Precision therapies and immunotherapies have revolutionized cancer care, resulting in significant gains in patient survival across tumor types. Despite this transformation in care, there is variability in the utilization of tumor molecular profiling. To standardize testing, we designed a pathologist-directed test ordering system at time of diagnosis utilizing a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel. We assessed actionability rates, therapy choices, and outcomes among 3,216 patients. 49% of cases had at least one actionable genomic biomarker-driven (GBD) approved and/or guideline-recommended targeted or immunotherapy and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an insilico 50 gene panel, 67% of tumors compared to 33% harbored actionable alterations. Among patients with 6-months or more of follow-up, over 52% received a targeted therapy or immunotherapy, versus 32% that received conventional chemotherapy alone, a phenomenon not previously observed. Statement of Significance This study represents the first report where precision therapies (targeted and immunotherapy) have overtaken traditional cytotoxic treatments in the routine community-based care of advanced cancer patients, resulting in better overall survival. This represents an important milestone in the evolution and adoption of precision oncology and highlights the importance of CGP. ### Competing Interest Statement All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: BP and CBB acknowledge institutional financial support from Illumina, Inc for the submitted work; BB and BS are employees and stockholders of Illumina, Inc; CW and HP are employees of Microsoft; BP has received research grants from Loxo@Lilly and Shimadzu Scientific; RL has grants and/or contracts with Bristol Myers Squibb, Incyte and AstraZeneca; BP has been paid for consulting Loxo@Lilly and Optum; CBB has been paid for consulting Sanofi, Agilent, Roche, and Incendia; RL has been paid for consulting at Bristol Myers Squibb, Merck, Vir, AstraZeneca, and CDR-Life; CBB has received payment or honoraria for a presentation from Abcam; CBB has received support from Illumina for travel; RL has received travel support from Bristol Myers Squibb; CBB has patents US20180322632A1 and US20200388033A1 planned and/or issued; WU participates on a Data Safety Monitoring board for AstraZeneca; CBB participates on Data Safety Monitoring boards for PrimeVax, BioAI and Lunaphore; RL participates on a Data Safety Monitoring board for Incyte; CBB has PrimeVax stock options; BP has been gifted early instrument access from Lunaphore; RL has been gifted materials and/or services from Celldex, Ubivac, Incyte, and Clinigen; no other relationships or activities that could appear to have influenced the submitted work. ### Funding Statement This study was funded in part by Illumina. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Providence gave ethical approval for this work. All research was performed under protocol 201900048 "Effect of Automatic Reflex Genomics Testing on Clinical and Economic Outcomes in Cancer" approved by Providence IRB. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes

Introduction Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment.Areas covered We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation.Expert opinion The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.

