 84.7−99.9%) in an independent cohort of 34 unknown samples. When CLL cells were reported as a percentage of total white blood cells, there was excellent correlation between the DNN and expert analysis [r > 0.999; Passing−Bablok slope = 0.997 (95% CI: 0.988−0.999) and intercept = 0.001 (95% CI: 0.000−0.001)]. Gating time was dramatically reduced to 12 s/case by DNN from 15 min/case by the manual process. The proposed DNN demonstrated high accuracy in CLL MRD detection and significantly improved workflow efficiency. Additional clinical validation is needed before it can be fully integrated into the existing clinical laboratory practice.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation

Background

COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment.

Objective

Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population.

Methods

We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient’s first positive COVID-19 nucleic acid test result.

Results

The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106).

Conclusions

Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19–positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Classification of Monocytes, Promonocytes and Monoblasts Using Deep Neural Network Models: An Area of Unmet Need in Diagnostic Hematopathology

The accurate diagnosis of chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) subtypes with monocytic differentiation relies on the proper identification and quantitation of blast cells and blast-equivalent cells, including promonocytes. This distinction can be quite challenging given the cytomorphologic and immunophenotypic similarities among the monocytic cell precursors. The aim of this study was to assess the performance of convolutional neural networks (CNN) in separating monocytes from their precursors (i.e., promonocytes and monoblasts). We collected digital images of 935 monocytic cells that were blindly reviewed by five experienced morphologists and assigned into three subtypes: monocyte, promonocyte, and blast. The consensus between reviewers was considered as a ground truth reference label for each cell. In order to assess the performance of CNN models, we divided our data into training (70%), validation (10%), and test (20%) datasets, as well as applied fivefold cross validation. The CNN models did not perform well for predicting three monocytic subtypes, but their performance was significantly improved for two subtypes (monocyte vs. promonocytes + blasts). Our findings (1) support the concept that morphologic distinction between monocytic cells of various differentiation level is difficult; (2) suggest that combining blasts and promonocytes into a single category is desirable for improved accuracy; and (3) show that CNN models can reach accuracy comparable to human reviewers (0.78 ± 0.10 vs. 0.86 ± 0.05). As far as we know, this is the first study to separate monocytes from their precursors using CNN.

Important Pathologic Considerations for Establishing the Diagnosis of Myelofibrosis

Diagnostic criteria for primary myelofibrosis as defined by the 2017 revised World Health Organization (WHO) classification system incorporate clinical and laboratory findings, including driver mutational status (JAK2, MPL, CALR. and triple negative). The WHO emphasized the role of histopathology in making an accurate diagnosis of primary myelofibrosis and successfully incorporated a fibrosis scoring system and scoring schemas for collagen fibrosis and osteosclerosis. These steps represent a significant addition to the standardization of myelofibrosis evaluation and minimize the risk for misdiagnosis. This article reviews important pathologic considerations along with highlights of potentially relevant pitfalls relevant to histopathological diagnosis of myelofibrosis.

Could Bordetella pertussis vaccine protect against coronavirus COVID-19?

According to the World Health Organization (WHO), as of today, there are 2.165.500 confirmed cases of the novel coronavirus disease (COVID-19) and 145.705 deaths in over 185 countries. Unfortunately, despite the tremendous efforts to develop a vaccine initiated by various leading health institutions all over the world, it may be 18 months before a vaccine against the coronavirus is publicly available. We are proposing a theory about testing the use of the Bordetella pertussis vaccine to protect against COVID-19. We deliver this theory to the scientific community, aiming to raise the concern about it, and to provide us with support by realistic and experimental evidence.

Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases

OBJECTIVES

To assess and improve the assistive role of a deep, densely connected convolutional neural network (CNN) to hematopathologists in differentiating histologic images of Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL).

METHODS

A total of 10,818 images from BL (n = 34) and DLBCL (n = 36) cases were used to either train or apply different CNNs. Networks differed by number of training images and pixels of images, absence of color, pixel and staining augmentation, and depth of the network, among other parameters.

RESULTS

Cases classified correctly were 17 of 18 (94%), nine with 100% of images correct by the best performing network showing a receiver operating characteristic curve analysis area under the curve 0.92 for both DLBCL and BL. The best performing CNN used all available training images, two random subcrops per image of 448 × 448 pixels, random H&E staining image augmentation, random horizontal flipping of images, random alteration of contrast, reduction on validation error plateau of 15 epochs, block size of six, batch size of 32, and depth of 22. Other networks and decreasing training images had poorer performance.

CONCLUSIONS

CNNs are promising augmented human intelligence tools for differentiating a subset of BL and DLBCL cases.

Identification and Characterization of Tumor-Initiating Cells in Multiple Myeloma

BACKGROUND

Treatment failures in cancers, including multiple myeloma (MM), are most likely due to the persistence of a minor population of tumor-initiating cells (TICs), which are noncycling or slowly cycling and very drug resistant.

