SPRINTR-Real

SPRINTR-Real, an observational study, is the back-bone of the SPRINTR concept. A clinically integrated procedure invites all men with a suspected prostate cancer, aiming for national coverage. With central collection of clinical follow-up data, clinical images, lab data, and molecular profiling it creates a high quality cohort for all precision medicine research as well as a study-ready population for clinical trials.
Read more about ongoing studies within SPRINTR-REAL 

Key features of SPRINTR-Real

Electronic consent module

Information and invitation are distributed together with the call for prostate biopsy. Participants consent electronically to usage of clinical information, images and molecular profiling data for research.

 Continuous clinical follow up

Data from national registers for diagnoses, drug prescriptions, and treatments are regularly retrieved to the study database together with images (MRI/PET) and labdata from the hospitals.

Biomarker workflow

At the pathology department, biopsy sections for molecular profiling (RNA, DNA, protein) and digital pathology are sampled within the clinical routine process, enabling rapid access to biomarkers for studies or clinical decisions.

Study-ready population

Participants consent to all collected data to be used to match them with clinical trials to which they can be selectively invited. Thereby, SPRINTR will facilitate the recruitment process  as well as increase patient access to clinical trials.

The SPRINTR-real ethical permit

Building on its broad and longterm ethical permit,SPRINTR creates a nation-wide reasearch platform that covers all steps from participant recruitment, data collection, biomarker profiling, access to datasets and selected patient cohorts. SPRINTR serves as an ideal platform for multiple research efforts and facilitates regulatory and administrative work.
The ethical permit is easily adapted to any other cancer precision medicine effort with the same goals.

                   SPRINTR study-ready population

SPRINTR will generate a continuously growing, well characterized cohort. Today, 27 diagnostic centers are involved in the study, of which nine are actively recruiting and the rest will start during the fall of 2026.
These sites will cover ~50% of all diagnosed cases in Sweden, and SPRINTR is aiming for at least one active center per health care region.

SPRINTR Biomarker workflow

SPRINTR initiates a prospective tissue collection procedure at pathology departments. Prostate biopsies are very small, and tissue at two different levels are used for diagnostic purposes. Tissue between these levels are normally discarded. SPRINTR instead secure this tissue for both histology and molecular profiling. Current workflow includes immunohistochemistry for Ki67, PSA and PTEN, DNA panel sequencing (GMS560), RNA sequencing and proteomics.

Selection of ongoing and planned research in SPRINTR-Real
  • SPRINTR-SCAN-FIRST

    An AI tool will be developed to predict PTEN and HRR status from histological readouts in the first 1000 men, including PTEN immunohistochemical scoring, RNA-seq, and DNA sequencing (WGS or panel-based sequencing).
    Andreas Josefsson, Umeå University, Andreas Røder, University of Copenhagen, Carolina Wählby, Uppsala University

  • PTEN-loss evaluation

    In this study PTEN loss will be assessed by complementary approaches, including immunohistochemical scoring, DNA sequencing, and RNA sequencing. A back-to-back analysis will show how to best identify PTEN loss as a biomarker for clinically relevant therapeutic options. 
    PI: Stina Nateghi Pettersson and Martin Sjöström, Lund University

  • SPRINTR-PSMA-PET 

    AI-based analysis of PSMA-PET imaging will be evaluated to extract clinically relevant features beyond standard radiological assessment and improve non-invasive tumor characterization. By first testing, we will assess its ability to stratify patients and predict treatment response, before prospective implementation in SPRINTR‑Real
    Elin Trägårdh, Lund University

  • Liquid biopsy for docetaxel response (draft)

    Serum thymidine kinase 1 (sTK1) will be evaluated as a liquid biopsy marker to identify men with a high‑proliferative phenotype and to monitor treatment response. Initial testing in SPRINT‑Retro will assess its ability to stratify patients and identify those most likely to benefit from docetaxel, before prospective validation in SPRINTR‑Real
    Johan Styrke (Umeå University), Stig Linder (IDL Diagnostics AB)

  • SPRINTR-MOLP

    Transcriptome-based molecular classification of tissue for tumor classification into MetA, MetB, and MetC phenotypes will be evaluated in SPRINTR-Retro followed by prospective validation in SPRINTR-Real regarding its prognostic and treatment predictive value. 
    Pernilla Wikström, Umeå University; Martin Sjöström, Lund University; Karin Welén, University of Gothenburg 

  • S100A9 as a marker for ADT response (draft)

    S100A9 - an inflammation-related biomarker linked to aggressive disease and poor outcomes in metastatic prostate cancer - will be evaluated as a blood-based early predictor (within 6-12 weeks) of response to androgen deprivation therapy in the metastatic setting.
    Marie Lundholm (Umeå University), Pernilla Wikström (Umeå University), Camilla Thellenberg (Umeå University) 

Study opportunities in SPRINTR-Real

The inherent comprehensive data collection and broad ethical permission makes SPRINTR-Real an excellent platform for many types of studies

Quality of life assessment

Patient reported outcome measurements from clinical routine are available for research in SPRINTR, and additional questionnaires can be used after separate ethical approval

Health economy evaluation

Precision medicine tools can be expensive. Evaluation of benefits and better outcomes in relation to costs can be performed in real-world data.

Biomarker research 

Biomarkers can be analyzed prospectively or retrospectively in SPRINTR samples or data and validated against patient treatment history and outcome

Digital Twins

The SPRINTR database will be uniquely optimal for the development of digital twin models, in which real patients can be matched on clinical and molecular characteristics to give real-world-based help in treatment decisions