
==== Front
JCO Precis Oncol
JCO Precis Oncol
po
PO
JCO Precision Oncology
2473-4284
Wolters Kluwer Health

38754057
PO.24.00075
10.1200/PO.24.00075
00108
ORIGINAL REPORTS
Precision Medicine
Real-World Impact of Comprehensive Genomic Profiling on Biomarker Detection, Receipt of Therapy, and Clinical Outcomes in Advanced Non–Small Cell Lung Cancer
https://orcid.org/0009-0005-2304-9792
Wallenta Law Jeanna MPH 1
Bapat Bela MA 2
https://orcid.org/0000-0002-4275-0569
Sweetnam Connor BS 1
https://orcid.org/0000-0002-6600-961X
Mohammed Hina MPH 1
https://orcid.org/0000-0003-1703-8032
McBratney Ashleigh MS 1
https://orcid.org/0000-0003-1852-5342
Izano Monika A. PhD, MS, MA 1
https://orcid.org/0000-0003-1030-3155
Scannell Bryan Molly PhD 1
Spencer Scott PhD 2
Schroeder Brock PhD 2
Hostin Damon MA 2
https://orcid.org/0000-0002-3894-0773
Simon George R. MD 3
https://orcid.org/0000-0002-9246-4560
Berry Anna B. MD 1
1 Syapse Holdings Inc, West Chester, PA
2 Illumina, San Diego, CA
3 Moffitt Cancer Center, Tampa, FL
Monika A. Izano, PhD; e-mail: Monika.Izano@syapse.com.
2024
16 5 2024
16 5 2024
8 e240007530 1 2024
13 3 2024
29 3 2024
© 2024 by American Society of Clinical Oncology
2024
American Society of Clinical Oncology
https://creativecommons.org/licenses/by-nc-nd/4.0/ Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: http://creativecommons.org/licenses/by-nc-nd/4.0/

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.

Comprehensive genetic testing improves survival of patients with advanced non–small cell lung cancer.

OPEN-ACCESSTRUE
==== Body
pmcINTRODUCTION

Non–small cell lung cancer (NSCLC) is a heterogeneous disease with a wide range of molecular subtypes and a growing list of therapy options.1 NSCLC tumors that are characterized by known oncogene drivers, such as BRAF, EGFR, KRAS and MET mutations, EGFR and ERBB2 amplifications, ALK rearrangements,2-6 and NTRK gene fusions,7 are effectively treated with therapies that target those alterations.1 Additionally, patients with high tumor mutational burden (TMB), microsatellite instability (MSI), and PD-L1 expression are likely to benefit from immunotherapy.8-12 Therapeutic choices thus rely on assessment of patients' specific tumor genetic profiles.

CONTEXT

Key Objective

This real-world study of patients with advanced non–small cell lung cancer receiving care in the US community health setting compared actionable biomarker detection, targeted therapy receipt, and overall survival (OS) among those tested with comprehensive genomic profiling (CGP) versus small panel testing (SP).

Knowledge Generated

Although the majority of patients received SP (80% v 20%), the proportion of patients with one or more actionable biomarkers (actionability) was significantly higher in CGP than in SP (32% v 14%; P < .001), and actionability was significantly associated with reduced risk of mortality. Among treated patients with actionability, recipients of matched therapies had improved OS in both the CGP and SP groups.

Relevance

Patient survival could be significantly improved by comprehensive testing that increases the detection of actionable biomarkers and subsequent use of matched therapies.

Approaches to characterize the biomarker profile for NSCLC include single-gene, sequential-gene, or limited-sized panels considered small panel testing (SP); or assessment of a large number of genes through comprehensive genomic profiling (CGP). CGP generates a more complete molecular portrait of the tumor13 and enables analyses of genomic signatures such as TMB and MSI. CGP can identify genomic alterations often missed with less comprehensive testing,14,15 and CGP-tested patients are more likely to be treated with beneficial targeted therapies.14-20 In addition, treatment guided by CGP has been shown to decrease health care costs13,21-23 and increase clinical trial enrollment. The Center for Medicare and Medicaid Services covers CGP testing starting in 2018 as do a growing number of commercial payers in the United States.

This study examines small panel and CGP testing strategies in real-world practice. Specifically, we evaluated the clinical impact of CGP versus SP on therapy matching and subsequent clinical outcomes in patients with locally advanced and metastatic NSCLC (aNSCLC) receiving care in the community health setting.

METHODS

Study Population

This was a retrospective study of patients with aNSCLC receiving cancer care in the Syapse Learning Health Network (LHN) of large community health systems across the United States. The database integrates structured and manually curated patient data from electronic health records (EHRs), laboratory and radiology/imaging systems, computerized order entry systems, and facility cancer registries. Eligible patients were diagnosed with aNSCLC (stages IIIB, IIIC, or IV at diagnosis, or recurrent locally advanced or metastatic disease) between January 1, 2015, and December 31, 2020 (index date); were age 18 years or older at initial NSCLC diagnosis; received small panel or CGP testing (defined below); and had ≥two distinct clinical encounters after the index date. Patients were followed through September 30, 2021 (study end date), allowing for a minimum potential follow-up of 9 months. This study received institutional review board exemption determination because of no patient contact and only use of secondary data sources.

Testing Type: Small Panel or CGP

Informed by the Medicare billing practices,24 for each instance of testing that a participant received any time after the 30 days before the initial NSCLC diagnosis, the testing panel was categorized into (1) small panel, if the panel interrogated between 1 and 52 genes, and (2) CGP, if the panel interrogated more than 52 genes (Data Supplement, Table S1). Billing codes (current procedural terminology) indicate tests interrogating >51 genes are classified as CGP; however, in our data, some laboratories used 51 or 52 gene panels. Thus, we adopted the >52 cutoff to classify true CGP tests. Tests were characterized according to the date the test was finalized; if the test date was missing, the date of specimen collection was used. Patients were categorized according to the most comprehensive testing they received during follow-up into small panel only or CGP recipients.

Timing of Most Comprehensive Testing

The patient journey was visualized in a Sankey diagram to show the timing of testing related to lines of treatment. Untreated patients tested after aNSCLC diagnosis were classified as tested before 1L (first-line regimen).