e20598 Background: Guideline-recommended molecular testing is essential for identifying appropriate targeted therapies (Rx) for treatment of mNSCLC patients (pts). Biomarker testing can be performed by single gene tests, small NGS panels (e.g., < 50 genes), or CGP approaches. There is little evidence on the rate of biomarker testing in the real world setting and the impact of different approaches on Rx utilization and cost of care. This study examined real-world utilization of biomarker testing among mNSCLC pts and outcomes with CGP and non-CGP testing. Methods: De-identified administrative claims data from the Optum Labs Data Warehouse were analyzed to identify newly diagnosed adult mNSCLC pts from 1/2018 to 8/2021; the date of the first claim indicating metastasis was the index date. Continuous enrollment in a commercial (COM) or Medicare Advantage (MA) plan with medical and pharmacy benefits for 12 mo prior to (baseline), and ≥6 mo post index date (follow-up) was required; pts with < 6 mo follow-up due to death were included. Initiation of a line of therapy (LOT1) during follow-up was required. We categorized pts based on receipt and type of biomarker testing prior to LOT1: CGP ( > 50 gene panel), non-CGP (5-50 gene panels or single gene testing), or no testing. Differences in receipt of targeted Rx, overall survival (OS), and total overall per patient per month (PPPM) costs during LOT1 were examined with multivariable regression analyses. Results: 9,945 mNSCLC pts (1,970 COM, 7,975 MA) were identified: 5,484 with no testing, 2,215 with CGP, and 2,246 with non-CGP testing prior to LOT1. Biomarker testing rates prior to LOT1 were low (45%) but increased during the study period from 42% to 48% (p <0.01). Testing rates were higher for the COM vs MA population (49% vs 44%, p <0.01). A higher percent of pts with CGP received targeted Rx compared to the non-CGP and no testing group (17% vs 11% and 5% respectively, p< 0.01); after adjustment, CGP pts were still more likely to receive targeted Rx. Compared to the no testing group, OS was more favorable for the CGP [HR 0.8, 95% CI 0.8-0.9] and non-CGP [HR 0.9, 95% CI 0.8-0.9] groups. There was no significant difference in PPPM costs between tested groups: CGP [CR 1.2, 95%CI 1.1-1.2] vs non-CGP [CR 1.1, 95%CI 1.1-1.2]. Conclusions: Rates of biomarker testing among mNSCLC pts are far from optimal despite well-established guideline recommendations and insurance coverage for testing. There was evidence of improved intermediate outcomes (receipt of targeted Rx) with CGP compared to non-CGP or no testing. In addition, OS was improved for tested pts compared to untested. Interventions to help improve biomarker testing are needed. Given the potential benefits of CGP testing (including assessment of biomarkers that cannot be evaluated using small panels), increasing CGP testing may improve outcomes.

e23109 Background: Immune checkpoint inhibitors (IO) have become a powerful precision therapy option to treat advanced stage cancer with biomarkers such as PD-L1, tumor mutational burden (TMB), and microsatellite instability (MSI) associated with improved patient response rates. Despite this, many patients do not receive genomic testing for all IO biomarkers. Providence, a large US community health system, developed a pathologist-directed testing protocol where comprehensive genomic profiling (CGP) was routinely used at time of diagnosis for advanced cancer patients. Methods: Advanced cancer patients who received CGP (ProvSeq 523) and IHC testing for PD-L1 between 2019-2023 were included in the study. Patients were required to be treated at Providence and were assessed for presence of IO biomarkers and subsequent therapy selection. Real world data were curated from patient charts and genomic laboratory data by employing a novel natural-language processing (NLP) approach to accelerate abstraction. Results: The study included 2,502 patients (53% female, median age 68y, 83% white). Top 3 tumor types tested were lung (40%), colorectal (9%), and breast (8%). Overall, 58% (N = 1,455) of patients had presence of ≥1 IO biomarker, with 46% (N = 1,155) being PD-L1 positive, and 27% (N = 682) being TMB-H. In PD-L1 negative patients (N = 1,347; 54%), 300 (22%) were TMB-H and 18 (1%) were MSI-H. 53% (N = 767) of patients who harbored an actionable IO biomarker received IO-based therapy. Fewer patients with an IO biomarker received chemotherapy compared to patients without an IO biomarker (23% vs 35%, p < 0.001). Patients who possessed ≥2 IO biomarkers received an IO precision therapy 68% of the time vs 47% in patients with 1 IO biomarker (p < 0.001). In PD-L1 negative patients, 26% (N = 78) of TMB-H patients received IO monotherapy compared to 5% (N = 49) of TMB-L patients. Conclusions: IO biomarker presence is associated with increased precision therapy use. CGP identified many TMB-H patients for IO monotherapy that would have been missed with PD-L1 testing alone. More than half of patients eligible for IO therapy don’t receive it; ongoing analyses will evaluate the impact of pathology-directed reflex testing on IO as well as targeted therapy approaches. [Table: see text]

