",isbn:"978-1-83962-547-3",printIsbn:"978-1-83962-546-6",pdfIsbn:"978-1-83962-548-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"e5ba02fedd7c87f0ab66414f3b07de0c",bookSignature:"Dr. John P. Tiefenbacher",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10765.jpg",keywords:"Managing Urbanization, Managing Development, Managing Resource Use, Drought Management, Flood Management, Water Quality Monitoring, Air Quality Monitoring, Ecological Monitoring, Modeling Extreme Natural Events, Ecological Restoration, Restoring Environmental Flows, Environmental Management Perspectives",numberOfDownloads:18,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 12th 2021",dateEndSecondStepPublish:"February 9th 2021",dateEndThirdStepPublish:"April 10th 2021",dateEndFourthStepPublish:"June 29th 2021",dateEndFifthStepPublish:"August 28th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"A geospatial scholar working at the interface of natural and human systems, collaborating internationally on innovative studies about hazards and environmental challenges. Dr. Tiefenbacher has published more than 200 papers on a diverse array of topics that examine perception and behaviors with regards to the application of pesticides, releases of toxic chemicals, environments of the U.S.-Mexico borderlands, wildlife hazards, and the geography of wine.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"73876",title:"Dr.",name:"John P.",middleName:null,surname:"Tiefenbacher",slug:"john-p.-tiefenbacher",fullName:"John P. Tiefenbacher",profilePictureURL:"https://mts.intechopen.com/storage/users/73876/images/system/73876.jfif",biography:"Dr. John P. Tiefenbacher (Ph.D., Rutgers, 1992) is a professor of Geography at Texas State University. His research has focused on various aspects of hazards and environmental management. Dr. Tiefenbacher has published on a diverse array of topics that examine perception and behaviors with regards to the application of pesticides, releases of toxic chemicals, environments of the U.S.-Mexico borderlands, wildlife hazards, and the geography of wine. More recently his work pertains to spatial adaptation to climate change, spatial responses in wine growing regions to climate change, the geographies of viticulture and wine, artificial intelligence and machine learning to predict patterns of natural processes and hazards, historical ethnic enclaves in American cities and regions, and environmental adaptations of 19th century European immigrants to North America's landscapes.",institutionString:"Texas State University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Texas State University",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"12",title:"Environmental Sciences",slug:"environmental-sciences"}],chapters:[{id:"76073",title:"Integrating Ecological Site Descriptions with Soil Morphology to Optimize Forest Management: Three Missouri Case Studies",slug:"integrating-ecological-site-descriptions-with-soil-morphology-to-optimize-forest-management-three-mi",totalDownloads:18,totalCrossrefCites:0,authors:[{id:"185895",title:"Dr.",name:"Michael",surname:"Aide",slug:"michael-aide",fullName:"Michael Aide"},{id:"269286",title:"Dr.",name:"Christine",surname:"Aide",slug:"christine-aide",fullName:"Christine Aide"},{id:"269287",title:"Dr.",name:"Indi",surname:"Braden",slug:"indi-braden",fullName:"Indi Braden"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194667",firstName:"Marijana",lastName:"Francetic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194667/images/4752_n.jpg",email:"marijana@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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1. Introduction
The solution to a well-known problem of a finite rectangular quantum well (QW) indicates that the energy position of the quantum subbands is determined by several parameters, namely, the width of the quantum well, the height of the barriers, and the carrier effective masses. The possibility of a rather simple way of monitoring the wavelength of intersubband transitions in a quantum well to create photodetectors has attracted the attention of researchers for a long time [1]. Improving the technology of the molecular beam epitaxy (MBE) has allowed converting these dreams into reality. The quantum well infrared photodetector (QWIP) was first demonstrated on the basis of the AlGaAs/GaAs heterostructure in 1987 [2]. Permanent improvement of the QWIP design, epitaxial growth technologies of the AlGaAs layers, and techniques for device manufacturing allow using the QWIP based on AlGaAs/GaAs heterostructures not only for military purposes but also for various civilian tasks [3, 4, 5, 6, 7].
In this paper, we have described the current state of the manufacturing technology in the Rzhanov Institute of Semiconductor Physics of SB RAS for the focal plane array (FPA) based on the AlGaAs/GaAs QWIP structure.
2. The physical model of the QWIP
A typical heterostructure intended to detect the infrared radiation (IR) is shown in Figure 1. Such heterostructures are commonly formed by a thin (4–6 nm) GaAs QW located between the wide band gap AlGaAs barrier layers. The thickness of these barrier layers (Lb) is much larger than the width (Lw) of the quantum well and selected in a range of 40–55 nm to suppress a dark current caused by the electron tunneling between the neighboring QWs. The barrier height Δ Ec is determined by the aluminum mole fraction (x) in the barriers. The energy position of the quantum levels depends not only on the quantum well thickness but also on x, as the barrier height is not infinite.
Figure 1.
The energy structure of the GaAs/AlGaAs QW.
In such a structure, the electron moves from a ground quantum level Ee1 to the first excited level Ee2 at the absorption of the photon with the energy ħω. In contrast to the bulk case, the probability of intersubband transitions depends on the direction (polarization) of the electric field in the incident electromagnetic wave. In this case, the matrix element of the intersubband transition from the i-th level to the j-th level obtained in the first order of the perturbation theory is proportional jk′e∙p̂ik [8], where e is the polarization vector of a electromagnetic wave and p̂ is the momentum operator. In case of the polarization of the electromagnetic wave in the QW plane (in x or y directions), the polarization vector is equal to e = (1, 0, 0) or e = (0, 1, 0), whereas the operator is equal to −iℏ∂∂x or −iℏ∂∂y, respectively. Thus, under the influence of this operator upon the total wave function
ΨikR=A−12ϕizeik∙rE1
such expressions as ℏkx, y〈jk′| ik〉 being zero at the i ≠ j resulted from the orthogonality of the wave functions are to be obtained. The constant A in the expression (Eq.(1)) is the area of the quantum well.
Conversely, when the electromagnetic wave is polarized along the z axis, the operator ep̂ is equal to −iℏ∂∂z and the expression jk′e∙p̂ik changes as:
jk′e∙p̂ik=−iℏA−12∫ϕj∗z∂∂zϕizdz∫eik−k′∙rd2rE2
The first integral in expression (Eq.(2)) is nonzero for i and j with different parities, and the last integral is nonzero for k = k′. Therefore, optical transitions in the QW occur only in the presence of polarization in an electromagnetic wave along the coordinate z, i.e., in the direction of the heterostructure growth.
The concentration of the two-dimensional electron gas (2DEG) in the photodetector structures should be large enough to locate the ground quantum level below the Fermi level. The required position of the Fermi level is achieved at the 2DEG concentration (N2D) approximately equal to (4−5) × 1011 cm−2. This electron density is distributed over the QW width in proportion to the square of the ground state wave function. According to the Poisson equation, such a large number of negative charges result in the appearance of a negative curvature of the conduction band bottom in the quantum well. It is worth mentioning that the middle part of the QW is maximally distorted, where the value of the square of the cosine is maximum. However, it is the middle part of the QW that is commonly doped to fill the QWs with electrons. A positive charge of the ionized donors bends the bottom of the QW back and downward. Consequently, the quantum wells remain almost rectangular shape under such a method of doping. The potential diagrams, the energy of the quantum levels, and wave functions in such rectangular AlGaAs/GaAs/AlGaAs quantum wells calculated by the nextnano program are shown Figure 2A [9].
Figure 2.
The calculated potential diagrams and wave functions for AlGaAs/GaAs/ AlGaAs QWs: (A) doped in the middle part of the quantum well; (B) with the one-side δ-doping with the 5-nm spacer.
The doping of the structure outside the GaAs QW (so-called the modulation-doped QWs) leads to significant changes in the QW shape (Figure 2B). Actually, the separation of electrons and ionized donors causes the appearance of an electric field and, therefore, a noticeable bending of the band next to the quantum well region. Therefore, the energy of the quantum levels changes. The calculated energies of the ground and first-excited quantum levels resulted from a self-consistent calculation proved to be equal to 3.2/140.9 meV and −3.1/133.7 meV for the QW doped in middle part and the modulation-doped QW, respectively. These values are given with respect to the Fermi level that lies at the energy ε = 0. As one can easily estimate, the shift in the quantum levels resulted from the changes in the doping location leads to a shift in the intersubband absorption band by 0.5 μm.
The nomograms of the dependence of the intersubband transition line upon the QW width and the barrier height were calculated by the self-consistent solution of the Schrödinger and Poisson equations to compare the rectangular and modulation-doped QWs. Figure 3 shows the calculated nomogram for the QW doped in the middle part. As one can see from Figure 3, λmax is weakly dependent on the QW width in the wavelength region (8–10 μm) worth examining, whereas the required wavelength of the IR absorption is more difficult to obtain in the modulation-doped QWs. One more parameter is added, and the energy of the intersubband transition from the ground to the first excited level depends not only on the well width and the barrier height (or the Al mole fraction in the barrier) but also on the doping level. Therefore, the calculation was made for the modulated-doped QW at a fixed QW width equal to 5.6 nm (Figure 4).
