Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Feb 1.
Published in final edited form as: Free Radic Biol Med. 2024 Dec 4;227:312–321. doi: 10.1016/j.freeradbiomed.2024.12.006

Extracellular vesicles released by ALL patients contain HNE-adducted proteins: implications of collateral damage

Jenni Ho 1,2, Suriyan Sukati 3, Tamara Taylor 4, Sherry Carter 4, Brittany Fuller 4, Amy Marmo 4, Caryn Sorge 4, John D’Orazio 2,4, D Allan Butterfield 2,5, Subbarao Bondada 2,7, Heidi Weiss 2,6, Daret K St Clair 1,2, Luksana Chaiswing 1,2,*
PMCID: PMC11786608  NIHMSID: NIHMS2041816  PMID: 39643137

Abstract

Off-target neuronal injury is a serious side-effect observed in cancer survivors. It has previously been shown that pediatric acute lymphoblastic leukemia (ALL) survivors have a decline in neurocognition compared to healthy age-matched counterparts. Elevated oxidative stress has been documented to be a mediator in off-target tissue damage in cancer survivors. Early detection of oxidative stress markers may provide an opportunity to prevent off-target tissue damage. Extracellular vesicles (EVs) have surfaced as a potential diagnostic tool due to molecular cargo they contain. We investigated the potential for EVs to be a sensitive indicator of oxidative stress and off-target tissue damage by isolating EVs from pediatric ALL patients throughout their first 2 months of treatment. EVs were measured throughout the collection points for: 1) number of EV particles generated using nanoparticle tracking analysis (NTA); 2) markers of neurons (NeuN), astrocyte activation (GFAP), neuronal stability (BDNF), 3) markers of pre-B cell ALL (CD19 and CD22); and ) 4-hydroxy-2-nonenal (HNE) adducted proteins. HNE protein adductions were measured in the patient sera and CSF. Pro-inflammatory cytokine levels were also measured in patient sera because of their contribution to oxidative stress and neuronal injury. Our results: 1) demonstrate EVs are a sensitive indicator of oxidative damage; 2) suggest EVs as a marker of a decline in neuronal stability; and 3) show the presence of leukemia has a greater contribution to pro-inflammatory cytokine production in the patient’s serum than the cancer treatment. Specifically, we observed a significant decrease in cytokine levels (e.g., TNF-α, IL-1β, IL-6, and IL-8) following the initiation of treatment, highlighting the influence of leukemia burden on systemic inflammation. The results support the utilization of EVs as a sensitive marker of oxidative stress and off-target tissue damage.

Graphical Abstract

graphic file with name nihms-2041816-f0006.jpg

1. Introduction

In 2022, there are projected to be 15,000 new cases of pediatric and adolescent cancer in the United States [1]. While the mortality rate for cancer patients has declined because of advances in therapies and detection, some therapies result in off-target tissue damage, including central nervous system (CNS) damage, due in part to increased oxidative stress. For example, radiation generates reactive oxygen species (ROS) to kill cancer cells [2], and 50% of chemotherapeutic agents have been associated with increased ROS generation [3]. Pediatric cancer patients are highly susceptible to off-target neuronal damage due to the various chemotherapies they receive [4].

Acute lymphoblastic leukemia (ALL) is the most diagnosed pediatric malignancy and they experience off-target tissue damage post-treatment, affecting their quality of life [1, 5]. Studies demonstrate a decline in neurocognition in one-third of pediatric ALL survivors [6–8]. Thus, it is critical to identify early markers of neurocognitive decline to mitigate this consequence [8].

Pediatric ALL patients receive different chemotherapy agents over their treatment regimen [9]. Previously, patients received cranial radiation to minimize the potential of CNS metastases [10]; currently, following the clinical study AALL0932, methotrexate (MTX, an anti-folate) and cytarabine (an antimetabolite) are given to the patients via intrathecal (IT) injection rather than cranial radiation [9]. Additional chemotherapy agents given to patients include pegasparagase, vincristine, cyclophosphamide, mercaptopurine, danorubicin, and steroids [9]. Despite the absence of cranial radiation for a majority of pediatric ALL patients, the decrease in neurocognitive function in pediatric ALL survivors remain [6], perhaps due to oxidative damage.

ROS in the brain induces glial cell activation, accumulation of misfolded proteins, and dysfunction of the mitochondria, lysosome, and protease [11, 12]. Generation of ROS, specifically the hydroxyl radical (•OH), leads to lipid peroxidation (LPO). LPO of omega-6 polyunsaturated fatty acids (PUFAs) leads to 4-hydroxy-2-nonenal (HNE) formation [13]. HNE is an electrophilic aldehyde that reacts with the cysteine, lysine, and histidine, making HNE the most reactive product of LPO [13]. HNE has been implicated in neurodegenerative diseases associated with protein aggregates [14, 15]. We hypothesize the overproduction of ROS from chemotherapy drugs leads to inhibition of the cell’s canonical methods of removing unwanted molecular products. Therefore, the cell needs another way to discard this molecular debris.

Extracellular vesicles (EVs) are lipid-bound organelles secreted from every cell in the body and can modulate downstream events following endocytosis [16, 17]. The lipid membrane of EVs protects their molecular content from enzymatic degradation, allowing it to be assessed for indications of dysregulation and for drug delivery [18–20]. Research highlighting the role these organelles play in both normal and pathophysiology is abundant [21–25]. For example, miRNAs isolated from cancer cell derived EVs can modulate the immune response [26], and EVs have been shown to promote a pro-tumorigenic environment by promoting epithelial-to-mesenchymal transition [22]. These studies highlight the role of EVs in normal and pathophysiology and the necessity to further explore their clinical potential.

Previously, our group characterized EVs as early indicators of cardiomyocyte damage in vivo following treatment with the ROS-generating chemotherapy doxorubicin [27]. We also demonstrated EVs are a more sensitive indicator of oxidative stress and astrocyte activation than brain tissue isolated from mice treated with cranial radiation [28]. These findings demonstrate that EVs are an early indicator of off-target tissue damage following doxorubicin and radiation, and highlight the consequences of oxidative stress. Based on these findings, we aimed to determine if EVs would contain markers of neuronal injury associated with the observed change in neurocognition in pediatric ALL survivors [7, 8].

In this study, we investigated the levels of HNE-adducted proteins and expression of neuronal proteins utilizing EVs derived from pediatric ALL patients during the first two months of their treatment, based on the idea that the presence of HNE-adducted proteins is the result of chemotherapy-induced oxidative stress. We found that EVs are a more sensitive indicator of changes in HNE-adducted proteins compared to the serum and cerebrospinal fluid (CSF). We also observed a decrease in the neuronal growth factor, brain-derived neurotrophic factor (BDNF), in the EVs throughout the treatment. Moreover, we observed the highest level of pro-inflammatory cytokines produced in the serum with the presence of leukemia cells. Our findings suggest the potential use of EVs as an indicator of oxidative damage and decreased neuronal stability in ALL survivors.

2. Materials and Methods

2.1. Patients

The University of Kentucky’s (UK) Medical Institutional Review Board reviewed and approved this clinical study prior to engagement or enrollment of any participants (IRB #46811). Pediatric ALL patients treated at the Dance Blue Hematology/Oncology Clinic were recruited to participate in the study between January 26, 2019, and March 14, 2022. All study participants and/or their legal guardians provided written informed consent. During routine blood draws, an additional 3 mL of blood was collected. Any surplus CSF following the patient’s routine care was collected. Samples were collected at five different time points during: day 1 and day 29 of induction therapy, and days 1, 8, and 15 during consolidation therapy (shown schematically below in Diagram 1). On day 1, samples were collected prior to the administration of chemotherapy agents. A total of 21 patients were recruited, with 17 of the 21 having both blood and CSF collected for all five time points. Details of the chemotherapy agents administered during induction and consolidation phases are in Table 1 and further detailed in Supplementary Data 1.

Diagram 1. Collection time points for patient samples.

Diagram 1.

Open in a new tab

The first sample was collected on their first day of treatment, before the introduction of any chemotherapy agents. Time point 2 was at the end of their first phase of therapy (induction phase), and the last 3 time points were collected during their second phase of therapy (consolidation phase).

Table 1.

The treatment information of induction and consolidation phases.

