Low pan-immune-inflammation-value predicts better chemotherapy response and survival in breast cancer patients treated with neoadjuvant chemotherapy

Blood-based biomarkers reflect systemic inflammation status and have prognostic and predictive value in solid malignancies. As a recently defined biomarker, Pan-Immune-Inflammation-Value (PIV) integrates different peripheral blood cell subpopulations. This retrospective study of collected data aimed to assess whether PIV may predict the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in Turkish women with breast cancer. The study consisted of 743 patients with breast cancer who were scheduled to undergo NAC before attempting cytoreductive surgery. A pre-treatment complete blood count was obtained in the two weeks preceding NAC, and blood-based biomarkers were calculated from absolute counts of relevant cell populations. The pCR was defined as the absence of tumor cells in both the mastectomy specimen and lymph nodes. Secondary outcome measures included disease-free survival (DFS) and overall survival (OS). One hundred seven patients (14.4%) had pCR. In receiver operating characteristic analysis, optimal cut-off values for the neutrophile-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte (PLR), PIV, and Ki-67 index were determined as ≥ 2.34, ≥ 0.22, ≥ 131.8, ≥ 306.4, and  ≥ 27, respectively. The clinical tumor (T) stage, NLR, MLR, PLR, PIV, estrogen receptor (ER) status, human epidermal growth factor receptor-2 (HER-2) status, and Ki-67 index were significantly associated with NAC response in univariate analyses. However, multivariate analysis revealed that the clinical T stage, PIV, ER status, HER-2 status, and Ki-67 index were independent predictors for pCR. Moreover, the low PIV group patients had significantly better DFS and OS than those in the high PIV group (p = 0.034, p = 0.028, respectively). Based on our results, pre-treatment PIV seems as a predictor for pCR and survival, outperforming NLR, MLR, PLR in predicting pCR in Turkish women with breast cancer who received NAC. However, further studies are needed to confirm our findings.

www.nature.com/scientificreports/ with a lower recurrence rate and more favorable survival outcomes 6 . Unfortunately, there is considerable interindividual variation in response to NAC amongst women with breast cancer, and several variables have been investigated in relation to this variability 7 .
In the last two decades, the relationship between chronic inflammation and cancer has become very popular, and both the diagnostic and therapeutic value of inflammatory markers have been studied extensively. Inflammation has been shown to promote tumor initiation and progression, whereas escape from immune surveillance may favor cancer invasiveness 8 . In the tumor microenvironment, neutrophils, monocytes-derived macrophages, and platelets have adverse prognostic significance by promoting tumoral angiogenesis and tumor growth, whereas tumor-infiltrating lymphocytes portend favorable outcomes [9][10][11] . Based on the assumption that peripheral blood cell populations can provide information about the intratumoral immune system status, peripheral blood-derived inflammation markers such as neutrophile-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), and platelet-lymphocyte ratio (PLR) was shown to have prognostic value in many solid organ malignancies [12][13][14] . In addition to their prognostic value, these markers were reported to predict the neoadjuvant chemotherapy response in breast cancer [15][16][17] .
In 2020, Fuca et al. reported that a novel systemic immune score called Pan-Immune-Inflammation-Value (PIV) performed better in predicting survival outcomes than other immune-inflammatory biomarkers such as NLR in advanced colorectal cancer patients 18 . However, PIV's predictive and prognostic value in breast cancer patients receiving NAC has not been studied. We, therefore, designed the current study to address these issues specifically.

Results
The general characteristics of the entire study sample (n = 743) are shown in Table 1. The median age was 48.0 years (range: 22.0-83.5 years). One hundred ninety-seven patients (26.5%) had T3/T4, and 37.5% had node-positive disease. More than two-thirds received chemotherapy regimens containing both anthracycline and taxane. Of patients, 14.4% had pCR.  Table 3 depicts the analyses of the association between the patients' characteristics and pCR.

