Skip to main content
Download PDF

IDH1 mutation is detectable in plasma cell-free DNA and is associated with survival outcome in glioma patients

BMC Cancer volume 24, Article number: 31 (2024) Cite this article

Abstract

Background

Circulating cell-free DNA (cfDNA, liquid biopsy) is a powerful tool to detect molecular alterations. However, depending on tumor characteristics, biology and anatomic localization, cfDNA detection and analysis may be challenging. Gliomas are enclosed into an anatomic sanctuary, which obstacles the release of cfDNA into the peripheral blood. Therefore, the advantages of using liquid biopsy for brain tumors is still to be confirmed. The present study evaluates the ability of liquid biopsy to detect IDH1 mutations and its correlation with survival and clinical characteristics of glioma patients.

Methods

Blood samples obtained from glioma patients were collected after surgery prior to the adjuvant therapy. cfDNA was extracted from plasma and IDH1 p.R132H mutation analysis was performed on a digital droplet PCR. χ2-test and Cohen k were used to assess the correlation between plasma and tissue IDH1 status, while Kaplan Meier curve and Cox regression analysis were applied to survival analysis. Statistical calculations were performed by MedCalc and GraphPad Prism software.

Results

A total of 67 samples were collected. A concordance between IDH1 status in tissue and in plasma was found (p = 0.0024), and the presence of the IDH1 mutation both in tissue (138.8 months vs 24.4, p < 0.0001) and cfDNA (116.3 months vs 35.8, p = 0.016) was associated with longer median OS. A significant association between IDH1 mutation both in tissue and cfDNA, age, tumor grade and OS was demonstrated by univariate Cox regression analysis. No statistically significant association between IDH1 mutation and tumor grade was found (p = 0.10).

Conclusions

The present study demonstrates that liquid biopsy may be used in brain tumors to detect IDH1 mutation which represents an important prognostic biomarker in patients with different types of gliomas, being associated to OS.

Peer Review reports

Background

Liquid biopsy recently emerged as a new approach to investigate the molecular profile of solid tumors by detecting gene alterations and obtaining potential prognostic and predictive biomarkers across different cancers [1,2,3]. Liquid biopsy has gained interest due to its advantages being a minimally invasive, sensitive, repeatable, and feasible alternative to tissue biopsy, having the ability to capture heterogeneity across multiple areas of tumors [2, 4,5,6]. The term “liquid biopsy” is mainly referred to the analysis of circulating free DNA (cfDNA) extracted from plasma, which contains a small amount of tumor DNA (ctDNA); however, this concept is also applied to different biological fluids such as blood, cerebrospinal fluid (CSF), urine, saliva, and to several analytes such as cell-free RNA, circulating tumor cells, extracellular vesicles (EVs), RNA and non-coding miRNA [7,8,9,10,11,12,13]. Nowadays, several national and international recommendations are available, regarding the use of liquid biopsy in clinical practice, which mainly refer to the use of plasma cfDNA, and suggest the use of alternative sources only in specific clinical trials and research studies [14, 15]. However, when using liquid biopsy, one of the major challenge is represented by the false negative results, mainly related to anatomic barriers and disease biological characteristics [16, 17]. While cfDNA has been detected in several types of cancers including breast, lung, pancreatic, melanoma and colorectal cancer [18,19,20,21,22,23,24,25,26,27], few studies have been able to identify cfDNA in peripheral blood in patients with glioma, due to cfDNA difficulties of crossing the blood–brain barrier (BBB), and since its release can change depending on histopathology, localization, and tumor grade [17, 28,29,30,31,32].

Ideally, the use of the liquid source close to the disease site would be the optimal solution; therefore, the CSF-derived cfDNA would be the best source of biological material to study the molecular profile of brain tumors through liquid biopsy [33,34,35,36,37,38]. Published studies using sensitive methods, such as digital PCR, demonstrated that higher concentrations of cfDNA may be found in CSF compared to plasma, suggesting that cfDNA in CSF could be used as a molecular marker to identify mutations and longitudinally monitor changes in brain tumors [39,40,41,42]. However, CSF is an invasive, complex, and uncomfortable approach, requiring specific expertise and associated with additional risks following its repeated use [34].

Among brain tumors, high-grade gliomas are considered the most common malignancies in adults and their classification has traditionally been based on histopathological findings supplemented by tissue-based tests, grade, and genomic alterations [43,44,45,46]. In particular, the presence of the IDH1 p.R132H amino acid substitution in tissue has been found to be the most common subtype with a 90% of prevalence among IDH1-mutant tumors [47,48,49,50]. Different studies suggested that IDH-mutant gliomas have a significantly improved prognosis, independently of age and grade, as compared to IDH-wild-type tumors [51,52,53,54]. Accordingly, the World Health Organization (WHO) classification of diffuse gliomas under the 2021 update has been dependent largely on IDH1 mutation status together with 1p/19q-codeletion [55,56,57]. In particular, the new classification includes astrocytoma, IDH-mutant (Grade 2, 3, 4), oligodendroglioma, IDH-mutant, and 1p/19q-codeleted (Grade 2, 3) and GBM, IDH-wildtype (Grade 4) [57].

Therefore, the use of IDH1 mutation status as a prognostic biomarker of tumor molecular evolution has allowed easier and more accurate risk stratification of glioma patients and could be a key tool to improve their personalization of treatments. Moreover, IDH-1 and -2 recently became a predictive biomarker of response to specific IDH inhibitors, such as vorasidenib for low grade glioma [58].