PURPOSE Therapeutic decision making for patients with advanced non–small cell lung cancer (aNSCLC) includes a growing number of options for genomic, biomarker-guided, targeted therapies. We compared actionable biomarker detection, targeted therapy receipt, and real-world overall survival (rwOS) in patients with aNSCLC tested with comprehensive genomic profiling (CGP) versus small panel testing (SP) in real-world community health systems. METHODS Patients older than 18 years diagnosed with aNSCLC between January 1, 2015, and December 31, 2020, who received biomarker testing were followed until death or study end (September 30, 2021), and categorized by most comprehensive testing during follow-up: SP (≤52 genes) or CGP (>52 genes). RESULTS Among 3,884 patients (median age, 68 years; 50% female; 73% non-Hispanic White), 20% received CGP and 80% SP. The proportion of patients with ≥one actionable biomarker (actionability) was significantly higher in CGP than in SP (32% v 14%; P < .001). Of patients with actionability, 43% (CGP) and 38% (SP) received matched therapies ( P = .20). Among treated patients, CGP before first-line treatment was associated with higher likelihood of matched therapy in any line (odds ratio, 3.2 [95% CI, 1.84 to 5.53]). CGP testing (hazard ratio [HR], 0.80 [95% CI, 0.72 to 0.89]) and actionability (HR, 0.84 [95% CI, 0.77 to 0.91]) were associated with reduced risk of mortality. Among treated patients with actionability, matched therapy receipt showed improved median rwOS in months in CGP (34 [95% CI, 21 to 49] matched v 14 [95% CI, 10 to 18] unmatched) and SP (27 [95% CI, 21 to 43] matched v 10 [95% CI, 8 to 14] unmatched). CONCLUSION Patients who received CGP had improved detection of actionable biomarkers and greater use of matched therapies, both of which were associated with significant increases in survival.

PURPOSE:Precision therapies and immunotherapies have revolutionized cancer care, with novel genomic biomarker-associated therapies being introduced into clinical practice rapidly, resulting in notable gains in patient survival. Despite this, there is significant variability in the utilization of tumor molecular profiling that spans the timing of test ordering, comprehensiveness of gene panels, and clinical decision support through therapy and trial recommendations. METHODS:To standardize testing, we designed a pathologist-directed test ordering system at the time of diagnosis using a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel and extensive clinical decision support tools. To comprehensively characterize the clinical impact of this protocol, we developed a novel natural language processing (NLP)-based approach to extract clinical features from physician chart notes. We assessed test actionability rates, therapy choice, and outcomes across a set of 3,216 patients with advanced cancer. RESULTS:We observed 49% of patients had at least one actionable genomic biomarker-driven-approved and/or guideline-recommended targeted or immunotherapy (IO) and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an in silico 50-gene panel, 67% of tumors compared with 33% harbored actionable alterations including clinical trials. Among patients with 6 months or more of follow-up, over 52% received a targeted therapy (TT) or IO, versus 32% who received conventional chemotherapy alone. Furthermore, patients receiving TT had significantly improved overall survival compared with patients receiving chemotherapy alone (P < .001). CONCLUSION:Overall, these data represent a major shift in standard clinical practice toward molecularly guided treatments (targeted and immunotherapies) over conventional systemic chemotherapy. As guidelines continue to evolve and more precision therapeutics gain approval, we expect this gap to continue to widen.

