Publications by authors named "Sylvia K Plevritis"

100 Publications

Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force.

JAMA 2021 03;325(10):988-997

Department of Radiology, Massachusetts General Hospital, Boston.

Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years.

Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies.

Design, Setting, And Participants: Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years.

Exposures: Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios.

Main Outcomes And Measures: Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms).

Results: Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases.

Conclusions And Relevance: Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less disparity in screening eligibility by sex and race/ethnicity.
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http://dx.doi.org/10.1001/jama.2021.1077DOI Listing
March 2021

Risk-Based lung cancer screening: A systematic review.

Lung Cancer 2020 09 12;147:154-186. Epub 2020 Jul 12.

Departments of Biomedical Data Science and of Radiology, Stanford University, Stanford, CA USA. Electronic address:

Lung cancer remains the leading cause of cancer related deaths worldwide. Lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer specific mortality. In 2013, the United States Preventive Services Task Force (USPSTF) recommended annual lung cancer screening with LDCT for smokers aged between 55 years to 80 years, with at least 30 pack-years of smoking exposure that currently smoke or who have quit smoking within 15 years. Risk-based lung cancer screening is an alternative approach that defines screening eligibility based on the personal risk of individuals. Selection of individuals for lung cancer screening based on their personal lung cancer risk has been shown to improve the sensitivity and specificity associated with the eligibility criteria of the screening program as compared to the 2013 USPSTF criteria. Numerous risk prediction models have been developed to estimate the lung cancer risk of individuals incorporating sociodemographic, smoking, and clinical risk factors associated with lung cancer, including age, smoking history, sex, race/ethnicity, personal and family history of cancer, and history of emphysema and chronic obstructive pulmonary disease (COPD), among others. Some risk prediction models include biomarker information, such as germline mutations or protein-based biomarkers as independent risk predictors, in addition to clinical, smoking, and sociodemographic risk factors. While, the majority of lung cancer risk prediction models are suitable for selecting high-risk individuals for lung cancer screening, some risk models have been developed to predict the probability of malignancy of screen-detected solidary pulmonary nodules or to optimize the screening frequency of eligible individuals by incorporating past screening findings. In this systematic review, we provide an overview of existing risk prediction models and their applications to lung cancer screening. We discuss potential strengths and limitations of lung cancer screening using risk prediction models and future research directions.
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http://dx.doi.org/10.1016/j.lungcan.2020.07.007DOI Listing
September 2020

Cost-Effectiveness Analysis of Lung Cancer Screening in the United States.

Ann Intern Med 2020 05;172(10):706-707

Harvard Medical School and Institute for Technology Assessment, Massachusetts General Hospital, Boston, Massachusetts (S.D.C., C.Y.K.).

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http://dx.doi.org/10.7326/L20-0072DOI Listing
May 2020

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line.

Nat Commun 2020 05 11;11(1):2345. Epub 2020 May 11.

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA.

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAF mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.
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http://dx.doi.org/10.1038/s41467-020-15956-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214418PMC
May 2020

A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

Genome Biol 2020 05 7;21(1):107. Epub 2020 May 7.

Department of Biomedical Data Science, Stanford University, Stanford, CA, 94035, USA.

Background: Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.

Result: To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.

Conclusion: These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.
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http://dx.doi.org/10.1186/s13059-020-02019-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206807PMC
May 2020

Disparities of National Lung Cancer Screening Guidelines in the US Population.

J Natl Cancer Inst 2020 11;112(11):1136-1142

Stanford Cancer Institute, Stanford University, Stanford, CA, USA.

Background: Current US Preventive Services Task Force (USPSTF) lung cancer screening guidelines are based on smoking history and age (55-80 years). These guidelines may miss those at higher risk, even at lower exposures of smoking or younger ages, because of other risk factors such as race, family history, or comorbidity. In this study, we characterized the demographic and clinical profiles of those selected by risk-based screening criteria but were missed by USPSTF guidelines in younger (50-54 years) and older (71-80 years) age groups.