Outcomes

Actionability

Clinical actionability of each biomarker was determined on the basis of the OncoKB database standing at the time of the study.25 We defined actionable as level 1 (US Food and Drug Administration–recognized), level 2 (standard of care), or level R1 (predictive of resistance). The full list of OncoKB gene alterations and relevant targeted therapies for NSCLC, by level of evidence, at the time of our analysis, is presented in the Data Supplement (Table S2).

Matched Therapy

We defined matched therapy as receipt of a targeted therapy or immunotherapy matched to an actionable alteration according to OncoKB levels 1 or 2 (Data Supplement, Table S2). Indicators of whether patients with actionable biomarkers received matched therapies were used in analysis; indicators were set to missing for patients without an actionable alteration. Some analyses included PD-L1–matched therapies in addition to OncoKB levels 1 or 2.

Overall Survival

Real-world overall survival (rwOS) was the time from the index date to the patient's date of death; patients who did not die were censored at the study end date. The date of death was determined by a validated mortality score that uses data captured in (1) hospital-based cancer registries; (2) the Social Security Death index; (3) online obituaries; and (4) health systems' EHR.26

Covariates

Patient characteristics included year of aNSCLC diagnosis; age at aNSCLC diagnosis (date of advanced disease – date of birth); race/ethnicity; sex; smoking history; anonymized indicator of the health system in which patients were treated; the median household income estimated from the 2018 American Community Survey; stage at initial diagnosis; dominant histology categorized as squamous, nonsquamous, or other; the presence of bone, brain, distant lymph nodes, liver, lung, or other metastasis at the time of metastatic disease diagnosis and the number of metastatic sites; Eastern Cooperative Oncology Group performance status (ECOG PS) documented between 30 days before through 7 days after the aNSCLC diagnosis, prioritizing reports closest to aNSCLC diagnosis and the highest of the scores reported on the same day; and comorbidity. Sex, race/ethnicity, smoking history (current, former, and never), and pack-years smoked were self-reported at initial NSCLC diagnosis. The Charlson comorbidity index, a weighted sum of the presence of comorbidities over a 12-month period before the aNSCLC diagnosis, assessed the comorbidity burden.27,28

Lines of therapy received in the advanced setting were algorithmically determined on the basis of antineoplastic systemic agents that were abstracted from the EHR by Certified Tumor Registrars. Patients' 1L included all drugs that started up to 30 days after the aNSCLC diagnosis. Subsequent lines of therapy were identified after discontinuation of all agents in the line of therapy, a treatment gap of >120 days, or the introduction of a new systemic antineoplastic agent after the first 30 days of a line.

Statistical Analysis

Frequency (N) and percentage (%) of total patients described categorical variables. The calculation of percentages included the missing category in the case of missing values. Continuous variables were described by the median and IQR. Among patients who received one or more lines of systemic therapy, multivariable logistic regression estimated the independent association of testing type before the first line of therapy with receipt of a matched therapy in the first line adjusting for year of aNSCLC diagnosis, initial NSCLC stage, histology, age, sex, race/ethnicity, estimated median income, smoking history, comorbidity, number of metastatic sites, ECOG PS, and health system. Adjusted odds ratios (ORs) and corresponding 95% CIs were reported.

The Kaplan-Meier (KM) product-limit estimator was used to evaluate the distributions of rwOS. KM plots included the number at risk, number of events and censored patients, median time to event (in months), and 95% CI of the median. Associations of testing type with rwOS were evaluated using multivariable Cox proportional hazards models; models were adjusted for year of aNSCLC diagnosis, initial NSCLC stage, histology, age, sex, race/ethnicity, estimated median income, smoking history, comorbidity, number of metastatic sites, ECOG, actionable biomarker detected, and health system. Models were additionally adjusted for the number of lines of therapy received in sensitivity analyses. We conducted all analyses using RStudio.29

RESULTS

Study Population Characteristics

The study included 3,884 patients with aNSCLC who received biomarker testing. The median age at diagnosis was 68 years, 50% of patients were female, and 73% were non-Hispanic White; approximately 80% of patients were diagnosed with NSCLC in 2017 or later, and 90% of patients were stage IIIa or above at initial NSCLC diagnosis (Table 1). When patients were grouped by the most comprehensive testing received, 779 (20%) of patients received CGP and 3,105 (80%) received a small panel as the most comprehensive test. Six hundred and three (77%) of CGP patients and 1,852 (60%) of small panel patients received any type of cancer-directed systemic therapy (P < .001; Table 2). Small panel and CGP recipients had similar distributions of year of initial diagnosis, sex, race/ethnicity, region of residence, median household income, comorbidity, PS, stage, age, histology, smoking history, sites, and number of metastases for patients (Table 1). Among the three health systems, a greater proportion of patients in health system three received CGP instead of a small panel for their most comprehensive testing (41%) compared with health systems one (23%) or two (10%).

