High-quality evidence comparing CGP with other standard targeted assays traditionally used in clinical practice is limited. The present retrospective study aimed to help fill this gap by comparing the actionability of identified mutations and subsequent recommended treatment between cancer patients (lung, breast, colorectal, melanoma, prostate) tested with CGP or SP testing. CGP-testing was found to be associated with the identification of more actionable genetic results across a number of advanced cancer types compared with patients who underwent SP testing. These findings help to further support the potential adoption and coverage of CGP in routine clinical practice beyond NSCLC.

Other published literature in both clinical and real-world settings have assessed the impact of CGP testing versus SP testing with findings generally consistent with the present study. In contrast, however, the majority focused on only patients with primary NSCLC. In one prospective single-center study with CGP testing of 134 patients, additional actionable alterations beyond those identified previously in SP tests were found in 31% of patients and could directly impact treatment decisions and clinical trial eligibility [31]. A similar study reported that, among 561 patients with NSCLC who had received negative SP test results, 46% received positive CGP results for actionable biomarkers missed on single-gene testing. In fact, initial SP testing was found to use up much of the tumor tissue sample, resulting in the inability to conduct CGP at all or higher sequencing failures and turnaround times [32].

Similar to the current study, Bapat et al. in 2022 found that CGP testing was associated with a significantly higher proportion of patients with > 1 actionable biomarker compared to SP (34% versus 15%; p < 0.001), in patients with advanced/metastatic NSCLC treated in a United States (US) community health setting. The proportion of patients receiving approved targeted therapy or immunotherapy (9% versus 3%; p < 0.001), and being eligible for clinical trials (56% versus 4%; p < 0.001) was also significantly higher with CGP than SP testing [33]. Wallenta and colleagues reported similar results in that CGP identified actionable biomarkers at more than twice the rate of single panel testing (32% versus 14%; p < 0.001), with a higher chance of receiving matched treatment in any line. Furthermore, patients who received matched therapies had improved survival compared with those who did not [26]. A major distinction of the present study compared with previously published literature is the inclusion of patients with breast cancer, colorectal cancer, prostate cancer, and melanoma.

The reported findings not only align with previous results in NSCLC as summarized above [26, 31,32,33], but also demonstrates that the benefits of CGP in terms of biomarker actionability extend to other cancer types as well. A more granular evaluation of actionable biomarker distributions provides further context for these findings. Across all tumor types, commonly observed actionable alterations—including KRAS, EGFR, and BRAF—were identified at similar frequencies between CGP- and SP-tested cohorts. However, CGP testing identified a broader range of actionable alterations across additional biomarker classes, including ERBB2, PIK3CA, and DNA damage repair genes such as BRCA2. Tumor-specific analyses demonstrated similar patterns, with CGP consistently capturing actionable alterations beyond those most frequently observed in SP testing.

The current coverage for CGP in the USA is limited, with coding practices not fully refined [34, 35]. Previous studies have reported low rates of reimbursement for CGP- or NGS-based testing, despite the inclusion of associated billing codes [35]. Variable coverage may also reinforce existing racial and socio-economic inequities in access to genetic testing [36,37,38]. The findings of more actionable genetic results and the receipt of a matched recommended treatment with CGP testing in the present study, in combination with other published analyses indicating minimal cost and budget impact, suggest that CGP testing warrants consideration for expanded coverage. Extending coverage of CGP and the removal of barriers such as prior authorization, could expand access to CGP testing to more patients with advanced or metastatic cancer, potentially contributing to significant added diagnostic, treatment, and potential economic value. Further studies are needed to quantify the economic value and clinical impact of expanded CGP access.