Precision therapies and immunotherapies have revolutionized cancer care, resulting in significant gains in patient survival across tumor types. Despite this transformation in care, there is variability in the utilization of tumor molecular profiling. To standardize testing, we designed a pathologist-directed test ordering system at time of diagnosis utilizing a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel. We assessed actionability rates, therapy choices, and outcomes among 3,216 patients. 49% of cases had at least one actionable genomic biomarker-driven (GBD) approved and/or guideline-recommended targeted or immunotherapy and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an insilico 50 gene panel, 67% of tumors compared to 33% harbored actionable alterations. Among patients with 6-months or more of follow-up, over 52% received a targeted therapy or immunotherapy, versus 32% that received conventional chemotherapy alone, a phenomenon not previously observed. Statement of Significance This study represents the first report where precision therapies (targeted and immunotherapy) have overtaken traditional cytotoxic treatments in the routine community-based care of advanced cancer patients, resulting in better overall survival. This represents an important milestone in the evolution and adoption of precision oncology and highlights the importance of CGP. ### Competing Interest Statement All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: BP and CBB acknowledge institutional financial support from Illumina, Inc for the submitted work; BB and BS are employees and stockholders of Illumina, Inc; CW and HP are employees of Microsoft; BP has received research grants from Loxo@Lilly and Shimadzu Scientific; RL has grants and/or contracts with Bristol Myers Squibb, Incyte and AstraZeneca; BP has been paid for consulting Loxo@Lilly and Optum; CBB has been paid for consulting Sanofi, Agilent, Roche, and Incendia; RL has been paid for consulting at Bristol Myers Squibb, Merck, Vir, AstraZeneca, and CDR-Life; CBB has received payment or honoraria for a presentation from Abcam; CBB has received support from Illumina for travel; RL has received travel support from Bristol Myers Squibb; CBB has patents US20180322632A1 and US20200388033A1 planned and/or issued; WU participates on a Data Safety Monitoring board for AstraZeneca; CBB participates on Data Safety Monitoring boards for PrimeVax, BioAI and Lunaphore; RL participates on a Data Safety Monitoring board for Incyte; CBB has PrimeVax stock options; BP has been gifted early instrument access from Lunaphore; RL has been gifted materials and/or services from Celldex, Ubivac, Incyte, and Clinigen; no other relationships or activities that could appear to have influenced the submitted work. ### Funding Statement This study was funded in part by Illumina. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Providence gave ethical approval for this work. All research was performed under protocol 201900048 "Effect of Automatic Reflex Genomics Testing on Clinical and Economic Outcomes in Cancer" approved by Providence IRB. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes

11154 Background: Despite concerted efforts to improve clinical trial participation (CTP), enrollment remains low. Perceived additional costs to the patient or health plan may be barriers to CTP; low biomarker testing rates may be another. This study assessed differences in characteristics of patients with CTP versus patients without CTP in an insured population with advanced cancer. Methods: A retrospective analysis was conducted using de-identified administrative claims data from commercially insured and Medicare Advantage (MA) enrollees in the Optum Labs Data Warehouse. Patients ≥18y with claims evidence of advanced cancer and systemic therapy between 01/01/2018 and 02/28/2022 were stratified into 2 groups: 1) with CTP: ≥1 claim with a CTP diagnosis (ICD10 Z00.6) and ≥1 claim with a Q modifier on or within 90 days after index date; and 2) without CTP: no claims with a CTP diagnosis or Q modifier. Patients with >1 primary cancer, T-cell therapy, or <360 days baseline or follow-up (unless death) enrollment were excluded. Biomarker testing, targeted therapy use, and healthcare costs per patient per month (PPPM) were assessed in the baseline period. Results: Of 61,490 patients identified, 1753 (3%) had CTP and 59,737 (97%) did not have CTP; 4% of commercial and 3% of MA patients had CTP. Most common tumor types were breast (27%; 2% had CTP), lung (16%; 3% had CTP), digestive tract (15%; 2% had CTP), and prostate (15%; 3% had CTP). Patients with CTP were younger and more frequently had documented biomarker test use in the baseline period than patients without CTP (Table). In unadjusted analyses, the CTP group had higher healthcare costs in the baseline period prior to CTP regardless of insurance type, driven by 51% higher systemic cancer therapy costs and 35% higher ambulatory visit costs. An ongoing propensity score matched analysis will evaluate the impact of CTP on cost of care in the follow-up period. Conclusions: Consistent with prior studies, the overall CTP rate among patients with advanced cancers was low (3%). Baseline costs (prior to CTP) were higher for patients that later enrolled in clinical trials than for patients without CTP. Further research is ongoing to assess differences in follow-up healthcare costs for patients with vs without CTP adjusting for baseline characteristics. [Table: see text]