TABLE 1. Patient Demographic and Clinical Characteristics

Characteristic	Total (N = 3,884)	CGP Ever (n = 779)	Small Panel Only Ever (n = 3,105)	
Health system, No. (%)				
 1	924 (24)	209 (27)	715 (23)	
 2	2,098 (54)	219 (28)	1,879 (61)	
 3	862 (22)	351 (45)	511 (16)	
Year of aNSCLC diagnosis, No. (%)				
 2015	170 (4.4)	39 (5.0)	131 (4.2)	
 2016	475 (12)	92 (12)	383 (12)	
 2017	560 (14)	82 (11)	478 (15)	
 2018	784 (20)	159 (20)	625 (20)	
 2019	1,033 (27)	208 (27)	825 (27)	
 2020	862 (22)	199 (26)	663 (21)	
Stage at initial diagnosis, No. (%)				
 I	147 (3.8)	34 (4.4)	113 (3.6)	
 II	82 (2.1)	23 (3.0)	59 (1.9)	
 III	676 (17)	125 (16)	551 (18)	
 IV	2,812 (72)	560 (72)	2,252 (73)	
 Unknown	167 (4.3)	37 (4.7)	130 (4.2)	
Histology, No. (%)				
 Squamous	818 (21)	125 (16)	693 (22)	
 Nonsquamous	2,659 (68)	576 (74)	2,083 (67)	
 Other	407 (10)	78 (10)	329 (11)	
Age at aNSCLC diagnosis, years, median (IQR)	68 (61-76)	67 (60-74)	69 (61-76)	
Race/ethnicity, No. (%)				
 Asian/Hawaiian/Pacific Islander	124 (3.2)	27 (3.5)	97 (3.1)	
 Black	591 (15)	101 (13)	490 (16)	
 Hispanic/Latino	211 (5.4)	40 (5.1)	171 (5.5)	
 Non-Hispanic White	2,842 (73)	566 (73)	2,276 (73)	
 Other or unknown	116 (3.0)	45 (5.8)	71 (2.3)	
Sex				
 Female	1,944 (50)	402 (52)	1,542 (50)	
 Male	1,935 (50)	375 (48)	1,560 (50)	
 Unknown/other	5 (0.1)	2 (0.3)	3 (<0.1)	
Median household income category in US dollars, No. (%)				
 <$30,000	96 (2.5)	19 (2.4)	77 (2.5)	
 $30,000 to <$50,000	1,278 (33)	246 (32)	1,032 (33)	
 $50,000 to <$75,000	1,719 (44)	335 (43)	1,384 (45)	
 $75,000 to <$100,000	557 (14)	108 (14)	449 (14)	
 ≥$100,000	139 (3.6)	29 (3.7)	110 (3.5)	
 Unknown	95 (2.4)	42 (5.4)	53 (1.7)	
Smoking history, No. (%)				
 Current smoker	1,303 (34)	257 (33)	1,046 (34)	
 Former smoker	1,886 (49)	369 (47)	1,517 (49)	
 Never smoker	466 (12)	130 (17)	336 (11)	
 Unknown	229 (5.9)	23 (3.0)	206 (6.6)	
Geographic region, No. (%)				
 Midwest	2,918 (75)	549 (70)	2,369 (76)	
 Northeast	76 (2.0)	10 (1.3)	66 (2.1)	
 South	807 (21)	183 (23)	624 (20)	
 Unknown	79 (2.0)	37 (4.7)	42 (1.4)	
 West	4 (0.1)	0 (0)	4 (0.1)	
Evidence of brain metastasis, No. (%)				
 No	2,853 (73)	551 (71)	2,302 (74)	
 Yes	1,031 (27)	228 (29)	803 (26)	
Evidence of liver metastasis, No. (%)				
 No	3,365 (87)	683 (88)	2,682 (86)	
 Yes	519 (13)	96 (12)	423 (14)	
Evidence of lung metastasis, No. (%)				
 No	2,573 (66)	481 (62)	2,092 (67)	
 Yes	1,311 (34)	298 (38)	1,013 (33)	
Evidence of bone metastasis, No. (%)				
 No	2,479 (64)	473 (61)	2,006 (65)	
 Yes	1,405 (36)	306 (39)	1,099 (35)	
Evidence of metastasis to other sites, No. (%)				
 No	2,671 (69)	523 (67)	2,148 (69)	
 Yes	1,213 (31)	256 (33)	957 (31)	
No. of metastatic sites, median (IQR)	1 (1-2)	1 (1-2)	1 (1-2)	
Charlson comorbidity index, No. (%)				
 0	933 (24)	153 (20)	780 (25)	
 1	632 (16)	94 (12)	538 (17)	
 2	284 (7.3)	41 (5.3)	243 (7.8)	
 3+	178 (4.6)	12 (1.5)	166 (5.3)	
 Unknown	1,857 (48)	479 (61)	1,378 (44)	
ECOG performance status, No. (%)				
 0	556 (14)	140 (18)	416 (13)	
 1	881 (23)	174 (22)	707 (23)	
 ≥2	647 (17)	89 (11)	558 (18)	
 Unknown	1,800 (46)	376 (48)	1,424 (46)	
Pack-years smoked among current/former smokers, median (IQR)	40 (22-50)	35 (20-50)	40 (23-50)	
Abbreviations: aNSCLC, advanced non–small cell lung cancer; ECOG, Eastern Cooperative Oncology Group.

TABLE 2. Actionable Alterations and Therapy Receipt Summarized by OncoKB Level by Most Comprehensive Test Received During Study

Variable	Total (N = 3,884), %	Small Panel Only (n = 3,105), %	CGP Ever (n = 779), %	P	
Actionable alteration					
 OncoKB 1 detected	17	14	31	<.001	
 OncoKB 2 detected	1.50	1.20	2.70	.003	
 OncoKB R1 detected	0.40	0.20	1.40	<.001	
 OncoKB 1 and/or 2 and/or R1 detected	17	14	32	<.001	
 PD-L1 detected	54	54	52	.2	
Therapy receipt					
 Any evidence of therapy	63	60	77	<.001	
 Matched therapy (OncoKB 1 or 2)	7	5	14	<.001	
 Any matched therapy (OncoKB 1, 2, or PD-L1)	31	29	39	<.001	
 Matched immunotherapy (TMB, MSI, PD-L1)	26	24	31	<.001	
 Matched immunotherapy for PD-L1	25	24	28	.072	
Abbreviations: CGP, comprehensive genomic profiling; MSI, microsatellite instability; TMB, tumor mutational burden.

Timing of Most Comprehensive Testing

Time of testing relative to the line of therapy is represented in the Data Supplement (Fig S1). One hundred and fifty-four (4%) patients who initially received SP and later received CGP in the follow-up are included in the CGP group. A higher proportion of patients receiving small panel were tested before 1L therapy initiation compared with CGP-tested patients (96% [N = 2,970/3,106] v 66% [511/779]; P < .001; Data Supplement, Fig S1).