Pre-Tx Induction D29 Consolidation D1 Consolidation D8 Consolidation D15
PT No. ARAC MTX PEG-Asp VINC Danorubicin MTX VINC Danorubicin Nelarabine ARAC MTX VINC Cyclophos 6-MP Nelarabine ARAC MTX PEG-Asp VINC Cyclophos 6-MP ARAC MTX PEG-Asp VINC 6-MP
1 X X X X X X X X X X X X X
2 X X X X X X X X X X X X X X X X X
3 X X X X X X X X X X X X X
5 X X X X X X X X X X
6 X X X X X X X X X X X X X X X X X
7 X X X X X X X X X X X X X X
8 X X X X X X X X X X X X X
9 X X X X X X X X X X X X X X X X X
10 X X X X X X X X X X X
11 X X X X X X X X X X X X X X X X
12 X X X X X X X X
13 X X X X X X X X X X X
14 X X X X X X X X X X
15 X X X X X X X X X X
17 X X X X X X X X X X
18 X X X X X X X X X X X X
19 X X X X X X X X X X X X X X
20 X X X X X X X X X X
21 X X X X X X X X X X X
22 X X X X X X X X X X X X X X
23 X X X X X X X X X X
Open in a new tab

PT No.=Patient number. ARAC=Cytarabine. Cyclophos=Cyclophosphamide. 6-MP= Mercaptopurine. MTX=Methotrexate. PEG-Asp=PEG-asparaginase. VINC=Vincristine.

2.2. Patient sample storage

Blood was collected in BD Vacutainer Serum 4.0 mL tubes (catalog #: 367812). Within 15 minutes of blood collection from the clinic, the blood clotted at room temperature for 30 minutes. The tube was then centrifuged at 4 °C for 10 minutes x 1700g for separation of serum. Serum was placed in a cryogenic vial and stored.

CSF was centrifuged at 4 °C for 10 minutes x 2000 g immediately following collection from the clinic. After centrifugation, CSF was allocated into cryogenic vials and stored.

2.3. EV isolation from patient sera

EVs were collected from patient sera using SmartSEC™ HT EV Isolation System for Serum & Plasma from System Biosciences (catalog #: SSEC096A-1). Serum was thawed on ice and centrifuged at 4 °C for 5 minutes at 2500 rpm to collect serum at the bottom of the vial. The supernatant was then transferred into a 1.5 mL Eppendorf tube and centrifuged at 4 °C for 15 minutes x 3000g. The supernatant was again transferred to a new 1.5 mL Eppendorf tube and centrifuged at 4 °C for 15 minutes x 12000g. The supernatant collected was used for the EV isolation following manufacturer’s protocol. In compliance with recommendations for the “Minimal information for the studies of extracellular vesicles” published by Thery et al. (MISEV2018), we probed for three markers of EVs (Supplementary Data 2A–C) and a contamination marker (Supplementary Data 2D) using protein immunoblotting (see below) [29]. Transmission electron microscopy (TEM) was performed to assess EV morphology (Supplementary Data 2E). Images of the whole capillaries for protein immunoblotting was recorded (Supplementary Data 2F–I).

2.4. Protein concentration quantification and concentration of EVs

Protein concentrations for the patient-derived EVs, EV lysates, CSF, and serum derived from patients were quantified by the BCA kit from ThermoFisher (catalog #: 23228). The manufacturer’s protocol was followed.

If a higher protein concentration (>1 μg/μL) for EVs was needed, EVs were placed in a centrifugal filter (Amicon Ultra-15, catalog #: UFC900324) from Millipore Sigma (St. Louis, MO), then centrifuged at 4 °C at 14000g for 10–60 minutes, depending on the starting protein concentration.

2.5. HNE-adducted protein immunoblotting

Concentrated EVs were lysed with 5x RIPA buffer (Alfa Aesar, catalog #: J62524) supplemented with EDTA and protease inhibitor cocktail (ThermoFisher Scientific, catalog #: 78430). Samples were provided to the Redox Metabolism Shared Resource Facility at UK’s Markey Cancer Center. The facility utilizes the standardized slot-blot immunoblotting method as previously described [30]. Images of the bands from the immunoblotting were saved in Adobe Photoshop and quantified using Scion Image. Each patient had all available time points quantified on the same plate for comparison.

2.6. Nanoparticle tracking analysis

Size distribution and particle number were measured by nanoparticle tracking analysis (NTA) using ZetaView® Basic NTA (Particle Metrix, Germany). EVs were diluted in PBS to obtain 20–200 particles in each frame. Eleven videos were recorded for each specimen for 0.5 seconds at room temperature. The conditions for NTA analysis were as follows: sensitivity = 70, shutter speed = 65, frame rate = 30, 1 cycle, minimum brightness = 20, maximum area = 1000, minimum area = 15, trace length = 30. The videos were analyzed using ZetaView software version 8.05.12.

2.7. Electron microscopy

For TEM, EVs were fixed by combining the sample 1:1 in 2% glutaraldehyde in 0.1 M sodium phosphate for 30 minutes at room temperature. Five μL of fixed EVs were then placed on parafilm. A copper grid was placed on the drop for 10 minutes. Excess liquid was removed by blotting, washed twice in sterile water, and then blotted again. Final product was stained in 1.5% uranyl acetate in water for 45 seconds to 1 minute, blotted, and then used. For immunogold labeling, the antibody from Abcam (catalog #: ab48506) was diluted at 1:25. EVs were fixed as described above and then probed with the antibody to detect HNE-adducted proteins. EVs were diluted either 1:2 or used without dilution to acquire a similar number of EV particles in each frame. Grids were photographed with a Hitachi H-600 electron microscope.

2.8. Protein expression measurement

Lysed EVs and CSF were separated using capillary electrophoresis with Jess by Protein Simple © technology (San Jose, CA, USA). The manufacturer’s protocol for Jess with Protein Normalization was followed for all protein immunoblotting. The primary antibodies and their dilutions used were: flotillin-1 (Flot-1) (1:20) from Bioss (catalog #: BS-7798R), HSC70 (1:20) from Santa Cruz (catalog #: sc-7298), CD63 (1:10) from Santa Cruz (catalog #: sc-5275), ApoA1 (1:50) from Cell Signaling (catalog #: 3350), CD22 (1:50) from Protein Tech (catalog #: 66103–1-Ig) CD19 (1:10) from Abcam (catalog #: ab227019), GFAP (1:20) from Aviva Systems (catalog #: OAEB01041), BDNF (1:50) from Abcam (catalog #: ab10505), and NeuN from Novus (catalog #: NBP1–92716). All secondary antibodies were provided by Protein Simple. Compass Software was used to analyze the data and quantify the area under the curve with the baseline set at a threshold of 1, a window of 15, and a stiffness of 5; peak find was set at a threshold of 10 and a width of 15; and dropped lines calculation was used. The area under the curve for each protein was normalized to the total protein from the same lane as internal control to account for variance in loading.

2.9. Quantification of serum pro-inflammatory cytokines

Seventy μL of serum was provided to the Biomarker Analysis Lab at UK’s Center for Clinical and Translational Science. Serum levels of IL-1β, IL-6, IL-8, and TNF-α were determined by electrochemiluminescence immunoassay (Meso Scale Diagnostic V-PLEX Human Pro-inflammatory Panel II (4-Plex), catalog #: K15053D-1). Manufacturer’s protocol was followed. Briefly, serum samples were centrifuged to pellet debris and diluted 2-fold in assay buffer. Samples and standards were added to wells of high binding carbon electrode-bearing MULTI-SPOT microplates and incubated at room temperature for 2 hours with constant shaking at 800 rpm. After washing, plates were incubated with a mixture of secondary antibodies for 2 hours with shaking. A final wash was performed, followed by the addition of MSD Read Buffer. Data were captured by a MESO QuickPlex SQ120 and results were extracted using Meso Scale Discovery analysis software.

2.10. Statistics

Descriptive statistics including means and standard error of the mean (SEM) are calculated using GraphPad Prism 9 software. SEM is calculated as the standard deviation divided by the square root of the sample size (SEM=SD/√n). Mean and SEM presented graphically at each time point of follow up for quantitative measurements of EV and protein concentrations, HNE-adducted protein levels, cytokines, EV size distribution and particle number from NTA. Comparisons across time points were performed using linear mixed models to account for repeated measurements over time per patient; pairwise comparisons between time points were tested from the linear model. Normality assumptions on outcomes were assessed and data were log transformed if model assumptions were not met.

A nonlinear mixed model was employed to determine the association of EV particle counts as a function of EV size, time point of measurement, and the interaction between these two variables. The EV particle count was modelled as a Poisson distribution with an adjustment for over dispersion and allowing for a random effect of patient curves. The covariance modelled incorporated all the treatment groups. Specific pairwise comparisons between time points were performed for several EV sizes.