Figure 3.
The nomogram showing the dependence of the wavelength upon the quantum well width and the barrier height (molar aluminum mole fraction) for a quantum well with the middle doped part.
Figure 4.
The nomogram showing the dependence of the wavelength upon the 2DEG concentration and the barrier height (aluminum mole fraction) in the modulation-doped QWs.
The dark current flowing in the thermal equilibrium through the photodetector under a bias is an important characteristic of a QWIP. Its value is frequently used as a QWIP quality criterion. In QWIPs, the physical reason for the appearance of a dark current is the thermionic electron emission from the ground-filled quantum level to continuum states both above and below the energy barrier Ec0 (Figure 1), due to its tunneling through the triangle barrier in the electric field.
The dark current density (Id) in the structure shown in Figure 1 consists of the thermoactivation and tunneling components, and it has the following general form [8]:
JE=q∙νEm∗πℏ2LW+LB∫ε1∞fεTTεEdEE3
where νE=μE1+μEνsat2 is the average electron drift velocity in the AlGaAs barrier layers, νsat = 2.9×106 cm/s is the saturation velocity in the multiple quantum well (MQW), μn = 103 cm2/V is the electron mobility in the AlGaAs with х≈ 0.3, ε1 is the energy of the ground level in the quantum well. The fourth multiplier in (Eq. (3)) determines the concentration of the carriers participating in the conductivity, f(ε) is the Fermi distribution function of the 2DEG, and Т(ε,E) is the probability of tunneling of the electrons from the GaAs layer to the states above the barrier. The electric field dependence of the Т(ε,E) reflects the effective barrier lowering for the hot electrons with a total energy ε ≅ Vb. We can assume with good accuracy that for ε > Vb, the coefficient is Т(ε,E) ≈ 1, whereas for ε < Vb, the coefficient is Т(ε,E) = 0.
The modeling with the help of nextnano has shown that the barrier lowering in an electric field can be well described by the expression:
VbE=Vb0−qELW2E4
Thus, the current density through the heterostructure is finally obtained as:
jE=q∙νEm∗πℏ2LW+LBexpVb0−qELW2−ε1+εFkTE5
where k is the Boltzmann constant and T is the temperature. To calculate the dependence of the current density upon the donor concentration, the dependence of the activation energy Vb0−ε1+εF upon the donor concentration for the QW of 5.7 nm width and the aluminum mole fraction in the barriers x = 0.25 was calculated by the nextnano program at T = 77 K. The doped middle part of the quantum well was 3.5-nm thick. The calculation results are shown in Figure 5.
Figure 5.
Dependence of the activation energy of the QWIP dark current upon the donor concentration.
As one can see from Figure 5, the barrier height decreases at the enhanced doping, which is caused by an increase in the Fermi level accompanied by an increase in the DEG concentration from 1.4 × 1011 to 3.4 × 1011 cm−2.
Figure 6 shows the calculated dependences of the dark current density upon the applied electric field for different donor concentrations.
Figure 6.
Dependence of the QWIP dark current upon the applied electric field for different donor concentrations. The donor concentration is indicated in units of 1018 cm−3 in the figure.
The optimum 2DEG concentration corresponding to the minimum value of the threshold power of the radiation (NEP) is estimated as follows. The value of NEP is given by:
NEP=InRE6
where In is the noise current, R is the responsivity. The noise current depends on the dark current Id as follows [10]:
In2=4qgnIdΔfE7
where gn is the coefficient of the noise gain and Δf is the width of the noise band. As one can see from the expression (Eq.(5)), the dark current increases exponentially at the increasing N2D. Thus, for the degenerate 2DEG, the current depends on the 2DEG concentration N2D (which is directly proportional to the donor concentration) as:
Id∼expN2Dπℏ2/m∗kT−1E8
In its turn, the responsivity is proportional to the absorption coefficient of a single QW α, which, in turn, is proportional to the DEG concentration [10]:
where ρ = πℏ2N2D/m∗kT. The minimum value of the expression given is achieved under the condition ρ = 1.6 that is N2D = 2.7 × 1011 or N = 5.1 × 1017 cm−3 for the quantum well of 5.3 nm width at the QWIP operating temperature of T = 70 K.
Thus, the maximum sensitivity of the GaAs/AlGaAs QWIP is expected to be around 8.6 μm to ensure the spectral range of the detector (8–10 μm). The calculations have shown that this condition is satisfied by such parameters of the doped QW as the well width LW = 5.3 nm and aluminum mole fraction x = 0.25. When the ground state energy level is ε1 = 66.8 meV, the energy of the first excited level is ε2 = 206.0 meV, and the barrier height is Vb = 211.0 meV that corresponds to a fairly accurate coincidence of the first-exited quantum level and the edge of the potential barrier.
3. Properties of the heteroepitaxial GaAs/AlGaAs MQW structures
Multiple quantum wells GaAs/AlGaAs were obtained by the MBE on the GaAs (100) substrate with a buffer consisting of a 0.4-μm GaAs layer. The doped GaAs layers of 1.5 μm thickness were used as a base contact, whereas the upper ohmic contact was provided by the doped GaAs of 1.2 μm thickness.
It has been found that the best structural perfection of epitaxial GaAs layers and the greatest value of the carrier mobility can be obtained at a growth under conditions ensuring a (3 × 6) surface structure having a stoichiometric composition on the growth surface and being intermediate between the “As-stabilized” (2 × 4) and “Ga-stabilized” (4 × 2) surfaces. At the growth under such conditions, the concentrations of the gallium and arsenic vacancies on the surface are minimal, which provides a minimum concentration of the intrinsic point defects in the crystal. The deviation of the growth conditions toward the gallium or arsenic stabilization leads to the enrichment of the epitaxial layers by the defects of anion or cation sublattices, respectively. Since the (3 × 6) surface is observed in the narrow ranges of the substrate temperature and V/III flow ratio, the growth conditions around the transition from (2 × 4) to (3 × 6) surface can be recommended in order to obtain good process reproducibility.
Such growth conditions being applied, the necessary homogeneity and concentration of the growth defects across the wafer can be achieved. The distribution of the growth defect (“oval” defect) density is shown in Figure 7. One can see that the average density of the defects of 100–200 pcs/cm2 can potentially lead to defectiveness of the photosensitive element matrix by no more than 0.2% (The number of pixels in the array of the photodetectors is 100–300 thousands). A small size of the growth defect in 4–6 microns suggests that even if one pixel is damaged, the neighboring pixels will remain unaffected.
Figure 7.
Distribution of surface defects of the epitaxial QWIP structure. The distribution of the density of defects (left) and defect sizes (right).
The structure of the samples was examined by the method of studying the cross section of structures by the transmission electron microscopy (TEM). The studies were performed by the JEM-4000EX electron microscope by JEOL (Japan). The samples for the TEM were made in the geometry of the cross section by grinding them with their subsequent thinning during etching by Ar+ ions with an energy of 3–4 keV. The survey was conducted at an accelerating voltage of 400 kV. Figure 8 shows the results of the TEM AlGaAs/GaAs heterostructure. The image represents no structural defects, which indicate a high quality of the heterostructures under study. The measurements carried out by the TEM methods demonstrate a good correspondence of the heterostructure layer thicknesses planned and obtained. Sharp changes in the intensity at the quantum well heterogeneities testify to fairly sharp and smooth transitions from one material to another.
Figure 8.
The cross section of the QWIP heterostructure.
The spectra of the piezomodulated reflection of the MQW structure in a visible spectral range at a liquid nitrogen temperature were measured to control the aluminum mole fraction in the AlGaAs barrier layers. Mechanical vibrations of the ceramic plate caused the modulation of the mechanical stresses in the structure, the modulations of the real Δεr and the imaginary Δεi parts of the dielectric constant, and as a consequence, the modulation of the reflection coefficient. The reflection variances ΔR are related to the variation of the dielectric constant by the Seraphin ratio [12]:
ΔRR=αΔεr+βΔεiE11
where α and β are Seraphin coefficients. The energy position of the peaks in the piezoreflection spectrum corresponds to the electronic transitions in the structure under study. Typical piezoreflection spectra of the MQW structure are shown in Figure 9A. The aluminum mole fraction in the barrier layer is calculated from the peak energy. The peak at the 1.484 eV corresponds to the transitions from the levels of residual acceptors (neutral carbon atoms) to the conduction band in the GaAs substrate. The band gap of the GaAs at the 77 K is Eg(GaAs) = 1.508 eV [13], and the binding energy of the carbon atom is Δ = 25 meV. Therefore, the calculated transition energy is expected to be Eg(GaAs)-Δ = 1.483 eV, which is in a good agreement with the experimental results. The peak at the 1.839 eV (or 674 nm) corresponds to the bound excitons in the AlXGa1-XAs barrier layers. The intermediate peaks between the 1.484 and 1.839 eV energies correspond to the transitions between the hole (light and heavy) and electron quantum levels in the MQWs [14]. Thus, the aluminum mole fraction in the barriers is determined from the energy position of the 1.839 eV line by a well-known relation between the bandgap width of the AlGaAs layer and the aluminum mole fraction (x) as Eg(x) = Eg(0) + 1.427x + 0.041x2 [15].
