Keywords
Brain Neoplasms - Survival Analysis - Biomarkers
Palavras-chave
Neoplasias Encefálicas - Análise de Sobrevida - Biomarcadores
INTRODUCTION
Brain metastasis (BM) are the most common malignant intracranial tumors, and their
incidence continues to rise over time. Advances in oncologic treatments and supportive
care provides patients with longer survival to the point of developing brain metastases
in end-stage disease.[1] Treatment of BM ranges from supportive care to multimodal strategies considering
surgery, fractioned radiotherapy, whole brain radiotherapy, stereotactic radiosurgery,
or chemotherapy. Treatment decisions are based on primary tumor site, metastatic tumor
characteristics, and patient performance status. Determining the most suitable treatment
option based on the patient prognosis can be challenging.[2]
Cancer is a disease state associated with marked systemic inflammation.[3] Previous studies have demonstrated that elevated levels of inflammatory biomarkers
can be associated with cancer progression and recurrence of solid tumors.[4] For example, the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio
(MLR), platelet-to-lymphocyte ratio (PLR), and red blood cell distribution width (RDW)
have shown promise as predictors of cancer survival in several different malignant
diseases.[5]
[6]
[7]
[8]
[9]
The use of validated prognostic tools can greatly aid clinicians in creating patient-centered
treatment plans. While the NLR, MLR, PLR, and RDW have demonstrated efficacy as prognostic
factors in many malignancy states, few studies have evaluated their performance specifically
for patients with BM.[6] This study aims to fill this gap by examining the relationship between these biomarkers
and patient outcomes in a prospective cohort.
METHODS
This report adheres to the Standards for the Reporting of Diagnostic Accuracy Studies
(STARD) statement. Although the STARD was developed for diagnostic accuracy studies,
it also provides a useful framework for reporting other studies evaluating the performance
of other clinical tests, including prognostic studies.[10]
Study design and patient selection
We retrospectively reviewed a database of 200 patients recruited prospectively and
consecutively for a recent diagnosis of BM, and primarily treated with surgical resection
between August 2010 and July 2016 at the Instituto do Câncer do Estado de São Paulo
of the Faculdade de Medicina of the Universidade de São Paulo (ICESP-FMUSP).
Patients were included in this study if they met the following criteria: 1) adult
patients (≥ 18 years old); 2) at least one BM treated by surgical resection; and 3)
did not receive any treatment for BM before surgery. One patient had a limited surgical
resection (< 50%) which was considered as an open biopsy. After surgical resection,
patients were referred for adjuvant therapy after multidisciplinary evaluation. The
patients were prospectively followed up and underwent magnetic resonance imaging (MRI)
every 4 months for a maximum follow-up period of 60 months, or until death. Follow-up
was lost on 16 patients, for whom the date of the last appointment was considered
for analysis.
Data collection and hematologic variables
Patients' epidemiological and clinical information were collected from an electronic
database, and their performance status was assessed using the American Society of
Anesthesiologists (ASA) and the Karnofsky performance status (KPS) classifications.
The graded prognostic assessment (GPA) oncologic prognostic score was used to adjust
for risk. The primary outcome of interest was 1-year survival.
Blood samples were collected no more than 30 days before surgery in ethylenediaminetetraacetic
acid (EDTA), and the complete blood count (CBC) was processed using an automated hematology
analyzer. The NLR, MLR, and PLR were respectively calculated by dividing the absolute
neutrophil, monocyte, and platelet counts, respectively, by the absolute lymphocyte
count. The RDW was provided by the hematology analyzer.
Statistical analysis
Categorical variables were presented as relative and absolute frequencies. Normally
distributed continuous data were presented as mean and standard deviation (SD), or
median and quartiles, as appropriate and indicated. The normality assumption was assessed
by skewness and kurtosis values, as well as graphical methods for each variable.
For the outcome of 1-year survival, potential categorical predictors were identified
using the Kaplan-Meier method, using the logrank (Mantel-Cox) test for the comparison
of the survival functions. Continuous variables were analyzed through a univariate
Cox regression. Variables with a univariate p-value less than 0.10 were included in a multivariate Cox regression model, and the
results were presented as hazard ratios (HR), with 95% confidence intervals (CI).
