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Background Genomic fusions are potent oncogenic drivers across cancer types and many are targetable. We demonstrate the clinical performance of DNA-based comprehensive genomic profiling (CGP) for detecting targetable fusions. Materials and Methods We analyzed targetable fusion genes in >450 000 tissue specimens profiled using DNA CGP (FoundationOne CDx, FoundationOne). Using a de-identified nationwide (US-based) non–small cell lung cancer (NSCLC) clinico-genomic database, we assessed outcomes in patients with nonsquamous NSCLC (NonSqNSCLC) who received matched therapy based on a fusion identified using DNA CGP. Lastly, we modeled the added value of RNA CGP for fusion detection in NonSqNSCLC. Results We observed a broad diversity of fusion partners detected with DNA CGP in conjunction with targetable fusion genes (ALK , BRAF , FGFR2 , FGFR3 , NTRK1/2/3 , RET , and ROS1). In NonSqNSCLC with oncogenic ALK , NTRK , RET , and ROS1 fusions detected by DNA CGP, patients treated with a matched tyrosine kinase inhibitor had better real-world progression-free survival than those receiving alternative treatment regimens and benefit was observed regardless of the results of orthogonal fusion testing. An estimated 1.3% of patients with NonSqNSCLC were predicted to have an oncogenic driver fusion identified by RNA, but not DNA CGP, according to a model that accounts for multiple real-world factors. Conclusion A well-designed DNA CGP assay is capable of robust fusion detection and these fusion calls are reliable for informing clinical decision-making. While DNA CGP detects most driver fusions, the clinical impact of fusion detection is substantial for individual patients and exhaustive efforts, inclusive of additional RNA-based testing, should be considered when an oncogenic driver is not clearly identified. [ABSTRACT FROM AUTHOR]

Background Pancreatic cancer (PC) represents an aggressive disease with median overall survival (OS) of less than 1 year in the front-line setting. FOLFIRINOX and gemcitabine and paclitaxel (GP) are standard of care options for these patients; however, optimal selection of therapy is challenging. Methods Comprehensive genomic profiling was performed on 8358 PC patients. Outcomes were available for 1149 metastatic PC patients treated with 1L FOLFIRINOX or GP. A scar-based measure of HRD was called using a machine learning-based algorithm incorporating copy number and indel features. Results A scar-based HRD signature (HRDsig) was identified in 9% of patients. HRDsig significantly co-occurred with biallelic alterations in BRCA1/2, PALB2, BARD1, and RAD51C/D , but encompassed a larger population than that defined by BRCA1/BRCA2/PALB2 (9% vs. 6%). HRDsig was predictive of 1L FOLFIRNOX chemotherapy benefit with doubled OS relative to gemcitabine and paclitaxel (GP) (rwOS aHR 0.37 [0.22-0.62]), including 25% of the population with long-term (2 year+) survival in a real-world cohort of patients. Less benefit from FOLFIRINOX was observed in the HRDsig(−) population. Predictive value was seen in both the BRCA1/2/PALB2 mutant and wildtype populations, suggesting additional value to mutational profiling. Conclusion A scar-based HRD biomarker was identified in a significant fraction of PC patients and is predictive of FOLFIRINOX benefit. Incorporating a biomarker like HRDsig could identify the right patients for platinum chemotherapy and potentially reduce FOLFIRINOX use by over 40%, minimizing toxicities with similar survival outcomes. Confirmatory studies should be performed. [ABSTRACT FROM AUTHOR]

Policymakers face difficult choices over which health interventions to publicly finance. We developed an approach to health benefits package design that accommodates explicit tradeoffs between improvements in health and provision of financial risk protection (FRP). We designed a mathematical optimization model to balance gains in health and FRP across candidate interventions when publicly financed. The optimal subset of interventions selected for inclusion was determined with bi‐criterion integer programming conditional on a budget constraint. The optimal set of interventions to publicly finance in a health benefits package varied according to whether the objective for optimization was population health benefits or FRP. When both objectives were considered jointly, the resulting optimal essential benefits package depended on the weights placed on the two objectives. In the Sustainable Development Goals era, smart spending toward universal health coverage is essential. Mathematical optimization provides a quantitative framework for policymakers to design health policies and select interventions that jointly prioritize multiple objectives with explicit financial constraints. [ABSTRACT FROM AUTHOR]

The use of biomarker testing to inform treatment decisions has emerged as a standard of care in multiple cancer types. However, the rates of patients with genomic testing results in hand to inform treatment decision-making remain variable. Here, we studied the impact of comprehensive genomic profiling (CGP) on clinical trial enrollment rates in patients with advanced-stage non-small cell lung, colorectal, breast, and prostate cancer using a real-world clinicogenomic database. On average, clinical trial enrollment in the therapy line immediately after CGP report receipt was 5.4%, which represents a 3.0 percentage point increase compared to therapy lines preceding CGP report receipt, supporting a meaningful association between CGP report availability and increased clinical trial enrollment., (© The Author(s) 2024. Published by Oxford University Press.)