METHODS

Gene expression profiling and real-time quantitative reverse transcription polymerase chain reaction were employed to define genes differentially expressed between the side-population cells, which contain the TICs, and the main population of MM cells derived from 11 MM patient samples. Self-renewal potential was analyzed by clonogenicity and drug resistance of CD24+ MM cells. Flow cytometry (n = 60) and immunofluorescence (n = 66) were applied on MM patient samples to determine CD24 expression. Therapeutic effects of CD24 antibodies were tested in xenograft MM mouse models containing three to six mice per group.

RESULTS

CD24 was highly expressed in the side-population cells, and CD24+ MM cells exhibited high expression of induced pluripotent or embryonic stem cell genes. CD24+ MM cells showed increased clonogenicity, drug resistance, and tumorigenicity. Only 10 CD24+ MM cells were required to develop plasmacytomas in mice (n = three of five mice after 27 days). The frequency of CD24+ MM cells was highly variable in primary MM samples, but the average of CD24+ MM cells was 8.3% after chemotherapy and in complete-remission MM samples with persistent minimal residual disease compared with 1.0% CD24+ MM cells in newly diagnosed MM samples (n = 26). MM patients with a high initial percentage of CD24+ MM cells had inferior progression-free survival (hazard ratio [HR] = 3.81, 95% confidence interval [CI] = 5.66 to 18.34, P < .001) and overall survival (HR = 3.87, 95% CI = 16.61 to 34.39, P = .002). A CD24 antibody inhibited MM cell growth and prevented tumor progression in vivo.

CONCLUSION

Our studies demonstrate that CD24+ MM cells maintain the TIC features of self-renewal and drug resistance and provide a target for myeloma therapy.

Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists

CONTEXT.—

Advancements in genomic, computing, and imaging technology have spurred new opportunities to use quantitative image analysis (QIA) for diagnostic testing.

OBJECTIVE.—

To develop evidence-based recommendations to improve accuracy, precision, and reproducibility in the interpretation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) for breast cancer where QIA is used.

DESIGN.—

The College of American Pathologists (CAP) convened a panel of pathologists, histotechnologists, and computer scientists with expertise in image analysis, immunohistochemistry, quality management, and breast pathology to develop recommendations for QIA of HER2 IHC in breast cancer. A systematic review of the literature was conducted to address 5 key questions. Final recommendations were derived from strength of evidence, open comment feedback, expert panel consensus, and advisory panel review.

RESULTS.—

Eleven recommendations were drafted: 7 based on CAP laboratory accreditation requirements and 4 based on expert consensus opinions. A 3-week open comment period received 180 comments from more than 150 participants.

CONCLUSIONS.—

To improve accurate, precise, and reproducible interpretation of HER2 IHC results for breast cancer, QIA and procedures must be validated before implementation, followed by regular maintenance and ongoing evaluation of quality control and quality assurance. HER2 QIA performance, interpretation, and reporting should be supervised by pathologists with expertise in QIA.

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

Machine learning (ML) offers opportunities to advance pathological diagnosis, especially with increasing trends in digitalizing microscopic images. Diagnosing leukemia is time-consuming and challenging in many areas globally and there is a growing trend in utilizing ML techniques for its diagnosis. In this review, we aimed to describe the literature of ML utilization in the diagnosis of the four common types of leukemia: acute lymphocytic leukemia (ALL), chronic lymphocytic leukemia (CLL), acute myeloid leukemia (AML), and chronic myelogenous leukemia (CML). Using a strict selection criterion, utilizing MeSH terminology and Boolean logic, an electronic search of MEDLINE and IEEE Xplore Digital Library was performed. The electronic search was complemented by handsearching of references of related studies and the top results of Google Scholar. The full texts of 58 articles were reviewed, out of which, 22 studies were included. The number of studies discussing ALL, AML, CLL, and CML was 12, 8, 3, and 1, respectively. No studies were prospectively applying algorithms in real-world scenarios. Majority of studies had small and homogenous samples and used supervised learning for classification tasks. 91% of the studies were performed after 2010, and 74% of the included studies applied ML algorithms to microscopic diagnosis of leukemia. The included studies illustrated the need to develop the field of ML research, including the transformation from solely designing algorithms to practically applying them clinically.