Figure 9.
(A) The spectra of the piezomodulated reflection of the MQW structure. (B) The photoluminescence spectra of the MQW structure. The YAG laser was used to excite the PL.
The measurement of the QW width was carried out by means of the photoluminescence (PL) spectroscopy at a liquid nitrogen temperature along a line corresponding to the transition from the ground electron level to the ground level of the heavy holes e1-h1. According to calculations in the approximation of a rectangular quantum well of a finite depth for a quantum well with a width of 5 nm and an aluminum mole fraction of 0.26, the energies of the electron and hole ground quantum levels are 70 and 15 meV, respectively. Consequently, the energy of the e1-h1 transition at a room temperature is expected about 1.6 eV. As one can see from Figure 9B, the measured maximum of the PL band e1-h1 is located at 1.61 eV, which indicates the grown thickness of the layers being sufficiently accurate.
To control the electron concentration in the grown structures with MQWs, the C-V characteristics were measured. Those measurements were carried out with the specially fabricated Schottky barriers of the TiAu with a diameter of 200 μm and a C-V bridge operating at frequencies of 1 to 100 kHz. The upper contact layer had been previously etched and then measured. According to the measured C-V dependences, the concentration dependences upon the depletion region depth N2D(W) are determined from the relation:
N2DW=2qεε0S2dC2dV−1E12
where q is the electron charge, C is the measured capacitance, V is the applied voltage, S is the area of the Schottky barrier, ε and ε0 are the permittivity of the semiconductor and vacuum, respectively. The depth W, where the concentration of free carriers is determined, is W = εε0S/C, whereupon, the dependence of the sheet concentration with precision to constant upon the depletion region depth ∆Γ(W) was obtained by integrating N2D(W) (Figure 10). As one can see from the figure, all the experimental data obtained from different sample points agree with a high accuracy, which indicates a high uniformity of the electron density in the quantum wells over the wafer area. It should be noted that this method can be duplicated by a capacitive method where the mercury probe and profilometer operating at a frequency of 1 MHz are applied to measure the derivative.
Figure 10.
Distribution of the sheet concentration of the charge carriers ΔΓ in the quantum wells for six different points. These points are located along the wafer radius.
To calculate parameters of the multiplexer (capacitance and integration time), it is necessary to know the field dependences of the dark current. The measured dependencies of the dark current upon the bias for the QWIP structure made up of 30 periods of QW and barriers (Lb = 40 nm) with the pixel area 20 × 20 μm are presented in the Figure 11. As one can see from the figure, the current asymmetry is observed, which results from the segregation of the impurity atoms during the structure growth.
Figure 11.
Field dependences of the dark current in the QWIP structure (the donor concentration is 2.5 × 1011 cm−2) for various current directions at the 77 K temperature.
4. Selecting the parameters of the FPA on the basis of the GaAs/AlGaAs QWIP
While choosing a number of QW periods in the QWIP structure, the following must be taken into account: (A) an increase in a number of structure periods leads to an increase in the absorption coefficient, but, in turn, (B) the overall probability of capturing photoexcited electrons back into the QW, (C) the growth time of the heterostructure, (D) the mesa depth, and (E) the magnitude of the voltage applied to the structure increase as well. As a result, the sensitivity of the QWIP increases very weakly with an increasing number of the GaAs/AlGaAs layer periods [11]. Therefore, a heterostructure with 30 periods of the GaAs QW has been chosen.
A 2D diffraction grating with the parameters determined by the spectral range and properties of the dielectric applied is required to provide the absorption of a normal-incident light and increase the quantum efficiency of the QWIP [16]. The required etching depth of the lattice (d) is determined by the relation d = λ/4n, where λ is the radiation wavelength and n is the refractive index of the GaAs. These parameters are λ = 8–9 μm and d = 0.7–0.75 μm for the wavelength range applied. The period of the diffraction grating L = 2.8 μm was chosen so that the direction of the first diffraction order was an angle of 60° with the normal to the sample wafer.
The thickness of the upper contact layer of n+ doped GaAs was increased up to 1200 nm to adapt the fabrication technology for ohmic contacts. At a lower thickness of the contact layer, the metal may penetrate into the QW region as a result of diffusion during a long (5 minutes) annealing.
The multiplexer capacity and integration time of the signal were estimated by the dependency dark current upon the bias at the 77 K (Figure 11). The current at the bias voltage equal to 0.5 V is about 10−10 A, which, in the presence of the capacitance in the multiplexer equal to 6 × 106 electrons, allows integrating the signal during 10 ms.
5. The fabrication technology for the FPA on the basis of the GaAs/AlGaAs QWIP
The focal plane arrays on the basis of GaAs/AlGaAs QWIP structures were fabricated by the complex of coordinated technological operations based on photolithography processes using functional dielectric and metallic layers, etching processes, and chemical treatments of the heterostructures in the regimes determined by the physicochemical properties of the MQW layers. The technology development was carried out at the FPA with 384 × 288 elements with the 25 microns pitch. The structure control was carried out by the optical and scanning electron (LEO-1430, SU8220) microscopes.
5.1. Formation of the diffraction grating and mesa structure
A diffraction grating and mesa structures were formed by dry anisotropic etching of the GaAs layer in a remote gas-discharged plasma (ICP RF) of the BCl3, argon, and nitrogen. The obtained diffraction grating holes are square shaped with rounded corners and vertical walls (Figure 12A). The view of the mesa structure walls formed by etching the GaAs at a given depth (2.4 μm) at the optimum ratios of reagent gases, the power of the RF and ICP generators, the reactor pressure, substrate temperature, heating, and etching time are shown in Figure 12B.
Figure 12.
SEM images of (A) the diffraction grid and (B) the gap (2 μm) between the mesa structures of the FPA 384 × 288 elements obtained by dry etching with the 0.5-μm SiO2 layer.
5.2. The fabrication of ohmic contacts
The ohmic contacts to the base and upper n+ doped GaAs layers (on the mesa surface) were fabricated by the Ge/Au/Ni/Au (20/20/20/100 nm) deposition [17] after the removal of the native oxide layer from the semiconductor surface by HCl:Н2O (1:8) and annealing during 5 minutes at the 385°C in a hydrogen (Figure 13).
Figure 13.
SEM image of the mesa structure of FPA 384 × 288 elements with a diffraction grating and the Ge/Au/Ni/Au (20/20/20/100 nm) metallization layer.
5.3. The mesa structure surface passivation
The SiO2 dielectric layer was deposited by a low-pressure chemical vapor deposition (LP CVD) method at temperatures of 195–250°C to passivate and protect the mesa structure surface. The low temperature of this process excludes the disorder of the surface stoichiometric composition due to the evaporation at high temperatures of the fifth group element. Depending on the synthesis conditions, the layers of LP CVD SiO2 (refractive index 1.46) have a dielectric constant of 5.9–6.5 and leakage currents of 6 ×·10−8–9·× 10−7A/cm2 (Е = 2.106 V/cm) at the room temperature with a water content of 2.5–3.3 volume percent, respectively. The SiO2 formation at the semiconductor surface results in coating the vertical walls of the mesa structures (Figure 12B).
5.4. Production of indium bumps on the FPA and silicon multiplexers
To assemble a FPA by cold welding, indium bumps were fabricated both on the GaAs/AlGaAs QWIP structure and silicon multiplexers. The Tl xLift photoresist was used to produce indium bumps (height of ∼5 μm) by inverse photolithography. The view and cleavage of a separate mesa structure with an indium bump of the FPA of 384 ×·288 elements produced by the developed technology on the GaAs/AlGaAs heterostructures are shown in Figure 14.
Figure 14.
SEM images (A) of the mesa structure and (B) mesa structure cleavage of the FPA with 288 × 384 pixels on the basis of GaAs/AlGaAs QWIP structures with indium bumps.
5.5. Hybrid assembly of the FPA
The FPA modules were assembled by cold welding of the indium bumps under pressure [18]. The FPA and multiplexer crystals were docked on the M9 setup of Laurier company. The fusion of the indium contacts was performed by heating the module up to the indium melting temperature with the succeeding cooling. The surface autoplanarization is provided during the crystal compression process, which is achieved by installing polyamide stoppers along the perimeter of one of the module elements—an array or multiplexer. The maximum limit mechanical load is determined experimentally from the measurement of the curves of the plastic flow of the indium bumps, their height, and area. The pressure required for the plastic flow of the contacts ranges from 0.3 to 0.9 kg/mm2. The total height of the indium bumps after the FPA assembly is 6–8 μm, which satisfies the requirements of the durability of the FPA hybrid assemblies [19].