Methods: We used data from the National Health Interview Survey, the CISNET Smoking History Generator, and results of logistic prediction models to simulate lifetime lung cancer risk-factor data for 100 000 individuals in the 1950-1960 birth cohorts. We calculated age-specific 6-year lung cancer risk for each individual from ages 50 to 90 years using the PLCOm2012 model and evaluated age-specific screening eligibility by USPSTF guidelines and by risk-based criteria (varying thresholds between 1.3% and 2.5%).

Results: In the 1950 birth cohort, 5.4% would have been ineligible for screening by USPSTF criteria in their younger ages but eligible based on risk-based criteria. Similarly, 10.4% of the cohort would be ineligible for screening by USPSTF in older ages. Notably, high proportions of blacks were ineligible for screening by USPSTF criteria at younger (15.6%) and older (14.2%) ages, which were statistically significantly greater than those of whites (4.8% and 10.8%, respectively; P < .001). Similar results were observed with other risk thresholds and for the 1960 cohort.

Conclusions: Further consideration is needed to incorporate comprehensive risk factors, including race and ethnicity, into lung cancer screening to reduce potential racial disparities.
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http://dx.doi.org/10.1093/jnci/djaa013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7669226PMC
November 2020

Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings.

JNCI Cancer Spectr 2019 Sep 23;3(3):pkz035. Epub 2019 May 23.

Departments of Radiology and of Biomedical Data Science.

Background: Numerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.

Methods: We assess the cost-effectiveness of 199 screening strategies that vary in terms of age and smoking eligibility criteria, using a microsimulation model. We simulate lung cancer-related events throughout the lifetime of US-representative current and former smokers. We conduct sensitivity analyses to test key model inputs and assumptions.

Results: The cost-effectiveness efficiency frontier consists of both annual and biennial screening strategies. Current guidelines are not on the frontier. Assuming 4% disutility associated with indeterminate findings, biennial screening for smokers aged 50-70 years with at least 40 pack-years and less than 10 years since smoking cessation is the cost-effective strategy using $100 000 willingness-to-pay threshold yielding the highest health benefit. Among all health utilities, the cost-effectiveness of screening is most sensitive to changes in the disutility of indeterminate findings. As the disutility of indeterminate findings decreases, screening eligibility criteria become less stringent and eventually annual screening for smokers aged 50-70 years with at least 30 pack-years and less than 10 years since smoking cessation is the cost-effective strategy yielding the highest health benefit.

Conclusions: The disutility associated with indeterminate findings impacts the cost-effectiveness of lung cancer screening. Efforts to quantify and better understand the impact of indeterminate findings on the effectiveness and cost-effectiveness of lung cancer screening are warranted.
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http://dx.doi.org/10.1093/jncics/pkz035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6947892PMC
September 2019

Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution.

Nat Commun 2019 12 6;10(1):5587. Epub 2019 Dec 6.

Department of Biomedical Data Science, Stanford University, Stanford, USA.

Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.
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http://dx.doi.org/10.1038/s41467-019-13441-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6898514PMC
December 2019

Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Pancreas 2019 Nov/Dec;48(10):1250-1258

Division of Gastroenterology and Hepatology, Stanford University, Stanford, CA.

A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.
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http://dx.doi.org/10.1097/MPA.0000000000001412DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282491PMC
September 2020

Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study.

Ann Intern Med 2019 12 5;171(11):796-804. Epub 2019 Nov 5.

Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (C.Y.K.).

Background: Recommendations vary regarding the maximum age at which to stop lung cancer screening: 80 years according to the U.S. Preventive Services Task Force (USPSTF), 77 years according to the Centers for Medicare & Medicaid Services (CMS), and 74 years according to the National Lung Screening Trial (NLST).

Objective: To compare the cost-effectiveness of different stopping ages for lung cancer screening.

