The prognostic value of the pretreatment lung immune prognostic index in patients with metastatic hormone-sensitive and castration-resistant prostate cancer

Introduction

Prostate cancer (PCa) is the most frequently diagnosed cancer in men (1). De novo metastatic PCa (mPCa) constitutes about 6% and 25% to 44% of the total PCa cases in the United States and Asia, respectively (1,2). Among the many factors promoting tumorigenesis and progression of PCa, inflammation plays a crucial role (3-5).

On the pan-cancer level, cancer-associated inflammation is considered a hallmark feature of carcinogenesis (6,7). Likewise, a chronic inflammatory response in preneoplastic and malignant prostates has also been implicated as a driver for PCa development and progression (3). Inflammation induces PCa in several ways. For example, immune cells in the tumor microenvironment release cytokines in response to inflammation, which causes DNA double-strand breaks and epithelial proliferation (3,8,9). Additionally, intraprostate inflammation can enhance the activation of the androgen receptor (AR) pathway and subsequently trigger the tumoral transformation of normal prostate cells (3,10).

Notably, inflammatory biomarkers, such as neutrophil and lymphocyte levels as well as derived neutrophil-to-lymphocyte ratio (dNLR), have been considered to have potential prognostic value in several cancers (11,12). In addition, lactate dehydrogenase (LDH), a blood indicator of inflammation and metabolism, is a well-known biomarker associated with the malignant progression of multiple solid tumors (13). The lung immune prognostic index (LIPI), which combines the dNLR and LDH levels, was first proposed to predict the effectiveness of immune checkpoint inhibitors (ICIs) in patients with metastatic non–small cell lung cancer (mNSCLC) in 2018 (14). LIPI was subsequently found to be an important prognostic biomarker irrespective of the treatment modality for patients with mNSCLC (15). To date, the prognostic value of LIPI has been demonstrated in several solid tumors, including gastric cancer, urothelial bladder cancer, and squamous esophageal cancer (16-20). However, there are currently no studies investigating the predictive value of LIPI for patients with PCa.

Therefore, in this study, we aimed to explore the association between LIPI and survival outcomes in patients with metastatic hormone-sensitive PCa (mHSPC) and metastatic castration-resistant PCa (mCRPC). We present the following article in accordance with the STARD reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-4318/rc).

Methods

Study design and patients

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional review board of West China Hospital (No. 20211703), and individual consent for this retrospective analysis was waived. A total of 502 with mHSPC and 185 patients with mCRPC who attended the West China Hospital between 2008 and 2021 were included in this study. After diagnosis, most patients with mHSPC (n=448, 89.2%) received maximal androgen blockade (MAB; medical or surgical castration plus bicalutamide 50 mg/day), and 10.8% of patients were treated with abiraterone (ABI; n=26, 5.2%; ABI 1,000 mg/day plus prednisone 10 mg/day) and/or docetaxel (DOC; n=33; 6.6%; 75 mg/m² q3w, plus prednisone 10 mg/day, maximized with 10 cycles). In the mCRPC cohort, all patients received ABI (1,000 mg/day plus prednisone 10 mg/day) as the first-line therapy.

LIPI was a novel categorical blood-based biomarker reflecting the peripheral inflammatory status. LIPI for all patients was calculated prior to the treatment. The pretreatment absolute white blood cell count, absolute neutrophil count, and LDH levels were collected in each case. According to the definition of LIPI proposed by Mezquita et al. (14), LIPI consists of 2 parameters: dNLR and LDH. The dNLR was calculated as follows: absolute neutrophil count/(absolute white blood cell count—absolute neutrophil count). When evaluating the LIPI status of a patient, a dNLR more than 3.0 and an LDH level higher than the upper limit of the normal value were considered 2 independent risk factors. LIPI status could be classified into 3 conditions: LIPI-good (0 factor), LIPI-intermediate (1 factor), and LIPI-poor (2 factors).

Other clinicopathological data of all patients were also collected, including age, prostate-specific antigen (PSA), International Society of Urological Pathology (ISUP) grading, visceral metastasis, Eastern Cooperative Oncology Group (ECOG) score, hemoglobin (HGB) levels, and alkaline phosphatase (ALP) levels.

End points

For patients with mHSPC, end points were CRPC-free survival (CFS) and overall survival (OS). CFS and OS were defined as the time from the initial diagnosis to mCRPC or death from any cause, respectively. mCRPC was defined according to the 2022 EAU guidelines (21).