The Lineage-Defining Transcription Factors SOX2 and NKX2-1 Determine Lung Cancer Cell Fate and Shape the Tumor Immune Microenvironment

The major types of non-small-cell lung cancer (NSCLC)-squamous cell carcinoma and adenocarcinoma-have distinct immune microenvironments. We developed a genetic model of squamous NSCLC on the basis of overexpression of the transcription factor Sox2, which specifies lung basal cell fate, and loss of the tumor suppressor Lkb1 (SL mice). SL tumors recapitulated gene-expression and immune-infiltrate features of human squamous NSCLC; such features included enrichment of tumor-associated neutrophils (TANs) and decreased expression of NKX2-1, a transcriptional regulator that specifies alveolar cell fate. In Kras-driven adenocarcinomas, mis-expression of Sox2 or loss of Nkx2-1 led to TAN recruitment. TAN recruitment involved SOX2-mediated production of the chemokine CXCL5. Deletion of Nkx2-1 in SL mice (SNL) revealed that NKX2-1 suppresses SOX2-driven squamous tumorigenesis by repressing adeno-to-squamous transdifferentiation. Depletion of TANs in SNL mice reduced squamous tumors, suggesting that TANs foster squamous cell fate. Thus, lineage-defining transcription factors determine the tumor immune microenvironment, which in turn might impact the nature of the tumor.

Ruxolitinib Therapy Followed by Reduced-Intensity Conditioning for Hematopoietic Cell Transplantation for Myelofibrosis: Myeloproliferative Disorders Research Consortium 114 Study

We evaluated the feasibility of ruxolitinib therapy followed by a reduced-intensity conditioning (RIC) regimen for patients with myelofibrosis (MF) undergoing transplantation in a 2-stage Simon phase II trial. The aims were to decrease the incidence of graft failure (GF) and nonrelapse mortality (NRM) compared with data from the previous Myeloproliferative Disorders Research Consortium 101 Study. The plan was to enroll 11 patients each in related donor (RD) and unrelated donor (URD) arms, with trial termination if ≥3 failures (GF or death by day +100 post-transplant) occurred in the RD arm or ≥6 failures occurred in the URD. A total of 21 patients were enrolled, including 7 in the RD arm and 14 in the URD arm. The RD arm did not meet the predetermined criteria for proceeding to stage II. Although the URD arm met the criteria for stage II, the study was terminated owing to poor accrual and a significant number of failures. In all 19 transplant recipients, ruxolitinib was tapered successfully without significant side effects, and 9 patients (47%) had a significant decrease in symptom burden. The cumulative incidences of GF, NRM, acute graft-versus-host disease (GVHD), and chronic GVHD at 24 months were 16%, 28%, 64%, and 76%, respectively. On an intention-to-treat basis, the 2-year overall survival was 61% for the RD arm and 70% for the URD arm. Ruxolitinib can be integrated as pretransplantation treatment for patients with MF, and a tapering strategy before transplantation is safe, allowing patients to commence conditioning therapy with a reduced symptom burden. However, GF and NRM remain significant.

Nuclear-Cytoplasmic Transport Is a Therapeutic Target in Myelofibrosis

PURPOSE

Myelofibrosis is a hematopoietic stem cell neoplasm characterized by bone marrow reticulin fibrosis, extramedullary hematopoiesis, and frequent transformation to acute myeloid leukemia. Constitutive activation of JAK/STAT signaling through mutations in JAK2, CALR, or MPL is central to myelofibrosis pathogenesis. JAK inhibitors such as ruxolitinib reduce symptoms and improve quality of life, but are not curative and do not prevent leukemic transformation, defining a need to identify better therapeutic targets in myelofibrosis.

EXPERIMENTAL DESIGN

A short hairpin RNA library screening was performed on JAK2^V617F-mutant HEL cells. Nuclear-cytoplasmic transport (NCT) genes including RAN and RANBP2 were among top candidates. JAK2^V617F-mutant cell lines, human primary myelofibrosis CD34⁺ cells, and a retroviral JAK2^V617F-driven myeloproliferative neoplasms mouse model were used to determine the effects of inhibiting NCT with selective inhibitors of nuclear export compounds KPT-330 (selinexor) or KPT-8602 (eltanexor).

RESULTS

JAK2^V617F-mutant HEL, SET-2, and HEL cells resistant to JAK inhibition are exquisitely sensitive to RAN knockdown or pharmacologic inhibition by KPT-330 or KPT-8602. Inhibition of NCT selectively decreased viable cells and colony formation by myelofibrosis compared with cord blood CD34⁺ cells and enhanced ruxolitinib-mediated growth inhibition and apoptosis, both in newly diagnosed and ruxolitinib-exposed myelofibrosis cells. Inhibition of NCT in myelofibrosis CD34⁺ cells led to nuclear accumulation of p53. KPT-330 in combination with ruxolitinib-normalized white blood cells, hematocrit, spleen size, and architecture, and selectively reduced JAK2^V617F-mutant cells in vivo.

CONCLUSIONS

Our data implicate NCT as a potential therapeutic target in myelofibrosis and provide a rationale for clinical evaluation in ruxolitinib-exposed patients with myelofibrosis.