Design: By using shared inputs for smoking behavior, costs, and quality of life, 4 independently developed microsimulation models evaluated the health and cost outcomes of annual lung cancer screening with low-dose computed tomography (LDCT).

Data Sources: The NLST; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; SEER (Surveillance, Epidemiology, and End Results) program; Nurses' Health Study and Health Professionals Follow-up Study; and U.S. Smoking History Generator.

Target Population: Current, former, and never-smokers aged 45 years from the 1960 U.S. birth cohort.

Time Horizon: 45 years.

Perspective: Health care sector.

Intervention: Annual LDCT according to NLST, CMS, and USPSTF criteria.

Outcome Measures: Incremental cost-effectiveness ratios (ICERs) with a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY).

Results Of Base-case Analysis: The 4 models showed that the NLST, CMS, and USPSTF screening strategies were cost-effective, with ICERs averaging $49 200, $68 600, and $96 700 per QALY, respectively. Increasing the age at which to stop screening resulted in a greater reduction in mortality but also led to higher costs and overdiagnosis rates.

Results Of Sensitivity Analysis: Probabilistic sensitivity analysis showed that the NLST and CMS strategies had higher probabilities of being cost-effective (98% and 77%, respectively) than the USPSTF strategy (52%).

Limitation: Scenarios assumed 100% screening adherence, and models extrapolated beyond clinical trial data.

Conclusion: All 3 sets of lung cancer screening criteria represent cost-effective programs. Despite underlying uncertainty, the NLST and CMS screening strategies have high probabilities of being cost-effective.

Primary Funding Source: CISNET (Cancer Intervention and Surveillance Modeling Network) Lung Group, National Cancer Institute.
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http://dx.doi.org/10.7326/M19-0322DOI Listing
December 2019

A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies.

J Natl Cancer Inst 2020 05;112(5):466-479

Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Zuid-Holland, the Netherlands.

Background: Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations.

Methods: Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis.

Results: Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%.

Conclusions: Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages.
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http://dx.doi.org/10.1093/jnci/djz164DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7225672PMC
May 2020

Change in Survival in Metastatic Breast Cancer with Treatment Advances: Meta-Analysis and Systematic Review.

JNCI Cancer Spectr 2018 Nov 24;2(4):pky062. Epub 2018 Dec 24.

Department of Medicine, Stanford University School of Medicine, Stanford, CA.

Background: Metastatic breast cancer (MBC) treatment has changed substantially over time, but we do not know whether survival post-metastasis has improved at the population level.

Methods: We searched for studies of MBC patients that reported survival after metastasis in at least two time periods between 1970 and the present. We used meta-regression models to test for survival improvement over time in four disease groups: recurrent, recurrent estrogen (ER)-positive, recurrent ER-negative, and de novo stage IV. We performed sensitivity analyses based on bias in some studies that could lead earlier cohorts to include more aggressive cancers.

Results: There were 15 studies of recurrent MBC (N = 18 678 patients; 3073 ER-positive and 1239 ER-negative); meta-regression showed no survival improvement among patients recurring between 1980 and 1990, but median survival increased from 21 (95% confidence interval [CI] = 18 to 25) months to 38 (95% CI = 31 to 47) months from 1990 to 2010. For ER-positive MBC patients, median survival increased during 1990-2010 from 32 (95% CI = 23 to 43) to 57 (95% CI = 37 to 87) months, and for ER-negative MBC patients from 14 (95% CI = 11 to 19) to 33 (95% CI = 21 to 51) months. Among eight studies (N = 35 831) of de novo stage IV MBC, median survival increased during 1990-2010 from 20 (95% CI = 16 to 24) to 31 (95% CI = 24 to 39) months. Results did not change in sensitivity analyses.

