Abstract
Precision oncology using biomarkers and genomic testing facilitates individualised diagnosis and consequently treatment for cancer patients. Standard of Care is conducted through assays of biomarkers for treatment selection. However, Whole Genome Sequencing (WGS) provides excessively more information about actionable mutations and complex interventions. Despite its potentials in cancer management, WGS is restricted by its high cost and limited capacity. Implementing WGS is complex because it influences several elements of the care delivery and deals with the dynamic changes in supply, demand, technology, and cost. In this study, we use a System Dynamics (SD) model to evaluate WGS implementation. We also use the developed SD model to conduct sensitivity analysis to derive insights for decision-making on genomic testing scenarios. Various scenarios have been compared by the number of diagnosed patients and total cost. These scenarios have been created through alterations in WGS referral system, capacity, price, and willingness rate as well as testing time across different hospitals. The results showed that referring more patients for WGS will increase the number of diagnosed patients (by 30%), however it will impose more cost on the system (by 20%) due to higher cost of WGS. Therefore, investment strategies toward reducing cost of WGS as well as reimbursement policies can be beneficial to make WGS more economic and accessible. This study shows how the developed SD model can contribute to providing insights for policy makers in finding the optimal way for implementing WGS.
Keywords: Whole genome sequencing, System dynamics, Sensitivity analysis, Precision oncology, Health policy
Introduction
In cancer, genome, via either inheriting genetic abnormalities or acquiring genomic mutations, plays a crucial role in growth of tumours and survival of the patients [1]. Therefore, genomic testing and biomarkers facilitate individualised diagnosis and treatment for cancer patients testing under precision oncology. Biomarkers are used to find the optimal treatment and avoid ineffective treatments, i.e., overtreatments. Genomic testing and biomarkers are complex interventions for risk prediction of developing aberrations and accurate diagnosis using genetic conditions that result in preventing cancer disease, survival, and promoting health [2].
Genomic testing has been employed in cancer care for several purposes such as testing healthy individuals to specify the risk of developing cancer (e.g., BRCA) or tumour testing for diagnostic purposes (e.g., PCR or FISH) to either define specific cancer subtypes based on genetic variations or identify variant patterns that are known to drive cancer growth. Genomic testing could be conducted as single tests, a panel testing for multiple molecular aberrations, or through Whole Genome Sequencing (WGS) [3, 4].
Compared to single tests, which may lead to conducting a cascade of tests to diagnose the cancer, and panel testing, with targeting few molecular aberrations, WGS conducts a massive parallel approach for sequencing of numerous genes at once within a single test. By sequencing the entire region of the genome, WGS is a comprehensive test allowing more biomarkers to be identified as well as patterns of genomic variants that drive tumour growth [5].
Testing for more prevalent biomarkers through WGS increases the likelihood of diagnosis and finding an early actionable target and decreases the number of conducted tests. However, WGS is costly and is restricted to central facilities and/or the academic setting as it may not be available in all types of hospitals. Despite identifying more biomarkers by WGS, the Standard of Care (SoC) diagnostics is sufficient for most patients [6, 7]. Therefore, comparing WGS with other genomic testing, as SoC, is challenging for finding the best time to conduct WGS testing, so the patients can start the treatment at the earliest time and the lowest cost possible. It is difficult to determine the clinical benefits of WGS because of the diversity of genomic drivers, the complexity of diagnostic and treatment consequences and their availability, suffering from data scarcity, and the dynamic nature of prices across laboratories and countries and over time [8].
To date, few studies have attempted to estimate the effect of WGS at the system level spanning multiple cancer types and multiple genes [9, 10]. In this study, we develop a System Dynamics (SD) model to simulate the current process of implementing WGS against the SoC diagnostics. This model facilitates evaluation of the process for cancer diagnosis at the system level. In addition, we conduct sensitivity analysis to validate the developed model as well as comparing various scenarios to derive insights for policy making.
