nesilin / evolution_TALL_adults

The evolution of relapse of adult T-cell acute lymphoblastic leukemia

CODE OF THE ANALYSIS PERFORMED IN THE T-ALL RELAPSE EVOLUTION IN ADULT PATIENTS PROJECT

The code is organized in the following folders:

filters/
	Here are the scripts that are used at the beginning to process the VCF to a MAF 
modules/
	Here are some functions and data (e.g. such as lists and dictionaries of the colors used) recurrently used along the analysis that can be imported. 
	Must be added to you $PYTHONPATH
processing/
	Here are some jupyter-notebooks that were used in some intermediate step between the filtered MAFs and the final results as a figures
notebook_figures/
	Here are jupyter-notebooks generating the figures of the paper
mut_rate_models/
	Here is the code used for the simulations and figures of the mutation rate increment models
ext_runs/
	Here are the scripts showing how external computational tools and software were run

Extra folders and files:

ext_files/
	Files that are necessary to run certain part of the analysis such as files required to run external tools
intermediate_files/
	Files generated at some point of the analysis that are the result of some intermediate 

Workspace

All patients had al least a tumoral and normal sample and, in some cases, two tumoral (primary and relapse) samples and a normal sample. Therefore, the data was store as:

cohort/
	patientID/
		DxTumorID_vs_normalID/
		ReTumorID_vs_normalID/ (sometimes)

This working directory structure was used in the code

STEPS TO REPRODUCE THE ANALYSIS

The following intructions are in the right order to obtain the same results

ALIGNMENT AND CALLING

1. Sarek pipeline v2.2.1 (intruccions to install and run --> https://github.com/nf-core/sarek)

   Run preprocessing step and Strelka in variantcalling step

2. FACETS (v0.5.6)

   ext_runs/run_FACETS/facets_cnv_wgs_hg19.sh

   the README file within the run_FACETS/ directory shows how to install it

3. DELLY (v.0.7.9)

   ext_runs/run_delly/run_delly.sh

   there is a yml file to install an enviroment with delly for conda

FILTER FROM STRELKA VCFs

The following list of scripts from filters/ shows the order in which the scripts were executed to obtain the MAF files

1. filters/process_strelka_v2.9.3_vcf.py

2. filters/refish_shared.py

3. filters/check_for_missed_MNVs_muts.py

4. ext_runs/run_vep/run_vep_cannonical.sh

5. filters/process_vep.py

6. filters/filter_snps_maf.py

7. filters/clonal_classification_maf.py

Within ext_runs/run_vep there is a README file with instructions on how to install vep.

The python scripts can be run within a conda environment of python 3.7. There is a yml file to build de environment (environment.yml)

conda env create -f environment.yml

PROCESSING

1. Run IntOGen v20191009

   In the FAQ section of IntOGen are the instructions to install it and run it locally https://www.intogen.org/faq

2. Processing of IntOGen results. Inspect list of driver genes and filter out possible FP

   processing/drivers_intogen.ipynb

3. Get a list of protein affecting mutations (SNVs and InDels) in driver genes

    processing/
    	driver_mutations_primary_ALL.ipynb
    	driver_mutations_TALL.ipynb	
   

4. Processing FACETS results, get private and shared copy number changes

   processing/cnv_overlapping_segments.ipynb

5. Annotate CNV. Add cytobands and check for driver gains and losses

    processing/
    	annotate_cnv_TALL.ipynb
    	driver_cnv_TALL.ipynb
   

6. Process SV variants and get the driver known ones

    processing/
    	SV_parser.ipynb
    	driver_SV_TALL.ipynb	
   

7. To get exons of NOTCH1 the mutations within this gene were re-annotated with VEP

   processing/re-annotate_NOTCH1_muts.ipynb

8. Run deconstructSigs to fit known signatures

   Inside the folder ext_runs/run_deconstructSig there is a README.txt file on how it was run

FIGURES AND RESULTS

The jupyter-notebooks within notebook_figures/ generate the figures of the paper separately.

The final figures were layed out with SVG editable software.

Figure 1

notebook_figures/
	number_mutations_cohort.ipynb
	UMAP_mutational_profile.ipynb
	signature_barplots_by_cohort.ipynb
	clustering_driver_combinations.ipynb

Figure 2

notebook_figures/
	table_driver_alterations_TALL.ipynb (also contains supplementary figure)
	NOTCH1_needle_plot.ipynb
	NOTCH1_pathway_change.ipynb

Figure 3

notebook_figures/
	counts_with_probability_exposure_assignments_phylotree.ipynb
	signature_barplots_evolution_part.ipynb

(signature_barplots_evolution_part.ipynb also contains supplementary figures)

Figure 4

notebook_figures/mut_rate_test_&_plot.ipynb

(also contains supplementary figures)

(missing figures are generated with R code in mut_rate_models/mutation_models.R)

Figure 5

notebook_figures/relapse_evolution_with_doubling_estimates.ipynb

(missing figures are generated with R code in mut_rate_models/relapse_fixation.R)

Additional files with supplementary figures:

Additional 3

notebook_figures/
	barplot_recalling_comparative.ipynb
	barplot_snps_filter.ipynb
	ccf_clonality_scatterplots.ipynb

Additional 1

notebook_figures/
	table_driver_mutations_primary_ALL.ipynb
	clinical_plot.ipynb
	plot_total_minor_copy_number.ipynb
	relapse_growth_model.ipynb

MUTATION RATE MODELS

The code in this section is written in R and can be found in mut_rate_models/mutation_models.R.

The Rscript performs the simulations and the figures with the summary of the results

TUMOR GROWTH SIMULATIONS

The simulations of the tumor growth were performed with Clonex: https://github.com/gerstung-lab/clonex.

The Rscript in mut_rate_models/relapse_fixation.R takes the results of Clonex and outputs the figures of the paper