For patients with mCRPC receiving ABI, end points were PSA response, PSA-progression-free survival (PSA-PFS), and OS. PSA response was defined as a more than or equal to 50% decline in the PSA level from the baseline after ABI therapy maintained for more than or equal to 4 weeks. PSA-PFS was the interval from ABI therapy to PSA progression, which was defined as 2 consecutive rises in the PSA level of 25% or more above the nadir (≥2 ng/mL) after the initiation of treatment. OS was the time from ABI therapy to death from any cause.

Statistics

The chi-squared test was used to compare categorical variables. CFS, PSA-PFS, and OS were assessed using the Kaplan-Meier curves and compared using the log-rank test. A propensity score matching (PSM) methodology with a caliper distance of 0.1 was performed based on all baseline factors to balance the baseline factors of the different groups. The value of clinicopathological factors in predicting CFS, PSA-PFS, and OS was analyzed using Cox proportional hazards model. Factors with a P value <0.05 in univariate analyses were further tested in multivariate analyses. The prognosis-predicting accuracy of different variables was evaluated using the concordance index (C-index).

All statistical analyses were performed using SPSS 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.1.0 (The R Foundation of Statistical Computing, Vienna, Austria). A P value <0.05 was considered statistically significant.

Results

Prognostic value of LIPI for patients with mHSPC

Patient characteristics

The baseline characteristics of the 502 patients with mHSPC are summarized in Table 1. Among the patients with mHSPC, 261 (52.0%), 172 (34.3%), and 69 (13.7%) were classified into the LIPI-good, LIPI-intermediate, and LIPI-poor groups, respectively. Cases in the LIPI-poor and LIPI-intermediate groups harbored a lower HGB level, a higher ALP level, and a higher ISUP grading than did those in the LIPI-good group, while other factors were comparable among these groups. After PSM, the baseline characteristics were well balanced among the different groups (Table S1). The median follow-up time was 46.9 months for the mHSPC cohort. Eventually, 378 (75.3%) patients progressed to mCRPC, while 193 (38.4%) patients died. The median CFS (mCFS) and OS (mOS) were 18.3 and 69.5 months, respectively.

Prognostic analysis of LIPI in mHSPC

In the mHSPC cohort, as shown in Figure 1A,1B, cases in the LIPI-good (mCFS: 25.7 months; mOS: 93.3 months), LIPI-intermediate (mCFS: 14.8 months; mOS: 51.9 months), and LIPI-poor groups (mCFS: 6.8 months; mOS: 18.5 months) had sequentially worse clinical outcomes in terms of both CFS and OS, with a P value <0.001 for the log-rank test of all pairwise comparisons. Similar results were observed in the analyses using the post-PSM cohort (Figure 1C,1D). We performed a subgroup analysis in patients with mHSPC treated with either MAB or androgen deprivation therapy (ADT) plus ABI/DOC. In the MAB subgroup, LIPI had strong power in predicting the survival outcomes of CFS (P<0.001) and OS (P<0.001) in the LIPI-good, LIPI-intermediate, LIPI-poor groups (CFS: 24.7 vs. 14.8 vs. 6.7 months; OS: 93.3 vs. 47.4 mo 18.4 months; Figure S1A,S1B). In the ABI/DOC subgroup, due to the small number of patients in the LIPI-poor group (n=5), we combined the LIPI-intermediate and LIPI-poor groups. The results showed that patients in the LIPI-intermediate and LIPI-poor groups had significantly shorter CFS than did those in the LIPI-good group (mCFS: 14.8 vs. 24.7 months; P=0.012; Figure S1C). However, in terms of OS, neither group of patients reached the median survival time (Figure S1D).

Figure 1 Kaplan-Meier curves showing the prognostic value of LIPI for patients with mHSPC. (A) CFS before PSM. (B) OS before PSM. (C) CFS after PSM. (D) OS after PSM. PSM, propensity score matching; LIPI, lung immune prognostic index; CFS, cancer-free survival; int., intermediate; OS, overall survival; mHSPC, metastatic hormone-sensitive prostate cancer.

