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Last data update: 2014.03.03

R: Function to compute the IHC4 prognostic score as published by...

ihc4	R Documentation

Function to compute the IHC4 prognostic score as published by Paik et al. in 2004.

Description

This function computes the prognostic score based on four measured IHC markers (ER, PGR, HER2, Ki-67), following the algorithm as published by Cuzick et al. 2011. The user has the option to either obtain just the shrinkage-adjusted IHC4 score (IHC4) or the overall score htat also combines the clinical score (IHC4+C)

Usage

ihc4(ER, PGR, HER2, Ki67,age,size,grade,node,ana,scoreWithClinical=FALSE, na.rm = FALSE)

Arguments

`ER`	ER score between 0-10, calculated as (H-score/30)
`PGR`	Progesterone Receptor score between 0-10
`HER2`	Her2/neu status (0 or 1).
`Ki67`	Ki67 score based on percentage of positively staining malignant cells
`age`	patient age
`size`	tumor size in cm.
`grade`	Histological grade, i.e. low (1), intermediate (2) and high (3) grade.
`node`	Nodal status.
`ana`	treatment with anastrozole
`scoreWithClinical`	`TRUE` to get IHC4+C score, `FALSE` to get just the IHC4 score.
`na.rm`	`TRUE` if missing values should be removed, `FALSE` otherwise.

Value

Shrinkage-adjusted IHC4 score or the Overall Prognostic Score based on IHC4+C (IHC4+Clinical Score)

Author(s)

Deena M.A. Gendoo

References

Jack Cuzick, Mitch Dowsett, Silvia Pineda, Christopher Wale, Janine Salter, Emma Quinn, Lila Zabaglo, Elizabeth Mallon, Andrew R. Green, Ian O. Ellis, Anthony Howell, Aman U. Buzdar, and John F. Forbes (2011) "Prognostic Value of a Combined Estrogen Receptor, Progesterone Receptor, Ki-67, and Human Epidermal Growth Factor Receptor 2 Immunohistochemical Score and Comparison with the Genomic Health Recurrence Score in Early Breast Cancer", Journal of Clinical Oncologoy, 29(32):4273–4278.

Examples

## load NKI dataset
data(nkis)
## compute shrinkage-adjusted IHC4 score
count<-nrow(demo.nkis)
ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
scoreWithClinical=FALSE, na.rm=TRUE)

## compute IHC4+C score
ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
age=demo.nkis[,"age"],size=demo.nkis[ ,"size"],grade=demo.nkis[ ,"grade"],node=demo.nkis[ ,"node"],
ana=sample(x=0:1,size=count,replace=TRUE), scoreWithClinical=TRUE, na.rm=TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(genefu)
Loading required package: survcomp
Loading required package: survival
Loading required package: prodlim
Loading required package: mclust
Package 'mclust' version 5.2
Type 'citation("mclust")' for citing this R package in publications.
Loading required package: limma
Loading required package: biomaRt
Loading required package: iC10
Loading required package: pamr
Loading required package: cluster
Loading required package: iC10TrainingData
Loading required package: AIMS
Loading required package: e1071
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:limma':

