There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical ...design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology.
Key Features:
Cutting-edge topics with appropriate technical background
Built around case studies which give the work a "hands-on" approach
Real examples of flaws in previously reported clinical trials and how to avoid them
Access to statistical code on the book’s website
Chapters written by internationally recognized statisticians from academia and pharmaceutical companies
Carefully edited to ensure consistency in style, level, and approach
Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others
Section I Introduction
Introduction to Clinical Trials - Susan Halabi, Stefan Michiels
Section II General Issues
Selection of Endpoints - Katherine S Panageas and Andrea Knezevic
Section III Early Development
Innovative Phase I Trials - Cody Chiuzan and Nathaniel O’Connell
Section IV Middle Development
Current Issues in Phase II Cancer Clinical Trials - Sin-Ho Jung
Design and Analysis of Immunotherapy Clinical Trials - Megan Othus
Adaptive Designs - William T. Barry
Section V Late Phase Clinical Trials
Sample Size Calculations for Phase III Trials in Oncology - Koji Oba, Aye Kuchiba
Non-inferiority Trial - Keyue Ding, Chris O’Callaghan
Design of Multi-arm, Multi-stage Trials in Oncology - James Wason
Multiple Comparisons, Multiple Primary Endpoints and Subpopulation Analysis - Ekkehard Glimm, Dong Xi, Paul Gallo
Cluster Randomized Trials - Catherine M. Crespi
Statistical Monitoring of Safety and Efficacy - Jay Herson, Chen Hu
Section VI Personalized Medicine
Biomarker-Based Phase II and III Clinical Trials in Oncology - Shigeyuki Matsui, Masataka Igeta, Kiichiro Toyoizumi
Genomic Biomarker Clinical Trial Designs - Richard Simon
Trial designs for rare diseases and small samples in oncology - Robert A. Beckman, Cong Chen, Martin Posch and Sarah Zohar
Statistical Methods for Biomarker and Subgroup Evaluation in Oncology Trials - Ilya Lipkovich, Alex Dmitrienko, Bohdana Ratitch
Developing and Validating Prognostic Models of Clinical Outcomes - Susan Halabi, Lira Pi, and Chen-Yen Lin
High-Dimensional Penalized Regression Models in Time-to-Event Clinical Trials - Federico Rotolo, Nils Ternes, Stefan Michiels
Sequential Multiple Assignment Randomized Trials - Kelly Speth, Kelley M. Kidwell
Section VII Advanced Topics
Assessing the value of surrogate endpoints - Xavier Paoletti, Federico Rotolo, Stefan Michiels
Competing Risks - Aurelien Latouche, Gang Li, Qing Yang
Cure models in cancer clinical trials - Catherine Legrand, Aurelie Bertrand
Interval Censoring - Yuan Wu
Methods for analysis of trials with changes from randomised treatment - Nicholas R. Latimer and Ian R. White
The analysis of adverse events in randomized clinical trials - Jan Beyersmann, Claudia Schmoor
Analysis of quality of life outcomes in oncology trials - Stephen Walters
Missing Data - Stephanie Pugh, James J. Dignam, Juned Siddique
"This highly anticipated book focuses on clinical trials in oncology, ranging from early, middle, and late phase trials to advanced topics such as precision medicine and immunotherapy. This textbook is expected to be extremely useful for statisticians and investigators who have been doing clinical trials for years, and for future clinical researchers and statisticians who are eager to learn about the design, conduct, analysis, and interpretation of clinical trials in oncology.
