PredictioR Nextflow Pipeline

Overview

The PredictioR Nextflow pipeline analyzes immunotherapy response data to identify biomarkers across multiple cancer types. The workflow is implemented in Nextflow for scalable workflow management and executed using Docker to ensure reproducible and portable analyses.

Input datasets are provided as Bioconductor SummarizedExperiment objects, enabling standardized handling of gene expression data, clinical annotations, and immunotherapy outcome variables.

The main workflow (main.nf) consists of three sequential analysis stages:

• Gene-level analysis
• Signature-level analysis
• Meta-analysis

Quickstart

• Step 1: Install Nextflow and Docker
• Step 2: Prepare input data
• Step 3: Run the PredictioR pipeline
• Step 4: Review and interpret outputs

Step 1: Install Nextflow and Docker

Nextflow

• Version: 24.04.2
• Setup: https://www.nextflow.io/docs/latest/install.html
• Documentation: https://www.nextflow.io/docs/latest/index.html
• Training: https://training.nextflow.io

Docker

• Purpose: Ensures reproducible execution by containerizing the full runtime environment
• Install: https://docs.docker.com/get-docker/
• PredictioR Docker image: bhklab/nextflow-env
• Docker Hub: https://hub.docker.com/r/bhklab/nextflow-env

Pull the image:

docker pull bhklab/nextflow-env

Step 2: Prepare clinical input data

Project Structure

Before running the pipeline, the project directory should contain:

• main.nf: Main Nextflow workflow
• nextflow.config: Pipeline configuration
• ICB_data/: Gene-level input data
• SIG_data/: Signature-level input data
• output/ Pipeline results (created automatically)

Gene-level input (ICB_data/)

• Contains Bioconductor SummarizedExperiment .rda files

• These objects enable standardized handling of:

  • Gene expression data
  • Clinical annotations
  • Immunotherapy outcome variables

Example input files:

• ICB_small_Hugo.rda
• ICB_small_Mariathasan.rda

Example datasets directory: https://github.com/bhklab/PredictioR/tree/main/data

SummarizedExperiment documentation: https://bioconductor.org/packages/devel/bioc/vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html

Signature-level input (SIG_data/)

• Contains .rda files storing a data frame named sig

Example signature files:

• CYT_Rooney.rda
• EMT_Thompson.rda
• PredictIO_Bareche.rda

Typical columns in sig:

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

Signature definitions are sourced from: https://github.com/bhklab/SignatureSets

Full signature metadata (50+ signatures) is available at: https://github.com/bhklab/SignatureSets/tree/main/data-raw

Please follow the same format for consistency.

Step 3: Run the PredictioR pipeline

Run the pipeline from the project root:

nextflow run main.nf -profile docker

Step 4: Review and interpret outputs

All results are written to:

• ./output/main_output/

Outputs are:

• Organized by study ID
• Stratified by analysis stage (gene-level, signature-level, meta-analysis)

Reference Resources

• GitHub repository: https://github.com/bhklab/PredictioR
• Associated publication: https://pubmed.ncbi.nlm.nih.gov/36055464/

Data Directory Configuration

Gene-level Analysis

• Input Data Directory: params.gene_data_dir = './ICB_data'
• Output Data Directory: params.out_dir = './output/main_output'
• Output Details: Results are stored in main_output, stratified by study ID.

Signature-level Analysis

• Input Data Directory: params.signature_data_dir = './SIG_data'
• Output Data Directory: params.out_dir = './output/main_output'
• Output Details: Results are stored in main_output, stratified by study ID.

Meta-analysis

• The meta-analysis step uses 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 are properly organized with all required and additional fields to maintain 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 (e.g., 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

• Sex
• Age
• Histology (histo)
• Cancer stage
• DNA and RNA metadata

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 (must match names in ./SIG_data)
• method: Method used for signature score calculation
• score function: Function used in the R script for scoring

For detailed information on the signatures used in the pipeline, refer to the signature information CSV (50+ signatures) available at: https://github.com/bhklab/SignatureSets/tree/main/data-raw

Additional Notes

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