In the present study, we assessed the feasibility of detecting plasma-cfDNA IDH1 mutation in gliomas and the results were correlated with survival and clinical characteristics of patients affected by gliomas.

Methods

Patients and data collection

The present retrospective pharmacogenetic study included patients with glioma referred to the University Hospital of Pisa (Italy) from 2016 to 2019.

Patients were selected according to the following criteria: histologic neuronal-glial tumors diagnosis according to the fourth edition of the WHO classification (2016); IDH1 status previously determined by Sanger sequencing of tumor DNA as per standard laboratory procedures; clinical data and follow-up available in the neuro-oncology database; and written informed consent before enrollment in the study [57].

Blood samples were collected after surgery prior to the adjuvant therapy. The study has been approved by the local Human Investigations Committee in accordance with the Declaration of Helsinki (Comitato Etico Area Vasta Nord Ovest Toscana, Prot. Number 560/2015).

Circulating free DNA extraction and IDH1 mutation analysis

Twelve ml of blood were collected in EDTA tubes and centrifuged for 10 min at 1900×g within 2 h from sampling to collect plasma and stored at − 80 °C until analysis. Plasma samples were centrifuged again for 15 min at 1900×g to remove cellular debris. cfDNA was isolated from 3 ml of plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Valencia, CA). The analysis of IDH1 p.R132H mutation was performed by the QX200 digital droplet PCR (Bio-Rad, Hercules, CA) as previously reported [59].

Statistical analysis

Categorical variables and patient clinical outcomes were described by absolute and relative frequencies, while quantitative factors such as age by mean ± standard deviation (STD). Overall survival (OS) was measured as the length of time from the diagnosis to death from any cause or last follow-up. To find which IDH genomic factors are related to OS, survival curves were estimated according to the Kaplan-Meier method, and differences between curves were calculated using the log-rank test. Univariate analysis was performed by Cox hazard regression model to evaluate independent risk factors for OS. The associations between IDH1 somatic mutation and the other categorical variables in the sample were assessed using χ2-test. Specificity and sensitivity were also calculated using ROC analysis. Agreement between IDH1 mutation detection in tissue and plasma was calculated with the Cohen κ test. Differences were considered significant at p < 0.05. All statistical calculations were performed with MedCalc Statistical Software version 14.8.1 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2014) and GraphPad Prism 9.0.0 (GraphPad Software, San Diego, California USA, www.graphpad.com).

Results

Patients’ characteristics

A total of 67 samples from patients affected by a glioma were collected; the average patient age at the time of first blood collection was 54 years old (range 28–84); sex ratio was 0.86 (31 males and 36 females). The majority of patients (64%) presented newly diagnosed tumors, while a subset (3%), comprising 2 cases of oligoastrocytoma and 1 of anaplastic astrocytoma, underwent analysis subsequent to a diagnosis of recurrence and prior to treatment initiation. Histopathological subtypes of brain tumors analyzed according with the fourth edition of the WHO Classification (2016) included oligodendroglioma, diffuse astrocytoma, anaplastic astrocytoma, ganglion astrocytoma, oligo-astrocytoma and glioblastoma (GBM), with the latter being the most frequent diagnosis in our cohort (64.1%). Table 1 reports the clinical characteristics of the cohort analyzed.

Table 1 Clinical characteristics of patients
Full size table

Tissue IDH1 mutational status was available for all patients included in the study; 21 patients (31.3%) were carriers of the IDH1 p.R132H mutation and 46 patients (68.7%) were IDH1 wild type. More specifically, the highest number of patients with IDH1 mutation in tissue was found in the anaplastic astrocytoma group (38.1%), following by diffuse astrocytoma (23.8%), GBM (14.3%), oligodendroglioma (14.3%), and oligoastrocytoma (9.5%).

Twelve out of twenty-one cases (57.2%) with IDH1 mutation were located in the frontal lobe, while 2/21 (9.5%) involved the temporal and parietal lobes, and 7/21 (33.3%) were multifocal. Moreover, 11/21 (52.4%) of patients underwent a subtotal resection (STR), whereas 10/21 (47.6%) patients had a gross total resection (GTR).

Based on the 2021 WHO Classification of Tumors of the Central Nervous System, brain tumors were classified into seven different subgroups [55]. According to this, patients were grouped in GBM (65.6%), oligodendroglioma (4.5%), astrocytoma grade 2 (7.5%), astrocytoma G III (11.9%), astrocytoma G IV (4.5%) and ganglion astrocytoma (1.5%). However, 1p19q co-deletion status was not available for 3 patients (4.5%) identified as oligoastrocytoma in the WHO 2016 classification. For this reason, the 2021 update could not be applied to these patients.

More specifically, the highest number of patients with IDH1 mutation in tissue was found in the astrocytoma grade (G) III (38.1%), following by astrocytoma G II (23.8%), oligodendroglioma (14.3%), astrocytoma G IV (9.5%), and oligoastrocytoma (9.5%) (Table 2).