    plotMA

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/genefu/ihc4.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ihc4
> ### Title: Function to compute the IHC4 prognostic score as published by
> ###   Paik et al. in 2004.
> ### Aliases: ihc4
> ### Keywords: prognosis
> 
> ### ** Examples
> 
> ## load NKI dataset
> data(nkis)
> ## compute shrinkage-adjusted IHC4 score
> count<-nrow(demo.nkis)
> ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
+ HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
+ scoreWithClinical=FALSE, na.rm=TRUE)
  PATIENT.1   PATIENT.2   PATIENT.3   PATIENT.4   PATIENT.5   PATIENT.6 
  64.039159   76.308614  142.204914  126.748433   44.282348   -6.445028 
  PATIENT.7   PATIENT.8   PATIENT.9  PATIENT.10  PATIENT.11  PATIENT.12 
  45.617602   65.831042   81.279802  126.748433   88.705701  -53.174536 
 PATIENT.13  PATIENT.14  PATIENT.15  PATIENT.16  PATIENT.17  PATIENT.18 
  41.904837   17.319540   12.993325   30.734913  140.270125  130.292267 
 PATIENT.19  PATIENT.20  PATIENT.21  PATIENT.22  PATIENT.23  PATIENT.24 
  47.598424   62.059818  118.304675   30.431965  124.901738  111.432684 
 PATIENT.25  PATIENT.26  PATIENT.27  PATIENT.28  PATIENT.29  PATIENT.30 
  71.636987   45.249209   55.223982  181.552908   90.967749   12.988442 
 PATIENT.31  PATIENT.32  PATIENT.33  PATIENT.34  PATIENT.35  PATIENT.36 
   8.488841   87.566040   55.664980  153.489604  109.852131   74.900138 
 PATIENT.37  PATIENT.38  PATIENT.39  PATIENT.40  PATIENT.41  PATIENT.42 
 129.057004   62.171357  114.408056  146.296467  -20.402258  153.374636 
 PATIENT.43  PATIENT.44  PATIENT.45  PATIENT.46  PATIENT.47  PATIENT.48 
  89.758209    6.297064   94.997255   26.034938    1.184557  126.202125 
 PATIENT.49  PATIENT.50  PATIENT.51  PATIENT.52  PATIENT.53  PATIENT.54 
  74.132709   70.752933  150.756972  -13.236041   97.933614   -8.130264 
 PATIENT.55  PATIENT.56  PATIENT.57  PATIENT.58  PATIENT.59  PATIENT.60 
  80.616902  104.418424   84.766405  131.287465   86.721730   -1.751154 
 PATIENT.61  PATIENT.62  PATIENT.63  PATIENT.64  PATIENT.65  PATIENT.66 
 146.820987   20.970861  103.115605  112.821166    7.426433    8.296315 
 PATIENT.67  PATIENT.68  PATIENT.69  PATIENT.70  PATIENT.71  PATIENT.72 
  67.380893   84.325978   83.477085   20.131736  100.960656   11.793658 
 PATIENT.73  PATIENT.74  PATIENT.75  PATIENT.76  PATIENT.77  PATIENT.78 
  53.000540   77.332648  112.633412   44.938955  117.831175  120.324336 
 PATIENT.79  PATIENT.80  PATIENT.81  PATIENT.82  PATIENT.83  PATIENT.84 
  81.754699  110.487823   89.818578   85.684404   60.338442   51.222140 
 PATIENT.85  PATIENT.86  PATIENT.87  PATIENT.88  PATIENT.89  PATIENT.90 
  49.727405   43.041237  117.831175  -31.099709    5.118285    3.996825 
 PATIENT.91  PATIENT.92  PATIENT.93  PATIENT.94  PATIENT.95  PATIENT.96 
  44.345344  -17.837867   88.365394   78.993682   88.071131   82.431161 
 PATIENT.97  PATIENT.98  PATIENT.99 PATIENT.100 PATIENT.101 PATIENT.102 
 109.414487   44.224829  -24.694741   99.765485  157.422718  -17.369051 
PATIENT.103 PATIENT.104 PATIENT.105 PATIENT.106 PATIENT.107 PATIENT.108 
 105.262855   53.344699   76.694305   24.196646   76.569755  -36.309051 
PATIENT.109 PATIENT.110 PATIENT.111 PATIENT.112 PATIENT.113 PATIENT.114 
 155.984055  -62.171036   59.770561   18.905514  146.039433  117.272068 
PATIENT.115 PATIENT.116 PATIENT.117 PATIENT.118 PATIENT.119 PATIENT.120 
  62.088933   11.270771  121.837142   19.651057   60.211809  129.077571 
PATIENT.121 PATIENT.122 PATIENT.123 PATIENT.124 PATIENT.125 PATIENT.126 
  67.597180   17.940532   57.402014   64.546449  115.842475   86.949142 
PATIENT.127 PATIENT.128 PATIENT.129 PATIENT.130 PATIENT.131 PATIENT.132 
  72.591152   64.219608  108.954193   54.134133   68.140252  126.080812 
PATIENT.133 PATIENT.134 PATIENT.135 PATIENT.136 PATIENT.137 PATIENT.138 
  73.574338   56.335940  145.893336   54.437467   19.281515   81.939875 
PATIENT.139 PATIENT.140 PATIENT.141 PATIENT.142 PATIENT.143 PATIENT.144 
  99.056129  -28.220671   74.453625   42.928822  159.811055  106.876936 
PATIENT.145 PATIENT.146 PATIENT.147 PATIENT.148 PATIENT.149 PATIENT.150 