The change-in-estimate criterion strategy was also employed as a sensitivity analysis
while not changing the direction or effect size of the relationship between the hematological
parameters and the outcome. The hematological parameter was then included in the regression
model as a continuous variable. The proportionality and linearity assumptions were
verified using graphical methods and the Schoenfeld and Martingale residuals, respectively.
Since age, preoperative KPS, and the number of BM lesions are considered in the GPA
score, these variables were not considered for inclusion in the multivariate models.
The cutoff values for the NLR, MLR, PLR, and RDW are not well established for solid
cancers, although recent studies have suggested thresholds of NLR < 4[9] and PLR < 150.[10] We built separate multivariate regression models to assess the predictive performance
of each of these biomarkers as continuous or categorical variables. Afterwards, we
refined the cut-off determination directed at the survival outcome, as proposed by
Budczies et al.[11] A Cox proportional hazard model was fitted to the dichotomized hematological and
survival parameters. The survival analysis was performed using the functions coxph and survfit from the R package survival.[12] The optimal cutoff was defined as the point with the most significant (logrank test)
split. Differences in survival were calculated from the mean survival times in the
good and poor prognosis groups. Mean survival times were estimated from the area under
the Kaplan-Meier curve using the maximum time that occurs in the data as the uniform
time endpoint. To assess the robustness of the cutoff value and avoid overestimation
of effect size, the HR, including 95% CI, was plotted against all possible cutoff
values. A wider range of cutoff values demonstrating significance would indicate more
robust findings.
Significance was tested through two-tailed tests at a significance level of p < 0.05 for all analyses. All analyses were conducted with the R (R Foundation for
Statistical Computing, Vienna, Austria), version 3.5.2. The sample size was based
on the available data, and no a priori power calculations were performed.
RESULTS
A total of 200 patients (mean age 56.1 ± 12.6 years, 55% female) met the eligibility
criteria and were included in the analysis ([Table 1]). The most common presenting malignancies were non-small cell lung cancer (NSCLC)
(33.0%), breast cancer (18.0%), and cancers of the gastrointestinal tract (13.5%).
A single BM was diagnosed in 52% of patients, three or more lesions in 30.5% during
initial diagnosis, and carcinomatous meningitis in 10%. The performance status assessment
by different scales is illustrated in [Table 1].
Table 1
Sample characteristics and univariate survival analysis
|
Variables
|
Total (n = 200)
|
Death at 1-year
|
p-value *
|
|
No (n = 69)
|
Yes (n = 131)
|
|
Age (years)
|
56.1 ± 12.6
|
54.5 ± 12.8
|
56.9 ± 12.6
|
0.196
|
|
Female
|
110 (55.0)
|
42 (60.9)
|
68 (51.9)
|
0.138
|
|
Synchronous presentation
|
72 (36.0)
|
19 (27.5)
|
53 (40.5)
|
0.260
|
|
Compartment
|
Infratentorial
|
54 (27.0)
|
20 (29.0)
|
34 (26.0)
|
0.814
|
|
Supratentorial
|
137 (68.5)
|
46 (66.7)
|
91 (69.5)
|
|
Both
|
9 (4.5)
|
3 (4.3)
|
6 (4.6)
|
|
Laterality
|
Right
|
62 (31.0)
|
30 (43.5)
|
32 (24.4)
|
0.034
|
|
Left
|
60 (30.0)
|
19 (27.5)
|
41 (31.3)
|
|
Both
|
78 (39.0)
|
20 (29.0)
|
58 (44.3)
|
|
Eloquent brain areas