Clinical Actionability

Overall, OncoKB actionable alterations were detected at more than twice the rate in the CGP group (32% v 14%; P < .001; Table 2). OncoKB level 1 alterations were detected among 31% of the patients in the CGP group and 14% of the patients in the small panel group; OncoKB level 2 alterations were detected among 2.7% and 1.2% of the patients in the CGP and small panel groups, respectively; OncoKB level R1 alterations were detected among 1.4% and 0.2% of the patients in the CGP and small panel groups, respectively (Table 2). The proportion of patients in each group for the full list of clinically actionable alterations is provided in the Data Supplement (Table S3). Specific alterations with higher detection with CGP compared with small panel were EGFR T790M (1.4% compared with 0.2%; P < .001), KRAS G12C (9.1% compared with 5.4%; P < .001), MSI (1.0% compared with <0.1%; P < .001), TMB (11.0% compared with 0%; P < .001), and ERBB2 oncogenic mutations (0.8% compared with 0%; P < .001; Data Supplement, Table S3).

Receipt of Matched Therapies

Among all patients, 39% of CGP and 29% of the small panel group received OncoKB level 1, 2, or PD-L1–matched therapy (P < .001; Table 2). Fourteen percent of CGP and 5% of the small panel group received matched therapies on the basis of OncoKB level 1 or 2 (P < .001), and 31% of CGP and 24% of small panel group received matched immunotherapy for TMB, MSI, or PD-L1 (P < .001). Notably, 28 (4%) of patients in the CGP group received matched immunotherapy for TMB or MSI compared with 0% of small panel patients (P < .001; Table 2). Among patients with actionable biomarkers on the basis of OncoKB level 1 or 2, 43% of CGP and 38% of the small panel group received matched therapies (P = .20). When the association of testing type before 1L with receipt of a matched therapy in 1L was evaluated by multivariate logistic regression, among patients who received 1L (N = 1,739), CGP had a statistically significant association with greater odds of receiving therapy on the basis of OncoKB level 1 or 2 (OR, 3.20 [95% CI, 1.84 to 5.53; Table 3). Factors significantly associated with reduced odds of receiving a matched therapy in the first line included later years of aNSCLC diagnosis (2015: reference; 2016: OR, 0.50 [95% CI, 0.18 to 1.44]; 2017: OR, 0.30 [95% CI, 0.10 to 0.94]; 2018: OR, 0.30 [95% CI, 0.11 to 0.85]; 2019: OR, 0.45 [95% CI, 0.18 to 1.20]; 2020: OR, 0.45 [95% CI, 0.18 to 1.23]), squamous/other histology (OR, 0.31 [95% CI, 0.13 to 0.62]), and current smoking (OR, 0.15 [95% CI, 0.07 to 0.31]) or former smoking (OR, 0.37 [95% CI, 0.21 to 0.64]).

TABLE 3. Multivariable Logistic Regression Evaluating Receipt of Matched Therapy in the First Line for OncoKB Level 1 and 2 Actionable Alterations in the aNSCLC Setting Among Patients Who Had Evidence of Testing Before 1L

Variable	Total (N = 1,739)	Matched Therapy per OncoKB Levels 1 and 2 (n = 94)	No Matched Therapy per OncoKB Levels 1 and 2 (n = 1,645)	OR (95% CI)	P	
Categorization of testing before 1L, No. (%)						
 Small panel only	1,477 (85)	61 (65)	1,416 (86)	—		
 CGP	262 (15)	33 (35)	229 (14)	3.2 (1.84 to 5.53)	<.001	
Year of aNSCLC diagnosis, No. (%)						
 2015	83 (4.8)	8 (8.5)	75 (4.6)	—		
 2016	215 (12)	14 (15)	201 (12)	0.5 (0.18 to 1.44)	.2	
 2017	234 (13)	8 (8.5)	226 (14)	0.3 (0.10 to 0.94)	.038	
 2018	388 (22)	16 (17)	372 (23)	0.3 (0.11 to 0.85)	.019	
 2019	448 (26)	24 (26)	424 (26)	0.45 (0.18 to 1.20)	.094	
 2020	371 (21)	24 (26)	347 (21)	0.45 (0.18 to 1.23)	.1	
Stage at initial NSCLC, No. (%)						
 I or II	92 (5.3)	2 (2.1)	90 (5.5)	—		
 III	311 (18)	3 (3.2)	308 (19)	0.83 (0.11 to 7.11)	.9	
 IV	1,277 (73)	87 (93)	1,190 (72)	2.99 (0.83 to 19.4)	.2	
 Unknown	59 (3.4)	2 (2.1)	57 (3.5)	1.5 (0.16 to 14.2)	.7	
Histology, No. (%)						
 Nonsquamous	1,214 (70)	86 (91)	1,128 (69)	—		
 Squamous/other	525 (30)	8 (8.5)	517 (31)	0.31 (0.13 to 0.62)	.002	
Age at aNSCLC, years, median (IQR)	67 (60-74)	66 (60-74)	67 (60-74)	1 (0.97 to 1.02)	.7	
Sex, No. (%)						
 Female	878 (50)	59 (63)	819 (50)	—		
 Male	857 (49)	35 (37)	822 (50)	0.71 (0.45 to 1.13)	.2	
 Unknown/other	4 (0.2)	0 (0)	4 (0.2)	0	>.9	
Race/ethnicity, No. (%)						
 Asian/Hawaiian/Pacific Islander	54 (3.1)	8 (8.5)	46 (2.8)	—		
 Black	239 (14)	14 (15)	225 (14)	0.62 (0.21 to 1.87)	.4	
 Hispanic/Latino	99 (5.7)	6 (6.4)	93 (5.7)	0.29 (0.08 to 1.01)	.053	
 Non-Hispanic White	1,296 (75)	62 (66)	1,234 (75)	0.45 (0.19 to 1.18)	.087	
 Other or unknown	51 (2.9)	4 (4.3)	47 (2.9)	0.31 (0.04 to 1.83)	.2	
Median income in US dollars, No. (%)						
 <$30,000	49 (2.8)	3 (3.2)	46 (2.8)	—		
 $30,000 to <$50,000	544 (31)	21 (22)	523 (32)	0.61 (0.18 to 2.87)	.5	
 $50,000 to <$75,000	786 (45)	48 (51)	738 (45)	0.96 (0.28 to 4.47)	>.9	
 $75,000 to <$100,000	262 (15)	15 (16)	247 (15)	0.75 (0.20 to 3.72)	.7	
 ≥$100,000	50 (2.9)	2 (2.1)	48 (2.9)	0.43 (0.05 to 3.12)	.4	
 Unknown	48 (2.8)	5 (5.3)	43 (2.6)	3.16 (0.45 to 23.7)	.2	
Smoking history, No. (%)						
 Never smoker	202 (12)	34 (36)	168 (10)	—		
 Current smoker	621 (36)	15 (16)	606 (37)	0.15 (0.07 to 0.31)	<.001	
 Former smoker	845 (49)	44 (47)	801 (49)	0.37 (0.21 to 0.64)	<.001	
 Unknown	71 (4.1)	1 (1.1)	70 (4.3)	0.15 (0.01 to 2.71)	.2	
CCI, No. (%)						
 0	482 (28)	31 (33)	451 (27)	—		
 1	285 (16)	14 (15)	271 (16)	0.99 (0.48 to 1.98)	>.9	
 ≥2	193 (11)	10 (11)	183 (11)	1.05 (0.45 to 2.29)	>.9	
 Unknown	779 (45)	39 (41)	740 (45)	0.72 (0.37 to 1.37)	.3	
ECOG, No. (%)						
 0	336 (19)	21 (22)	315 (19)	—		
 1	487 (28)	23 (24)	464 (28)	0.9 (0.46 to 1.77)	.8	
 ≥2	242 (14)	13 (14)	229 (14)	1.23 (0.54 to 2.69)	.6	
 Unknown	674 (39)	37 (39)	637 (39)	0.9 (0.48 to 1.69)	.7	
No. of metastatic sites, (%)						
 0	223 (13)	2 (2.1)	221 (13)	—		
 1	827 (48)	43 (46)	784 (48)	2.16 (0.33 to 30.5)	.5	
 ≥2	689 (40)	49 (52)	640 (39)	2.89 (0.43 to 41.1)	.3	
Health system, No. (%)						
 1	406 (23)	27 (29)	379 (23)	—		
 2	960 (55)	48 (51)	912 (55)	1.23 (0.67 to 2.32)	.5	
 3	373 (21)	19 (20)	354 (22)	1.09 (0.49 to 2.48)	.8	
Abbreviations: aNSCLC, advanced NSCLC; CCI, Charlson Comorbidity Index; CGP, comprehensive genomic profiling; ECOG, Eastern Cooperative Oncology Group; NSCLC, non–small cell lung cancer; OR, odds ratio.

