Economic Evaluation of Inavolisib Combined With Palbociclib–Fulvestrant for PIK3CA-Mutated,HR+/HER2− Advanced Breast Cancer in USA

Abstract

Objective

Inavolisib, a novel highly selective PI3Kα inhibitor, is approved for treating PIK3CA-mutated, hormone receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer (PIK3CA-mutated, HR+/HER2− ABC). Given its significant efficacy, as confirmed by the updated INAVO120 III trial, but also its high price, a comprehensive evaluation of its value is warranted. The purpose of this study was to investigate whether inavolisib plus palbociclib–fulvestrant is cost-effective for patients with PIK3CA-mutated, HR+/HER2− ABC from the U.S. healthcare system perspective.

Methods

A Markov model comprising three health states was developed to simulate the progression of PIK3CA-mutated, HR+/HER2− ABC. Parametric survival models, fitted to and extrapolated from survival data, were used to estimate long-term clinical outcomes. Lifetime costs and health outcomes were calculated. Willingness-to-pay (WTP) thresholds were set at $100,000, $150,000, and $200,000 per quality-adjusted life year QALY to reflect general and more affluent regional standards. One-way and probabilistic sensitivity analyses were conducted to assess model robustness, and subgroup analyses explored the variation in benefits across patient clinical characteristics.

Results

Compared with placebo, inavolisib provided an additional 0.6 QALYs while increasing costs by $160,490.07, resulting in an incremental cost-effectiveness ratio (ICER) of $268,457.82 per QALY. Sensitivity analysis identified the utility of the progression-free state as the most sensitive factor in the model. Within the WTP threshold range of $100,000 to $200,000 per QALY, the inavolisib regimen was not considered cost-effective. To achieve cost-effectiveness at WTP thresholds of $100,000, $150,000, and $200,000 per QALY, the per-cycle price of inavolisib would need to be reduced to 59.5%, 72.0%, and 86.5% of its current price, respectively.

Conclusion

For patients with PIK3CA-mutated HR+/HER2− ABC, the inavolisib regimen is not cost-effective in the U.S. healthcare setting. Negotiating price reductions and adjusting decision thresholds based on patient characteristics may be viable strategies to meet the extensive treatment demand in the U.S.

Keywords

inavolisib PIK3CA-mutated HR+/HER2− advanced breast cancer cost-effectiveness

Introduction

In the United States, breast cancer is the second leading cause of cancer death among women, with over 280,000 new cases diagnosed each year.¹ Although modern therapies can effectively prevent the recurrence of metastatic disease, about 15% of patients are eventually diagnosed with distant metastatic recurrence.² Hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer represents the most frequent subtype, comprising 68% of all breast cancer cases.³ For early-stage patients with this type of cancer, endocrine therapy is the standard treatment. While around 30% of early-stage patients initially respond to endocrine therapy, resistance inevitably emerges over time; notably, approximately 50% of patients with advanced or metastatic disease exhibit resistance to endocrine therapy.⁴

Genetic alterations underlying this resistance include activating mutations in the PIK3CA gene, which are present in approximately 40% of HR+/HER2− breast cancers.^4,5 Activating mutations in the PIK3CA gene not only lead to abnormal activation of the PI3Kα enzyme, resulting in continuous PI3K (phosphoinositide 3-kinase)/AKT (serine/threonine kinase)/mTOR (mechanistic target of rapamycin) pathway signaling and uncontrolled tumor growth, but also serve as a key driver of endocrine therapy resistance.^4,6 Preclinical studies have demonstrated that concurrently targeting the PI3K pathway, Cyclin-Dependent Kinase 4 and 6 (CDK4/6), and estrogen receptor can enhance treatment response and delay the emergence of resistance in PIK3CA-mutated breast cancer models.^7,8 This rationale has prompted the clinical development of targeted agents, including inavolisib—a potent, orally bioavailable PI3Kα inhibitor with a dual mechanism of action. On one hand, it shuts down the downstream PI3K/AKT/mTOR signaling pathway through enzyme inhibition, thereby suppressing tumor cell proliferation.⁹ On the other hand, it directly degrades the mutant p110α protein (the catalytic subunit of the PI3Kα enzyme encoded by the PIK3CA gene), blocking signal transmission at its source and thereby reducing the risk of drug resistance.¹⁰ However, the clinical development of PI3Kα inhibitors has faced challenges, as exemplified by previous combination regimens of alpelisib with CDK4/6 inhibitors, which were limited by adverse events including grade ≥3 hyperglycemia in 32.7% of patients.¹¹ Biochemical assays demonstrated that inavolisib exhibits over 300-fold greater selectivity for p110α (the alpha isoform of PI3K) compared to the p110β, δ, and γ isoforms.¹⁰ This high selectivity profile translates to a widened therapeutic window, enabling enhanced efficacy when combined with standard treatment strategies, as demonstrated in the GO39374 trial.¹²

