Patient Characteristics
A total of 581 patients were evaluated and documented in REDCap across four centers. In turn, overall demographic scores and biases across centers were assessed. Overall, mean age was 61 years [range: 20–89] (Table 1). Males slightly exceeded females in representation across centers (Fig. 1A). The distribution of ethnic origin directly related to the site of collection. North American sites represented predominately Caucasians and Black/African Americans with non-Latino ethnicity, while South American sites were of Caucasian and mixed ancestry with Latino origin (Fig. 1B-C). All patients had biopsy confirmed GB with resection of tumor at primary surgery. There was a slight predominance of left sided lesions primarily occurring in the frontal, temporal, and parietal lobes across sites (Fig. 1D-E). On average, mean tumor diameter was 4.34 cm [range: 0.1–9.5] with most without midline shift on imaging (Table 1). It was however noted that several clinical features were not collected in sites outside OSU (Supplementary Fig. 1). When tested, IHC studies showed primarily ATRX locus intact (98%) and p53 mutation (78%) as defined by positivity in over 10% of cells. Predominant molecular features were EGFR amplification (56%) and unmethylated MGMT promoter status (57%).
Table 1
Summarization of collected feature across study group and per site. Features listed as N/A were not collected at the site. Data is reported as either total (percent from collected patients) or mean [range].
Clinical Feature | Overall | OSU | UMMC | Barretos | FLENI |
N (%) or mean [range] | N = 581 | N = 377 | N = 57 | N = 94 | N = 53 |
Age at Primary Surgery (years) | 60.64 [20–89] | 61.97 [20–89] | 61.49 [25–80] | 54.13 [29–76] | 61.81 [20–78] |
Gender | Male | 342 (58.86%) | 219 (58.09%) | 36 (63.16%) | 54 (57.45%) | 33 (62.26%) |
Female | 239 (41.14%) | 158 (41.91%) | 21 (36.84%) | 40 (42.55%) | 20 (37.74%) |
Race | Native American | 1 (0.17%) | 0 (0%) | 1 (1.75%) | 0 (0%) | 0 (0%) |
Asian | 4 (0.69%) | 4 (1.06%) | 0 (0%) | 0 (0%) | 0 (0%) |
Black or African | 30 (5.16%) | 15 (3.98%) | 15 (26.32%) | 0 (0%) | 0 (0%) |
Caucasian | 467 (80.38%) | 351 (93.10%) | 40 (70.18%) | 76 (80.85%) | 0 (0%) |
More than One Race | 14 (2.41%) | 1 (0.27%) | 0 (0%) | 13 (13.83%) | 0 (0%) |
Unknown | 12 (2.07%) | 6 (1.59%) | 1 (1.75%) | 5 (5.32%) | 0 (0%) |
N/A | 53 (9.12%) | 0 (0%) | 0 (0%) | 0 (0%) | 53 (100%) |
Ethnicity | Hispanic/Latino | 152 (26.16%) | 5 (1.33%) | N/A | 94 (100%) | 53 (100%) |
Non-Hispanic/Latino | 372 (64.03%) | 372 (98.67%) | N/A | 0 (0%) | 0 (0%) |
N/A | 57 (9.81%) | 0 (0%) | 57 (100%) | 0 (0%) | 0 (0%) |
Vital Status | Alive | 47 (8.09%) | 23 (6.10%) | 15 (26.32%) | 4 (4.26%) | 5 (9.43%) |
Deceased | 530 (91.22%) | 354 (93.90%) | 42 (73.68%) | 90 (95.74%) | 44 (83.02%) |
Overall Survival (days) | 539.6 [31-3685] | 560.4 [52-2787] | 477.5 [45-3685] | 533.8 [94-1471] | 365.9 [31–930] |
Age at Death (years) | 62.39 [21–90] | 64.08 [21–90] | N/A | 56.07 [31–76] | 61.61 [46–80] |
Weight (kg) | 87.29 [45–214] | 87.29 [45–214] | N/A | N/A | N/A |
Height (cm) | 172.5 [147–196] | 172.5 [147–196] | N/A | N/A | N/A |
BMI | 29.15 [17.8–66.1] | 29.15 [17.8–66.1] | N/A | N/A | N/A |
CCI Score | 3.70 [0–11] | 3.70 [0–11] | N/A | N/A | N/A |
Lesion Side | Left | 244 (42.0%) | 197 (52.25%) | N/A | 47 (50%) | N/A |
Right | 213 (36.66%) | 170 (45.09%) | N/A | 43 (45.74%) | N/A |
Both | 14 (2.41%) | 10 (2.65%) | N/A | 4 (4.26%) | N/A |
N/A | 110 (18.93%) | 0 (0%) | 57 (100%) | 0 (0%) | 53 (100%) |
