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Abstract

Background

IDH1 mutations are common in many cancers, however, their role in promoting the Warburg effect remains elusive. This study elucidates the putative involvement of mutant-IDH1 in regulating hypoxia-inducible factor (HIF1-α) and Sine-Oculis Homeobox-1 (SIX-1) expression.

Methodology

Genetic screening was performed using the ARMS-PCR in acute myeloid leukemia (AML), brain, and breast cancer (BC) cohorts, while transcript expression was determined using qPCR. Further, a meta-analysis of risk factors associated with the R132 mutation was performed.

Results

Approximately 32% of AML and ∼60% of glioma cases were mutants, while no mutation was found in the BC cohort. ‘AA’ and TT’ were associated with higher disease risk (OR = 12.18 & 4.68) in AML and had significantly upregulated IDH1 expression. Moreover, downregulated HIF1-α and upregulated SIX-1 expression was also observed in these patients, suggesting that mutant-IDH1 may alter glucose metabolism. Perturbed IDH1 and HIF-α levels exhibited poor prognosis in univariate and multivariate analysis, while age and gender were found to be contributory factors as well. Based on the ROC model, these had a good potential to be used as prognostic markers. A significant variation in frequencies of R132 mutations in AML among different populations was observed. Cytogenesis (R² = 12.2%), NMP1 mutation status (R² = 18.5%), and ethnic contributions (R² = 73.21%) were critical moderators underlying these mutations. Women had a higher risk of R132 mutation (HR = 1.3, P < 0.04). The pooled prevalence was calculated to be 0.29 (95% CI 0.26-0.33, P < 0.01), indicating that IDH1 mutations are a significant prognostic factor in AML.

Conclusion

IDH1 and HIF1-α profiles are linked to poor survival and prognosis, while high SIX-1 expression in IDH1 mutants suggests a role in leukemic transformation and therapy response in AML.

Plain Language Summary

IDH1 mutations are common in many types of cancer, but scientists have not fully understood how they contribute to the Warburg effect - a process that alters glucose metabolism in cells. In this study, we evaluate the association between mutant-IDH1 and HIF1 as well as SIX-1 gene expression. We analyzed genetic data from patients with brain cancer, breast cancer, and acute myeloid leukemia (AML), and found that roughly 32% of AML cases and 60% of glioma cases had IDH1 mutations, while no mutations were found in breast cancer. Patients with mutant genotypes had a higher risk of disease and showed upregulated IDH1 expression. They also had downregulated HIF1 and upregulated SIX-1 expression, suggesting that mutant-IDH1 can change glucose metabolism in cancer cells. Patients with abnormal IDH1 and HIF1 levels were more likely to have a poor prognosis. Further, we identified several risk factors that can influence IDH1 mutations, including cytogenesis, NMP1 mutation status, and ethnicity. The researchers calculated that IDH1 mutations are a significant factor in predicting outcomes for AML.

Keywords

R132 IDH1 HIF1 SIX1 metabolism stem cells metanalysis NPM1 ethnicity

Background

Isocitrate dehydrogenases (IDHs) are mitochondrial or cytosolic enzymes that help in oxidative decarboxylation of isocitrate into α-ketoglutarate (KG) during glucose metabolism. Through this process, NAD/NADP+ is reduced into NADH/NADPH.

Isocitrate + NAD(P) ------> α-ketoglutarate + NAD(P)H

Since the synthesis of NADPH is catalyzed by IDHs, aberrant IDH expression may partially be responsible for the cellular oxidative damage. NADPH is not only an essential cofactor to maintain GSH (reduced glutathione) turnover in the cells through the activities of glutathione reductase, but also important for the activities of NADPH-dependent thioredoxin system.¹

In humans, five IDH genes are reported namely IDH1, IDH2, IDH3A, IDH3B, and IDH3G², which give rise to 3 catalytic isozymes IDH1, IDH2, and IDH3. Cells that are deficient in IDH1 proteins exhibit pronounced oxidative damage upon exposure to free radicals and reactive oxygen species (ROS)^3,4 suggesting its potential role in maintaining cellular redox state.⁵

Malignant tumors undergo metabolic reprogramming to overcome the energy barrier, hence considerable alterations in glycolysis under normoxic conditions are expected to maintain cellular bioenergetics. Members of the IDH1 family are crucial in transducing these activities. The aerobic glycolysis, commonly called the Warburg effect, is considered to bring a characteristic shift in cellular respiration. Implications of IDH1 mutations to promote the Warburg effect in association with other glycolytic genes like hypoxia-inducible factor (HIF1-α) and sine-oculis homeobox (SIX-1) remained largely unattended.

