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DOE OSTI2023

Plasma proteomics of SARS-CoV-2 infection and severity reveals impact on Alzheimer’s and coronary disease pathways

Identification of proteins dysregulated by COVID-19 infection is critically important for better understanding of its pathophysiology, building prognostic models, and identifying new targets. Plasma proteomic profiling of 4,301 proteins was performed in two independent datasets and tested for the association for three COVID-19 outcomes (infection, ventilation, and death). We identified 1,449 proteins consistently associated in both datasets with any of these three outcomes. We subsequently created highly accurate models that distinctively predict infection, ventilation, and death. These proteins were enriched in specific biological processes including cytokine signaling, Alzheimer’s disease, and coronary artery disease. Mendelian randomization and gene network analyses identified eight causal proteins and 141 highly connected hub proteins including 35 with known drug targets. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes, reveal their relationship to Alzheimer’s disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States

Spring phenology of temperate ecosystems is highly sensitive to climate change, generating various impacts on many important terrestrial surface biophysical processes. Although various prognostic models relying on environmental variables of temperature and photoperiod have been developed for spring phenology, comprehensive ecosystem-scale evaluations over large landscapes and long-time periods remain lacking. Further, environmental variables other than temperature and photoperiod might also importantly constrain spring phenology modelling but remain under-investigation. To address these issues, we leveraged around 20-years datasets of environmental variables (from Daymet and GLDAS products) and the spring phenology metric (i.e., the greenup date) respectively derived from MODIS and PhenoCams across 108 sites in the Northern and Eastern United States. We firstly cross-compared MODIS-derived greenup date with official PhenoCams product with high accuracy (R 2 = 0.70). Then, we evaluated the three prognostic models (i.e., Growing Degree Date (GDD), Sequential (SEQ) and optimality-based (OPT)) with MODIS-derived spring phenology, assessed the model residuals and their associations with soil moisture, rainfall, and solar radiation, and revised the two photoperiod-relevant models (SEQ, OPT) by replacing the daylength variable with solar radiation, which was found to contribute the most to model residuals. We found that 1) all models demonstrated good capability in characterizing spring phenology, with OPT performing the best (RMSE = 8.04 ± 5.05 days), followed by SEQ (RMSE = 10.57 ± 7.77 days) and GDD (RMSE = 10.84 ± 8.42 days), 2) all models displayed high model residuals showing tight correlation with solar radiation (r = 0.45–0.75), and 3) the revised models that included solar radiation significantly performed better with an RMSE reduction by 22.08%. Such results are likely because solar radiation better constrains early growing season plant photosynthesis than photoperiod, supporting the hypothesis of spring phenology as an adaptive strategy to maximize photosynthetic carbon gain (approximated by solar radiation) while minimizing frost damage risk (captured by temperature). Collectively, our study here reveals the underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems, and suggests ways to improve spring phenology modelling and other phenology-related ecological processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Multi-walled carbon nanotube-induced gene expression in the mouse lung: Association with lung pathology

Due to the fibrous shape and durability of multi-walled carbon nanotubes (MWCNT), concerns regarding their potential for producing environmental and human health risks, including carcinogenesis, have been raised. This study sought to investigate how previously identified lung cancer prognostic biomarkers and the related cancer signaling pathways are affected in the mouse lung following pharyngeal aspiration of well-dispersed MWCNT. A total of 63 identified lung cancer prognostic biomarker genes and major signaling biomarker genes were analyzed in mouse lungs (n = 80) exposed to 0, 10, 20, 40, or 80 {mu}g of MWCNT by pharyngeal aspiration at 7 and 56 days post-exposure using quantitative PCR assays. At 7 and 56 days post-exposure, a set of 7 genes and a set of 11 genes, respectively, showed differential expression in the lungs of mice exposed to MWCNT vs. the control group. Additionally, these significant genes could separate the control group from the treated group over the time series in a hierarchical gene clustering analysis. Furthermore, 4 genes from these two sets of significant genes, coiled-coil domain containing-99 (Ccdc99), muscle segment homeobox gene-2 (Msx2), nitric oxide synthase-2 (Nos2), and wingless-type inhibitory factor-1 (Wif1), showed significant mRNA expression perturbations at both time points. It was also found that the expression changes of these 4 overlapping genes at 7 days post-exposure were attenuated at 56 days post-exposure. Ingenuity Pathway Analysis (IPA) found that several carcinogenic-related signaling pathways and carcinogenesis itself were associated with both the 7 and 11 gene signatures. Taken together, this study identifies that MWCNT exposure affects a subset of lung cancer biomarkers in mouse lungs. - Research Highlights: > Multi-Walled Carbon Nanotubes affect lung cancer biomarkers in mouse lungs. > The results suggest potentially harmful effects of MWCNT exposure on human lungs. > The results could potentially be used for the medical surveillance of workers.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Serum biomarkers for diagnosis and prediction of type 1 diabetes

