***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Purpose : A new generation of molecularly targeted agents is entering the definitive stage of clinical evaluation . Many of these drugs benefit only a subset of treated patients and may be overlooked by the traditional , broad- eligibility approach to r and omized clinical trials . Thus , there is a need for development of novel statistical methodology for rapid evaluation of these agents . Experimental Design : We propose a new adaptive design for r and omized clinical trials of targeted agents in setting s where an assay or signature that identifies sensitive patients is not available at the outset of the study . The design combines prospect i ve development of a gene expression – based classifier to select sensitive patients with a properly powered test for overall effect . Results : Performance of the adaptive design , relative to the more traditional design , is evaluated in a simulation study . It is shown that when the proportion of patients  sensitive to the new drug is low , the adaptive design substantially reduces the chance of false rejection of effective new treatments . When the new treatment is broadly effective , the adaptive design has power to detect the overall effect similar to the traditional design . Formulas are provided to determine the situations in which the new design is advantageous . Conclusion : Development of a gene expression – based classifier to identify the subset of sensitive patients  can be prospect ively incorporated into a r and omized phase III design without compromising the ability to detect an overall effect The National Lung Matrix Trial is currently the largest stratified medicine study in lung cancer . Utilizing a next-generation sequencing screening platform and an adaptive umbrella trial design , we will explore the activity of multiple biomarker/targeted therapy options in order to exp and the precision medicine opportunities for patients with non-small-cell lung cancer PURPOSE Biomarkers can add substantial value to current medical practice by providing an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient to guide patient-specific treatment selection . We discuss and evaluate various clinical trial design s for the validation of biomarker-guided therapy . METHODS Design s for predictive marker validation are broadly classified as retrospective ( ie , using data from previously well-conducted r and omized controlled trials [ RCTs ] ) versus prospect i ve ( enrichment , unselected , hybrid , or adaptive analysis ) . We discuss the salient features of each design in the context of real trials . RESULTS Well- design ed retrospective analysis from well-conducted prospect i ve RCTs can bring forward effective treatments to marker-defined subgroups of patients in a timely manner ( eg , KRAS and colorectal cancer ) . Enrichment design s are appropriate when preliminary evidence suggest that patients with or without that marker profile do not benefit from the treatments in question ; however , this may sometimes leave questions unanswered ( eg , trastuzumab and breast cancer ) . An unselected design is optimal where preliminary evidence regarding treatment benefit and assay reproducibility is uncertain ( eg , epidermal growth factor receptor and lung cancer ) . Hybrid design s are appropriate when preliminary evidence demonstrate the efficacy of certain treatments for a marker-defined subgroup , making it unethical to r and omly assign patients with that marker status to other treatments ( eg , multigene assay and breast cancer ) . Adaptive analysis design s allow for prespecified marker-defined subgroup analyses of data from an RCT . CONCLUSION The implementation of these design strategies will lead to a more rapid clinical validation of biomarker-guided therapy Purpose : Many anticancer therapies benefit only a subset of treated patients and may be overlooked by the traditional broad eligibility approach to design phase III clinical trials . New biotechnologies such as microarrays can be used to identify the patients that are most likely to benefit from anticancer therapies .  However , due to the high-dimensional nature of the genomic data , developing a reliable classifier by the time the definitive phase III trail is design ed may not be feasible . Experimental Design : Previously , Freidlin and Simon ( Clinical Cancer Research , 2005 ) introduced the adaptive signature design that combines a prospect i ve development of a sensitive patient classifier and a properly powered test for overall effect in a single pivotal trial . In this article , we propose a cross-validation extension of the adaptive signature design that optimizes the efficiency of both the classifier development and the validation components of the design . Results : The new design is evaluated through simulations and is applied to data from a r and omized breast cancer trial . Conclusion : The cross-validation approach is shown to considerably improve the performance of the adaptive signature design . We also describe approaches to the estimation of the treatment effect for the identified sensitive sub population . Clin Cancer Res ; 16(2 ) ; UNLABELLED The Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination ( BATTLE ) trial represents the first completed prospect i ve , biopsy-m and ated , biomarker-based , adaptively r and omized study in 255 pretreated lung cancer patients . Following an initial equal r and omization period , chemorefractory non-small cell lung cancer ( NSCLC ) patients were adaptively r and omized to erlotinib , v and etanib , erlotinib plus bexarotene , or sorafenib , based on relevant molecular biomarkers analyzed in fresh core needle biopsy specimens . Overall results include a 46 % 8-week disease control rate ( primary end point ) , confirm prespecified hypotheses , and show an impressive benefit from sorafenib among mutant-KRAS patients . BATTLE establishes the feasibility of a new paradigm for a personalized approach to lung cancer clinical trials . SIGNIFICANCE The BATTLE study is the first completed prospect i ve , adaptively r and omized study in heavily pretreated NSCLC patients that m and ated tumor profiling with " real-time " biopsies , taking a substantial step toward realizing personalized lung cancer therapy by integrating real-time molecular laboratory findings in delineating specific patient population s for individualized treatment Advancements made in molecularly targeted therapeutics have focussed increasing attention on biomarker-adaptive clinical trial design s as a means of improving the efficiency of drug development and advancing medical care . Here , we focus on r and omized clinical trial ( RCT ) design s aim ed at developing/validating clinical ly relevant pharmacogenomic biomarkers capable of identifying patients sensitive to a given therapy(s ) , and thereby , selecting the best therapy for a given patient . The term ‘ biomarker-adaptive ’ refers to biomarker trials that incorporate adaptive clinical trial method ology to modify the trial according to the accumulating outcome data . While some adaptive methods ( e.g. , interim monitoring ) have been an integral part of traditional clinical trial design for decades , these methods can be particularly advantageous in biomarker studies that evaluate molecularly targeted therapies . Here , we briefly review the main clinical trial methods that are used in design ing adaptive trials . We then discuss how these methods can be used to improve the efficiency of biomarker RCT design Increased survival is a common goal of cancer clinical trials . Owing to the long periods of observation and follow-up to assess patient survival outcome , it is difficult to use outcome -adaptive r and omization in these trials . In practice , often information about a short-term response is quickly available during or shortly after treatment , and this short-term response is a good predictor for long-term survival . For example , complete remission of leukemia can be achieved and measured after a few cycles of treatment . It is a short-term response that is desirable for prolonging survival . We propose a new design for survival trials when such short-term response information is available . We use the short-term information to ' speed up ' the adaptation of the r and omization procedure . We establish a connection between the short-term response and the long-term survival through a Bayesian model , first by using prior clinical information , and then by dynamically updating the model according to information accumulated in the ongoing trial . Interim monitoring and final decision making are based upon inference on the primary outcome of survival . The new design uses fewer patients , and can more effectively assign patients to the better treatment arms . We demonstrate these properties through simulation studies Background : Response to treatments is highly heterogeneous in cancer . Increased availability of biomarkers and targeted treatments has led to the need for trial design s that efficiently test new treatments in biomarker-stratified patient subgroups . Methods : We propose a novel Bayesian adaptive r and omisation ( BAR ) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment ’s effect . The design is motivated in part by two phase II trials that are currently in development . The design starts by r and omising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active . At interim analyses , data from treated patients are used to up date the allocation probabilities . If the linked treatments are effective , the allocation remains high ; if ineffective , the allocation changes over the course of the trial to unlinked treatments that are more effective . Results : Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct , but also performs well when alternative pairings are true . The design is consistently more powerful than parallel-groups stratified trials . Conclusions : This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously Predictive biomarkers to guide therapy for cancer patients are a cornerstone of precision medicine . Discussed herein are considerations regarding the design and interpretation of such predictive biomarker studies . These considerations are important for both planning and interpreting prospect i ve studies and for using specimens collected