Invited Article: Molecular biomarkers characterization for human brain glioma grading using visible resonance Raman spectroscopy Open Access

Nowadays, nearly 700 000 people are living with a primary brain and central nerve system (CNS) tumor in the USA.¹ About 79 000 new cases of primary brain and CNS tumors will be diagnosed in the USA in 2018, of which about one third are malignant tumors (cancer). Brain cancer (malignant brain tumor) is an advanced disease with an average 5-year survival rate of about 34%.^1–4 Glioma accounts for about one third of all tumors in the brain and is the most common primary malignant brain tumor (∼75%).^1–4 It is one of the global refractory tumors due to (a) its strong heterogeneity, and (b) its frequently disrupting brain functional areas and causing epilepsy. The types of glioma mainly include astrocytoma, oligodendroglioma, and ependymoma, where astrocytoma is the most common type of glioma. Grade IV astrocytoma, i.e., glioblastoma (GBM), is the most aggressive primary brain tumor and makes up about half of all gliomas.⁴ Although the survival of a brain glioma patient is affected by multiple factors, the accurate tumor boundary identification and complete resection of the tumor are two essential factors.

The current clinical routine diagnosis of brain tumors is performed by using biopsy and histopathology, which is considered the gold standard. This method requires freezing the biopsy tissue and reagent preparation prior to microscopic analysis. It also requires skilled technicians and expert histopathologists to perform the diagnosis, as well as considerable time before results are available. Besides the gold-standard method, there is a panoply of alternative methods available for cancer detection. For instance, magnetic resonance imaging (MRI), computed tomography (CT), and microscopic neurosurgical technology have emerged, which have played a huge role in neurosurgeries. However, for the diagnosis and treatment of malignant brain tumors, it seems there has not been a breakthrough. Most patients with malignant gliomas (77%) died within one year after diagnosis.^5,6 It is critical and important to develop a real-time in situ diagnostic and margin assessment method for the early screening and more accurate intraoperative resection which are the key factors to increase the survival rate of patients.

The current brain tumor treatment starts with surgical resection which is the most critical first step in the comprehensive treatment of glioma. The main goal of the operation is total resection of the tumor, which is then followed by further treatment after surgery. However, early postoperative MRI reviews confirmed that only about 65% of gliomas can achieve a total resection because the current micro-surgery technology lacks accurate and timely detection. Recent studies showed that for 75% tumor resection, there was no clear distinction between higher and lower grade glioma cases. This is the major reason of high rates of recurrence and mortality.^7–12 

Surgery is the first choice for glioma patients and doctors because it can stop the growth and spread of tumors. During brain cancer surgery, identifying cancer margins, i.e., recognizing where a tumor ends and normal brain tissue begins, is a major challenge. Effective and complete resection will reduce the recurrence rate of the tumor. On one hand, if cancer cells are left behind, the seeds of tumor recurrence and spread will be left behind. On the other hand, surgical injury to the normal healthy tissue causes nervous system problems. Therefore, accurate judgment of the tumor margin is very important. Neurosurgeons suffer from the problem that the current technology is not able to accurately identify the boundaries between brain tumors and normal tissues quickly and timely during surgery. They are particularly desperate for a complementary technique to both screen for the type and grade of the tumor during surgery and detect the form and extent of tumor cell invasion and the exact boundaries between the tumor and normal tissue.

We propose a new optical molecular histopathology method for real-time evaluation of the normal control (the negative margin of gliomas) and glioma grade II, grade III, and grade IV tissues by using visible resonance Raman (VRR) spectroscopy techniques^13–16 based on the native molecular characteristics in the Raman spectra of human brain tissues. This work is to continue the new progress of Alfano’s group which created the optical biopsy field.¹⁷ It is found that the additional two molecular biomarkers, i.e., RR peaks at 1129 cm⁻¹ and 1338 cm⁻¹, from the four types of brain tissues are significantly different in intensity by using the VRR technique. This result may help a surgeon better decide surgical margins of tumors.^13,16

Twenty-one human brain glioma tissues were measured including six (6) grade II, five (5) grade II-III, and ten (10) grade III-IV specimens. The specimens were obtained from the General Hospital of the Air Force in Beijing, China. The reports of the histopathologic analysis of cancer tissues by standard pathology and immunohistochemistry methods were available for each specimen. The experimental procedures were approved by the committee of the General Hospital of the Air Force.

