A large-panel gene expression analysis was conducted to identify biomarkers associated with the effectiveness of adding palbociclib to fulvestrant.
The PALOMA-3 ( ClinicalTrials.gov identifier: ...NCT01942135) trial randomly assigned 521 endocrine-pretreated patients with metastatic breast cancer to receive palbociclib plus fulvestrant or placebo plus fulvestrant. Primary analysis was first conducted on 10 genes on the basis of pathway biology and evidence from previous studies followed by a systematic panel-wide search among 2,534 cancer-related genes. The association of gene expression with the effect of palbociclib on progression-free survival (PFS) was evaluated using Cox proportional hazards regression analysis, with gene expression as a continuous variable or dichotomized by median. An independent breast cancer cohort from the Preoperative Palbociclib (POP) Clinical Trial ( ClinicalTrials.gov identifier: NCT02008734) was used for validation, in 61 patients with primary breast cancer treated with 2 weeks of palbociclib.
In the PALOMA-3 trial, 302 patients had tumor tissue analyzed (palbociclib arm, 194 patients; placebo arm, 108 patients). Palbociclib efficacy was lower in patients with high versus low cyclin E1 (
) mRNA expression (median PFS: palbociclib arm, 7.6
14.1 months; placebo arm, 4.0
4.8 months, respectively; interaction
unadjusted = .00238; false discovery rate-adjusted
= .0238).
mRNA was more predictive in metastatic than in archival primary biopsy tissue samples. No significant interaction was found between treatment and expression levels of CDK4, CDK6, cyclin D1, and RB1. Palbociclib was efficacious in both luminal A and luminal B tumors. High
mRNA expression was associated with poor antiproliferative activity of palbociclib in the POP trial (
= .005).
Addition of palbociclib to fulvestrant demonstrated efficacy in all biomarker groups, although high
mRNA expression was associated with relative resistance to palbociclib.
The development of precision medicine for the management of metastatic breast cancer is an appealing concept; however, major scientific and logistical challenges hinder its implementation in the ...clinic. The identification of driver mutational events remains the biggest challenge, because, with the few exceptions of ER, HER2, PIK3CA and AKT1, no validated oncogenic drivers of breast cancer exist. The development of bioinformatic tools to help identify driver mutations, together with assessment of pathway activation and dependency should help resolve this issue in the future. The occurrence of secondary resistance, such as ESR1 mutations, following endocrine therapy poses a further challenge. Ultra-deep sequencing and monitoring of circulating tumour DNA (ctDNA) could permit early detection of the genetic events underlying resistance and inform on combination therapy approaches. Beside these scientific challenges, logistical and operational issues are a major limitation to the development of precision medicine. For example, the low incidence of most candidate genomic alterations hinders randomized trials, as the number of patients to be screened would be too high. We discuss these limitations and the solutions, which include scaling-up the number of patients screened for identifying a genomic alteration, the clustering of genomic alterations into pathways, and the development of personalized medicine trials.
The aim of the current study was to conduct a pooled analysis of studies that have investigated the prognostic value of tumor-infiltrating lymphocytes (TILs) in early-stage triple negative breast ...cancer (TNBC).
Participating studies had evaluated the percentage infiltration of stromally located TILs (sTILs) that were quantified in the same manner in patient diagnostic samples of early-stage TNBC treated with anthracycline-based chemotherapy with or without taxanes. Cox proportional hazards regression models stratified by trial were used for invasive disease-free survival (iDFS; primary end point), distant disease-free survival (D-DFS), and overall survival (OS), fitting sTILs as a continuous variable adjusted for clinicopathologic factors.
We collected individual data from 2,148 patients from nine studies. Average age was 50 years (range, 22 to 85 years), and 33% of patients were node negative. The average value of sTILs was 23% (standard deviation, 20%), and 77% of patients had 1% or more sTILs. sTILs were significantly lower with older age ( P = .001), larger tumor size ( P = .01), more nodal involvement ( P = .02), and lower histologic grade ( P = .001). A total of 736 iDFS and 548 D-DFS events and 533 deaths were observed. In the multivariable model, sTILs added significant independent prognostic information for all end points (likelihood ratio χ
, 48.9 iDFS; P < .001; χ
, 55.8 D-DFS; P < .001; χ
, 48.5 OS; P < .001). Each 10% increment in sTILs corresponded to an iDFS hazard ratio of 0.87 (95% CI, 0.83 to 0.91) for iDFS, 0.83 (95% CI, 0.79 to 0.88) for D-DFS, and 0.84 (95% CI, 0.79 to 0.89) for OS. In node-negative patients with sTILs ≥ 30%, 3-year iDFS was 92% (95% CI, 89% to 98%), D-DFS was 97% (95% CI, 95% to 99%), and OS was 99% (95% CI, 97% to 100%).