It is noteworthy that the INAVO120 III clinical trial (NCT04191499), at the recent data cutoff (July10, 2025), was the first to compare the efficacy and safety of inavolisib plus palbociclib–fulvestrant versus placebo plus palbociclib–fulvestrant in patients with PIK3CA-mutated, HR+/HER2− locally advanced or metastatic breast cancer following prior endocrine therapy.¹³ In this global Phase III trial, the inavolisib regimen demonstrated significant efficacy compared with placebo, with a statistically significant and clinically meaningful improvement in overall survival (OS) (34.0 vs 27.0 months, hazard ratio [HR] 0.67; 95% CI, 0.48 to 0.94). However, this clinical gain was accompanied by a higher incidence of grade 3 or 4 adverse events compared to the placebo arm (90.7% vs. 84.7%). In the United States, value assessment frameworks such as that developed by the Institute for Clinical and Economic Review (ICER) have been used to guide coverage decisions and clinical practice.¹⁴ ICER’s framework integrates cost-effectiveness analysis with budget impact assessment, typically using WTP thresholds ranging from $100,000 to $150,000 per QALY to evaluate long-term value for money.¹⁵ Within this framework, the clinical promise of PI3Kα inhibition must be carefully weighed against its economic implications, as interventions with incremental cost-effectiveness ratios (ICERs) exceeding $150,000 per QALY are often considered not cost-effective from a U.S. payer perspective. For instance, Alpelisib, the first approved PI3Kα inhibitor, when combined with fulvestrant in patients with PIK3CA-mutated, HR+/HER2− advanced breast cancer, yielded an incremental cost-effectiveness ratio (ICER) of $340,153.30 per QALY.¹⁶ From a U.S. payer perspective, this regimen was not deemed cost-effective at current prices, despite a clinically meaningful 7.9-month median survival extension. This prior evidence raises a critical concern: whether inavolisib, despite its substantial clinical efficacy, may face a similar cost-effectiveness challenge in the same patient population, particularly given its associated high costs and modestly increased toxicity burden. To date, economic evidence specific to inavolisib in this setting remains lacking.

To address this evidence gap, the present study evaluates the cost-effectiveness of the inavolisib-based regimen versus placebo from a U.S. healthcare payer perspective among patients with PIK3CA-mutated HR+/HER2− advanced breast cancer following endocrine therapy. By integrating clinical efficacy, safety, and economic data, this analysis provides essential economic evidence to inform clinical decision-making and support the value-based management of metastatic breast cancer.

Methods

Patients and Regimens

In this trial, 325 patients with HR–positive, HER2-negative locally advanced or metastatic breast cancer were randomly assigned to receive inavolisib plus palbociclib–fulvestrant (inavolisib group) or placebo plus palbociclib fulvestrant (placebo group). Genetic testing revealed PIK3CA mutations in all patients, and the tumor PIK3CA mutation status in the majority of patients was assessed primarily by circulating tumor DNA (ctDNA) testing. Patients eligible for enrollment had experienced disease recurrence or progression within 12 months of completing adjuvant endocrine therapy. Additional inclusion criteria included a fasting blood glucose level below 126 mg/dL (7.0 mmol/L), an HbA1c (glycated hemoglobin) level below 6.0%, and measurable disease as defined by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1.¹⁷ In inavolisib group, patients received inavolisib (at a dose of 9 mg, administered orally, once daily on days 1 to 28 of each 28-day cycle), each given in combination with palbociclib (at a dose of 125 mg, administered orally, once daily on days 1 to 21 of each 28-day cycle) and fulvestrant (at a dose of 500 mg, administered intramuscularly, on days 1 and 15 of cycle 1, and approximately every 4 weeks thereafter) until the occurrence of disease progression, unacceptable toxic effects, withdrawal of consent, or death. Patients in the placebo group received a once-daily placebo, with the dosing schedules for the placebo itself as well as the other two agents mirroring those of the inavolisib group. Following disease progression, 74.8% of patients in the inavolisib group and 75.7% in the control group received post-progression therapies, while the remaining individuals received palliative care. The second-line treatments included chemotherapy, antibody–drug conjugates, PI3K inhibitors, mTOR kinase inhibitors, and CDK4/6 inhibitors. The detailed distribution of patients receiving each regimen is provided in the appendix of the INAVO120 trial.¹³

In this economic assessment, no individual patient-level data were used for analysis. As it does not meet the criteria for human research, review or exemption from an institutional review board is not required, nor is approval from an ethics committee necessary.