Table 2 Detection of IDH1 mutation in tissue and plasma according to the WHO classification 2016 and 2021 update
Full size table

Detection of IDH1 p.R132H mutation in plasma cfDNA

Sixty-seven samples were analyzed for IDH1 p.R132H mutation in cfDNA and tissue; in particular, 10 samples (15%) were positive both in tissue and in cfDNA (T+/P+), while five patients (7.3%) were IDH1 wild type in tumor tissue but positive in cfDNA (T−/P+). Finally, 11 patients were found to have IDH1 mutated tumor tissue and no IDH1 mutation in plasma (16.4%) (Fig. 1a). A Chi-squared test was conducted and a statistically significant difference across the two groups was observed (p = 0.0024), indicating a potential relationship between the two categorical variables (tissue and plasma). Compared to tissue, the sensitivity and specificity for IDH1 mutation in plasma was 47.6% (CI 25.7–70.2) and 89.1% (CI 76.6–96.4), respectively. Moreover, a fair agreement between IDH1 mutation status detected in tissue and plasma was found, with a Cohen κ of 0.40 (95% CI, 0.16–0.64). Figure 1b reports the IDH1 Fractional Abundance (AF), expressed as percentage, in T+/P+ and T−/P+ group of patients. In particular, for T+/P+ patients, the median AF% was 0.17 (0.12–1.60), whereas for the T−/P+ group, the median AF% was 0.16 (0.06–0.75).

Fig. 1
figure 1

Concordance table for the presence of the IDH1 mutation both in tissue and in plasma (A). Mutant amplification ratio expressed as allelic fraction (AF) of IDH1 p.R132H mutation detected by ddPCR (B)

Full size image

Table 3 summarizes the characteristics of discordant cases (T−/P+, T+/P-). Among T−/P+ group, the blood sample of 1 patient was collected during the follow-up, in concomitance of the progression of the disease.

Table 3 Clinical characteristics of IDH1 mutant discordant cases
Full size table

Considering the T+/P+ group (n = 10), the highest number of patients with IDH1 mutations in cfDNA were found in the diffuse astrocytoma (40%), following the anaplastic astrocytoma (30%), oligodendroglioma (20%), and GBM (10%) (Table 2). According to the 2021 WHO Classification, the highest number of patients with IDH1 mutation in plasma was found in the astrocytoma G II (40%), and G III (30%), oligodendroglioma (20%), astrocytoma G IV (10%) (Table 2). Six of ten patients (60%) underwent a STR and 4/10 (40%) had a GTR.

Focusing on the T−/P+ group (n = 5), all patients presented a GBM high grade tumor (Table 2) involving the temporal lobe (2/5, 40%), frontal lobe (1/5, 20%), parietal lobe (1/5, 20%), or multiple sites (1/5, 20%). Interestingly, all the patients underwent a STR (100%).

Overall, a correlation analysis of the presence of IDH1 mutation in cfDNA with the histological grade of tumor was also performed (Fig. 2a). Five of 11 low-grade gliomas had the IDH1 p.R132H mutation in tissue and plasma. Among the 56 patients with high-grade glioma, the mutation was detected in the plasma of 5 patients (8.9%) with an IDH1 p.R132H mutant tumor. A Chi-squared test was conducted to evaluate the potential association between tumor grade and the presence of IDH1 mutation in cfDNA. No statistically significant difference across the two examined groups was observed (p = 0.10).

Fig. 2
figure 2

IDH1 mutation frequencies detected in cfDNA according to tumor grade (A). IDH1 mutation expressed as allelic fraction (AF) detected in cfDNA according to tumor grade (B)

Full size image

Figure 2B reported the correlation analysis of the histological grade with the presence of the IDH1 (AF%) mutation in plasma cfDNA, no statistically significant association was found (p = 0.8).

Figure 3 reports the IDH1 alteration rate in plasma (Fig. 3A) and the median AF% (IC 95%) per patient stratified by histologic subtype (Fig. 3B). Diffuse astrocytoma, oligodendroglioma and glioblastoma revealed greater AF% than other subtypes.

Fig. 3
figure 3

IDH1 mutation frequencies detected in cfDNA according to histological subtype (A). IDH1 mutation expressed as allelic fraction (AF) detected in cfDNA according to histological subtype (B)

Full size image

IDH1 mutation in plasma and tissue predicts overall survival in glioma tumors

Median overall survival of the entire population was 40.3 months. Considering the patients whose IDH1 status was assessed on tissue, as expected, median OS was significantly longer in patients with IDH1 p.R132H mutation vs IDH wild type (138.8 months vs 24.4; p < 0.0001; Fig. 4A). Accordingly, also for patients with the IDH1 assessment on cfDNA, median OS was significantly longer in patients with IDH1 p.R132H mutation vs IDH wild type (116.3 months vs 35.8, p = 0.016; Fig. 4B).

Fig. 4
figure 4

Overall survival of glioma patients stratified as per IDH1 mutational status detected in tissue (A) and cfDNA (B)

Full size image

We conducted univariate cox regression analysis using clinical variables such as age, gender, tumor grade, type of surgery and IDH1 mutation status finding a statistically significant association between IDH1 mutation both in tissue and cfDNA, age, tumor grade and OS (Table 4). Sample size was unfortunately not large enough to allow a multivariate analysis.

Table 4 Univariate analysis for overall survival
Full size table

Considering patients with wild-type IDH1 status in tissue but a mutant IDH1 status in plasma (n = 5), median OS was 65.8 months. Despite a subgroup analysis was not performed due to the limited sample size, the longer PFS may confirm the presence of the IDH1 mutation, or the presence of a higher level of tumor molecular heterogeneity.