|
55 (27.5)
|
13 (18.8)
|
42 (32.1)
|
0.005
|
|
Number of BM
|
1 (1–3)
|
1 (1–3)
|
2 (1 -3)
|
0.32
|
|
ASA
|
2 (2–3)
|
2 (2–3)
|
2 (2–3)
|
< 0.001
|
|
KPS
|
Preoperative
|
60 (50–80)
|
70 (60–80)
|
60 (50–70)
|
< 0.001
|
|
Postoperative
|
80 (60–90)
|
90 (80–100)
|
70 (50–90)
|
< 0.001
|
|
GPA
|
1.5 (1–2)
|
2 (1–2.5)
|
1.5 (1–2)
|
0.001
|
|
ECOG
|
2 (1–3)
|
2 (1–3)
|
3 (1–4)
|
< 0.001
|
|
Number of resected BM
|
1 (1–1)
|
1 (1–1)
|
1 (1–1)
|
0.757
|
|
Extent of resection
|
Biopsy
|
1 (0.5)
|
0 (0.0)
|
1 (0.8)
|
0.069
|
|
Partial
|
21 (10.5)
|
4 (5.8)
|
17 (13.0)
|
|
Gross total
|
178 (89.0)
|
65 (94.2)
|
113 (86.3)
|
|
En bloc resection
|
41 (20.5)
|
17 (24.6)
|
24 (18.3)
|
0.471
|
|
Radiotherapy
|
126 (63.0)
|
57 (82.6)
|
69 (52.78)
|
< 0.001
|
|
Whole-brain
|
89 (44.5)
|
33 (47.8)
|
56 (42.7)
|
|
|
RS/FSR (surgical bed)
|
40 (20.0)
|
25 (36.2)
|
15 (11.5)
|
|
|
RS/FSR (concurrent BM)
|
14 (7.0)
|
8 (11.6)
|
6 (4.6)
|
|
Abbreviations: ASA, American Society of Anesthesiologists; BM, brain metastasis; GPA, graded prognostic
assessment; KPS, Karnofsky performance status; ECOG, Eastern Cooperative Oncology
Group; RS/FRS, Radiosurgery/Fractionated Stereotactic Radiotherapy.
Notes: Categorical variables are presented as n (%). Continuous variables are presented as median and quartiles, except for age (mean ± standard
deviation). *Survival analysis.
All patients underwent surgical treatment; 89% underwent gross total resection, and
20.5% underwent en bloc resection. Adjuvant radiotherapy was administered to 63% of
the patients. There were no intraoperative deaths; however, 3 patients died in the
first postoperative week, and 7.5% died within 4 weeks. The median postoperative KPS
score was 80 (quartiles 60–90), which was significantly improved from the preoperative
score (p < 0.001). During follow-up, progression of primary disease was the most common cause
of death. A total 131 patients died within the first year.
In the univariate survival analysis, GPA, ASA, preoperative hemoglobin, tumor location,
laterality, radiotherapy, postoperative KPS, and recurrence were potential predictors.
Thus, these variables were included in the adjusted models to assess the predictive
performance for each blood-based parameter ([Table 2]). The NLR was significantly associated with 1-year outcome (HR 2.19, 95% CI 1.13–4.25,
p = 0.021), while the MLR was not (HR 2.05, 95% CI: 0.98–4.26, p = 0.055). In a multivariate model including all blood-based biomarkers concurrently,
we opted to not include the MLR due to a high correlation with the NLR (r = 0.626,
p < 0.001). In this model, only the NLR maintained significance (HR 2.66, 95% CI: 1.17–6.01,
p = 0.019) ([Figure 1]).
Figure 1 Neutrophil-to-lymphocyte ratio (NLR) histogram. Vertical line: optimal cutoff 3.832.
Table 2
Multivariate survival analysis
|
Blood-based parametera
|
Models with one blood-based parameter at a timeb
|
Model with all parametersc
|
|
HR (95% CI)
|
p-value
|
HR (95% CI)
|
p-value
|
|
RDW-CV
|
1.66 (0.08–33.55)
|
0.741
|
0.15 (0.0–4.97)
|
0.285
|
|
NLR
|
2.19 (1.13–4.25)
|
0.021
|
2.66 (1.17–6.01)
|
0.019
|
|
MLR
|
2.05 (0.98–4.26)
|
0.055
|
−
|
−
|
|
PLR
|
1.68 (0.76–3.7)
|
0.198
|
0.7 (0.26–1.92)
|
0.492
|
Abbreviations: CI, confidence interval; HR, hazard ratios; MLR, monocyte-to-lymphocyte; NLR, neutrophil-to-lymphocyte;
PLR, platelet-to-lymphocyte ratio; RDW-CV, red-cell distribution width coefficient
of variation.
