DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic

Rapid Breast Cancer Disease Progression Following Cyclin Dependent Kinase 4 and 6 Inhibitor Discontinuation.

Background: CDK 4 and 6 inhibitors (CDK4/6i), which arrest unregulated cancer cell proliferation, show clinical efficacy in breast cancer. Unexpectedly, a patient treated on a CDK4/6i-based trial, as first-line therapy in metastatic breast cancer, developed rapid disease progression following discontinuation of study drug while receiving standard second-line therapy off trial. We thus sought to expand this observation within a population of patients treated similarly at The University of Texas MD Anderson Cancer Center. Methods: Using an IRB-approved protocol, 4 patients previously enrolled on CDK4/6i trials were analyzed for outcomes after discontinuing study drug. These patients were treated on a randomized trial of first-line endocrine therapy +/- a CDK4/6i. Rapid disease progression was defined as progression occurring within 4 months of CDK4/6i discontinuation. Results: In total, 4 patients developed rapid disease progression and died; 2 of whom died within 6 months of CDK4/6i discontinuation. Conclusion: This case series suggests a potential for rapid disease progression following CDK4/6i discontinuation. However, the clinical course following progression must be validated in large CDK4/6i clinical trials and standard-of-care cohorts. If confirmed, such observations may alter the algorithm for subsequent therapy in patients with disease progression on CDK4/6i. Nevertheless, the need remains to define a mechanistic basis for this rapid progression and formulate alternative therapeutic strategies

Invariant NKT cells contribute to chronic lymphocytic leukemia surveillance and prognosis

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant CD5(+) B lymphocytes in blood, bone marrow and lymphoid organs. CD1d-restricted invariant Natural Killer T (iNKT) cells are innate-like T lymphocytes strongly implicated in tumor surveillance. We investigated the impact of iNKT cells in the natural history of the disease both in Eμ;-Tcl1 (Tcl1) CLL mouse model and 68 CLL patients. We found that Tcl1-CLL cells express CD1d and iNKT cells critically delay the disease onset, but become functionally impaired upon disease progression. In patients, disease progression correlates also with high CD1d expression on CLL cells and impaired iNKT cells. Conversely, disease stability correlates with negative/low CD1d expression on CLL cells and normal iNKT cells, suggesting an indirect leukemia control. iNKT cells indeed hinder CLL survival in vitro by restraining CD1d-expressing Nurse Like Cells, a relevant pro-leukemia macrophage population. Finally, multivariate analysis identifies iNKT cell frequency as independent predictor of disease progression. Together, these results support iNKT cell contribution to CLL immune-surveillance and highlight iNKT cell frequency as prognostic marker for disease progression

IFNα and IFNγ Impede Marek’s Disease Progression

Marek’s disease virus (MDV) is an alphaherpesvirus that causes Marek’s disease, a malignant lymphoproliferative disease of domestic chickens. While MDV vaccines protect animals from clinical disease, they do not provide sterilizing immunity and allow field strains to circulate and evolve in vaccinated flocks. Therefore, there is a need for improved vaccines and for a better understanding of innate and adaptive immune responses against MDV infections. Interferons (IFNs) play important roles in the innate immune defenses against viruses and induce upregulation of a cellular antiviral state. In this report, we quantified the potent antiviral effect of IFNα and IFNγ against MDV infections in vitro. Moreover, we demonstrate that both cytokines can delay Marek’s disease onset and progression in vivo. Additionally, blocking of endogenous IFNα using a specific monoclonal antibody, in turn, accelerated disease. In summary, our data reveal the effects of IFNα and IFNγ on MDV infection and improve our understanding of innate immune responses against this oncogenic virus

The association between serum biomarkers and disease outcome in influenza A(H1N1)pdm09 virus infection: results of two international observational cohort studies