We strongly recommend this textbook for four reasons. First, it covers multiple stages of clinical trials in oncology, from early, middle, to late development. Second, it examines various designs of clinical trials, including traditional study designs, flexible designs, and SMART (Sequential Multiple Assignment Randomized Trials) designs. Third, it gives insights into unique aspects of clinical trials in oncology compared with other therapeutic areas, such as time-to-event endpoints and censoring. Fourth, it consists of different types of materials that are suitable to different groups of readers, with some materials for readers who like to have an aerial view of the practical considerations and the other materials for readers who like to have deep understanding to motivate their theoretical research. In the following, we explain these four reasons in detail...To summarize, because of the above four reasons, we strongly recommend this book to clinical researchers and statisticians who are interested in the development, design, conduct and analysis of oncology clinical trials. This book is well-balanced between practical considerations and statistical theories involved in oncology clinical trials. We believe that this book will help advance the design and analysis of oncology clinical trials with the ultimate goal to improve the care of oncology patients and their quality of life." - Man Jin and Yixin Fang , Journal of Biopharmaceutical Statistics , November 2019
"This book offers a comprehensive presentation of the statistical methods and issues connected with clinical trials in oncology...I would recommend this book to those who are new to the field of clinical trials in oncology and those who would like to learn about its specifics. The book covers a vast range of topics, which on its own illustrates how broad and dynamic the statistical methodology applied in oncology is...Individual chapters of the book are written by different authors. Therefore, each chapter is written by someone who is an expert in their field and can enrich the description of the methods with much appreciated insight on what is really used in practice and what the advantages and disadvantages of the methods are...Struggling with understanding some medical terms I found using the National Cancer Institute Dictionary of Cancer Terms very helpful when reading the book." - Eva Kielkowská , ISCB News , July 2020
Susan Halabi , Ph.D. is Professor of Biostatistics and Bioinformatics, Duke University, USA. She has extensive experience in the design and analysis of clinical trials in oncology. Dr. Halabi is a fellow of the American Statistical Association, the Society of Clinical Trials, and the American Society of Clinical Oncology. She serves on the Oncologic Drugs Advisory Committee for the Food and Drug Administration.
Stefan Michiels , Ph.D. is Head of the Oncostat team of the Center for research in epidemiology and population health (INSERM U1018, University Paris-Saclay, University Paris-Sud) at Gustave Roussy, Villejuif, France. His areas of expertise are clinical trials, meta-analyses and prediction models in oncology. Stefan is the currently the chair of the biostatisticians at Unicancer, a French collaborative cancer clinical trials group. Stefan holds a PhD in Biostatistics from the School of Public Health at the University Paris-Sud and Master Degrees in Statistics and in Applied Mathematics from the University of Leuven. His previous positions include the Université Libre de Bruxelles- Institut Jules Bordet (Belgium), the National Cancer Institute (France) and the University of Leuven (Belgium). He is currently member of the editorial board of the Journal of the National Cancer Institute and Annals of Oncology.
Breast cancer in young women is associated with poor prognosis. We aimed to define the role of gene expression signatures in predicting prognosis in young women and to understand biological ...differences according to age.
Patients were assigned to molecular subtypes estrogen receptor (ER)(+)/HER2(-); HER2(+), ER(-)/HER2(-)) using a three-gene classifier. We evaluated whether previously published proliferation, stroma, and immune-related gene signatures added prognostic information to Adjuvant! online and tested their interaction with age in a Cox model for relapse-free survival (RFS). Furthermore, we evaluated the association between candidate age-related genes or gene sets with age in an adjusted linear regression model.
A total of 3,522 patients (20 data sets) were eligible. Patients aged 40 years or less had a higher proportion of ER(-)/HER2(-) tumors (P < 0.0001) and were associated with poorer RFS after adjustment for breast cancer subtype, tumor size, nodal status, and histologic grade and stratification for data set and treatment modality (HR = 1.34, 95% CI = 1.10-1.63, P = 0.004). The proliferation gene signatures showed no significant interaction with age in ER(+)/HER2(-) tumors after adjustment for Adjuvant! online. Further analyses suggested that breast cancer in the young is enriched with processes related to immature mammary epithelial cells (luminal progenitors, mammary stem, c-kit, RANKL) and growth factor signaling in two independent cohorts (n = 1,188 and 2,334).
