Papers by Francesco Stingo
International journal of radiation oncology, biology, physics, 2016
PURPOSE We sought to investigate the ability of mid-treatment (18)F-fluorodeoxyglucose positron e... more PURPOSE We sought to investigate the ability of mid-treatment (18)F-fluorodeoxyglucose positron emission tomography (PET) studies to objectively and spatially quantify esophageal injury in vivo from radiation therapy for non-small cell lung cancer. METHODS AND MATERIALS This retrospective study was approved by the local institutional review board, with written informed consent obtained before enrollment. We normalized (18)F-fluorodeoxyglucose PET uptake to each patient's low-irradiated region (<5 Gy) of the esophagus, as a radiation response measure. Spatially localized metrics of normalized uptake (normalized standard uptake value [nSUV]) were derived for 79 patients undergoing concurrent chemoradiation therapy for non-small cell lung cancer. We used nSUV metrics to classify esophagitis grade at the time of the PET study, as well as maximum severity by treatment completion, according to National Cancer Institute Common Terminology Criteria for Adverse Events, using multivari...
Blood, 2014
INTRODUCTION: Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm that belongs to the ca... more INTRODUCTION: Chronic myelomonocytic leukemia (CMML) is a myeloid neoplasm that belongs to the category of myelodysplastic syndrome / myeloproliferative neoplasms (MDS / MPN). The International Prognostic Scoring System for Myelodysplastic Syndromes (IPSS) classification and its revised version (IPSS-R) addressed patients with newly diagnosed, untreated MDS and excluded CMML. While numerous investigators have attempted to devise a prognostic risk scoring system for CMML, no system has been generally accepted for this entity. A CMML-specific prognostic scoring (CPSS) system proposed by Such, et al [Blood. 2013; 11;121(15):3005-15] defines 4 different prognostic risk categories for estimating both overall survival (OS) and risk for AML transformation; the alternative version replaces RBC transfusion dependency with hemoglobin levels. AIM: The aim of the study is to validate the alternative CPSS scoring system on the CMML patient cohort at UT MD Anderson Cancer Center (UTMDACC). METHOD...
Scientific reports, Jan 20, 2017
Personalized cancer therapy seeks to tailor treatment to an individual patient's biology. The... more Personalized cancer therapy seeks to tailor treatment to an individual patient's biology. Therefore, a means to characterize radiosensitivity is necessary. In this study, we investigated radiosensitivity in the normal esophagus using an imaging biomarker of radiation-response and esophageal toxicity, esophageal expansion, as a method to quantify radiosensitivity in 134 non-small-cell lung cancer patients, by using K-Means clustering to group patients based on esophageal radiosensitivity. Patients within the cluster of higher response and lower dose were labelled as radiosensitive. This information was used as a variable in toxicity prediction modelling (lasso logistic regression). The resultant model performance was quantified and compared to toxicity prediction modelling without utilizing radiosensitivity information. The esophageal expansion-response was highly variable between patients, even for similar radiation doses. K-Means clustering was able to identify three patient su...
Statistics in Biosciences, 2016
In this paper, we propose a Bayesian hierarchical approach to infer network structures across mul... more In this paper, we propose a Bayesian hierarchical approach to infer network structures across multiple sample groups where both shared and differential edges may exist across the groups. In our approach, we link graphs through a Markov random field prior. This prior on network similarity provides a measure of pairwise relatedness that borrows strength only between related groups. We incorporate the computational efficiency of continuous shrinkage priors, improving scalability for network estimation in cases of larger dimensionality. Our model is applied to patient groups with increasing levels of chronic obstructive pulmonary disease severity, with the goal of better understanding the break down of gene pathways as the disease progresses. Our approach is able to identify critical hub genes for four targeted pathways. Furthermore, it identifies gene connections that are disrupted with increased disease severity and that characterize the disease evolution. We also demonstrate the superior performance of our approach with respect to competing methods, using simulated data.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, Jan 5, 2015
Several recent studies have demonstrated the potential for quantitative imaging features to class... more Several recent studies have demonstrated the potential for quantitative imaging features to classify non-small cell lung cancer (NSCLC) patients as high or low risk. However applying the results from one institution to another has been difficult because of the variations in imaging techniques and feature measurement. Our study was designed to determine the effect of some of these sources of uncertainty on image features extracted from computed tomography (CT) images of non-small cell lung cancer (NSCLC) tumors. CT images from 20 NSCLC patients were obtained for investigating the impact of four sources of uncertainty: Two region of interest (ROI) selection conditions (breathing phase and single-slice vs. whole volume) and two imaging protocol parameters (peak tube voltage and current). Texture values did not vary substantially with the choice of breathing phase; however, almost half (12 out of 28) of the measured textures did change significantly when measured from the average images...