Related works
The use of simulation techniques especially SD has gained a lot of attention in healthcare and cancer management recently. Kenzie, et al. [11] conducted a systematic review on studies that used SD modelling for cancer prevention and control. They reviewed 32 studies that addressed various topics such as chemotherapy treatments, interventions, and environmental contaminations with focus on cancer treatment, prevention, and detection. In 2024, several studies have been conducted using SD simulation modelling to address different problems for various cancer types. Wongseree, et al. [12] evaluated cost-effectiveness of screening strategies for colorectal cancer in Thailand. Wang, et al. [13] simulated the implementation of breast cancer screening for optimising the coverage rate in China. Marjanovic, et al. [14] developed a conceptual model to map the key influencing factors on the uptake rate for lung cancer screening in Australia.
However, the use of SD modelling and simulation techniques in precision oncology and genome sequencing is scarce. van de Ven, et al. [9] developed a discrete event simulation model for evaluating implementation of WGS for lung cancer. Soltanolkottabi, et al. [6] proposed models for WGS implementation using game theory and SD. Khorshidi, et al. [3] introduced SD and its advantages for precision oncology and genome sequencing and proposed a conceptual model. Fagery, et al. [15] developed a SD model for integrating Multi-Cancer Early Detection (MCED), a liquid biopsy test, into cancer screening system in Australia.
Methods
The method, we use in this study, is SD simulation modelling to evaluate economic and clinical aspects of WGS implementation for cancer diagnosis via different scenarios. The reason of developing a SD model is to consider the patients’ flow and delay in different parts of the healthcare system in an operational level. In this section, we describe the system and the problem that we are going to investigate. Then, we present the developed SD model with defining the parameters and equations. Finally, we explore and discuss the model via sensitivity analyses.
Case description
This paper is based on an earlier work in the Netherlands in lung cancer patients. Previous work has implemented ABM/DES to model patients diagnosed with stage IV of Non-Small-Cell Lung Cancer (NSCLC). In this study, we draw from these papers which are publicly available but rather use a SD approach [9, 10]. We propose using SD for this case due to being less data intensive [3] which aligns with the situation of assessing new health technologies like WGS when there is not much available of their adoption. This study can be categorised as health technology assessment (HTA). When a patient is diagnosed with cancer, they will go through a set of biomarker tests until their diagnostic pathway is completed subsequently their treatment can be started. The assumption is that a patient will visit the closest hospital, and the diagnosis pathway differs based on the type of hospital that could be a general, teaching, or an academic hospital. This assumption gives a randomness degree and equal probability for all patients across the country. General hospitals have SoC panels while teaching hospitals can conduct a complete set of genomic tests (as SoC) for cancer diagnosis. Academic hospitals conduct the same test set as teaching hospitals, on top of the SoC, they can offer WGS testing biopsy for referred patients. If a patient visits a general hospital first and their cancer is not identified through genomic testing, they will be referred to a teaching hospital for more tests. If the diagnosis fails in the teaching hospitals, they will be referred to academic hospitals for WGS testing. Thus, hospitals need to send samples to other hospitals to conduct the test which takes additional time and leads delay. At academic hospitals, the referral to WGS is based on the physician’s (clinician’s) prescription (preference) and the patient’s preference. The patients will be consulted to get their consent if they are referred for WGS testing by a clinician. Academic hospitals send patients for WGS biopsy sampling. If the percentage of tumour cells in the WGS sampling is less than an acceptable threshold, the patient will be referred to SoC again. If the WGS sample is acceptable, the biopsy sample will be sent to WGS facilities for conducting WGS testing. After conducting WGS, reports that include massive analyses of whole genomes are generated. These reports are the significant reasons of cost and delays in conducting WGS. These reports will be sent to molecular tumour board (MTB) for interpretation.