Propyl hydroxylase domain-containing proteins (PHDs), which are α-KG-dependent dioxygenases, can destabilize HIF1-α through post-translational proline hydroxylation. The IDH1 mutation at position 132 may disrupt HIF1-α levels, but this remains largely unexplored. Reports on HIF1-α expression in acute myeloid leukemia (AML) are inconsistent, likely due to its non-solid tumor nature. However, hypoxia-induced SIX-1 expression in gliomas,⁶ suggests a potential link between SIX family of transcription factors and IDH1 regulation.⁷ While SIX-1’s direct involvement in IDH1 expression regulation remains unknown, SIX-1 does regulate the transcription of Warburg genes like glucose transporter-1 (GLUT1), phosphofructokinase (PFK), enolase-1 (ENO1), PKM, aldolase A (ALDOA), and phosphoglycerate kinase-1 (PGK1).⁸ Recently, HIF1-α was shown to upregulate SIX-1 expression by suppressing miR-548a, enhancing glycolysis and tumor cell growth in breast cancer.⁹ Notably, SIX-1 is involved in leukemia stem cell (LSC) maintenance,¹⁰ and IDH1 mutations have been found in preleukemic hematopoietic stem cells (preL-HSC)¹¹ propagating clonal expansion. This suggests that co-overexpression of IDH1 and SIX-1 may drive clonal hematopoiesis and leukemic transformation under hypoxia. Analyzing the profiles of these critical genes will provide valuable clinical insights for effective therapy.

Cytogenetically, the IDH1 gene is located on chromosome-2 at q33.3 from 208,236,227 base pair (bp) to 208,255,143 bp (GRCh38.p12). A mutation of arginine “R” at position 132 of IDH1 into histidine “H” or serine “S “will result in the formation of 2-hydroxyglutarate (2HG) instead of α-ketoglutarate (αKG).^12,13 2HG competitively inhibits αKG disturbing αKG-dependent dioxygenase activities¹⁴ of the TET and Jumanji-C (JmjC) family of demethylases.^15,16 These somatic mutations affect cell division and promote tumorigenesis.^12,17

IDH1 is reported 75% mutated in glioblastoma multiform (GBM) tumors and in ˜20% of AML cases,¹⁵ but rarely in prostrate¹⁸ and lung cancer.¹⁹ However, they are not common in breast carcer. Several reports have indicated an increased risk of AML following adjuvant chemotherapy for breast and brain cancers.^20-22 Frequently administered adjuvant chemotherapy includes alkylating agents like Temozolomide. While these treatment regimens may improve survival outcomes, they can also induce metabolic stress in IDH1-mutant cancers,²³ which may lead to treatment-induced AML in survivors. Due to the clinical significance of AML, brain and breast cancers, the current study set out to analyze IDH1 mutations to investigate a potential metabolic link triggering oncogenic transformations in these cancers. The current study investigates the status of two IDH1 mutations at position R132 L (c.395 G>T) and R132H (c.395 G>A) in breast, brain, and AML cancers in the local Pakistani population. Moreover, an association of IDH1 mutation in various clinicopathological attributes of AML as well as HIF1-α and SIX-1 was also analyzed to evaluate its diagnostic potential and impact on overall prognosis in AML.

Methodology

Sample Collection and DNA Extraction

The study was conducted with prior approval from the Ethical Review Board (ERB) of COMSTAS University (CUI) Islamabad via the following approval numbers: CUI/Bio/ERB/5-21/24 for Acute Myeloid Leukemia (AML), CIIT-09-10-14 & CUI/Bio/ERB/2017/20 for Breast Cancer (BC) as previously reported in Ref. 24, and CUI/Bio/ERB/2017/21 for Brain cancer (samples received from Prof. Ishrat Mahjabeen). The AML cohort included 79 cases and 50 healthy controls, while the breast cohort consisted of 30 BC tumors along with their adjacent normal controls (ANCT), and the brain tumor cohort was comprised of 10 glioma cases along with their blood controls. All participants were from local subcontinent ethnicity. Most of the AML cases recruited were Punjabi, while 4 cases were from Pashtoon backgrounds. Cases were recruited from the Pakistan Institute of Medical Sciences (PIMS) Islamabad and Jinnah General Hospital Lahore, Pakistan. A written informed consent was obtained from every patient, before sample collection. Biopsy samples were collected in RNAlater® stabilization solution (Thermo Fisher, Waltham, USA) at the time of surgery and blood samples were collected in EDTA (Ethylenediaminetetraacetic acid) tubes and stored at −20°C until further use.

∼150-200 mg tissue and 0.5-1 ml blood were used to extract genomic DNA using the standard phenol-chloroform method.²⁵ The quantity and quality of extracted DNA were checked by gel electrophoresis and spectrophotometry respectively.

Mutation Profiling of IDH1 Gene

To screen reported IDH1 SNPs (rs121913500) from G>A and G>T) primers for tetra-ARMS (tetra-primed Amplification-refractory mutation system) PCR (polymerase chain reaction) were designed (Table S1).

A PCR reaction was performed in 20ul reaction volume with a 1:1 ratio of inner and outer primers (0.3pmol/uL). FirePol® PCR mix (Solisbiodyne Cat# 04-12-00S15) with 1.5 M MgCl₂ concentration and ∼100 ng/ul of genomic DNA was used as a PCR template. The thermal cyclic conditions were a single cycle at 95°C for 1min (initial denaturation); followed by 35 cycles at 95°C for 30 sec (denaturation), 55°C for 45 sec (annealing), and 72°C for 1 min (extension); and a single cycle at 72°C for 10 min (final extension).