Type 1 diabetes (T1D) culminates in the autoimmune destruction of the pancreatic ß-cells, leading to insufficient production of insulin and development of hyperglycemia. Serum biomarkers including glucose, glycated molecules, c-peptide, and autoantibodies have been well established to diagnose T1D from other subtypes of diabetes. However, these molecules often mark a late stage of the disease when ~90% of the pancreatic insulin-producing ß-cells have already been lost. With the prevalence of T1D increasing worldwide and because of the physical and psychological burden induced by this disease, there is a great need for prognostic biomarkers to predict T1D development or progression. This would allow us to identify individuals at high risk for early prevention and intervention. Therefore, considerable efforts have been dedicated to the understanding of disease etiology and the discovery of novel biomarkers in the last few decades. The advent of high-throughput and sensitive ‘-omics’ technologies for the study of proteins, nucleic acids, and metabolites have allowed large scale profiling of protein expression and gene changes in T1D patients relative to disease-free controls. In this review, we briefly discuss the classical diagnostic biomarkers of T1D and mainly focus on the novel biomarkers that are identified as markers of ß-cell destruction and screened with the use of state-of-the-art ‘-omics’ technologies.

Yi, Lian↗

DOE OSTI2018

CHD4-mediated loss of E-cadherin determines metastatic ability in triple-negative breast cancer cells

Highlights: • CHD4 is associated with poor prognosis in triple-negative breast cancer patients. • Increased CHD4 is positively correlated with cell motility and drug resistance of TNBC cells. • CHD4 should be a potential prognostic biomarker in TNBC patient survival. Triple-negative breast cancer (TNBC) is a subtype of cancer with aggressive behaviors (high recurrence and metastasis rate) and poor prognosis. Therefore, studying the determining factors that lead to malignant TNBCs is necessary to develop personalized therapy and improve survival rates. In this study, we first analyzed levels of chromodomain helicase DNA binding protein 4 (CHD4) in 60 TNBC patients by immunohistochemical staining. We then clarified the role of CHD4 in TNBC and non-TNBC cell lines. Our clinical data indicated that higher CHD4 expression is positively correlated with metastatic stage, tumor recurrence, and survival status. Consistent with the clinical analytical data, our in vitro data also indicated that high level of CHD4 is positively correlated with malignant behaviors in TNBC cells, such as cell motility and mortality. For further analyses, we found that E-cadherin, N-cadherin and fibronetin are involved in CHD4-mediated epithelial-mesenchymal transition (EMT). Silencing of CHD4 also increased drug sensitivity to cisplatin and PARP1 inhibitor, especially in TNBC cells. Altogether, our findings showed that CHD4 is not only a potential prognostic biomarker for TNBC patient survival, but is also a powerful candidate in the development of new anti-cancer agents in TNBC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

AIM2 is a potential therapeutic target in human renal carcinoma and suppresses its invasion and metastasis via enhancing autophagy induction

Highlights: • Low AIM2 expression was correlated with RCC patient survival. • AIM2 suppressed the proliferation, migration and invasion of renal cancer cells. • AIM2 enhanced the expression of autophagy-related genes. • AIM2 exerts the anti-tumor effect via enhancing the induction of autophagy. Absent in melanoma 2 (AIM2) as a tumor suppressor is recently recognized to play an important role in many human solid tumors. However, the biological significance and function of AIM2 in renal cell carcinoma (RCC) remain unknown. In this study, we proposed to investigate the clinicopathological and prognostic significance of AIM2 in RCC patient specimens, to analyze the correlation between AIM2 expression and disease progression, and to further investigate the role and mechanism of AIM2 in malignant behaviors of renal carcinoma by cellular and animal models. The correlation analysis showed that low AIM2 expression was significantly correlated with lymph node metastasis, poor 5-year overall (P < 0.05) and disease-specific survival (P < 0.05) in RCC patients. Over-expression of AIM2 in 786-O or OSRC-2 cells suppressed the cell proliferation, migration and invasion as compared with control. The increased AIM2 expression enhanced the expression of autophagy-related genes (Bcl-2, Beclin-1, LC3-Ⅱ, and ATG-5) and increased the ratio of LC3-Ⅱ/LC3-Ⅰin renal carcinoma cells. Of note, the blockade of autophagy by 3-Methyladenine (3-MA) abrogated the inhibition of cell migration and invasion by over-expressing AIM2, indicated that AIM2 suppressed the malignant behaviors of renal carcinoma by enhancing the induction of autophagy. Furthermore, 3-MA abolished the prevention of tumor growth on nude mouse established by AIM2-OSRC-2 cells. Our results suggested that AIM2 might serve as a novel prognostic indicator as well as a potential therapeutic target for renal carcinoma treatment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