Clinical Outcomes

In KM analyses, CGP recipients had better survival than small panel only recipients (median rwOS, 18 [17-22] v 10 [9-11] months; Data Supplement, Fig S2). In multivariable Cox proportional hazards models, CGP testing (hazard ratio [HR], 0.80 [95% CI, 0.72 to 0.89]) and having an OncoKB level 1 or 2 actionable biomarker detected (HR, 0.84 [95% CI, 0.77 to 0.91]) were statistically significantly associated with a reduced hazard of mortality (Table 4). Year of aNSCLC diagnosis (2015: reference; 2016: HR, 1.31 [95% CI, 1.07 to 1.60]; 2017: HR, 1.31 [95% CI, 1.07 to 1.60]; 2018: HR, 1.45 [95% CI, 1.18 to 1.77]; 2019: HR, 1.22 [95% CI, 1.00 to 1.49]; 2020: HR, 1.25 [95% CI, 1.02 to 1.55]), de novo metastatic disease (HR, 1.32 [95% CI, 1.07 to 1.63]), squamous (HR, 1.19 [95% CI, 1.08 to 1.31]) or other histology (HR, 1.3 [95% CI, 1.15 to 1.46]), older age at aNSCLC diagnosis (HR, 1.01 [95% CI, 1.01 to 1.02]), male sex (HR, 1.19 [95% CI, 1.10 to 1.28]), greater income (HR, 1.4 [95% CI, 1.00 to 1.96]), current smoking (HR, 1.18 [95% CI, 1.02 to 1.37]) and former smoking (HR, 1.37 [95% CI, 1.19 to 1.57]) were statistically significantly associated with greater mortality risk. Findings were similar in a model that additionally adjusted for the number of lines of therapy received in the advanced setting (Data Supplement, Table S4).

TABLE 4. Multivariable Cox Proportional HRs and 95% CIs for the Association of CGP Testing With OS Among All Patients With Follow-Up Starting at aNSCLC Diagnosis