Molecular Fingerprinting of Anatomically and Temporally Distinct B-Cell Lymphoma Samples by Next-Generation Sequencing to Establish Clonal Relatedness

CONTEXT.—

B-cell lymphomas exhibit balanced translocations that involve immunoglobulin loci and result from aberrant V(D)J recombination, class switch recombination, or somatic hypermutation. Although most of the breakpoints in the immunoglobulin loci occur in defined regions, those in the partner genes vary; therefore, it is unlikely that 2 independent clones would share identical breakpoints in both partners. Establishing whether a new lesion in a patient with history of lymphoma represents recurrence or a new process can be relevant. Polymerase chain reaction (PCR)-based clonality assays used in this setting rely only on evaluating the length of a given rearrangement. In contrast, next-generation sequencing (NGS) provides the exact translocation breakpoint at single-base resolution.

OBJECTIVE.—

To determine if translocation breakpoint coordinates can serve as a molecular fingerprint unique to a distinct clonal population.

DESIGN.—

Thirty-eight follicular lymphoma/diffuse large B-cell lymphoma samples collected from different anatomic sites and/or at different time points from 18 patients were analyzed by NGS. For comparison, PCR-based B-cell clonality and fluorescence in situ hybridization studies were performed on a subset of cases.

RESULTS.—

IGH-BCL2 rearrangements were detected in all samples. The breakpoint coordinates on derivative chromosome(s) were identical in all samples from a given patient, but distinct between samples derived from different patients. Additionally, 5 patients carried a second rearrangement also with conserved breakpoint coordinates in the follow-up sample(s).

CONCLUSIONS.—

Breakpoint coordinates in the immunoglobulin and partner genes can be used to establish clonal relatedness of anatomically/temporally distinct lesions. Additionally, an NGS-based approach has the potential to detect secondary translocations that may have prognostic and therapeutic significance.

US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised

April 12, 2017 marked a significant day in the evolution of digital pathology in the United States, when the US Food and Drug Administration announced its approval of the Philips IntelliSite Pathology Solution for primary diagnosis in surgical pathology. Although this event is expected to facilitate more widespread adoption of whole slide imaging for clinical applications in the United States, it also raises a number of questions as to the means by which pathologists might choose to incorporate this technology into their clinical practice. This article from the College of American Pathologists Digital Pathology Committee reviews frequently asked questions on this topic and provides answers based on currently available information.

Digital image analysis agrees with visual estimates of adult bone marrow trephine biopsy cellularity

INTRODUCTION

Evaluation of cellularity is an essential component of bone marrow trephine biopsy examination. The standard practice is to report the results as visual estimates (VE). Digital image analysis (DIA) offers the promise of more objective measurements of cellularity.

METHODS

Adult bone marrow trephine biopsy sections were assessed for cellularity by VE. Sections were scanned using an Aperio AT2 Scanscope and analyzed using a Cytonuclear (version 1.4) algorithm on halo software. Intraclass correlation (ICC) was used to assess relatedness between VE and DIA, and between MRI and DIA for a separate subset of patients. Trephine biopsy sections from a subset of patients with bone marrow biopsies uninvolved by malignancy were assessed for age-related changes.

RESULTS

Interobserver VE agreement was good to excellent. The ICC value was 0.81 for VE and DIA, and 0.50 for MRI and DIA. Linearity studies showed no statistically significant trend for age-related changes in cellularity in our cohort (r = -.29, P = .06).

CONCLUSIONS

Agreement was good between VE and DIA. It may be possible to use DIA or VE to measure cellularity in the appropriate clinical scenario. The limited sample size precludes similar determinations for MRI calculations. Further studies examining healthy donors are necessary before making definitive conclusions regarding age and cellularity.

Gestational Trophoblastic Disease: Clinical and Imaging Features

Gestational trophoblastic disease (GTD) is a spectrum of both benign and malignant gestational tumors, including hydatidiform mole (complete and partial), invasive mole, choriocarcinoma, placental site trophoblastic tumor, and epithelioid trophoblastic tumor. The latter four entities are referred to as gestational trophoblastic neoplasia (GTN). These conditions are aggressive with a propensity to widely metastasize. GTN can result in significant morbidity and mortality if left untreated. Early diagnosis of GTD is essential for prompt and successful management while preserving fertility. Initial diagnosis of GTD is based on a multifactorial approach consisting of clinical features, serial quantitative human chorionic gonadotropin (β-hCG) titers, and imaging findings. Ultrasonography (US) is the modality of choice for initial diagnosis of complete hydatidiform mole and can provide an invaluable means of local surveillance after treatment. The performance of US in diagnosing all molar pregnancies is surprisingly poor, predominantly due to the difficulty in differentiating partial hydatidiform mole from nonmolar abortion and retained products of conception. While GTN after a molar pregnancy is usually diagnosed with serial β-hCG titers, imaging plays an important role in evaluation of local extent of disease and systemic surveillance. Imaging also plays a crucial role in detection and management of complications, such as uterine and pulmonary arteriovenous fistulas. Familiarity with the pathogenesis, classification, imaging features, and treatment of these tumors can aid in radiologic diagnosis and guide appropriate management. ^©RSNA, 2017.