Discussion

Our study examined the feasibility of using liquid biopsy for patients affected by a brain tumor, and the association between the mutational status of IDH1 in plasma, survival outcomes and clinical characteristics of a cohort of glioma patients. In our study, a statistically significant concordance between IDH1 mutation detection in tissue and cfDNA was found (p = 0.0024), suggesting that liquid biopsy is a technique capable of integrating tissue biopsy, especially in elderly patients or patients with tumors close to critical areas. In particular, IDH1 mutation was detected in the cfDNA of 10 out of 21 patients harboring an IDH1 mutant tumor, and in 5 patients with IDH1 wild type tumor tissue (n = 41), suggesting that liquid biopsy may support tissue biopsy in the detection of IDH1 mutation. Similarly, previous studies were able to identify the IDH1 mutation using a digital PCR technology in the plasma of 50–60% of patients with IDH-mutant gliomas [29, 30]. Interestingly, all patients with IDH1 wild type tumor but with detectable IDH1 mutation in cfDNA had a subtotal resection, and a residual tumor burden was still present in the brain. Therefore, a shedding of cfDNA in the circulation could be expected as a consequence of this type of surgery [60, 61]. For this reason, our analysis could more accurately represent the entire heterogeneity of the tumor compared to tissue biopsy, explaining the detection of the IDH1 mutation in the cfDNA of a subgroup of patients negative on tissue. On the other hand, in our cohort eleven patients presented IDH1 mutated tumor tissue and no IDH1 mutation in plasma, suggesting that the blood-brain barrier could limit the release of cfDNA and consequently, the detection of IDH1 mutation. Recently, a meta-analysis highlighted the complexity of cfDNA analysis to detect the mutational status in glioma patients [28]. cfDNA analysis resulted to have high specificity (0.98; 95% CI 0.96–0.99) but a relatively moderate sensitivity (0.69; 95% CI 0.66–0.73) and a high grade of heterogeneity (I2 = 73.1%, p < 0.05) of sample source and assay methods [28]. These latter could affect the sensitivity of the cfDNA and the accuracy of the findings, suggesting that cfDNA could be used only as an auxiliary tool for molecular assessment of glioma [62].

In our study, patients were divided into different groups depending on the brain tumor’s histopathological subtypes and tumor grade, finding a higher rate of IDH1 mutation (AF%) in the diffuse astrocytoma, glioblastoma and oligodendroglioma. However, the association between tumor grade and the presence of IDH1 mutation (p = 0.10) was not statistically significant. Specifically, the majority of patients with a low-grade brain tumor correlated with the presence of the p.R132H mutation, while the high-grade group, which counted about 82% of patients, did not show IDH1 mutation. This is in accordance with previous studies, which reported that IDH1 mutation occurs in the majority of low-grade gliomas and less frequently in high-grade gliomas [50, 63, 64]. Accordingly, Yan et al. confirmed that IDH1 mutations are more frequent in G II–III astrocytomas and oligodendrogliomas and less frequently in GBM, underlining the correlation between the presence of IDH1 mutation and tumor grade [63].

To date, few data have reported the association between IDH1 mutations with tumor localization [65]. In the present study, we found that the majority of gliomas (57.2%) with IDH1 mutations were located in the frontal lobe, followed by gliomas affecting multiple sites (33.3%) and temporal or parietal tumors (9.5%), suggesting a potential relationship between IDH1 mutations and tumor localization.

Interestingly, our study evaluated the role of the IDH1 mutation detected in tissue and plasma and the survival outcome. Patients carrying IDH1 mutation p.R132H have a higher survival rate than patients not carrying the mutation. Accordingly, several studies highlighted the positive correlation between IDH1 p.R132H mutation and survival of patients with gliomas [66,67,68]. Sanson et al. highlighted that the IDH1 codon 132 mutation is associated with the genomic profile of the tumor and constitutes an independent prognostic marker in G II to IV gliomas (p = 0.00021) [66]. Similarly, Polivka et al. affirmed that patients with IDH1 p.R132H mutation had a significantly longer OS than patients with wild-type IDH1 (270 versus 130 days; p < 0.024) [67].

A survival rate of 65.8 months was found in patients with a wild type IDH1 status in tissue but a mutant IDH1 status in plasma (T−/P+), which is approximately twice as long than the median OS of tissue (24.4 months) or plasma (35.8 months) IDH1 wild type patients of our cohort, assuming the real presence of the IDH1 mutation. However, due to the small sample size, appropriate statistical analyses could not be performed.

In our study, the evaluation of the O-6-methylguanine-DNA methyltransferase (MGMT) status was not available for all patients. In this context, different studies suggested the importance of combined IDH1 and MGMT analysis to predict survival in patients with different types of gliomas [65, 69]. Patients harboring IDH1 mutation and MGMT methylation had the more favorable outcomes, followed by patients with IDH1 mutation and unmethylated MGMT promoter, and patients without IDH1 mutation and unmethylated MGMT promoter [69]. There is compelling evidence that liquid biopsy, especially using cfDNA, offers an accurate and accessible approach to capture the landscape of brain tumor-related molecular alterations, allowing diagnosis and characterization of glioma patients [35, 38]. Liquid biopsy is already used in several tumors, especially for treatment monitoring, and recent several studies reported its usefulness in the management of glioma patients [38, 70,71,72,73]. Moreover, liquid biopsy has found applicability as potential minimally invasive alternative to traditional tissue surgery, especially in difficult scenarios (i.e unavailable tissue, poor clinical conditions of patients), bypassing its spatial and temporal biases, and for the research of diagnostic and prognostic biomarkers, enabling clinicians to improve the neuro-oncology traditional monitoring of glioma patients [32, 33, 35,36,37, 74,75,76]. Moreover, the assessment of the IDH-1 and -2 status will become crucial since vorasidenib, a new oral brain-penetrant IDH-1/2 inhibitor inhibitor, showed linical activity in IDH-mutant gliomas [58]; therefore, its assessment will be relevant in order to treat patients with appropriate targeted treatments.