5.6. The GaAs substrate removal from the FPA assembly
The technology of the substrate removal after the assembly of the FPA consisted of the successive processes of the mechanical grinding aimed at removing the main thickness of the GaAs substrate, chemical mechanical polishing and chemical dynamic polishing, in order to obtain a mirror-smooth surface of the array crystal. The processes of the chemical selective etching of the GaAs and heterostructure layers were applied to remove the GaAs substrate from the FPA surface completely (Figure 15) [20, 21, 22].
Figure 15.
The photo of the FPA assembly with 384 × 288 elements (A) before and (B) after the removal of the GaAs substrate (650 μm).
5.7. Technical characteristics of the multiplexer
The silicon multiplexers by Integral Joint Stock Company (IZ640FD format 640 × 512) made by the CMOS technology and meeting the QWIP requirements were used as a part of the FPA assembly [23].
The storage capacitance in every cell of the IZ640FD multiplexer was approximately 8 × 106 electrons with a reading noise of 1000 electrons. The adjustable integration time could vary from 100 microseconds to the entire duration of the frame scanning. The electric power consumed at a frame rate of 100 Hz did not exceed 120 mW. The electric power consumed at a frame rate of 100 Hz did not exceed 140 mW in the four output mode and 90 mW at a switch into one output mode, respectively.
The schematic diagram of the input block of the multiplexer is shown in Figure 16, where D is the photoresistor (detector), VD is the detector supply voltage, VB is the voltage specifying the detector bias voltage, VA is the voltage specifying the level of anti-burglary, VS is the skimming voltage, C1 is the integration capacitance in pixel, C2 is the storage capacity in pixel, C3 is the storage capacity in column, K1, K2, and K3 are the keys, A is the amplifier with a controlled gain, and B is the buffer. The signal integration occurs simultaneously on all the array elements, and then, the voltage from the capacitances C2 is line-by-line read out by connecting the key K2 to the column capacitance C3 and the column amplifier A.
Figure 16.
Schematic diagram of the input multiplexer for the FPA.
6. Opto-electronic characteristics of the FPA assembly
At the final stage, the opto-electronic characteristics of the fabricated FPA assembly were determined. For this purpose, the assembly was placed in a nitrogen cryostat with an entrance window made of the ZnSe. The operating temperature of 65 K was achieved due to the pressure pumped down by a vacuum pump. A cooled diaphragm provided a relative aperture of 1:2. To measure the sensitivity of the FPA, the module illumination was made by an extended-type absolutely black body. It should be noted that the high parameters of the FPA assembly both the absolute values of the signals and their homogeneity with respect to the array elements are supposed to be essential.
The distribution histogram of the total current (dark + photo signal from the 300 K background) for the FPA assembly thinned up to 170 μm is shown in Figure 17A. The integration time of the signal was chosen to be 9 ms. Figure 17B shows the distribution histogram of the temperature sensitivity of the ST at the 300 K background upon the pixels of the FPA module BM20. Its average value is rather high and equal to 23.2 mV/K. Figure 17C shows the distribution of the noise voltage Vn at the output of the photoreceptor module BM20 at a background of 300 K. All the histograms are rather narrow, which demonstrates the high uniformity of the array parameters. The noise equivalent temperature difference NETD = Vn/ST of the FPA module BM20 is shown in Figure 17D. The average value of a NETD for nondefective pixels at the FPA temperature of 67 K is 22.2 mK. A number of defective elements with a NETD over 70 mK is 0.15%. A typical spectrum of photosensitivity of a 640 × 512 FPA is shown in Figure 18. An example of the IR image detected by a 640 × 512 FPA assembly module equipped by the germanium lens with D/F = 1:2 aperture is shown in Figure 19.
Figure 17.
(A) The histogram of the total current distribution of the 640 × 512 FPA module BM20. (B) The histogram of the temperature sensitivity at a background of 300 K of the 640 × 512 FPA module BM20. (C) The histogram of the noise voltage of the 640 × 512 FPA module BM20. (D) Experimentally measured NEΔT histogram of the 640 × 512 FPA module BM20.
Figure 18.
Photosensitivity spectrum of the FPA.
Figure 19.
The example of an IR image. The temperature is 65 K. The integration time is 6 ms. The working elements are 99.6%.
The developed technology for the FPA assembly is reproducible and has a rather high yield percentage of the suitable products well seen from Figure 20 showing the scatter of the noise equivalent temperature differences and a number of defective elements for the 640 × 512 FPA assembly manufactured on the 5 MBE grown heterostructures.
Figure 20.
(A) Scatter of NETD and (B) a number of defective elements in the 640x512 FPAs produced on the five grown QWIP structures.
6.1. Integrated detector cooler assembly
The fabricated and tested 640 × 512 FPA were installed in the body of a vacuum cryostat integrally coupled with a microcryogenic system. Resulted integrated detector cooler assembly (IDCA) is showed in Figure 21. A vacuum cryostat performs thermal isolation of the array from the environment to guarantee effective cooling of the photodetector up to the operating temperature (T = 68–72 K). The radiation from the detected objects is fed to the FPA through an input window made up of the germanium with the antireflection in the range of 8–10 μm and a cooled diaphragm with a relative aperture F/2 designed to reduce the background illumination. A low pressure in the vacuum cryostat is provided by a getter, whose reactivation is carried out by passing an electric current through it. Typical operating temperatures for the FPA based on the QWIP with the wavelength range 8–10 μm are 68–72 K. Thus, the powerful microcryogenic systems ensuring a cooling capacity at an operating temperature of 70 K not less than 0.4 W and the power consumption not more than 20 W are needed to provide the required temperature in a full range of climatic conditions.
Figure 21.
The photo of the QWIP IDCA.
As one can see from Table 1, the parameters of the developed 640 × 512 QWIP IDCA are comparable with those of the Sofradir products.
ISP SB RAS (Russia) BM20
Sofradir (France) SIRIUS-LW
Array format
640 × 512
640 × 512
Pixel pitch, μm
20
20
Peak sensitivity, μm
8.5–8.6
8.5
FWHM, μm
0.8–1
1
NETD, mK
<35
<35
Operability, %
>99.5
99.9%
Integration time, ms
6
7
Operating temperature, K
72
70–73
Table 1.
Comparative characteristics of the GaAs/AlGaAs QWIP IDCA by the ISP of the SB RAS (Novosibirsk) and Sofradir (France).
7. Conclusions
The technology of manufacturing the AlGaAs/GaAs QWIP FPA has been discussed. The parameters of a FPA of 640 × 512 format with a 20-μm pitch for a spectral range of 8–10 μm have been described. At an operating temperature of 72 K, the temperature resolution of the QWIP FPA is less than 35 mK. The frame rate is 100 Hz. A number of defect elements in the array do not exceed 0.5%. It is shown that the parameters of the QWIP FPA fabricated by Rzhanov Institute of Semiconductor Physics of SB RAS meet the world class standards.