3. Results

3.1. Therapy-induced changes in HNE-adducted protein levels in serum but not in CSF

To gain insight into the oxidative state in ALL patients from the tumor-bearing state to the initial remission stage as assessed by the absence of bone marrow blast cells (Supplementary Data 1), we determined the level of HNE-adducted proteins in the serum and CSF. First, we measured the total protein in the serum and CSF at all time points (see Diagram 1). No statistically significant difference in the protein concentration of the serum (Fig. 1A) or CSF (Fig. 1B) was observed. Next, we measured the HNE-adducted protein levels in the serum (Fig. 1C), where we observed a statistically significant decrease in the level of HNE-adducted proteins from pre-treatment to induction day 29. This level remained low throughout the remainder of the patient’s therapy. Since CSF is used in the clinic to monitor potential CNS metastases, we also measured the level of HNE-adducted proteins in the CSF. Quantification of the signal from the immunoblotting of the CSF (Fig. 1D) showed very low levels of HNE-adducted proteins in the CSF, with no statistically significant change throughout the treatment.

Figure 1. Characterization of patient sera and CSF.

Figure 1.

Open in a new tab

(A) Average serum protein concentration (μg/μL) for each time point. Each individual point represents a measurement from an individual patient. (B) Average CSF protein concentration (μg/μL) for each collection time point. (C) Relative changes in HNE-adducted proteins in serum. Each patient’s individual fold change of HNE-adducted proteins in the serum was averaged to determine overall fold change in the serum. Induction day 29 and consolidation days 1, 8, and 15 all statistically decreased compared to pre-treatment (p<0.05). An increase from induction day 29 to consolidation days 1, 8, and 15 was also observed (p<0.05). (D) HNE-adducted proteins in CSF. Average values provided for quantification of HNE-adducted proteins in the CSF are shown. *denotes p<0.05 when compared to pre-treatment and # denotes p<0.05 when compared to induction day 29. N.S.=No statistically significant difference

3.2. EV HNE-adducted proteins increased during consolidation phase

Our group has demonstrated the ability of EVs to be a sensitive indicator of off-target tissue injury and oxidative stress [27, 28]. Therefore, we aimed to determine whether EVs isolated from serum of ALL patients would contain HNE-adducted proteins, suggesting increased oxidative stress. First, we isolated EVs from patient sera using SmartSEC™ HT EV Isolation System (Materials and Methods). Following isolation, the EVs were lysed using a lysis buffer and heat treatment at 95°C to release the contents, ensuring that the detected HNE-adducted proteins originated from intact, circulating EVs rather than from non-EV serum components. Subsequently, measurement of EV protein concentration was performed. As shown in Fig. 2A, we observed a statistically significant increase of EV protein concentration during the consolidation phase to induction day 29. A statistically significant decrease from pre-treatment to induction day 29 was also observed, and an increase from pre-treatment to consolidation day 8 and 15.

Figure 2. Characterization of EVs isolated from ALL patients.

Figure 2.

Open in a new tab

(A) Average EV protein concentration (μg/μL) for each time point, with each individual value represented. Compared to induction day 29, a statistically significant increase in EV protein concentration was observed during all three time points during the consolidation phase (p<0.05). (B) The number of EV particles generated was measured using NTA. The number of EVs generated by induction day 29 was statistically increased compared to pre-treatment and compared to consolidation phase (p<0.05). (C) Median diameter of the EV particles was measured by NTA. No difference in size was observed throughout the various sample collection time points. (D) The size distribution comparing the number of particles (y-axis) to the size of the EVs in nanometers (x-axis) to visualize the relationship between particle size and EV number. (E) Each patient’s individual fold change for HNE-adducted proteins measured in the EVs was averaged to analyze the overall fold change of HNE adductions throughout the course of treatment. (F) TEM demonstrating the presence of EVs on the grid with variations in size and morphology (arrows, F1), along with immunogold labeling indicating HNE-adducted proteins on the EVs (arrowheads, F2). *denotes p<0.05 when compared to pre-treatment and # denotes p<0.05 when compared to induction day 29. N.S.=No statistically significant difference.

The highest number of EVs generated was observed at induction day 29 (Fig. 2B), which was significantly increased compared to all other time points. However, no difference in the size of EVs was observed throughout treatment (Fig. 2C). Size distribution to visualize the relationship between EV size and number is shown in Fig. 2D. Finally, HNE-adducted proteins were measured in the EV lysates. While there was a decrease in HNE-adducted proteins from pre-treatment to induction day 29, we observe the HNE-adducted proteins during consolidation phase surpass the amount during pre-treatment (Fig. 2E). Furthermore, to visualize HNE-adducted proteins are located on the EVs, we performed immunogold labeling of HNE-adducted protein on intact EVs and observed the staining with TEM (Figs. 2F1–F2). As show in Fig. 2F, HNE-adducted protein (gold particles) are located on the intact EV of ALL patient from consolidation day 8.

3.3. Decrease in neuronal growth factor observed in EV lysates

Because EVs contain molecular components unique to their cell of origin. Since we observed an increase in HNE-adducted proteins in the EVs during consolidation therapy, we investigated changes in EV content associated with cancer and potential off-target tissue damage. Due to the small sample volumes and the limited quantity of ALL patient-derived EVs, we were unable to perform flow cytometry in this study. According to the MIFlowCyt-EV2020 guidelines [31], a concentration of 1.0 × 108−9 EVs/mL is required to achieve an optimal flow rate of 50–200 events per second during flow cytometry analysis. Additionally, the antibody staining process for EVs, particularly for detecting neuronal damage markers, often involves multiple washing steps. These washing steps can lead to a significant loss of EVs, potentially affecting the detection sensitivity and yield of the final analysis. Instead, we relied on Jess Automated Western Blot, which required only 3 ul of 0.3–0.5 ug/ul of proteins for EV characterization. As shown in Figs. 3A and 3B, there is no significant difference in the level of CD22 and CD19, two bone marrow cell surface markers used by the clinic in the pre-B cell ALL patients was observed (Supplementary Data 1).

Figure 3. Immunoblotting of protein markers in EV lysates.

Figure 3.

Open in a new tab

For all graphs in this figure, individual fold change was measured, followed by averaging the overall fold change across the different collection time points. Quantification of CD22 (A) and CD19 (B) as cell surface markers of leukemia. A significant increase of CD22 was detected in the EVs at consolidation day 15 compared to induction day 29 (p<0.05). (C) A significant decrease in GFAP expression was observed in the EV lysates at consolidation days 1 and 8 compared to induction day 29 (p<0.05). (D) An insignificant decline in the neuronal marker, NeuN, in the EVs was observed during consolidation phase compared to pre-treatment or induction day 29. (E) A statistically significant decrease in BDNF was observed in the EV lysates during both induction and consolidation phase collection points compared to pre-treatment (p<0.05). *denotes p<0.05 when compared to pre-treatment and # denotes p<0.05 when compared to induction day 29. N.S.=No statistically significant difference

We and others have previously found an increase in glial fibrillary acidic protein (GFAP) in EVs [28], a marker of glial cell activation [32, 33]. Comparing the level of GFAP in the EV samples, a significant decrease was observed at consolidation days 1 and 8 compared to pretreatment and induction day 29 (p<0.05) (Fig. 3C). A similar trend was observed in the GFAP expression in the CSF, though no significant difference was observed (Supplementary Data 3A). Next, we measured neuronal nuclear protein (NeuN), a universal marker of neurons [34]. In the EVs, NeuN decreased throughout the treatment course though no statistical significance detected (Fig. 3D); no significant difference was observed for NeuN measured in the CSF (Supplementary Data 3B). Brain-derived neurotrophic factor (BDNF, ProBDNF), a neuronal growth factor, was statistically decreased at all four later time points compared to pre-treatment (p<0.05) (Fig. 3E), but was not detectable in the CSF (data not shown).

3.4. Decrease in pro-inflammatory cytokines in serum after therapy

TNF-α has been shown to promote HNE-mediated neuronal damage in the brain, contributing to cognitive decline [35]. Inflammation has been established to be elevated in numerous cancers [36]; furthermore, peripheral inflammation has been shown to cause blood-brain barrier breakdown, resulting in increased susceptibility to neuronal dysregulation [37]. Therefore, we compared the levels of selected pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, and IL-8) in the serum of ALL patients to determine if the presence of cancer or exposure to chemotherapy agents contributes more to the cytokines levels in the serum. TNF-α and IL-1β have been shown to stimulate astrocyte activation, which may contribute to ROS production, leading to further neuronal injury [11, 38, 39]. IL-6 can modulate blood-brain barrier function, and IL-8 has been observed to be an indicator of decreased gray matter in vivo [40, 41].