Conclusions

Our study confirms that liquid biopsy and cfDNA could be a complementary methods to tissue biopsy in the detection of IDH1 mutation, which can be used as a strong prognostic and predictive biomarker for a favorable clinical outcome in patients with glioma. The potential use of liquid biopsy opens an innovative field of circulating biomarkers research with a relevant impact on the characterization, prognosis, and clinical management of brain cancer.

Availability of data and materials

The data presented in this study are available in this article.

Abbreviations

cfDNA:

circulating free DNA

CSF:

cerebrospinal fluid

EVs:

extracellular vesicles

BBB:

blood–brain barrier

WHO:

World Health Organization

OS:

Overall survival

GBM:

glioblastoma

AF:

Allelic Fraction

References

  1. Donaldson J, Park BH. Circulating tumor DNA: measurement and clinical utility. Annu Rev Med. 2018;69:223–34.

    Article  PubMed  CAS  Google Scholar 

  2. Ossandon MR, Agrawal L, Bernhard EJ, Conley BA, Dey SM, Divi RL, et al. Circulating tumor DNA assays in clinical Cancer research. J Natl Cancer Inst. 2018;110(9):929–34.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Diaz LA Jr, Bardelli A. Liquid biopsies: genotyping circulating tumor DNA. J Clin Oncol. 2014;32(6):579–86.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mader S, Pantel K. Liquid biopsy: current status and future perspectives. Oncol Res Treat. 2017;40(7–8):404–8.

    Article  PubMed  CAS  Google Scholar 

  5. Poulet G, Massias J, Taly V. Liquid biopsy: general concepts. Acta Cytol. 2019;63(6):449–55.

    Article  PubMed  CAS  Google Scholar 

  6. Crowley E, Di Nicolantonio F, Loupakis F, Bardelli A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat Rev Clin Oncol. 2013;10(8):472–84.

    Article  PubMed  CAS  Google Scholar 

  7. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017;14(9):531–48.

    Article  PubMed  CAS  Google Scholar 

  8. Shohdy KS, West HJ. Circulating tumor DNA testing-liquid biopsy of a Cancer. JAMA Oncol. 2020;6(5):792.

    Article  PubMed  Google Scholar 

  9. Pascual J, Attard G, Bidard FC, Curigliano G, De Mattos-Arruda L, Diehn M, et al. ESMO recommendations on the use of circulating tumour DNA assays for patients with cancer: a report from the ESMO precision medicine working group. Ann Oncol. 2022;33(8):750–68.

    Article  PubMed  CAS  Google Scholar 

  10. Michela B. Liquid biopsy: a family of possible diagnostic tools. Diagnostics (Basel). 2021;11(8).

  11. Urabe F, Kosaka N, Ito K, Kimura T, Egawa S, Ochiya T. Extracellular vesicles as biomarkers and therapeutic targets for cancer. Am J Phys Cell Physiol. 2020;318(1):C29–39.

    Article  CAS  Google Scholar 

  12. Vasconcelos MH, Caires HR, Abols A, Xavier CPR, Line A. Extracellular vesicles as a novel source of biomarkers in liquid biopsies for monitoring cancer progression and drug resistance. Drug Resist Updat. 2019;47:100647.

    Article  PubMed  Google Scholar 

  13. Taverna S, Giallombardo M, Gil-Bazo I, Carreca AP, Castiglia M, Chacartegui J, et al. Exosomes isolation and characterization in serum is feasible in non-small cell lung cancer patients: critical analysis of evidence and potential role in clinical practice. Oncotarget. 2016;7(19):28748–60.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Rolfo C, Mack P, Scagliotti GV, Aggarwal C, Arcila ME, Barlesi F, et al. Liquid biopsy for advanced NSCLC: a consensus statement from the International Association for the Study of Lung Cancer. J Thorac Oncol. 2021;16(10):1647–62.

    Article  PubMed  CAS  Google Scholar 

  15. Passiglia F, Pilotto S, Facchinetti F, Bertolaccini L, Del Re M, Ferrara R, et al. Treatment of advanced non-small-cell lung cancer: the 2019 AIOM (Italian Association of Medical Oncology) clinical practice guidelines. Crit Rev Oncol Hematol. 2020;146:102858.

    Article  PubMed  CAS  Google Scholar 

  16. Heidrich I, Ackar L, Mossahebi Mohammadi P, Pantel K. Liquid biopsies: potential and challenges. Int J Cancer. 2021;148(3):528–45.

    Article  PubMed  CAS  Google Scholar 

  17. Arvanitis CD, Ferraro GB, Jain RK. The blood-brain barrier and blood-tumour barrier in brain tumours and metastases. Nat Rev Cancer. 2020;20(1):26–41.

    Article  PubMed  CAS  Google Scholar 

  18. Rolfo C, Mack PC, Scagliotti GV, Baas P, Barlesi F, Bivona TG, et al. Liquid biopsy for advanced non-Small cell lung Cancer (NSCLC): a statement paper from the IASLC. J Thorac Oncol. 2018;13(9):1248–68.