Notes:
aAll parameters were log-transformed for distribution normalization, except for hemoglobin.
All of them were inserted on the models as continuous variables; bAll models adjusted for GPA (age, preoperative KPS, number of CNS metastases, and
presence of extracranial metastases), ASA, preoperative hemoglobin, eloquence of the
affected area, laterality, radiotherapy, postoperative KPS, and recurrence; cModel adjusted for GPA (age, preoperative KPS, number of CNS metastases, and presence
of extracranial metastases), ASA, preoperative hemoglobin, eloquence of the affected
area, laterality, radiotherapy, postoperative KPS, and recurrence, besides the parameters
on the table (hemoglobin, RDW-CV, NLR, and PLR). Finally, the MLR was not included
due to high correlation with NLR.
[Figure 2] presents the analyses aimed at determining the optimal NLR cutoff value. The HR
and differences in mean survival times (months) are each plotted against each possible
preoperative NLR cutoff. A total 132 of 171 possible cutoffs (77.2%) were significant—the
larger the range of significant cutoff values, the more robust the finding. In our
series, the optimal NLR cutoff value, based on HR and differences in mean survival,
was 3.832. We compared the survival curves and effect sizes according to preoperative
NLR cutoff values for solid tumors, as suggested by Templeton et al.,[9]
[15] and our suggested optimal cutoff value. Both presented significant results in survival
analysis; however, our value showed a higher HR = 2.07 (1.31–3.25; p = 0.0014) ([Figure 3]).
Figure 2 (A) Hazard ratios (HR) and 95% confidence intervals (CI) according to preoperative NLR
cutoff; (B) Difference in mean survival (months) and 95% CI according to preoperative NLR cutoff.
A total 132 of 171 possible cutoffs (77.2%) were significant. Vertical line: optimal
cutoff 3.832.
Figure 3 Survival curves and effect sizes (HR and CI) according to preoperative NLR cutoff
(A: cutoff 3.832; B: cutoff 4.0).
DISCUSSION
The results of this study demonstrated a significant association between preoperative
NLR levels and patient survival at 1-year after surgery for BM. Of all the hematologic
inflammatory markers we analyzed, only the preoperative NLR was found to be an independent
predictor of survival in our adjusted models. More specifically, shorter survival
time after surgical resection of BM can be expected for patients with a preoperative
NLR > 3.83.
These findings align with previous works testing the clinical utility of similar cutoff
values for inflammatory biomarkers. The NLR has been used as a predictor of late recurrence,
treatment response and poor prognosis in different solid cancers.[5]
[8] Templenton et al.[9]
[13] conducted a systematic review of 100 studies (n = 40,559 patients) that included different solid tumors and suggested a NLR cutoff
of 4.0 for overall survival (OS). While the biological processes underlying these
findings are not fully understood, there is some evidence of a tumor-promoting effect
of neutrophils during metastasis progression by an increasing number of metastatic
initiating cancer cells. Notably, Wculek and Malanchi[14] showed that neutrophil depletion in lungs reduced local metastasis on a breast cancer
experimental model.
The other hematologic biomarkers tested in our study did not perform as well as the
NLR. The prognostic role of the PLR is hypothesized on increased host inflammatory
response with higher secretion of thrombopoietic cytokines, such as IL-6. Platelet
recruitment and activation is involved in the process of tumor growth and angiogenesis.