This pooled data analysis confirms the strong prognostic role of sTILs in early-stage TNBC and excellent survival of patients with high sTILs after adjuvant chemotherapy and supports the integration of sTILs in a clinicopathologic prognostic model for patients with TNBC. This model can be found at www.tilsinbreastcancer.org .
Summary Background High quantities of tumour-infiltrating lymphocytes (TILs) in primary HER2-positive breast cancer are associated with improved prognosis and response to therapy. We aimed to ...investigate the prognostic role of host antitumour immunity as represented by baseline quantities of TILs in patients with advanced HER2-positive breast cancer treated with either pertuzumab or placebo in addition to trastuzumab and docetaxel. Methods CLEOPATRA was a randomised phase 3 study comparing the addition of either pertuzumab or placebo to first-line therapy with trastuzumab and docetaxel for patients with locally recurrent, unresectable, or metastatic HER2-positive breast cancer. We assessed the quantity of stromal TILs in prospectively collected tumour samples and investigated their association with progression-free survival, overall survival, clinicopathological characteristics, and pertuzumab treatment. We estimated hazard ratios (HR) and 95% CIs with multivariate Cox regression models fitting stromal TILs as a continuous variable (per 10% increment). The CLEOPATRA trial is registered with ClinicalTrials.gov , number NCT00567190. Findings Tumour samples from 678 (84%) of 808 participants were evaluable for TILs, including 519 (77%) archival samples, 155 (23%) freshly obtained samples (collected 45 days or fewer before randomisation), and four samples of unknown archival status. Median follow-up was 50 months (IQR 41–54) for progression-free survival and 51 months (IQR 46–57) for overall survival. 519 progression-free survival events occurred and 358 patients died. The median TIL value was 10% (IQR 5–30). Freshly obtained tumour samples had significantly lower TIL values than did archival samples (10·00% 95% CI 5·00–20·00 vs 15·00% 5·00–35·00; p=0·00036). We detected no significant association between TIL values and progression-free survival (adjusted HR 0·95, 95% CI 0·90–1·00, p=0·063). However, for overall survival, each 10% increase in stromal TILs was significantly associated with longer overall survival (adjusted HR 0·89, 95% CI 0·83–0·96, p=0·0014). The treatment effect of pertuzumab did not differ significantly by stromal TIL value for either progression-free survival (pinteraction =0·23) or overall survival (pinteraction =0·21). Interpretation In patients with advanced HER2-positive breast cancer treated with docetaxel, trastuzumab, and pertuzumab or placebo, higher TIL values are significantly associated with improved overall survival, suggesting that the effect of antitumour immunity extends to the advanced setting. Future clinical studies in this cancer subtype should consider TILs as a stratification factor and investigate whether therapies that can augment immunity could potentially further improve survival. Funding F Hoffmann-La Roche–Genentech and the Breast Cancer Research Foundation.
Previous preclinical and clinical data suggest that the immune system influences prognosis and response to chemotherapy (CT); however, clinical relevance has yet to be established in breast cancer ...(BC). We hypothesized that increased lymphocytic infiltration would be associated with good prognosis and benefit from immunogenic CT-in this case, anthracycline-only CT-in selected BC subtypes.
We investigated the relationship between quantity and location of lymphocytic infiltrate at diagnosis with clinical outcome in 2009 node-positive BC samples from the BIG 02-98 adjuvant phase III trial comparing anthracycline-only CT (doxorubicin followed by cyclophosphamide, methotrexate, and fluorouracil CMF or doxorubicin plus cyclophosphamide followed by CMF) versus CT combining doxorubicin and docetaxel (doxorubicin plus docetaxel followed by CMF or doxorubicin followed by docetaxel followed by CMF). Readings were independently performed by two pathologists. Disease-free survival (DFS), overall survival (OS), and interaction with type of CT associations were studied. Median follow-up was 8 years.
There was no significant prognostic association in the global nor estrogen receptor (ER) -positive/human epidermal growth factor receptor 2 (HER2) -negative population. However, each 10% increase in intratumoral and stromal lymphocytic infiltrations was associated with 17% and 15% reduced risk of relapse (adjusted P = .1 and P = .025), respectively, and 27% and 17% reduced risk of death in ER-negative/HER2-negative BC regardless of CT type (adjusted P = .035 and P = .023), respectively. In HER2-positive BC, there was a significant interaction between increasing stromal lymphocytic infiltration (10% increments) and benefit with anthracycline-only CT (DFS, interaction P = .042; OS, P = .018).
In node-positive, ER-negative/HER2-negative BC, increasing lymphocytic infiltration was associated with excellent prognosis. Further validation of the clinical utility of tumor-infiltrating lymphocytes in this context is warranted. Our data also support the evaluation of immunotherapeutic approaches in selected BC subtypes.