Variable	Alive (N = 1,186)	Deceased (N = 2698)	HR (95% CI)	P	
CGP testing ever, No. (%)					
 No	906 (76)	2,199 (82)	—		
 Yes	280 (24)	499 (18)	0.8 (0.72 to 0.89)	<.001	
Year of aNSCLC diagnosis, No. (%)					
 2015	33 (2.8)	137 (5.1)	—		
 2016	73 (6.2)	402 (15)	1.31 (1.07 to 1.60)	.008	
 2017	116 (9.8)	444 (16)	1.31 (1.07 to 1.60)	.01	
 2018	183 (15)	601 (22)	1.45 (1.18 to 1.77)	<.001	
 2019	361 (30)	672 (25)	1.22 (1.00 to 1.49)	.053	
 2020	420 (35)	442 (16)	1.25 (1.02 to 1.55)	.033	
Stage at initial NSCLC, No. (%)					
 I	49 (4.1)	98 (3.6)	—		
 II	29 (2.4)	53 (2.0)	1.15 (0.82 to 1.61)	.4	
 III	268 (23)	408 (15)	1 (0.80 to 1.27)	>.9	
 IV	788 (66)	2,024 (75)	1.32 (1.07 to 1.63)	.009	
 Unknown	52 (4.4)	115 (4.3)	1.36 (1.03 to 1.79)	.031	
Histology, No. (%)					
 Nonsquamous	879 (74)	1,780 (66)	—		
 Other	93 (7.8)	314 (12)	1.3 (1.15 to 1.46)	<.001	
 Squamous	214 (18)	604 (22)	1.19 (1.08 to 1.31)	<.001	
Age at aNSCLC, years, median (IQR)	66 (59-74)	69 (61-77)	1.01 (1.01 to 1.02)	<.001	
Sex, No. (%)					
 Female	651 (55)	1,293 (48)	—		
 Male	531 (45)	1,404 (52)	1.19 (1.10 to 1.28)	<.001	
 Unknown/other	4 (0.3)	1 (<0.1)	0.36 (0.05 to 2.58)	.3	
Race/ethnicity, No. (%)					
 Non-Hispanic White	809 (68)	2,033 (75)	—		
 Asian/Hawaiian/Pacific Islander	51 (4.3)	73 (2.7)	0.88 (0.69 to 1.12)	.3	
 Black	182 (15)	409 (15)	0.93 (0.83 to 1.04)	.2	
 Hispanic/Latino	95 (8.0)	116 (4.3)	0.68 (0.56 to 0.83)	<.001	
 Other or unknown	49 (4.1)	67 (2.5)	1.03 (0.75 to 1.41)	.9	
Median income in US dollars, No. (%)					
 <$30,000	39 (3.3)	57 (2.1)	—		
 $30,000 to <$50,000	366 (31)	912 (34)	1.26 (0.96 to 1.65)	.1	
 $50,000 to <$75,000	538 (45)	1,181 (44)	1.19 (0.91 to 1.57)	.2	
 $75,000 to <$100,000	161 (14)	396 (15)	1.23 (0.92 to 1.65)	.2	
 ≥$100,000	35 (3.0)	104 (3.9)	1.4 (1.00 to 1.96)	.051	
 Unknown	47 (4.0)	48 (1.8)	0.79 (0.50 to 1.25)	.3	
Smoking history, No. (%)					
 Never smoker	201 (17)	265 (9.8)	—		
 Current smoker	441 (37)	862 (32)	1.18 (1.02 to 1.37)	.03	
 Former smoker	517 (44)	1,369 (51)	1.37 (1.19 to 1.57)	<.001	
 Unknown	27 (2.3)	202 (7.5)	2.89 (2.27 to 3.68)	<.001	
CCI, No. (%)					
 0	312 (26)	621 (23)	—		
 1	168 (14)	464 (17)	1.17 (1.03 to 1.32)	.012	
 2	71 (6.0)	213 (7.9)	1.18 (1.01 to 1.38)	.041	
 ≥3	39 (3.3)	139 (5.2)	1.14 (0.94 to 1.38)	.2	
 Unknown	596 (50)	1,261 (47)	0.91 (0.81 to 1.02)	.1	
ECOG, No. (%)					
 0	239 (20)	317 (12)	—		
 1	286 (24)	595 (22)	1.34 (1.17 to 1.54)	<.001	
 ≥2	94 (7.9)	553 (20)	2.48 (2.15 to 2.86)	<.001	
 Unknown	567 (48)	1,233 (46)	1.45 (1.27 to 1.64)	<.001	
No. of metastatic sites, (%)					
 0	184 (16)	359 (13)	—		
 1	628 (53)	1,231 (46)	1.16 (0.97 to 1.38)	.1	
 ≥2	374 (32)	1,108 (41)	1.64 (1.37 to 1.96)	<.001	
Actionable biomarker detected, No. (%)					
 No	374 (32)	1,144 (42)	—		
 Yes	812 (68)	1,554 (58)	0.84 (0.77 to 0.91)	<.001	
Health system, No. (%)					
 1	304 (26)	620 (23)	—		
 2	614 (52)	1,484 (55)	0.96 (0.86 to 1.06)	.4	
 3	268 (23)	594 (22)	1.18 (1.03 to 1.36)	.018	
Abbreviations: 1L, first-line regimen; aNSCLC, advanced NSCLC; CCI, Charlson Comorbidity Index; CGP, comprehensive genomic profiling; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; NSCLC, non–small cell lung cancer; OS, overall survival.

When survival was assessed separately for those who received systemic therapy and those who did not, CGP-tested patients had better survival than small panel–tested patients in both treated (median rwOS, 22 [18-25] v 15 [14-16] months) and untreated patients (median rwOS, 10 [6-15] v 4 [4-5] months; Fig 1). Among the subset of treated patients with an OncoKB level 1 or 2 actionable biomarker detected, patients who received matched therapy had improved survival compared with patients who received unmatched therapies only in both the CGP-tested (median rwOS, 34 [21-49] matched therapy v 14 [10-18] unmatched therapy) and small panel–tested groups (median rwOS, 27 [21-43] matched therapy v 10 [8-14] unmatched therapy; Fig 2). Additional analyses including receipt of therapy for PD-L1 are provided in the Data Supplement (Fig S3).

FIG 1. Overall survival from aNSCLC diagnosis by most comprehensive testing received during the study and systemic therapy received during the study period. aNSCLC, advanced non–small cell lung cancer; CGP, comprehensive genomic profiling.

FIG 2. Overall survival from aNSCLC diagnosis by most comprehensive testing received during the study and matched therapy for OncoKB level 1 or 2, among patients with actionable alterations. aNSCLC, advanced non–small cell lung cancer; CGP, comprehensive genomic profiling.

DISCUSSION

In this retrospective study that compared patients with CGP testing to patients with SP, in a real-world, large community health system setting, patients with CGP testing had actionable alterations detected at more than twice the rate of patients with SP, and a significantly higher proportion of patients received matched targeted or immunotherapy. We further found that patients treated with matched therapies had improved survival compared with patients not treated with matched therapies. Although significant benefit of matched therapy is seen regardless of testing approach, a significantly higher proportion of CGP-tested patients have actionable biomarker findings and were placed on matched therapy. Unfortunately, more than half of the patients with actionable biomarker findings did not receive matched therapies, which was associated with worse OS, highlighting an ongoing care gap.

