Tumor Profiler Center Analysis Pipeline
The TPC performs different types of measurements that collectively describe the precise genomic, biochemical, cellular and functional state of different cell types in the tumor and their response to known treatments.
For the Clinical Loop, the profiling technologies are selected based on their ability to provide part of a multi-level representation of the tumor or its microenvironment, as well as their potential to deliver robust and clinically relevant insights in short turnaround times.
Various deep profiling technologies are used in the Exploratory Research Loop. The generated big data sets are analysed with utlimate goal to identify druggable cancer targets and biomarkers for cancer prognosis.
Digital Pathology evaluates the detailed cellular composition of the tumor including immune-cell markers with a focus on immune checkpoint expression, the tumor’s ecosystem and its interaction with its microenvironment. Digital image analysis is applied to obtain unbiased, large scale quantitative data for key immune markers on all samples. This includes absolute quantification of each cell population as well as spatial information to account for tumor heterogeneity and to enable tumor infiltration analyses. Digital pathology images are then used to select the optimal regions for highly multiplexed assessment by imaging mass cytometry.
Targeted NGS (tissue/circulating DNA-based)
Targeted Next-Generation Sequencing (NGS) allows personalised treatment by suggesting off-label treatments according to the genetic profile of the tumor. A report with clinically actionable variants and treatment recommendations is provided. To complement targeted NGS analyses, circulating tumor DNA (ctDNA) is being studied as a minimally invasive tool to monitor disease progression and recurrence.
Single-cell genomics approaches
Single-cell genomics approaches generate a high-resolution map of the tumor microenvironment, characterise tumor cell heterogeneity, establish each tumor’s evolutionary history, and take advantage of insights into cancer genomics and transcriptomics acquired over the past decades. The Single-Cell Genomics and Transcriptomics platform uses droplet-based scDNA-seq and scRNA-seq protocols to identify copy number changes in tumor subclones and their evolutionary relationships. Different cell populations are quantified and their gene expression patterns elucidated. This approach probes the cellular heterogeneity of the tumor and its microenvironment in an untargeted and genome-wide fashion on the level of individual cells.
Bulk-RNA-seq data characterise a sample’s transcriptomic status and allow the determination of signaling pathway status, splicing landscape, and existence of novel putative epitopes for targeted treatment. These data provide information about alternative splicing, alternative promoters, gene-fusions, RNA-editing and other RNA aberrations and complement the other transcriptome and proteome profiling technologies. Additionally, it provides a common basis for cross-comparison and joint analyses with samples from other studies.
To assess posttranslational modifications affecting proteins involved in signaling pathways, the clinical proteotyping platform converts a clinical sample into a permanent digital proteotype map. This digital proteotype map contains information about the state of the proteome at the time of the measurement (“proteotype”), which includes the identity and quantity of 3000-5000 proteins, as well as their post-translational modifications. Such digital proteotype maps derived from clinical specimens can be analyzed, re-analyzed, compared, and mined in silico to detect and quantify peptide/protein patterns across large clinical cohorts.
Mass Cytometry (CyTOF)
Mass cytometry allows the simultaneous quantification of over 50 proteins, phosphorylation sites, and transcripts on the single-cell level based on the use of metal-labeled reporters. With its high dynamic range, low detection limit, high-throughput capability and relatively low cost, this method has proven to be particularly well-suited for the analysis of highly heterogeneous tumor samples.
Imaging Mass Cytometry (IMC)
IMC allows the quantification of over 50 markers with subcellular resolution in a single tissue section. This enables the identification of tissue morphology and structural features such as cell-cell interactions, vessels, stroma, tumor tissue phenotypes, and a variety of immune cell types on selected regions of the different tumor samples. IMC provides high dimensional single-cell phenotyping as its main readout, thus allowing quantification of the proportions of immune and tumor cell populations. This is of particular value for predicting the success of therapies that depend on direct cell-cell interactions, such as immune checkpoint
Pharmacoscopy focuses on cancer-cell-specific drug effectiveness using cell death as a readout. It uses high-throughput microscopy to measure ex vivo responses to targeted therapies in small liquid biopsy samples from individual patients. The method provides a ranking of drug sensitivity for many drugs and their combinations based on a functional relative toxicity measurement of cancer versus healthy cells. The readout can be directly used to identify effective therapies for individual patients as well as to improve the understanding of the cellular and molecular systems that determine drug response variability.
4i Drug Response Profiling (4i DRP)
The 4i Drug Response Profiling technology utilises an innovative multiplexed immunofluorescence protocol termed 4i (iterative indirect immunofluorescence imaging) to map the changes in proliferation or survival signaling pathways upon drug treatment. 4i technology is an advanced approach to precision medicine that uses large-scale image processing, computer vision and machine learning to achieve an in-depth molecular characterisation of patient responses and drug resistance emergence in tumor biopsies at single-cell resolution. The multivariate analysis allows deciphering cell-to-cell phenotypic variability in relation to different drug treatments and generates predictive models of drug response.
Molecular Research Report
The study requires a technical and organisational framework for the collection and centralisation of molecular and clinical data and for structured reporting to tumor boards. The clinical and molecular data are collected, stored, and analyzed in a customized research data management system. A multidisciplinary team jointly generates the molecular research report (MRR) based on the collected data and technology-specific analyses. The MRR is accessible via an interactive web application that provides an overview of potential treatment suggestions along with the specific evidence supporting each option and facilitates discussions between technology experts and clinicians in the pre-Tumor Board.
A summary of the MRR and the treatment suggestions from the pre-Tumor Board is used as a molecular summary report for supporting treatment decisions at the Tumor Board.
Beyond the clinically driven investigation, the TPC carries out a deep, discovery-driven analysis of individual and combined technologies to identify novel features that improve understanding of tumor biology and predict treatment responses. It applies tools developed previously (Hypoxia score, neo-epitopes) and newly developed tools.