Ovarian cancer is the fifth most common cause of cancer deaths in women in the United States. Numerous gene signatures of patient prognosis have been proposed, but diverse data and methods make these ...difficult to compare or use in a clinically meaningful way. We sought to identify successful published prognostic gene signatures through systematic validation using public data.
A systematic review identified 14 prognostic models for late-stage ovarian cancer. For each, we evaluated its 1) reimplementation as described by the original study, 2) performance for prognosis of overall survival in independent data, and 3) performance compared with random gene signatures. We compared and ranked models by validation in 10 published datasets comprising 1251 primarily high-grade, late-stage serous ovarian cancer patients. All tests of statistical significance were two-sided.
Twelve published models had 95% confidence intervals of the C-index that did not include the null value of 0.5; eight outperformed 97.5% of signatures including the same number of randomly selected genes and trained on the same data. The four top-ranked models achieved overall validation C-indices of 0.56 to 0.60 and shared anticorrelation with expression of immune response pathways. Most models demonstrated lower accuracy in new datasets than in validation sets presented in their publication.
This analysis provides definitive support for a handful of prognostic models but also confirms that these require improvement to be of clinical value. This work addresses outstanding controversies in the ovarian cancer literature and provides a reproducible framework for meta-analytic evaluation of gene signatures.
Abstract Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to ...incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model’s potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.
While progression from normal prostatic epithelium to invasive cancer is driven by molecular alterations, tumor cells and cells in the cancer microenvironment are co-dependent and co-evolve. Few ...human studies to date have focused on stroma. Here, we performed gene expression profiling of laser capture microdissected normal non-neoplastic prostate epithelial tissue and compared it to non-transformed and neoplastic low-grade and high-grade prostate epithelial tissue from radical prostatectomies, each with its immediately surrounding stroma. Whereas benign epithelium in prostates with and without tumor were similar in gene expression space, stroma away from tumor was significantly different from that in prostates without cancer. A stromal gene signature reflecting bone remodeling and immune-related pathways was upregulated in high compared to low-Gleason grade cases. In validation data, the signature discriminated cases that developed metastasis from those that did not. These data suggest that the microenvironment may influence prostate cancer initiation, maintenance, and metastatic progression.Stromal cells contribute to tumor development but the mechanisms regulating this process are still unclear. Here the authors analyze gene expression profiles in the prostate and show that stromal gene signature changes ahead of the epithelial gene signature as prostate cancer initiates and progresses.
Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models focus ...on a few specific syndromes; however, recent evidence from multi-gene panel testing shows that many syndromes are overlapping, motivating the development of models that incorporate family history on several cancers and predict mutations for a comprehensive panel of genes.
We present PanelPRO, a new, open-source R package providing a fast, flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. It includes a customizable database with default parameter values estimated from published studies and allows users to select any combinations of genes and cancers for their models, including well-established single syndrome BayesMendel models (BRCAPRO and MMRPRO). This leads to more accurate risk predictions and ultimately has a high impact on prevention strategies for cancer and clinical decision making. The package is available for download for research purposes at
https://projects.iq.harvard.edu/bayesmendel/panelpro
.
Genetic mutations that increase cancer risk can be passed down from parents to their children, which can affect families across many generations. In these families, multiple members may be affected by different types of cancer, and these cancers often develop at an early age. Unaffected family members are often referred to genetic counselling, where they can explore their own risk of cancer. Clinicians and genetic counselors can provide recommendations to minimize cancer risk and inform personal choices on how to manage that risk, such as opting for preventative surgeries or participating in regular screening.
In genetic counselling sessions, highly trained clinicians and specialists use software that takes an individual’s family history of cancer and uses it to estimate their individual risk of carrying certain genetic mutations. These estimates can in turn help to predict their future risk of cancer. Many existing software packages are limited to estimating risks based on mutations in well-known cancer-related genes, such as
BRCA1
and
BRCA2
in breast and ovarian cancer. However, emerging evidence suggests that many of the genes associated with cancer risk work as part of a complex and overlapping network. Since current risk-profiling software packages are only designed to consider such genes in isolation, they cannot generate the most robust, accurate or comprehensive cancer risk profiles.
To address this challenge, Lee, Liang et al. have developed a new risk-profiling software that can integrate a large number of gene mutations and a wide range of potential cancer types to provide more accurate estimates of individual cancer risk. This software, called PanelPRO, uses evidence identified from extensive literature reviews to model the complex interplay between genes and cancer risk. The software not only calculates risks based on known genes, but also allows other developers to integrate new cancer-related genes that may be identified in the future. Importantly, the software is compatible with genetic counselling applications, since it returns answers within seconds when reasonable family and gene database sizes are used.
