Spatial Proteomics Pipeline for Precision Oncology

A precision oncology centre required a sophisticated computational framework to transform complex spatial proteomics data into actionable clinical insights. Traditional bulk analysis methods failed to capture the spatial complexity of tumour micro-environments, limiting their clinical utility. The Akoya PhenoCycler IO60 panel provides 60-marker spatial resolution across tissue samples, but required robust computational infrastructure to extract meaningful clinical guidance. The challenge involved processing Akoya's evolving qptiff file format, implementing automated batch processing for clinical-scale throughput, and developing validated scoring algorithms that directly inform oncology treatment decisions.

• Develop complete upstream processing pipeline with qptiff to quantification capability

• Create eight clinically actionable scoring metrics for treatment guidance

• Achieve processing time under 2 hours for upstream components

• Implement automated batch processing system with parallel execution capability

• Validate scoring algorithms against known immunological states

• Enable processing capacity exceeding 50 samples weekly for clinical deployment

• Provide quantitative foundations for immunotherapy selection and combination strategies

1. Data Architecture Analysis

• Evaluated Akoya's multi-layer qptiff file format containing 60-marker phenocycler data, H&E staining, original photography, and slide barcoding

• Developed novel extraction methodology to isolate clinically relevant imaging components whilst preserving data integrity

• Integrated Bio-Formats software suite for qptiff to ome.tiff conversion

• Configured MCMICRO pipeline optimised for IO60 specifications

• Established validation framework using LAG-3 high and LAG-3 low tumour populations to compare immunosuppressive versus standard tumour micro-environments

2. Technical Implementation

• Implemented QuPath integration for format conversion with compression and validation protocols

• Configured MCMICRO Nextflow pipeline for image segmentation and quantification, bypassing pre-processed alignment steps

• Deployed Seurat as optimal spatial analysis platform following comprehensive evaluation

• Developed sophisticated tiling approach with user-adjustable parameters for different tissue architectures

• Constructed eight clinical scoring algorithms addressing immune activity, therapy targets, tumour growth, immune evasion, delivery barriers, treatment complexity, target accessibility, and microenvironment classification

• Integrated SLURM scheduler for parallel processing capability across multiple compute nodes

• Implemented automated quality control protocols identifying regions with compromised segmentation

3. Solution Delivery

• Created comprehensive upstream processing pipeline with automated error handling

• Developed five fully validated clinical scoring algorithms demonstrating discriminatory power

• Built automated marker positivity detection using False Discovery Rate statistics

• Established advanced visualisation capabilities with logarithmic scaling for clinical interpretation

• Deposited complete codebase on GitHub with comprehensive technical and user documentation

• Provided training materials and clinical deployment guidelines

• Dockerised complete pipeline and dependencies for seamless transfer

Quantitative Outcomes

• Achieved 1.5-hour upstream processing time, exceeding target of under 2 hours

• Delivered 3-4 hour complete pipeline processing time per sample containing approximately 216,000 cells

• Established 48+ samples daily processing capacity through parallel execution via SLURM

• Demonstrated system reliability exceeding 99% with less than 1% failure rate

• Validated five algorithms successfully discriminating between LAG-3 high (immunosuppressive) and LAG-3 low (standard tumour) conditions

Qualitative Benefits

• Transformed complex spatial proteomics data into quantitative clinical decision support metrics

• Enabled precision oncology treatment selection based on detailed tumour microenvironment characterisation

• Provided spatial analysis capabilities revealing cell-to-cell interactions invisible to bulk methods

• Established foundations for biomarker discovery and clinical trial stratification

• Delivered production-ready solution substantially exceeding initial performance specifications

"The spatial biology pipeline developed by Umbizo represents a significant advancement in our precision oncology capabilities. The system successfully transforms our 60-marker PhenoCycler data into actionable clinical insights that directly inform treatment decisions."

Client Clinical Team

• Upstream Pipeline Development and Bio-Formats Integration: 2 weeks

• MCMICRO Configuration and Optimisation: 1 week

• Downstream Analysis Framework and Seurat Implementation: 2 weeks

• Clinical Scoring Algorithm Development: 3 weeks

• Validation and Quality Assurance Testing: 2 weeks

• Documentation and Training Materials: 1 week

• Total Project Duration: 11 weeks

Future enhancements will focus on incorporating advanced sub-cellular segmentation capabilities as they become available in MCMICRO distributions, enabling full implementation of target accessibility scoring. Machine learning integration represents a significant opportunity, with the rich spatial data providing excellent foundations for predictive modelling and automated pattern recognition.