TNF-α (Fig. 4A), IL-1β (Fig. 4B), IL-8 (Fig. 4C), and IL-6 (Fig. 4D) were measured by Meso Scale Diagnostics, which utilizes a sandwich ELISA to quantify cytokines. The highest levels of all cytokines evaluated were observed at pre-treatment, suggesting the presence of the leukemia as the major contributing factor to increased pro-inflammatory cytokine production.

Figure 4. Quantification of pro-inflammatory cytokines in patient sera.

Figure 4.

Open in a new tab

The amount of TNF-α (A), IL-1β (B), IL-8 (C), and IL-6 (D) in patients. All values are given as pg of cytokine per mL of serum.

4. Discussion

Off-target tissue injury, especially in the brain, is a serious consequence observed in cancer survivors, and pediatric ALL patients are particularly susceptible to these complications. Detection of redox dysregulation and markers associated with off-target tissue damage may provide opportunities to mitigate these negative outcomes, especially with non-invasive liquid biopsy. Here, we demonstrate the potential of EVs to be a sensitive indicator of oxidative stress, indexed by the presence of HNE-adducted proteins, throughout the treatment of pediatric ALL patients. While an initial decrease in HNE-adducted proteins was observed in the EVs from pretreatment to induction day 29, an increase surpassing the level measured at pre-treatment was observed during the consolidation phase that was not observed in the serum or CSF. Thus, quantifying HNE-adducted protein in isolated EVs complemented with measuring brain tissue-specific damage could provide insight into off-target effects occurring in patients, overcoming the limitations of detecting these negative outcomes at a time when intervention may provide little benefit.

Oxidative stress has been implicated in a number of human diseases [42] and its contribution in neurodegenerative disease etiology has been well-established [15, 43]. LPO is generated by •OH attacking omega-6 PUFAs, where the most toxic by-product of LPO is HNE [13]. Therefore, measuring HNE-adducted proteins indicates oxidative stress and can serve as a non-invasive indicator of collateral damage to normal tissues. Previous work has demonstrated the role HNE plays in disrupting neuronal and glial cell glutamate transporters, particularly, glial glutamate transporter 1 (GLT-1) [44]. The dysregulation of proper GLT-1 function can lead to increased neuronal sensitivity to glutamate, inducing neuronal damage and death [44]. Therefore, the increase in HNE-adducted proteins observed in our patient-derived EVs warrants further validation for use in monitoring collateral damage.

The decrease in HNE-adducted protein detected in both the serum (Fig. 1C) and EVs (Fig. 2D) from pre-treatment to induction day 29 corresponds with the eradication of the leukemia (Supplementary Data 1), suggesting the presence of leukemia may have a role in oxidative damage leading to neuronal toxicity. Importantly, while the level of HNE-adductions during consolidation therapy remained low similar to induction day 29 in the serum, the HNE-adducted proteins in EVs during the consolidation phase surpassed the level measured at pre-treatment, despite the absence of leukemia in the patients (Supplementary Data 1). We hypothesize this increase in EV HNE-adducted proteins during the consolidation phase is a result of prolonged exposure to the chemotherapies these patients are receiving. While the treatment regimen for each patient differed based on characteristics of their disease, a number of the chemotherapies used in our patient population have been shown to utilize ROS to exert their anticancer effects, such as mercaptopurine [45] cytarabine [46], daunorubicin [47, 48], and MTX [49]. We reason that exposure to these ROS-inducing drugs may be a source of oxidative stress contributing to elevated levels of HNE-adducted proteins observed during consolidation phase in the EVs.

Previous research, including our own, has demonstrated that EVs can serve as indicators of tissue-specific injury and oxidative stress, even without direct damage to the tissue of origin (e.g., detecting cardiomyocyte injury markers in the absence of direct heart injury). This underscores the role of EVs as systemic indicators of off-target effects, such as neuronal damage, particularly when CNS-specific markers like NeuN and GFAP are present in the EV cargo. It is important to note that the loss of NeuN from CNS was correlated with the increase in the number of apoptotic cells (detected by TUNEL) [50]. In addition, elevated GFAP is thought to represent reactive astrogliosis, an inflammatory response of activated astrocytes to brain insults such as aberrant accumulation of protein aggregates, neuronal damage, and brain vascular injury [51]. These markers are typically confined to the CNS and appear outside the CNS only in cases of neuronal injury. As shown in Figure 3E, the decline in the tissue-specific protein marker, BDNF, compare to pre-treatment, suggests a potential decrease in neuronal stability after chemotherapy. Although not significantly difference, we also observed elevated levels of neuronal markers, NeuN, in EVs, influenced by cancer, compared to induction Day 29. Please note, the observed increase in neuronal markers during pre-treatment likely results from the direct impact of circulating ALL cells crossing the blood-brain barrier (BBB) and the indirect effects of increased cytokines and oxidative products like HNE associated with cancer (Figures 4 and 1C). Furthermore, cancer-derived EVs may promote axonogenesis [52, 53] and interact directly with the CNS, potentially contributing to cognitive impairment. The lack of similar changes in HNE-adducted proteins and BDNF in cerebrospinal fluid (CSF) and serum, compared to significant alterations in EVs, suggests that EVs may be more sensitive indicators of systemic oxidative stress and neuronal injury than traditional biomarkers. On the contrary, during treatment, as the leukemia burden diminishes, the levels of cancer-derived EVs and related cytokines decline, suggesting that the observed changes in EV content are more likely driven by the cancer itself rather than the treatment alone. It is also important to note that while the treatment phase is approximately 2 weeks, patients may have been exposed to cancer and cancer-derived EVs for several months before diagnosis, often without obvious symptoms or abnormal blood test results, potentially contributing to the higher presence of cytokines and neuron damage markers from cancer-derived EVs. In summary, while cancer cells do contribute to the overall EV population, the specific changes we identified in patient-derived EVs reflect oxidative stress and neuronal injury linked to both cancer and chemotherapy.

The highest level of pro-inflammatory cytokines observed in our patients occurred during pre-treatment, supporting that the presence of leukemia has the greatest influence on the production of pro-inflammatory cytokines [36]. Pro-inflammatory cytokines contribution to neuronal damage has been of interest to our group [35, 54, 55]. We proposed a mechanism of cancer therapy-induced cognitive impairment via the generation of TNF-α systemically, which can then cross the blood-brain barrier via receptor-facilitated endocytosis and lead to several downstream consequences in the brain. Evidence of this scenario has previously been published by our group, where we have demonstrated: 1) microglia activation can lead to further TNF-α release in the brain [56]; 2) mitochondrial respiration is a mediator of CNS injury induced by TNF-α [57]; and 3) inhibition of TNF-α prevented CNS injury in vivo, utilizing a well-established doxorubicin treated model known to induce CNS injury [58]. It is important to note the trend observed in the amount of TNF-α detected in the serum in the current study (Fig. 4A); while it significantly decreased from pre-treatment to induction day 29, interestingly, the trend observed overall from pre-treatment to consolidation day 15 strongly correlates with the amount of HNE-adducted proteins detected in the serum (Fig. 1C). Several studies demonstrate that peripheral cancers produce inflammatory cytokines TNF- α, IL-1B, IL-6, IL-8, etc. which enter the circulation and travel to the BBB [59–61], attenuating the BBB and directly influencing neuronal plasticity. The constant production of cytokines activates meningeal and choroid plexus immune cells, promotes inflammatory glial polarization, and ultimately inflicts pathological structural and biochemical changes in neuron populations central to cognitive function. Indeed, neuroinflammation caused by circulating cytokines during cancer progression parallels the emerging concept of inflammation in primary neurodegenerative diseases [62]. For example, once TNF-α crosses the BBB, it can activate the astrocytic TNF-α receptor 1 (TNFR1), resulting in hippocampal synaptic alterations and subsequent memory impairment [63]. Terrando and colleagues demonstrated that TNF- α sustains cognitive decline in mice [64] by acting as an upstream target of IL-1B production in the brain and synergizes with MyD88 signaling. While we observed a decrease in cytokine levels following treatment initiation, we interpret this as a reduction in systemic inflammation rather than a direct indication of reduced neuronal injury. Moreover, the decrease in cytokine levels could due to steroid treatment including dexamethasone and prednisone during cancer treatments, these steroids have been shown to significantly reduced the release of TNF- α, without impairing the antitumor activity [65, 66]. Recent study has shown that patients with AML/MDS are highly symptomatic and experience cognitive impairment and fatigue before the initiation of their treatment which is correlated with levels of circulating cytokines. Patients who obtained a complete response tended to have better fine motor control with a trended to increase fatigue levels, and lower circulating IL-1 levels [67]. Thus, we propose that the observed trend may reflect a reduction in cancer-associated inflammation, but it does not necessarily imply a decrease in off-target effects, which may still be driven by ROS generation and oxidative stress during chemotherapy. Furthermore, the trend for both HNE-adducted proteins (Fig. 1C) and TNF-α (Fig. 4A) in the sera is similar to the GFAP in the EVs (Fig. 3C), suggesting elevation in HNE-adducted proteins and TNF-α may modulate astrocyte activation. Together, these data suggest TNF-α plays a role in elevated oxidative stress in the brain, highlighting the ability of TNF-α to mediate neuronal dysregulation.