Proliferation-related prognostic gene signatures can aid treatment decision-making for young women. However, breast cancer arising at a young age seems to be biologically distinct beyond subtype distribution. Separate therapeutic approaches such as targeting RANKL or mammary stem cells could therefore be needed.
Summary Background We aimed to assess the clinical validity of circulating tumour cell (CTC) quantification for prognostication of patients with metastatic breast cancer by undertaking a pooled ...analysis of individual patient data. Methods We contacted 51 European centres and asked them to provide reported and unreported anonymised data for individual patients with metastatic breast cancer who participated in studies between January, 2003, and July, 2012. Eligible studies had participants starting a new line of therapy, data for progression-free survival or overall survival, or both, and CTC quantification by the CellSearch method at baseline (before start of new treatment). We used Cox regression models, stratified by study, to establish the association between CTC count and progression-free survival and overall survival. We used the landmark method to assess the prognostic value of CTC and serum marker changes during treatment. We assessed the added value of CTCs or serum markers to prognostic clinicopathological models in a resampling procedure using likelihood ratio (LR) χ2 statistics. Findings 17 centres provided data for 1944 eligible patients from 20 studies. 911 patients (46·9%) had a CTC count of 5 per 7·5 mL or higher at baseline, which was associated with decreased progression-free survival (hazard ratio HR 1·92, 95% CI 1·73–2·14, p<0·0001) and overall survival (HR 2·78, 95% CI 2·42–3·19, p<0·0001) compared with patients with a CTC count of less than 5 per 7·5 mL at baseline. Increased CTC counts 3–5 weeks after start of treatment, adjusted for CTC count at baseline, were associated with shortened progression-free survival (HR 1·85, 95% CI 1·48–2·32, p<0·0001) and overall survival (HR 2·26, 95% CI 1·68–3·03) as were increased CTC counts after 6–8 weeks (progression-free survival HR 2·20, 95% CI 1·66–2·90, p<0·0001; overall survival HR 2·91, 95% CI 2·01–4·23, p<0·0001). Survival prediction was significantly improved by addition of baseline CTC count to the clinicopathological models (progression-free survival LR 38·4, 95% CI 21·9–60·3, p<0·0001; overall survival LR 64·9, 95% CI 41·3–93·4, p<0·0001). This model was further improved by addition of CTC change at 3–5 weeks (progression-free survival LR 8·2, 95% CI 0·78–20·4, p=0·004; overall survival LR 11·5, 95% CI 2·6–25·1, p=0·0007) and at 6–8 weeks (progression-free survival LR 15·3, 95% CI 5·2–28·3; overall survival LR 14·6, 95% CI 4·0–30·6; both p<0·0001). Carcinoembryonic antigen and cancer antigen 15-3 concentrations at baseline and during therapy did not add significant information to the best baseline model. Interpretation These data confirm the independent prognostic effect of CTC count on progression-free survival and overall survival. CTC count also improves the prognostication of metastatic breast cancer when added to full clinicopathological predictive models, whereas serum tumour markers do not. Funding Janssen Diagnostics, the Nuovo-Soldati foundation for cancer research.
Lorlatinib is a third-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor with proven efficacy in patients with ALK-rearranged lung cancer previously treated with first- and ...second-generation ALK inhibitors. Beside compound mutations in the
kinase domain, other resistance mechanisms driving lorlatinib resistance remain unknown. We aimed to characterize the mechanisms of resistance to lorlatinib occurring in patients with
-rearranged lung cancer and design new therapeutic strategies in this setting.
Resistance mechanisms were investigated in 5 patients resistant to lorlatinib. Longitudinal tumor biopsies were studied using high-throughput next-generation sequencing. Patient-derived models were developed to characterize the acquired resistance mechanisms, and Ba/F3 cell mutants were generated to study the effect of novel
compound mutations. Drug combinatory strategies were evaluated
and
to overcome lorlatinib resistance.