\n',keywords:"GaAs/AlGaAs quantum well infrared photodetector, focal plane array, multiplexer, dark current, noise equivalent temperature difference",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/57362.pdf",chapterXML:"https://mts.intechopen.com/source/xml/57362.xml",downloadPdfUrl:"/chapter/pdf-download/57362",previewPdfUrl:"/chapter/pdf-preview/57362",totalDownloads:1178,totalViews:765,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"May 11th 2017",dateReviewed:"September 26th 2017",datePrePublished:"December 20th 2017",datePublished:"April 4th 2018",dateFinished:"October 27th 2017",readingETA:"0",abstract:"In this article, we present an overview of a focal plane array (FPA) with 640 × 512 pixels based on the AlGaAs quantum well infrared photodetector (QWIP). The physical principles of the QWIP operation and their parameters for the spectral range of 8–10 μm have been discussed. The technology of the manufacturing FPA based on the QWIP structures with the pixels 384 × 288 and 640 × 512 has been demonstrated. The parameters of the manufactured 640 × 512 FPA with a step of 20 μm have been given. At the operating temperature of 72 K, the temperature resolution of QWIP focal plane arrays is less than 35 mK. The number of defective elements in the matrix does not exceed 0.5%. The stability and uniformity of the FPA have been demonstrated.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/57362",risUrl:"/chapter/ris/57362",book:{slug:"two-dimensional-materials-for-photodetector"},signatures:"Michael A. Dem'yanenko, Dmitry G. Esaev, Aleksandr I. Toropov,\nNatalia A. Valisheva, Sergey A. Dvoretsky, Dmitry V. Dmitriev, Dmitry\nV. Gulyaev, Vladimir A. Fateev, Igor V. Marchishin, Dmitry Yu\nProtasov, Anatoly P. Savchenko, Victor N. Ovsyuk and Konstantin\nZhuravlev",authors:[{id:"96746",title:"Dr.",name:"Sergey",middleName:null,surname:"Dvoretsky",fullName:"Sergey Dvoretsky",slug:"sergey-dvoretsky",email:"dvor@isp.nsc.ru",position:null,institution:null},{id:"210910",title:"Dr.",name:"Konstantin",middleName:null,surname:"Zhuravlev",fullName:"Konstantin Zhuravlev",slug:"konstantin-zhuravlev",email:"zhur@isp.nsc.ru",position:null,institution:null},{id:"220672",title:"Dr.",name:"Michael",middleName:null,surname:"Demyanenko",fullName:"Michael Demyanenko",slug:"michael-demyanenko",email:"demyanenko@isp.nsc.ru",position:null,institution:null},{id:"220673",title:"Dr.",name:"Dmitry",middleName:null,surname:"Esaev",fullName:"Dmitry Esaev",slug:"dmitry-esaev",email:"esaev@isp.nsc.ru",position:null,institution:null},{id:"220674",title:"Mr.",name:"Igor",middleName:null,surname:"Marchishin",fullName:"Igor Marchishin",slug:"igor-marchishin",email:"march@isp.nsc.ru",position:null,institution:null},{id:"220675",title:"Dr.",name:"Natalia",middleName:null,surname:"Valisheva",fullName:"Natalia Valisheva",slug:"natalia-valisheva",email:"valisheva@isp.nsc.ru",position:null,institution:null},{id:"220676",title:"Dr.",name:"Aleksander",middleName:null,surname:"Toropov",fullName:"Aleksander Toropov",slug:"aleksander-toropov",email:"toropov@isp.nsc.ru",position:null,institution:null},{id:"220677",title:"Mr.",name:"Dmitry",middleName:null,surname:"Dmitriev",fullName:"Dmitry Dmitriev",slug:"dmitry-dmitriev",email:"ddmitriev@isp.nsc.ru",position:null,institution:null},{id:"220678",title:"Dr.",name:"Dmitry",middleName:null,surname:"Protasov",fullName:"Dmitry Protasov",slug:"dmitry-protasov",email:"protasov@isp.nsc.ru",position:null,institution:null},{id:"220679",title:"Dr.",name:"Dmitry",middleName:null,surname:"Gulyaev",fullName:"Dmitry Gulyaev",slug:"dmitry-gulyaev",email:"gulyaev@isp.nsc.ru",position:null,institution:null},{id:"222336",title:"Prof.",name:"Victor",middleName:null,surname:"Ovsyuk",fullName:"Victor Ovsyuk",slug:"victor-ovsyuk",email:"acelle@isp.nsc.ru",position:null,institution:null},{id:"222338",title:"Mr.",name:"Anatoly",middleName:null,surname:"Savchenko",fullName:"Anatoly Savchenko",slug:"anatoly-savchenko",email:"sap@isp.nsc.ru",position:null,institution:null},{id:"222341",title:"Ms.",name:"Vladimir",middleName:null,surname:"Fateev",fullName:"Vladimir Fateev",slug:"vladimir-fateev",email:"vafateev@isp.nsc.ru",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The physical model of the QWIP",level:"1"},{id:"sec_3",title:"3. Properties of the heteroepitaxial GaAs/AlGaAs MQW structures",level:"1"},{id:"sec_4",title:"4. Selecting the parameters of the FPA on the basis of the GaAs/AlGaAs QWIP",level:"1"},{id:"sec_5",title:"5. The fabrication technology for the FPA on the basis of the GaAs/AlGaAs QWIP",level:"1"},{id:"sec_5_2",title:"5.1. Formation of the diffraction grating and mesa structure",level:"2"},{id:"sec_6_2",title:"5.2. The fabrication of ohmic contacts",level:"2"},{id:"sec_7_2",title:"5.3. The mesa structure surface passivation",level:"2"},{id:"sec_8_2",title:"5.4. Production of indium bumps on the FPA and silicon multiplexers",level:"2"},{id:"sec_9_2",title:"5.5. Hybrid assembly of the FPA",level:"2"},{id:"sec_10_2",title:"5.6. The GaAs substrate removal from the FPA assembly",level:"2"},{id:"sec_11_2",title:"5.7. Technical characteristics of the multiplexer",level:"2"},{id:"sec_13",title:"6. Opto-electronic characteristics of the FPA assembly",level:"1"},{id:"sec_13_2",title:"6.1. Integrated detector cooler assembly",level:"2"},{id:"sec_15",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'West LC, Eglash SJ. First observation of an extremely large-dipole infrared transition within the conduction band of a GaAs quantum well. Applied Physics Letters. 1985;46:1156. DOI: 10.1063/1.95742'},{id:"B2",body:'Levine BF, Choi KK, Bethea CG, Walker J, Maliл RJ. New 10 μm infrared detector using intersubband absorption in resonant tunneling GaAlAs superlattices. Applied Physics Letters 1987;50:1092. DOI: http://dx.doi.org/10.1063/1.97928'},{id:"B3",body:'Rafol SB, Cho E, Lim W. Characterization of very large format 1K×1K LWIR QWIP focal plane array. Proceedings of SPIE. 2007;6678:6678X-1. 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DOI: http://dx.doi.org/10.1117/12.2228348'},{id:"B8",body:'Davies JH. The Physics of Low-Dimensional Semiconductors: An Introduction. Cambridge: Cambridge University Press; 1998. 438 p'},{id:"B9",body:'Nextnano. Software for semiconductor nanodevices [Internet]. 2017. Available from: www.nextnano.de [Accessed: 2017-10-25]'},{id:"B10",body:'Schneider H, Liu HC. Quantum Well Infrared Photodetectors: Physics and Applications. Heidelberg: Springer; 2007. 248 p. DOI: 10.1007/978-3-540-36324-8'},{id:"B11",body:'Levine BF. Quantum well infrared photodetectors. Journal of Applied Physics. 1993;74(8):R1. DOI: http://dx.doi.org/10.1063/1.354252'},{id:"B12",body:'Seraphin O, Bottka N. Band-structure analysis from electro-reflectance studies. Physical Review. 1966;145:628. DOI: https://doi.org/10.1103/PhysRev.145.628'},{id:"B13",body:'Lee YR, Ramdas AK, Moretti AL, Chambers FA, Devane GP, Ram-Mohan LR. 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DOI: 10.1016/S0040-6090(97)00439-2'},{id:"B18",body:'Klimenko AG, Voinov VG, Novoselov AR, Nedosekina TN, Vasilyev VV, Zakhariash TI, Ovsyuk VN. Soft indium bumps for indium bump bonding FPAs on CdHgTe. Optoelectronics Instrumentation and Data Processing C/C of Avtometriia. 1998;4:87'},{id:"B19",body:'Smith HI. Fabrication techniques for surface-acoustic-wave and thin-film optical devices. Proceedings of the IEEE. 1974;62:1361. DOI: 10.1109/PROC.1974.9627'},{id:"B20",body:'Zhao R, Lau WS, Chong TC, Li MF. A comparison of the selective etching characteristics of conventional and low-temperature-grown GaAs over AlAs by various etching solutions. Japanese Journal of Applied Physics. 1996;35:22. DOI: https://doi.org/10.1143/JJAP.35.22'},{id:"B21",body:'Spooner F, Quinn W, Hanes L, Woolsey S, Smith K, Maison J. A reproducible, high yield, robust wet etch etch-stop process using organic acid – peroxide solutions. [On-line Digest Table. The International Conference on Compound Semiconductor Manufacturing Technology (Sharing Ideas Throughout the Industry)]. [Internet]. Available from: http://csmantech.org/OldSite/Digests/2004/2004Papers/8.9.pdf [Accessed: 25 Oct 2017]'},{id:"B22",body:'Chang EY, Lai Y-L, Lee YS, Chen SH. A GaAs/AlAs wet selective etch process for the gate recess of GaAs power metal-semiconductor field-effect transistors. Journal of the Electrochemical Society. 2001;148:G4. DOI: 10.1149/1.1344555'},{id:"B23",body:'“INTEGRAL” Joint Stock Company [Internet]. Available from: HYPERLINK “http://www.integral.by/en” www.integral.by/en [Accessed: Oct 25, 2017]'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Michael A. Dem'yanenko",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Dmitry G. Esaev",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Aleksandr I. Toropov",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Natalia A. Valisheva",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Sergey A. Dvoretsky",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Dmitry V. Dmitriev",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Dmitry V. Gulyaev",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Vladimir A. Fateev",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
'},{corresp:null,contributorFullName:"Igor V. Marchishin",address:null,affiliation:'
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
Rzhanov Institute of Semiconductor Physics of SB RAS, Novosibirsk, Russia
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Noori, Christopher S. Woodhead and Robert J. 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1. Introduction
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The WHO 2016 classification integrates molecular and histological features in the diagnosis of gliomas. Among numerous genomic alterations, the isocitrate dehydrogenase (IDH) mutation is one of the most important genetic alterations found in this kind of tumor. As IDH mutation is a ubiquitous mutation in lower grade gliomas, the development of molecular target therapies against IDH mutations is expected. Here, we review IDH-mutant gliomas, focusing on their role in tumorigenesis and as novel therapeutic targets.