The amplified PCR products were analyzed on 2% agarose gel prepared in 1X TAE (Tris-Acetate-EDTA) and stained with ethidium bromide. 100 bp DNA marker was run along with PCR products to determine the band size. The amplified products were visualized and photographed by a gel documentation system (BioDocAnalyze-Biometra). The sequencing was performed from commercially available sequencers in Macrogen, Korea. Blast online tool (BioEdit version 7.0.5) was used to align and analyze the sequences. The results were analyzed statistically, and genetic as well as allelic frequencies were calculated, and risk analysis was performed for each mutation.

RNA Extraction and Real-Time PCR

RNA was extracted from tissue samples using TRIzol® reagent (Invitrogen, Carlsbad, CA Catalog # 15596026) as per the manufacturer’s instructions. RNA was stored at −80°C until further analysis. Afterwards, cDNA was synthesized using FIREScript RT cDNA synthesis KIT (Solis BioDyne, Tartu, and Estonia Catalog #06-15-00050). Quantitative PCR (qPCR) was performed using EvaGreen PCR Master Mix (Solis BioDyne, Tartu, Estonia Catalog # 08-24-00001). Primers were synthesized from Macrogen Korea. Gene-specific primers for IDH1, HIF1-α, SIX-1, and MKI67 (Marker of Proliferation Ki-67) were used to measure transcript expression, β-actin was used as an internal control for quantitative real-time PCR analysis using the Syber green dye base approach. The details of primer sequences are given in Table S1. qPCR was performed on Step One Plus Real-Time PCR system (Applied Biosystems). Thermo-cycler conditions were 95°C for 10 min (initial denaturation) followed by 40 cycles of 95°C for 15 seconds, 54°C for 60 seconds, and 72°C for 20 seconds with a final extension of 72°C for 1 min, and the results were interpreted using Δ ΔCT values. The relative expression of all the genes was assessed using Livak’s ∆∆Ct method.²⁶

Data Analysis

Statistical analysis was performed using ‘R-program’ version 4.2.2. For analysis of transcript profiles, expression data of the target genes (IDH1/HIF1-α/SIX-1/MKI67) were normalized against the internal control gene (β-actin). The correlation among different clinicopathological factors was assessed by the Pearson Correlation Coefficient test. Depending on the experiment, the statistical significance was determined on 95% confidence intervals (CIs) using the Mann-Whitney test, an analysis of variance (ANOVA), and specific comparisons were made by Tukey’s honest significant difference (HSD) test. The values of P < 0.05 were considered as significant. Risk analysis was performed by calculating ODD’s ratio (OR) for individual mutations and survival analysis was performed by Kaplan-Meier analysis. Univariate and multivariate analysis was also performed for risk stratification. Further, the predictive potential of molecular markers was determined by drawing the receiver operating characteristic (ROC) curve. Additionally, we conducted a power analysis to determine if our sample size was sufficient to provide adequate statistical power for this study. To assess the effect size, we calculated Cohen’s d value, which was used to determine the observed power in this analysis. This power analysis helped us evaluate whether our sample size was sufficient to detect the observed effect size with adequate statistical power.

Meta-Analysis

A literature search was done in PubMed and Science Direct databases to find relevant studies on IDH1 mutations in AML from 2010 to 2023. Databases were searched using multiple keywords like “Isocitrate dehydrogenase 1, IDH1, mutation(s), AML, c.395 G>A, R132H, c.395 G>T, R132 L, and R132”. Additional publications were identified by examining the bibliography from the pertinent articles. Eligible studies included primary research on IDH1 mutation in AML along with data on demographic and molecular attributes of the study cohort. Meta-analyses were conducted using R version 4.3.1 with the ‘dmetar’ and ‘meta’ packages and the Standard Mean Difference (SMD) was used to combine effect sizes. Cochran’s Q and I² statistics were used to assess heterogeneity and funnel plots and Egger’s test was used to assess the risk of publication bias.

Results

IDH1 Mutations Were More Frequent in AML and Brain Cohorts but not in BC

79 AML subjects along with 50 healthy controls; 30 BC cases along with their adjacent normal controls and 10 brain tumor subjects along with their blood controls were analyzed to screen 2 reported SNPs (R132 L (c.395 G>T) and R132H (c.395 G>A) in IDH1 gene. The demographic and clinic pathological attributes of the AML cohort are given in Table 1 and Table S2.

Table 1.

Demographic Attributes of AML Cohort.