There seems to exist a correlation between thrombocytosis and shorter survival time
in different solid tumors, in addition to recent findings of higher PLR scores for
metastatic patients compared with locoregional disease.[8]
[10]
[15]
[16]
Several biological events related to the cancer activity might implicate in high RDW
levels. Higher levels of inflammatory cytokines are associated with increased metabolic
activity, increased cellular proliferation, and, consequently, higher RDW rates. Furthermore,
the RDW is directly related to the individual nutritional status.[7]
[17] Generalized hypovitaminosis (iron, folate, vitamin B12) in patients with advanced
and uncontrolled cancers is an alternative hypothesis that could explain the role
of the RDW as a prognosis predictor.[5]
[17] Consistent results of PLR and RDW as outcome predictors are related to advanced-stage
cancers or uncontrolled systemic disease. Although brain metastasis is a hallmark
of cancer disease progression, the majority of patients in this study had a good performance
status preoperatively, possibly suggesting there was adequate control of the disease
before brain metastasis detection. Therefore, we hypothesize that neither the PLR
nor RDW were sensitive enough for significant results in this clinical setting.
The incidence of BM from solid cancers has been paradoxically rising due to improvements
in diagnostic methods, increased availability of neuroimaging, and advances in systemic
treatment.[1]
[7] The BM represents the end stage of a cancer disease; prognosis should be carefully
considered for decision-making on additional treatments at this point. The decision
on whether to treat a BM is challenging in neuro-oncology, as it represents not only
a tumor with mass effect, but also a systemic uncontrolled disease in progression
that must be considered for individualized decision. Currently, the decision to treat
is based on patients' performance status, the number and size of tumors, and treatment
responsiveness of the primary cancer.[1]
[18] New biomarkers are under investigation to support decision-making in this setting.
Several blood-based biomarkers have been proposed for gliomas with promising results.[10]
[13]
[19]
[20]
[21]
[22] Our results promote the use of preoperative NLR in decision-making, and its contribution
for prognostic predictive models for brain metastasis.
Other studies have analyzed the hematologic prognostic markers based on recent insights
on cancer-immune system interactions. The NLR, PLR, and RDW were tested in several
types of metastatic tumors, as well as in different brain tumors. Starzer et al.[23] addressed the role of systemic inflammation on cancer progression. In their retrospective
analysis with more than one thousand patients, lower NLR, PLR, and MLR were associated
with longer OS in patients with BM. Mitsuya et al.[6] used the cutoff of NLR > 5 with promising results in a retrospective analysis with
105 BM patients. Cacho-Diaz et al.[24] applied the NLR cutoff > 4.5, with significant correlation with mortality in those
patients as well. Both studies have included only patients with uncontrolled primary
cancer and high systemic inflammation. Some authors hypothesized that the involvement
of the brain, as a critical and sensitive organ, is the main trigger of the systemic
inflammation process that might influence the OS. Marini et al.[25] studied predictive factors on OS in patients with glioblastoma—a primary brain cancer
with very low risk of distant metastasis. In this study, the NLR > 4 cutoff was significantly
related to worse OS in multivariate analysis, similar to other studies with BM.
Our study has several limitations that should be considered when interpreting the
results. First, the study included patients with BM from different primary tumors;
this was not included as a covariate in our models, given the limited sample size
of the cohort. Second, our analyses did not consider the varying doses of corticosteroids
that patients received, although this may have had effects on hematologic parameters.
Third, we only included patients without any prior treatment for BM (radiotherapy/radiosurgery,
immunotherapy etc.). While this was done to reduce treatment confounding effects,
we recognize it limits the external validity of the study and favors the proof of
concept. However, our study has a representative sample of patients with cancer and
BM, and proposes an accessible test for treatment decision making in patients with
BMs.
In conclusion, our analyses demonstrated that the preoperative NLR is associated with
1-year survival outcomes after surgery for BM, and may be an important predictor of
survival, irrespective of primary cancer site. These findings further support the
trend of considering serum inflammatory markers in oncologic treatment decisions.
Given the complexity of this disease and its burden on patients, clinicians are tasked
with carefully formulating robust treatment plans. Although additional validation
studies are warranted, we believe the low cost and accessibility of accessing the
NLR distribution rates favors its utility as a prognostic aide when making decisions
for the management of BM. Future studies should continue to explore and validate the
use of other biomarkers as prognostic factors in metastatic diseases.