Conclusion: By bridging studies over time, we demonstrated improvements in survival for recurrent and de novo stage IV MBC overall and across ER-defined subtypes since 1990. These results can inform patient-doctor discussions about MBC prognosis and therapy.
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http://dx.doi.org/10.1093/jncics/pky062DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305243PMC
November 2018

A radiogenomic dataset of non-small cell lung cancer.

Sci Data 2018 10 16;5:180202. Epub 2018 Oct 16.

Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.

Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
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http://dx.doi.org/10.1038/sdata.2018.202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190740PMC
October 2018

Caution Needed for Analyzing the Risks of Second Cancers.

J Thorac Oncol 2018 09;13(9):e172-e173

Department of Medicine, Stanford University School of Medicine, Stanford, California; Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.

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http://dx.doi.org/10.1016/j.jtho.2018.04.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086830PMC
September 2018

Contributions of Screening and Treatment to Mortality From Breast Cancer-Reply.

JAMA 2018 06;319(22):2336

Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC.

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http://dx.doi.org/10.1001/jama.2018.4261DOI Listing
June 2018

-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma.

Cancer Res 2018 07 14;78(13):3445-3457. Epub 2018 May 14.

Department of Radiology, Stanford University School of Medicine, Stanford, California.

Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with FDG-PET scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in adenocarcinoma, they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAF). Among these adenocarcinoma genes correlated to glucose uptake, we focused on glutamine-fructose-6-phosphate transaminase 2 (), which codes for the glutamine-fructose-6-phosphate aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGFβ treatment, upregulated HBP genes, including , with less change in genes driving glycolysis, pentose phosphate pathway, and TCA cycle. Our work provides new evidence of histology-specific tumor stromal properties associated with glucose uptake in NSCLC and identifies as a critical regulator of tumor metabolic reprogramming in adenocarcinoma. These findings implicate the hexosamine biosynthesis pathway as a potential new therapeutic target in lung adenocarcinoma. .
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http://dx.doi.org/10.1158/0008-5472.CAN-17-2928DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030462PMC
July 2018

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

Proc Natl Acad Sci U S A 2018 05 13;115(18):E4294-E4303. Epub 2018 Apr 13.

Department of Radiology, Center for Cancer Systems Biology, Stanford University, Stanford, CA 94305;

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.
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http://dx.doi.org/10.1073/pnas.1711365115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5939057PMC
May 2018

A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010.

Med Decis Making 2018 04;38(1_suppl):89S-98S

Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.

We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predict future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient's age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance, Epidemiology, and End Results) historic stage, detection mode at time of detection, preclinical tumor course, and death age and cause of death (breast cancer v. other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET), including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2 status, and death from causes other than breast cancer. In this article, predicted mortality outcomes are compared assuming proportional v. nonproportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 2010, particularly after 1995. Our work indicates that population-level simulation modeling may have a broader role in assessing the time dependence of treatment effects.
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http://dx.doi.org/10.1177/0272989X17737508DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538507PMC
April 2018

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

Med Decis Making 2018 04;38(1_suppl):3S-8S

Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.
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http://dx.doi.org/10.1177/0272989X17737507DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862043PMC
April 2018

Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology.

Med Decis Making 2018 04;38(1_suppl):112S-125S

Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.

Background: Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models.

Methods: To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers.

Results: The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer's screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions.

Conclusions: The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.
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http://dx.doi.org/10.1177/0272989X17743244DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862068PMC
April 2018

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Med Decis Making 2018 04;38(1_suppl):140S-150S

Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.

Background: The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results.

Methods: Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial.

Results: The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial.

Conclusions: The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.
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http://dx.doi.org/10.1177/0272989X17718168DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862071PMC
April 2018

Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.

Med Decis Making 2018 04;38(1_suppl):9S-23S

Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA.

Background: Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality.

Method And Results: In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.

Conclusion: These data are intended to enhance the transparency of the breast CISNET models.
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http://dx.doi.org/10.1177/0272989X17700624DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862072PMC
April 2018

Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment.