	All cases (n = 79)	IDH1 (R132) Mutated (n = 25, 31.23%)		Wild Type (n = 54, 68%)	P Value
Variable		G>A (n= 18, 22.8%)	G>T (n= 7, 9%)
Mean Age (yr)	28.76	29.94 (6-60)	28.28 (24-36)	28.42 (6-60)	1.12E-14_(wilcox)
<40 (n = 69)	26	25.9	28.3	25.7
>40 (n = 10)	47	50		46.7
F/M ratio	39/40	9/9	2/5	28/26	0.6296_(Fisher)
FAB classification n(%)
M2	34 (30)	7 (9)	2 (3)	15 (19)	0.03242_(Fisher)
M3	27 (34)	2 (3)	2 (3)	23 (29)
M4	7 (9)	4 (5)	0 (0)	7 (9)
M5	11 (14)	4 (5)	0 (0)	6 (8)
M7	7 (9)	1 (1)	3 (4)	3 (4)
HBV/HCV Infection					0.6743_(Fisher)
Yes	3	0	0	3
No	76	18	7	51
Chemotherpy					0.5531_(Fisher)
Cytosar	22	7	1	14
Cytosar + ATRA	2	0	0	2
Cytosar + Doxorubicin	7	0	1	6
Cytosar + Etoposide	7	3	0	4
ATRA + Daunorubicin + Cytosar	4	1	0	3
Induction Therapy					0.5231_(Fisher)
Yes	70	17	7	46
No	9	1	0	8
Other Molecular Feature (mean ± SD)
IDH1 expression	2.52 ±1.40	1.91 ± 0.67	1.05 ± 0.57	2.92 ± 1.47	2.11E-13_(wilcox)
HIF1-ΑExpression	0.008 ± 0.06	2.14e-05 ± 3.0 e-05	0.08 ± 0.002	0.001 ± 0.002	1.17E-14_(wilcox)
SIX-1 expression	1.75 ± 1.8	1.18 ± 0.86	1.52 ± 1.6	1.97 ± 2.13	1.17E-14_(wilcox)
MKI67 expression	10.24 ± 3.035	9.98 ± 2.94	9.67 ± 2.96	10.41 ± 3.12	1.17E-14_(wilcox)

Two allelic mutations (R132) in the IDH1 gene at c.395 (G>T/A) were analyzed in study cohorts using the ARMS-PCR. The mutation-specific amplicon of 291bp and 288 bp for the ‘T’ and ‘A’ allele respectively were observed in AML and brain tumors but not in the BC cohort (Figure 1A). The absence of mutated alleles in BC was further validated through Sanger sequencing.

Figure 1.

Mutation analysis of IDH1 c.395 (G>T/A) in AML, brain, and BC cohorts. (A) 2% agarose gel resolving tetra-ARMS-PCR products. Arrowheads represent the presence of relevant mutant alleles as marked above the wells; (B) bar chart representing genotypic frequencies; (C) forest plot of odds ratio (OR) associated with IDH1 genotypes. Homozygous ‘AA’ was found to have higher disease risk followed by the ‘TT’ genotype in AML; (D) Relative IDH1 expression on log2 scale among various genotypic groups of the AML study cohort.

Frequency of ‘GA’ and ‘GT’ genotypes in the AML cohort was found to be ∼12.8% and ∼5.1% respectively, whereas ∼10% and ∼4% cases were homozygous mutants for ‘AA’ and ‘TT’ genotypes respectively (Table S3, Figure 2B). In brain cancer, 60% of cases were homozygous for ‘AA’ and 20% were heterozygous for the ‘GA’ genotype, while no cases for ‘TT’ or ‘TA’ genotype were observed.

Figure 2.

Expression comparison among various study cohorts. (A) expression profiles of IDH1, HIF1-α, SIX-1, and MKI67 (KI67) in AML study cohort; (B) Mean comparison using Tukey test; (C) box plot of IDH1, HIF1-α, and SIX-1 expression among IDH1 genotypes.

Consistently, significantly different IDH1 expression was observed in AML compared to the control cohort. Patients with the ‘AA’ and ‘GA’ genotypes have higher expression compared to “TA’ and ’TT’ (Figure 1D). Homozygous ‘AA’ was found to have the highest odds ratio (OR) of 12.18, followed by homozygous ‘TT’ (OD = 4.68) highlighting a high disease susceptibility associated with these genotypes. However, heterozygous mutants (GA/GT) were observed to have lesser associated risk compared to homozygous mutants (Figure 1C). The “A’ allele had a significantly high relative allele risk (P < 0.05) assuming dominant (OR = 7.2) and additive models (OR = 10.57). The recessive model however was not significant despite having a high OR = 12.17 (Table 2). On the other hand, for G>T transition all 3 models were found to have ∼ 3-fold increase in disease risk.

Table 2.

Genotype Relative Risk Models for IDH1 Mutations in AML.