  60.830866   45.591530   -8.954339   77.775902  170.205622   38.188466 
> 
> ## compute IHC4+C score
> ihc4(ER=sample(x=1:10, size=count,replace=TRUE),PGR=sample(x=1:10, size=count,replace=TRUE),
+ HER2=sample(x=0:1,size=count,replace=TRUE),Ki67=sample(x=1:100, size=count,replace=TRUE),
+ age=demo.nkis[,"age"],size=demo.nkis[ ,"size"],grade=demo.nkis[ ,"grade"],node=demo.nkis[ ,"node"],
+ ana=sample(x=0:1,size=count,replace=TRUE), scoreWithClinical=TRUE, na.rm=TRUE)
  PATIENT.1   PATIENT.2   PATIENT.3   PATIENT.4   PATIENT.5   PATIENT.6 
 92.2572249 -45.2197362  80.3567552  48.5915557  59.3907138  -2.4676088 
  PATIENT.7   PATIENT.8   PATIENT.9  PATIENT.10  PATIENT.11  PATIENT.12 
129.2898749  96.7936944  46.9194777 148.0034047 100.3370692  55.8875419 
 PATIENT.13  PATIENT.14  PATIENT.15  PATIENT.16  PATIENT.17  PATIENT.18 
 49.9560928  28.3283077  85.0959363 115.1270148  41.3089113  45.9855162 
 PATIENT.19  PATIENT.20  PATIENT.21  PATIENT.22  PATIENT.23  PATIENT.24 
 64.6800293  74.1327087  73.5859882  98.5011549 107.7071647  -2.5866940 
 PATIENT.25  PATIENT.26  PATIENT.27  PATIENT.28  PATIENT.29  PATIENT.30 
 44.8265676  -2.5941524 122.8109671  70.7079249  78.4597566 132.2904912 
 PATIENT.31  PATIENT.32  PATIENT.33  PATIENT.34  PATIENT.35  PATIENT.36 
 65.7783912  87.7079148 161.6199249 109.5196310 -13.2593765  71.3962382 
 PATIENT.37  PATIENT.38  PATIENT.39  PATIENT.40  PATIENT.41  PATIENT.42 
 83.3962144  81.0786041  35.7173041  26.2439335  81.9398749  34.7234404 
 PATIENT.43  PATIENT.44  PATIENT.45  PATIENT.46  PATIENT.47  PATIENT.48 
131.9524620  29.4687697  31.8590334 -24.6562670  97.4984553  10.6396647 
 PATIENT.49  PATIENT.50  PATIENT.51  PATIENT.52  PATIENT.53  PATIENT.54 
 83.1114591  64.1583310  77.0848275 109.3923138 117.5939297 135.1986249 
 PATIENT.55  PATIENT.56  PATIENT.57  PATIENT.58  PATIENT.59  PATIENT.60 
-13.2894596  69.3349419  47.1018869  39.9968676   9.2960041 111.6608786 
 PATIENT.61  PATIENT.62  PATIENT.63  PATIENT.64  PATIENT.65  PATIENT.66 
 -7.1728341  66.1527133 163.3318080 -31.1094362   1.1607659  37.8455144 
 PATIENT.67  PATIENT.68  PATIENT.69  PATIENT.70  PATIENT.71  PATIENT.72 
118.6042334  13.6408882   0.8622049 167.4421967  80.8587675  37.7149659 
 PATIENT.73  PATIENT.74  PATIENT.75  PATIENT.76  PATIENT.77  PATIENT.78 
152.9011363  61.7592612  22.0894659 154.0286310  74.7285944  82.4941557 
 PATIENT.79  PATIENT.80  PATIENT.81  PATIENT.82  PATIENT.83  PATIENT.84 
 84.9867300 147.7749777   2.7660113  87.5660404  13.5123060 144.5631520 
 PATIENT.85  PATIENT.86  PATIENT.87  PATIENT.88  PATIENT.89  PATIENT.90 
 64.9216553 110.9924882 -24.2746596  69.3715552 104.6449707 175.2510162 
 PATIENT.91  PATIENT.92  PATIENT.93  PATIENT.94  PATIENT.95  PATIENT.96 
180.2831080   8.6217882 139.7942697 138.7640992  12.0373557 113.7844692 
 PATIENT.97  PATIENT.98  PATIENT.99 PATIENT.100 PATIENT.101 PATIENT.102 
 95.3872749 109.5309412 105.8056442  55.9962992  88.1787077  42.7613705 
PATIENT.103 PATIENT.104 PATIENT.105 PATIENT.106 PATIENT.107 PATIENT.108 
  4.4499275  81.8012140  44.4572777 168.7403675 -18.1675388  55.0377697 
PATIENT.109 PATIENT.110 PATIENT.111 PATIENT.112 PATIENT.113 PATIENT.114 
 31.9333249  67.8581419  22.8829144  94.5587566  62.9410144 119.3810237 
PATIENT.115 PATIENT.116 PATIENT.117 PATIENT.118 PATIENT.119 PATIENT.120 
130.2922671 160.9434464  83.4445138   3.5468777  47.8389041 139.7910138 
PATIENT.121 PATIENT.122 PATIENT.123 PATIENT.124 PATIENT.125 PATIENT.126 
 38.9355138  74.3892692   6.8833060  22.1088866 144.3077675 -28.6003725 
PATIENT.127 PATIENT.128 PATIENT.129 PATIENT.130 PATIENT.131 PATIENT.132 
 85.2634663 100.0627591 130.4639241  45.7031412 101.6482119  42.6947671 
PATIENT.133 PATIENT.134 PATIENT.135 PATIENT.136 PATIENT.137 PATIENT.138 
 50.0348647 115.5653182  97.6854692  78.2791993 -15.6264725  49.9629162 
PATIENT.139 PATIENT.140 PATIENT.141 PATIENT.142 PATIENT.143 PATIENT.144 
101.4927080  93.2417993  74.2166148 119.2794553  96.6982786  49.2088591 
PATIENT.145 PATIENT.146 PATIENT.147 PATIENT.148 PATIENT.149 PATIENT.150 
 48.4062419  62.1827591  50.8216291  14.3269024  53.8523657  80.4424614 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>