Analytic Model

A Markov model was constructed in TreeAge Pro 2024 utilizing data from the INAVO120 trial to simulate disease progression. The model consisted of three mutually exclusive health states: progression-free survival (PFS), progressed survival (PS), and death. All patients entered the model in the PFS state and were randomized to either the treatment or control arm. The model adopted a unidirectional transition structure, allowing patients to transition only from the PFS state to either the PS state or death over each Markov cycle (Supplemental Material Figure S1). In alignment with the trial’s dosing regimens, a 28-day cycle length was adopted for the Markov model, and transitions were permitted each cycle. To ensure that 99% of the cohort transitions to the death state, we set the time horizon to 30 years. This study evaluated total costs, QALYs, and the ICERs as primary outcomes, employing semicircular correlation analysis to estimate costs and survival rates. Costs were estimated from the U.S. payer’s perspective, incorporating only direct medical expenses. A 3% discount rate per year was applied for both cost and effectiveness in the U.S.¹⁸ Consistent with empirical evidence, we established WTP thresholds at $100,000, $150,000 and $200,000 in the current analysis, the highest recommended threshold by Neumann et al.¹⁹

Kaplan-Meier survival data for OS and PFS were available from the INAVO120 trial at the updated data cutoff (July 2025). Data points were extracted from the OS and PFS curves using GetData Graph Digitizer software (version 2.25) and were then used to fit parametric survival models. To enhance model accuracy, we produced pseudo-individual patient data using the approach established by Hoyle et al. The log-logistic distribution was identified as providing the most appropriate fit to the pseudo-individual patient data, based on statistical goodness-of-fit tests using the Akaike (AIC) and Bayesian (BIC) information criteria, when compared to the exponential, log-logistic, Weibull and log-normal distributions. The graphical comparison and goodness-of-fit statistics results are presented in Supplemental Materials Figure S2 and Table S2. We used the parameters to calculate survival rate, which is S(t)= 1/(1+(θt)κ), where t is time. The two parameters of Log-logistic distribution, scale parameters (θ) and shape parameters (κ) are shown in Table 1.

Table 1.

Log-Logistic Parameters

Parameters values
Log-logistic survival model of PFS
Inavolisib	θ=0.056956 κ=1.372
Placebo	θ=0.134949 κ=1.372
Log-logistic survival model of OS
Inavolisib	θ=0.0287 κ=1.703
Placebo	θ=0.035951 κ=1.5

θ: scale; κ: shape; ECF/ECX: docetaxel, oxaliplatin, leucovorin, fluorouracil; FLOT: epirubicin, cisplatin, fluorouracil or capecitabine.

Utility and Cost

For each subject, expected QALYs were estimated by multiplying the cumulative duration in each health state by the specific utility value assigned to that state. The following utility values were used: 0.85 (0.64-1.00) for the PFS state and 0.52 (0.39-0.65) for the PS state. Utility values were assumed to be identical for all patients in the same health state, regardless of treatment arm. To account for the impact of treatment-related Grade III/IV adverse events (AEs), the utility value of the progression-free state was adjusted by subtracting the disutility associated with AEs. Disutility parameters were derived from published literature, and the corresponding AE incidence rates were obtained from the results reported in the INAVO120 trial.^16,20 Consistent with clinical practice and expert suggestions, the model incorporated grade III/IV AEs with an incidence rate of at least 5%, specifically hyperglycemia, stomatitis, neutropenia, thrombocytopenia, and anemia. The per-cycle utility decrement per patient was calculated as the sum of each AE’s incidence rate multiplied by its corresponding disutility value. Each AE was assumed to incur a one-time utility loss only in the cycle in which it occurred, thereby avoiding double counting. Throughout the model’s time horizon, QALYs were discounted at an annual rate of 3%.

From the U.S. payer’s perspective, the cost analysis was focused exclusively on direct medical costs. Direct costs included expenses for PIK3CA mutation testing, pharmaceutical costs in the progression-free state, subsequent treatment costs after disease progression, management costs of serious adverse events (SAEs), follow-up examinations, supportive care, and end-of-life care. The proportions of patients receiving subsequent therapy after disease progression are available in the supplementary materials of the INAVO120 trial. According to the supplementary appendix of study, any PI3K inhibitor or mTOR kinase inhibitor may be used following disease progression, depending on the treatment approach.¹³ Hence, we hypothesize that everolimus is an mTOR kinase inhibitor and alpelisib is a PI3K inhibitor. The cost of managing adverse events was calculated as the product of the per-event cost and incidence rate, based on the same adverse event categories applied in the utility analysis. Table 2 lists all direct costs used in the model.

Table 2.