Neurogenesis and neuronal stability is critical during development [68]. Our cohort of patients had a mean age of 5 years (Supplementary Data 1). BDNF has been well-established to be a critical player in neuronal plasticity, neurogenesis, and memory [69]. In the EVs of our patients, a decrease in BDNF protein expression was observed throughout the treatment course (Fig. 3E); moreover, a decrease in the neuronal marker, NeuN, was also observed to decrease throughout the time points, though not statistically significant (Fig. 3D). BDNF was not detectable in the CSF, suggesting EVs are a sensitive indicator of the neuronal stability. This observation, in combination with the ability of EVs to demonstrate an increase in HNE-adducted proteins during the consolidation phase which was not detectable in the serum or the CSF, support the ability of EVs to provide insight into off-target tissue effects and redox dysregulation.

In summary, our study explored the potential of EVs as an early indicator of oxidative stress and off-target tissue injury, focusing on the brain, due to ALL being diagnosed at a critical development age. However, we acknowledge some limitations of our study. First, we provide no direct proof of a mechanism by which EVs mediate neuronal injury; however, the insights provided here are built upon numerous previous studies [27, 35, 55–58]. Second, while we interpret the changes in protein markers in EVs as indicative off-target tissue damage, successfully characterizing the neurological effects induced by chemotherapy would require long-term psychological and neurological follow-up. Nevertheless, we have demonstrated the ability of EVs to be sensitive indicator of oxidative stress and decreased expression of the neuronal growth factor, BDNF. Overall, this unique clinical study provides insight into the potential clinical and translational utilization of EVs.

Supplementary Material

1

Supplementary Data 1. Patient characterization. Tab 1 (”Patient Characteristics”) contains information regarding each individual’s age at pre-treatment (column B), sex (column C), race (column D), ethnicity (column E), ALL subtype (pre-B cell vs. T cell, column F), any known genetic defects (column G), weight in kilograms at each collection time point (columns I to L), and bone marrow blast percentage at each collection time point (columns M to Q). Tab 2 (“Chemotherapy and Steroids Used”) includes the different chemotherapies used at the collection time point for the patients. The chemotherapies present are: cytarabine (ARAC), methotrexate (MTX), pegaspargase (PEG-Asp), vincristine (VINC), cyclophosphamide (Cyclophos), 6-mercaptopurine (6-MP), and danorubicin. Additionally, the steroid received by each patient during the induction phase is listed. Tab 3 (“Bone Marrow Markers”) includes the various markers detected by the clinical team during pre-treatment and on induction day 29 for each patient. No markers were detected during the consolidation phase. The graphs represent the percentage of patients that had each marker present during pre-treatment and on induction day 29, excluding patients 7 and 12 due to these patients being the only 2 T-cell ALL subtypes.

Supplementary Data 2. Characterization of EVs isolated from patient sera based on MISEV2018. Based on MISEV2018 recommendations, protein immunoblotting was used to measure three markers associated with EVs in EVs isolated from patient sera: Flot-1 (A), HSC70 (B), and CD63 (C). A serum contamination marker (ApoA1) was also measured in the EVs (D). Morphology was assessed using TEM (E) (arrows = EVs).

Supplementary Data 3. Protein immunoblotting in patient CSF. GFAP (A) and NeuN (B) were measured in the CSF of patients using capillary electrophoresis with Jess by Protein Simple. Each patient’s individual fold change for each protein was quantified, followed by averaging all individual patients’ fold change to analyze the overall fold change for these markers. Each protein was normalized by the protein normalization of that capillary to account for variation in protein loading. N.S.=No statistically significant difference

2

Highlights:

Key Points:

  • EVs were shown to be a sensitive indicator of oxidative stress in response to cancer and chemotherapy via the presence of HNE-adducted proteins.

  • Quantifying HNE-adducted protein levels and tissue-specific damage in EVs could elucidate off-target effects in pediatric ALL patients.

5. Acknowledgments

The authors would like to acknowledge the Redox Metabolism Shared Resource Facility (Michael Alstott) for the HNE immunoblotting and the Biostatistics and Bioinformatics Shared Resource Facility at the Markey Cancer Center, and Jennifer Moylan of the Biomarker Analysis Lab at the Center for Clinical and Translational Science at the University of Kentucky, for the outstanding service.

This work was supported, in part, by the National Institutes of Health (5R01CA217934), the National Institute of Environmental Health Sciences (T32ES07266), the National Cancer Institute Cancer Center Support Grant (P30 CA177558, R01 CA251663), and the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health (UL1TR001998).

Footnotes

7.

Conflicts of Interest

The authors declare no conflict of interest.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