HNRNPUL1 inhibits cisplatin sensitivity of esophageal squamous cell carcinoma through regulating the formation of circMAN1A2

Highlights: • HNRNPUL1 is an independent prognostic factor of ESCC patients receiving platinum-based chemotherapy. • Knockdown of HNRNPUL1 enhances cisplatin sensitivity of ESCC cells. • HNRNPUL1 inhibits the cisplatin sensitivity of ESCC cells by regulating the formation of circMAN1A2. Cisplatin (CDDP) is widely used for chemotherapy of esophageal squamous cell carcinoma (ESCC) but the drug resistance limits its therapeutic benefit. Heterogeneous nuclear ribonucleoprotein U-like 1 (HNRNPUL1) belongs to the family of RNA-binding proteins (RBPs) and is involved in DNA damage repair. To investigate whether and how HNRNPUL1 affects CDDP resistance of ESCC, we evaluated the expression of HNRNPUL1 and found that it was associated with recurrence in ESCC patients receiving postoperative platinum-based chemotherapy and was an independent prognostic factor for disease-free survival (DFS). Besides, we showed that the reduced expression of HNRNPUL1 enhanced the CDDP sensitivity of ESCC cells. Furthermore, RNA immunoprecipitation coupled with high-throughput sequencing (RIP-seq) were performed and a range of HNRNPUL1-binding RNAs influenced by CDDP treatment were identified followed by bioinformatics analysis. In terms of mechanism, we found that HNRNPUL1 inhibited CDDP sensitivity of ESCC cells by regulating the CDDP sensitivity-inhibited circular RNA (circRNA) MAN1A2 formation. Taken together, our results first demonstrated the role of HNRNPUL1 in CDDP resistance of ESCC and suggested that HNRNPUL1 may be a potential target of ESCC chemotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Single-Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer

An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from European Center for Medium-Range Weather Forecasts Reanalysis 5th Generation high resolution realization reanalysis data over the summertime northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. The resulting model, Machine-Assisted Reanalysis Boundary Layer Emulation, skillfully reproduces the boundary layer structure and cloud properties of the reanalysis data in 7-day single-column prognostic simulations over withheld testing periods. Radiative heating profiles are well simulated, and the mean climatology and variability of the stratocumulus to cumulus transition are accurately reproduced. Lastly, Machine-Assisted Reanalysis Boundary Layer Emulation more closely tracks the reanalysis than does a comparable configuration of the underlying forecast model.

58 GEOSCIENCES↗

DOE OSTI2020

Impact of a New Sea Ice Thermodynamic Formulation in the CESM2 Sea Ice Component

Abstract The sea ice component of the Community Earth System Model version 2 (CESM2) contains new “mushy‐layer” physics that simulates prognostic salinity in the sea ice, with consequent modifications to sea ice thermodynamics and the treatment of melt ponds. The changes to the sea ice model and their influence on coupled model simulations are described here. Two simulations were performed to assess the changes in the vertical thermodynamics formulation with prognostic salinity compared to a constant salinity profile. Inclusion of the mushy layer thermodynamics of Turner et al. (2013, https://doi.org/10.1002/jgrc.20171 ) in a fully coupled Earth system model produces thicker and more extensive sea ice in the Arctic, with relatively unchanged sea ice in the Antarctic compared to simulations using a constant salinity profile. While this is consistent with the findings of uncoupled ice‐ocean model studies, the role of the frazil and congelation growth is more important in fully coupled simulations. Melt pond drainage is also an important contribution to simulated ice thickness differences as also found in the uncoupled simulations of Turner and Hunke (2015; https://doi.org/10.1002/2014JC010358 ). However, it is an interaction of the ponds and the snow fraction that impacts the surface albedo and hence the top melt. The changes in the thermodynamics and resulting ice state modify the ice‐ocean‐atmosphere fluxes with impacts on the atmosphere and ocean states, particularly temperature.