A phase 1 study of the Janus kinase 2 (JAK2)V617F inhibitor, gandotinib (LY2784544), in patients with primary myelofibrosis, polycythemia vera, and essential thrombocythemia

Mutations in Janus kinase 2 (JAK2) are implicated in the pathogenesis of Philadelphia-chromosome negative myeloproliferative neoplasms, including primary myelofibrosis, polycythemia vera, and essential thrombocythemia. Gandotinib (LY2784544), a potent inhibitor of JAK2 activity, shows increased potency for the JAK2^V617F mutation. The study had a standard 3+3 dose-escalation design to define the maximum-tolerated dose. Primary objectives were to determine safety, tolerability, and recommended oral daily dose of gandotinib for patients with JAK2^V617F-positive myelofibrosis, essential thrombocythemia, or polycythemia vera. Secondary objectives included estimating pharmacokinetic parameters and documenting evidence of efficacy by measuring clinical improvement. Thirty-eight patients were enrolled and treated (31 myelofibrosis, 6 polycythemia vera, 1 essential thrombocythemia). The maximum-tolerated dose of gandotinib was 120mg daily, based on dose-limiting toxicities of blood creatinine increase or hyperuricemia at higher doses. Maximum plasma concentration was reached 4h after single and multiple doses, and mean half-life on day 1 was approximately 6h. Most common treatment-emergent adverse events were diarrhea (55.3%) and nausea (42.1%), a majority of which were of grade 1 severity. Best response of clinical improvement was achieved by 29% of myelofibrosis patients. A ≥50% palpable spleen length reduction was observed at any time during therapy in 20/32 evaluable patients. Additionally, ≥50% reduction in the Total Symptom Myeloproliferative Neoplasm Symptom Assessment Form Score was seen in 11/21 (52%) and 6/14 patients (43%) receiving ≥120mg at 12 and 24 weeks respectively. Gandotinib demonstrated an acceptable safety and tolerability profile, and findings at the maximum-tolerated dose of 120mg supported further clinical testing. Clinicaltrials.gov identifier: NCT01134120.

Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters

CONTEXT

- There is growing interest in the use of digital pathology, especially whole slide imaging, for diagnostic purposes. Many issues need to be considered when incorporating this technology into a clinical laboratory. The College of American Pathologists (CAP) established a Digital Pathology Committee to support the development of CAP programs related to digital pathology. One of its many initiatives was a panel discussion entitled "Implementing Whole-Slide Imaging for Clinical Use: What to Do and What to Avoid," given for 3 years at the CAP annual meetings starting in 2014.

OBJECTIVES

- To review major issues to consider when implementing whole slide imaging for clinical purposes as covered during the panel discussion.

DESIGN

- The views expressed and recommendations given are based primarily on the personal experience of the authors as early adopters of this technology. It is not intended to be an exhaustive review of digital pathology.

RESULTS

- Implementation is best approached in phases. Early efforts are directed toward identifying initial clinical applications and assembling an implementation team. Scanner selection should be based on intended use and budget. Recognizing pathologist concerns over the use of digital pathology for diagnostic purposes, ensuring adequate training, and performing appropriate validation studies will enhance adoption. Once implemented, the transition period from glass slide to image-based diagnostics will be associated with challenges, especially those related to a hybrid glass slide-digital slide workflow.

CONCLUSIONS

- With appropriate preparation, planning, and stepwise implementation, whole slide imaging can be used safely and reliably for frozen sections, consultation, quality assurance, and primary diagnosis.

Effect of applied energy in renal sympathetic denervation with magnetic resonance guided focused ultrasound in a porcine model

Background

Past catheter-based and focused ultrasound renal denervation studies have indicated that procedure efficacy is related to the number of ablations performed or the amount of energy used for the ablation. This study extends those prior results and investigates energy level effects on the efficacy of MR guided focused ultrasound renal denervation performed in a porcine model.

Methods

Twenty-four normotensive pigs underwent unilateral denervation at three intensity levels. The applied intensity level was retrospectively de-rated to account for variability in animal size. Efficacy was assessed through evaluating the norepinephrine present in the kidney medulla and through histological analysis. The treatment was performed under MRI guidance including pre- and post-procedure T1-weighted and quantitative T1 and T2 imaging. During treatment, the temperature in the near field of the ultrasound beam was monitored in real time with MR temperature imaging. Energy delivery in the regions surrounding the renal artery was independently confirmed through an invasive fiberoptic temperature probe placed in the right renal artery.

Results

Animals that underwent denervation at a de-rated acoustic intensity of greater than 1.2 kW/cm² had a significantly lower norepinephrine concentration in the kidney indicating successful denervation. Images obtained during the treatment indicated no tissue changes in the kidneys as a function of the procedure but there were significant T1 changes present in the right lumbar muscles, although only one animal had indication of muscle damage at the time of necropsy.