Univariate Cox regression analysis further revealed that the 2 components of LIPI (i.e., LDH and dNLR) and other clinicopathological features, including ECOG score, ISUP grading, visceral metastases, and HGB and ALP levels, were predictors of CFS and OS (Tables 2,3). Multivariate Cox regression analysis confirmed that LIPI, ISUP grading, visceral metastases, and HGB and ALP levels were independently associated with CFS, while these factors, apart from ALP, were also adversely related to OS (Tables 2,3). The accuracy of different features in predicting CFS and OS was evaluated using the C-index (Table 4). LIPI showed stronger predictive power against other features. Furthermore, the inclusion of LIPI into the basic model developed using the factors with predictive power in multivariate Cox regression could further increase the C-index of the model from 0.672 to 0.712 for predicting CFS and from 0.679 to 0.746 for predicting OS.

The robustness of the prognostic significance of LIPI was then explored among patients of various subgroups (Figure 2). The results showed that LIPI was capable of predicting CFS and OS in patients of almost all subgroups except those with visceral metastases or treated with ABI or DOC (who showed a prognostic trend).

Figure 2 Forest plot showing the prognostic value of LIPI among patients with mHSPC of various subgroups. LIPI, lung immune prognostic index; mHSPC, metastatic hormone-sensitive prostate cancer; IUSP, International Society of Urological Pathology; ECOG, Eastern Cooperative Oncology Group; PSA, prostate specific antigen; HGB, hemoglobin; ALP, alkaline phosphatase; ABI, abiraterone; DOC, docetaxel; MAB, maximum androgen blockade.

The prognostic value of LIPI for patients with mCRPC

Patient characteristics

The baseline characteristics of the 158 patients with mCRPC are summarized in Table 1. In the mCRPC cohort, 70 (44.3%), 69 (43.7%), and 19 (12.0%) patients were placed in the LIPI-good, LIPI-intermediate, and LIPI-poor groups, respectively. Consistent with the mHSPC cohort, men in the LIPI-intermediate and LIPI-poor groups had lower HGB and higher ALP levels. Considering the small number of patients in the LIPI-poor group, cases of the LIPI-intermediate and LIPI-poor groups were merged and matched with men in the LIPI-good group using the PSM algorithm (Table S1). All patients received ABI as first-line mCRPC therapy with a median follow-up time of 35.9 months. Among the 158 cases, PSA response was achieved in 96 (60.8%) patients, whereas PSA progression and death occurred in 115 (72.8%) and 68 (43.0%) patients, respectively. The median PSA-PFS (mPSA-PFS) and mOS were 10.0 and 41.8 months, respectively.

Prognostic analysis of LIPI in mCRPC

For patients with mCRPC, LIPI also had strong power in predicting the therapeutic efficacy and survival outcomes of the ABI treatment. Specifically, among men in the LIPI-good, LIPI-intermediate, and LIPI-poor groups, a clear difference in PSA response rate [71.4% (50/70) vs. 56.5% (39/69) vs. 36.8% (7/19); P=0.015] and PSA-PFS (mPSA-PFS: 14.9 vs. 9.3 vs. 3.1 months; P<0.001) was observed (Figure 3A,3B). As for OS, patients in the LIPI-poor group had a worse OS compared to those in the LIPI-intermediate group (mOS: 14.6 vs. 32.3 months; P=0.009) and LIPI-good group (mOS: 14.6 vs. 53.4 months; P<0.001), while men in the LIPI-intermediate group had a numerically shorter OS than did those in the LIPI-good group (mOS: 32.3 vs. 53.4 months; P=0.091; Figure 3C). Similar results were also found in the post-PSM cohort, in which the LIPI-good, LIPI-intermediate, and LIPI-poor groups had a ladder-shaped worse clinical outcome (Figure 3D-3F).

Figure 3 The prognostic value of LIPI for patients with mCRPC. (A) PSA response rate before PSM. (B) PSA-PFS before PSM. (C) OS before PSM. (D) PSA response rate after PSM. (E) PSA-PFS after PSM. (F) OS after PSM. PSM, propensity score matching; int., intermediate; PSA, prostate-specific antigen; LIPI, lung immune prognostic index; PFS, progression-free survival; OS, overall survival; mCRPC, metastatic castration-resistant prostate cancer.

Univariate Cox regression indicated that, besides LIPI, factors including ECOG score, ISUP grading, PSA, and CFS were also related to patients’ prognoses (Tables 2,3). After adjustments were made for other prognosticators, multivariate Cox regression confirmed that LIPI was an independent prognosticator predicting the efficacy of ABI (Tables 2,3). Additionally, we found that LIPI had a better discriminatory ability compared with other features and could further improve the C-index of the basic model from 0.603 to 0.649 for predicting CFS and from 0.703 to 0.729 for predicting OS (Table 4). Due to the limited sample size of patients with mCRPC, subgroup analysis was only performed in the mHSPC cohort.