    Article  PubMed  Google Scholar 

  19. Del Re M, Bordi P, Rofi E, Restante G, Valleggi S, Minari R, et al. The amount of activating EGFR mutations in circulating cell-free DNA is a marker to monitor osimertinib response. Br J Cancer. 2018;119(10):1252–8.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Del Re M, Petrini I, Mazzoni F, Valleggi S, Gianfilippo G, Pozzessere D, et al. Incidence of T790M in patients with NSCLC progressed to Gefitinib, Erlotinib, and Afatinib: a study on circulating cell-free DNA. Clin Lung Cancer. 2020;21(3):232–7.

    Article  PubMed  Google Scholar 

  21. Clatot F. Review ctDNA and breast Cancer. Recent Results Cancer Res. 2020;215:231–52.

    Article  PubMed  CAS  Google Scholar 

  22. Groot VP, Mosier S, Javed AA, Teinor JA, Gemenetzis G, Ding D, et al. Circulating tumor DNA as a clinical test in resected pancreatic Cancer. Clin Cancer Res. 2019;25(16):4973–84.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Schwaederle M, Chattopadhyay R, Kato S, Fanta PT, Banks KC, Choi IS, et al. Genomic alterations in circulating tumor DNA from diverse Cancer patients identified by next-generation sequencing. Cancer Res. 2017;77(19):5419–27.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Zill OA, Banks KC, Fairclough SR, Mortimer SA, Vowles JV, Mokhtari R, et al. The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced Cancer patients. Clin Cancer Res. 2018;24(15):3528–38.

    Article  PubMed  CAS  Google Scholar 

  25. Gracie L, Pan Y, Atenafu EG, Ward DG, Teng M, Pallan L, et al. Circulating tumour DNA (ctDNA) in metastatic melanoma, a systematic review and meta-analysis. Eur J Cancer. 2021;158:191–207.

    Article  PubMed  CAS  Google Scholar 

  26. Marczynski GT, Laus AC, Dos Reis MB, Reis RM, Vazquez VL. Circulating tumor DNA (ctDNA) detection is associated with shorter progression-free survival in advanced melanoma patients. Sci Rep. 2020;10(1):18682.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Malla M, Loree JM, Kasi PM, Parikh AR. Using circulating tumor DNA in colorectal Cancer: current and evolving practices. J Clin Oncol. 2022;40(24):2846–57.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Kang Y, Lin X, Kang D. Diagnostic value of circulating tumor DNA in molecular characterization of glioma: a meta-analysis. Medicine (Baltimore). 2020;99(33):e21196.

    Article  PubMed  CAS  Google Scholar 

  29. Boisselier B, Gallego Perez-Larraya J, Rossetto M, Labussiere M, Ciccarino P, Marie Y, et al. Detection of IDH1 mutation in the plasma of patients with glioma. Neurology. 2012;79(16):1693–8.

    Article  PubMed  CAS  Google Scholar 

  30. Cabezas-Camarero S, Garcia-Barberan V, Perez-Alfayate R, Casado-Farinas I, Sloane H, Jones FS, et al. Detection of IDH1 mutations in plasma using BEAMing Technology in Patients with gliomas. Cancers (Basel). 2022;14(12).

  31. Nassiri F, Chakravarthy A, Feng S, Shen SY, Nejad R, Zuccato JA, et al. Detection and discrimination of intracranial tumors using plasma cell-free DNA methylomes. Nat Med. 2020;26(7):1044–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Piccioni DE, Achrol AS, Kiedrowski LA, Banks KC, Boucher N, Barkhoudarian G, et al. Analysis of cell-free circulating tumor DNA in 419 patients with glioblastoma and other primary brain tumors. CNS. Oncol. 2019;8(2):CNS34.

    CAS  Google Scholar 

  33. Yi Z, Qu C, Zeng Y, Liu Z. Liquid biopsy: early and accurate diagnosis of brain tumor. J Cancer Res Clin Oncol. 2022;148(9):2347–73.

    Article  PubMed  Google Scholar 

  34. Simonelli M, Dipasquale A, Orzan F, Lorenzi E, Persico P, Navarria P, et al. Cerebrospinal fluid tumor DNA for liquid biopsy in glioma patients' management: close to the clinic? Crit Rev Oncol Hematol. 2020;146:102879.

    Article  PubMed  Google Scholar 

  35. Sareen H, Garrett C, Lynch D, Powter B, Brungs D, Cooper A, et al. The role of liquid biopsies in detecting molecular tumor biomarkers in brain Cancer patients. Cancers (Basel). 2020;12(7).

  36. Eibl RH, Schneemann M. Liquid biopsy and primary brain tumors. Cancers (Basel). 2021;13(21).

  37. Ray A. Liquid biopsy in gliomas- a review. Neurol India. 2020;68(6):1295–300.

    PubMed  Google Scholar 

  38. Orzan F, De Bacco F, Lazzarini E, Crisafulli G, Gasparini A, Dipasquale A, et al. Liquid biopsy of cerebrospinal fluid enables selective profiling of glioma molecular subtypes at first clinical presentation. Clin Cancer Res. 2023;29(7):1252–66.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Pan W, Gu W, Nagpal S, Gephart MH, Quake SR. Brain tumor mutations detected in cerebral spinal fluid. Clin Chem. 2015;61(3):514–22.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. De Mattos-Arruda L, Mayor R, Ng CKY, Weigelt B, Martinez-Ricarte F, Torrejon D, et al. Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat Commun. 2015;6:8839.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Seoane J, De Mattos-Arruda L, Le Rhun E, Bardelli A, Weller M. Cerebrospinal fluid cell-free tumour DNA as a liquid biopsy for primary brain tumours and central nervous system metastases. Ann Oncol. 2019;30(2):211–8.