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2. Discovery of IDH mutations in cancers
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The presence of an isocitrate dehydrogenase (IDH) mutation was first discovered in colorectal cancers [1]. Parsons et al. [2] found mutations of the IDH1 (2q.33) in 12% of the glioblastomas (GBMs). Other large scale studies validated that IDH1 and IDH2 (IDH) mutations were found in the majority of secondary GBM and lower grade (WHO grade II and III) gliomas, whereas these were rarely found in adult primary and pediatric GBMs [2, 3, 4]. Almost all of the IDH1 mutations occur at codon 132, >90% of them exhibit a c.395G>A (R132H) substitution, followed by R132C [3, 5, 6]. Although the frequency was low, IDH2 mutations were also identified at codon 172 in gliomas [4, 7].
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Besides, IDH mutation was found in hematopoietic cancers, including acute myeloid leukemia (AML; 10–15%, IDH2) [8, 9], angioimmunoblastic T-cell lymphoma (AITL, 20%) [10], chondrosarcoma (~50%) [11, 12, 13], intrahepatic cholangiocarcinoma (15–20%, IDH1) [13], and at lower frequency in other hematopoietic and solid cancers, such as B-acute lymphoblastic leukemia (B-ALL), esophageal cancer, colorectal cancer, melanoma, prostate carcinoma, and breast adenocarcinoma [1, 4, 14, 15, 16].
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3. Tumorigenesis of IDH-mutant gliomas
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3.1 Genomic characteristics of IDH-mutant glioma
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The discovery of IDH mutations allowed the distinction of primary GBM, which is genetically characterized by TERT promoter mutation, gene alteration of epidermal growth factor receptor (EGFR), phosphatase and tensin homolog (PTEN) mutation or deletion, trisomy 7, monosomy 10, and cyclin-dependent kinase inhibitor 2A (CDKN2A) homozygous deletion, from secondary GBM (GBM, IDH-mutant) [3, 5, 17, 18].
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In astrocytic tumors, most of the tumor cells have co-mutations in IDH1, TP53, and ATRX. Moreover, WHO 2016 [19] defined the presence of IDH mutation and co-deletion of chromosome1p and 19q as necessary for the diagnosis of oligodendroglial tumors. Also, in oligodendroglial tumors, TERT promoter mutation is almost always present (>95%), while CIC and FUBP1 are commonly (>40%) observed. These genetic abnormalities for astrocytic and oligodendroglial tumors are mutually exclusive [20, 21, 22, 23, 24]. Importantly, the IDH mutation is the earliest genetic alteration observed; it is commonly retained during tumor progression [25, 26, 27, 28], but in a subset of mutants, IDH1 was either deleted or amplified at tumor recurrence [29], indicating the critical role of IDH mutation for tumorigenesis. It has also been shown that IDH mutations do not select or create ATRX or TERT promoter mutations [30].
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3.2 Developmental hierarchy in IDH-mutant gliomas
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Two recent large scale single cell RNA-sequencing studies revealed a developmental hierarchy in IDH1-mutant gliomas [31, 32]. Accordingly, IDH1-mutant astrocytoma and oligodendroglioma shared a similar developmental hierarchy, consisting of three subpopulations of malignant cells: nonproliferative astrocytic and oligodendrocytic cells, proliferative, and undifferentiated neural stem/progenitor cells. In contrast, tumor micro environment (TME) was different in the abundance microglia/macrophage cells between astrocytic and oligodendroglial tumors. TME also differs between astrocytic tumors of different grades. Though TME and genomic alterations are distinctive, evidence indicates the existence of common progenitor cells in IDH1-mutant gliomas. In higher grade tumors, undifferentiated glioma stem/progenitor cells were increased [32]. In addition, almost all proliferating cancer cells were composed of subpopulations of undifferentiated cells (stem-like) in oligodendroglioma [31], suggesting a significant role for undifferentiated cells in cell proliferation and malignant progression.
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3.3 IDH-mutant xenograft model
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Although IDH1 mutation induced proliferation in vitro [33], IDH1 mutation did not promote xenograft formation [34, 35, 36]. Intriguingly, Bardella et al. [37] demonstrated that IDH1R132H overexpression in the murine subventricular zone induced the formation of early gliomagenesis, where stem and transit amplifying/progenitor cell populations were expanded, indicating the pivotal role of IDH1 mutation in glioma formation. Moreover, Wakimoto et al. demonstrated that “tertiary mutations,” such as PIK3CA mutation, PDGFRA amplification, and MYC amplification, promote IDH1-mutant glioma formation in xenograft models. Importantly, tumor harboring tertiary mutations were associated with unfavorable prognosis in IDH1-mutant glioma patients [38]. Recently, large genomic analyses demonstrated that malignant progression in IDH1-mutant glioma is associated with the PI3K pathway and MYC activation [39, 40]. Thus, IDH mutation induces gliomagenesis, whereas tertiary mutations are critical to promote tumor progression in lower grade gliomas (Figure 1).
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Figure 1.
Genomic alteration and tumor microenvironment in IDH-mutant astrocytic and oligodendroglial tumors.
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4. The 2016 WHO classification
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The 2016 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) integrated phenotypic and genotypic parameters for CNS tumor classification. According to this classification, all diffusely infiltrating gliomas are grouped as diffuse astrocytic and oligodendroglial tumors. These tumors were histologically and genetically classified based on the presence of IDH mutation, co-deletion of chromosome1p and 19q, or ATRX and TP53 mutations. Accordingly, gliomas are classified as follows: (1) diffuse astrocytoma (WHO grade II) or anaplastic astrocytoma (AA, WHO grade III): IDH-mutant, -wildtype, or not otherwise specified (NOS); (2) oligodendroglioma (WHO grade II) or anaplastic oligodendroglioma (WHO grade III): IDH-mutant and 1p/19q-codeleted or NOS; (3) oligoastrocytoma (grade II) and anaplastic oligoastrocytoma (WHO grade III): NOS; (4) GBM (WHO grade IV): IDH-mutant, -wildtype, or NOS; and (5) diffuse midline glioma (WHO grade IV): H3K27M-mutant.
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IDH-wildtype GBM (about 90% of cases) is known as primary GBM, while IDH-mutant GBM (about 10% of cases) corresponds to secondary GBM [19].
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5. Epidemiology of IDH-mutant gliomas
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5.1 Age distribution of IDH-mutant gliomas
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According to some statistical analyses, the IDH-mutant GBM or anaplastic astrocytoma patients were more than 20 years younger than those with IDH-wildtype GBM [4]. In contrast, IDH-mutant GBM patients were only 4 years older than those with IDH1-mutant grade II and III astrocytoma [41]. This indicates that IDH-mutant glioma arises earlier than IDH-wildtype glioma (mostly GBM).
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5.2 Prognosis of IDH-mutant gliomas
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Parsons et al. [2] initially demonstrated that IDH1-mutant GBM patients survived about threefold longer than those with IDH1-wildtype GBM. Other groups verified that IDH1 mutation is a favorable prognostic biomarker in gliomas [4, 42, 43]. In addition to GBM, large amounts of clinical studies indicated that the IDH mutation was an independent prognostic factor in grade II and III gliomas [4, 28, 43, 44, 45, 46, 47]. Notably, the prognosis of IDH1-mutant GBM is better than of IDH1-wildtype AA [48]. Also, a prospective randomized study (NOA-04) revealed that IDH1 mutation, hypermethylation of the O6-methylguanine DNA-methyltransferase (MGMT) promoter, age, extent of resection, and oligodendroglial histology are independent prognostic factors in anaplastic gliomas [44]. Among them, the impact of IDH1 mutation conferred a stronger favorable prognosis than 1p/19q co-deletion, MGMT promoter methylation, and histology [44]. Collectively, IDH1 mutation is a convincing prognostic factor in gliomas, irrespective of tumor grade and histology.
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5.3 Prognostic classification for gliomas
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Suzuki et al. [28] distinguished lower grade gliomas on the basis of the presence of IDH1 mutation, TP53 mutation, and 1p/19q co-deletion. Accordingly, tumors were classified into three groups: type I (IDH1-mutant with 1p/19q co-deletion; favorable prognostic group), type II (IDH1-mutant with TP53 mutation; intermediate prognostic group), and type III (IDH1-wildtype; poor prognostic group). Eckel-Passow et al. [47] classified gliomas into five groups based on the mutation status of IDH1 and TERT promoter and on 1p/19q co-deletion. This group also demonstrated that TERT promoter mutations and ATRX alterations provided additional information for a tailored prognostic classification [49]. Besides, Arita et al. [50] proposed a classification of grade II–IV gliomas based on the mutations in IDH and the hotspot in TERT promoter.
