Netie

Inferring the evolution of neoantigen-T cell interactions in tumors

Introduction

The Bayesian Hierarchical Model named Neoantgien-T cell interaction estimation (Netie) is developed to investigate the neoantigens observed in the patient tumors to estimate the history of the impact of the host immune pressure on the evolution of the tumor clones. The estimation results will reveal whether the host immune system has been conferring strong or weaker selection pressure on the tumor clones over the time of tumor development. This may give us a peak into the future of how the mutations and clones will evolve for that patient. The model is based on pyclone/sciclone/phylowgs estimation results.

Please refer to our lab’s website for more bioinformatics software https://qbrc.swmed.edu/labs/wanglab/software.php.

Installation of the netie package:

To install our package, you may simply execute the following codes.

# install.packages("devtools") 

devtools::install_github("tianshilu/netie", subdir = "netie") # don't forget to specify subdir!

Dependencies

PyClone (or PhyloWGS or SciClone); R(version>3.6)

Guided Tutorial

Command:

netie(input_data,sigma_square = 100000 ,
      alpha = 10,beta = 2,sigma_p_sqr = 0.1,sigma_a_sqr = NULL,max_iter =100000,
      cellular_clock='variant_allele_frequency',
      cellular_prevalence_min=0.02,
      keep_mutations_number=2,
      keep_neoantigen_encoding_mutations_number=1,
      multi_sample = T)

• input_data: a list with each element being a data frame corresponding to each patient.

  Each data frame consists of 7 columns and each row is for one mutation. The 7 columns are mutation ID, sample ID, cluster ID, cellular prevalence, variant allele prevalence, variant allele frequency, and neoantigen load with column names as “mutation_id”,“sample_id”,“cluster_id”,“cellular_prevalence”,“variant_allele_frequency”, and “neoantigen_load”.

  Please use PyClone or other similar software (https://github.com/tianshilu/Phylogenetic-Tree) to get information of cluster id and cellular prevalence (we recommend keeping mutations with sequencing depth more than 50 for clustering and netie inference). Please arrange the output of the mutation clustering software as a table in plain text format with 6 columns: mutation_id, sample_id, cluster_id, cellular_prevalence, variant_allele_frequency, neo_load (please see an example of the input of netie below).

  Please use the QBRC mutation calling pipeline (https://github.com/tianshilu/QBRC-Somatic-Pipeline) to call mutations from the whole exome sequenicng data; and the QBRC neoantigen calling pipeline (https://github.com/tianshilu/QBRC-Neoantigen-Pipeline) to call neoantigens from the whole exome sequencing and RNA sequencing data. Output from other somatic mutation/neoantigen calling pipelines can also be organized into the input of netie

examples of input_data:

example_input1= read.table('example_input1.txt', header=T,sep='\t',stringsAsFactors = F)
head(example_input1)

##                   mutation_id       sample_id cluster_id cellular_prevalence
## 1 TCGA-D3-A2JG-06 7 142881273 TCGA-D3-A2JG-06          0           0.3670081
## 2  TCGA-D3-A2JG-06 5 71016376 TCGA-D3-A2JG-06          0           0.3893199
## 3 TCGA-D3-A2JG-06 11 74015355 TCGA-D3-A2JG-06          0           0.3963911
## 4 TCGA-D3-A2JG-06 12 95914926 TCGA-D3-A2JG-06          0           0.3966120
## 5 TCGA-D3-A2JG-06 7 140453136 TCGA-D3-A2JG-06          0           0.3990956
## 6  TCGA-D3-A2JG-06 1 47746773 TCGA-D3-A2JG-06          0           0.3993479
##   cellular_prevalence_std variant_allele_frequency neo_load
## 1              0.07829626                0.1771429        0
## 2              0.09846122                0.2000000        0
## 3              0.11910942                0.2864583        0
## 4              0.12477982                0.3121387        5
## 5              0.12635598                0.3260870        1
## 6              0.11804737                0.2951807        0

library(ggplot2)

ggplot(example_input1,aes(variant_allele_frequency,neo_load))+ geom_point(colour = "seagreen3", size = 3) +labs(x = "Variant Allele Frequency",y="#Neoantigen per mutation")

example_input2= read.table('example_input2.txt', header=T,sep='\t',stringsAsFactors = F)
head(example_input2)

##                         mutation_id       sample_id cluster_id
## 1 TCGA-BF-A3DN-01 GL000205.1 117561 TCGA-BF-A3DN-01          0
## 2       TCGA-BF-A3DN-01 1 153270485 TCGA-BF-A3DN-01          0
## 3         TCGA-BF-A3DN-01 2 1926514 TCGA-BF-A3DN-01          0
## 4        TCGA-BF-A3DN-01 4 47663875 TCGA-BF-A3DN-01          0
## 5       TCGA-BF-A3DN-01 17 11840675 TCGA-BF-A3DN-01          0
## 6        TCGA-BF-A3DN-01 7 93108821 TCGA-BF-A3DN-01          0
##   cellular_prevalence cellular_prevalence_std variant_allele_frequency neo_load
## 1           0.3441747              0.06237205                0.1290323        0
## 2           0.3450515              0.03324016                0.1538462        0
## 3           0.3470656              0.05007811                0.1372549        0
## 4           0.3529826              0.05362554                0.1600000        0
## 5           0.3537355              0.03360195                0.1655172        3
## 6           0.3588706              0.06354249                0.3085714        0

ggplot(example_input1,aes(variant_allele_frequency,neo_load))+ geom_point(colour = "blue", size = 3) +labs(x = "Variant Allele Frequency",y="#Neoantigen per mutation")

• sigma_square, alpha, beta, sigma_p_sqr, sigma_a_sqr: hyperparameters for prior distributions. Please refer to the paper for more details.

• max_iter: the maximum iterations of Markov chain Monte Carlo allowed.

• cellular_clock: choose to use cellular prevalence (CP) or variant allele frequency (VAF) as the indicator of developmental time

• cellular_prevalence_min: the minimal cutoff on the range of CP (or VAF) of the mutations found in a clone, for this clone to be considered in the model; the default is 0.02.

• keep_mutations_number: the minimum number of somatic mutations that a tumor clone must have, for it to be considered in the model.

• keep_neoantigen_encoding_mutations_number: the minimum number of neoantigen-encoding somatic mutations that a tumor clone must have, for it to be considered in the model.

• multi_sample: TRUE if the samples in the input data list are to be treated as the multi-sampling data from one single patient.

Two example input datasets can be found here: https://github.com/tianshilu/Netie/tree/main/example

Output

The output is a list with the data of the estimated anti-tumor selection pressure for each clone (ac) and for the whole tumor (a). The output list is consisting of two lists (all_parameters and final_parameters). all_parameters is the inferred variables ac and a for each MCMC iterations and final_parameters is the posterior means of the variables of the second half of MCMC iterations. ac indicates the inferred anti-tumor selection pressure for clone c. a indicates the inferred anti-tumor selection pressure for the whole tumor.

Two example output results can be found here: https://github.com/tianshilu/Netie/tree/main/example

load('example_output1.RData')
print(example_output$a) 

## [1] 1.577462

load('example_output2.RData')
print(example_output$a)

## [1] 3.885662