    Article  PubMed  CAS  Google Scholar 

  42. Miller AM, Shah RH, Pentsova EI, Pourmaleki M, Briggs S, Distefano N, et al. Tracking tumour evolution in glioma through liquid biopsies of cerebrospinal fluid. Nature. 2019;565(7741):654–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Perry A, Wesseling P. Histologic classification of gliomas. Handb Clin Neurol. 2016;134:71–95.

    Article  PubMed  Google Scholar 

  44. Lapointe S, Perry A, Butowski NA. Primary brain tumours in adults. Lancet. 2018;392(10145):432–46.

    Article  PubMed  Google Scholar 

  45. Wesseling P, Capper D. WHO 2016 classification of gliomas. Neuropathol Appl Neurobiol. 2018;44(2):139–50.

    Article  PubMed  CAS  Google Scholar 

  46. Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med. 2008;359(5):492–507.

    Article  PubMed  CAS  Google Scholar 

  47. Picca A, Berzero G, Di Stefano AL, Sanson M. The clinical use of IDH1 and IDH2 mutations in gliomas. Expert Rev Mol Diagn. 2018;18(12):1041–51.

    Article  PubMed  CAS  Google Scholar 

  48. Sonoda Y, Kumabe T, Nakamura T, Saito R, Kanamori M, Yamashita Y, et al. Analysis of IDH1 and IDH2 mutations in Japanese glioma patients. Cancer Sci. 2009;100(10):1996–8.

    Article  PubMed  CAS  Google Scholar 

  49. Liu Y, Lang F, Chou FJ, Zaghloul KA, Yang C. Isocitrate dehydrogenase mutations in glioma: genetics, biochemistry, and clinical indications. Biomedicines. 2020;8(9).

  50. Cohen AL, Holmen SL, Colman H. IDH1 and IDH2 mutations in gliomas. Curr Neurol Neurosci Rep. 2013;13(5):345.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Khan I, Waqas M, Shamim MS. Prognostic significance of IDH 1 mutation in patients with glioblastoma multiforme. J Pak Med Assoc. 2017;67(5):816–7.

    PubMed  Google Scholar 

  52. Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J, Simon M, et al. Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol. 2010;120(6):707–18.

    Article  PubMed  Google Scholar 

  53. Xia L, Wu B, Fu Z, Feng F, Qiao E, Li Q, et al. Prognostic role of IDH mutations in gliomas: a meta-analysis of 55 observational studies. Oncotarget. 2015;6(19):17354–65.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Pappula AL, Rasheed S, Mirzaei G, Petreaca RC, Bouley RA. A genome-wide profiling of glioma patients with an IDH1 mutation using the catalogue of somatic mutations in Cancer database. Cancers (Basel). 2021;13(17).

  55. Stoyanov GS, Lyutfi E, Georgieva R, Georgiev R, Dzhenkov DL, Petkova L, et al. Reclassification of glioblastoma Multiforme according to the 2021 World Health Organization classification of central nervous system tumors: a single institution report and practical significance. Cureus. 2022;14(2):e21822.

    PubMed  PubMed Central  Google Scholar 

  56. Sledzinska P, Bebyn M, Szczerba E, Furtak J, Harat M, Olszewska N, et al. Glioma 2021 WHO classification: the superiority of NGS over IHC in routine diagnostics. Mol Diagn Ther. 2022;26(6):699–713.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-Oncology. 2021;23(8):1231–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Mellinghoff IK, van den Bent MJ, Blumenthal DT, Touat M, Peters KB, Clarke J, et al. Vorasidenib in IDH1- or IDH2-mutant low-grade glioma. N Engl J Med. 2023;389(7):589–601.

    Article  PubMed  CAS  Google Scholar 

  59. Crucitta S, Ruglioni M, Novi C, Manganiello M, Arici R, Petrini I, et al. Comparison of digital PCR systems for the analysis of liquid biopsy samples of patients affected by lung and colorectal cancer. Clin Chim Acta. 2023;541:117239.

    Article  PubMed  CAS  Google Scholar 

  60. Nørøxe DS, Østrup O, Yde CW, Ahlborn LB, Nielsen FC, Michaelsen SR, et al. Cell-free DNA in newly diagnosed patients with glioblastoma - a clinical prospective feasibility study. Oncotarget. 2019;10(43):4397–406.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426–37.

    Article  PubMed  CAS  Google Scholar 

  62. Husain A, Mishra S, Siddiqui MH, Husain N. Detection of IDH1 mutation in cfDNA and tissue of adult diffuse glioma with allele-specific qPCR. Asian Pac J Cancer Prev. 2023;24(3):961–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009;360(8):765–73.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Deng L, Xiong P, Luo Y, Bu X, Qian S, Zhong W, et al. Association between IDH1/2 mutations and brain glioma grade. Oncol Lett. 2018;16(4):5405–9.

    PubMed  PubMed Central  Google Scholar 

  65. Yan W, Zhang W, You G, Bao Z, Wang Y, Liu Y, et al. Correlation of IDH1 mutation with clinicopathologic factors and prognosis in primary glioblastoma: a report of 118 patients from China. PLoS One. 2012;7(1):e30339.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Sanson M, Marie Y, Paris S, Idbaih A, Laffaire J, Ducray F, et al. Isocitrate dehydrogenase 1 codon 132 mutation is an important prognostic biomarker in gliomas. J Clin Oncol. 2009;27(25):4150–4.