Lesion Location | Frontal | 163 (28.06%) | 142 (37.67%) | N/A | 21 (22.34%) | N/A |
Temporal | 161 (27.71%) | 119 (31.56%) | N/A | 42 (44.68%) | N/A |
Parietal | 97 (16.70%) | 77 (20.42%) | N/A | 20 (21.28%) | N/A |
Occipital | 23 (3.96%) | 22 (5.84%) | N/A | 1 (1.07%) | N/A |
Brain Stem | 14 (2.41%) | 14 (3.71%) | N/A | 0 (0%) | N/A |
Cerebellum | 2 (0.34%) | 2 (0.53%) | N/A | 0 (0%) | N/A |
Mixed | 11 (1.89%) | 1 (0.27%) | N/A | 10 (10.64%) | N/A |
N/A | 110 (18.93%) | 0 (0%) | 57 (100%) | 0 (0%) | 53 (100%) |
Lesion Size (cm) | 4.34 [0.1–9.5] | 4.18 [0.1–9.2] | N/A | N/A | N/A |
Midline Shift | Yes | 141 (24.27%) | 141 (37.40%) | N/A | N/A | N/A |
No | 221 (38.04%) | 221 (58.62%) | N/A | N/A | N/A |
N/A | 219 (37.69%) | 15 (3.98%) | 57 (100%) | 94 (100%) | 53 (100%) |
ATRX Status | Intact | 427 (73.49%) | 341 (90.45%) | N/A | 86 (91.49%) | N/A |
Loss | 9 (1.55%) | 3 (0.80%) | N/A | 6 (6.38%) | N/A |
N/A | 145 (24.96%) | 33 (8.75%) | 57 (100%) | 2 (2.13%) | 53 (100%) |
p53 Mutation (> 10% of Cells) | Negative | 72 (12.39%) | 72 (19.10%) | N/A | N/A | N/A |
Positive | 256 (44.06%) | 256 (67.90%) | N/A | N/A | N/A |
N/A | 253 (43.55%) | 49 (13.00%) | 57 (100%) | 94 (100%) | 53 (100%) |
Ki67 (%) | 31.84 [4–95] | 31.84 [4–95] | N/A | N/A | N/A |
EGFR Amplification | Yes | 173 (29.78%) | 173 (45.89%) | N/A | N/A | N/A |
No | 136 (23.41%) | 136 (36.07%) | N/A | N/A | N/A |
N/A | 272 (46.82%) | 68 (18.04%) | 57 (100%) | 94 (100%) | 53 (100%) |
MGMT Status | Hypermethylated | 189 (32.53%) | 155 (41.11%) | N/A | 34 (36.17%) | N/A |
Unmethylated | 249 (42.86%) | 221 (58.62%) | N/A | 28 (29.79%) | N/A |
N/A | 143 (24.61%) | 1 (0.27%) | 57 (100%) | 32 (34.04%) | 53 (100%) |
WBC Post-Surgical (x10^9/L) | 9.78 [2.20-63.77] | 9.96 [2.49–63.77] | 9.76 [2.30–24.80] | 8.72 [2.20–18.60] | 10.57 [5.10–20.50] |
Neutrophils Post-Surgical (x10^9/L) | 7.21 [0.21–22.82] | 7.37 [1.00-21.50] | 7.26 [0.21–22.82] | 6.02 [0.97-16.00] | 8.53 [2.35–18.25] |
Lymphocytes Post-Surgical (x10^9/L) | 1.65 [0.07–54.2] | 1.65 [0.08–54.2] | 1.59 [0.07–4.68] | 1.80 [0.12–5.60] | 1.38 [0.38–3.54] |
Neutrophil: Lymphocyte Ratio Post-Surgical | 7.51 [0.06–137.90] | 8.00 [0.06-115.13] | 6.84 [0.85–30.67] | 5.53 [1.01–137.90] | 9.19 [1.12-32] |
Platelets Post-Surgical (x10^9/L) | 236.5 [43–593] | 231.6 [64–593] | 246.2 [43–522] | 247.9 [84–467] | N/A |
Steroids Post-Surgical (mg/day) | 2.84 [0–24] | 2.67 [0–24] | 0.31 [0–2] | N/A | 6.87 [0–16] |
Radiation Dose (Gy) | 55.59 [5.34-75] | 55.82 [5.34-75] | N/A | 54.66 [25–66] | N/A |
Radiation Fractions | 26.8 [1–50] | 26.74 [1–50] | N/A | 27.07 [5–33] | N/A |
ChemoRT Treatment Time (Days) | 42.2 [0-336] | 38.14 [0–90] | N/A | 62.09 [0-336] | N/A |
Adjuvant Temozolomide Dose (mg) | 238.1 [0-500] | 238.1 [0-500] | N/A | N/A | N/A |
Adjuvant Temozolomide Cycles | 3.00 [0–19] | 3.072 [0–19] | N/A | 2.70 [1–12] | N/A |
Time to Enhancement (days) | 278.6 [41-2037] | 278.6 [41-2037] | N/A | N/A | N/A |
Enhancement Status | Recurrent | 241 (41.48%) | 241 (63.94%) | N/A | N/A | N/A |
Reactive | 104 (17.90%) | 104 (27.59%) | N/A | N/A | N/A |
Stable | 32 (5.51%) | 32 (8.49%) | N/A | N/A | N/A |
N/A | 204 (35.11%) | 0 (0%) | 57 (100%) | 94 (100%) | 53 (100%) |
ChemoRT overall followed a traditional 60 Gray-30 fractions radiation plan, but some patients received hypo-fractionated regimen or did not complete ChemoRT (Radiation Plan: 55.59 [range: 5.34-75]; Radiation Fractions: 26.8 [range: 1–50]). Adjuvant TMZ therapy was completed at 3 cycles on average [range: 0–19]. Symptomatic management of edema with steroids at time of CBC collection was on average 2.84 mg/day with a broad range of use [range: 0–24]. Outcomes of patients were assessed as both PFS and OS from date of primary surgery. Among centers, median OS ranged from 12–16 months without significant difference among groups while median PFS was 6 months at OSU (Fig. 2).
