Abstract
Purpose:
Chronic Myeloid Leukemia (CML) is characterized by the BCR-ABL1 fusion gene and is effectively managed with tyrosine kinase inhibitors (TKIs). However, therapeutic resistance and the persistence of leukemic stem cells pose challenges to achieving long-term remission. Tumor Necrosis Factor-alpha (TNF-α), a pro-inflammatory cytokine, is implicated in leukemogenesis and resistance, yet its clinical relevance in Indian CML cohorts remains underexplored.
Objective:
This study investigates the pharmacodynamic and prognostic role of serum TNF-α levels in chronic-phase CML patients receiving first-line imatinib, aiming to evaluate its utility as a biomarker for treatment response.
Methods:
We conducted a prospective observational study on 40 CML patients treated at Safdarjung Hospital, New Delhi. Blood samples were taken before treatment and 5–7 months after starting imatinib. ELISA was used to quantify TNF-α levels, and qRT-PCR was used to monitor BCR-ABL1 transcripts. We analyzed clinical and hematologic parameters using appropriate statistical methods.
Results:
Imatinib treatment significantly reduced serum TNF-α (259.5 to 129.8 pg/mL; p < 0.0001), mirroring the observed decrease in BCR-ABL1 transcripts. Basically, if TNF-α stuck around (r = 0.87), patients responded poorly, whereas good outcomes correlated with its quicker clearance (r = 0.45).
Conclusion:
Our data suggest that TNF-α represents a clinically relevant pharmacodynamic and prognostic biomarker for patients with chronic-phase CML receiving imatinib therapy. A decline in TNF-α levels was associated with a favorable therapeutic outcome, whereas failure of TNF-α to decline was indicative of an inferior outcome. TNF-α does not replace qRT-PCR–based monitoring of BCR-ABL1; however, it may be useful as an adjunctive prognostic marker, especially in resource-poor environments, pending appropriate prospective confirmation.
1 Introduction
CML, a myeloproliferative disease, arises from the Philadelphia chromosome, t(9;22)(q34;q11), yielding BCR-ABL1 and its unchecked tyrosine kinase activity (). This oncogenic protein is mainly responsible for oncogenesis, as it activates downstream signaling pathways such as PI3K/AKT/mTOR, JAK/STAT, RAS/ERK, and NF-κB, leading to proliferation, apoptosis resistance, and genomic instability (, ). Recent findings suggest reactive oxygen species ROS play an important role in maintaining oncogenic signals resistance and the tumor microenvironment ().
In India CML is more common and patients are diagnosed about ten years sooner than Western patients with a typical diagnosis age of 38 to 40 years (). Imatinib and other tyrosine kinase inhibitors have changed how CML is treated turning it into a manageable chronic disease with many patients achieving complete cytogenic and major molecular responses (, ). Reports still indicate that 10 to 15% of patients show primary resistance to these tyrosine kinase inhibitors while another 20 to 30% develop secondary resistance or treatment intolerance ().
Treating CML is tough; leukemic stem cells (LSCs) persist because they bypass TKI targeting by activating BCR-ABL1-independent survival signals (). Relapse in this disease often stems from what’s known as minimal residual disease (MRD).
Researchers are now very interested in pro-inflammatory cytokines such as Tumor Necrosis Factor-alpha (TNF-alpha) in recent biomarkers because it may help predict and treat leukemia. TNF-alpha activates the NF-κB and p38 MAPK pathways through the TNFR1/2 receptor promoting LSC survival, microenvironment remodeling and treatment resistance (, ). Gallipoli et al. noted that autocrine TNF-alpha signaling may sustain CML progenitor cells and inhibit TKI-induced apoptosis (). Herrmann et al. observed improved TKI sensitivity that decreased LSCs when TNF-alpha is neutralized in CML models (13).
Despite existing evidence thorough observation of TNF-alpha dynamics in CML patients particularly within the Indian population remains unconducted even though India has a relatively young patient population with high disease prevalence. ELISA is a very economical tool for measuring TNF-alpha that could supplement modern molecular measurement.
We investigated TNF-alpha’s impact on treatment outcomes in chronic phase CML patients taking imatinib, focusing on its pharmacodynamic and prognostic significance at elevated serum levels. We want to explore TNF-alpha as a potential marker for treatment effectiveness.
