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Cancers
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The Application of Biostatistics in Cancers
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The Application of Biostatistics in Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 17452

Special Issue Editors

Dr. Luciano Cascione

E-Mail Website
Guest Editor
1. Institute of Oncology Research, Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6500 Bellinzona, Switzerland
2. Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
Interests: bioinformatics; biostatistics; non-coding RNAs; lymphoma; high-throughput sequencing data analysis; diagnostic and prognostic biomarkers discovery
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Denti

E-Mail Website
Guest Editor
Department of Statistics, University of California, Irvine, CA 92697, USA
Interests: bayesian statistics, bayesian nonparametric, mixture models, model-based clustering, latent variable models, dimensionality reduction and intrinsic dimension
Dr. Antonietta Mira

E-Mail Website
Guest Editor
Data Science Lab, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
Interests: statistical models to life science; Monte Carlo simulation; Approximate Bayesian Computation

Special Issue Information

Dear Colleagues,

Cancer research relies heavily on a broad application of statistical methods, ranging from the epidemiological methods necessary for establishing links between exposures and cancer risks to the development of elaborate designs of clinical trials for testing potential drugs. In recent years, sophisticated machine learning techniques, which in many cases are synonymous with statistical models, have also been used for the discovery of novel cancer subtypes based on -omics technologies, as well as the prediction of patient trajectories based on big clinical data.

For this Special Issue in Cancers, we invite submissions related to the development of or novel applications of statistical methodology with a specific aim of addressing challenges in cancer research. The focus of the submissions should be on the methodology, but with a clear and useful application on cancer research data.

Dr. Cascione Luciano
Dr. Francesco Denti
Dr. Antonietta Mira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biostatistics
  • statistical methodology
  • cancer

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Published Papers (6 papers)