Basic Parameters Input to the Model and the Ranges for Sensitivity Analyses

Parameters	Median	Range	Distribution	Reference
Expenditures on Drugs
Fulvestrant per 500 mg	134.56	107.648-161.472	LogNormal	21
Palbociclib per 125 mg	783.9	627.12-752.544	LogNormal	22
Inavolisib per 9mg	816.68	653.34-980.02	LogNormal	22
Alpelisib per 300 mg	850.1	680.08-1020.12	LogNormal	21
PIK3CA mutation screening^a	261.56	209.24-313.87	Gamma	24
Imaging^a	89.74	71.79-107.68	Gamma	24
Outpatient visit^a	280.52	224.42-336.63	Gamma	16
Administration^a	39.93	31.94-47.92	Gamma	24
Terminal care^a	3141.63	2513.31-3769.96	Gamma	16
Follow-up of patients^a	1313.57	1050.86-1576.28	Gamma	25
Laboratory tests^a	545.16	436.13-654.2	Gamma	26
Expenditures on main adverse events in USA (Grade 3 or 4)
Hyperglycemia^a	288.15	230.52-345.79	Gamma	27
Neutropenia^a	7987.61	7188.85-8786.38	Gamma	28
Thrombocytopenia^a	12846.08	9634.56-16057.6	Gamma	20
Anemia^a	544.52	490.07-598.98	Gamma	28
Stomatitis^a	116.31	93.04-139.57	Gamma	29
Expenditures on subsequent treatments
Chemotherapy per cycle^a	632.29	505.83-758.75	Gamma	25
Everolimus per cycle^a	1534.4	1227.52-1841.28	Gamma	21
CDK4/6 inhibitor per cycle^a	14213.2	11370.56-17055.84	Gamma	26
Targeted therapy per cycle^a	17.3	13.84-20.76	Gamma	26
Best supportive care per cycle^a	1,477.22	1181.77-1772.66	Gamma	30
Discount rate in the US	0.03	0–7%	LogNormal	26
Utility			Beta
PFS	0.837	0.753-0.921	Beta	31
PS	0.52	0.39-0.65	Beta	31
Risk for main adverse events in inavolisib group (Grade 3 or 4)
Hyperglycemia	0.068	0.0544-0.0816	Beta	13
Neutropenia	0.826	0.0448-0.0672	Beta	13
Thrombocytopenia	0.137	0.6608-0.9912	Beta	13
Anemia	0.068	0.1096-0.1644	Beta	13
Stomatitis	0.056	0.0544-0.0816	Beta	13
Risk for main adverse events in placebo group (Grade 3 or 4)
Thrombocytopenia	0.804	0.6432-0.9648	Beta	13
Anemia	0.049	0.0392-0.0588	Beta	13
Stomatitis	0.018	0.0144-0.0216	Beta	13
Distribution of Subsequent treatment in inavolisib group
Chemotherapy	0.554	0.4432-0.6648	Beta	13
Antibody–drug conjugate	0.012	0.0096-0.0144	Beta	13
PI3K inhibitor (Alpelisib)	0.06	0.048-0.072	Beta	13
mTOR kinase inhibitor (Everolimus)	0.096	0.0768-0.1152	Beta	13
CDK4/6 inhibitor	0.096	0.0768-0.1152	Beta	13
Distribution of Subsequent treatment in placebo group
Chemotherapy (any)	0.725	0.58-0.87	Beta	13
Antibody–drug conjugate (any)	0.009	0.0072-0.0108	Beta	13
PI3K inhibitor (any)	0.101	0.0808-0.1212	Beta	13
mTOR kinase inhibitor (everolimus)	0.092	0.0736-0.1104	Beta	13
CDK4/6 inhibitor	0.046	0.0368-0.0552	Beta	13

^aAll costs were inflated to 2025 U.S. dollars using the Medical Care Consumer Price Index (Medical CPI). The adjustment was performed using the following formula: Adjusted Cost = Original Cost (Year Y) × (CPI_2025 / CPI_Year Y) where CPI_2025 is the annual average Medical CPI for 2025, and CPI_Year Y is the annual average Medical CPI for the original cost year.²³

Sensitivity Analysis

The robustness of our model was tested through one-way sensitivity analyses. A tornado diagram illustrates how variations in individual parameters influence the results. Details regarding the parameters’ median values, distributions, and the ranges used for these analyses are provided in Table 2. For parameters without such data, we applied a hypothetical range of ±20% around the base-case value. A probabilistic sensitivity analysis (PSA) was conducted to assess parameter uncertainty of the model through 10,000 Monte Carlo simulations. The results are presented in the form of cost-effectiveness acceptability curves (CEACs) to predict the probability of cost-effectiveness of treating according to the study protocol at WTP thresholds of $100,000–$200,000 per QALY. A treatment strategy was considered cost-effective if it either: (1) yielded more QALYs with cost savings, or (2) increased QALYs at a higher cost but with an ICER below the predefined WTP threshold.

Subgroup Analysis

In the subgroup analysis, all patient subgroups reported in the INAVO120 trial were included. It was assumed that all subgroups shared the same baseline survival function for both OS and PFS, with the only differentiating factor being the application of subgroup-specific hazard ratios (HRs). Specifically, the survival function for a given subgroup was derived by applying the corresponding HR_S to the overall population survival function, as follows:

S_{subgroup} (t) = {[S_{overall} (t)]}^{HRs}

where S_overall(t) represents the survival function for the overall population (derived from the Kaplan-Meier curve or parametric extrapolation of the INAVO120 trial), and HRs is the hazard ratio for the subgroup relative to the overall population. This approach was applied separately to OS and PFS using their respective HRs. Subgroup economic evaluations explored the potential cost-effectiveness of inavolisib in patients with different clinical characteristics.