BACKGROUND Prospective studies establishing the temporal relationship between the degree of inflammation and human influenza disease progression are scarce. To assess predictors of disease progression among patients with influenza A(H1N1)pdm09 infection, 25 inflammatory biomarkers measured at enrollment were analyzed in two international observational cohort studies. METHODS Among patients with RT-PCR-confirmed influenza A(H1N1)pdm09 virus infection, odds ratios (ORs) estimated by logistic regression were used to summarize the associations of biomarkers measured at enrollment with worsened disease outcome or death after 14 days of follow-up for those seeking outpatient care (FLU 002) or after 60 days for those hospitalized with influenza complications (FLU 003). Biomarkers that were significantly associated with progression in both studies (p<0.05) or only in one (p<0.002 after Bonferroni correction) were identified. RESULTS In FLU 002 28/528 (5.3%) outpatients had influenza A(H1N1)pdm09 virus infection that progressed to a study endpoint of complications, hospitalization or death, whereas in FLU 003 28/170 (16.5%) inpatients enrolled from the general ward and 21/39 (53.8%) inpatients enrolled directly from the ICU experienced disease progression. Higher levels of 12 of the 25 markers were significantly associated with subsequent disease progression. Of these, 7 markers (IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10), all with ORs for the 3(rd) versus 1(st) tertile of 2.5 or greater, were significant (p<0.05) in both outpatients and inpatients. In contrast, five markers (sICAM-1, IL-8, TNF-α, D-dimer, and sVCAM-1), all with ORs for the 3(rd) versus 1(st) tertile greater than 3.2, were significantly (p≤.002) associated with disease progression among hospitalized patients only. CONCLUSIONS In patients presenting with varying severities of influenza A(H1N1)pdm09 virus infection, a baseline elevation in several biomarkers associated with inflammation, coagulation, or immune function strongly predicted a higher risk of disease progression. It is conceivable that interventions designed to abrogate these baseline elevations might affect disease outcome

Association of specific biotypes in patients with Parkinson disease and disease progression

Objective: To identify biotypes in patients with newly diagnosed Parkinson disease (PD) and to test whether these biotypes could explain interindividual differences in longitudinal progression. Methods: In this longitudinal analysis, we use a data-driven approach clustering PD patients from the Parkinson's Progression Markers Initiative (n = 314, age 61.0 ± 9.5, years 34.1% female, 5 years of follow-up). Voxel-level neuroanatomic features were estimated with deformation-based morphometry (DBM) of T1-weighted MRI. Voxels with deformation values that were significantly correlated (p < 0.01) with clinical scores (Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale Parts I–III and total score, tremor score, and postural instability and gait difficulty score) at baseline were selected. Then, these neuroanatomic features were subjected to hierarchical cluster analysis. Changes in the longitudinal progression and neuroanatomic pattern were compared between different biotypes. Results: Two neuroanatomic biotypes were identified: biotype 1 (n = 114) with subcortical brain volumes smaller than heathy controls and biotype 2 (n = 200) with subcortical brain volumes larger than heathy controls. Biotype 1 had more severe motor impairment, autonomic dysfunction, and much worse REM sleep behavior disorder than biotype 2 at baseline. Although disease durations at the initial visit and follow-up were similar between biotypes, patients with PD with smaller subcortical brain volume had poorer prognosis, with more rapid decline in several clinical domains and in dopamine functional neuroimaging over an average of 5 years. Conclusion: Robust neuroanatomic biotypes exist in PD with distinct clinical and neuroanatomic patterns. These biotypes can be detected at diagnosis and predict the course of longitudinal progression, which should benefit trial design and evaluation

Leveraging Disease Progression Learning for Medical Image Recognition

Unlike natural images, medical images often have intrinsic characteristics that can be leveraged for neural network learning. For example, images that belong to different stages of a disease may continuously follow a certain progression pattern. In this paper, we propose a novel method that leverages disease progression learning for medical image recognition. In our method, sequences of images ordered by disease stages are learned by a neural network that consists of a shared vision model for feature extraction and a long short-term memory network for the learning of stage sequences. Auxiliary vision outputs are also included to capture stage features that tend to be discrete along the disease progression. Our proposed method is evaluated on a public diabetic retinopathy dataset, and achieves about 3.3% improvement in disease staging accuracy, compared to the baseline method that does not use disease progression learning

Label-free high-throughput photoacoustic tomography of suspected circulating melanoma tumor cells in patients in vivo

Significance: Detection and characterization of circulating tumor cells (CTCs), a key determinant of metastasis, are critical for determining risk of disease progression, understanding metastatic pathways, and facilitating early clinical intervention. Aim: We aim to demonstrate label-free imaging of suspected melanoma CTCs. Approach: We use a linear-array-based photoacoustic tomography system (LA-PAT) to detect melanoma CTCs, quantify their contrast-to-noise ratios (CNRs), and measure their flow velocities in most of the superficial veins in humans. Results: With LA-PAT, we successfully imaged suspected melanoma CTCs in patients in vivo, with a CNR >9. CTCs were detected in 3 of 16 patients with stage III or IV melanoma. Among the three CTC-positive patients, two had disease progression; among the 13 CTC-negative patients, 4 showed disease progression. Conclusions: We suggest that LA-PAT can detect suspected melanoma CTCs in patients in vivo and has potential clinical applications for disease monitoring in melanoma