		Odds Ratio	95 % CI	z statistic	Significance Level
		Odds Ratio	95 % CI	z statistic	Significance Level
rs121913500 G>A
Dominant	AA + GA Vs GG	7.2	1.5915 to 32.5728	2.563	0.0104
Recessive	AA Vs GA + GG	12.1773	0.6870 to 215.8513	1.704	0.0884
Additive	A Vs G (AA Vs GA Vs GG)	10.5763	2.4482 to 45.6896	3.159	0.0016
rs121913500 G>T
Dominant	TT + GT Vs GG	3.1304	0.6475 to 15.1354	1.419	0.1558
Recessive	TT Vs GT + GG	4.6821	0.2367 to 92.6044	1.014	0.3107
Additive	T vs G (TT vs GT Vs GG)	4.0833	0.8941 to 18.6479	1.816	0.0694

IDH1 and SIX-1 Expression was Found Upregulated while HIF1-α was Downregulated in AML Cohorts

Our analysis revealed that IDH1 and SIX-1 expression profiles were significantly upregulated in the AML cohort compared to controls, while HIF1-α expression was downregulated (Figure 2A). This suggests a potential interplay between these genes in regulating the tumor microenvironment and modulating energy metabolism. Notably, elevated SIX-1 and IDH1 levels may impact patient responses to therapy and drive clonal hematopoiesis. Consistent with this, we observed higher IDH1 expression in patients undergoing induction therapy, which was also associated with elevated SIX-1 expression (Supplementary Figure 1). This further supports our hypothesis. Additionally, we found a statistically significant upregulation in the expression of the proliferative marker gene MKI67 (KI67) in the AML cohort, reflecting the aggressive behavior of these tumors.

Subsequently to elucidate significant differences among expression profiles of IDH1 (overall, mutated, and wild type), HIF1-α, SIX-1, and MKI67 post hoc analysis was performed using Honest Significant Difference (HSD)/Tukey test. A significant difference in mean expression was observed among SIX-1-IDH1, SIX-1-HIF1-α, IDH1-HIF1-α, IDH1(M)-HIF1-α as well as mutated and wild-type IDH1 groups (Figure 2B). These results indicate that mutations in IDH1 genes may affect glucose metabolism in AML. The expression trend of IDH1, HIF1-α, and SIX-1 among IDH1 genotypes are shown in Figure 2C.

Deregulation of IDH1 and HIF1-α is Associated with Poor Survival Outcomes in AML

A Pearson’s correlation analysis was performed to evaluate the association among various molecular, demographic, and clinico-pathological attributes of the study cohort. IDH1 expression had a strong negative association with R132 mutation status while gender, staging had a moderately negative association. HIF1-α and SIX-1 were positively associated with IDH1 expression but had a negative association with IDH1 mutations (Figure 3A). Kaplan-Meier (log-rank) analysis showed poor overall survival (OS) among patients with downregulated IDH1 (median OS: 976 vs 1306 days) and HIF1-α (median OS: 1206 vs 1338 days) expression profiles (P < 0.003 and 0.007 respectively) (Figure 3B & C). In the multivariate Cox regression model too, upregulated IDH1 and HIF1- α showed significantly good prognosis (−0.99 and −1.03, Figure 3D), while gender and age were also found to be the contributory factors towards overall prognosis in AML.

Figure 3.

Survival outcome and ROC curve for IDH1 status in AML. (A) pearson’s correlation among various molecular and demographic attributes of the cohort; (B-C) Kaplan-Meier analysis for IDH1, HIF1-α expressionrespectively; (D) Cox regression models; (E-G) ROC curve for IDH1, HIF1-α, and SIX-1 expression.

IDH1 profiles showed better capabilities in predicting AML phenotypes in ROC curve analysis Figure 3E). The partial area under the curve (AUC) within the range of 90–70% specificity was calculated as 84.7%, while the sensitivity of the predictor in this range was observed as 88.3%. In contrast, the AUC for HIF1-α and SIX1 was ∼76 % and 71 % respectively (Figure 3F-G), highlighting their good potential to be used as a prognostic marker.

Demographic and Genetic Factors may Contribute to IDH1-R132 Mutations in AML

13 studies with a total of 2388 subjects were included in the meta-analysis (Table S4). Five studies originated from Europe,^27-31 4 from Asia,^32-35 and 4 from the United States.^36-39 The cumulative frequency of IDH1 R132 mutations was 9.6 % varying between 4.3–12.4%.

To evaluate any potential publication bias among the studies funnel plot was generated which showed no asymmetry (Figure 4A). Eggers’ tests also validated this with an observed ‘t’ value close to zero indicating the absence of potential publication bias (t = 0.52 P = 0.61; Figure 4B).

Figure 4.

Meta-analysis of IDH1 mutations in AML. (A) Funnel plot to study publication bias; (B) Results of Egger’s test; (C) GOSH plot for heterogeneity patterns; (D) Forest plot of IDH1 Mutation incidence among reported studies; (E) Identification of influential studies.

The between-study heterogeneity variance was estimated at ^{^}τ² = 0.002 (95%CI: 0.0003-0.007), with an I² value of 62.4% (95% CI: 28–80%). The prediction interval ranged from g = 0.18 to 0.4, indicating that minute ethnic and other demographic effects cannot be ruled out for genetic analysis. Patterns of heterogeneity were also analyzed by plotting Graphic Display of Heterogeneity (GOSH) plots (Figure 4C).