8. References

  • 1.Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin 72:7–33, 2022. + [DOI] [PubMed] [Google Scholar]
  • 2.Hall EJ. Radiobiology for the radiologist. Hagerstown, Md. : Medical Dept., Harper & Row, [1973] ©1973; 1973. [Google Scholar]
  • 3.Chen Y, Jungsuwadee P, Vore M, Butterfield DA, St Clair DK. Collateral damage in cancer chemotherapy: oxidative stress in nontargeted tissues. Mol Interv 7:147–156, 2007. [DOI] [PubMed] [Google Scholar]
  • 4.Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer Journal 7:e577–e577, 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Siegel DA, Henley SJ, Li J, Pollack LA, Van Dyne EA, White A. Rates and Trends of Pediatric Acute Lymphoblastic Leukemia - United States, 2001–2014. MMWR Morb Mortal Wkly Rep 66:950–954, 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Cheung YT, Sabin ND, Reddick WE, Bhojwani D, Liu W, Brinkman TM, Glass JO, Hwang SN, Srivastava D, Pui CH, Robison LL, Hudson MM, Krull KR. Leukoencephalopathy and long-term neurobehavioural, neurocognitive, and brain imaging outcomes in survivors of childhood acute lymphoblastic leukaemia treated with chemotherapy: a longitudinal analysis. Lancet Haematol 3:e456–e466, 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Campbell LK, Scaduto M, Sharp W, Dufton L, Van Slyke D, Whitlock JA, Compas B. A meta-analysis of the neurocognitive sequelae of treatment for childhood acute lymphocytic leukemia. Pediatric Blood & Cancer 49:65–73, 2007. [DOI] [PubMed] [Google Scholar]
  • 8.Hardy SJ, Krull KR, Wefel JS, Janelsins M. Cognitive Changes in Cancer Survivors. American Society of Clinical Oncology Educational Book 795–806, 2018. [DOI] [PubMed] [Google Scholar]
  • 9.Angiolillo AL, Schore RJ, Kairalla JA, Devidas M, Rabin KR, Zweidler-McKay P, Borowitz MJ, Wood B, Carroll AJ, Heerema NA, Relling MV, Hitzler J, Lane AR, Maloney KW, Wang C, Bassal M, Carroll WL, Winick NJ, Raetz EA, Loh ML, Hunger SP. Excellent Outcomes With Reduced Frequency of Vincristine and Dexamethasone Pulses in Standard-Risk B-Lymphoblastic Leukemia: Results From Children’s Oncology Group AALL0932. Journal of Clinical Oncology 39:1437–1447, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Vora A, Andreano A, Pui CH, Hunger SP, Schrappe M, Moericke A, Biondi A, Escherich G, Silverman LB, Goulden N, Taskinen M, Pieters R, Horibe K, Devidas M, Locatelli F, Valsecchi MG. Influence of Cranial Radiotherapy on Outcome in Children With Acute Lymphoblastic Leukemia Treated With Contemporary Therapy. J Clin Oncol 34:919–926, 2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Andersen JK. Oxidative stress in neurodegeneration: cause or consequence? Nat Med 10 Suppl:S1825, 2004. [DOI] [PubMed] [Google Scholar]
  • 12.Johansson AC, Appelqvist H, Nilsson C, Kågedal K, Roberg K, Ollinger K. Regulation of apoptosisassociated lysosomal membrane permeabilization. Apoptosis 15:527–540, 2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Dalleau S, Baradat M, Gueraud F, Huc L. Cell death and diseases related to oxidative stress: 4hydroxynonenal (HNE) in the balance. Cell Death Differ 20:1615–1630, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Perluigi M, Coccia R, Butterfield DA. 4-Hydroxy-2-nonenal, a reactive product of lipid peroxidation, and neurodegenerative diseases: a toxic combination illuminated by redox proteomics studies. Antioxid Redox Signal 17:1590–1609, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Di Domenico F, Tramutola A, Butterfield DA. Role of 4-hydroxy-2-nonenal (HNE) in the pathogenesis of alzheimer disease and other selected age-related neurodegenerative disorders. Free Radic Biol Med 111:253–261, 2017. [DOI] [PubMed] [Google Scholar]
  • 16.van Niel G, D’Angelo G, Raposo G. Shedding light on the cell biology of extracellular vesicles. Nat Rev Mol Cell Biol 19:213–228, 2018. [DOI] [PubMed] [Google Scholar]
  • 17.Shah R, Patel T, Freedman JE. Circulating Extracellular Vesicles in Human Disease. N Engl J Med 379:2180–2181, 2018. [DOI] [PubMed] [Google Scholar]
  • 18.Shao H, Chung J, Balaj L, Charest A, Bigner DD, Carter BS, Hochberg FH, Breakefield XO, Weissleder R, Lee H. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat Med 18:1835–1840, 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Vader P, Mol EA, Pasterkamp G, Schiffelers RM. Extracellular vesicles for drug delivery. Adv Drug Deliv Rev 106:148–156, 2016. [DOI] [PubMed] [Google Scholar]
  • 20.Teng F, Fussenegger M. Shedding Light on Extracellular Vesicle Biogenesis and Bioengineering. Adv Sci (Weinh) 8:2003505, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, Curry WT Jr., Carter BS, Krichevsky AM, Breakefield XO. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol 10:1470–1476, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Peinado H, Alec kovic M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, Garcia-Santos G, Ghajar CM, Nitadori-Hoshino A, Hoffman C, Badal K, Garcia BA, Callahan MK, Yuan J, Martins VR, Skog J, Kaplan RN, Brady MS, Wolchok JD, Chapman PB, Kang Y, Bromberg J, Lyden D. Corrigendum: Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med 22:1502, 2016. [DOI] [PubMed] [Google Scholar]
  • 23.Al-Nedawi K, Meehan B, Micallef J, Lhotak V, May L, Guha A, Rak J. Intercellular transfer of the oncogenic receptor EGFRvIII by microvesicles derived from tumour cells. Nat Cell Biol 10:619–624, 2008. [DOI] [PubMed] [Google Scholar]
  • 24.Lachenal G, Pernet-Gallay K, Chivet M, Hemming FJ, Belly A, Bodon G, Blot B, Haase G, Goldberg Y, Sadoul R. Release of exosomes from differentiated neurons and its regulation by synaptic glutamatergic activity. Mol Cell Neurosci 46:409–418, 2011. [DOI] [PubMed] [Google Scholar]
  • 25.Thery C, Ostrowski M, Segura E. Membrane vesicles as conveyors of immune responses. Nat Rev Immunol 9:581–593, 2009. [DOI] [PubMed] [Google Scholar]
  • 26.Alexander M, Hu R, Runtsch MC, Kagele DA, Mosbruger TL, Tolmachova T, Seabra MC, Round JL, Ward DM, O’Connell RM. Exosome-delivered microRNAs modulate the inflammatory response to endotoxin. Nature Communications 6:7321, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Yarana C, Carroll D, Chen J, Chaiswing L, Zhao Y, Noel T, Alstott M, Bae Y, Dressler EV, Moscow JA, Butterfield DA, Zhu H, St Clair DK. Extracellular Vesicles Released by Cardiomyocytes in a Doxorubicin-Induced Cardiac Injury Mouse Model Contain Protein Biomarkers of Early Cardiac Injury. Clin Cancer Res 24:1644–1653, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Sukati S, Ho J, Chaiswing L, Sompol P, Pandit H, Wei W, Izumi T, Chen Q, Weiss H, Noel T, Bondada S, Allan Butterfield D, St. Clair DK. Extracellular vesicles released after cranial radiation: An insight into an early mechanism of brain injury. Brain Research 1782:147840, 2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Thery C, Witwer KW, Aikawa E, Alcaraz MJ, Anderson JD, Andriantsitohaina R, Antoniou A, Arab T, Archer F, Atkin-Smith GK, Ayre DC, Bach JM, Bachurski D, Baharvand H, Balaj L, Baldacchino S, Bauer NN, Baxter AA, Bebawy M, Beckham C, Bedina Zavec A, Benmoussa A, Berardi AC, Bergese P, Bielska E, Blenkiron C, Bobis-Wozowicz S, Boilard E, Boireau W, Bongiovanni A, Borras FE, Bosch S, Boulanger CM, Breakefield X, Breglio AM, Brennan MA, Brigstock DR, Brisson A, Broekman ML, Bromberg JF, Bryl-Gorecka P, Buch S, Buck AH, Burger D, Busatto S, Buschmann D, Bussolati B, Buzas EI, Byrd JB, Camussi G, Carter DR, Caruso S, Chamley LW, Chang YT, Chen C, Chen S, Cheng L, Chin AR, Clayton A, Clerici SP, Cocks A, Cocucci E, Coffey RJ, Cordeiro-da-Silva A, Couch Y, Coumans FA, Coyle B, Crescitelli R, Criado MF, D’Souza-Schorey C, Das S, Datta Chaudhuri A, de Candia P, De Santana EF, De Wever O, Del Portillo HA, Demaret T, Deville S, Devitt A, Dhondt B, Di Vizio D, Dieterich LC, Dolo V, Dominguez Rubio AP, Dominici M, Dourado MR, Driedonks TA, Duarte FV, Duncan HM, Eichenberger RM, Ekstrom K, El Andaloussi S, Elie-Caille C, Erdbrugger U, Falcon-Perez JM, Fatima F, Fish JE, Flores-Bellver M, Forsonits A, Frelet-Barrand A, Fricke F, Fuhrmann G, Gabrielsson S, Gamez-Valero A, Gardiner C, Gartner K, Gaudin R, Gho YS, Giebel B, Gilbert C, Gimona M, Giusti I, Goberdhan DC, Gorgens A, Gorski SM, Greening DW, Gross JC, Gualerzi A, Gupta GN, Gustafson D, Handberg A, Haraszti RA, Harrison P, Hegyesi H, Hendrix A, Hill AF, Hochberg FH, Hoffmann KF, Holder B, Holthofer H, Hosseinkhani B, Hu G, Huang Y, Huber V, Hunt S, Ibrahim AG, Ikezu T, Inal JM, Isin M, Ivanova A, Jackson HK, Jacobsen S, Jay SM, Jayachandran M, Jenster G, Jiang L, Johnson SM, Jones JC, Jong A, Jovanovic-Talisman T, Jung S, Kalluri R, Kano SI, Kaur S, Kawamura Y, Keller ET, Khamari D, Khomyakova E, Khvorova A, Kierulf P, Kim KP, Kislinger T, Klingeborn M, Klinke DJ 2nd, Kornek M, Kosanovic MM, Kovacs AF, Kramer-Albers EM, Krasemann S, Krause M, Kurochkin IV, Kusuma GD, Kuypers S, Laitinen S, Langevin SM, Languino LR, Lannigan J, Lasser C, Laurent LC, Lavieu G, Lazaro-Ibanez E, Le Lay S, Lee MS, Lee YXF, Lemos DS, Lenassi M, Leszczynska A, Li IT, Liao K, Libregts SF, Ligeti E, Lim R, Lim SK, Line A, Linnemannstons K, Llorente A, Lombard CA, Lorenowicz MJ, Lorincz AM, Lotvall J, Lovett J, Lowry MC, Loyer X, Lu Q, Lukomska B, Lunavat TR, Maas SL, Malhi H, Marcilla A, Mariani J, Mariscal J, Martens-Uzunova ES, Martin-Jaular L, Martinez MC, Martins VR, Mathieu M, Mathivanan S, Maugeri M, McGinnis LK, McVey MJ, Meckes DG Jr., Meehan KL, Mertens I, Minciacchi VR, Moller A, Moller Jorgensen M, Morales-Kastresana A, Morhayim J, Mullier F, Muraca M, Musante L, Mussack V, Muth DC, Myburgh KH, Najrana T, Nawaz M, Nazarenko I, Nejsum P, Neri C, Neri T, Nieuwland R, Nimrichter L, Nolan JP, Nolte-’t Hoen EN, Noren Hooten N, O’Driscoll L, O’Grady T, O’Loghlen A, Ochiya T, Olivier M, Ortiz A, Ortiz LA, Osteikoetxea X, Ostergaard O, Ostrowski M, Park J, Pegtel DM, Peinado H, Perut F, Pfaffl MW, Phinney DG, Pieters BC, Pink RC, Pisetsky DS, Pogge von Strandmann E, Polakovicova I, Poon IK, Powell BH, Prada I, Pulliam L, Quesenberry P, Radeghieri A, Raffai RL, Raimondo S, Rak J, Ramirez MI, Raposo G, Rayyan MS, Regev-Rudzki N, Ricklefs FL, Robbins PD, Roberts DD, Rodrigues SC, Rohde E, Rome S, Rouschop KM, Rughetti A, Russell AE, Saa P, Sahoo S, Salas-Huenuleo E, Sanchez C, Saugstad JA, Saul MJ, Schiffelers RM, Schneider R, Schoyen TH, Scott A, Shahaj E, Sharma S, Shatnyeva O, Shekari F, Shelke GV, Shetty AK, Shiba K, Siljander PR, Silva AM, Skowronek A, Snyder OL 2nd, Soares RP, Sodar BW, Soekmadji C, Sotillo J, Stahl PD, Stoorvogel W, Stott SL, Strasser EF, Swift S, Tahara H, Tewari M, Timms K, Tiwari S, Tixeira R, Tkach M, Toh WS, Tomasini R, Torrecilhas AC, Tosar JP, Toxavidis V, Urbanelli L, Vader P, van Balkom BW, van der Grein SG, Van Deun J, van Herwijnen MJ, Van Keuren-Jensen K, van Niel G, van Royen ME, van Wijnen AJ, Vasconcelos MH, Vechetti IJ Jr., Veit TD, Vella LJ, Velot E, Verweij FJ, Vestad B, Vinas JL, Visnovitz T, Vukman KV, Wahlgren J, Watson DC, Wauben MH, Weaver A, Webber JP, Weber V, Wehman AM, Weiss DJ, Welsh JA, Wendt S, Wheelock AM, Wiener Z, Witte L, Wolfram J, Xagorari A, Xander P, Xu J, Yan X, Yanez-Mo M, Yin H, Yuana Y, Zappulli V, Zarubova J, Zekas V, Zhang JY, Zhao Z, Zheng L, Zheutlin AR, Zickler AM, Zimmermann P, Zivkovic AM, Zocco D, Zuba-Surma EK. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7:1535750, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Aluise CD, St Clair D, Vore M, Butterfield DA. In vivo amelioration of adriamycin induced oxidative stress in plasma by gamma-glutamylcysteine ethyl ester (GCEE). Cancer Lett 282:25–29, 2009. [DOI] [PubMed] [Google Scholar]