Diverse biological mechanisms leading to lorlatinib resistance were identified. Epithelial-mesenchymal transition (EMT) mediated resistance in two patient-derived cell lines and was susceptible to dual SRC and ALK inhibition. We characterized three
kinase domain compound mutations occurring in patients, L1196M/D1203N, F1174L/G1202R, and C1156Y/G1269A, with differential susceptibility to ALK inhibition by lorlatinib. We identified a novel bypass mechanism of resistance caused by
loss-of-function mutations, conferring sensitivity to treatment with mTOR inhibitors.
This study shows that mechanisms of resistance to lorlatinib are diverse and complex, requiring new therapeutic strategies to tailor treatment upon disease progression.
The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional ...data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. We evaluated different penalizations in a Cox model to select grouped variables in order to further penalize variables that, in addition to having a low effect, belong to a group with a low overall effect; and to favor the selection of variables that, in addition to having a large effect, belong to a group with a large overall effect. We considered the case of prespecified and disjoint groups and proposed diverse weights for the adaptive lasso method. In particular we proposed the product Max Single Wald by Single Wald weighting (MSW*SW) which takes into account the information of the group to which it belongs and of this biomarker. Through simulations, we compared the selection and prediction ability of our approach with the standard lasso, the composite Minimax Concave Penalty (cMCP), the group exponential lasso (gel), the Integrative L1-Penalized Regression with Penalty Factors (IPF-Lasso), and the Sparse Group Lasso (SGL) methods. In addition, we illustrated the methods using gene expression data of 614 breast cancer patients. The adaptive lasso with the MSW*SW weighting method incorporates both the information in the grouping structure and the individual variable. It outperformed the competitors by reducing the false discovery rate without severely increasing the false negative rate.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Multiple independent studies have shown that tumor-infiltrating lymphocytes (TIL) are prognostic in breast cancer with potential relevance for response to immune-checkpoint inhibitor therapy. ...Although many groups are currently evaluating TIL, there is no standardized system for diagnostic applications. This study reports the results of two ring studies investigating TIL conducted by the International Working Group on Immuno-oncology Biomarkers. The study aim was to determine the intraclass correlation coefficient (ICC) for evaluation of TIL by different pathologists. A total of 120 slides were evaluated by a large group of pathologists with a web-based system in ring study 1 and a more advanced software-system in ring study 2 that included an integrated feedback with standardized reference images. The predefined aim for successful ring studies 1 and 2 was an ICC above 0.7 (lower limit of 95% confidence interval (CI)). In ring study 1 the prespecified endpoint was not reached (ICC: 0.70; 95% CI: 0.62–0.78). On the basis of an analysis of sources of variation, we developed a more advanced digital image evaluation system for ring study 2, which improved the ICC to 0.89 (95% CI: 0.85–0.92). The Fleiss' kappa value for <60 vs ≥60% TIL improved from 0.45 (ring study 1) to 0.63 in RS2 and the mean concordance improved from 88 to 92%. This large international standardization project shows that reproducible evaluation of TIL is feasible in breast cancer. This opens the way for standardized reporting of tumor immunological parameters in clinical studies and diagnostic practice. The software-guided image evaluation approach used in ring study 2 may be of value as a tool for evaluation of TIL in clinical trials and diagnostic practice. The experience gained from this approach might be applicable to the standardization of other diagnostic parameters in histopathology.
The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes ...with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
To investigate the association between chemotherapy response and gene expression modules describing important biologic processes and druggable oncogenic pathways in breast cancer (BC) subtypes.
We ...searched for publicly available gene expression studies evaluating anthracycline with or without taxane-based neoadjuvant chemotherapy and identified eight studies with 996 patients. We computed 17 gene modules and calculated odds ratios (ORs) for pathologic complete response (pCR) for one-unit increases in scaled modules with and without adjustment for clinicopathologic characteristics. Added predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) and integrated discrimination index (IDI). We used the false discovery rate (FDR) to adjust for multiple testing.