Using a random effect model effect sizes of R132 mutation frequency were observed. The incidence varied from 11% to 55%. The variation among effect sizes can be attributed to ethnic and other demographics of the population. The pooled prevalence of 0.29 (95% confidence interval (CI) 0.26-0.33, P < 0.01) indicates that IDH1 mutations are a significant prognostic factor in AML patients. (Figure 4D). Further 2 influential studies (Wagner K, 2010; Marucci G,2010) responsible for this heterogeneity were identified. Removal of outliers resulted in I² = 12% (Figure 4E).

The Frequency of IDH1 Mutations Differs Across Various Geographical Regions

Subgroup analysis was performed to understand the nature of heterogeneity and to perform risk stratification among various factors like geographic location, age groups, Nucleophosmin-1 (NPM1) mutation status, and cytogenetics (normal or abnormal karyotyping, Figure 5A–C). Studies of European origin showed a greater effect size compared to American and Asian studies (Figure 5A), highlighting ethnic contributions in acquiring IDH1 mutations. The moderator analysis presented in Table 3 suggests that ∼ 26. 13% of the variability in our data can be attributed to between-study heterogeneity, while the remaining ∼36% observed heterogeneity can be explained by ethnic contributions. This is also evident from the high R² value of 73. 21%, that ethnicity is an important risk factor. Among other factors, cytogenetic outcome (R2 = 12.21%) and NPM1 mutation status (R2 = 18.57%) may also influence R-132 mutation frequency (Table 3).

Figure 5.

Regression analysis of demographic and molecular factors on IDH1 mutation frequency in AML. (A-C) Impact of ethnic, age, and cytogenic contributions in IDH1 mutations; (D) Forest plot of gender-associated risk ratios in AML.

Table 3.

Test of Heterogeneity and Effect of Moderator on IDH1-R132 Mutation.

	Test of Heterogeneity Random Effects Model (HK)						Moderator Analysis
	K = 12;	Q = 29.23;	P = 0.0021;	I² = 62.40%	CI = 29.7-79.9	^{^}τ² = 0.002
Sub Groups	g	95%CI	P	I²	95%CI	Psubgroup	τ²_unexplained	I²_explained	R²	P
Cytogenic findings						0.19	0.018	57.43	12.21	0.26
Normal	0.31	0.26-0.35	1.2.E−07	0.62	0.21-0.82
Abnormal	0.55	0.38-0.72	1.0.E−01	0.42	0.29-0.79
Region						0.0014	5E-04	26.13	73.21	0.033
Asian	0.27	0.18-0.35	2.0.E−03	0.4	0.00-0.79
European	0.35	0.30-0.39	2.8.E−05	0	0.00-0.78
American	0.25	0.15-0.36	9.5.E−03	0.38	0.00-0.80
Age						0.39	0.002	63.00	0.00	0.49
<50	0.31	0.22-0.40	1.45.E−03	0.49	0.00-0.83
>50	0.29	0.23-0.34	1.25.E−05	0.71	0.36-0.86
NPM1 Mutation Status						0.008	0.002	60.00	18.57	0.006
High	0.28	0.23-0.33	8.61.E−06	0.71	0.36-0.86
Low	0.35	0.27-0.43	2.69.E−03	0	0.00-0.89

Eight studies reported the distribution of IDH1 mutation frequency among gender-based cohorts. To analyze any gender-specific risk associated with these mutations, the hazard ratio (HR) was calculated. Common as well as random-effects model was used to analyze gender risks. There was no between-study heterogeneity (I² = 0%; P = 0.68). The combined analysis from both (Random and fixed) models showed significant risk associated with female gender (HR = 1.34, 95% CI: 1.01-1.78; P = 0.04; Figure 5D). This indicates that the gender composition of the population may increase the risk of AML with R132 mutations.

Discussion

Isocitrate dehydrogenase-1 is an NADP-dependent enzyme that carries out the reductive carboxylation of isocitrate. IDH1 produces α-ketoglutarate from isocitrate while reducing NADP to NADPH. Hence, this enzyme protects the cell from oxidative damage by producing NADPH in the cytosol. The α-KG produced by IDH1 acts as a co-factor of many important enzymes including the TET (ten-eleven translocation) family of proteins.^15,16 It has been reported that the IDH1 enzyme gains neomorphic activity due to mutations in the active site of the enzyme. The mutant IDH1 produces an oncometabolite R-2HG instead of α-KG. The mutations in exon-4 (codon 132) of IDH1 have been reported commonly in several cancers.

Unlike other cancers, IDH1 mutations have been reported rarely in breast cancer and to date, only 3 studies have reported these mutations. The first one was the case carrying the IDH1-R132 C mutation (The Cancer Genome Atlas, n.d.). It had combined features of both invasive mucinous and ductal carcinoma however, the receptor status for the patient was not available. The second case of breast cancer harboring IDH1 mutation was reported by Ang et al, (2012). They reported a patient with concurrent invasive lobular and papillary carcinoma that had IDH1-R132H mutation.⁴⁰ Fathi (2014) reported the third case of HR-positive breast adenocarcinoma harboring IDH1-R132 L mutation. The patient subsequently developed metastatic breast cancer as indicated by high 2-HG levels in serum and urine.⁴¹ Whereas numerous other studies with larger sample sizes don’t report IDH1 mutations in breast cancer. In the current study, no mutation at c.395 in the IDH1 gene was observed in breast cancer cohorts, while brain tumor and AML study cohorts showed point mutations at this position. These findings highlight a cancer-specific incidence of IDH1 mutations.