Supplementary Figures 1-7 - PDF file 117K, Supplementary Figure 1. Cohorts used for BCI model training and validation Supplementary Figure 2. Performance of H:I and MGI for early (0-5 years) and late (> 5 years) distant recurrence in Stockholm ER+, LN- untreated patients. A, C: H:I, 0-5 years and > 5 years. B, D: MGI, 0-5 years and > 5 years. Supplementary Figure 3. BCI risk groups for prediction of overall distant recurrence in Stockholm TAM and Multi-institutional cohorts, ER+, LN- patients. Supplementary Figure 4. Continuous BCI score for overall (0 - 10 years) rate of distant recurrence in Stockholm TAM cohort. Supplementary Figure 5. BCI risk groups for prediction of early and late distant recurrence in the combined StockholmTAM and Multi-institutional cohort; ER+, LN-, tamoxifen-treated patients. Supplementary Figure 6. Continuous BCI score for early (0 - 5 years) and late (> 5 years) rate of distant recurrence in the combined Stockholm TAM and Multi-institutional cohort (ER+, LN-, tamoxifen-treated patients). Supplementary Figure 7. Prediction of tamoxifen benefit by H/I. A: no benefit of tamoxifen in H/I-low patients. B: benefit of tamoxifen in H/I-high patients

6622 Background: An ever-increasing number of biomarker-guided therapies, some with pan-cancer indications have expanded the oncologist’s toolbox for fighting advanced cancer. Despite this, not all advanced cancer patients receive tumor genomic testing, or are tested for a limited number of targets. Still others are tested too late in their care journey to benefit from precision therapy. To assess the impact of removing testing barriers, we developed a reflex testing protocol where comprehensive genomic profiling (CGP) was routinely ordered by pathologists at time of diagnosis for advanced cancer patients. Methods: Reflex CGP testing was primarily initiated by the pathologist at the time of advanced cancer diagnosis. Testing was performed between 2019 and 2021 via CGP using the ProvSeq 523 lab-developed test, and testing was performed at no cost to the patient. Post-CGP, stage 4 patients were followed for ≥ 12 months. We assessed time to therapy, therapy selection, and overall survival (OS). Therapies were stratified by presence of biomarkers associated with approved targeted therapies (TT), presence of biomarkers for immunotherapies (IO), and/or therapies that were guideline based (GB) and not associated with a specific biomarker. As much key patient information is only consistently available in free-text medical charts, we implemented a novel natural-language processing (NLP) approach based on deep learning and large language models to accelerate abstraction. Results: A cohort of 1,423 advanced cancer patients met the study criteria. The median age was 66 years, 53% were female, and 82% white. The 3 most tested tumor types were 22% non-small cell lung cancer, 16% colorectal cancer, and 12% breast. Overall, 49% (N=704) of patients had a biomarker result considered actionable for an approved TT or IO. Median (IQR) time-to-treatment initiation post-CGP was 19 (2-70) days. In patients with no actionable TT or IO biomarkers (N=719), 63% were treated with chemotherapy-based regimens, 11% with GB, 17% with unmatched TT, and 8% with unmatched IO. Of patients who had only an actionable TT biomarker (N=287), 18% received matched TT and 13% received GB. 36% of patients with only an actionable IO biomarker (N=317) received matched IO monotherapy. 48% of patients with both actionable TT and IO biomarkers (N=100) received matched TT or IO monotherapy, and 5% received GB. Across all tumor types, patients receiving a TT had better OS compared to patients receiving chemotherapy with 12 months OS (%) of 70.1 (95% CI=64.4 - 76.3) for TT-treated patients, compared to 62.9 (95% CI=58.8 - 67.3) for chemotherapy only. Conclusions: CGP-guided precision therapy use is associated with significantly higher survival in a reflex testing population. A reflex protocol can overcome key barriers to the use and timing of genomic testing to improve access to these life-extending treatment modalities.