Conclusions

While MR guided focused ultrasound renal denervation was found to be safe and effective in this normotensive animal model, the results indicated the need to incorporate patient-specific details in the treatment planning of MRgFUS renal denervation procedure.

Impact of circulating tumor cell (CTC) nucleus size on outcomes with abiraterone acetate (AA) therapy in men with metastatic castration-resistant prostate cancer (mCRPC)

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Background: CTC enumeration but not CTC morphology has been reported to predict outcomes to treatment in men with mCRPC. Recently Chen JF et. al (Cancer, 2015) showed an association with nuclear size and incidence of visceral disease in metastatic prostate cancer. In this study, we investigate the impact of CTC nucleus size on outcomes in men treated with AA for mCRPC. Methods: In a cohort of men with mCRPC treated with first-line AA, who had CTCs identified by CellSearch (CS) analysis prior to initiating treatment, we retrospectively quantified the nuclear size of CTCs by ImageJ/Fiji 1.46 software and correlated with progression free survival (PFS) on AA. We analyzed with univariate in addition to pre-specified multivariable analysis adjusted for Gleason score and baseline log PSA to assess independent predictive value of CTC nuclear size on PFS. Median PFS was calculated by Kaplan-Meier analysis and p-values were determined from Cox proportional hazards model. Results: 22 men treated with AA for mCRPC were included. Median nucleus size was 23.8 µm. Patients were divided in to 2 cohorts: small nuclear cohort (CTC nucleus size < 23.8 µm) vs large nuclear cohort (CTC nucleus size ≥23.8 µm). There was a non-significant trend towards worsened PFS (5.8 versus 6.8 months) in the larger nuclear size arm (Table). Conclusions: In this cohort of men with CRPC treated with AA, there is a non-significant trend towards decreased PFS associated with larger CTC nucleus size. Data are hypothesis generating and require further interrogation in a larger cohort. [Table: see text]

MYC Drives Progression of Small Cell Lung Cancer to a Variant Neuroendocrine Subtype with Vulnerability to Aurora Kinase Inhibition

Loss of the tumor suppressors RB1 and TP53 and MYC amplification are frequent oncogenic events in small cell lung cancer (SCLC). We show that Myc expression cooperates with Rb1 and Trp53 loss in the mouse lung to promote aggressive, highly metastatic tumors, that are initially sensitive to chemotherapy followed by relapse, similar to human SCLC. Importantly, MYC drives a neuroendocrine-low "variant" subset of SCLC with high NEUROD1 expression corresponding to transcriptional profiles of human SCLC. Targeted drug screening reveals that SCLC with high MYC expression is vulnerable to Aurora kinase inhibition, which, combined with chemotherapy, strongly suppresses tumor progression and increases survival. These data identify molecular features for patient stratification and uncover a potential targeted treatment approach for MYC-driven SCLC.

POWERPIINC (PreOperative Window of Endocrine TheRapy Provides Information to Increase Compliance) trial: Changes in tumor proliferation index and quality of life with 7 days of preoperative tamoxifen

OBJECTIVES

A decrease in Ki67 during neoadjuvant therapy predicts response to tamoxifen. Previous trials have shown a decreased Ki67 in breast tumors with as little as two or more weeks of preoperative tamoxifen. Shortening the preoperative treatment time in window of opportunity clinical trials makes these trials more attractive to women. POWERPIINC examined the effect of 7 days of preoperative tamoxifen on breast tumor proliferation and patient symptoms.

METHODS

Women with untreated stage I/II, ER-positive, invasive breast cancer with no contraindications to tamoxifen were enrolled. Women received 20 mg of tamoxifen for 7 days up to the day of surgery. Proliferation was assessed by Ki67 immunohistochemistry before and after 7 days of tamoxifen. Symptoms and QOL were assessed by the FACT-ES and MENQOL. Adherence was measured by pill counts.

RESULTS

52 women were enrolled, and 44 were evaluable for Ki67. The median age was 58.5 years, and the median tumor diameter was 1.2 cm. Most women (73%) were post-menopausal. Most tumors were PR positive (88%) and HER2-negative (92%). The Ki67 decreased by a geometric mean of 40% (95% CI 29%-63%), and 73% (95% CI 57%-85%) of women had tumors with decreased proliferation (p = 0.0001 by paired t-test). Adherence to taking tamoxifen during the preoperative period was 100%. Women reported minimal bother from psychosocial or physical symptoms at baseline or on the day of surgery.

CONCLUSION

Seven days of tamoxifen showed a similar relative decrease in Ki67 as that reported for longer courses, was acceptable to women, and could be considered for window of opportunity studies.