2 Materials and methods
2.1 Study design
At Safdarjung Hospital’s Hematology Department in New Delhi, we observed forty chronic-phase CML patients, all 16 or older, in a prospective study. All patients provided written informed consent. The Institutional Ethics Committee approved the study which followed the Declaration of Helsinki. For initial imatinib therapy, we included newly diagnosed chronic-phase CML patients. This study excluded patients who had prior malignancies autoimmune or inflammatory disorders active infections or treatment non-compliance.
We collected 3–5 mL peripheral blood samples in EDTA vacutainers at baseline, prior to therapy, and then again at 5–7 months post-imatinib initiation. Plasma was separated by centrifugation at 3000 rpm for 10 minutes and stored at –80 °C until analysis. We used the Qiagen RNeasy Mini Kit to isolate total RNA from whole blood, assessing purity via NanoDrop spectrophotometry. For cDNA conversion, we used the TRUPCR® BCR-ABL1 kit; subsequently, qRT-PCR amplification of BCR-ABL1 and ABL1 transcripts was performed on a Rotor-Gene Q thermocycler, employing TaqMan probes. Gene expression, assessed via ΔCt, was reported as MMR-IS%, indicating major molecular response.
The TNF-α levels in plasma were measured with a Fine Test Human TNF-α ELISA Kit (Catalog No. EH0302) according to the manufacturer’s recommendations. All samples were measured in duplicate, and absorbance was read at 450 nm on a microplate reader. The final TNF-α concentrations were calculated from the standard curve provided with the kit. All samples were run in duplicate within the same plate, and the average values were used for calculations. Patient samples all lay within the manufacturer’s established linear range of detection (31.25–2000 pg/mL); no samples thus required extrapolation above this range. TNF-α levels were interpolated from a standard curve using known TNF-α concentrations.
2.2 Response stratification
Patients with MMR, BCR-ABL1 ≤0.1% IS within 5–7 months, were classified as Good Responders according to the ELN 2020 criteria. Bad Responders were defined as patients who did not reach MMR. This stratification was applied before analyses of TNF-α correlations in order to use standardized response definitions.
2.3 Statistical analysis
Statistical analyses were performed using GraphPad Prism 9.3.0. Paired comparisons between baseline and follow-up values were conducted using the Wilcoxon Signed Rank Test. One-sample Wilcoxon tests were used to assess deviation from a theoretical median. Pearson correlation coefficients were calculated for hematological parameters. A p-value < 0.05 was considered statistically significant.
Multiple comparisons were performed across hematologic and molecular parameters. Given the exploratory nature of this biomarker study and the modest number of primary comparisons (n<20), no formal multiple-comparison adjustment was applied. However, findings should be interpreted as hypothesis-generating and require external validation.
3 Results
3.1 Patient characteristics and sample overview
Forty patients with chronic-phase CML were included. The median age was 40 years (range 22–56). Baseline and follow-up blood samples were analyzed to evaluate TNF-α modulation and its association with molecular response to imatinib therapy.
3.2 TNF-α levels decline significantly following imatinib therapy
A Wilcoxon signed-rank test revealed a significant reduction in serum TNF-α levels post-treatment (p < 0.0001). The mean TNF-α concentration declined from 259.5 pg/mL at baseline to 129.8 pg/mL at follow-up (Figure 1), suggesting a decrease in systemic inflammation in response to therapy.
Figure 1
3.3 Concordant molecular response observed via BCR-ABL1 transcript reduction
BCR-ABL1 transcript levels showed a substantial decrease post-treatment (mean copy number reduced from 118,472 to 21,642; Ct value increased from 23.4 to 27.3) (Table 1). Median MMR-IS% improved, reflecting effective molecular response.
Table 1
| Parameter | Baseline (Mean ± SD) | Follow-up (Mean ± SD) |
|---|---|---|
| TNF-α (pg/mL) | 259.5 ± 94.1 | 129.8 ± 66.4 |
| BCR-ABL1 copies | 118,472.8 | 21,642.6 |
| Ct value | 23.42 ± 4.15 | 27.29 ± 3.72 |
Comparison of molecular and clinical parameters at baseline and follow-up.
Exploratory analyses showed no strong correlation between baseline TNF-α levels and baseline WBC count or platelet count (all correlations weak-to-moderate and not statistically robust). These findings suggest that TNF-α may capture aspects of the inflammatory milieu that are not fully redundant with conventional disease burden metrics.
3.4 Statistical deviation confirmed via one-sample Wilcoxon test
Significant differences (p < 0.0001) from the theoretical median (zero) were observed for all key parameters—TNF-α, Ct value, and MMR—at both time points, confirming strong biological changes with therapy (Figure 2).