This study highlights that a majority of patients did not receive CGP testing among patients diagnosed between 2015 and 2020. In a study of patients diagnosed with metastatic NSCLC between 2018 and 2021, only 14% received CGP testing.30 Greater accessibility of in-house, SP, and shorter turnaround times than reference laboratory CGP testing may explain the low GCP testing uptake in the community setting. Consistent with our findings, a recent study conducted among patients with advanced cancer in a clinical trial recruitment program in Australia demonstrated improved survival among patients with CGP-detected actionable biomarkers who received targeted treatments.31 The improved actionability leading to greater uptake of matched therapies in both the Australian study and our study suggest a need for greater uptake of CGP to enhance patient benefit. In addition, among patients with actionable biomarkers, only a subset received matched therapies in this study. One potential explanation for this is patient contraindications such as rapid progression, dwindling PS, or comorbidities. It is also possible that actionable mutations were identified before targetable therapies being available for the particular mutation.25,32-35

This study provides insight into real-world clinical practice patterns and characterizes the treatment and survival trajectory of patients diagnosed with aNSCLC in the community setting. Highlighting the patient experience in the community setting is essential as approximately 85% of patients with cancer receive care in the community setting.36 The LHN may include a more diverse population than would typically be seen in a clinical trial or academic setting. The differences in uptake of CGP testing across the LHN health systems may reflect variability in practices regarding reflex testing and whether testing is performed in-house or sent out to reference laboratories. It is possible that health systems with available, in-house, small panels preferentially ordered these, while health systems that used reference laboratories preferentially ordered CGP.

A potential limitation of this study is immortal time bias. Patients who live longer may have had more opportunities to receive CGP testing compared with patients who do not live as long.37 Therefore, it is possible that by grouping patients according to the most comprehensive biomarker testing received during follow-up that patients who live longer are overrepresented in the CGP group. This might explain the improved survival in CGP versus small panel recipients, even if they did not receive any systemic therapy. However, it is pertinent to consider that the mechanism through which CGP would act to improve survival is via improved and more complete detection of actionable alterations and subsequent treatment with matched therapies—both of which are reported in this study.

Identification and characterization of molecular biomarkers has revolutionized the management of lung cancer, enabling personalized treatment. This study shows that CGP testing can unlock personalized treatment options and improve downstream clinical outcomes. Therefore, broadening the access and uptake of CGP testing enabling patients to receive molecularly driven precision therapies is paramount to improving outcomes in patients with aNSCLC.

PRIOR PRESENTATION

SUPPORT

AUTHOR CONTRIBUTIONS

Conception and design: Jeanna Wallenta Law, Bela Bapat, Monika A. Izano, Scott Spencer, Brock Schroeder, Damon Hostin, George R. Simon, Anna B. Berry

Financial support: Bela Bapat

Administrative support: Damon Hostin

Provision of study materials or patients: George R. Simon

Collection and assembly of data: Jeanna Wallenta Law, Connor Sweetnam, Hina Mohammed, Ashleigh McBratney, Monika A. Izano, Scott Spencer, Brock Schroeder, Anna B. Berry

Data analysis and interpretation: All authors

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

Presented in part at the European Society for Medical Oncology Congress in Madrid, Spain, October 20-24, 2023.

Supported by Illumina. Funding source provided input on study design, review of the report, manuscript development, and the decision to submit the article for publication.

Jeanna Wallenta Law

Employment: Syapse

Stock and Other Ownership Interests: Syapse

Travel, Accommodations, Expenses: Merck

Bela Bapat

Stock and Other Ownership Interests: Illumina

Connor Sweetnam

Employment: Syapse

Stock and Other Ownership Interests: Syapse

Hina Mohammed

Employment: Syapse Holdings, Syapse

Stock and Other Ownership Interests: Syapse

Research Funding: Merck (Inst), AstraZeneca (Inst), Illumina (Inst), Bristol Myers Squibb (Inst)

Ashleigh McBratney

Employment: Syapse

Stock and Other Ownership Interests: Tempus, Syapse

Patents, Royalties, Other Intellectual Property: Patent application 17/546,049: Artificial Intelligence Driven Therapy Curation and Prioritization

Monika A. Izano

Employment: Syapse

Stock and Other Ownership Interests: Syapse

Research Funding: Syapse

Molly Scannell Bryan

Employment: Syapse

Research Funding: Syapse

Scott Spencer

Employment: Illumina

Stock and Other Ownership Interests: Illumina

Research Funding: Illumina

Brock Schroeder

Employment: Illumina

Stock and Other Ownership Interests: Illumina

Damon Hostin

Employment: Illumina

Stock and Other Ownership Interests: Illumina

George R. Simon

Consulting or Advisory Role: AstraZeneca, Onc.AI, Genprex, Reflexion Medical

Speakers' Bureau: AstraZeneca, OncLive

Anna B. Berry

Employment: Syapse

Stock and Other Ownership Interests: Syapse

No other potential conflicts of interest were reported.
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REFERENCES