  • 31.Welsh JA, Van Der Pol E, Arkesteijn GJA, Bremer M, Brisson A, Coumans F, Dignat-George F, Duggan E, Ghiran I, Giebel B, Gorgens A, Hendrix A, Lacroix R, Lannigan J, Libregts S, Lozano-Andres E, Morales-Kastresana A, Robert S, De Rond L, Tertel T, Tigges J, De Wever O, Yan X, Nieuwland R, Wauben MHM, Nolan JP, Jones JC. MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments. J Extracell Vesicles 9:1713526, 2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Chatterjee P, Pedrini S, Stoops E, Goozee K, Villemagne VL, Asih PR, Verberk IMW, Dave P, Taddei K, Sohrabi HR, Zetterberg H, Blennow K, Teunissen CE, Vanderstichele HM, Martins RN. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Translational Psychiatry 11:27, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Middeldorp J, Hol EM. GFAP in health and disease. Prog Neurobiol 93:421–443, 2011. [DOI] [PubMed] [Google Scholar]
  • 34.Gusel’nikova VV, Korzhevskiy DE. NeuN As a Neuronal Nuclear Antigen and Neuron Differentiation Marker. Acta Naturae 7:42–47, 2015. [PMC free article] [PubMed] [Google Scholar]
  • 35.Ren X, Keeney JTR, Miriyala S, Noel T, Powell DK, Chaiswing L, Bondada S, St Clair DK, Butterfield DA. The triangle of death of neurons: Oxidative damage, mitochondrial dysfunction, and loss of choline-containing biomolecules in brains of mice treated with doxorubicin. Advanced insights into mechanisms of chemotherapy induced cognitive impairment (“chemobrain”) involving TNF-alpha. Free Radic Biol Med 134:1–8, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Coussens LM, Werb Z. Inflammation and cancer. Nature 420:860–867, 2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Galea I. The blood–brain barrier in systemic infection and inflammation. Cellular & Molecular Immunology 18:2489–2501, 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Hyvarinen T, Hagman S, Ristola M, Sukki L, Veijula K, Kreutzer J, Kallio P, Narkilahti S. Co-stimulation with IL-1beta and TNF-alpha induces an inflammatory reactive astrocyte phenotype with neurosupportive characteristics in a human pluripotent stem cell model system. Sci Rep 9:16944, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Ye L, Huang Y, Zhao L, Li Y, Sun L, Zhou Y, Qian G, Zheng JC. IL-1β and TNF-α induce neurotoxicity through glutamate production: a potential role for neuronal glutaminase. J Neurochem 125:897–908, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Zhang J, Sadowska GB, Chen X, Park SY, Kim JE, Bodge CA, Cummings E, Lim YP, Makeyev O, Besio WG, Gaitanis J, Banks WA, Stonestreet BS. Anti-IL-6 neutralizing antibody modulates blood-brain barrier function in the ovine fetus. Faseb j 29:1739–1753, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Willette AA, Coe CL, Birdsill AC, Bendlin BB, Colman RJ, Alexander AL, Allison DB, Weindruch RH, Johnson SC. Interleukin-8 and interleukin-10, brain volume and microstructure, and the influence of calorie restriction in old rhesus macaques. Age (Dordr) 35:2215–2227, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Pizzino G, Irrera N, Cucinotta M, Pallio G, Mannino F, Arcoraci V, Squadrito F, Altavilla D, Bitto A. Oxidative Stress: Harms and Benefits for Human Health. Oxid Med Cell Longev 2017:8416763, 2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Butterfield DA, Halliwell B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat Rev Neurosci 20:148–160, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Lauderback CM, Hackett JM, Huang FF, Keller JN, Szweda LI, Markesbery WR, Butterfield DA. The glial glutamate transporter, GLT-1, is oxidatively modified by 4-hydroxy-2-nonenal in the Alzheimer’s disease brain: the role of Abeta1–42. J Neurochem 78:413–416, 2001. [DOI] [PubMed] [Google Scholar]
  • 45.Rehman SU, Zubair H, Sarwar T, Husain MA, Ishqi HM, Nehar S, Tabish M. Redox cycling of Cu(II) by 6-mercaptopurine leads to ROS generation and DNA breakage: possible mechanism of anticancer activity. Tumor Biology 36:1237–1244, 2015. [DOI] [PubMed] [Google Scholar]
  • 46.Iacobini M, Menichelli A, Palumbo G, Multari G, Werner B, Del Principe D. Involvement of oxygen radicals in cytarabine-induced apoptosis in human polymorphonuclear cells. Biochem Pharmacol 61:1033–1040, 2001. [DOI] [PubMed] [Google Scholar]
  • 47.Boland MP, Foster SJ, O’Neill LA. Daunorubicin activates NFkappaB and induces kappaB-dependent gene expression in HL-60 promyelocytic and Jurkat T lymphoma cells. J Biol Chem 272:12952–12960, 1997. [DOI] [PubMed] [Google Scholar]
  • 48.Al-Aamri HM, Ku H, Irving HR, Tucci J, Meehan-Andrews T, Bradley C. Time dependent response of daunorubicin on cytotoxicity, cell cycle and DNA repair in acute lymphoblastic leukaemia. BMC Cancer 19:179, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Phillips DC, Woollard KJ, Griffiths HR. The anti-inflammatory actions of methotrexate are critically dependent upon the production of reactive oxygen species. Br J Pharmacol 138:501–511, 2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Davoli MA, Fourtounis J, Tam J, Xanthoudakis S, Nicholson D, Robertson GS, Ng GY, Xu D. Immunohistochemical and biochemical assessment of caspase-3 activation and DNA fragmentation following transient focal ischemia in the rat. Neuroscience 115:125–136, 2002. [DOI] [PubMed] [Google Scholar]
  • 51.Abdelhak A, Foschi M, Abu-Rumeileh S, Yue JK, D’Anna L, Huss A, Oeckl P, Ludolph AC, Kuhle J, Petzold A, Manley GT, Green AJ, Otto M, Tumani H. Blood GFAP as an emerging biomarker in brain and spinal cord disorders. Nat Rev Neurol 18:158–172, 2022. [DOI] [PubMed] [Google Scholar]
  • 52.Olson B, Marks DL. Pretreatment Cancer-Related Cognitive Impairment-Mechanisms and Outlook. Cancers (Basel) 11:2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Madeo M, Colbert PL, Vermeer DW, Lucido CT, Cain JT, Vichaya EG, Grossberg AJ, Muirhead D, Rickel AP, Hong Z, Zhao J, Weimer JM, Spanos WC, Lee JH, Dantzer R, Vermeer PD. Cancer exosomes induce tumor innervation. Nat Commun 9:4284, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Aluise CD, Miriyala S, Noel T, Sultana R, Jungsuwadee P, Taylor TJ, Cai J, Pierce WM, Vore M, Moscow JA, St Clair DK, Butterfield DA. 2-Mercaptoethane sulfonate prevents doxorubicin-induced plasma protein oxidation and TNF-alpha release: implications for the reactive oxygen species-mediated mechanisms of chemobrain. Free Radic Biol Med 50:1630–1638, 2011. [DOI] [PubMed] [Google Scholar]
  • 55.Hayslip J, Dressler EV, Weiss H, Taylor TJ, Chambers M, Noel T, Miriyala S, Keeney JT, Ren X, Sultana R, Vore M, Butterfield DA, St Clair D, Moscow JA. Plasma TNF-alpha and Soluble TNF Receptor Levels after Doxorubicin with or without Co-Administration of Mesna-A Randomized, Cross-Over Clinical Study. PLoS One 10:e0124988, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Tangpong J, Sompol P, Vore M, St Clair W, Butterfield DA, St Clair DK. Tumor necrosis factor alphamediated nitric oxide production enhances manganese superoxide dismutase nitration and mitochondrial dysfunction in primary neurons: an insight into the role of glial cells. Neuroscience 151:622–629, 2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Tangpong J, Cole MP, Sultana R, Estus S, Vore M, St Clair W, Ratanachaiyavong S, St Clair DK, Butterfield DA. Adriamycin-mediated nitration of manganese superoxide dismutase in the central nervous system: insight into the mechanism of chemobrain. J Neurochem 100:191–201, 2007. [DOI] [PubMed] [Google Scholar]
  • 58.Tangpong J, Cole MP, Sultana R, Joshi G, Estus S, Vore M, St Clair W, Ratanachaiyavong S, St Clair DK, Butterfield DA. Adriamycin-induced, TNF-alpha-mediated central nervous system toxicity. Neurobiol Dis 23:127–139, 2006. [DOI] [PubMed] [Google Scholar]
  • 59.Tsao N, Hsu HP, Wu CM, Liu CC, Lei HY. Tumour necrosis factor-alpha causes an increase in blood-brain barrier permeability during sepsis. J Med Microbiol 50:812–821, 2001. [DOI] [PubMed] [Google Scholar]
  • 60.de Vries HE, Blom-Roosemalen MC, van Oosten M, de Boer AG, van Berkel TJ, Breimer DD, Kuiper J. The influence of cytokines on the integrity of the blood-brain barrier in vitro. J Neuroimmunol 64:37–43, 1996. [DOI] [PubMed] [Google Scholar]
  • 61.Daniels BP, Holman DW, Cruz-Orengo L, Jujjavarapu H, Durrant DM, Klein RS. Viral pathogen-associated molecular patterns regulate blood-brain barrier integrity via competing innate cytokine signals. mBio 5:e01476–01414, 2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, Jacobs AH, Wyss-Coray T, Vitorica J, Ransohoff RM, Herrup K, Frautschy SA, Finsen B, Brown GC, Verkhratsky A, Yamanaka K, Koistinaho J, Latz E, Halle A, Petzold GC, Town T, Morgan D, Shinohara ML, Perry VH, Holmes C, Bazan NG, Brooks DJ, Hunot S, Joseph B, Deigendesch N, Garaschuk O, Boddeke E, Dinarello CA, Breitner JC, Cole GM, Golenbock DT, Kummer MP. Neuroinflammation in Alzheimer’s disease. Lancet Neurol 14:388–405, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Habbas S, Santello M, Becker D, Stubbe H, Zappia G, Liaudet N, Klaus FR, Kollias G, Fontana A, Pryce CR, Suter T, Volterra A. Neuroinflammatory TNFalpha Impairs Memory via Astrocyte Signaling. Cell 163:1730–1741, 2015. [DOI] [PubMed] [Google Scholar]
  • 64.Terrando N, Monaco C, Ma D, Foxwell BM, Feldmann M, Maze M. Tumor necrosis factor-alpha triggers a cytokine cascade yielding postoperative cognitive decline. Proc Natl Acad Sci U S A 107:20518–20522, 2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Walz A, Andratschke M, Wollenberg B, Lindhofer H, Zeidler R. Prednisolone reduces TNF-alpha release by PBMCs activated with a trifunctional bispecific antibody but not their anti-tumor activity. Anticancer Res 25:4239–4243, 2005. [PubMed] [Google Scholar]
  • 66.Machuca C, Mendoza-Milla C, Cordova E, Mejia S, Covarrubias L, Ventura J, Zentella A. Dexamethasone protection from TNF-alpha-induced cell death in MCF-7 cells requires NF-kappaB and is independent from AKT. BMC Cell Biol 7:9, 2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Meyers CA, Albitar M, Estey E. Cognitive impairment, fatigue, and cytokine levels in patients with acute myelogenous leukemia or myelodysplastic syndrome. Cancer 104:788–793, 2005. [DOI] [PubMed] [Google Scholar]
  • 68.Sorrells SF, Paredes MF, Cebrian-Silla A, Sandoval K, Qi D, Kelley KW, James D, Mayer S, Chang J, Auguste KI, Chang EF, Gutierrez AJ, Kriegstein AR, Mathern GW, Oldham MC, Huang EJ, Garcia-Verdugo JM, Yang Z, Alvarez-Buylla A. Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults. Nature 555:377–381, 2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Miranda M, Morici JF, Zanoni MB, Bekinschtein P. Brain-Derived Neurotrophic Factor: A Key Molecule for Memory in the Healthy and the Pathological Brain. Front Cell Neurosci 13:363, 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