Figure 2
These findings highlight that treatment not only reduces molecular disease burden, as reflected by Ct values and MMR, but also modulates inflammatory pathways through changes in TNF-α. Together, these shifts underscore the therapeutic impact across both molecular and immunological domains and support the potential of TNF-α as a complementary biomarker for monitoring treatment response.
3.5 Correlation analysis reveals hematologic shifts
At baseline, a moderate correlation was observed between WBC and Hb (r = 0.49) and WBC and neutrophils (r = 0.43), as shown in Figure 3. Post-treatment, platelet and neutrophil counts correlated strongly (r = 0.60), indicating a coordinated hematologic recovery. Other correlations were weak or negligible.
Figure 3
The detailed statistical analysis raw excel file has been provided in Supplementary Tables 1 and 2.
At baseline, moderate correlations between WBC with hemoglobin and neutrophils reflected the underlying leukemic burden and its impact on hematopoiesis. Following therapy, the emergence of a strong correlation between platelet and neutrophil counts suggests a coordinated recovery of myeloid and megakaryocytic lineages, indicative of bone marrow regeneration and treatment efficacy. The absence of other meaningful correlations highlights the selective restoration of hematologic parameters most affected by disease and therapy.
Evaluated the correlation between baseline TNF levels (log_TNF_T0) and follow-up TNF levels (log_TNF_T6) in two groups of patients stratified by treatment response.
Although statistical significance was achieved, the small sample size of poor responders (n=8) necessitates cautious interpretation. The strong correlation (r=0.87, p=0.005) observed in this group demonstrates an interesting trend but with a wide 95% confidence interval (0.42-0.98) (Figure 4A), indicating substantial uncertainty. These findings should be considered preliminary and require validation in larger prospective cohorts before clinical application (Figure 4A).
In contrast, Good Responders (n = 31) demonstrated a moderate positive correlation (r = 0.45, p = 0.0095) (Figure 4B). Although the association was weaker compared to bad responders, the correlation remained statistically significant, with the 95% CI ranging from 0.12 to 0.69. This suggests that baseline TNF levels were less predictive of follow-up levels in good responders, likely reflecting a reduction in TNF signaling after treatment.
Figure 4
This indicates that persistence of TNF elevation is strongly linked to poor treatment response. In bad responders, TNF levels at baseline serve as a strong predictor of follow-up levels, suggesting inadequate suppression of TNF-driven pathways. Conversely, in good responders, although a moderate correlation exists, the weaker association implies that therapeutic intervention may partially mitigate TNF activity. Collectively, these results support the role of TNF as a potential biomarker of treatment resistance and highlight its importance in patient stratification.
The paired boxplots (Figure 5) compare log-transformed TNF levels at baseline (T0) and follow-up (T6) within each response group. In bad responders, TNF levels remained unchanged over time (p = 0.78), whereas good responders showed a downward trend that did not reach statistical significance (p = 0.14). This highlights the within-group dynamics of TNF during treatment.
Figure 5
The violin plot (Figure 6) depicts the distribution of net TNF change (ΔTNF, T0–T6) between groups. While bad responders demonstrated minimal fluctuation centered around zero, good responders exhibited a broader range of TNF modulation, including marked reductions and a few increases, indicating heterogeneity in treatment-associated TNF shifts.
Figure 6
Baseline characteristics were broadly comparable between good responders and bad responders (Table 2). Median baseline WBC and hemoglobin values were identical across groups, reflecting overlapping distributions rather than systematic baseline differences. Baseline TNF−α levels tended to be higher in bad responders, but considerable overlap in distributions was observed. These findings suggest that persistence of TNF−α at follow−up is not solely explained by obvious baseline clinical heterogeneity.”
Table 2
| Parameter | Good responders (n=31) | Bad responders (n=8) |
|---|---|---|
| Median age (years) | 40 | 40 |
| Baseline WBC (×109/L) | 101.725 | 101.725 |
| Baseline Hb (g/dL) | 9.75 | 9.75 |
| Median TNF-α (pg/mL) | 34.88 | 36.48 |
Baseline clinical and inflammatory parameters according to treatment response.
Baseline hematologic parameters are presented as medians calculated for each response cohort. Identical median values, as observed in baseline WBC and hemoglobin, reflect overlapping distributions between good and poor responders and are not due to transcription or data duplication errors.
Although between-group differences were not statistically significant, the combined analyses underscore that TNF remains stable in poor responders but is more variably regulated in good responders, suggesting a potential but inconsistent role in therapeutic response.