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Research

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14 pages, 330 KiB  
Article
Spline Analysis of Biomarker Data Pooled from Multiple Matched/Nested Case–Control Studies
by Yujie Wu, Mitchell Gail, Stephanie Smith-Warner, Regina Ziegler and Molin Wang
Cancers 2022, 14(11), 2783; https://doi.org/10.3390/cancers14112783 - 3 Jun 2022
Viewed by 1599
Abstract
Pooling biomarker data across multiple studies enables researchers to obtain precise estimates of the association between biomarker measurements and disease risks due to increased sample sizes. However, biomarker measurements often vary significantly across different assays and laboratories; therefore, calibration of the local laboratory [...] Read more.
Pooling biomarker data across multiple studies enables researchers to obtain precise estimates of the association between biomarker measurements and disease risks due to increased sample sizes. However, biomarker measurements often vary significantly across different assays and laboratories; therefore, calibration of the local laboratory measurements to a reference laboratory is necessary before pooling data. We propose two methods for estimating the dose–response curves that allow for a nonlinear association between the continuous biomarker measurements and log relative risk in pooling projects of matched/nested case–control studies. Our methods are based on full calibration and internalized calibration methods. The full calibration method uses calibrated biomarker measurements for all subjects, even for people with reference laboratory measurements, while the internalized calibration method uses the reference laboratory measurements when available and otherwise uses the calibrated biomarker measurements. We conducted simulation studies to compare these methods, as well as a naive method, where data are pooled without calibration. Our simulation and theoretical results suggest that, in estimating the dose–response curves for biomarker-disease relationships, the internalized and full calibration methods perform substantially better than the naive method, and the full calibration approach is the preferred method for calibrating biomarker measurements. We apply our methods in a pooling project of nested case–control studies to estimate the association of circulating Vitamin D levels with risk of colorectal cancer. Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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10 pages, 732 KiB  
Article
Utility of Continuous Disease Subtyping Systems for Improved Evaluation of Etiologic Heterogeneity
by Ruitong Li, Tomotaka Ugai, Lantian Xu, David Zucker, Shuji Ogino and Molin Wang
Cancers 2022, 14(7), 1811; https://doi.org/10.3390/cancers14071811 - 2 Apr 2022
Cited by 2 | Viewed by 2504
Abstract
Molecular pathologic diagnosis is important in clinical (oncology) practice. Integration of molecular pathology into epidemiological methods (i.e., molecular pathological epidemiology) allows for investigating the distinct etiology of disease subtypes based on biomarker analyses, thereby contributing to precision medicine and prevention. However, existing approaches [...] Read more.
Molecular pathologic diagnosis is important in clinical (oncology) practice. Integration of molecular pathology into epidemiological methods (i.e., molecular pathological epidemiology) allows for investigating the distinct etiology of disease subtypes based on biomarker analyses, thereby contributing to precision medicine and prevention. However, existing approaches for investigating etiological heterogeneity deal with categorical subtypes. We aimed to fully leverage continuous measures available in most biomarker readouts (gene/protein expression levels, signaling pathway activation, immune cell counts, microbiome/microbial abundance in tumor microenvironment, etc.). We present a cause-specific Cox proportional hazards regression model for evaluating how the exposure–disease subtype association changes across continuous subtyping biomarker levels. Utilizing two longitudinal observational prospective cohort studies, we investigated how the association of alcohol intake (a risk factor) with colorectal cancer incidence differed across the continuous values of tumor epigenetic DNA methylation at long interspersed nucleotide element-1 (LINE-1). The heterogeneous alcohol effect was modeled using different functions of the LINE-1 marker to demonstrate the method’s flexibility. This real-world proof-of-principle computational application demonstrates how the new method enables visualizing the trend of the exposure effect over continuous marker levels. The utilization of continuous biomarker data without categorization for investigating etiological heterogeneity can advance our understanding of biological and pathogenic mechanisms. Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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15 pages, 1784 KiB  
Article
Integration of Baseline Metabolic Parameters and Mutational Profiles Predicts Long-Term Response to First-Line Therapy in DLBCL Patients: A Post Hoc Analysis of the SAKK38/07 Study
by Sofia Genta, Guido Ghilardi, Luciano Cascione, Darius Juskevicius, Alexandar Tzankov, Sämi Schär, Lisa Milan, Maria Cristina Pirosa, Fabiana Esposito, Teresa Ruberto, Luca Giovanella, Stefanie Hayoz, Christoph Mamot, Stefan Dirnhofer, Emanuele Zucca and Luca Ceriani
Cancers 2022, 14(4), 1018; https://doi.org/10.3390/cancers14041018 - 17 Feb 2022
Cited by 7 | Viewed by 2388
Abstract
Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We [...] Read more.
Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We evaluated the combined impact of mutation profiling and baseline PET/CT functional parameters on the outcome of DLBCL patients treated with the R-CHOP14 regimen in the SAKK38/07 clinical trial (NCT00544219). The concomitant presence of mutated SOCS1 with wild-type CREBBP and EP300 defined a group of patients with a favorable prognosis and 2-year progression-free survival (PFS) of 100%. Using an unsupervised recursive partitioning approach, we generated a classification-tree algorithm that predicts treatment outcomes. Patients with elevated metabolic tumor volume (MTV) and high metabolic heterogeneity (MH) (15%) had the highest risk of relapse. Patients with low MTV and favorable mutational profile (9%) had the lowest risk, while the remaining patients constituted the intermediate-risk group (76%). The resulting model stratified patients among three groups with 2-year PFS of 100%, 82%, and 42%, respectively (p < 0.001). Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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17 pages, 2008 KiB  
Article
Dynamic Prediction of Near-Term Overall Survival in Patients with Advanced NSCLC Based on Real-World Data
by Xuechen Wang, Kathleen Kerrigan, Sonam Puri, Jincheng Shen, Wallace Akerley and Benjamin Haaland
Cancers 2022, 14(3), 690; https://doi.org/10.3390/cancers14030690 - 29 Jan 2022
Cited by 3 | Viewed by 1863
Abstract
Patients with terminal cancers commonly receive aggressive and sub-optimal treatment near the end of life, which may not be beneficial in terms of duration or quality of life. To improve end-of-life care, it is essential to develop methods that can accurately predict the [...] Read more.
Patients with terminal cancers commonly receive aggressive and sub-optimal treatment near the end of life, which may not be beneficial in terms of duration or quality of life. To improve end-of-life care, it is essential to develop methods that can accurately predict the short-term risk of death. However, most prediction models for patients with cancer are static in the sense that they only use patient features at a fixed time. We proposed a dynamic prediction model (DPM) that can incorporate time-dependent predictors. We apply this method to patients with advanced non-small-cell lung cancer from a real-world database. Inverse probability of censoring weighted AUC with bootstrap inference was used to compare predictions among models. We found that increasing ECOG performance status and decreasing albumin had negative prognostic associations with overall survival (OS). Moreover, the negative prognostic implications strengthened over the patient disease course. DPMs using both time-independent and time-dependent predictors substantially improved short-term prediction accuracy compared to Cox models using only predictors at a fixed time. The proposed model can be broadly applied for prediction based on longitudinal data, including an estimation of the dynamic effects of time-dependent features on OS and updating predictions at any follow-up time. Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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19 pages, 1909 KiB  
Article
Correlation of Body Mass Index with Oncologic Outcomes in Colorectal Cancer Patients: A Large Population-Based Study
by Chong-Chi Chiu, Chung-Han Ho, Chao-Ming Hung, Chien-Ming Chao, Chih-Cheng Lai, Chin-Ming Chen, Kuang-Ming Liao, Jhi-Joung Wang, Yu-Cih Wu, Hon-Yi Shi, Po-Huang Lee, Hui-Ming Lee, Li-Ren Yeh, Tien-Chou Soong, Shyh-Ren Chiang and Kuo-Chen Cheng
Cancers 2021, 13(14), 3592; https://doi.org/10.3390/cancers13143592 - 17 Jul 2021
Cited by 12 | Viewed by 3602
Abstract
It has been acknowledged that excess body weight increases the risk of colorectal cancer (CRC); however, there is little evidence on the impact of body mass index (BMI) on CRC patients’ long-term oncologic results in Asian populations. We studied the influence of BMI [...] Read more.
It has been acknowledged that excess body weight increases the risk of colorectal cancer (CRC); however, there is little evidence on the impact of body mass index (BMI) on CRC patients’ long-term oncologic results in Asian populations. We studied the influence of BMI on overall survival (OS), disease-free survival (DFS), and CRC-specific survival rates in CRC patients from the administrative claims datasets of Taiwan using the Kaplan–Meier survival curves and the log-rank test to estimate the statistical differences among BMI groups. Underweight patients (<18.50 kg/m2) presented higher mortality (56.40%) and recurrence (5.34%) rates. Besides this, they had worse OS (aHR:1.61; 95% CI: 1.53–1.70; p-value: < 0.0001) and CRC-specific survival (aHR:1.52; 95% CI: 1.43–1.62; p-value: < 0.0001) rates compared with those of normal weight patients (18.50–24.99 kg/m2). On the contrary, CRC patients belonging to the overweight (25.00–29.99 kg/m2), class I obesity (30.00–34.99 kg/m2), and class II obesity (≥35.00 kg/m2) categories had better OS, DFS, and CRC-specific survival rates in the analysis than the patients in the normal weight category. Overweight patients consistently had the lowest mortality rate after a CRC diagnosis. The associations with being underweight may reflect a reverse causation. CRC patients should maintain a long-term healthy body weight. Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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Review

