# Using R: Breast Cancer

## Introduction

Breast cancer is a malignant condition originating from the proliferation of abnormal breast cells. It ranks as the second most prevalent cancer among women on a global scale, and it can also affect men. In recent times, there has been a notable surge in the utilization of computational methodologies and tools within the field of cancer research. Among these, the programming language R has gained substantial prominence due to its robust capacities for statistical computing and graphical representation. Below are several applications highlighting the utility of R in the realm of breast cancer informatics:

**1. Data Preprocessing:** R serves as a valuable tool for conducting data preprocessing, a fundamental phase within the realm of cancer informatics.
This critical step encompasses the cleansing, transformation, and organization of data prior to analysis.
In R, packages from the **tidyverse** can be used for data cleaning, transformation and manipulation.

**2. Survival Analysis:** Survival analysis is a statistical method used to analyse the time it takes for an event to occur, such as death or disease progression.
R has several packages that can be used for survival analysis in breast cancer, including **survival** or **survminer**.

**3. Gene Expression Analysis:** Gene expression analysis can help identify genes that are overexpressed or underexpressed in breast cancer.
R has several packages, including **limma** and **DESeq2**, which can be used for differential gene expression analysis.
These packages can help identify genes that are differentially expressed between different subtypes of breast cancer.

**4. Machine Learning:** Machine learning algorithms can be used to identify patterns in breast cancer data that can help predict patient outcomes.
R has several packages for machine learning, including **caret** or **randomForest**.
These packages can be used to develop predictive models based on patient data, including gene expression data, clinical data, and imaging data.

**5. Data Visualization:** R is an excellent tool for data visualization, which is essential in cancer informatics.
R has several packages, including **ggplot2** and **lattice**, that can be used to create informative and visually appealing plots and graphs.
These packages can be used to create plots of gene expression data, survival curves, and other types of data relevant to breast cancer.

In the following, individual methods mentioned above are explained using breast cancer as an example. Afterwards, you should be able to perform individual analyses using R and apply them to a medical problem.

## Data preprocessing

Here's an example of how to preprocess the Breast Cancer dataset **gbsg: Breast cancer data sets used in Royston and Altman**, which is included in the survival package in R:
To generate the dataset you can use the following code.
Further information regarding abbreviations can be found in the legend at the following link **gbsg: Breast cancer data sets used in Royston and Altman**.

`data(cancer, package="survival")`

gbsg

Next, we need to preprocess the data to make it suitable for survival analysis. In particular, we need to:

- Remove any missing or incomplete data.
- Convert the factor variables to numeric variables.
- Create a Surv object that specifies the survival time and censoring status.

The `na.omit`

R function removes all incomplete cases of a data object (typically of a data frame, matrix or vector)

`# remove any missing or incomplete data`

gbsg <- na.omit(gbsg)

# convert factor variables to numeric variables

gbsg$nodes <- ifelse(gbsg$nodes >= 1, 1, 0)

gbsg$er <- ifelse(gbsg$er >= 1 , 1, 0)

Our example converts the "nodes" column into either 1 or 0 values depending on if the patient has one or more positive lymph nodes. Similarly, we convert the estrogen receptor (ER) status into 1 or 0 depending on if the ER status is above zero.

`ifelse`

is particularly useful when you want to transform a categorical variable.
For example, if you have a factor variable called gender with levels "male" and "female", you can use ifelse to create a new variable called gender_num that takes the value 1 for "male" and 0 for "female":

Next, we can subset the data to include only the variables of interest. For example, we might want to include only the age, tumour size, and nodal status variables:

`# subset the data`

gbsg.subset <- gbsg[, c("age", "size", "nodes")]

Now the dataset **gbsg.subset** only contains the following three columns and could look like this:

Finally, we might want to normalize the data to remove any systematic biases or differences in scale between the variables. One common way to do this is to perform z-score normalization:

Z-score normalization (also known as standardization) is a data preprocessing technique that rescales the values in a numerical variable so that they have a mean of zero and a standard deviation of one. The rescaled values are known as z-scores.

`# z-score normalization`

gbsg.norm <- scale(gbsg.subset)

The gbsg.norm object now contains the normalized variables, which can be used for further analysis.

Note that these preprocessing steps are just a few examples of the many ways to preprocess data for survival analysis. The specific steps will depend on the nature of the data and the research question.

## Survival Analysis

In our example we will utilize the `survival`

and `tidyverse`

package:

`install.packages("survival")`

library(survival)

install.packages("tidyverse")

library(tidyverse)

Next on, you can load the dataset that you wish to analyse.
In this course, we will be using the **rotterdam: Breast cancer data set used in Royston and Altman**.
It is included in the `survival`

package and may be loaded using the `data()`

function:

`data(cancer, package="survival")`

rotterdam

First we create a survival object using the Surv function from the survival package. The first argument should be the time-to-event variable (in this case, days to death or last follow-up) and the second argument should be the event variable (in this case, whether the patient died or not).