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Among IDH-mutant astrocytic tumors, CDKN2A/B homozygous deletion was demonstrated to be an unfavorable prognostic molecular marker [51]. Similarly, another group demonstrated that PIK3R1 mutation and altered retinoblastoma pathway genes, including RB1 and CDKN2A, were independent predictors of poor survival in astrocytic tumors. In oligodendrogliomas, NOTCH pathway inactivation and PI3K pathway activation were associated with poor prognosis [52, 53]. Collectively, these molecular markers could predict prognosis in glioma patients.
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6. The mechanism of tumorigenesis in IDH1-mutant gliomas
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6.1 IDH mutation drives production of oncometabolite D-2-hydroxyglutarate
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In humans, IDH is composed of three types of isozymes (IDH1, IDH2, and IDH3). IDH1 is located in the cytoplasm and peroxisomes, whereas IDH2 and IDH3 are localized in the mitochondria and are involved in the TCA cycle. IDH1 and IDH2 are NADP+ dependent, whereas IDH3 is NAD+ dependent. IDH converts isocitrate into α-ketoglutarate (α-KG). No mutation in IDH3 has been detected in human cancers. If IDH is mutated, it blocks normal enzymatic activity and instead produces D-2-hydroxyglutarate (2-HG) from α-KG in an NADPH dependent manner, irrespective of the substituted amino acid [54, 55, 56]. Compared with IDH-wildtype cells, the 2-HG level in IDH-mutant cells is 50–100-fold higher [54, 57]. IDH mutations are almost always heterozygous, and both mutant and wildtype IDH1 alleles are required for 2-HG production in glioma cells [58].
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6.2 IDH-mutation induced epigenetic alterations
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6.2.1 IDH-mutation inducible DNA hypermethylator phenotype
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Since the structure of 2-HG is similar to that of α-KG, 2-HG inhibits a variety of α-KG-dependent dioxygenases [59, 60]. Among them, 10–11 translocation-2 (TET2) induces global demethylation of DNA by catalyzing the conversion of 5-methylcytosine (5-mC) to 5-hydroxymethylcytosine (5-hmC). Forced mutant IDH1 caused increased 5mC concentrations, instead of decreased 5hmC [37, 61]. IDH mutation also promotes methylation of DNA by TET2 inhibition, resulting in a glioma CpG island methylator phenotype (G-CIMP), a specific DNA methylation pattern in IDH-mutant tumor cells [61, 62, 63]. Indeed, forced overexpression of mutant IDH (IDH1R132H and IDH2R172K) produced high concentrations of 2-HG and increased global 5-mC levels [61]. Similarly, TET2 mutations, which are mutually exclusive to IDH mutations, induce a global hypermethylation signature [61]. Turcan et al. [64] demonstrated that a G-CIMP-like phenotype and G-CIMP positive proneural glioblastomas were formed after the introduction of an IDH1 mutation into normal human astrocytes (NHA). These data indicate that mutant IDH induced TET2 suppression, followed by G-CIMP, in cancer cells. Consistent with IDH-mutant glioma patients, glioma patients with G-CIMP are younger at diagnosis and survive longer than those without G-CIMP [62]. Intriguingly, about 10% of G-CIMP tumors were relapsed as G-CIMP low tumors with poor clinical outcome [65].
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The Cancer Genome Atlas (TCGA) performed comprehensive transcriptome analysis. Accordingly, GBM was classified into four groups (classic, mesenchymal, proneural, and neural groups). Aberrations and gene expression of EGFR and NF1 define the classical and mesenchymal subtypes, whereas tumors with an IDH1 mutation were classified within the proneural group. The proneural group is also accompanied by a PDGFRA gene abnormality and the G-CIMP feature [66]. DNA methylation induced by the IDH1 mutation caused hypermethylation at cohesion and CCCTC-binding factor (CTCF) binding sites and compromised the binding of the insulator protein. As a result, loss of CTCF at a domain permits a constitutive enhancer to interact aberrantly with the receptor tyrosine kinase gene PDGFRA [67].
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6.2.2 IDH mutation promotes global histone methylation
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IDH mutation is also known to increase histone methylation. Lysine methylation of histone tails modifies chromatin structure and regulates gene expression. By competition with α-KG, 2-HG inhibits histone demethylases including members of the Jumonji transcription factor family (JMJD2A, JMJD2C/KDM4C, and JHDM1A/FBXL11), resulting in histone hypermethylation [68]. Indeed, hypermethylation in H3K4me1, H3K4me3, H3K9me2, H3K27me2, H3K79me2, H3K27me3, H3K9me3, and H3K36me3 was observed in cells with exogenous 2-HG or mutant IDH1 induction [60, 63, 64, 69]. Sasaki et al. [63] also demonstrated that IDH1R132H knock in mice showed significantly increased early hematopoietic progenitors, histone hypermethylation, and DNA methylation. Interestingly, the elevation of H3K9me3 levels was observed earlier than the DNA methylation change in NHA upon IDH1R132H induction [69], suggesting that histone methylation may be an early event in IDH1-mutant cancers. The hypermethylation of histones blocks cell differentiation in cancer cells [60, 63, 64, 69]. Using a histone demethylating agent or a specific mutant IDH1 inhibitor, suppressed cell differentiation can be restored [70, 71]. Besides, 2-HG impairs collagen maturation, which leads to basement membrane aberrations that play a part in glioma progression [72]. Taken together, these data show that DNA hypermethylation and histone methylation promote tumorigenesis through a wide range of gene function changes (Figure 2).
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Figure 2.
Biological role of IDH mutation to induce gliomagenesis.
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6.3 IDH mutation inducible metabolic alterations
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In addition to the epigenetic changes, IDH1 mutation is known to alter hypoxia inducible factor 1α (HIF-1α) activity. Under oxidative conditions, α-KG-dependent prolyl hydroxylases (PHDs), which form the Egl nine homolog (EglN) families, induce HIF-1α hydroxylation. Hydroxylated protein is then bound by the von Hippel-Lindau tumor suppressor protein (VHL), ubiquitylated, and degraded via proteasome. In contrast, under hypoxia, the hydroxylation reaction is inhibited and HIF-1α is upregulated. HIF-1α then activates the transcription of several genes to mediate a switch from oxidative to glycolytic metabolism and induces angiogenesis by regulating the expression of vascular endothelial growth factor (VEGF) [73, 74]. Koivunen et al. [33] demonstrated that IDH1 mutation attenuates HIF-1α through the activation of HIF prolyl 4-hydroxylase (EGLN), enhancing the proliferation and soft agar growth of NHA.
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While several studies demonstrated that the IDH1 mutation induced aerobic glycolysis via HIF-1α activity [59, 75], other group reported that HIF-1α responsive genes, including lactate dehydrogenase (LDHA) were downregulated; silenced LDHA was associated with increased methylation of the LDHA promoter [76]. Another group showed that IDH1 mutation reduces pyruvate flux to lactate and suppresses monocarboxylate transporters MCT1 and MCT4, which mediate lactate transmembrane transport [77]. IDH mutation also alters pyruvate metabolism, including pyruvate dehydrogenase and pyruvate carboxylase enzymes, resulting in anaplerosis of the TCA cycle [78, 79].
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Cancer cells are known to depend on reductive carboxylation (RC) of glutamine-derived α-KG for de novo lipogenesis under hypoxia [80]. It is worth noticing that the RC pathway is inhibited by IDH mutation [55]. Under hypoxia, IDH1 mutation upregulated the contribution of glutamine to lipogenesis [81, 56].
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Altered amino acids, glutathione, choline derivatives, and tricarboxylic acid (TCA) cycle intermediates were observed in IDH-mutant cells [82, 83]. Glutamate dehydrogenase (GDH)1 and GDH2 were overexpressed in IDH1-mutant tumors, and the orthotopic growth of an IDH1-mutant glioma is inhibited by a double GDH1/2 knockdown [84]. Another group demonstrated that GDH2 was critical for IDH1-mutation induced metabolic alterations and IDH1-mutant glioma growth [85]. The presence of 2-HG also inhibited ATP synthase and mTOR signaling [41].
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Importantly, branched-chain amino acid transaminase (BCAT), which catalyzes the α-KG to glutamate conversion, was expressed at lower levels in IDH1-mutant gliomas than in IDH1-wildtype [86, 87]. As a result, the glutamate level was decreased, and cell proliferation and invasiveness were suppressed in IDH-mutant gliomas [87].