Result

Base-Case Results

Expected direct medical costs and QALYs for the treatment strategies (PIK3CA-guided inavolisib plus palbociclib-fulvestrant, and placebo plus palbociclib-fulvestrant) in the base-case analysis are presented in Table 3. After accounting for quality of life, the inavolisib group was associated with higher costs ($446,715 vs. $286,225) and a greater gain in QALYs (2.76 vs.2.16) relative to placebo group. Compared to placebo group, the inavolisib group resulted in an incremental gain of 0.6 QALYs at an additional cost of $160,490 yielding an ICER of $268,457.8/QALY gained. This ICER value is significantly higher than the WTP in the United States.

Table 3.

Summary of Cost (US Dollars) and Outcome Results in the Base-Case Analysis

		Inavolisib	Placebo	Difference
QALYs	PFS	2.06	1.05	1.01
QALYs	PS	0.7	1.11	-0.41
Total		2.76	2.16	0.6
Cost	PFS	393517.04	193694.11	199822.93
Cost	PS	53891.61	92621.48	-38729.87
Total		446715.65	286225.58	160490.07
ICER, $/QALY				268457.82

One-Way Sensitivity Analysis

The outcomes of the one-way sensitivity analysis were presented in a tornado diagram (Figure 1). The five most influential parameters were the utility of progression-free state, the cost of inavolisib, utilities of post progression state, discount rate, and the cost of Alpelisib. The utility of progression-free state was the most sensitive parameter affecting the ICER, causing the ICER to range from $206,664/QALY to $461,748/QALY. The analyses suggested that all parameters in defined ranges failed to make ICER lower than the $200,000/QALY WTP of threshold selected for affluent regions, and none of the parameters resulted in an ICER reaching the $100,000/QALY WTP threshold established for general regions.

Figure 1.

Tornado diagram for univariable sensitivity analyses. ICER incremental cost-effectiveness ratio.

Probabilistic Sensitivity Analysis

Considering the uncertainty of the model parameters, we conducted 10,000 iterations by varying all parameters simultaneously in a Monte Carlo simulation for probabilistic sensitivity analysis (PSA). Figure 2 shows the PSA results via a CEAC. The probability of inavolisib plus palbociclib-fulvestrant being cost-effective was 0% at a WTP threshold of $100,000/QALY, 3.3% at $150,000/QALY, and only 22.23% even when the threshold increased to $200,000 per QALY. The probability of inavolisib regimen being cost-effective exceeds 50% only when the WTP threshold surpasses $252,000/QALY. The inavolisib regimen reached cost-effectiveness in most cases (about 90%), and became cost-effective in virtually all cases at a WTP threshold above $573,000/QALY.

Figure 2.

Cost-effectiveness acceptability curves of inavolisib versus placebo group in patients with PIK3CA-mutated, HR+/HER2− locally advanced or metastatic breast cancer following prior endocrine therapy.

Scenario Analysis

We conducted a scenario analysis modeling progressive price reductions for inavolisib to explore its potential to achieve cost-effectiveness thresholds. In our probabilistic sensitivity analysis, we used WTP thresholds of $100,000, $150,000, and $200,000 per QALY, assessing the impact of drug price on the cost-effectiveness probability of the inavolisib regimen at these different values (Figure 3). When the price of inavolisib per cycle was reduced to 59.5 % and 42.8%, the proportions of simulations with cost-effectiveness for inavolisib regimen were 50% and 90%, respectively, at the WTP of $100,000/QALY. To achieve a 50% and 90% probability of cost-effectiveness, the per-cycle price of inavolisib must be reduced to 72% and 48% of its current price at the $150,000/QALY WTP threshold, and to 86.5% and 54% at the $200,000/QALY threshold, respectively. This scenario analysis demonstrates that to meet standard US cost-effectiveness benchmarks, a substantial reduction in the drug’s acquisition cost is essential.

Figure 3.

Probability of cost-effectiveness of inavolisib versus placebo group at different costs of inavolisib’price. QALY, quality-adjusted life-year; WTP, willingness-to-pay.

Subgroup Analysis

At the WTP threshold of $150,000/QALY, adding inavolisib was most cost-effective among the following subgroups, in descending order: patients with 2 organ metastases at enrollment (39.2%), patients outside of Asia, North America, or Western Europe (32.21%), HR-positive patients (14.71%). When the threshold was increased to $200,000/QALY, the list of potentially cost-effective subgroups expanded to include patients with two organ metastases at enrollment (81.91%), patients from regions outside Asia, North America, or Western Europe (77.41%),HR-positive patients (57.62%), patients aged ≤65 years (39.01%). The results of the subgroup analyses are presented in Supplemental Material Table S3.

Discussion

This is the first study to evaluate the cost-effectiveness of inavolisib plus palbociclib–fulvestrant versus placebo plus palbociclib–fulvestrant in patients with PIK3CA-mutated, HR+/HER2-advanced breast cancer following prior endocrine therapy by developing an economic model. In this population, the inavolisib treatment group demonstrated superior modeled efficacy outcome (i.e.,QALYs) compared to the control group, with an ICER of approximately $268,457.82/QALY gained for inavolisib. However, from the perspective of the U.S. healthcare system, inavolisib may not offer a cost-effective advantage under the current circumstances.