Medical Physics, 2013
For nonsmall cell lung cancer (NSCLC) patients, quantitative image features extracted from comput... more For nonsmall cell lung cancer (NSCLC) patients, quantitative image features extracted from computed tomography (CT) images can be used to improve tumor diagnosis, staging, and response assessment. For these findings to be clinically applied, image features need to have high intra and intermachine reproducibility. The objective of this study is to identify CT image features that are reproducible, nonredundant, and informative across multiple machines. Methods: Noncontrast-enhanced, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. Two machines ("M1" and "M2") used cine 4D-CT and one machine ("M3") used breath-hold helical 3D-CT. Gross tumor volumes (GTVs) were semiautonomously segmented then pruned by removing voxels with CT numbers less than a prescribed Hounsfield unit (HU) cutoff. Three hundred and twenty eight quantitative image features were extracted from each pruned GTV based on its geometry, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix. For each machine, features with concordance correlation coefficient values greater than 0.90 were considered reproducible. The Dice similarity coefficient (DSC) and the Jaccard index (JI) were used to quantify reproducible feature set agreement between machines. Multimachine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering based on the average correlation between features across multiple machines. Results: For all image types, GTV pruning was found to negatively affect reproducibility (reported results use no HU cutoff). The reproducible feature percentage was highest for average images (M1 = 90.5%, M2 = 94.5%, M1∩M2 = 86.3%), intermediate for end-exhale images (M1 = 75.0%, M2 = 71.0%, M1∩M2 = 52.1%), and lowest for breath-hold images (M3 = 61.0%). Between M1 and M2, the reproducible feature sets generated from end-exhale images were relatively machinesensitive (DSC = 0.71, JI = 0.55), and the reproducible feature sets generated from average images were relatively machine-insensitive (DSC = 0.90, JI = 0.87). Histograms of feature pair correlation distances indicated that feature redundancy was machine-sensitive and image type sensitive. After hierarchical clustering, 38 features, 28 features, and 33 features were found to be reproducible and nonredundant for M1∩M2 (average images), M1∩M2 (end-exhale images), and M3, respectively. When blinded to the presence of test-retest images, hierarchical clustering showed that the selected features were informative by correctly pairing 55 out of 56 test-retest images using only their reproducible, nonredundant feature set values. Conclusions: Image feature reproducibility and redundancy depended on both the CT machine and the CT image type. For each image type, the authors found a set of cross-machine reproducible, nonredundant, and informative image features that would be useful for future image-based models. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multimachine reproducibility and are the best candidates for clinical correlation.