Discussion

LIPI was first proposed to predict the effectiveness of ICIs in patients with mNSCLC (14) and was subsequently validated in patients with other tumors treated with ICIs (16,18,19). Researchers later found that LIPI also had the potential to predict the prognosis of patients with different cancers reeving non-ICI therapies (15,17,20,22). The present study is the first to report the utility of LIPI in patients with advanced PCa. Our findings indicated that the clinical outcomes of patients with mHSPC deteriorated as their LIPI score elevated, which was robust among cases of the various subgroups. Moreover, a higher LIPI score was also associated with an unfavorable PSA response, shorter PSA-PFS, and OS for patients with mCRPC treated with ABI.

LIPI is a biomarker that is based on cancer-associated inflammation and metabolism. Inflammation plays a key role in carcinogenesis and tumor progression (6,7). Many cancers arise from chronic inflammatory irritation, and there are shared molecular mechanisms and signaling pathways between inflammation and carcinogenesis, such as increased proliferation rate and angiogenesis (6). In terms of PCa, inflammation has also been implicated as a driver of tumorigenesis (3). Intraprostatic inflammation can trigger the release of free radicals and cytokines that cause DNA damage and prostatic epithelial injury (23). Additionally, intraprostate inflammation can enhance the activation of the AR pathway (10). Taken together, these effects eventually induce PCa growth and progression. Given that inflammation and PCa are closely related, it is not surprising that LIPI was found to be a strong prognostic predictor in this study.

LIPI consists of two critical inflammatory and metabolism parameters: dNLR and LDH. dNLR (absolute neutrophil count/[absolute white blood cell count—absolute neutrophil count]) is considered to be marker of systematic inflammation and has been demonstrated to be a prognosis predictor in several types of cancer (24-26). For PCa, Shu et al. (27) reported that high dNLR was associated with poor biochemical recurrence-free survival in patients with high-risk localized PCa undergoing radical prostatectomy. Additionally, data from 2 randomized phase III trials also showed that dNLR was a prognostic biomarker for men with mCRPC receiving first-line chemotherapy (28). Another component of LIPI, LDH, is not only a biomarker related to inflammation but also an enzyme related to metabolism. LDH, as a glycolytic enzyme, can be released by rapidly growing tumors. The high LDH level was demonstrated to be a classic biomarker associated with the malignant progression of multiple solid tumors (13). As for PCa, 2 recent meta-analyses (n=12,224 and n=9,813) demonstrated the association between a high level of LDH and the poor prognosis of patients with mPCa (29,30). The current study found that the integration of both dNLR and LDH using LIPI could further enhanced the predictive accuracy of these inflammatory and metabolism biomarkers.

LIPI is a low-cost, easily accessible biomarker with good prognostic value. The calculation of LIPI is based on routine blood testing and biochemistry and is thus highly practicable, especially in developing countries. However, LIPI still has some drawbacks: as a blood parameter, it might be affected by patients’ internal environment and treatments. For example, in this study, we found that LIPI had a weaker discriminatory ability in patients with mCRPC than in patients with mHSPC. This may be because the blood biochemical baseline of patients with mCRPC was affected by the treatments in the mHSPC stage.

This study had some limitations. First, it was a retrospective study with a relatively small sample size; specifically, we could not conduct a subgroup analysis for patients with mCRPC. Second, this study did not assess the impact of dynamic changes in LIPI score due to a lack of data. Third, the combination of intermediate and poor groups in the patients with mHSPC receiving ABI and DOC and mCRPC PSM cohort might have affected the accuracy of LIPI.

Conclusions

This study is the first to investigate the utility of LIPI for patients with advanced PCa. This study demonstrated that LIPI was not only a strong predictor of poor prognosis for patients with mHSPC receiving hormone therapy but was also associated with unfavorable clinical outcomes for patients with mCRPC treated with ABI. These findings suggest that LIPI could potentially facilitate risk classification and clinical decision-making for patients with metastatic PCa.

Acknowledgments

Funding: This work was supported by the National Natural Science Foundation of China (NSFC; Nos. 82172785, 82103097, 81974398, 81902577, and 81872107), the Science and Technology Support Program of Sichuan Province (No. 2021YFS0119), and the 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. 0040205301E21).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-4318/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-4318/dss

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-4318/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-4318/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by institutional review board of West China Hospital (No. 20211703), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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