One-way sensitivity analysis identified the utility value for the PFS state as the most influential parameter in the model. As the PFS utility increased from 0.64 to 1.00, the ICER decreased from $461,748 to $206,664 per QALY, demonstrating a strong inverse relationship between the ICER and this utility parameter. Because the inavolisib group spent substantially more time in the PFS state than the placebo group (17.3 months vs. 7.3 months), improvements in the PFS utility value led to a disproportionately greater QALY gain for the treatment regimen, while the gain for the control egimen was relatively limited. If patients maintain a good quality of life during the progression-free period, the economic value of the inavolisib group would be substantially enhanced. Conversely, if the actual quality of life during PFS is lower (e.g., approaching the lower bound of the 95% confidence interval at 0.64), potentially due to treatment-related toxicity or other factors, the ICER would increase markedly, rendering the regimen not cost-effective. Given that the PFS utility value was identified as the most sensitive parameter, future research should prioritize the use of standardized, preference-based instruments such as the EQ-5D or SF-6D to systematically collect real-world health-related quality of life data from patients receiving inavolisib.³² Such efforts would enable economic evaluations to be updated in line with real-world clinical outcomes, thereby providing more robust evidence to inform pricing and reimbursement decisions.

This study also focuses on the impact of price fluctuations of inavolisib, because its cost is the second factor affecting the results of sensitivity analysis, and the drug is still in the early stage of market supply, so its price may have potential room for price adjustment over time. Sensitivity analysis indicated that as the WTP threshold increases, the likelihood of inavolisib being cost-effective significantly rises. This finding is consistent with the earlier research on alpelisib by Wenhua Wu et al.¹⁶ Given the heavy disease burden and steadily rising healthcare expenditures, we followed the recommendation of several economists and organizations by setting a maximum WTP threshold of US $200,000/QALY.^2,19 Scenario analyses using different WTP thresholds demonstrated that the inavolisib regimen is still unlikely to be considered cost-effective under the current U.S. WTP when compared to the placebo regimen, based on results of long-term health benefits and cost differences. The findings demonstrate that at WTP thresholds of $100,000, $150,000, and $200,000 per QALY, the inavolisib group becomes cost-effective when its price is reduced to 59.5%, 72.0%, and 86.5% of the current level, respectively, with a cost-effectiveness probability exceeding 50%. Therefore, appropriately adjusting the price of inavolisib could be an effective approach to enhancing the economic value of this treatment regimen and could thereby secure a more favorable formulary position.

Moreover, we applied suitable HRs to determine whether the cost-effectiveness of inavolisib group varies in subgroups. The subgroup analysis demonstrated that the inavolisib regimen was most likely to be cost-effective in patients with no more than two organ metastases at enrollment. At WTP thresholds of $150,000/QALY and $200,000/QALY, the probabilities of cost-effectiveness reached 39.2% and 81.91%, respectively. Other potential subgroups that could achieve cost-effectiveness include patients outside of Asia, North America, or Western Europe and those with an ER-positive and PR-positive status. Their probabilities of cost-effectiveness each exceeded 50% at a WTP threshold of $200,000/QALY. Based on these findings, a benefit analysis could help identify which patients are better candidates for the treatment. From an economic perspective, this precision screening based on patient characteristics provides a basis for reimbursement decisions to better match the benefits of specific patient groups. Notably, several health technology assessment agencies have recognized the use of thresholds based on prevalence adjustment or disease severity, rather than applying a uniform threshold across all indications, which is consistent with our findings.¹⁵

Although CDK4/6 inhibitors (i.e., palbociclib) combined with endocrine therapy have been the first-line treatment for PIK3CA-mutated, HR+/HER2- ABC, drug resistance remains a significant challenge.³³ Given the multiple resistance mechanisms, such as alterations in the PI3K/AKT/mTOR signaling pathway and ESR1 mutations, we should perform precise molecular testing upon disease progression, focus on mutations within these pathways, and provide targeted treatment.³⁴ Compared to capivasertib, which targets the midstream of the pathway, and everolimus, which acts downstream, inavolisib offers a novel upstream intervention with potentially stronger efficacy.^35,36 In contrast, alpelisib, also a PI3K inhibitor, is associated with more prominent toxicities such as hyperglycemia, diarrhea, and rash, and has received only a Category 2A recommendation in the NCCN guidelines, whereas inavolisib has achieved a Category 1 recommendation in the latest guidelines. ³⁷ It should be noted that the patient populations in the INAVO120 and SOLAR-1 trials differed. A head-to-head phase III trial, INAVO121, is currently underway to directly compare the efficacy and safety of inavolisib versus alpelisib in patients who have progressed on prior CDK4/6 inhibitor therapy.