In the data we used this should be the following two columns:

- dtime: days to death or last follow-up
- death: 0= alive, 1= dead

`# This will incloud our two variables: dtime and the event that the patient died (1)`

surv_object <- with(rotterdam, Surv(dtime, death))

Next we create a Kaplan-Meier survival curve using the `survfit`

function from the survival package and visualize it using the `plot`

function

`# Create one survival analysis using surv_object ~ 1`

surv_prob <- survfit(surv_object ~ 1, data = rotterdam)

plot(surv_prob, xlab = "Time (Days)", ylab = "Survival Probability", main = "Kaplan-Meier Curve for Breast Cancer")

This should generate the following graphic:

As expected, the probability of survival decreases as the period of disease increases

In the following we use the Cox proportional hazards model to investigate the relationship between predictor variables and time-to-event outcomes.
In this case we use the survival object created above and add the predictor variables of interest.
For example **age, meno, size and nodes**.
Getting more detailed information on the abbreviations and all the information contained in the dataset, it is advisable to consult the above-mentioned website, including the legend.
For the sake of simplicity, only four variables are briefly explained below.

age: age at surgery

meno: menopausal status (0= premenopausal, 1= postmenopausal)

size: tumour size, a factor with levels <=20 20-50 >50

nodes: number of positive lymph nodes

`# Create a Cox proportional hazards model using the coxph function from the survival package.`

cox_model <- coxph(surv_object ~ age + meno + size + nodes, data = rotterdam)

Output:

coef exp(coef) se(coef) z Pr(>|z|)

age 0.010972 1.011032 0.003744 2.931 0.00338 **

meno 0.115715 1.122676 0.097337 1.189 0.23452

size20-50 0.477329 1.611764 0.065093 7.333 2.25e-13 ***

size>50 0.882872 2.417834 0.090818 9.721 < 2e-16 ***

nodes 0.074264 1.077091 0.004760 15.603 < 2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

**Hazard ratio:**It represents the relative hazard of the outcome in one group (e.g., treatment group) compared to another group (e.g., control group) with a different level of the predictor variable, while holding all other variables constant. For example, a hazard ratio of 1.5 for a treatment group compared to a control group would indicate that the hazard of the outcome is 50% higher in the treatment group compared to the control group, while holding all other variables constant. Hazard ratios are important for identifying which predictor variables are most strongly associated with the outcome and can help guide clinical decision-making and treatment strategies.

The amount of data may initially overwhelm you. The five lines are briefly explained below:

**First column "Coefficients":**provides the coefficients for each predictor variable in the model. These coefficients represent the estimated change in the hazard ratio for each unit increase in the predictor variable, holding all other variables constant.**Second column "exp(coef)":**provides the exponentiated coefficients (i.e., the hazard ratios) for each predictor variable. These hazard ratios represent the estimated change in the hazard of the outcome (in this case, death) for a one-unit increase in the predictor variable, holding all other variables constant.**Third column "se(coef)":**provides the standard errors of the coefficients.**Fourth column "z":**provides the z-statistics and associated p-values for each predictor variable. The z-statistic is the coefficient divided by its standard error, and the p-value represents the probability of observing a z-statistic as extreme as the one observed in the data, assuming the null hypothesis (i.e., no effect of the predictor variable on the outcome).**Fifth column "Pr(>|z|)":**provides the p-values for each predictor variable. These p-values are equivalent to the ones in the fourth column, but are presented in a more conventional format.

In conclusion, the hazard ratios mentioned above show that the size of the primary tumour has a decisive influence on the probability of survival. The presence of a tumour with size 20-50 is associated with a 1.6-fold increased probability of reduced survival and size larger than 50 even with a 2.4-fold increased probability of reduced survival. Also of interest: lymph node involvement has little effect on survival probability compared to the size of the primary tumour.

## Sources & Further Reading

Elsheakh DN, Mohamed RA, Fahmy OM, Ezzat K, Eldamak AR. Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors. Biosensors (Basel). 2023;13(1):87. Published 2023 Jan 4. doi:10.3390/bios13010087

Chiao JY, Chen KY, Liao KY, Hsieh PH, Zhang G, Huang TC. Detection and classification the breast tumors using mask R-CNN on sonograms. Medicine (Baltimore). 2019;98(19):e15200. doi:10.1097/MD.0000000000015200

Akselrod-Ballin A, Chorev M, Shoshan Y, et al. Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms. Radiology. 2019;292(2):331-342. doi:10.1148/radiol.2019182622

West M, Blanchette C, Dressman H, et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A. 2001;98(20):11462-11467. doi:10.1073/pnas.201162998

Finak G, Mayer B, Fulp W, et al. DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis. Gates Open Res. 2018;2:31. Published 2018 Jul 10. doi:10.12688/gatesopenres.12832.2

Bose M, Benada J, Thatte JV, et al. A catalog of curated breast cancer genes. Breast Cancer Res Treat. 2022;191(2):431-441. doi:10.1007/s10549-021-06441-y

Nagel A, Szade J, Iliszko M, et al. Clinical and Biological Significance of ESR1 Gene Alteration and Estrogen Receptors Isoforms Expression in Breast Cancer Patients. Int J Mol Sci. 2019;20(8):1881. Published 2019 Apr 16. doi:10.3390/ijms20081881