Executive summary

Goal of the NDS

The goal of the Swiss Personalized Oncology (SPO)-NDS is to build and extend the existing infrastructure, datasets, and know-how developed over the first 3 years of the Swiss Personalized Oncology SPHN Driver project.

The SPO-NDS will aim to consolidate the clinical data acquisition, finalizing the extraction of the core data set (120 structured data entry types) at the 5 university hospitals and at the Swiss Group for Clinical Cancer Research (SAKK), and pursuing the natural language processing (NLP) applications to enrich the data set.

In addition, high value cohorts have been and will be assembled in cancer types treated with immuno-oncology (IO) therapies that will allow comparative immunomics between highly IO sensitive tumors (e.g., melanoma, lung-cancer, triple-negative breast cancer (TNBC)) to less sensitive (e.g., MSI-CRC, hormone receptor-positive breast cancer).

Using the momentum of the Tumor Profiler (TuPro) initiative, single-cell and -omics data will now be at the center of the program including NGS, CyTOF/IMC, digital pathology, as well as functional drug screening to drive therapeutic decisions within the existing molecular tumor boards. The patients will readily benefit from these efforts as the infrastructure and the -omics data will be directly employed to support treatment decisions within the molecular tumor boards nationwide. In addition, tumors will be collected for subsequent analysis in a discovery program within the lighthouse project, using single-cell RNA seq, single-cell TCR seq, spatial transcriptomics, and bulk RNA seq.

The lighthouse project will focus on identifying the mechanisms of primary and acquired immunotherapy resistance within and between tumors with different immune-reactivities.

Our strategy is to use a comparative immune-oncology (IO) approach at the single-cell and multi-omics levels to identify shared features of primary and acquired resistance to IO in a subset of the molecular tumor board patients meeting strict inclusion criteria. In particular, we will compare the cancer-immune ecosystems of patients and tumors that respond well to IO to those who fail to mount antitumor immune responses to IO. In turn, these analyses are expected to identify predictive biomarkers to be used in precision oncology tumor boards, also directly benefiting patients.

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