Morphological evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer is gaining momentum as evidence strengthens the clinical relevance of this immunological biomarker. TILs in the ...post-neoadjuvant residual disease setting are acquiring increasing importance as a stratifying marker in clinical trials, considering the raising interest on immunotherapeutic strategies after neoadjuvant chemotherapy. TILs in ductal carcinoma in situ, with or without invasive carcinoma, represent an emerging area of clinical breast cancer research. The aim of this report is to update pathologists, clinicians and researchers on TIL assessment in both the post-neoadjuvant residual disease and the ductal carcinoma in situ settings. The International Immuno-Oncology Working Group proposes a method for assessing TILs in these settings, based on the previously published International Guidelines on TIL Assessment in Breast Cancer. In this regard, these recommendations represent a consensus guidance for pathologists, aimed to achieve the highest possible consistency among future studies.
The survival benefit with adjuvant chemotherapy for patients with resected stage II-III non-small-cell lung cancer (NSCLC) is modest. Efforts to develop prognostic or predictive biomarkers in these ...patients have not yielded clinically useful tests. We report findings from the Lung Adjuvant Cisplatin Evaluation (LACE)-Bio-II study, in which we analyzed next-generation sequencing and long-term outcomes data from > 900 patients with early-stage NSCLC treated prospectively in adjuvant landmark clinical trials. We used a targeted gene panel to assess the prognostic and predictive effect of mutations in individual genes, DNA repair pathways, and tumor mutation burden (TMB).
A total of 908 unmatched, formalin-fixed, paraffin-embedded, resected lung cancer tumor specimens were sequenced using a targeted panel of 1,538 genes. Stringent filtering criteria were applied to exclude germline variants and artifacts related to formalin fixation. Disease-free survival, overall survival, and lung cancer-specific survival (LCSS) were assessed in Cox models stratified by trial and adjusted for treatment, age, sex, performance score, histology, type of surgery, and stage.
Nonsynonymous mutations were identified in 1,515 genes in 908 tumor samples. High nonsynonymous TMB (> 8 mutations/Mb) was prognostic for favorable outcomes (ie, overall survival, disease-free survival, and LCSS) in patients with resected NSCLC. LCSS benefit with adjuvant chemotherapy was more pronounced in patients with low nonsynonymous TMBs (≤ 4 mutations/Mb). Presence of mutations in DNA repair pathways, tumor-infiltrating lymphocytes, TP53 alteration subtype, and intratumor heterogeneity was neither prognostic nor predictive. Statistically significant effect of mutations in individual genes was difficult to determine due to high false-discovery rates.
High nonsynonymous TMB was associated with a better prognosis in patients with resected NSCLC. In addition, the benefit of adjuvant chemotherapy on LCSS was more pronounced in patients with low nonsynonymous TMBs. Studies are warranted to confirm these findings.
Abstract
Background
We conducted a meta-analysis in nonmetastatic breast cancer patients treated by neoadjuvant chemotherapy (NCT) to assess the clinical validity of circulating tumor cell (CTC) ...detection as a prognostic marker.
Methods
We collected individual patient data from 21 studies in which CTC detection by CellSearch was performed in early breast cancer patients treated with NCT. The primary end point was overall survival, analyzed according to CTC detection, using Cox regression models stratified by study. Secondary end points included distant disease–free survival, locoregional relapse–free interval, and pathological complete response. All statistical tests were two-sided.
Results
Data from patients were collected before NCT (n = 1574) and before surgery (n = 1200). CTC detection revealed one or more CTCs in 25.2% of patients before NCT; this was associated with tumor size (P < .001). The number of CTCs detected had a detrimental and decremental impact on overall survival (P < .001), distant disease–free survival (P < .001), and locoregional relapse–free interval (P < .001), but not on pathological complete response. Patients with one, two, three to four, and five or more CTCs before NCT displayed hazard ratios of death of 1.09 (95% confidence interval CI = 0.65 to 1.69), 2.63 (95% CI = 1.42 to 4.54), 3.83 (95% CI = 2.08 to 6.66), and 6.25 (95% CI = 4.34 to 9.09), respectively. In 861 patients with full data available, adding CTC detection before NCT increased the prognostic ability of multivariable prognostic models for overall survival (P < .001), distant disease–free survival (P < .001), and locoregional relapse–free interval (P = .008).
Conclusions
CTC count is an independent and quantitative prognostic factor in early breast cancer patients treated by NCT. It complements current prognostic models based on tumor characteristics and response to therapy.
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.
Abstract
Background
Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical ...frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues.
Methods
We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability.
Results
Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates.
The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results.
Conclusions
Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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