Discussion
In the present study, we demonstrated for the first time that a new inflammatory score, PIV, was one of the independent predictors for pCR to NAC like the well-studied other clinicopathological factors such as T stage, ER status, HER-2 status, and Ki-67 index in breast cancer. Additionally, PIV outperformed other blood-derived inflammation markers in predicting pCR, and it also had a prognostic value for DFS and OS.
In general, white blood cell count reflects an individual's systemic and/or local inflammatory status 11 . Neutrophils are known to regulate the tumor microenvironment and produce cytokines, chemokines, and growth factors that may promote angiogenesis as well as tumor cell proliferation and migration 19 . The M2 phenotype tumor-associated macrophages (TAMs) deriving from circulating monocytes exist within the tumor microenvironment and promote metastasis and immunosuppression 20,21 . It was reported that peripheral monocyte count was associated with the density of the TAMs, and high absolute monocyte count predicted poor survival in cancer patients 22,23 . Platelets are other cells contributing to cancer-favored inflammation by various mechanisms. For example, the activated platelets inhibit the interaction between tumor cells and cytolytic immune cells by forming a layer around tumor cells and support tumor growth via the secretion of several factors such as TGF-β 24,25 . Hence, high platelet counts were reported to be associated with adverse outcomes in breast cancer 26 . In contrast, lymphocytes are responsible for antitumor-specific immune response-including T-lymphocyte tumor infiltration and cytotoxic T-lymphocyte-mediated antitumor activity 11,27 . Starting from these findings, NLR, MLR, and PLR, indexes reflecting the balance between inflammation and immunoreaction in cancer, were reported to have predictive value in NAC response in many breast cancer studies, supporting our findings of univariate analysis [15][16][17]28 .
Although the scientific evidence supporting the predictive value of the blood-derived inflammation indexes has been expanding, there are conflicting reports about which index provides the best prediction for the efficacy of NAC in breast cancer 29 www.nature.com/scientificreports/ factor of pCR among blood-derived inflammation markers in multivariate analysis 29 . In another study conducted by Peng et al., multivariate analysis of 808 breast cancer patients showed that the lymphocyte-monocyte ratio was the only independent predictive factor for the efficacy of NAC among these inflammatory markers 32 . In addition, Hu et al. stated that PLR had superior efficacy to NLR in predicting NAC response 33 . When these studies are evaluated together with scholars presenting negative results 34 , it has emerged offering a combination www.nature.com/scientificreports/ of these markers 35,36 . The combination of NLR and PLR was reported to predict NAC response more precisely than NLR and PLR alone, claiming that the combination of different biomarkers could better define the patients' inflammatory status 35,36 . PIV is a new blood-based biomarker integrating different peripheral blood immune cell subpopulationsneutrophil, platelet, monocyte, and lymphocyte. Due to its potential to represent comprehensively patient's immunity and systemic inflammation, PIV was proposed as a stronger predictor of outcomes in advanced cancer patients receiving cytotoxic chemotherapy, immunotherapy, and targeted therapy 18,[37][38][39] . Recently, Ligorio reported that PIV was firmly associated with survival and outperformed NLR, PLR, and MLR in predicting survival in patients with HER-2 positive advanced breast cancer 39 . In line with the studies mentioned above, we showed that patients with low PIV scores had better survival outcomes. Furthermore, to our knowledge, this is the first study reporting that PIV was a more reliable predictor of pCR after NAC than other blood-based markers in breast cancer patients.
Meta-analyses and systematic reviews have shown that numerous factors-including age, genetic polymorphisms, tumor-infiltrating lymphocytes, programmed death-ligand 1, ER, progesterone receptor, and HER2 Table 3. Association between the patients' characteristics and pCR. pCR pathological complete response, NLR neutrophil-to-lymphocyte ratio, MLR monocyte-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio, PIV Pan-immune-inflammation-value, ER estrogen receptor, HER-2 human epidermal growth factor receptor-2. a Mann-Whitney test; b Pearson's Chi-squared test. Data are given as counts (percentages) unless otherwise indicated. Bold numbers indicate statistical significance. www.nature.com/scientificreports/ expression status-may be predictive of response to NAC in women with breast cancer 7,40-42 . However, most of these variables become available only following detailed pathological and genetic investigations. There is, therefore, an urgent need for reliable prognostic tools grounded on simple pre-treatment variables. In this scenario, PIV-an easy-to-drive biomarker originating from routine complete blood count-may help clinicians to predict treatment responses after prospective validation and confirmation of our results by further studies. Some limitations of our study merit comment, including the retrospective design, the presentation of singlecenter experience, and the inclusion of women of Turkish descent only. Furthermore, although we excluded the patients with hematological disorders and those receiving immunomodulatory treatment, various other conditions may influence the blood-based biomarkers.
Based on our results, pre-treatment PIV seems to have a significant predictive value and outperform NLR, MLR, PLR in predicting pCR in Turkish women with breast cancer who received NAC. In addition, PIV has a prognostic impact on survival. However, further studies are needed to confirm our findings.

Materials and methods
Study population. Figure 2 shows the profile of our study. The electronic records of patients admitted to the Department of Oncology or the Department of General Surgery, Uludag University Medical Center (Bursa, Turkey) between January 2008 and December 2019 due to breast cancer were reviewed. Among patients who underwent NAC before attempting cytoreductive surgery, patients who were aged < 18 years, received immu- Table 4. Univariate and multivariate logistic regression analysis for the predictors of pathological complete response. OR odds ratio, CI confidential interval, RC reference category, NLR neutrophil-to-lymphocyte ratio, MLR monocyte-to-lymphocyte ratio, PLR platelet-to-lymphocyte ratio, PIV Pan-immune-inflammation-value, ER estrogen receptor, HER-2 human epidermal growth factor receptor-2, IDC invasive ductal carcinoma, AplusT anthracycline plus taxane. The multivariate logistic regression model is significant (p < 0.001). Bold numbers indicate statistical significance.   Outcomes. The primary outcome measure was pCR to NAC. The pCR was defined as the absence of tumor cells in both the mastectomy specimen and the sampled or dissected regional lymph nodes. Secondary outcome measures included DFS and OS. DFS was calculated as the time (in months) from curative surgery until recurrence or death, whichever occurred first. OS was calculated as the time (in months) from breast cancer diagnosis to death. www.nature.com/scientificreports/ Statistical analysis. The optimal cut-off points for NLR, MLR, PLR, PIV, and Ki-67 index were determined using ROC curve analysis, taking pCR as the endpoint of interest. The general characteristics of the study patients are presented using descriptive statistics (median, ranges, counts, and percentages). The Pearson's Chisquared test (categorical variables) or the Mann-Whitney U test (continuous variables) were used to analyze the association between pCR and the variables. Binary logistic regression analysis was employed for multivariate analysis, including the factors having a p-value below 0.25 in univariate analysis. Survival curves were plotted using the Kaplan-Meier method and compared with the log-rank test. All calculations were performed using SPSS, version 22.0 (IBM, Armonk, NY, USA) and MedCalc Statistical Software trial version 20.009 (MedCalc Software bv, Ostend, Belgium; www. medca lc. org; 2021). Two-tailed p values < 0.05 were considered statistically significant.