4 Discussion
This study demonstrates a significant reduction in serum TNF-α levels in chronic-phase CML patients following imatinib therapy, paralleling declines in BCR-ABL1 transcripts. However, persistence of TNF-α elevation was strongly linked to poor treatment response, with baseline levels serving as a strong predictor of follow-up levels in non-responders, suggesting inadequate suppression of TNF-driven pathways. In contrast, good responders showed only a moderate correlation, indicating that therapy may partially attenuate TNF activity. Collectively, these findings highlight TNF-α as a potential biomarker of treatment resistance and a valuable tool for patient stratification in CML.
Imatinib therapy in chronic-phase CML correlates with diminished serum TNF-α, mirroring BCR-ABL1 transcript declines. Persistent TNF-α elevation correlated with poor treatment outcomes, and baseline levels predicted follow-up levels in non-responders, pointing to insufficient TNF pathway suppression. Good responders, on the other hand, exhibited merely moderate correlation, suggesting therapy’s potential to partially attenuate TNF activity. Essentially, TNF-α emerges as a biomarker for treatment resistance and may prove valuable for patient stratification in CML.
But, the study design incorporates only a single follow-up timepoint at 5–7 months. TNF-α kinetics may include rapid suppression phases followed by potential rebounds or compensatory cytokine shifts not captured in this cross sectional assessment. Future studies incorporating serial sampling at months 1, 3, 6, and 12 are necessary to characterize TNF-α trajectories and predict resistance emergence.
Preclinical studies have implicated TNF-α in the survival and persistence of leukemic stem and progenitor cells via TNFR-mediated activation of NF-κB and p38 MAPK pathways (, ). In the present clinical cohort, we did not directly quantify LSCs (e.g., CD34+CD38- populations) or LSC-related gene expression. Therefore, our findings should be interpreted as clinically correlative and consistent with, but not directly demonstrative of, TNF-α–driven LSC persistence. Future studies incorporating phenotypic and molecular LSC assessment will be critical to mechanistically link TNF-α dynamics with stem cell burden in vivo (, 13).
Similar mechanisms have been observed in solid tumors, where RNA- and protein-regulating molecules such as the NONO protein enhance malignant survival through Akt/MAPK/β-catenin signaling (14). These parallels underscore how diverse oncogenic pathways converge on inflammatory and survival circuits, supporting the rationale for exploring TNF-α as a therapeutic and prognostic biomarker in CML.
Gallipoli et al. demonstrated that autocrine production of TNF-α enhances the survival and expansion of CML stem and progenitor cells while diminishing their sensitivity to TKIs (15). This pro-survival cytokine loop provides a protective microenvironment that sustains minimal residual disease (MRD) and hinders complete remission. Supporting this, Herrmann et al. found that neutralizing TNF-α in preclinical models significantly enhanced imatinib efficacy and reduced the burden of LSCs (16).
Our findings corroborate these observations in an Indian clinical cohort. Patients showed elevated baseline TNF-α levels, which significantly declined after 5–7 months of imatinib treatment, aligning with a decrease in BCR-ABL1 transcript levels. These results highlight the interplay between cytokine modulation and molecular response and suggest that TNF-α dynamics may be a surrogate marker for disease burden and therapeutic effectiveness.
The correlation between TNF-α and hematologic recovery parameters (e.g., WBC, platelets, and neutrophils) further supports its involvement in the disease’s inflammatory milieu and its resolution with therapy. Elevated TNF-α has been associated with poor prognosis and resistance in hematologic malignancies, including AML and multiple myeloma (17, 18), while in CML, increased TNF-α has been linked with advanced phases and reduced survival (19).
From a translational perspective, ELISA-based TNF-α quantification offers a cost-effective and accessible monitoring tool, particularly in low-resource settings where frequent molecular testing may not be feasible. In such environments, TNF-α could be an early warning indicator of suboptimal response or resistance, prompting timely therapeutic adjustments.
Despite these promising findings, several limitations must be acknowledged. The study focused exclusively on chronic-phase CML patients undergoing first-line imatinib therapy. The behavior of TNF-α in accelerated or blast phase disease, or in patients treated with second-generation TKIs, remains to be investigated. Furthermore, while statistical associations between TNF-α reduction and treatment response were evident, mechanistic causality cannot be inferred from this study alone.