American Journal of Hematology, 2014
Purpose-The frequency of RAS mutations in chronic myelomonocytic leukemia (CMML) suggests that ac... more Purpose-The frequency of RAS mutations in chronic myelomonocytic leukemia (CMML) suggests that activation of the MAPK pathway is important in CMML pathogenesis. Accordingly, we hypothesized that mutations in other members of the MAPK pathway might be overrepresented in RAS wt CMML. Methods-We performed next generation sequencing analysis on 70 CMML patients with known RAS mutation status using the TruSeq Amplicon Cancer Panel kit (Illumina, San Diego, CA). Results-The study group included 37 men and 33 women with a median age of 67.8 years (range, 28-86 years). Forty patients were RAS wt and 30 were RAS mut ; the latter included KRAS=17; NRAS=12; KRAS+NRAS=1. Next-generation sequencing showed 5 patients (7.1% of total group; 12.5% of RAS wt group) with RAS wt who had BRAF mutations. All BRAF mut patients had CMML-1; 2 (40%) with MPN-CMML and 3 with MDS-CMML. The BRAF mutations were of missense type and involved exon 11 in 1 patient and exon 15 in 4 patients. All BRAF mut patients had CMML-1 with low-risk cytogenetic findings, and none of the BRAF mut CMML cases were therapy-related. Two (40%) of the 5 patients with BRAF mut patients transformed to acute myeloid leukemia during follow up. Multivariate Cox proportional hazard regression modeling suggests that BRAF mut status is associated with overall survival (p=0.04). Additionally, the RAS mut group tended to have worse OS compared to the RAS wt group.
Journal of the American Statistical Association, 2020
Integrative network modeling of data arising from multiple genomic platforms provides insight int... more Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.
JNCI: Journal of the National Cancer Institute, 2021
Background Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific o... more Background Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1–negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1–negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1–negative LUAD. Methods Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1–negative and 4 TTF-1–positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided. Resu...
Cancer, Jan 20, 2015
Ibrutinib is active in patients with relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL)... more Ibrutinib is active in patients with relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL). In patients treated with ibrutinib for R/R CLL, del(17p), identified by interphase fluorescence in situ hybridization (FISH), is associated with inferior progression-free survival despite equivalent initial response rates. Del(17p) is frequently associated with a complex metaphase karyotype (CKT); the prognostic significance of CKT in ibrutinib-treated patients has not been reported. This study reviewed 88 patients treated for R/R CLL at The University of Texas MD Anderson Cancer Center with investigational ibrutinib-based regimens from 2010 to 2013. Pretreatment FISH and lipopolysaccharide-stimulated metaphase cytogenetic analysis were performed on bone marrow. An adequate pretreatment metaphase karyotype was available for 56 of the 88 patients. The karyotype was complex in 21 of the 56 cases; 17 of the 21 had del(17p) according to FISH. The overall response rate, including partial re...
British journal of haematology, Jan 30, 2015
The relative benefit of combination therapy with CD20 monoclonal antibodies (mAbs) compared to le... more The relative benefit of combination therapy with CD20 monoclonal antibodies (mAbs) compared to lenalidomide monotherapy is unknown; lenalidomide has been shown to potentiate rituximab-induced antibody-dependent cellular cytotoxicity (ADCC) in a mouse model.(Wu, et al 2008) Pre-treatment with a CD20 mAb could also reduce the likelihood of tumour flare reaction (TFR).
Statistics and Computing, 2014
We present a new Bayesian approach for undirected Gaussian graphical model determination. We prov... more We present a new Bayesian approach for undirected Gaussian graphical model determination. We provide some graph theory results for local updates that facilitate a fast exploration of the graph space. Specifically, we show how to locally update, after either edge deletion or inclusion, the perfect sequence of cliques and the perfect elimination order of the nodes associated to an oriented, directed acyclic version of a decomposable graph. Building upon the decomposable graphical models framework, we propose a more flexible methodology that extends to the class of nondecomposable graphs. Posterior probabilities of edge inclusion are interpreted as a natural measure of edge selection uncertainty. When applied to a protein expression data set, the model leads to fast estimation of the protein interaction network.