Introduction: Cardiovascular disease (CVD) continues to be the leading cause of death in the US and globally. Clinical practice guidelines recommend genetic testing for the diagnosis of select CVDs to improve disease management, reduce incidence of cardiac events, and positively impact major cardiac adverse events. Professional guidelines also recommend pharmacogenomic (PGx) testing for patients being prescribed certain medications. Research Question: What is the utilization of genetic diagnosis and PGx testing in patients with CVD in the US? Methods: This study used data from the Optum Labs Data Warehouse, composed of de-identified administrative claims data for both commercially insured and Medicare Advantage enrollees to assess rates of genetic testing among adult patients diagnosed with 4 major types of CVD conditions (arrhythmias (N=518,316), familial hypercholesterolemia (FH) (N=38,068), cardiomyopathies (CM) (N=74,597), and aortopathies (N=58,316)) and PGx testing rates in patients prescribed CVD medications with PGx test recommendations (per CPIC guidelines) between January 1, 2017 to December 31, 2021. Patients were required to have ≥12 months of continuous enrollment prior (baseline) to diagnosis (index date) and ≥12 months post-index period unless they died (follow-up). Genetic and PGx testing was captured using Current Procedural Terminology (CPT®) codes in both baseline and follow-up periods. Results: Low genetic testing utilization was found across the 4 major types of CVD conditions. Genetic testing rates were 1.3% in the arrythmia cohort, 1.9% in the CM cohort, 2.0% in the aortopathy cohort and 1.9% in the FH cohort. PGx testing rates in patients prescribed with CVD relevant medications were 0.6% (N=2,094/377,111) in the arrhythmia cohort, 0.8% (N=501/60,632) in the CM cohort, 0.7% (N=247/37,344) in the aortopathy cohort, and 1.3% (N=329/25,835) in FH cohort. Conclusions: Despite clinical practice guidelines, genetic and PGx testing in patients with CVD conditions remains underutilized. Future work will help us understand the barriers to guideline adherence both for genetic diagnosis and PGx testing that could impact clinical management and patient outcomes.

Supplemental Table 2 shows univariate and multivariate Cox regression analyses of overall 15-year and post-5-year prognostic performance of BCIN+.

6633 Background: Guideline-recommended molecular testing has become essential for biomarker-guided clinical decision making, particularly for patients with advanced (adv) disease. Several biomarkers have indications that are tumor type-agnostic (starting with MSI in 2017, NTRK in 2018, TMB in 2020, and RET and BRAF in 2022). Biomarker testing is covered by insurers, both Medicare (covers comprehensive genomic profiling [CGP] and non-CGP panels) and commercial (at least non-CGP). This study aimed to understand utilization of biomarker testing across tumor types. Methods: This retrospective analysis used de-identified administrative claims from Optum Labs Data Warehouse. Adult Commercial (COM) and Medicare Advantage (MA) enrollees diagnosed with any 1 of 6 adv cancer types from 1/2018 to 8/2021 were identified; the date of the first claim indicating adv cancer was the index date. Continuous enrollment for 12 months prior to (baseline), and ≥6 months post-index date, unless they died (follow-up) was required. Biomarker testing was captured using Current Procedural Terminology (CPT) codes indicating CGP (> 50 gene panels), non-CGP (at most 5-50 gene panels), or CPT code 81479 (unlisted molecular pathology procedure) during the study period. The primary analysis evaluates testing in the follow-up only, with secondary analyses evaluating testing including the baseline (to account for testing prior to the index date). Results: We identified 16,931 breast (BC), 16,838 non-small cell lung (NSCLC), 8,755 colorectal (CRC), 4,244 pancreatic (PC), 2,610 ovarian (OC), and 1,231 gastric (GC) adv cancer patients meeting study criteria. Overall biomarker testing rates in the follow-up period were: 37% NSCLC, 19% BC, 41% CRC, 35% PC, 51% OC, 35% GC. The Table shows testing rates by cancer, insurance type, and panel size during follow-up. Testing rates were lower among MA patients compared to COM patients. Even considering baseline and follow-up periods, overall biomarker testing rates were low, and lower among MA compared to COM : 48% NSCLC (53% COM, 47% MA), 27% BC (34% COM, 22% BC), 56% OC (61% COM, 53% MA),42% PC (53% COM, 38% MA), 47% CRC (56% COM, 42% MA), 41% GC (54% COM, 35% MA). Conclusions: Considering guideline recommendations, rates of biomarker testing across tumor types are far from optimal despite insurance coverage for testing. Identification of barriers to biomarker testing and interventions to overcome these are needed to improve adherence to biomarker testing guidelines. [Table: see text]

Supplemental Figure 4 shows the prognostic performance of BCIN+ for overall 15-year (A,C) and late post-5-year (B,D) distant recurrence in HER2-negative patients with 1-3 positive nodes.