Model development
In this section, SD modelling is used to simulate the system for the initial cancer diagnosis and staging. SD captures the complexity of systems using causal loops and enhances the understanding of systems’ nonlinearity. SD deals with dynamic behaviour of systems through modelling processes over time and considering delays within causal (feedback) loops. In SD, the modelling occurs via representing the system using causal loop diagrams (CLD), as a conceptual model, and stock and flow structures, as a simulation model [3, 16, 17]. CLDs are useful to capture the structure of systems and represent the way in which a system works by considering casual relationships and determining the critical feedback loops [18]. Stock and flow structure are formulated based on differential equations where stocks are unit accumulations (e.g., patients receiving services), and flows are rates added in or subtracted out of these stocks [19]. We use both CLD and stock and flow structure to model the defined problem.
Causal loop diagram (CLD)
In this section, we present the conceptual model for the problem using CLD. Figure 1 shows the CLD model for representing the system for diagnosing stage IV NSCLC [6]. The model shows that the patients enter the system, go to hospitals and receive services at general, teaching and academic hospitals for cancer diagnosis. Then, they pass through pathways that is described in the problem. Finally, the diagnosed patients exit the system. The causal relationships demonstrate how different components in the system interact with each other. The model is developed to simulate the case description based on published literature [9, 10]. Also, the model is validated by experts who are familiar with the Netherlands’ health system.
Fig. 1.
CLD for SoC and WGS pathways in NSCLC diagnosis
There is a balancing loop B1 between WGS and SoC when patients bypass WGS and are referred to SoC directly as well as referring back to SoC when WGS fails. This ensures diagnosis continues even when WGS is limited and prevents unrealistic growth of WGS. Although the diagram depicts reinforcing structures, they do not form loops as patient/physician willingness and WGS capacity, in this model, are defined exogenously (Table 1) due to lack of data and evidence.
Table 1.
Model boundary
| Endogenous variables | Exogenous variables | Excluded variables |
|---|---|---|
|
WGS SoC Referrals Hospital services MTB |
Patient will for WGS Physician will for WGS WGS capacity Tumour cell percentage |
Investment Demand |
Stock and flow structure
We develop a stock and flow structure model based on the presented CLD to simulate the described problem. Figure 2 shows the simulation model. This model is created using VenSim software. It contains 7 stock variables for patients in General Hospital, Teaching Hospital, Academic Hospital, SoC, WGS, WGS Facility and MTB. These stocks are scaled by incoming and outgoing flows (14 flows) that define the dynamic changes in patient numbers in these stocks. These stock variables are formulated using Eqs. (1–7). In the simulation model and these equations, GH, TH and AH refer to General, Teaching and Academic Hospitals respectively. Also, 
, 
, 
, 
, 
, 
and 
are the initial values for stock values. We set all these initial values as zero to provide a neutral baseline, zero initial values give a clean starting point when the actual starting values are unknown, and make it easier to compare scenarios.It should be mentioned the flow “Reports” refers to the patients that their WGS test has been reported to MTB.
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Fig. 2.
The stock and flow model for SoC and WGS pathways
For the flow of patients go to each hospital, we assumed that 15 patients visit hospitals in average each day. These patients select the hospitals based on proximity. As 60% of hospitals are general hospitals, 29% of hospitals are teaching hospitals, and the rest 11% are academic hospitals [6], we randomly assign patients to each hospital by multiplying these percentages with the average daily number of patients (15). The formulations for the rest of flows are presented in Eqs. (8–17). Equation 8 calculates the number of patients who are diagnosed at general hospitals and go out of the system. This calculation happens through multiplication of the number of patients in general hospitals by diagnosis rate (0.58) of tests conducted in general hospitals. Then, this multiplication is divided by average test time (15 days) at general hospitals. Similarly, the number of diagnosed patients at teaching hospitals are calculated based on TH diagnosis rate (0.6) and delayed by TH test time (15 days) using Eq. 9. The rest of patients at general hospitals who are not diagnosed there would be referred to teaching hospitals (Eq. 10). These patients would be delayed by both GH test time and Refer time to TH (5 days). The rest of patients at teaching hospitals who are not diagnosed there would be referred to academic hospitals (Eq. 11) and would be delayed by both TH test time and Refer time to AH (5 days). At academic hospitals, the patients based on their willingness to WGS (0.9) and their physician’s willingness to WGS (0.9) will be sent for WGS biopsy (Eq. 12). The rest of patients at academic hospitals are sent for SoC (Eq. 13). If the WGS biopsy fails to have sufficient tumour cells, the patient will be referred to SoC (Eq. 14). The likelihood of having sufficient tumour cells in WGS biopsy is set as 0.66. The patients at SoC will be diagnosed and go out the system (Eq. 15) with a delay of AH test time (18 days). The patients whose WGS biopsy has sufficient tumour cells will be sent to WGS facility if the number of patients does not exceed the capacity (5 patients per day) of the WGS facility (Eq. 16). For those patients, the reports of their WGS test will be provided (Eq. 17) with the delay of WGS test time (14 days) and would be sent to MTB. Then, these patients will be diagnosed at MTB and go out of the system.