Community oncologists who deal with many cancer types on a daily basis may struggle to stay current with which small-gene panel to order for which cancers. They may also be limited by insurance coverage determinations to define panel choices. Often, the concern for delay in processing over insurance denials can lead to a smaller panel being used inappropriately or suboptimal treatment pathways relative to CGP-guided targeted treatments. Routine use of CGP for metastatic cancer could potentially reduce these testing inefficiencies. Also, existing small-gene panels can evolve into bigger gene panels. For example, ThyroSeq expanded from a 7-gene panel in 2011 to a 56 gene panel in 2014, and a 112 gene panel in 2017 [39]. Using CGP will avoid wasted time and effort in validating and marketing each new panel individualized for each cancer type as new data emerges on the significance of specific mutations. Patients often drive testing as they seek eligibility for clinical trials. This leads to unnecessary duplication where a small-gene panel has to be updated to CGP for a comprehensive assessment of clinical trial eligibility. In an older study examining the impact of CGP, the authors identified 61.3% of 333 patients having an off-label therapy available, and 77.9% were potentially eligible for a clinical trial based on CGP results [40]. The timeliness of CGP is important as well, where a delay in receiving results can impact the feasibility of using the results. Tredan et al. performed a randomized controlled trial of CGP versus a limited panel of 87 genes demonstrating an increase of 14.8% in molecular-based recommended therapies [41]. However, out of 192 patients with molecular-based recommended therapies, only 43 received directed therapy. The authors emphasize the importance of “tight timeframes” for these patients who have failed multiple treatments and are deteriorating. Early, routine use of CGP in patients diagnosed with metastatic cancer would avoid last minute tests at a critical time in decision-making where survival time is limited.

A major strength of this analysis is the use of real-world data which typically allows for the inclusion of a broader patient population than captured in clinical trials or a single academic institution [42, 43]. Thus, our findings may be more representative of the target population in real-world clinical practice. Additionally, the present analysis used clinically rich EMR data and employed data curation methodologies to obtain a real-world sample. The use of EMR data has not previously been used widely in this space; however, the consistency with published literature helps to validate the current findings as well as the novel data and methods employed [26, 31,32,33]. Furthermore, prior to this study, limited research had been published comparing CGP to SP testing across advanced cancer types, with the present study being the first published evidence for some.

This study has important limitations inherent in the use of routinely collected EMR data. Because this was a retrospective study based on routinely documented results in unstructured EMR notes, detailed analytical characteristics of the underlying molecular assays (e.g., gene content, genomic footprint, depth of coverage, and bioinformatic pipelines) were not consistently available across patients and could not be standardized. Accordingly, CGP and SP testing were defined operationally using CPT codes and manual chart abstraction of reported biomarkers and results, reflecting how EMR systems selectively capture genomic test data. In addition, actionability frameworks differ across clinical guidelines [44]. Because the present analysis relied on unstructured EMR documentation, we used a single framework (OncoKB) to enable consistent abstraction and comparison across cohorts [25]. Future research using richer structured molecular data should examine concordance across multiple frameworks.

Notably, a high degree of missing data (more than 50%) was present for baseline variables (race, ethnicity, pathology). These variables were excluded from population adjustment, which limits our ability to assess whether findings differ across demographic subgroups. Furthermore, heterogeneity and gaps within the EMR data lead to challenges in identifying eligible patients, resulting in fewer-than-intended patients being included in the final analyses. The small sample has reduced statistical power and precision and could limit the generalizability of findings, particularly for less-represented tumor types. Additionally, as the data for these analyses span from 2018 to 2022, they may not fully reflect current testing patterns. Given the small sample size, it was not feasible to obtain reliable temporal trends. Additional analyses with higher sample size, perhaps by obtaining data from multiple sources or databases, should be conducted in the future to further expand on these findings. EMR information is designed to support the delivery of health care and billing rather than research purposes, which can create challenges. Importantly, the dataset lacked clinical outcomes (e.g., progression-free survival, overall survival) and cost data, preventing assessment of whether increased actionable biomarker detection translates to improved patient outcomes or represents good economic value. As such, EMR-derived data may contain gaps and coding errors. Despite this, the value of EMR data for research has been well established and successfully used in a wide range of investigations, studies, and disease areas [45, 46]. The present study also employed a comprehensive and iterative process to address potential informational biases, such as confirming the identification of CGP testing with CPT codes as well as a review of the medical notes for validation.

It is also important to acknowledge that SP testing may remain clinically appropriate in certain scenarios. For example, when clinical presentation strongly suggests a specific actionable mutation with high pretest probability (such as EGFR mutations in never-smoking East-Asian patients with lung adenocarcinoma [47]), rapid SP testing may provide timely results to guide immediate treatment decisions. Additionally, SP testing may be preferred when tissue quantity is limited and a specific, high-probability target needs to be evaluated first. The decision between CGP and SP testing should consider clinical context, urgency of treatment initiation, tissue availability, and the probability of identifying actionable alterations beyond commonly targeted mutations. Our findings suggest that CGP offers advantages in detecting a broader range of actionable biomarkers, but testing strategy should be individualized based on clinical circumstances.