***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 Mutations affecting the KRAS gene are established predictive markers of outcome with anti-epithelial growth factor receptor ( EGFR ) antibodies in advanced colorectal cancer ( CRC ) . The relevance of these markers for anti-vascular endothelial growth factor ( VEGF ) therapy is controversial . This analysis was performed to assess the predictive and prognostic impact of KRAS and BRAF gene mutation status in patients receiving capecitabine with bevacizumab ( CG ) or capecitabine without bevacizumab in the phase III AGITG MAX ( Australasian Gastrointestinal Trials Group MAX ) study . PATIENTS AND METHODS Mutation status was determined for 315 ( 66.9 % ) of the original 471 patients . Mutation status was correlated with efficacy outcomes ( response rate , progression-free survival [ PFS ] , and overall survival [ OS ] ) , and a predictive analyses was undertaken . RESULTS Mutations in KRAS and BRAF genes were observed in 28.8 % and 10.6 % of patients , respectively . KRAS gene mutation status ( wild type [ WT ] v mutated [ MT ] ) had no prognostic impact for PFS ( hazard ratio [ HR ] , 0.89 ; CI , 0.69 to 1.14 ) or OS ( HR , 0.97 ; CI , 0.73 to 1.28 ) . BRAF mutation status ( WT v MT ) was not prognostic for PFS ( HR , 0.80 ; CI , 0.54 to 1.18 ) but was prognostic for OS ( HR , 0.49 ; CI , 0.33 to 0.73 ; P = .001 ) . By using the comparison of capecitabine versus capecitabine and bevacizumab ( CB ) and CB plus mitomycin ( CBM ) , KRAS gene mutation status was not predictive of the effectiveness of  bevacizumab for PFS or OS ( test for interaction P = .95 and 0.43 , respectively ) . Similarly , BRAF gene mutation status was not predictive of the effectiveness of bevacizumab for PFS or OS ( test for interaction P = .46 and 0.32 , respectively ) . CONCLUSION KRAS gene mutation status was neither prognostic for OS nor predictive of bevacizumab outcome in patients with advanced CRC . BRAF gene mutation status was prognostic for OS but was not predictive of outcome with bevacizumab PURPOSE Mutations within the KRAS proto-oncogene have predictive value but are of uncertain prognostic value in the treatment of advanced colorectal cancer . We took advantage of PETACC-3 , an adjuvant trial with 3,278 patients with stage II to III colon cancer , to evaluate the prognostic value of KRAS and BRAF tumor mutation status in this setting . PATIENTS AND METHODS  Formalin-fixed paraffin-embedded tissue blocks ( n = 1,564 ) were prospect ively collected and DNA was extracted from tissue sections from 1,404 cases . Planned analysis of KRAS exon 2 and BRAF exon 15 mutations was performed by allele-specific real-time polymerase chain reaction .  Survival analyses were based on univariate and multivariate proportional hazard regression models . RESULTS KRAS and BRAF tumor mutation rates were 37.0 % and 7.9 % , respectively , and were not significantly different according to tumor stage . In a multivariate analysis containing stage , tumor site , nodal status , sex , age , grade , and microsatellite instability ( MSI ) status , KRAS mutation was associated with grade ( P = .0016 ) , while BRAF mutation was significantly associated with female sex ( P = .017 ) , and highly significantly associated with right-sided tumors , older age , high grade , and MSI-high tumors ( all P < 10(-4 ) ) . In univariate and multivariate analysis , KRAS mutations did not have a major prognostic value regarding relapse-free survival ( RFS ) or overall survival ( OS ) . BRAF mutation was not prognostic for RFS , but was for OS , particularly in patients with MSI-low ( MSI-L ) and stable ( MSI-S ) tumors ( hazard ratio , 2.2 ; 95 % CI , 1.4 to 3.4 ; P = .0003 ) . CONCLUSION In stage II-III colon cancer , the KRAS mutation status does not have major prognostic value . BRAF is prognostic for OS in MS-L/S tumors Purpose : Alterations in the RAS-RAF-MAP2 K (MEK)-MAPK signaling pathway are major drivers in colorectal carcinogenesis . In colorectal cancer , BRAF mutation is associated with microsatellite instability ( MSI ) , and typically predicts inferior prognosis . We examined the effect of BRAF mutation on survival and treatment efficacy in patients with stage III colon cancer . Methods : We assessed status of BRAF c.1799T > A ( p . V600E ) mutation and MSI in 506 stage III colon cancer patients enrolled in a r and omized adjuvant chemotherapy trial [ 5-fluorouracil and leucovorin ( FU/LV ) vs. irinotecan ( CPT11 ) , FU and LV ( IFL ) ;  CALGB 89803 ] . Cox proportional hazards model was used to assess the prognostic role of BRAF mutation , adjusting for clinical features , adjuvant chemotherapy arm , and MSI status . Results : Compared with 431 BRAF wild-type patients , 75 BRAF-mutated patients experienced significantly worse overall survival [ OS ; log-rank P = 0.015 ; multivariate HR = 1.66 ; 95 % CI : 1.05–2.63 ] . By assessing combined status of BRAF and MSI , it seemed that BRAF-mutated MSS ( microsatellite stable ) tumor was an unfavorable subtype , whereas BRAF wild-type MSI-high tumor was a favorable subtype , and BRAF-mutated MSI-high tumor and BRAF wild-type MSS tumor were intermediate subtypes . Among patients with BRAF-mutated tumors , a nonsignificant trend toward improved OS was observed for IFL versus FU/LV arm ( multivariate HR = 0.52 ; 95 % CI : 0.25–1.10 ) . Among patients with BRAF wild-type cancer , IFL conferred no suggestion of benefit beyond FU/LV alone ( multivariate HR = 1.02 ; 95 % CI : 0.72–1.46 ) . Conclusions : BRAF mutation is associated with inferior survival in stage III colon cancer . Additional studies are necessary to assess whether there is any predictive role of BRAF mutation for irinotecan-based therapy . Clin Cancer Res ; 18(3 ) ; 890–900 . © 2011 AACR Purpose Mutations in KRAS and BRAF genes were associated with treatment failure from EGF receptor inhibitors  in colorectal cancer ( CRC ) patients . However , whether these mutations were associated with survival in patients not treated with EGF receptor inhibitors  remained controversial . Moreover , few data were available in Chinese . Therefore , we aim ed to evaluate the impact of KRAS and BRAF mutations on the survival of Chinese CRC patients . Methods CRC patients who underwent surgery between October 2003 and April 2006 were included in this study . They were observed until June 30 , 2010 or when death was ascertained by the National Death Registration System . Prospect ively collected fresh frozen tumor tissues were used for detection of mutations at codons 12 , 13 , and 61 of the KRAS gene and exons 11 and 15 of BRAF gene by PCR amplification followed by direct sequencing . Results Among the 314 patients ,  KRAS and BRAF mutations were detected in 65 ( 20.7 % ) and 12 ( 3.8 % ) patients , respectively . KRAS mutations appeared to occur more frequently in females than males ( p = 0.039 ) and in non-smokers ( p = 0.039 ) . KRAS and BRAF mutations were significantly associated with the proximal location of cancer ( p = 0.017 and 0.001 , respectively ) . In the univariate analysis , KRAS and BRAF mutations were not associated with survival . However , BRAF mutations were associated with a significantly worse overall survival ( hazard ratio = 3.91 , 95 % confidence interval 1.31 to 11.66 , p = 0.014 ) in the multivariate Cox proportional hazard model after adjustment for all direct clincopathological variables . Conclusion BRAF mutations , but not KRAS mutations , were associated with a worse outcome in Chinese CRC patients Networks of investigators have begun sharing best practice s , tools and methods for analysis of associations between genetic variation and common diseases . A Network of Investigator Networks has been set up to drive the process , sponsored by the Human Genome Epidemiology Network . A workshop is planned to develop consensus guidelines for reporting results of genetic association studies . Published literature data bases will be integrated , and unpublished data , including ' negative ' studies , will be captured by online journals and through investigator networks . Systematic review s will be exp and ed to include more meta-analyses of individual-level data and prospect i ve meta-analyses . Field synopses will offer regularly up date d overviews A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been 
    Output:
    
BRAF mutation appears to be associated with distinct , unfavourable clinicopathological characteristics in colorectal cancer