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11 pages, 699 KiB  
Review
A Brief Overview of Adaptive Designs for Phase I Cancer Trials
by Anshul Saxena, Muni Rubens, Venkataraghavan Ramamoorthy, Zhenwei Zhang, Md Ashfaq Ahmed, Peter McGranaghan, Sankalp Das and Emir Veledar
Cancers 2022, 14(6), 1566; https://doi.org/10.3390/cancers14061566 - 18 Mar 2022
Cited by 6 | Viewed by 3005
Abstract
Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise [...] Read more.
Phase I studies are used to estimate the dose-toxicity profile of the drugs and to select appropriate doses for successive studies. However, literature on statistical methods used for phase I studies are extensive. The objective of this review is to provide a concise summary of existing and emerging techniques for selecting dosages that are appropriate for phase I cancer trials. Many advanced statistical studies have proposed novel and robust methods for adaptive designs that have shown significant advantages over conventional dose finding methods. An increasing number of phase I cancer trials use adaptive designs, particularly during the early phases of the study. In this review, we described nonparametric and algorithm-based designs such as traditional 3 + 3, accelerated titration, Bayesian algorithm-based design, up-and-down design, and isotonic design. In addition, we also described parametric model-based designs such as continual reassessment method, escalation with overdose control, and Bayesian decision theoretic and optimal design. Ongoing studies have been continuously focusing on improving and refining the existing models as well as developing newer methods. This study would help readers to assimilate core concepts and compare different phase I statistical methods under one banner. Nevertheless, other evolving methods require future reviews. Full article
(This article belongs to the Special Issue The Application of Biostatistics in Cancers)
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