We acknowledge several limitations in this study. First, although validated extrapolation techniques were employed, uncertainty remains regarding outcomes beyond the follow-up period of the INAVO120 trial. Second, the utility values used in this model were derived from published literature and may not fully capture the health-related quality of life specific to the patient population under investigation. Third, treatment pathways in the trial setting were protocol-driven and may not fully capture the heterogeneity of real-world clinical practice in the United States. As such, uncertainty inherent in the treatment sequencing assumptions remains and may influence long-term cost and outcome estimates. In addition, the impact of adverse drug events was not fully captured, as less severe adverse events were excluded. The sensitivity analysis confirmed the robustness of the findings, showing that AEs exerted only a modest influence across the two treatment strategies. Finally, as a newly approved drug in the United States, the market price of inavolisib has not yet stabilized, and evolving U.S. healthcare policy represents an important source of uncertainty in its pharmacoeconomic evaluation. Furthermore, the head-to-head phase III trial comparing inavolisib with the similar agent alpelisib—the INAVO121 study—is still ongoing. Therefore, ongoing monitoring of relevant healthcare policies and timely updates to model inputs are warranted.

Conclusions

To our knowledge, from the perspective of US payers, evaluating the use of inavolisib combined with palbociclib-fulvestrant in patients with PIK3CA-mutated, HR+/HER2- ABC is not cost-effective compared with the placebo receiving palbociclib-fulvestrant. Although inavolisib has shown significant benefits in clinical trials, its cost needs to be reduced to provide more favorable economic benefits and improve patient accessibility.

Supplemental Material

Supplemental Material - Economic Evaluation of Inavolisib Combined With Palbociclib–Fulvestrant for PIK3CA-Mutated, HR+/HER2− Advanced Breast Cancer in USA

Supplemental Material for Economic Evaluation of Inavolisib Combined With Palbociclib–Fulvestrant for PIK3CA-Mutated, HR+/HER2− Advanced Breast Cancer in USA by Hanqing Zeng, Li-Ying Song, Ren Guo, Dan Ding, Xiaohui Zeng and Qiao Liu in Technology in Cancer Research & Treatment

Footnotes

ORCID iD

Hanqing Zeng

Ethical Considerations

This study does not involve human participants or animal subjects.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Provincial Natural Science Foundation [Grant numbers 2021JJ80080].

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

No additional data available.*

Provenance and Peer Review

Not commissioned; externally peer reviewed.

Trial Registration Number

NCT04191449.

Supplemental Material

Supplemental material for this article is available online.

References

Giaquinto

Sung

Newman

, et al. Breast cancer statistics. CA Cancer J Clin. 2024;74(6):477-495.

Jaber Chehayeb

Hood

Wang

, et al. Treatment Sequencing Patterns and Associated Direct Medical Costs of Metastatic Breast Cancer Care in the United States, 2011 to 2021. JAMA Netw Open. 2022;5(11):e2244204.

Martinez-Saez

Chic

Pascual

, et al. Frequency and spectrum of PIK3CA somatic mutations in breast cancer. Breast Cancer Res. 2020;22(1):45.

Gao

Zhang

Kang

. Mechanisms of endocrine resistance in hormone receptor-positive breast cancer. Front Oncol. 2024;14:1448687.

Mosele

Stefanovska

Lusque

, et al. Outcome and molecular landscape of patients with PIK3CA-mutated metastatic breast cancer. Ann Oncol. 2020;31:377-386.

Reinhardt

Stuckrath

Hartung

, et al. PIK3CA-mutations in breast cancer. Breast Cancer Res Treat. 2022;196:483-493.

Hanker

Sudhan

Arteaga

. Overcoming endocrine resistance in breast cancer. Cancer Cell. 2020;37:496-513.

Vitale

Martorana

Stella

, et al. PI3K inhibition in breast cancer: Identifying and overcoming different flavors of resistance. Crit Rev Oncol Hematol. 2021;162:103334.

Van haesebroeck

Burke

Madsen

. Precision Targeting of Mutant PI3Kα in Cancer by Selective Degradation. Cancer Discov. 2022;12(1):20-22.

10.

Hanan

Braun

M-G

Heald

, et al. Discovery of GDC-0077 (Inavolisib), a Highly Selective Inhibitor and Degrader of Mutant PI3Kalpha. J Med Chem J Med Chem. 2022;65(24):16589-16621.

11.

Andre

Ciruelos

Rubovszky

, et al. Alpelisib for PIK3CA-Mutated, Hormone Receptor–Positive Advanced Breast Cancer. N Engl J Med. 2019;380(20):1929-1940.

12.

Bedard

Jhaveri

Accordino

, et al. Inavolisib plus letrozole or fulvestrant in PIK3CA-mutated, hormone receptor positive, HER2-negative advanced or metastatic breast cancer (GO39374): an open label, multi centre, dose-escalation and dose expansion phase 1/1b study. Eur J Cancer. 2025;221:115397.

13.

Komal

Seock-Ah

Saura

, et al. Overall Survival with Inavolisib in PIK3CA-Mutated Advanced Breast Cancer. N Engl J Med. 2025;393(2):151-161.

14.

Pearson

. The ICER Value Framework: Integrating Cost Effectiveness and Affordability in the Assessment of Health Care Value. Value Health. 2018;21(3):258-265.

15.