from completed r and omized clinical trials . Specific issues addressed are differentiation between qualitative and quantitative predictive effects , challenges due to sample size requirements for predictive biomarker assessment , and consideration of additional factors relevant to clinical utility assessment , such as toxicity and cost of new therapies as well as costs and potential morbidities associated with routine use of biomarker-based tests BACKGROUND To investigate the correlation of TargetPrint with local and central immunohistochemistry/fluorescence in situ hybridization assessment of estrogen ( ER ) , progesterone ( PgR ) , and human epidermal growth factor receptor 2 ( HER2 )  in the first 800 patients enrolled in the MINDACT trial . PATIENTS AND METHODS Data from local ( N = 800 ) and central ( N = 626 ) assessment s of receptor status  were collected and compared with TargetPrint results . RESULTS For ER , the positive agreement ( the percentage of central pathology positive assessment s that were also TargetPrint/local laboratory positive ) for TargetPrint in comparison to central ized assessment was 98 % with a negative agreement ( the percentage of central pathology negative assessment s that were also TargetPrint/local laboratory negative ) of 96 % . For PgR , the positive agreement was 83 % with a negative agreement of 92 % . For HER2 , the positive agreement was 75 % with a negative agreement of 99 % . Even though the local assessment showed higher positive agreement for PgR ( 89 % ) and higher positive agreement for HER2 ( 85 % ) , the range of discordant local versus central assessment s were as high as 6.7 % for ER , 12.9 % for PgR , and 4.3 % for HER2 . CONCLUSION TargetPrint and local assessment of ER , PgR , and HER2 show high concordance with central assessment in the first 800 MINDACT patients . However , there are concerns about the higher discordance rates for some local sites . TargetPrint can improve the reliability of hormone receptor and HER2 testing for those centers with a lower rate of concordance with the reference laboratory , with the limitation of a positive agreement of 75 % for HER2 . TargetPrint consequently has important implication s for treatment decisions in clinical practice and is a reliable alternative to local assessment for ER . CLINICAL TRIALS NUMBER NCT00433589 BACKGROUND Many molecularly targeted anticancer agents entering the definitive stage of clinical development benefit only a subset of treated patients . This may lead to missing effective agents by the traditional broad- eligibility r and omized trials due to the dilution of the overall treatment effect . We propose a statistically rigorous biomarker-adaptive threshold phase III design for setting s in which a putative biomarker to identify patients who are sensitive to the new agent is measured on a continuous or grade d scale . METHODS The design combines a test for overall treatment effect in all r and omly assigned patients with the establishment and validation of a cut point for a prespecified biomarker of the sensitive sub population . The performance of the biomarker-adaptive design , relative to a traditional design that ignores the biomarker , was evaluated in a simulation study . The biomarker-adaptive design was also used to analyze data from a prostate cancer trial . RESULTS In the simulation study , the biomarker-adaptive design preserved the power to detect the overall effect when the new treatment is broadly effective . When the proportion of sensitive patients as identified by the biomarker is low , the proposed design provided a substantial improvement in efficiency compared with the traditional trial design . Recommendations for sample size planning and implementation of the biomarker-adaptive design are provided . CONCLUSIONS A statistically valid test for a biomarker-defined subset effect can be prospect ively incorporated into a r and omized phase III design without compromising the ability to detect an overall effect if the intervention is beneficial in a broad population BACKGROUND Developments in biotechnology have stimulated the use of predictive biomarkers to identify patients who are likely to benefit from a targeted therapy . Several r and omized phase III design s have been introduced for development of a targeted therapy using a diagnostic test . Most such design s require biomarkers measured before treatment . In many cases , it has been very difficult to identify such biomarkers . Promising c and i date biomarkers can sometimes be effectively measured after a short run-in period on the new treatment . METHODS We introduce a new design for phase III trials with a c and i date predictive pharmacodynamic biomarker measured after a short run-in period . Depending on the therapy and the biomarker performance , the trial would either r and omize all patients but perform a separate analysis on the biomarker-positive patients or only r and omize marker-positive patients after the run-in period . We evaluate the proposed 
    Output:
    
The authors conclude that the proposed approaches may readily help research ers to design biomarker trials , but novel approaches are still needed