Unsupervised Analysis of Patient Reveals Capabilities of CBCs in Predicting Overall Survival Outcome and Time to Enhancement
Utilizing the various collected clinical datapoints, we sought to evaluate whether specific clinical features were correlated with relevant outcomes in patients that have not been previously integrated in clinical practice. Specifically, we posited that applying an unsupervised machine learning approach could represent clinical relationships amongst features to allow us to visualize novel findings predictive in GB patient prognosis. To do so, our multidimensional dataset was reduced using principal components analysis (PCA) to stratify patients by these clinical metrics and visualized using PCA eigenvector plotting (Fig. 3). The directionality of eigenvectors (arrows) amongst other eigenvectors represents direct correlations through same arrow directionality, inverse correlations through opposite directionality, and no correlation through orthogonal directionality. With these considerations, we identified relationships of features to relevant clinical outcomes. Notably, the directionality of eigenvectors for OS and PFS occurred similarly [upper right quadrant] with inverse directionality to CBC-related measures—notably WBC and neutrophil measure in the lower left quadrant (Fig. 3). In contrast, enhancement status was shown on the left side of the plot, but other vectors had less robust directional relationships—with the most prominent being inverse directionality of adjuvant TMZ dosage in the upper left quadrant. Nevertheless, as our eigenvector plot uncovered novel variations amongst clinical features, we further explored these findings using predictive modeling.
Cox regression modeling was applied to predict OS based on our clinical features. Based on our PCA eigenvector plot, we theorized that CBC related metrics would be significant to predicting survival time. Univariate models were first performed over the study population to assess which features were found to have significance. In total, 11 separate features were found to be significant [Patient Age, CCI score, MGMT methylation status, WBC count, Neutrophil count, NLR, Radiation Dose, Radiation Fractions, Overall Radiation Time, Adjuvant TMZ Cycles, and Adjuvant TMZ Dose] (Table 2). Taking these features, a multivariate cox regression model was constructed to then assess which features independently contribute significantly to predicting survival outcome. Only 5 clinical features were found to be relevant [Patient Age, MGMT methylation status, Neutrophil count, Radiation Dose, and Adjuvant TMZ Cycles] (Table 3). While the contribution of neutrophil count was significant in our multivariate model, the hazard ratio (HR) was small [1.064 (1.013–1.117)]. In consequence, although a small, but significant risk to poorer survival was evidenced by increased neutrophil load, we next sought to explore causes to the observed difference our cox modeling showed against our PCA eigenvector plot.
Table 2
Univariate cox model results. Influence of clinical features to overall survival was evaluated using univariate cox regression. Features indirectly related to OS were omitted. Significant features are marked by asterisk.