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We also define two metrics, total cost and diagnosed patients, for evaluating the performance of the system under different scenarios. Total cost includes GH cost, TH cost, SoC cost and WGS cost (Eq. 18). The GH cost is the multiplication of patients going to general hospitals by GH testing cost (334.14 euros). For TH cost, the TH testing cost (536.02 euros) is multiplied by the summation of the patients going to teaching hospitals and the patients referred to teaching hospitals from general hospitals. The SoC cost is calculated via multiplying AH testing cost (536.02 euros) by the number of patients who are referred to SoC from academic hospitals or from WGS. The WGS cost calculates the number of patients who are sent to WGS facility multiplied by the WGS price (2925.25 euros). Also, diagnosed patients is formulated in Eq. 19 which includes the number of patients has been diagnosed at general and teaching hospitals as well as academic hospitals through either SoC or WGS (at MTB).All parameter values including rates, time duration, cost and price have been derived from Netherlands Cancer Registry [20] and the published benchmarks for the Netherlands’ context mostly using [9, 10].
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18 |
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19 |
Sensitivity analysis
In this paper, we aim to investigate what happens if the estimated parameters or the system structure change. This investigation is for validating the proposed model’s performance, analysing the sensitivity of the system under variations, and projecting the outcomes (defined metrics) for different scenarios. The parameters, which we investigate in this sensitivity analysis, are WGS capacity, WGS test time, WGS price (for reimbursement purposes) as well as average time in general ad teaching hospitals along with the willingness of patients and Physicians for WGS. These parameters and their variation ranges have been determined through brainstorming amongst researchers. For the system structure, we check the scenario if patients are referred directly from general hospitals to academic hospitals and compare it with the current procedure.
To conduct the sensitivity analysis, we transformed the developed model in VenSim into R using readsdr package [21]. In R, an SD model can be run using deSolve package [22] which is used for solving differential equations. We run the developed SD model under different conditions to conduct the sensitivity analysis in R.
Results
In this section, we firstly run the proposed SD model (Figure 2), then conduct the sensitivity analysis on the proposed model. We run the model for 1 year (365 days). The output of the running model is presented in Figure 3 in terms of both total cost (a) and number of diagnosed patients (b). Both metrics show increasing trends till reaching a stable point after about 100 days. These figures can be helpful in comparing different scenarios and situations.
Fig. 3.
Total cost and diagnosed patients for the current situation using the stock and flow model
In the following subsections, we investigate the sensitivity of the model over changing parameters and system structure.
WGS capacity
In the current situation, available capacity for WGS facility is 5 patients per day. We alter this parameter to see what happens if the capacity expanded or in some cases due to lack of resources the capacity decreases. We run the model under the conditions that the capacity is 1, 3, 5, 7 and 10 patients per day. Figure 4 shows the metrics, total cost (a) and diagnosed patients (b), under different capacities. Figure 4(b) is provided with a zoomed section to make the difference in the number of diagnosed patients visible. The outputs indicate that the system is sensitive to the change of capacity when the capacity is very low (equals to one patient per day). Under other conditions, the metrics remain the same which can infer that the current capacity works well for the system (in its current configuration). For the case of capacity equals to 1, as the WGS capacity reduces significantly, the number of diagnosed patients per day decreases. Subsequently, the fewer the WGS testing, the lower the total cost.