Consistent with earlier reports ‘A’ allele was more prevalent in both brain and AML cohorts than the ‘T’ allele. This indicates that the transition of a purine base with another purine (G>A) is a more common and convenient method of genome alteration in various cancers than transversion ie, replacement of a purine with a pyrimidine (G>T) base. Since chemical mutagens can easily cause base analogs that may lead to mutagenesis.

Due to significant heterogeneity in AML and the complexity of several molecular factors the impact of IDH1 mutations on AML prognosis remained contentious.^42,43 Thus, besides the availability of several treatment protocols, patients’ responses to therapy remain largely unpredictable.⁴⁴ Our metanalysis findings revealed that R132 mutations alone can’t predict prognostic outcomes rather factors like ethnic contribution, age, gender, NPMI mutations, and abnormal karyotyping are also crucial risk factors, implicating the overall survival of AML patients. This suggests that the impact of IDH1 mutations is context-dependent and co-occurrence with NPM1 mutations,⁴⁵ and O-6-Methylguanine-DNA Methyltransferase (MGMT) methylation^46,47 appears to confer more favorable outcomes. Clinical analysis performed on AML patients in the current study suggests that perturbed IDH1 levels accompanied by impaired HIF1- α status may result in poor survival outcomes for patients, while SIX-1 upregulation is associated with good prognosis in AML. The post-hoc power analysis (Supplementary Figure 2) revealed an observed statistical power of approximately 1, indicating a very high likelihood of detecting the effect as statistically significant.

As cancer cells exhibit increased glucose uptake to fulfill elevated energy demands of the proliferating cells, it results in considerable alterations in cellular bioenergetics. Aerobic glycolysis (Warburg effect) is considered a characteristic shift in cellular respiration⁴⁸ The altered IDH1 profiles may perturb the expression of other glycolytic genes like HIF1-α and SIX-1 affecting the overall prognosis of AML.

PHD domain-containing proteins, which are α-KG-dependent dioxygenases, destabilize HIF1-α through post-translational proline hydroxylation. In AML, mutated IDH1 may disrupt HIF1-α levels, but the exact impact of IDH1 mutations on HIF1-α expression has been unclear. While previous studies showed that IDH1 mutations are associated with elevated HIF1-α levels in glioma,⁴⁹ recent reports have suggested that IDH1 mutations may reduce HIF1-α expression by activating EGLN1.⁵⁰ This finding was supported by an analysis of 288 anaplastic glioma cases.⁵¹ Our findings in AML patients corroborate this hypothesis, providing further evidence for the complex relationship between IDH1 mutations and HIF1-α expression.

SIX-1, a transcription factor of the Sine oculis homeobox (Six) family, is a development-related gene. It has been linked to the transformation of normal hematopoietic progenitors into leukemia. Its role in maintaining leukemia stem cells (LSC) has been shown recently.¹⁰ It has been reported to be upregulated in numerous cancers including AML¹⁰ implicating poor prognosis in these patients. Interestingly, aberrations in the IDH1 gene, among others, have also been reported in premalignant disorders, including myelodysplastic syndromes (MDS),⁵² and have been shown to drive transformation and clonal expansion of pre-leukemic hematopoietic stem cells (preL-HSC).¹¹

Notably, mutant IDH1 in AML exhibits resistance to chemotherapy and IDH inhibitors (IDHi).^52-54 Our study reveals elevated expression of SIX-1 and IDH1 in AML, considering the role of SIX1 in LSC maintenance elevated SIX-1 levels in IDH-mutant cells may drive clonal hematopoiesis, maintaining a pool of stem cells resistant to conventional chemotherapy. This implies a need for alternative therapeutic strategies in these patients. Our findings show increased SIX-1 expression in patients with elevated IDH1 expression (see Supplementary Figure 1). Moreover, elevated IDH1 expression was observed in patients undergoing induction therapy, implying a contribution to chemoresistance development in AML blast cells. However, further investigation in vitro is required to confirm this.

The heterogeneity of AML blasts, comprising a mix of cells with varying potencies, makes it unlikely that any single therapeutic regimen will be effective. However, the ability of SIX-1 to discriminate AML occurrence as a binary classifier offers a promising approach to understanding the developmental plasticity of these blasts. As recently suggested,⁵⁵ by identifying the specific myelopoietic pathway indicated by quantitative SIX-1 expression, targeted therapies can be developed to exploit this differentiation tendency, potentially inducing differentiation and reducing the leukemic burden. Further research is crucial to explore the quantitative and lineage-specific potential of SIX-1 and IDH1 in guiding personalized AML treatment strategies.