Feature | Beta | HR (95% CI) | Wald Test | P-value |
Age at Surgery | 0.01944 | 1.02 (1.011–1.028) | 21 | 4.20E-06* |
Gender | -0.01552 | 0.9846 (0.8232–1.178) | 0.03 | 0.87 |
Race | -0.002198 | 0.9978 (0.9908–1.005) | 0.37 | 0.54 |
Ethnicity | 0.1583 | 1.172 (0.9444–1.453) | 2.1 | 0.15 |
Weight | 0.002215 | 1.002 (0.9979–1.007) | 0.99 | 0.32 |
Height | 0.004985 | 1.005 (0.9954–1.015) | 1 | 0.31 |
BMI | 0.004967 | 1.005 (0.9892–1.021) | 0.38 | 0.54 |
CCI Score | 0.05545 | 1.057 (1.013–1.103) | 6.5 | 0.011* |
Lesion Side | 0.03169 | 1.032 (0.868–1.228) | 0.13 | 0.72 |
Lesion Lobe | -0.01187 | 0.9882 (0.913–1.07) | 0.09 | 0.77 |
ATRX | 0.1074 | 1.113 (0.5269–2.353) | 0.08 | 0.78 |
p53 | -0.1035 | 0.9017 (0.6851–1.187) | 0.55 | 0.46 |
Ki67 | 0.00311 | 1.003 (0.9966-1.01) | 0.88 | 0.35 |
EGFR | -0.2294 | 0.795 (0.6275–1.007) | 3.6 | 0.057 |
MGMT | -0.4272 | 0.6523 (0.533–0.7983) | 17 | 3.40E-05* |
Lesion Size | 0.01855 | 1.019 (0.9616–1.079) | 0.4 | 0.53 |
Midline Shift | -0.1176 | 0.8891 (0.7115–1.111) | 1.1 | 0.3 |
WBC | 0.02951 | 1.03 (1.013–1.047) | 13 | 0.00036* |
Platelets | 0.000202 | 1 (0.9991–1.001) | 0.13 | 0.72 |
Neutrophils | 0.04722 | 1.048 (1.023–1.074) | 14 | 0.00015* |
Lymphocytes | 0.01214 | 1.012 (0.9767–1.049) | 0.44 | 0.51 |
NLR | 0.01261 | 1.013 (1.005–1.021) | 9.3 | 0.0023* |
Steroids | 0.02151 | 1.022 (0.9942-1.05) | 2.4 | 0.12 |
Radiation Dose | -0.05025 | 0.951 (0.9413–0.9608) | 92 | 9.80E-22* |
Radiation Fractions | -0.05064 | 0.9506 (0.9373–0.9641) | 50 | 1.70E-12* |
Radiation Time | -0.003487 | 0.9965 (0.9931-1) | 3.9 | 0.049* |
TMZ Cycles | -0.1612 | 0.8511 (0.8216–0.8816) | 80 | 3.30E-19* |
TMZ Dose | -0.00238 | 0.9976 (0.9969–0.9984) | 41 | 1.80E-10* |
Center | -0.05162 | 0.9497 (0.8576–1.052) | 0.98 | 0.32 |
Table 3
Multivariate cox model results. Influence of clinical features to OS was evaluated using multivariate cox regression from features in Table 3. Significant features are marked by asterisk.
Feature | Beta | HR (95% CI) | P-value |
Age at Surgery | 0.02665 | 1.027 (1.011–1.044) | 0.0011* |
CCI Score | -0.01702 | 0.9831 (0.9198–1.051) | 0.62 |
MGMT | -0.4308 | 0.65 (0.4904–0.8615) | 0.0027* |
WBC | 0.01937 | 1.02 (0.9909–1.049) | 0.18 |
Neutrophils | 0.062 | 1.064 (1.013–1.117) | 0.013* |
NLR | -0.01111 | 0.989 (0.975–1.003) | 0.13 |
Radiation Dose | -0.05578 | 0.9457 (0.9233–0.9687) | 5.30E-06* |
Radiation Fractions | 0.0134 | 1.013 (0.9697–1.059) | 0.55 |
Radiation Time | 0.005447 | 1.005 (0.9876–1.024) | 0.55 |
TMZ Cycles | -0.205 | 0.8146 (0.7653–0.8671) | 1.20E-10* |
TMZ Dose | 0.0003521 | 1 (0.9993–1.001) | 0.53 |
While cox regression modeling validated the assumption that CBCs have predictive capabilities to survival time, we further examined the discordance of our strong correlations seen in the PCA eigenvector plot against the smaller HRs calculated in our cox models. Specifically, we hypothesized the differences seen in our results may be underscored by robust survival differences present in the extremes of our CBC metrics. Specifically, based on our univariate cox results, we explored survival differences in patients when stratified by WBC count, neutrophil count, and neutrophil:lymphocyte ratio. CBC measures were evaluated by stratifying populations into quartiles with the lower 25% (lo in blue) and upper 25% (hi in red) of patients evaluated. In both overall WBC load and neutrophil load, PFS was significantly worse in the hi group relative to the lo group (p = 0.0082 and p = 0.039, respectively) (Fig. 4A-B). Furthermore, evaluation of OS using WBC load and neutrophils showed similar trends between groups (p = 0.00042 and p = 0.0007, respectively). However, while NLR did show significant survival difference in OS (p = 0.0081), the difference in PFS between groups did not reach our threshold of significance (Fig. 4C). Combined with the previous analyses, our findings strongly support the use of CBCs in predicting both OS and PFS in patients. Specifically, the evaluation of routinely drawn WBC and neutrophil count at time of pre-ChemoRT planning is shown to be correlative with poorer survival outcomes in patients with high load compared to patients with low load.