Bailey, David A.↗

DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy

Chromosomal instability (CIN) is a hallmark of cancer that contributes to tumour heterogeneity and other malignant properties. Aberrant centromere and kinetochore function causes CIN through chromosome missegregation, leading to aneuploidy, rearrangements and micronucleus formation. Here we develop a Centromere and kinetochore gene Expression Score (CES) signature that quantifies the centromere and kinetochore gene misexpression in cancers. High CES values correlate with increased levels of genomic instability and several specific adverse tumour properties, and prognosticate poor patient survival for breast and lung cancers, especially early-stage tumours. They also signify high levels of genomic instability that sensitize cancer cells to additional genotoxicity. Thus, the CES signature forecasts patient response to adjuvant chemotherapy or radiotherapy. Our results demonstrate the prognostic and predictive power of the CES, suggest a role for centromere misregulation in cancer progression, and support the idea that tumours with extremely high CIN are less tolerant to specific genotoxic therapies.

Science & Technology - Other Topics↗

DOE OSTI2022

A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

The vertical-velocity skewness in the atmospheric boundary layer without buoyancy and Coriolis effects

One of the main features of near-neutral atmospheric boundary layer (ABL) turbulence is the positive vertical velocity skewness $Sk_w$ above the roughness sublayer or the buffer region in smooth-walls. The $Sk_w$ variations are receiving renewed interest in many climate-related parameterizations of the ABL given their significance to cloud formation and to testing sub-grid schemes for Large Eddy Simulations (LES). The vertical variations of $Sk_w$ are explored here using wind tunnel and flume experiments collected above smooth, rough, and permeable-walls in the absence of buoyancy and Coriolis effects. These laboratory experiments form a necessary starting point to probe the canonical structure of $Sk_w$ as they deal with a key limiting case (i.e., near-neutral conditions). Diagnostic models based on cumulant expansions, realizability constraints, and constant mass flux approach routinely employed in the convective boundary layer as well as prognostic models based on third-order budgets are used to explain variations in $Sk_w$ for the idealized laboratory conditions. The failure of flux-gradient relations to model $Sk_w$ from the gradients of the vertical velocity variance σ$_w^2$ are explained and corrections based on models of energy transport offered. Novel links between the diagnostic and prognostic models are also featured, especially for the inertial term in the third-order budget of the vertical velocity fluctuation. The co-spectral properties of w′/σ w vs w′ 2 /σ$_w^2$ are also presented for the first time to assess the dominant scales governing $Sk_w$ in the inner and outer layers, where w′ is the fluctuating vertical velocity and σ w is the vertical velocity standard deviation.>

Boundary layer flow↗

DOE OSTI2015

SU-E-I-71: Quality Assessment of Surrogate Metrics in Multi-Atlas-Based Image Segmentation

Purpose: With the ever-growing data of heterogeneous quality, relevance assessment of atlases becomes increasingly critical for multi-atlas-based image segmentation. However, there is no universally recognized best relevance metric and even a standard to compare amongst candidates remains elusive. This study, for the first time, designs a quantification to assess relevance metrics’ quality, based on a novel perspective of the metric as surrogate for inferring the inaccessible oracle geometric agreement. Methods: We first develop an inference model to relate surrogate metrics in image space to the underlying oracle relevance metric in segmentation label space, with a monotonically non-decreasing function subject to random perturbations. Subsequently, we investigate model parameters to reveal key contributing factors to surrogates’ ability in prognosticating the oracle relevance value, for the specific task of atlas selection. Finally, we design an effective contract-to-noise ratio (eCNR) to quantify surrogates’ quality based on insights from these analyses and empirical observations. Results: The inference model was specialized to a linear function with normally distributed perturbations, with surrogate metric exemplified by several widely-used image similarity metrics, i.e., MSD/NCC/(N)MI. Surrogates’ behaviors in selecting the most relevant atlases were assessed under varying eCNR, showing that surrogates with high eCNR dominated those with low eCNR in retaining the most relevant atlases. In an end-to-end validation, NCC/(N)MI with eCNR of 0.12 compared to MSD with eCNR of 0.10 resulted in statistically better segmentation with mean DSC of about 0.85 and the first and third quartiles of (0.83, 0.89), compared to MSD with mean DSC of 0.84 and the first and third quartiles of (0.81, 0.89). Conclusion: The designed eCNR is capable of characterizing surrogate metrics’ quality in prognosticating the oracle relevance value. It has been demonstrated to be correlated with the performance of relevant atlas selection and ultimate label fusion.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer

Purpose: To develop an intra-tumor partitioning framework for identifying high-risk subregions from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and CT imaging, and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods: In this institutional review board-approved retrospective study, we analyzed the pre-treatment FDG-PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra-tumor partitioning method was developed based on a two-stage clustering process: first at patient-level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor, which were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (p < 0.001). Conclusion: We propose a robust intra-tumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-R-07: Radiomics of CT Features and Associations and Correlation with Outcomes Following Lung SBRT

Objective: To investigate the prognostic significance of image gradients and in predicting clinical outcomes in a patients with non-small cell lung cancer treated with stereotactic body radiotherapy (SBRT) on 71 patients with 83 treated lesions. Methods: The records of patients treated with lung SBRT were retrospectively reviewed. When applicable, SBRT target volumes were modified to exclude any overlap with pleura, chestwall, or mediastinum. The ITK software package was utilized to generate quantitative measures of image intensity, inhomogeneity, shape morphology and first and second-order CT textures. Multivariate and univariate models containing CT features were generated to assess associations with clinicopathologic factors. Results: On univariate analysis, tumor size (HR 0.54, p=0.045) sumHU (HR 0.31, p=0.044) and short run grey level emphasis STD (HR 0.22, p=0.019) were associated with regional failure-free survival; meanHU (HR 0.30, p=0.035), long run emphasis (HR 0.21, p=0.011) and long run low grey level emphasis (HR 0.14, p=0.005) was associated with distant failure-free survival (DFFS). No features were significant on multivariate modeling however long run low grey level emphasis had a hazard ratio of 0.12 (p=0.061) for DFFS. Adenocarcinoma and squamous cell carcinoma differed with respect to long run emphasis STD (p=0.024), short run low grey level emphasis STD (p<0.001), and long run low grey level emphasis STD (p=0.024). Multivariate modeling of texture features associated with tumor histology was used to estimate histologies of 18 lesions treated without histologic confirmation. Of these, MVA suggested the same histology as a prior metachronous lung malignancy in 3/7 patients. Conclusion: Extracting radiomics features on clinical datasets was feasible with the ITK package with minimal effort to identify pre-treatment quantitative CT features with prognostic factors for distant control after lung SBRT.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Development and validation of a next-generation sequencing-based multigene assay to predict the prognosis of estrogen receptor-positive, HER2-negative breast cancer

Purpose: Multigene assays provide useful prognostic information regarding hormone receptor (HR)-positive breast cancer. Next-generation sequencing (NGS)-based platforms have numerous advantages including reproducibility and adaptability in local laboratories. Here we aimed to develop and validate an NGS-based multigene assay to predict the distant recurrence risk. Experimental Design: In total, 179 genes including 30 reference genes highly correlated with the 21-gene recurrence score (RS) algorithm were selected from public databases. Targeted RNA-sequencing was performed using 250 and 93 archived breast cancer samples with a known RS in the training and verification sets, respectively, to develop the algorithm and NGS–Prognostic Score (NGS-PS). The assay was validated in 413 independent samples with long-term follow-up data on distant metastasis. Results: In the verification set, the NGS-PS and 21-gene RS displayed 91.4% concurrence (85/93 samples). In the validation cohort of 413 samples, area under the receiver operating characteristic curve plotted using NGS-PS values classified for distant recurrence was 0.76. The best NGS-PS cut-off value predicting distant metastasis was 20. Furthermore, 269 and 144 patients were classified as low- and high-risk patients in accordance with the cut-off. Five- and 10-year estimates of distant metastasis–free survival (DMFS) for low- versus high-risk groups were 97.0% versus 77.8% and 93.2% versus 64.4%, respectively. The age-related HR for distant recurrence without chemotherapy was 9.73 (95% CI, 3.59–26.40) and 3.19 (95% CI, 1.40–7.29) for patients aged ≤50 and >50 years, respectively. Conclusions: The newly developed and validated NGS-based multigene assay can predict the distant recurrence risk in ER-positive, HER2-negative breast cancer.

60 APPLIED LIFE SCIENCES↗