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Related Experiment Video

Updated: Jul 2, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Advancing maturity modeling for precision oncology.

Ariella Hoffman-Peterson¹, Megh Marathe², Mark S Ackerman¹

¹University of Michigan, Ann Arbor, MI, USA.

Journal of Clinical and Translational Science

|February 22, 2024

Summary

This summary is machine-generated.

Precision oncology maturity is currently low but evolving. Advancing this learning health system requires understanding complex infrastructure needs and closing learning loops for better cancer care.

Keywords:

Precision oncology learning cycle learning health systems maturity models molecular tumor boards

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Area of Science:

Oncology
Health Systems Science
Bioinformatics

Background:

Precision oncology aims to tailor cancer treatment to individual patients.
Learning Health Systems (LHS) integrate evidence into practice.
Mapping the maturity of precision oncology as an LHS is crucial for its advancement.

Purpose of the Study:

To assess the maturity of precision oncology as a Learning Health System.
To understand the current state of practice, tools, informatics, barriers, and facilitators.
To identify strategies for enhancing maturity in the field.

Main Methods:

Conducted semi-structured interviews with 34 professionals across academic medical centers and a Next Generation Sequencing company.
Interviewees included clinicians, pathologists, and program managers involved in Molecular Tumor Boards (MTBs).
Analyzed interview data to understand variations in maturity within precision oncology.

Main Results:

Precision oncology maturity is currently low but evolving across practice, tools, and informatics.
Resource-intensive and complex sociotechnical infrastructure are key factors influencing maturity.
Closing learning loops is essential for advancing the field.

Conclusions:

The study defines and contextualizes the current maturity of precision oncology.
Provides a framework for examining LHS maturity and developing maturity models.
Offers insights into future strategies for advancing precision oncology.