Fig. 4.
Total cost and diagnosed patients under different WGS capacities using the stock and flow model
WGS test time
Currently, the average WGS test time is 14 days. We change this parameter to investigate the increase and decrease of the WGS testing time. We run the model under the conditions that the test time is 1, 7, 14, 20, 50 and 100 days. Figure 5 shows total cost (a) and diagnosed patients (b) under different conditions. As expected, the total cost does not change as the patients already had their WGS biopsy and the WGS cost was paid. However, in terms of the number of diagnosed patients, the system is sensitive to the change of WGS test time. The longer the WGS test time, the lower the number of diagnosed patients.
Fig. 5.
Total cost and diagnosed patients under different WGS testing times using the stock and flow model
WGS price
Investigating the system performance under various WGS price helps policymakers to decide on the level of reimbursement as well as the investment level to reduce WGS cost and make it more accessible. In the current situation, the WGS price is set as 2925.25 euros. We alter this parameter to examine if the price drops to 2000, 1400, 1200 and 600 euros or price, in a special case, surges to 3500 euros. Figure 6 shows the metrics, total cost (a) and diagnosed patients (b), under different prices. As expected, the number of diagnosed patients does not change as the model considers sending patients for WGS testing independent from the WGS price. However, the total cost is significantly associated with the change of WGS price. The higher the WGS price, the higher the total cost.
Fig. 6.
Total cost and diagnosed patients under different WGS prices using the stock and flow model
General hospital test time
Using this simulation model, we also can examine parameters other than WGS-related parameters. For example, the average test time in general hospitals, which currently is 15 days, can influence the number of patients is referred to other hospitals and WGS facility and their referring time as well as the number of patients who are diagnosed in general hospitals. We change this parameter to investigate the increase and decrease of the general hospital testing time. We run the model under the conditions that the test time is 1, 7, 15, 20 and 35 days. Figure 7 shows total cost (a) and diagnosed patients (b) under different conditions. The outputs reveal that both metrics are sensitive to the change of GH test time. As testing time in general hospitals get shorter, the total cost becomes lower, and the number of diagnosed patients increases. Due to a relatively high diagnosis rate in general hospitals, lower GH test time results in faster and more diagnoses in lower costs.
Fig. 7.
Total cost and diagnosed patients under different GH testing times using the stock and flow model
Patient and physician willingness for WGS
We also check the sensitivity of the system based on changes in the willingness of patients to take WGS and physicians to prescribe WGS testing. Currently, willingness rate for both patients and physicians is set as 0.9. We run the model under the conditions that both willingness rates change within [0, 1] range. Figure 8 shows decreasing willingness for WGS reducing total cost (a) and the number of diagnosed patients (b). The bigger cycles and red colour show the bigger values in terms of cost and patient diagnosis. The values for the cost and the number of diagnosed patients are the accumulated values over a year.
Fig. 8.
Total cost and diagnosed patients under various patients’ and physicians’ willingness rates using the model
System structure
The SD simulation model provides an opportunity for us to project the scenarios by altering the system structure and the flow of patients. The current system structure, which is described in section 2.1 and modelled in Figures 1 and 2, is structure 1. Structure 2 represents the situation that undiagnosed patients in general hospital are referred directly to an academic hospital. In this flow (structure), patients who went to general hospital initially would have a higher chance of receiving WGS testing. Figure 9 shows the updated stock and flow model to accommodate system structure 2. In the updated model, the patients can be referred directly from general hospitals to academic hospitals. To simulate structure 2, the outflow from general hospitals to teaching hospitals will be zero and undiagnosed patients go to academic hospitals by a new input flow which is created for patients who are referred from general hospital to academic hospitals.
