Making Informed Choices On Incorporating Chemoprevention into carE (MiCHOICE, SWOG 1904): Design and methods of a cluster randomized controlled trial.

INTRODUCTION: Women with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS) have a significantly increased risk of breast cancer, which can be substantially reduced with antiestrogen therapy for chemoprevention. However, antiestrogen therapy for breast cancer risk reduction remains underutilized. Improving knowledge about breast cancer risk and chemoprevention among high-risk patients and their healthcare providers may enhance informed decision-making about this critical breast cancer risk reduction strategy. METHODS/DESIGN: We are conducting a cluster randomized controlled trial to evaluate the effectiveness and implementation of patient and provider decision support tools to improve informed choice about chemoprevention among women with AH or LCIS. We have cluster randomized 26 sites across the U.S. through the SWOG Cancer Research Network. A total of 415 patients and 200 healthcare providers are being recruited. They are assigned to standard educational materials alone or combined with the web-based decision support tools. Patient-reported and clinical outcomes are assessed at baseline, after a follow-up visit at 6 months, and yearly for 5 years. The primary outcome is chemoprevention informed choice after the follow-up visit. Secondary endpoints include other patient-reported outcomes, such as chemoprevention knowledge, decision conflict and regret, and self-reported chemoprevention usage. Barriers and facilitators to implementing decision support into clinic workflow are assessed through patient and provider interviews at baseline and mid-implementation. RESULTS/DISCUSSION: With this hybrid effectiveness/implementation study, we seek to evaluate if a multi-level intervention effectively promotes informed decision-making about chemoprevention and provide valuable insights on how the intervention is implemented in U.S. TRIAL REGISTRATION: NCT04496739.

Fragmentomic analysis of circulating tumor DNA-targeted cancer panels.

BACKGROUND: The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner. PATIENTS AND METHODS: We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets. RESULTS: In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99. CONCLUSIONS: To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.