    Article  PubMed  CAS  Google Scholar 

  67. Polivka J, Polivka J Jr, Rohan V, Pesta M, Repik T, Pitule P, et al. Isocitrate dehydrogenase-1 mutations as prognostic biomarker in glioblastoma multiforme patients in West Bohemia. Biomed Res Int. 2014;2014:735659.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Wang J, Zhao YY, Li JF, Guo CC, Chen FR, Su HK, et al. IDH1 mutation detection by droplet digital PCR in glioma. Oncotarget. 2015;6(37):39651–60.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Minniti G, Scaringi C, Arcella A, Lanzetta G, Di Stefano D, Scarpino S, et al. IDH1 mutation and MGMT methylation status predict survival in patients with anaplastic astrocytoma treated with temozolomide-based chemoradiotherapy. J Neuro-Oncol. 2014;118(2):377–83.

    Article  CAS  Google Scholar 

  70. Muralidharan K, Yekula A, Small JL, Rosh ZS, Kang KM, Wang L, et al. Promoter mutation analysis for blood-based diagnosis and monitoring of gliomas. Clin Cancer Res. 2021;27(1):169–78.

    Article  PubMed  CAS  Google Scholar 

  71. Fontanilles M, Marguet F, Beaussire L, Magne N, Pépin LF, Alexandru C, et al. Cell-free DNA and circulating TERT promoter mutation for disease monitoring in newly-diagnosed glioblastoma. Acta Neuropathol Commun. 2020;8(1):179.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Husain A, Mishra S, Hadi R, Sahu A, Kumari S, Rastogi M, et al. Dynamics of cell-free DNA in predicting response in adult diffuse glioma on chemoradiotherapy. Cancer Gene Ther. 2022;268-269:55–63.

    Article  CAS  Google Scholar 

  73. Riviere-Cazaux C, Dong X, Mo W, Luo S, Wang A, Du P, et al. Biom-16. Longitudinal glioma cerebrospinal fluid cell-free dna for monitoring and treatment response assessment. Neuro-Oncology. 2023;25(Supplement_5):v7-v.

    Article  Google Scholar 

  74. Shankar GM, Balaj L, Stott SL, Nahed B, Carter BS. Liquid biopsy for brain tumors. Expert Rev Mol Diagn. 2017;17(10):943–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Saenz-Antonanzas A, Auzmendi-Iriarte J, Carrasco-Garcia E, Moreno-Cugnon L, Ruiz I, Villanua J, et al. Liquid biopsy in glioblastoma: opportunities, applications and challenges. Cancers (Basel). 2019;11(7).

  76. Pasqualetti F, Rizzo M, Franceschi S, Lessi F, Paiar F, Buffa FM. New perspectives in liquid biopsy for glioma patients. Curr Opin Oncol. 2022;34(6):705–12.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

None.

Funding

This study was supported by a grant (n. 539901_2022) from the University of Pisa to RD.

Author information

Author notes

  1. Stefania Crucitta, Francesco Pasqualetti and Alessandra Gonnelli contributed equally to this work.

Authors and Affiliations

  1. Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy

    Stefania Crucitta, Martina Ruglioni, Giovanna Irene Luculli, Romano Danesi & Marzia Del Re

  2. Radiation Oncology, Department of Medicine and Oncology, University of Pisa, Pisa, Italy

    Francesco Pasqualetti, Alessandra Gonnelli, Martina Cantarella & Fabiola Paiar

  3. Department of Oncology, University of Oxford, Oxford, UK

    Francesco Pasqualetti

  4. Division of Pathology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Pisa, Italy

    Valerio Ortenzi, Cristian Scatena & Antonio Giuseppe Naccarato

  5. Department of Oncology and Hemato-Oncology, University of Milano, Via Festa del Perdono, 7, Milano, 20122, Italy

    Romano Danesi

Authors
  1. Stefania Crucitta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesco Pasqualetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandra Gonnelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martina Ruglioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giovanna Irene Luculli
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Martina Cantarella
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Valerio Ortenzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cristian Scatena
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fabiola Paiar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Antonio Giuseppe Naccarato
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Romano Danesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Marzia Del Re
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.D.R., S. C, F.P., designed the study. F.P., M. C, F.P. collected the data. M.D.R., S.C., F.P., A.G., M.R., G.I.L., M.C. analyzed and interpreted the data. M.D.R., S.C., F.P., M.R. drafted the original manuscript. M.D.R., F.P., S.C., M.C., V.O., C.S., F.P. A.G.N. and R.D. reviewed and edited the final manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Romano Danesi.

Ethics declarations

Ethics approval and consent to participate

All patients gave their signed informed consent before blood collection and data analysis. This study was conducted in accordance with the Declaration of Helsinki and approved by the local Ethics Committee of Area Vasta Nord-Ovest, Tuscany Region, Italy.

Consent for publication

Not applicable.

Competing interests

MDR consultant/speaker: Astellas, Astra Zeneca, Celgene, Novartis, Pfizer, Bio-Rad, Janssen, Sanofi-Aventis, Roche, Lilly, MSD and Ipsen. RD consultant/speaker: Ipsen, Novartis, Pfizer, Sanofi Genzyme, AstraZeneca, Janssen, Gilead, Lilly, and EUSA Pharma. All other authors report no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Crucitta, S., Pasqualetti, F., Gonnelli, A. et al. IDH1 mutation is detectable in plasma cell-free DNA and is associated with survival outcome in glioma patients. BMC Cancer 24, 31 (2024). https://doi.org/10.1186/s12885-023-11726-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-023-11726-0

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us