Bone marrow fibrosis in myelofibrosis: pathogenesis, prognosis and targeted strategies

Bone marrow fibrosis is a central pathological feature and World Health Organization major diagnostic criterion of myelofibrosis. Although bone marrow fibrosis is seen in a variety of malignant and non-malignant disease states, the deposition of reticulin and collagen fibrosis in the bone marrow of patients with myelofibrosis is believed to be mediated by the myelofibrosis hematopoietic stem/progenitor cell, contributing to an impaired microenvironment favoring malignant over normal hematopoiesis. Increased expression of inflammatory cytokines, lysyl oxidase, transforming growth factor-β, impaired megakaryocyte function, and aberrant JAK-STAT signaling have all been implicated in the pathogenesis of bone marrow fibrosis. A number of studies indicate that bone marrow fibrosis is an adverse prognostic variable in myeloproliferative neoplasms. However, modern myelofibrosis prognostication systems utilized in risk-adapted treatment approaches do not include bone marrow fibrosis as a prognostic variable. The specific effect on bone marrow fibrosis of JAK2 inhibition, and other rationally based therapies currently being evaluated in myelofibrosis, has yet to be fully elucidated. Hematopoietic stem cell transplantation remains the only curative therapeutic approach that reliably results in resolution of bone marrow fibrosis in patients with myelofibrosis. Here we review the pathogenesis, biological consequences, and prognostic impact of bone marrow fibrosis. We discuss the rationale of various anti-fibrogenic treatment strategies targeting the clonal hematopoietic stem/progenitor cell, aberrant signaling pathways, fibrogenic cytokines, and the tumor microenvironment.

Bone marrow fibrosis in myelofibrosis: pathogenesis, prognosis and targeted strategies

Bone marrow fibrosis is a central pathological feature and World Health Organization major diagnostic criterion of myelofibrosis. Although bone marrow fibrosis is seen in a variety of malignant and non-malignant disease states, the deposition of reticulin and collagen fibrosis in the bone marrow of patients with myelofibrosis is believed to be mediated by the myelofibrosis hematopoietic stem/progenitor cell, contributing to an impaired microenvironment favoring malignant over normal hematopoiesis. Increased expression of inflammatory cytokines, lysyl oxidase, transforming growth factor-β, impaired megakaryocyte function, and aberrant JAK-STAT signaling have all been implicated in the pathogenesis of bone marrow fibrosis. A number of studies indicate that bone marrow fibrosis is an adverse prognostic variable in myeloproliferative neoplasms. However, modern myelofibrosis prognostication systems utilized in risk-adapted treatment approaches do not include bone marrow fibrosis as a prognostic variable. The specific effect on bone marrow fibrosis of JAK2 inhibition, and other rationally based therapies currently being evaluated in myelofibrosis, has yet to be fully elucidated. Hematopoietic stem cell transplantation remains the only curative therapeutic approach that reliably results in resolution of bone marrow fibrosis in patients with myelofibrosis. Here we review the pathogenesis, biological consequences, and prognostic impact of bone marrow fibrosis. We discuss the rationale of various anti-fibrogenic treatment strategies targeting the clonal hematopoietic stem/progenitor cell, aberrant signaling pathways, fibrogenic cytokines, and the tumor microenvironment.

CD229 is expressed on the surface of plasma cells carrying an aberrant phenotype and chemotherapy-resistant precursor cells in multiple myeloma

Multiple Myeloma (MM) is a plasma cell (PC) malignancy, which despite significant therapeutic advances, is still considered incurable. This is due to the persistence of chemotherapy-resistant minimal residual disease in the patients' bone marrow (BM) after an effective induction therapy. Immunotherapies targeting surface molecules expressed on the bulk of tumor cells and the chemotherapy-resistant, myeloma-propagating cells could play a central role in this clinical setting. We recently described surface molecule CD229 as a potential therapeutic target for MM. In our current study we assessed the expression of CD229 on different PC subtypes and on cells with a myeloma-propagating phenotype in a total of 77 patients with PC dyscrasias independently at 2 different cancer centers. We found that CD229 was strongly and homogeneously overexpressed on the PC of patients with monoclonal gammopathy of undetermined significance (MGUS), smoldering myeloma, MM, and PC leukemia. CD229 was particularly overexpressed on those PC showing an abnormal phenotype such as expression of CD56. Most importantly, CD229 was also highly expressed on those cells in the patients' BM displaying the phenotype of chemotherapy-resistant and myeloma-propagating cells. In conclusion, our combined findings suggest that immunotherapies targeting CD229 will not only be effective for the bulk of tumor cells but will also help to eradicate chemotherapy-resistant cells remaining in the patients' BM after induction treatment. Hopefully, the design of CD229-specific monoclonal antibodies or chimeric antigen receptor-transduced T cells will help to achieve prolonged remissions or even cures in MM patients.