SIX-1 has been reported to regulate the Warburg effect in human malignancies,⁹ however, its regulation remained unclear. Recent studies have shown that SIX-1 expression can be induced by hypoxia⁶ and our results highlight an interaction between SIX-1 and HIF1-α, which is negatively associated with SIX-1 expression. Moreover, IDH1 upregulation has been linked to SIX4 expression profiles,⁷ indicating that SIX family proteins regulate IDH1 expression. Whether SIX-1 is also regulating IDH1 remained still unclear. Our results suggest a positive correlation between IDH1 and SIX-1 in AML, implying a potential regulatory role for SIX-1 in IDH1 expression.

Current study illuminates the clinical significance of IDH1 mutations in AML patients, identifying key molecular and demographic factors that shape overall prognosis. Unlike breast cancer, where IDH1 mutations are rare, they are more prevalent in AML. The association of IDH1 with SIX-1 and HIF1-α in AML patients offers a novel insight on the clinical landscape and may influence treatment outcomes. Notably, IDH1 and HIF1-α are linked to poor prognosis, whereas SIX-1 is not. Our meta-analysis reveals that R132 mutations in IDH1 alone are insufficient to predict prognostic outcomes in AML, and that co-occurring factors like NPM1 mutations, MGMT methylation, and ethnic contribution are also crucial considerations. This study highlights the context-dependent impact of IDH1 mutations and underscores the need for further research into the regulation of IDH1 expression and its interplay with other molecular factors.

We acknowledge that our study’s findings are based on a cross-sectional and retrospective design with a limited sample size, necessitating validation through larger cohorts and longitudinal analyses. Monitoring breast and brain cancer cases with IDH1 mutations over time could evaluate the incidence of AML following specific chemotherapeutic treatments. Moreover, our study’s geographic scope is limited, potentially restricting its representation of diverse populations. Furthermore, the absence of data on MGMT methylation status and NPM1 mutation status limits the study’s ability to fully explore the clinical implications of IDH1 mutations. However, our findings warrant further investigation in tissue culture models expressing IDH1 mutants to confirm their prognostic potential and elucidate their association with SIX-1 in propagating leukemic stem cells (LSCs) and contributing to chemoresistance. Future studies should address these limitations to provide a more comprehensive understanding of IDH1 mutations in AML.

Supplemental Material

Supplemental Material - Aberrant HIF1- α and SIX1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations

Supplemental Material for Aberrant HIF1- α and SIX1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations by Tariq Ali, Rohma Usman, Syed Alasar Shah, Aamir Parvez, Summayya Anwar, Zahid Muneer, and Muhammad Saeed in Cancer Control

Footnotes

Acknowledgments

The authors especially acknowledge and are thankful to all the participants as well as the supporting staff at Pakistan Institute of Medical Sciences (PIMS); Holy Family Hospitals Islamabad and Jinnah General Hospital Lahore, Pakistan. The authors are especially thankful to Dr Ishrat Mahjabeen for providing brain tissue samples and for her expert opinions and input to improve the quality of this work. The authors also express their gratitude to the members of the Cancer Genetics & Epigenetics Laboratory, Biosciences (CUI), Islamabad, Pakistan, for their kind support and cooperation. The authors are thankful to COMSATS University for providing infrastructure and partial funds to undertake this research.

Author Contributions

Tariq Ali (TA), Rohma Usman (RU): Performed the experiments; Syed Alasar Shah (SAS), Summayya Anwar (SA): performed meta-analysis; Aamir Parvez (AP) & Zahid Munir: Analyzed experimental data; Muhammad Saeed (MS). Funding, experiment design, analysis, paper writeup, Approved version to be published.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

ORCID iD

Muhammad Saeed

Data Availability Statement

All data generated or analyzed during this study are included in this article and supplementary information.*

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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Aberrant HIF1- α and SIX-1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations

Abstract

Background

Methodology

Results

Conclusion

Plain Language Summary

Keywords

Background

Isocitrate + NAD(P) ------> α-ketoglutarate + NAD(P)H

Methodology

Sample Collection and DNA Extraction

Mutation Profiling of IDH1 Gene

RNA Extraction and Real-Time PCR

Data Analysis

Meta-Analysis

Results

IDH1 Mutations Were More Frequent in AML and Brain Cohorts but not in BC

IDH1 and SIX-1 Expression was Found Upregulated while HIF1-α was Downregulated in AML Cohorts

Deregulation of IDH1 and HIF1-α is Associated with Poor Survival Outcomes in AML

Demographic and Genetic Factors may Contribute to IDH1-R132 Mutations in AML

The Frequency of IDH1 Mutations Differs Across Various Geographical Regions

Discussion

Supplemental Material

Supplemental Material - Aberrant HIF1- α and SIX1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations

Footnotes

Acknowledgments

Author Contributions

Declaration of Conflicting Interests

Funding

Ethical Statement

ORCID iD

Data Availability Statement

Supplemental Material

References

Supplementary Material