WBC Load is Reflective of Intrinsic Tumor Microenvironment Changes Present in Glioblastoma
As the measures of WBC load evaluate circulating immune counts, we posited these differences in peripheral immune activity may correlate with intrinsic tumor microenvironment differences found in primary GB events. To assess this, we selected PDL-1 IHC staining done in a subset of patient during clinical evaluation. Amongst our study population, 57 cases had been evaluated by neuropathology for PDL-1 and representative images were collected from cases and segmented using computer vision to quantify staining (Fig. 5A). Due to the membranous staining of PDL-1 however, staining was quantified as the ratio of DAB-positive PDL-1 staining against hematoxylin nuclear staining to control for cellularity of tissue. In turn, increased detection of PDL-1 staining is represented by an increased ratio. With this approach, we predicted the detection of PDL-1 staining ratio to be elevated in the WBC-hi group due to the associated poor prognostic outcome of increased PDL-1 staining. In fact, it was observed that the WBC-hi group showed higher ratios of PDL-1 DAB to hematoxylin staining when compared to WBC-lo (p = 0.027, Fig. 5B). Furthermore, assessing the two sides of the ratio comparison it was seen that while the amount of DAB pixels detected in an image was higher in the WBC-hi group (p = 0.037), the detection of hematoxylin pixels did not vary between groups (p = 0.63) (Fig. 5C-D). In conclusion, it could be inferred the increased detection of PDL-1 staining ratio in the WBC-hi group was not a product of increased cellularity as the distribution of hematoxylin was not different. Overall, these findings infer that an increase in PDL-1 staining was correlated with the WBC-hi group which showed poorer survival outcomes in patients—validating that the observed differences in WBC load are correlative to initial immune activity present in GB lesions at resection.
Steroid Tapering is Highly Heterogenous Following Surgery and May Influence WBC Load
Although our analyses found strong correlation of CBC load to survival outcomes, we further evaluated potential clinical confounders which may influence CBC levels prior to ChemoRT. As shown in the analyses of our study population, heterogeneity in patient demographics, lesion characteristics, and patient management was present (Table 1). In turn, an assessment to identify whether specific clinical features significantly varied between our hi and lo populations was critical. As patient age, CCI score, and MGMT methylation status were predictive of survival in our univariate cox model, we assessed these factors in addition to other clinical features that were shown to be correlative to CBC measures in the eigenvector plot [lesion size, steroid intake, and BMI] (Fig. 3& Table 2). Comparing WBC-hi and lo groups, MGMT methylation status distribution was not found to significantly vary between groups (X2 = 0.88, p-value = 0.35). However, while patient age, CCI score, lesion size and BMI were found to not vary between groups, a significant variation in steroid dosing between groups was present (p = 2.4e-05), with the WBC-hi group showing a higher mean daily steroid intake compared to those in the lo group (Fig. 6A-E). Evaluating the distribution of steroid doses given to patients at time of post-surgical CBC, most patients received low doses of steroids less than 6mg a day (Hi: 76.9%; Lo: 89.5%); however, a larger percent of patient from the WBC-lo fully tapered off steroids (Hi: 24.2%; Lo: 65.1%). Nevertheless, assay of patient distribution shows 55.9% of patients across both groups remain on steroids at follow-up prior to initiating therapy (Fig. 6F). Overall, this finding highlights the additional confounding role steroid intake may have on the survival differences seen between CBC load, but also emphasizes the heterogeneity of steroid dosing in patients following GB resection.