Validation of break-apart and fusion MYC probes using a digital fluorescence in situ hybridization capture and imaging system

INTRODUCTION

Detection of MYC translocations using fluorescence in situ hybridization (FISH) is important in the evaluation of lymphomas, in particular, Burkitt lymphoma and diffuse large B-cell lymphoma. Our aim was to validate a digital FISH capture and imaging system for the detection of MYC 8q24 translocations using LSI-MYC (a break-apart probe) and MYC 8;14 translocation using IGH-MYC (a fusion probe).

MATERIALS AND METHODS

LSI-MYC probe was evaluated using tissue sections from 35 patients. IGH-MYC probe was evaluated using tissue sections from forty patients. Sections were processed for FISH and analyzed using traditional methods. FISH slides were then analyzed using the GenASIs capture and analysis system.

RESULTS

Results for LSI-MYC had a high degree of correlation between traditional method of FISH analysis and digital FISH analysis. Results for IGH-MYC had a 100% concordance between traditional method of FISH analysis and digital FISH analysis.

CONCLUSION

Annotated whole slide images of H and E and FISH sections can be digitally aligned, so that areas of tumor within a section can be matched and evaluated with a greater degree of accuracy. Images can be archived permanently, providing a means for examining the results retrospectively. Digital FISH imaging of the MYC translocations provides a better diagnostic tool compared to traditional methods for evaluating lymphomas.

Prognostic Impact of Tumor Microenvironment in Diffuse Large B-Cell Lymphoma Uniformly Treated With R-CHOP Chemotherapy

OBJECTIVES

We evaluated the prognostic impact of cell-of-origin classification as well as intratumoral regulatory T cells (Tregs), macrophages, and microvessel density (MVD) on 115 patients (74 in the training set and 41 in the validation set) diagnosed with de novo diffuse large B-cell lymphoma (DLBCL) and uniformly treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy.

METHODS

The prognostic impact of Tregs, macrophages, and MVD was evaluated using FOXP3, CD68, and CD34 immunohistochemical stains, respectively. In addition, we designed a scoring system where 1 point was awarded per each adverse prognostic factor, including non-germinal center B-cell-like subtype, FOXP3 17% or more, CD68 less than 2%, and MVD less than 800 vessels/mm(2) RESULTS: Although only MVD was statistically significant on multivariate analysis, the scoring system significantly segregated patients into low- and high-risk groups. Patients having two or more adverse prognostic factors (high-risk group) demonstrated significantly worse event-free and progression-free survivals in the training set and event-free survival in the validation set.

CONCLUSIONS

The concomitant evaluation of cell of origin along with tumor microenvironment components identifies patients with DLBCL treated with R-CHOP chemotherapy portraying a worse prognosis.

Therapy Effect: Impact on Bone Marrow Morphology

This article highlights the most common morphologic features identified in the bone marrow after chemotherapy for hematologic malignancies, growth-stimulating agents, and specific targeted therapies. The key is to be aware of these changes while reviewing post-therapeutic bone marrow biopsies and to not mistake reactive patterns for neoplastic processes. In addition, given the development and prevalent use of targeted therapy, such as tyrosine kinase inhibitors and immune modulators, knowledge of drug-specific morphologic changes is required for proper bone marrow interpretation and diagnosis.

Renal sympathetic denervation using MR-guided high-intensity focused ultrasound in a porcine model

Background

Initial catheter-based renal sympathetic denervation (RSD) studies demonstrated promising results in showing a significant reduction of blood pressure, while recent data were less successful. As an alternative approach, the objective of this study was to evaluate the feasibility of using magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) to perform RSD in a porcine model.

Methods

An intravascular fiber optic temperature probe was used to confirm energy delivery during MRgHIFU. This technique was evaluated both in a vascular phantom and in a normotensive pig model. Five animals underwent unilateral RSD using MRgHIFU, and both safety and efficacy were assessed. MRI was used to evaluate the acoustic window, target sonications, monitor the near-field treatment region using MR thermometry imaging, and assess the status of tissues post-procedure. An intravascular fiber optic temperature probe verified energy delivery. Animals were sacrificed 6 to 9 days post-treatment, and pathological analysis was performed. The norepinephrine present in the kidney medulla was assessed post-mortem.

Results

All animals tolerated the procedure well with no observed complications. The fiber optic temperature probe placed in the target renal artery confirmed energy delivery during MRgHIFU, measuring larger temperature rises when the MRgHIFU beam location was focused closer to the tip of the probe. Following ablation, a significant reduction (p = 0.04) of cross-sectional area of nerve bundles between the treated and untreated renal arteries was observed in all of the animals with treated nerves presenting increased cellular infiltrate and fibrosis. A reduction of norepinephrine (p = 0.14) in the kidney medulla tissue was also observed. There was no indication of tissue damage in arterial walls.

Conclusions

Performing renal denervation non-invasively with MRgHIFU was shown to be both safe and effective as determined by norepinephrine levels in a porcine model. This approach may be a promising alternative to catheter-based strategies.