To verify the RR data, two confocal micro-Raman systems were used in the experiments. The RR spectra were acquired using the Jobin Yvon (JY-HR-800 France) confocal micro-Raman spectrometer with a 532 nm laser beam for excitation. The excitation light beam was shone on the surface of a sample. The spot diameter of the focused laser beam on the sample position was about 1 µm. The laser power at the sample position was 0.9 mW. The typical integration time was 30 s. All spectra were acquired at the room temperature.¹³ The second system was the WITec-alpha-300R Raman microscope and imaging system (WITec: Wissenschaftliche Instrumente und Technologie GmbH, Ulm, Germany) equipped with an ultrahigh throughput spectrometer (UHTS 300) and a front-illuminated CCD (Andor) cooled to −60 °C. Using the 532 nm excitation laser with a system resolution down to the optical diffraction limit of 200 nm, RR spectra were collected with 1 s integration time and 30 accumulations. The spectra were collected over the spectral range of 400–4000 cm⁻¹. The excitation source was a 532 nm solid-state diode laser (Verdi-2, Coherent Company, Santa Clara, CA, USA) with a maximum output of 50 mW. With a Zeiss 50× NA-0.55 objective, a diffraction-limited beam spot was focused with a diameter of 1 µm and the laser power was controlled to be under 3.5 mW of true power. All the spectra were collected at ambient room temperature. The final spectral resolution was 2 cm⁻¹ in the range of interest. The Raman and photoluminescence data analyses were performed with WITec Project plus software.

Twenty-eight (28) RR spectra were collected using the WITec300R Raman system. Fifty-nine (59) RR spectra were collected using the HR800 Raman system. Figure 1 shows the representative baseline-removed RR spectra collected from human brain normal controls (the negative margin of glioma), glioma tumors of grade II (low grade), and malignant glioma tumors of grade III (in pathological analysis, grade II-III was considered as grade III) and grade IV (in pathological analysis, grade III-IV glioma was considered as grade IV) using WITec-300R. Two new resonance Raman bands at 1129 cm⁻¹ and 1338 cm⁻¹ were observed.

A peculiar phenomenal 1129 cm⁻¹ peak prominently increased, as shown in Fig. 1. We propose that this peak is derived from the combined contribution of lactic acid, phosphatidic acid, and glucose.^18–20 The change in the peak intensity increased with glioma grades. It may explain the metabolic process of lactic acid, lipids, and glucose under the glycolysis effect. A new report indicated that lactic acid can serve as a major source of carbon and energy for the tricarboxylic acid (TCA) cycle based on the experiment with mice.²⁰ It means that lactic acid is actually an important energy-supplying substance in the human body. It was found in the experiment that the amount of circulating lactic acid is the highest among all metabolites and the cancer cells are mainly fed by lactic acid, not glucose.²⁰ 

In fact, both intense sharp peaks at 1129 cm⁻¹ and 2938 cm⁻¹ (not shown here) suggested contribution from lactate.²¹ Lactate is an end product of glycolysis and increases rapidly particularly as a result of hypoxia and ischemia which produce lactate at a rate 200 times higher than it is in normal tissues. Glycolysis is an anaerobic metabolic process, from which cancer cells can get energy and increase biomass rapidly. Normal cells do not apply glycolysis when there is adequate oxygen supply. The Warburg effect suggested that cancer is not always caused by genetic mutations; instead, cancer is primarily a metabolic disease. The root of cancer is a metabolic problem. If the mitochondrial metabolic function is impaired, metabolic changes will occur. The energy metabolism in cancer cells and normal cells is different. The energy source of cancer cells is glucose which is the main source of energy. RR peaks of 1129 cm⁻¹ to 1131 cm⁻¹ with clear enhancements were found in all grades of glioma, especially in high grades (grade III and grade IV) of glioma tissues when compared to the normal and the negative control brain tissues. The significant overlap and shift between RR peaks of 1129 cm⁻¹ to 1131 cm⁻¹ reveals a new sign that the cancer cells are in the growing process requiring new own lipid to rapidly form to support the cancer cells.^22–26 The RR peak of 1129 cm⁻¹ is also the characteristic peak of phosphatidic acid. Phosphatides are mainly from unsaturated fatty acids and found to have obviously increased in high grade gliomas. These new molecular spectral biomarkers are from lactate and phosphatides. They may provide the evidence for the Warburg effect.

