PredictioR Nextflow Pipeline

Overview

The PredictioR Nextflow pipeline is designed to analyze immunotherapy responses and identify biomarkers across various cancers. It utilizes Nextflow for workflow management and Docker for reproducibility, focusing on handling SummarizedExperiment objects for in-depth biomarker analysis.

The main.nf script integrates three key analysis steps:

1. Gene Level Analysis
2. Signature Level Analysis
3. Meta Analysis

Software Requirements and Installation Instructions

Nextflow

• Version: 24.04.2
• Installation and Resources:
  • Setup Instructions: For detailed installation steps, please refer to the Nextflow Setup Guide.
  • Documentation and Resources:
    • General Documentation
    • Nextflow Training

Docker

• Purpose: Ensures computational reproducibility by containerizing the environment.
• Installation Guide: Install Docker
• PredictioR Docker Image:

  docker pull bhklab/nextflow-env

  • Docker Hub: bhklab/nextflow-env

Reference Resources

• GitHub Repository: PredictioR GitHub Repository
• Key Publication: PubMed Reference, PMID: 36055464

Data Directory Configuration

Gene Level Analysis

• Input Data Directory:

  params.gene_data_dir = './ICB_data'

• Example Data Files: Includes files such as ICB_small_Hugo.rda, ICB_small_Mariathasan.rda, which are SummarizedExperiment objects. These files are located within the ICB_data directory at the bhklab PredictioR data repository.
  • Output Data Directory:

  params.out_dir = './output/main_output'

• Output Details: The results of the Gene Level Analysis are stored in the main_output directory, stratified by their study ID for clarity and ease of reference.

Signature Level Analysis

• Input Data Directory:

  params.signature_data_dir = './SIG_data'

• Example Data Files: Files like CYT_Rooney.rda, EMT_Thompson.rda, PredictIO_Bareche.rda are data frames with columns like:

  • signature_name: Name of the signature
  • gene_name: Name of the gene
  • weight: Weight assigned to each gene within the signature

  To see other columns, these files are also sourced from the bhklab SignatureSets GitHub repository. The .rda files are stored in the object sig as data frames. Please follow the same format for consistency.

• Output Data Directory:

  params.out_dir = './output/main_output'

• Output Details: The results of the Signature Level Analysis are stored in the main_output directory, stratified by their study ID for clarity and ease of reference.

Meta Analysis

• Input Data Directory:
  • The meta-analysis step uses the results from both gene-level and signature-level analyses.
  • Input Directories:
    • Gene level: ./output/main_output
    • Signature level: ./output/main_output
• Output Data Directory:

  params.out_dir = './output/main_output'

Input Data Specifications

• ICB Data Information

  This table summarizes each dataset by study and treatment type, along with cancer types, clinical and molecular data availability, and relevant PMID references. Required columns include 'treatment' and 'cancer type'.

  Dataset	Patients [#]	Cancer type	Treatment	Clinical endpoints	Molecular data	PMID
  ICB_small_Hugo	27	Melanoma	PD-1/PD-L1	OS	RNA	26997480
  ICB_small_Liu	121	Melanoma	PD-1/PD-L1	PFS/OS	RNA/DNA	31792460
  ICB_small_Miao	33	Kidney	PD-1/PD-L1	PFS/OS	RNA/DNA	29301960
  ICB_small_Nathanson	24	Melanoma	CTLA4	OS	RNA/DNA	27956380
  ICB_small_Padron	45	Pancreas	PD-1/PD-L1	PFS/OS	RNA	35662283
  ICB_small_Riaz	46	Melanoma	PD-1/PD-L1	OS	RNA/DNA	29033130
  ICB_small_Van_Allen	42	Melanoma	CTLA4	PFS/OS	RNA/DNA	26359337
  ICB_small_Mariathasan	195	Bladder	PD-1/PD-L1	OS	RNA/DNA	29443960

  Ensure that clinical data is properly organized with all required and additional fields to ensure the integrity of the analysis.

  • Required Columns:

    • patientid: Unique identifier for patients
    • treatmentid: Details of the treatment regimen
    • response: Patient response to treatment (Responder 'R', Non-responder 'NR')
    • tissueid: Standardized cancer type
    • survival_time_pfs: Time to progression-free survival, Example: 2.6 months
    • survival_time_os: Time to overall survival
    • survival_unit: Measurement units for survival times, typically months
    • event_occurred_pfs: Binary indicator of event occurrence during PFS (1,0)
    • event_occurred_os: Binary indicator of event occurrence during OS (1,0)

  • Additional Recommended Fields: Include sex, age, histo (histological type), stage of cancer, dna, and rna details among others as necessary.

• Signature Information

  This table summarizes each signature name by study and PMID references, the method for computing the signature score, and the corresponding score function.

  Signature	DNA/RNA	RNA Type	Method	Cancer Type	Score Function	PMID
  ADO_Sidders	RNA	Count RNA-seq/TPM	GSVA	Multiple	geneSigGSVA	31953314
  APM_Thompson	RNA	log CPM	GSVA	Lung, melanoma	geneSigGSVA	33028693
  APM_Wang	RNA	Microarray	GSVA	Multiple	geneSigGSVA	31767055
  Bcell_Budczies	RNA	Microarray	GSVA	Lung	geneSigGSVA	33520406
  Bcell_Helmink	RNA	log FPKM	GSVA	Melanoma, kidney	geneSigGSVA	31942075
  Blood_Friedlander	RNA	Microarray	GSVA	Melanoma	geneSigGSVA	28807052
  C-ECM_Chakravarthy	RNA	Normalized counts	ssGSEA	Multiple	geneSigssGSEA	30410077
  CCL5-CXCL9_Dangaj	RNA		GSVA	Multiple	geneSigGSVA	31185212
  CD39-CD8Tcell_Chow	RNA	RNA-seq count	GSVA	Lung	geneSigGSVA	36574773

  Required Columns:

  • signature: Name of the signature, same names located in './SIG_data'
  • method: Used for signature score calculation
  • score function: Specifying the function that should be used in the R script

  For detailed information on the signatures used in the pipeline, refer to the signature(there are more than 50) information CSV available at: Signature Information CSV.

Running the Pipeline

Run the pipeline with the configured parameters using Nextflow:

nextflow run main.nf

Additional Notes

• Necessary R packages and dependencies are installed as specified in load_libraries.R and included in the BHK Docker.
• Customize the nextflow.config file to specify any additional parameters or configurations required for your specific analysis needs