1. National Comprehensive Cancer Network: NCCN Clinical Practice Guidelines in Oncology Non Small Cell Lung Cancer. Version 7.2021. nccn.org
2. Shigematsu H , Gazdar AF : Somatic mutations of epidermal growth factor receptor signaling pathway in lung cancers. Int J Cancer 118 :257-262, 2006 16231326
3. Herbst RS , Heymach JV , Lippman SM : Lung cancer. N Engl J Med 359 :1367-1380, 2008 18815398
4. Cappuzzo F , Jänne PA , Skokan M , et al : MET increased gene copy number and primary resistance to gefitinib therapy in non-small-cell lung cancer patients. Ann Oncol 20 :298-304, 2009 18836087
5. Martelli MP , Sozzi G , Hernandez L , et al : EML4-ALK rearrangement in non-small cell lung cancer and non-tumor lung tissues. Am J Pathol 174 :661-670, 2009 19147828
6. Pawelczyk K , Piotrowska A , Ciesielska U , et al : Role of PD-L1 expression in non-small cell lung cancer and their prognostic significance according to clinicopathological factors and diagnostic markers. Int J Mol Sci 20 :824, 2019 30769852
7. Hsiao SJ , Sireci AN , Pendrick D , et al : Clinical utilization, utility, and reimbursement for expanded genomic panel testing in adult oncology. JCO Precis Oncol 10.1200/PO.20.00048
8. Kerr KM , Bibeau F , Thunnissen E , et al : The evolving landscape of biomarker testing for non-small cell lung cancer in Europe. Lung Cancer 154 :161-175, 2021 33690091
9. Willis C , Bauer H , Au TH , et al : Real-world survival analysis by tumor mutational burden in non-small cell lung cancer: A multisite U.S. study. Oncotarget 13 :257-270, 2022 35111281
10. Ricciuti B , Wang X , Alessi JV , et al : Association of high tumor mutation burden in non–small cell lung cancers with increased immune infiltration and improved clinical outcomes of PD-L1 blockade across PD-L1 expression levels. JAMA Oncol 8 :1160-1168, 2022 35708671
11. André T , Shiu K , Kim T , et al : Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. N Engl J Med 383 :2207-2218, 2020 33264544
12. Marabelle A , Fakih M , Lopez J , et al : Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: Prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol 21 :1353-1365, 2020 32919526
13. Harvey MJ , Cunningham R , Sawchyn B , et al : Budget impact analysis of comprehensive genomic profiling in patients with advanced non-small-cell lung cancer. JCO Precis Oncol 10.1200/PO.20.00540
14. Ali SM , Hensing T , Schrock AB , et al : Comprehensive genomic profiling identifies a subset of crizotinib-responsive ALK-rearranged non-small cell lung cancer not detected by fluorescence in situ hybridization. Oncologist 21 :762-770, 2016 27245569
15. Schrock AB , Frampton GM , Herndon D , et al : Comprehensive genomic profiling identifies frequent drug-sensitive EGFR exon 19 deletions in NSCLC not identified by prior molecular testing. Clin Cancer Res 22 :3281-3285, 2016 26933124
16. Johnson DB , Dahlman KH , Knol J , et al : Enabling a genetically informed approach to cancer medicine: A retrospective evaluation of the impact of comprehensive tumor profiling using a targeted next-generation sequencing panel. Oncologist 19 :616-622, 2014 24797823
17. Sicklick JK , Leyland-Jones B , Kato S , et al : Personalized, molecularly matched combination therapies for treatment-na. J Clin Oncol 35 , 2017 (suppl 15; abstr 2512)
18. Schwaederle M , Parker BA , Schwab RB , et al : Precision oncology: The UC San Diego Moores Cancer Center PREDICT experience. Mol Cancer Ther 15 :743-752, 2016 26873727
19. Wheler JJ , Janku F , Naing A , et al : Cancer therapy directed by comprehensive genomic profiling: A single center study. Cancer Res 76 :3690-3701, 2016 27197177
20. Haslem DS , Van Norman SB , Fulde G , et al : A retrospective analysis of precision medicine outcomes in patients with advanced cancer reveals improved progression-free survival without increased health care costs. JCO Oncol Pract 13 :e108-e119, 2017
21. Sheffield BS , Eaton K , Emond B , et al : Cost savings of expedited care with upfront next-generation sequencing testing versus single-gene testing among patients with metastatic non-small cell lung cancer based on current Canadian practices. Curr Oncol 30 :2348-2365, 2023 36826141
22. Tsai Y-L , Chang CJ : Budget impact analysis of comprehensive genomic profiling in advanced non–small cell lung cancer in Taiwan. Value Health Reg Issues 35 :48-56, 2023 36863067
23. Muthusamy B , Raskina K , Lofgren KT , et al : Quantifying the value of multigene testing in resected early stage lung adenocarcinoma. J Thorac Oncol 18 :476-486, 2023 36494074
24. Billing and coding: MolDX: Targeted and comprehensive genomic profile next generation sequencing testing in cancer (A55197). https://www.cms.gov/medicare-coverage-database/view/article.aspx?articleId=55197
25. Chakravarty D , Gao J , Phillips SM , et al : OncoKB: A precision oncology knowledge base. JCO Precis Oncol 10.1200/PO.17.00011
26. Lerman MH , Holmes B , St Hilaire D , et al : Validation of a mortality composite score in the real-world setting: Overcoming source-specific disparities and biases. JCO Clin Cancer Inform 10.1200/CCI.20.00143
27. Charlson ME , Pompei P , Ales KL , et al : A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 40 :373-383, 1987 3558716
28. Quan H , Sundararajan V , Halfon P , et al : Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 43 :1130-1139, 2005 16224307
29. RStudio Team: RStudio: Integrated development environment for R, 2020. http://www.rstudio.com/
30. Byfield SD , Bapat B , Becker L , et al : Real-world analysis of commercially insured and Medicare advantage patients with advanced cancer and rates of molecular testing. J Clin Oncol 41 (suppl 16; abstr 6633)
31. O’Haire S , Degeling K , Franchini F , et al : Comparing survival outcomes for advanced cancer patients who received complex genomic profiling using a synthetic control arm. Target Oncol 17 :539-548, 2022 36063280
32. Malone ER , Oliva M , Sabatini PJB , et al : Molecular profiling for precision cancer therapies. Genome Med 12 :8, 2020 31937368
33. Colomer R , Mondejar R , Romero-Laorden N , et al : When should we order a next generation sequencing test in a patient with cancer? EClinicalMedicine 25 :100487, 2020 32775973
34. Marcus L , Lemery SJ , Keegan P , et al : FDA approval summary: Pembrolizumab for the treatment of microsatellite instability-high solid tumors. Clin Cancer Res 25 :3753-3758, 2019 30787022
35. Bradford D , Larkins E , Mushti SL , et al : FDA approval summary: Selpercatinib for the treatment of lung and thyroid cancers with RET gene mutations or fusions. Clin Cancer Res 27 :2130-2135, 2021 33239432
36. National Cancer Institute: Bringing research to the community to reduce cancer disparities. https://www.cancer.gov/research/areas/disparities/chanita-hughes-halbert-clinical-trials-community-access
37. Backenroth D , Snider J , Shen R , et al : Accounting for delayed entry in analyses of overall survival in clinico-genomic databases. Cancer Epidemiol Biomarkers Prev 31 :1195-1201, 2022 35027431