The second molecular biomarker that is magically enhanced as the glioma grade increases is the RR peak at 1338 cm⁻¹. This peak is most likely derived from adenosine triphosphate (ATP)/reduced nicotinamide adenine dinucleotide (NADH).^27,28

To show the change in lactate acid/phosphatidic acid and ATP/NADH vs. glioma grades, the peak intensities of 1129 cm⁻¹ and 1338 cm⁻¹ in each VRR spectrum were first rescaled using the peak intensity of 1004 cm⁻¹ as a normalization factor. The peak of 1004 cm⁻¹ is contributed by phenylalanine of protein and was shown to be relatively stable in its intensity and position in different environments.^13,16 This normalization provides an internal control so that the relative peak intensities reflect the actual chemical composition changes.

Figure 2 shows a process of changes in lactate acid/phosphatidic acid and ATP/NADH vs. glioma grades, respectively. These two intrinsic vibrational modes as predictors may provide a new diagnosis method for the disorder of proteins/lipids in the gliomas.

Therefore, we found that both lactic acid and ATP components increase with the grades of malignancy of gliomas (grade IV as highest). It is noteworthy that in the most aggressive brain tumors of glioma grade IV (GBM), the peak intensity at 1129 cm⁻¹ exceeds that at 1587 cm⁻¹ [Fig. 1(d)]. The 1587 cm⁻¹ peak in glioma tissues shows a sharp increase compared with the control tissues. Here we note that the 1587 cm⁻¹ band arises from tryptophan, mitochondria, and other conponents.²⁹ It can be concluded that (1) RR spectral bands centered at 1129 cm⁻¹ and 1338 cm⁻¹ can be used as molecular biomarkers to measure glioma grades [Figs. 1(b)–1(d)] and identify the margins of gliomas from controls [Fig. 1(a)] and (2) the metabolic process of glioma cells (Fig. 2) fully revealed the Warburg hypothesis.

In this study, glioma grading was only based on the characterization of the spectral fingerprints of molecular biomarkers such as lactate and lipids. Due to the heterogeneity of cancer, and complexity in the spectral data, we have also proposed to use machine learning algorithms to utilize the whole spectral profiles and detect characteristic features for differentiating different types of tissues, which may be more robust than using a few particular Raman bands.^13,30,31 Potentially, both biomarker fingerprints and whole-spectra features can be combined to achieve an optimal criterion for cancer diagnosis. More work will be reported in future papers.

We observed two VRR peaks at 1129 cm⁻¹ and 1338 cm⁻¹, which are attributed to lactate and most of unsaturated lipid of phosphatidic acid, and ATP/NADH, respectively. This finding may provide the evidence of the hypothesized root of cancer named the Warburg effect. These new molecular spectral biomarkers may be used as prognostic molecular markers in clinical applications. Combining early research results,^13,16 we plan to establish a whole set of accurate criteria and risk warning models for molecular classifications of gliomas and individualized diagnosis that may lead to the first precise surgical technique system for gliomas in functional areas. The new prototype is being tested in ex vivo experiments. It will help brain surgeons improve the accuracy of identifying brain tumor boundaries.

The authors acknowledge the support in part from the General Hospital of the Air Force in China and the IUSL at the City College of the City University of New York in the USA.