Fig. 9.
The stock and flow model for structure 2
Figure 10 presents the metric values, total cost (a) and diagnosed patients (b), under different system structures. As structure 2 refers more patients to academic hospitals and thus WGS testing, the number of diagnosed patients is higher, however the total cost is higher too.
Fig. 10.
Total cost and diagnosed patients under various system structures using the stock and flow model
Conclusions, discussion, and further research
In this paper, we demonstrate a SD model for implementing WGS and biomarker testing for diagnosing stage IV of NSCLC (Non-Small-Cell Lung Cancer) for the case study of the Netherlands. This study shows how the developed simulation model can be used to define different scenarios and evaluate them via sensitivity analysis. These scenarios can be defined by altering the parameters and changing the system structure (healthcare guidelines or patients’ flow). This study provides insights for researchers and academics in a way that SD simulation modelling has potentials through scenario evaluation that can be applied for a wide range of healthcare projects specifically emerging cancer-related technologies.
The outputs show that conducting more WGS testing (referring more patients for WGS) will improve the number of diagnosed patients, however it will impose more cost on the system due to higher cost of WGS. So, the implication for policy makers is that investment strategies toward reducing cost of WGS as well as reimbursement policies can be beneficial in terms of making WGS more economic and accessible. More investment can contribute to allocate more resource and capacities to WGS that results in reducing the WGS testing time and subsequently reducing the time to diagnosis and increasing the number of diagnosed patients. Also, WGS service providers can invest on reducing the WGS price to be adopted in healthcare systems.
More willingness amongst patients and physicians towards choosing WGS will increase the rate of WGS testing. Similarly, if we put a shortcut from general hospitals to academic hospitals, it will increase the chance of having WGS testing. The higher the rate of WGS testing, the more the number of diagnosed patients. On the other hand, we should not ignore other parameters in the system. For example, by reducing the GH testing time, the total cost decreases, and the number of diagnosed patients improves.
This study is limited to the assumptions of van de Ven, et al. [9, 10] in developing the SD simulation model. For example, the willingness for WGS is estimated and modelled independently, while patients’ willingness (and maybe physician’s willingness) can be affected by WGS price. For future research, more of these interdependency relationships should be considered and formulated in the simulation model. Also, for future direction, the model should be expanded to consider mortality rates, the impact of social determinants on the WGS demand and the relationship between investment and service capacity. In the current study, willingness and capacity variables are defined exogenously while they should be defined endogenously in the expanded model.
In future research, different scenarios can be defined via changing multiple parameters and structure alterations simultaneously to find optimal scenarios. This will pave a way to develop scenario-based optimisation [23] models. Also, simulation-based optimisation [24] can be used to optimise parameters and suggest optimal scenarios.
Acknowledgements
This research is underpinned by the Health Economics Platform of The Advanced Genomics Collaboration (TAGC) —a partnership between Illumina and The University of Melbourne. Learn more at tagcaustralia.com. We also would like to acknowledge Erik Koffijberg and Michiel van de Ven for sharing their work on Discrete Event Simulation model.
Author contributions
H.K. wrote the main manuscript. All authors contributed to conceptualising the model. H.K. and M.S. contributed to the model development. H.K. run the model, conducted the analyses and derived the outputs. M.IJ. provided supervision. All authors reviewed the manuscript.
Funding
This research received no external funding.
Data availability
All data generated or analysed during this study are included in this published article. The R codes and analysed data during the current study are available in the GitHub repository at [https://github.com/hadi1453/WGS-SD] (https:/github.com/hadi1453/WGS-SD). Extra information can be asked from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated or analysed during this study are included in this published article. The R codes and analysed data during the current study are available in the GitHub repository at [https://github.com/hadi1453/WGS-SD] (https:/github.com/hadi1453/WGS-SD). Extra information can be asked from the corresponding author on reasonable request.