Institute for Clinical and Economic Review . Value Assessment Framework. 2025. Available from: https://icer.org/our-approach/methods-process/value-assessment-framework/. Accessed September 2025.

16.

Lin

Cai

, et al.

Is Alpelisib Plus Fulvestrant Cost-Effective for Treating PIK3CA-Mutation, HR+/HER2- Advanced Breast Cancer in the USA?

Clin Drug Investig. 2023;43(12):939-948.

17.

Eisenhauer

Therasse

Bogaerts

, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228-247.

18.

Rui

Wang

Wei

You

JHS

. HRR deficiency-guided use of talazoparib plus enzalutamide in patients with metastatic castration-resistant prostate cancer: a cost-effectiveness analysis. Ther Adv Med Oncol. 2025;17:1-15.

19.

Neumann

Cohen

Weinstein

. Updating Cost-Effectiveness-The Curious Resilience of the $50,000-Per-QALY Threshold. N Engl J Med. 2014;371(9):796-797.

20.

Chen

Han

Liu

, et al. Economic Evaluation of Sacituzumab Govitecan for the Treatment of Metastatic Triple-Negative Breast Cancer in China and the US. Front Oncol. 2021;28:11.

21.

Centers for Medicare and Medicaid Services . ASP drug pricing files 2025. Available from: https://www.cms.gov/medicare/medicare-part-bdrug-average-sales-price/2025-asp-drug-pricing-files. Accessed September 2025.

22.

IBM Micromedex RED BOOK [database online] . Micromedex Solutions. Available from: https://www.ibm.com/products/micromedex-red-book. Accessed June 2025.

23.

US Bureau of Labor Statistics . Consumer Price Index for All Urban Consumers (CPI-U): Medical Care. [Internet]. Available from: https://data.bls.gov/timeseries/CUUR0000SAM. Accessed Febuary 2026.

24.

Centers for Medicare & Medicaid Services . Overview of the Medicare Physician Fee Schedule (MPFS). Available from: https://www.cms.gov/medicare/physician-fee-schedule/search/overview. Accessed June 2025.

25.

Yang

Han

Tian

Liao

Yan

. Cost-effectiveness of ribociclib for hormone receptor-positive HER2-negative advanced breast cancer. Cancer Manag Res. 2020;12:12905-12913.

26.

Mistry

May

Suri

, et al. Cost-effectiveness of ribociclib plus letrozole versus palbociclib plus letrozole and letrozole monotherapy in the first-line treatment metastatic breast cancer: a US payer perspective. J Manag Care Spec Pharm. 2018;24:514-523.

27.

Sarfaty

Leshno

Gordon

, et al. Cost effectiveness of nivolumab in advanced renal cell carcinoma. Eur Urol. 2018;73:628-634.

28.

Pan

Ren

Yang

. Cost-effectiveness of talazoparib for patients with germline BRCA1/2 mutated HER2-negative advanced breast cancer in China and the US. Sci Rep. 2024;14(1):13935.

29.

Rashid

Koh

Baca

Lin

Malecha

Masaquel

. Economic burden related to chemotherapy-related adverse events in patients with metastatic breast cancer in an integrated health care system Breast Cancer. Dove Med Press. 2016;8:173-181.

30.

Lang

Chai

Tao

Liao

Liu

. Cost-effectiveness of sacituzumab govitecan versus chemotherapy in advanced or metastatic triple-negative breast cancer. Breast. 2023;68:173-180.

31.

Prajakta

Damgacioglu

Deshmukh

, et al. Cost Effectiveness of CDK4/6 Inhibitors in the First-Line Treatment of HR+/HER2− Metastatic Breast Cancer in Postmenopausal Women in the USA. Pharmacoeconomics. 2023;41(6):709-718.

32.

Kennedy-Martin

Slaap

Herdman

, et al. Which multi-attribute utility instruments are recommended for use in cost-utility analysis? A review of national health technology assessment (HTA) guidelines. Eur J Health Econ. 2020;21:1245-1257.

33.

Huang

Zheng

Sun

Jie

. CDK4/6 inhibitor resistance mechanisms and treatment strategies (Review). Int J Mol Med. 2022;50(4):128.

34.

Saatci

Huynh-Dam

Sahin

. Endocrine resistance in breast cancer: from molecular mechanisms to therapeutic strategies. J Mol Med. 2021;99:1691-1710.

35.

Eberlein

Williamson

Hopcroft

, et al. Capivasertib combines with docetaxel to enhance anti-tumour activity through inhibition of AKT-mediated survival mechanisms in prostate cancer. Br J Cancer. 2024;130(8):1377-1387.

36.

Miricescu

Totan

Stanescu-Spinu

I-I

Badoiu

Stefani

Greabu

. PI3K/AKT/mTOR Signaling Pathway in Breast Cancer: From Molecular Landscape to Clinical Aspects. Int J Mol Sci. 2020;22(1):173.

37.

National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology: breast cancer guideline. J Natl Compr Canc Netw. 2025; (https://www.nccn.org/). [Accessed June 2025]