PanelPRO is a new, modern, flexible and efficient software package that provides an important advance towards modelling the vast genetic and biological complexity that contributes to inherited cancer risk. This software is designed to provide a more accurate and comprehensive estimate of cancer risk for individuals with family histories of cancer.
As an open-source software, it is freely available for research purposes, and can be licensed by software companies and healthcare organizations to integrate electronic patient records and rapidly identify at-risk individuals across larger patient groups. Ultimately, this software has the potential to improve cancer prevention strategies and optimize the personalized decision-making processes around cancer risk.
The COVID-19 mortality rate is higher in the elderly and in those with pre-existing chronic medical conditions. The elderly also suffer from increased morbidity and mortality from seasonal influenza ...infections; thus, an annual influenza vaccination is recommended for them. In this study, we explore a possible county-level association between influenza vaccination coverage in people aged 65 years and older and the number of deaths from COVID-19. To this end, we used COVID-19 data up to 14 December 2020 and US population health data at the county level. We fit quasi-Poisson regression models using influenza vaccination coverage in the elderly population as the independent variable and the COVID-19 mortality rate as the outcome variable. We adjusted for an array of potential confounders using different propensity score regression methods. Results show that, on the county level, influenza vaccination coverage in the elderly population is negatively associated with mortality from COVID-19, using different methodologies for confounding adjustment. These findings point to the need for studying the relationship between influenza vaccination and COVID-19 mortality at the individual level to investigate any underlying biological mechanisms.
Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile ...and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 1.44-2.96; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 1.51-5.05; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.
To evaluate whether the use of Bayesian adaptive randomized (AR) designs in clinical trials for glioblastoma is feasible and would allow for more efficient trials.
We generated an adaptive ...randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR designs with more conventional trial designs by using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary end point was the number of patients needed to achieve a desired statistical power.
If our phase II trials had been a single, multiarm trial using AR design, 30 fewer patients would have been needed compared with a multiarm balanced randomized (BR) design to attain the same power level. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power and in more patients being randomly assigned to effective treatment arms. For a 140-patient trial with a control arm, two ineffective arms, and one effective arm with a hazard ratio of 0.6, a median of 47 patients would be randomly assigned to the effective arm compared with 35 in a BR trial design.
Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics, and a relatively short time for events, Bayesian AR designs are attractive for clinical trials in glioblastoma.
•The grayROC extends the ROC to classifiers that allow for cases, falling in an indeterminacy zone, to not be unclassified.•It is based on bounds on sensitivity and specificity derived accounting for ...the indeterminacy.•A simple optimization allows to focus on visualization of most interesting cases, avoiding indeterminacy when not needed.
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This work extends Receiver Operating Characteristic (ROC) curve to the situation where some cases, falling in an intermediate ”indeterminacy zone” of the predictor, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds for this case; and summarization of the large number of possibilities arising from different choices of indeterminacy zones.
Recently, the majority of protein coding genes were sequenced in a collection of pancreatic cancers, providing an unprecedented opportunity to identify genetic markers of prognosis for patients with ...adenocarcinoma of the pancreas.
We previously sequenced more than 750 million base pairs of DNA from 23,219 transcripts in a series of 24 adenocarcinomas of the pancreas. In addition, 39 genes that were mutated in more than one of these 24 cancers were sequenced in a separate panel of 90 well-characterized adenocarcinomas of the pancreas. Of these 114 patients, 89 underwent pancreaticoduodenectomy, and the somatic mutations in these cancers were correlated with patient outcome.
When adjusted for age, lymph node status, margin status, and tumor size, SMAD4 gene inactivation was significantly associated with shorter overall survival (hazard ratio, 1.92; 95% confidence interval, 1.20-3.05; P = 0.006). Patients with SMAD4 gene inactivation survived a median of 11.5 months, compared with 14.2 months for patients without SMAD4 inactivation. By contrast, mutations in CDKN2A or TP53 or the presence of multiple (> or =4) mutations or homozygous deletions among the 39 most frequently mutated genes were not associated with survival.
SMAD4 gene inactivation is associated with poorer prognosis in patients with surgically resected adenocarcinoma of the pancreas.
Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has ...not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R package
), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.
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