Publication

Oncology care exists in a dynamic environment of rapidly evolving health technology, increasing complexity
and cost of care, and continuing skepticism about whether rapidly introduced tools such as genomic profiling
result in clinically significant improved outcomes. The introduction of new technologies and therapies (e.g.,
genomic testing, targeted therapies, and immunotherapy) on a faster regulatory track based on studies of
smaller, stratified patient populations has led to a demand for a broader evidence base to inform clinical
practice, particularly with respect to precision medicine in oncology.

Many stakeholders are now leveraging the joint capabilities of genomics and health information technology
(HIT) to build that evidence base. Under the aegis of a genomics-driven, continuous-learning model, some
industry leaders are working with clinicians, health system executives, payers, and others to collect real-time
clinical data. This data will drive scientific discovery, shape clinical practice, and enable more informed
discussions on efficacy and cost effectiveness in care delivery. On November 3-4, 2015, led by the
Sustainable Predictive Oncology Therapeutic and Diagnostics (SPOTDx) working group, leaders from a
number of healthcare institutions gathered to discuss several of these collaborative efforts. Those efforts
include:

• NCI-MATCH (Molecular Analysis for Therapy Choice)

• SPECTA (Screening Patients for Efficient Clinical Trial Access, a program of the European Organisation

• for Research and Treatment of Cancer)

• The Molecular Evidence Development Consortium (MED-C)

• The Targeted Agent and Profiling Utilization Registry Study (TAPUR)

• The Syapse-Intermountain collaboration

• Geisinger’s MyCode Community Health Initiative

Meeting participants identified similarities, differences, gaps, and potential synergies across these initiatives. They questioned, “How do we optimize the learning we get from these studies? What evidentiary requirements are needed to support the useful and meaningful practice of genomic medicine?” Participants also explored ways to bridge research and clinical care in service to oncology patients seeking improved health outcomes. This synthesis integrates the November meeting discussions as well as post-meeting debriefs across participants and the broader landscape of stakeholders. See Appendix A for list of participants and contributors to the discussions. It highlights key themes (listed below) and details potential next steps:

• Next-generation sequencing (NGS). The excitement and interest in NGS is palpable, but questions remain about its “readiness, usefulness, and impact” on clinical care management and patient outcomes. Understanding the evidence that is being generated across a landscape of genomic-driven studies is a critical step in addressing these questions. (Page 2)

• The quality and reliability of technological and clinical data. Healthcare leaders must improve the quality and reliability of technological and clinical data before value can accrue from genomics-driven studies. (Page 6)

• Data sharing. The ability to share data across initiatives may be useful where there is an overlap in data elements, shared nomenclature, and agreed standards. (Page 7)

• The importance of leadership and incentives. The challenges of integrating genomic tools into a continuous-learning system are not problems of technology but of leadership, incentives, and will. (Page 8)

• Progress on oncology outcomes. Progress on oncology outcomes will require integrated solutions that include, but do not solely depend on, genomic tools. (Page 9)

• The necessity of new models of leadership and collaboration. New models of leadership and collaboration are needed to advance the genomics and real-world registry agenda. (Page 10)