Clinical characteristics and treatment patterns of patients with NTRK fusion-positive solid tumors: A multisite cohort study at US academic cancer centers.

BACKGROUND: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are rare oncogenic drivers prevalent in 0.3% of solid tumors. They are most common in salivary gland cancer (2.6%), thyroid cancer (1.6%), and soft-tissue sarcoma (1.5%). Currently, there are 2 US Food and Drug Administration-approved targeted therapies for NTRK gene fusions: larotrectinib, approved in 2018, and entrectinib, approved in 2019. To date, the real-world uptake of tyrosine receptor kinase inhibitor (TRKi) use for NTRK-positive solid tumors in academic cancer centers remains largely unknown. OBJECTIVE: To describe the demographics, clinical and genomic characteristics, and testing and treatment patterns of patients with NTRK-positive solid tumors treated at US academic cancer centers. METHODS: This was a retrospective chart review study conducted in academic cancer centers in the United States. All patients diagnosed with an NTRK fusion-positive (NTRK1, NTRK2, NTRK3) solid tumor (any stage) and who received cancer treatment at participating sites between January 1, 2012, and July 1, 2023, were included in this study. Patient demographics, clinical characteristics, genomic characteristics, NTRK testing data, and treatment patterns were collected from electronic medical records and analyzed using descriptive statistics as appropriate. RESULTS: In total, 6 centers contributed data for 55 patients with NTRK-positive tumors. The mean age was 49.3 (SD = 20.5) years, 51% patients were female, and the majority were White (78%). The median duration of time from cancer diagnosis to NTRK testing was 85 days (IQR = 44-978). At the time of NTRK testing, 64% of patients had stage IV disease, compared with 33% at cancer diagnosis. Prevalent cancer types in the overall cohort included head and neck (15%), thyroid (15%), brain (13%), lung (13%), and colorectal (11%). NTRK1 fusions were most common (45%), followed by NTRK3 (40%) and NTRK2 (15%). Across all lines of therapy, 51% of patients (n = 28) received a TRKi. Among TRKi-treated patients, 71% had stage IV disease at TRKi initiation. The median time from positive NTRK test to initiation of TRKi was 48 days (IQR = 9-207). TRKis were commonly given as first-line (30%) or second-line (48%) therapies. Median duration of therapy was 610 (IQR = 182-764) days for TRKi use and 207.5 (IQR = 42-539) days for all other first-line therapies. CONCLUSIONS: This study reports on contemporary real-world NTRK testing patterns and use of TRKis in solid tumors, including time between NTRK testing and initiation of TRKi therapy and duration of TRKi therapy.

Next-Generation Sequencing Trends among Adult Patients with Select Advanced Tumor Types: A Real-World Evidence Evaluation.

There are limited data on the prevalence of next-generation sequencing (NGS) in the United States, especially in light of the increasing importance of identifying actionable oncogenic variants due to molecular biomarker-based therapy approvals. This retrospective study of adult patients with select metastatic solid tumors and central nervous system tumors from the Optum Clinformatics Data Mart US health care claims database (January 1, 2014, to June 30, 2021; N = 63,209) examined NGS use trends over time. A modest increase in NGS was observed across tumor types from 2015 (0.0% to 1.5%) to 2021 (2.1% to 17.4%). A similar increase in NGS rates was also observed across key periods; however, rates in the final key period remained <10% for patients with breast, colorectal, head and neck, soft tissue sarcoma, and thyroid cancers, as well as central nervous system tumors. The median time to NGS from diagnosis was shortest among patients with non-small-cell lung cancer and longest for patients with breast cancer. Predictors of NGS varied by tumor type; test rates for minorities in select tumor types appeared comparable to the White population. Despite improving payer policies to expand coverage of NGS and molecular biomarker-based therapy approvals, NGS rates remained low across tumor types. Given the potential for improved patient outcomes with molecular biomarker-based therapy, further efforts to improve NGS rates are warranted.

Value-Based Analysis of Therapies in Refractory Metastatic Colorectal Cancer in US.