This study focused exclusively on TNF−α as a single pro−inflammatory cytokine with strong mechanistic support in CML biology. Other cytokines, including IL−6, IL−1β, IFN−γ, and MCP−1, were not evaluated because of financial and logistic limitations of an extended panel of cytokines. As a result, we cannot definitively conclude whether TNF−α is uniquely informative or primarily reflects global inflammatory resolution with therapy. Alternatively, TNF-A may play a synergistic or complex interrelated interactions with other cytokines that need further studies. Future work incorporating multiplex cytokine profiling will be required to determine the specificity and incremental value of TNF−α in comparison to a broader inflammatory panel.
Future research should aim to validate these findings in larger, multi-center cohorts and explore whether persistent TNF-α elevation predicts relapse or treatment failure. Additionally, the potential therapeutic utility of combining TKIs with TNF-α inhibitors (e.g., etanercept or infliximab) warrants further investigation, as preclinical studies have suggested synergistic benefits in overcoming resistance and eradicating LSCs (16, 20).
5 Conclusion
Our study highlights the potential of TNF-α as a clinically relevant biomarker in chronic-phase CML patients receiving imatinib therapy. The observed post-treatment decline, coupled with its persistence in poor responders, emphasizes its dual utility as both a pharmacodynamic and prognostic marker.
TNF-α levels may serve as a supplementary biomarker complementing BCR-ABL1 monitoring. This exploratory study demonstrates TNF-α dynamics parallel treatment response but cannot replace qRT-PCR-based molecular monitoring as the gold standard. In resource-limited settings where molecular testing is unavailable, TNF-α measurement could provide supplementary prognostic information, though clinical utility requires prospective validation.
Statements
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by INSTITUTIONAL ETHICS COMMITTEE OF NATIONAL INSTITUTE OF PATHOLOGY, (ICMR). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
GJ: Data curation, Investigation, Writing – original draft, Writing – review & editing. SS: Methodology, Writing – review & editing. HD: Methodology, Validation, Writing – review & editing. NK: Data curation, Project administration, Writing – review & editing. PK: Data curation, Investigation, Writing – review & editing. MK: Data curation, Investigation, Writing – review & editing. RK: Formal analysis, Software, Writing – review & editing. BN: Supervision, Writing – review & editing. SZ: Supervision, Writing – review & editing. AdJ: Supervision, Writing – review & editing. AnJ: Supervision, Writing – review & editing. SC: Supervision, Writing – review & editing. PT: Supervision, Writing – review & editing. AS: Supervision, Writing – review & editing. AM: Conceptualization, Project administration, Supervision, Writing – review & editing. BR: Project administration, Supervision, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. ICMR-Extramural Funding.
Acknowledgments
The authors sincerely thank the Department of Hematology at Vardhman Mahavir Medical College (VMMC) and Safdarjung Hospital for their invaluable collaboration and support. We are especially grateful for their assistance with patient sample collection and meticulous data curation, which was essential for the successful completion of this study.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frhem.2026.1709697/full#supplementary-material
Supplementary Table 1(Excel file) provides detailed patient data, including sample information, molecular parameters, and other relevant variables supporting the analyses presented in this study. This file is available online alongside the published article.
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Summary
Keywords
BCR-ABL1, chronic myeloid leukemia (CML), cytokine biomarker, ELISA, imatinib, qRT-PCR, TNF-α, tyrosine kinase inhibitor (TKI)
Citation
Joseph GT, Swain SK, Dhanda H, Kushwaha N, Kumari P, Khushalani MP, Kumar R, Noor B, Zaheer S, Jain A, Jain A, Chaudhry S, Tanwar P, Singh A, Rishi B and Misra A (2026) Evaluating role of TNF-α in tyrosine kinase inhibitors-treated chronic myeloid leukemia. Front. Hematol. 5:1709697. doi: 10.3389/frhem.2026.1709697
Received
20 September 2025
Revised
06 January 2026
Accepted
21 January 2026
Published
12 February 2026
Volume
5 - 2026
Edited by
Andrea Duminuco, Gaspare Rodolico Ospedale, Italy
Reviewed by
Amar Kumar, University of Illinois Chicago, United States
Priya Sriskandarajah, Guy’s and St Thomas’ NHS Foundation Trust, United Kingdom
Updates
Copyright
© 2026 Joseph, Swain, Dhanda, Kushwaha, Kumari, Khushalani, Kumar, Noor, Zaheer, Jain, Jain, Chaudhry, Tanwar, Singh, Rishi and Misra.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Bhavika Rishi, bhavz83@gmail.com; Aroonima Misra, dr.aroo.2402@gmail.com
These authors share first authorship
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.