High immune module scores were associated with increased pCR probability in all BC subtypes. High module scores of chromosomal instability, phosphatase and tensin homolog (PTEN) loss, and E2F3 transcription factor were associated with increased pCR probability in estrogen receptor (ER) -negative/human epidermal growth factor receptor 2 (HER2) -negative and ER-positive/HER2-negative but not in HER2-positive tumors (interactions between HER2 and each of these modules for their association with pCR: P < .05; FDR, 0.17; trend for interaction between HER2 and PTEN). High values of insulin-like growth factor 1 activation module were associated with increased pCR probability only in ER-positive/HER2-negative tumors (interaction between insulin-like growth factor 1 and ER: P = .002; FDR, 0.03). When adding the immune module to clinicopathologic characteristics, we observed substantial increases in predictive accuracy for pCR in the HER2-positive subtype (IDI, 0.093; P = .004; increase in AUC from 0.760 to 0.836).
Different processes and pathways are associated with pCR in different BC subtypes.
CDK4/6 inhibitors combined with endocrine therapy have demonstrated higher antitumor activity than endocrine therapy alone for the treatment of advanced estrogen receptor-positive breast cancer. Some ...of these tumors are de novo resistant to CDK4/6 inhibitors and others develop acquired resistance. Here, we show that p16 overexpression is associated with reduced antitumor activity of CDK4/6 inhibitors in patient-derived xenografts (n = 37) and estrogen receptor-positive breast cancer cell lines, as well as reduced response of early and advanced breast cancer patients to CDK4/6 inhibitors (n = 89). We also identified heterozygous RB1 loss as biomarker of acquired resistance and poor clinical outcome. Combination of the CDK4/6 inhibitor ribociclib with the PI3K inhibitor alpelisib showed antitumor activity in estrogen receptor-positive non-basal-like breast cancer patient-derived xenografts, independently of PIK3CA, ESR1 or RB1 mutation, also in drug de-escalation experiments or omitting endocrine therapy. Our results offer insights into predicting primary/acquired resistance to CDK4/6 inhibitors and post-progression therapeutic strategies.
Purpose Phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha ( PIK3CA) mutations are frequently observed in primary breast cancer. We evaluated their prognostic relevance by ...performing a pooled analysis of individual patient data. Patients and Methods Associations between PIK3CA status and clinicopathologic characteristics were tested by applying Cox regression models adjusted for age, tumor size, nodes, grade, estrogen receptor (ER) status, human epidermal growth factor receptor 2 (HER2) status, treatment, and study. Invasive disease-free survival (IDFS) was the primary end point; distant disease-free survival (DDFS) and overall survival (OS) were also assessed, overall and by breast cancer subtypes. Results Data from 10,319 patients from 19 studies were included (median OS follow-up, 6.9 years); 1,787 patients (17%) received chemotherapy, 4,036 (39%) received endocrine monotherapy, 3,583 (35%) received both, and 913 (9%) received none or their treatment was unknown. PIK3CA mutations occurred in 32% of patients, with significant associations with ER positivity, increasing age, lower grade, and smaller size (all P < .001). Prevalence of PIK3CA mutations was 18%, 22%, and 37% in the ER-negative/HER2-negative, HER2-positive, and ER-positive/HER2-negative subtypes, respectively. In univariable analysis, PIK3CA mutations were associated with better IDFS (HR, 0.77; 95% CI, 0.71 to 0.84; P < .001), with evidence for a stronger effect in the first years of follow-up (0 to 5 years: HR, 0.73; 95% CI, 0.66 to 0.81; P < .001; 5 to 10 years: HR, 0.82; 95% CI, 0.68 to 0.99; P = .037); > 10 years: (HR, 1.15; 95% CI, 0.84 to 1.58; P = .38; P heterogeneity = .02). In multivariable analysis, PIK3CA genotype remained significant for improved IDFS ( P = .043), but not for the DDFS and OS end points. Conclusion In this large pooled analysis, PIK3CA mutations were significantly associated with a better IDFS, DDFS, and OS, but had a lesser prognostic effect after adjustment for other prognostic factors.
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