BACKGROUND: Recent phase 2 trials have provided data supporting regorafenib dose optimization (ReDO) and trifluridine/tipiracil (TAS-102) with bevacizumab (TAS-BEV) as treatment options in refractory metastatic colorectal cancer (mCRC). Historically, regorafenib standard dose (RSD) and TAS-102 have been utilized as third-line options in mCRC. Given the incorporation of ReDO and TAS-BEV as treatment options, we sought to evaluate relative cost-effectiveness of ReDO vs. RSD, TAS-102, and TAS-BEV for mCRC from a payer perspective. METHODS: A Markov model was constructed to estimate total costs and quality-adjusted life-years (QALYs) for ReDO, RSD, TAS-102, and TAS-BEV. Clinical parameters were obtained from phase 2 and 3 trials for comparators. Health state utility values were from the RSD phase 3 clinical trial. Incremental cost-effectiveness ratios (ICERs) were utilized to compare treatments. Model robustness was checked with one-way and probabilistic sensitivity analyses. RESULTS: In the base case, ReDO was dominant over TAS-BEV (ie provided a higher QALY at a lower cost). ReDO produced an ICER of $104,308 per QALY relative to RSD and $37,966 relative to TAS-102. In one-way sensitivity analyses, monthly drug cost of TAS-BEV was the most influential parameter determining relative cost-effectiveness between TAS-BEV and ReDO. When TAS-102 and RSD were independently compared to ReDO, the most influential parameters were related to duration of OS and PFS and costs of managing AEs. CONCLUSIONS: The optimum dosing strategy for regorafenib has improved its benefit-to-toxicity ratio and relative cost-effectiveness compared to RSD, TAS-102, and TAS-BEV.

A Minimal Information Model for Potential Drug-Drug Interactions.

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Cytochrome P-450 gene and drug interaction analysis in patients referred for pharmacogenetic testing.

PURPOSE: The results of a study of variant cytochrome P-450 (CYP) alleles and associated risks of drug-drug interactions (DDIs) and altered drug metabolism are reported. METHODS: The records of a pharmacogenetic testing laboratory were retrospectively analyzed to identify patients tested for polymorphisms of genes coding for five CYP isozymes important in drug metabolism (CYP2D6, CYP2C9, CYP2C19, CYP3A4, and CYP3A5) over a 16-month period. Based on the results of phenotyping, the patients were categorized by expected CYP isozyme activity (e.g., normal or poor metabolizer, expresser or nonexpresser). Using proprietary Web-based software, researchers analyzed phenotyping data and medication lists submitted by patients to determine the potential for DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs). RESULTS: In the mixed-race study population of more than 22,000 male and female patients (age range, 1-108 years; mean, 60 years), phenotypes associated with alterations of CYP metabolic pathways were common. Among patients in whom phenotypes for all five isozymes of interest were determined (n = 14,578), about 93% were not categorized as normal metabolizers of all five proteins. In many cases, potential interaction threats were rated by clinicians as severe enough to warrant implementation or consideration of a medication regimen change or dose adjustment. Analysis of patient-provided medication lists indicated frequent use of medications posing DDI, DGI, or DDGI risks. CONCLUSION: In a mixed-race population of over 20,000 U.S. patients, CYP gene polymorphisms associated with DDIs and other interaction threats were prevalent, and most individuals were not categorized as normal metabolizers of all five CYP isozymes of interest.

Fluvoxamine-associated oscillopsia and a role for personalized medication dosing.

A 60-year-old woman reported horizontal "shimmering" movement while reading crossword puzzles when using fluvoxamine, bupropion, quetiapine, lithium, and levothyroxine. This visual disturbance, likely oscillopsia, started after the fluvoxamine was added and waned as the fluvoxamine was tapered, disappearing after the drug was discontinued. Genetic testing to explore how the patient metabolizes these medications combined with YouScript® interaction analysis suggest that she may have had abnormally high plasma concentrations of fluvoxamine during this time. Oscillopsia may be a novel dose-dependent side effect of fluvoxamine. Genetic testing combined with YouScript has the potential to discover novel drug side effects, elucidate drug interactions and guide future prescribing decisions.