Med Decis Making 2018 04;38(1_suppl):32S-43S

Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.

Background: As molecular subtyping of breast cancer influences clinical management, the evaluation of screening and adjuvant treatment interventions at the population level needs to account for molecular subtyping. Performing such analyses are challenging because molecular subtype-specific, long-term outcomes are not readily accessible; these markers were not historically recorded in tumor registries. We present a modeling approach to estimate historical survival outcomes by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status.

Method: Our approach leverages a simulation model of breast cancer outcomes and integrates data from two sources: the Surveillance Epidemiology and End Results (SEER) databases and the Breast Cancer Surveillance Consortium (BCSC). We not only produce ER- and HER2-specific estimates of breast cancer survival in the absence of screening and adjuvant treatment but we also estimate mean tumor volume doubling time (TVDT) and mean mammographic detection threshold by ER/HER2-status.

Results: In general, we found that tumors with ER-negative and HER2-positive status are associated with more aggressive growth, have lower TVDTs, are harder to detect by mammography, and have worse survival outcomes in the absence of screening and adjuvant treatment. Our estimates have been used as inputs into model-based analyses that evaluate the effects of screening and adjuvant treatment interventions on population outcomes by ER and HER2 status developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group. In addition, our estimates enable a re-assessment of historical trends in breast cancer incidence and mortality in terms of contemporary molecular tumor characteristics.

Conclusion: Our approach can be generalized beyond breast cancer and to more complex molecular profiles.
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http://dx.doi.org/10.1177/0272989X17743236DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635303PMC
April 2018

Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012.

JAMA 2018 01;319(2):154-164

Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC.

Importance: Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden.

Objective: To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu).

Design, Setting, And Participants: Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated.

Exposures: Screening mammography and treatment.

Main Outcomes And Measures: The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment.

Results: In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%).

Conclusions And Relevance: In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.
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http://dx.doi.org/10.1001/jama.2017.19130DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5833658PMC
January 2018

Distinguishing between CISNET model results versus CISNET models.

Cancer 2018 03 26;124(5):1083-1084. Epub 2017 Dec 26.

Carbone Cancer Center, Department of Population Health Science, School of Medicine and Public Health, University of Wisconsin at Madison, Madison, Wisconsin.

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http://dx.doi.org/10.1002/cncr.31150DOI Listing
March 2018

Re: Think before you leap.

Int J Cancer 2018 04 28;142(7):1507-1509. Epub 2017 Dec 28.

Department of Radiology, Stanford University, Palo Alto, CA.

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http://dx.doi.org/10.1002/ijc.31183DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6013033PMC
April 2018

Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative F FDG-PET/CT metrics.

Oncotarget 2017 Aug 10;8(32):52792-52801. Epub 2017 May 10.

Department of Radiology, Stanford University, Stanford, CA, USA.

This study investigated the relationship between epidermal growth factor receptor () and Kirsten rat sarcoma viral oncogene homolog () mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the and gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker ( < 0.001) or low pack-year smoking history ( = 0.002) and female gender ( = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations ( = 0.018). The maximum standardized uptake value (SUV) of FDG uptake in lung lesions was a predictive factor of the mutations ( = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor ( < 0.02) of mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor ( < 0.001) for the mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUV were both predictive for the mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the and mutations in lung cancer.
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http://dx.doi.org/10.18632/oncotarget.17782DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581070PMC
August 2017

Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications.

Radiology 2018 01 20;286(1):307-315. Epub 2017 Jul 20.

From the Stanford Center for Biomedical Informatics Research, Department of Medicine (M.Z., O.G.), Department of Radiology (A.L., S.E., A.G., S.K.P., D.L.R., S.N.), Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B.S.), and Department of Pathology (K.C.J., G.J.B.), Stanford University, 1265 Welch Rd, Stanford, CA 94305-5479.

Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.
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http://dx.doi.org/10.1148/radiol.2017161845DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749594PMC
January 2018