Supplementary Data 1. Patient characterization. Tab 1 (”Patient Characteristics”) contains information regarding each individual’s age at pre-treatment (column B), sex (column C), race (column D), ethnicity (column E), ALL subtype (pre-B cell vs. T cell, column F), any known genetic defects (column G), weight in kilograms at each collection time point (columns I to L), and bone marrow blast percentage at each collection time point (columns M to Q). Tab 2 (“Chemotherapy and Steroids Used”) includes the different chemotherapies used at the collection time point for the patients. The chemotherapies present are: cytarabine (ARAC), methotrexate (MTX), pegaspargase (PEG-Asp), vincristine (VINC), cyclophosphamide (Cyclophos), 6-mercaptopurine (6-MP), and danorubicin. Additionally, the steroid received by each patient during the induction phase is listed. Tab 3 (“Bone Marrow Markers”) includes the various markers detected by the clinical team during pre-treatment and on induction day 29 for each patient. No markers were detected during the consolidation phase. The graphs represent the percentage of patients that had each marker present during pre-treatment and on induction day 29, excluding patients 7 and 12 due to these patients being the only 2 T-cell ALL subtypes.

Supplementary Data 2. Characterization of EVs isolated from patient sera based on MISEV2018. Based on MISEV2018 recommendations, protein immunoblotting was used to measure three markers associated with EVs in EVs isolated from patient sera: Flot-1 (A), HSC70 (B), and CD63 (C). A serum contamination marker (ApoA1) was also measured in the EVs (D). Morphology was assessed using TEM (E) (arrows = EVs).

Supplementary Data 3. Protein immunoblotting in patient CSF. GFAP (A) and NeuN (B) were measured in the CSF of patients using capillary electrophoresis with Jess by Protein Simple. Each patient’s individual fold change for each protein was quantified, followed by averaging all individual patients’ fold change to analyze the overall fold change for these markers. Each protein was normalized by the protein normalization of that capillary to account for variation in protein loading. N.S.=No statistically significant difference

NIHMS2041816-supplement-1.xlsx (47.6KB, xlsx)
2
NIHMS2041816-supplement-2.pdf (336.2KB, pdf)

RESOURCES