550 Background: Biomarker testing to direct individualized therapy options can optimize cancer patient outcomes, particularly for patients with advanced disease. Guideline-recommended molecular testing, comprehensive genomic profiling [CGP] and non-CGP testing, is covered by both Medicare and commercial private insurers (at least non-CGP).. This study examined real-world utilization of biomarker testing among cancer patients with metastatic disease who received anti-cancer systemic therapy. Methods: A retrospective analysis was conducted using de-identified administrative claims data from the Optum Labs Data Warehouse. Adults identified with 1 of 6 advanced cancer types from 1/2018 to 8/2021 were identified; index date was the first claim date for advanced disease. Patients with diagnosis codes indicating only lymph node involvement around the primary cancer were excluded. Continuous enrollment in a commercial (COM) or Medicare Advantage (MA) health plan with both medical and pharmacy benefits was required for 12 months prior to the index date (baseline), and ≥6 months after the index date (follow-up); patients with <6 months follow-up due to death were included. Receipt of anti-cancer systemic therapy during the follow-up was required. Biomarker testing was captured using Current Procedural Terminology (CPT®) codes indicating CGP (> 50 gene panels) or non-CGP (≤50 gene panels or single gene tests) during the study period. Rates of biomarker testing by cancer and insurance type, and receipt of targeted therapy and/ were assessed. Results: Result: There were 27,434 metastatic cancer patients meeting study criteria: 10,320 non-small cell lung (NSCLC), 5,525 breast (BC), 5,429 colorectal (CRC), 2,314 ovarian (OC), 872 gastric (GC) and 2,974 pancreatic (PC) cancer patients. 66% of patients were MA enrollees. The median age was 70 years and ranged from 67 years for BC patients to 71 years for NSCLC and PC patients. The median follow-up was 383 days, ranging from 246 days (for PC patients) to 564 days (for BC patients). Overall biomarker testing rates in the follow-up period were: 47% for NSCLC, 34% BC, 58% CRC, 61% OC, 48% GC, and 47% PC. Testing rates were lower among MA patients compared to COM patients (46% vs 53%). Receipt of targeted/monoclonal antibody therapy was higher (47% vs 33%, p<0.01) among patients with biomarker testing compared to those without. Conclusions: Rates of biomarker testing across metastatic tumor types are far from optimal despite guideline recommendations and insurance coverage for testing, and may affect quality of care. To improve adherence to biomarker testing guidelines, interventions to help overcome obstacles to biomarker testing are needed. Future analysis with this cohort will examine patient management and outcomes by receipt of testing, timing of testing and type of therapy received.

Supplemental Figure 3 shows the discriminative performance of BCIN+ by ROC analysis for 15-year distant recurrence in patients with 1-3 positive nodes.

Supplemental Table 1 shows the clinical and pathologic characteristics for patients with early vs late distant recurrences.

Supplementary Tables 1-2 - PDF file 101K, Supplementary Table 1. Kaplan-Meier estimates of overall distant recurrence free survival (DRFS) for 3 BCI risk groups in Stockholm TAM-treated and Multi-institutional cohorts. Supplementary Table 2. Overall distant recurrence: Univariate and multivariate Cox model analysis after stepwise variable selection by AIC of likelihood of overall distant recurrence in Stockholm TAM and Multi-institutional cohorts for all ER+ and ER+, HER2- patients

Supplemental Figure 5 shows the prognostic performance of BCIN+ for overall 15-year and late post-5-year distant recurrence in subsets of patients with differing numbers of positive nodes.