Journal of hematology & oncology, Jan 4, 2014
Background De novo acute myeloid leukemia (AML) with concurrent DNMT3A, FLT3 and NPM1 mutations (... more Background De novo acute myeloid leukemia (AML) with concurrent DNMT3A, FLT3 and NPM1 mutations (AML DNMT3A/FLT3/NPM1 ) has been suggested to represent a unique AML subset on the basis of integrative genomic analysis, but the clinical features of such patients have not been characterized systematically. MethodsWe assessed the features of patients (n¿=¿178) harboring mutations in DNMT3A, FLT3 and/or NPM1, including an index group of AML DNMT3A/FLT3/NPM1 patients.ResultsPatients with AML DNMT3A/FLT3/NPM1 (n¿=¿35) were significantly younger (median, 56.0 vs. 62.0 years; p¿=¿0.025), mostly women (65.7% vs. 46.9%; p¿=¿0.045), and presented with a higher percentage of bone marrow blasts (p¿<¿0.001) and normal cytogenetics (p¿=¿0.024) in comparison to other groups in this study. Among patients…
Journal of Statistical Planning and Inference, 2011
A generalization of the Probit model is presented, with the extended skew-normal cumulative distr... more A generalization of the Probit model is presented, with the extended skew-normal cumulative distribution as a link function, which can be used for modelling a binary response variable in the presence of selectivity bias. The estimate of the parameters via ML is addressed, and inference on the parameters expressing the degree of selection is discussed. The assumption underlying the model is that the selection mechanism influences the unmeasured factors and does not affect the explanatory variables. When this assumption is violated, but other conditional independencies hold, then the model proposed here is derived. In particular, the instrumental variable formula still applies and the model results at the second stage of the estimating procedure.
Biometrics, 2015
Efforts to personalize medicine in oncology have been limited by reductive characterizations of t... more Efforts to personalize medicine in oncology have been limited by reductive characterizations of the intrinsically complex underlying biological phenomena. Future advances in personalized medicine will rely on molecular signatures that derive from synthesis of multifarious interdependent molecular quantities requiring robust quantitative methods. However, highly parameterized statistical models when applied in these settings often require a prohibitively large database and are sensitive to proper characterizations of the treatment-by-covariate interactions, which in practice are difficult to specify and may be limited by generalized linear models. In this article, we present a Bayesian predictive framework that enables the integration of a high-dimensional set of genomic features with clinical responses and treatment histories of historical patients, providing a probabilistic basis for using the clinical and molecular information to personalize therapy for future patients. Our work represents one of the first attempts to define personalized treatment assignment rules based on large-scale genomic data. We use actual gene expression data acquired from The Cancer Genome Atlas in the settings of leukemia and glioma to explore the statistical properties of our proposed Bayesian approach for personalizing treatment selection. The method is shown to yield considerable improvements in predictive accuracy when compared to penalized regression approaches.
Bayesian Analysis, 2013
Graphical model learning and inference are often performed using Bayesian techniques. In particul... more Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distributions involved in this second step have been studied in depth, the ones used in the first step have not been explored in as much detail. In this paper, we will study the prior and posterior distributions defined over the space of the graph structures for the purpose of learning the structure of a graphical model. In particular, we will provide a characterisation of the behaviour of those distributions as a function of the possible edges of the graph. We will then use the properties resulting from this characterisation to define measures of structural variability for both Bayesian and Markov networks, and we will point out some of their possible applications.
Statistical Methods & Applications
In this section we provide a reply to some of the comments concerning similarities and difference... more In this section we provide a reply to some of the comments concerning similarities and differences between graphical models and random graphs raised by Michael Schweinberger (MS) and Maria Prosperina Vitale, Giuseppe Giordano, and Giancarlo Ragozini (VGR). As MS pointed out, graphical models and random graphs, despite their prominent differences in theory, methodology, and computation, can have interesting
Journal of the American Statistical Association, 2018
ABSTRACT We consider the problem of modeling conditional independence structures in heterogenous ... more ABSTRACT We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariates—termed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariates—which allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We provide theoretical justifications of our modeling endeavor, in terms of graphical model selection consistency. We demonstrate the performance of our method through rigorous simulation studies. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks. Supplementary materials for this article are available online.
Scientific reports, Apr 3, 2017
Radiomics is the use of quantitative imaging features extracted from medical images to characteri... more Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non-small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment r...
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Papers by Francesco Stingo