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7. Role of extensive resection in IDH1-mutant gliomas
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There is a huge amount of evidence showing that surgical resection has a pivotal role in survival benefit of glioma patients. Extensive resection is known to prolong survival in low grade glioma and also in GBM (IDH1-wildtype) [88, 89, 90, 91]. In IDH1-mutant gliomas, an MRI study demonstrated that IDH1-mutant tumors were rarely located in high risk areas of the brain and show unilateral patterns of growth, sharp tumor margins, and less contrast enhancement [92, 93]. Indeed, radiographic atlas revealed IDH1-mutant gliomas were frequently located at frontal lobe [94]. A diffusion-tensor imaging study demonstrated that IDH-mutant GBM has a less invasive phenotype than IDH-wildtype GBM [95]. Intriguingly, patients with IDH1-wildtype gliomas had a reduced neurocognitive function and lower performance score than those with IDH1-mutant gliomas [96]. In addition, lesion volume was not associated with neurocognitive function for patients with IDH1-mutant tumors, but associated for those with IDH1-wildtype tumors [96]. Consequently, IDH1-mutant gliomas may be relatively less invasive to the surrounding eloquent area than IDH-wildtype GBM.
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In addition, Beiko et al. [97] reported that extensive resection, including nonenhancing area, prolonged survival in IDH1-mutant anaplastic astrocytoma and glioblastoma. They also mentioned, since IDH1-mutant gliomas were predominantly located at frontal lobe, that maximal resection was relatively amenable. Another group independently demonstrated that gross total resection extended survival in grade III IDH1-mutant gliomas without 1p/19q co-deletion [98]. In contrast, survival advantage was controversial in grade II astrocytoma [99, 100]. These results suggest that for IDH1-mutant gliomas, especially grade III astrocytoma, maximal resection should be considered.
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8. Prediction of IDH status
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To establish IDH status-based treatment strategies, including surgery, advanced preoperative or intraoperative molecular analysis is important. Magnetic resonance spectroscopy (MRS) can be used to detect 2-HG and glutamate changes [101, 102, 103, 104, 105, 106, 107]. A recent MRS study demonstrated that 2-HG peaks rapidly decrease in accordance with tumor regression, whereas they increase with tumor progression in IDH-mutant gliomas [108], suggesting that 2-HG concentration, measured by MRS, may be a reliable approach to evaluate disease states in IDH-mutant gliomas. In addition, several MR techniques, including diffusion tensor imaging and MR methods for determining relative cerebral blood volume, have been proposed to detect mutant IDH1 noninvasively [109, 110, 111]. Moreover, T2-FLAIR mismatch sign was found as a highly specific imaging marker for IDH-mutant astrocytoma [112, 113, 114]. Intraoperative technologies to assess IDH1 mutation have also been established [115, 116, 117]. These advanced technologies may allow the development of tailored surgical strategies for IDH-mutant gliomas. Other group demonstrated that urinary 2-HG is increased in patients with IDH1-mutant gliomas [118]. These findings indicate the possibility of application of indirectly assessed 2-HG as a clinical biomarker.
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9. Treatment vulnerability in IDH-mutant gliomas
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9.1 Radiotherapy for IDH-mutant gliomas
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It has been shown that there is a higher relative sensitivity to radiotherapy and concurrent temozolomide (TMZ) in IDH1-mutant GBM patients than in those with IDH1-wildtype GBM [119], although there is no prospective clinical evidence of radiation therapy to extend survival in glioma patients with IDH1 mutation. As described above, IDH mutation inhibits NADPH and glutamate production, resulting in reduced glutathione levels and increased reactive oxygen species (ROS) [120, 121, 122, 123]. Conversely, radiosensitivity in IDH1-mutant tumors was diminished by IDH1 inhibitor [124]. These findings support selective vulnerability to radiation therapy in IDH-mutant gliomas.
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9.2 Chemotherapeutic evidence for IDH-mutant gliomas
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9.2.1 Temozolomide
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Current standard management of GBM consists of surgical tumor resection, following local radiotherapy with temozolomide treatment [125]. Additionally, adjuvant TMZ prolonged survival in anaplastic astrocytoma [126]. Several studies demonstrated IDH1-mutation as a predictive biomarker for TMZ sensitivity in low grade gliomas and secondary GBM [127, 128].
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Cytotoxicity of TMZ is provoked by the formation of O6-methylguanine (O6G)-DNA adducts. O6G-DNA adducts induce DNA strand break and apoptosis through the O6G-thymine-mediated mismatch repair pathway [129, 130]. It has also been established that the activation of DNA repairing pathways, including methylguanine methyltransferase (MGMT) repair enzyme, together with mismatch repair (MMR) system proteins deficiency, such as mutation-induced MSH2 and MSH6, result in drug resistance [131, 132, 133]. MGMT promoter methylation is highly methylated in IDH1-mutant gliomas, particularly oligodendrogliomas, compared with IDH-wildtype [43].
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Some preclinical studies demonstrated that forced IDH mutation sensitized cells to chemotherapy by increased ROS [134, 135, 136]. Conversely, forced IDH1 mutation revealed that IDH1 mutation-induced temozolomide (TMZ) resistance and rapid G2 cell cycle arrest through increased RAD-51-mediated homologous recombination (HR) [137, 138]. Importantly, among DNA adducts, O6G represents less than 10%, while the majority of TMZ-induced DNA lesions are N7-methylguanine (60–80%) and N3-methyladenine (10–20%) adducts, which are immediately repaired through poly(ADP-ribose)polymerase (PARP)-dependent base excision repair (BER) [129, 139, 140]. We have recently shown that there are lower steady state NAD+ levels in IDH1-mutant gliomas [141], and that TMZ immediately induces NAD+ consumption through PARP activation-mediated BER in IDH1-mutant gliomas [142]. Besides, Lu et al. [143] reported that the PARP associated DNA repair pathway was extensively compromised in IDH1-mutant cells due to decreased NAD+ availability, thus, cells were sensitive to TMZ, suggesting that deregulated NAD+ metabolism may be related with chemosensitivity. Taken together, these studies show that IDH mutation may increase susceptibility to chemotherapy; however, it remains unclear if IDH mutation itself promotes TMZ sensitivity.
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In contrast, TMZ-induced hypermethylation is a critical problem. Long-term TMZ exposure induces MMR inactivation, followed by DNA hypermutation phenotype. Among numerous mutations, gene alterations in RB and AKT-mTOR pathways promoted malignant progression in IDH1-mutant gliomas [27].
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9.2.2 Other chemotherapeutic agents
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Sulkowski et al. [144] demonstrated that 2-HG inhibits KDM4A and KDM4B, histone demethylases that play a critical role in double strand repair. As a result, IDH1 mutation suppresses HR and induces PARP inhibitor sensitivity. Additionally, IDH1-mutant downregulates the DNA double strand break sensor ATM by altering histone methylation, resulting in impaired DNA repair. As a result, IDH1 mutation causes DNA damage susceptibility to radiation and daunorubicin and reduces self-renewal of hematopoietic stem cells in acute myeloid leukemia [145].
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10. Novel therapeutic target in IDH1-mutant tumors
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10.1 Specific IDH inhibitor
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In 2013, specific inhibitors for IDH1 and IDH2 mutations were discovered [70, 146]. In IDH2-mutant AML cells, an IDH2R140Q inhibitor induced both histone and DNA demethylation [147]. These effects reversed blocked cell differentiation and resulted in cytotoxicity in vitro [146, 147]. It is interesting to note that histone hypermethylation is more rapidly reversed than DNA hypermethylation [147]. In IDH1-mutant AML cells, differentiation and DNA demethylation were also induced by a next generation IDH1 inhibitor [148]. Since the IDH2 mutation is crucial for proliferation and maintenance of leukemia cells [149], an IDH inhibitor may be used as a novel and efficient chemotherapeutic agent against IDH-mutant AML cells. Indeed, clinical trials demonstrated durable response for IDH1/2-mutant refractory AML patients [150, 151].
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In IDH1-mutant glioma cells, Rohle et al. [70] reported that a specific IDH1 inhibitor, AGI-5198, blocked 2-HG production, histone demethylation, cell differentiation, and inhibited cell growth in endogenous IDH1-mutant glioma cells. Other group demonstrated that BAY 1436032, a pan inhibitor of IDH1 mutation, promoted mild cytotoxic effects in vivo [152]. In contrast, we established that, even with a long-term IDH1 inhibitor treatment, 2-HG depletion does not induce demethylation of global-DNA and histones, cell differentiation, nor cytotoxicity [141]. Studies using another IDH1 inhibitor also revealed minimal cytotoxicity despite a rapid decrease in 2-HG levels in glioma cells [153, 154]. Similarly, treatment with an IDH1 inhibitor did not contribute to cytotoxicity, and the CpG island methylation status as well as histone trimethylation levels were largely retained in malignant glioma and chondrosarcoma [155, 156]. Intriguingly, in immortalized human astrocytes with an inducible IDH1R132H expression system, a specific IDH1 inhibitor induced demethylation and inhibited tumorigenesis when forced expression was prior or concomitant to inhibitor treatment, but these effects were not observed if the treatment was delayed [157]. These results indicate that 2-HG depletion or blocked mutant IDH1 might be insufficient to control tumor growth and reprogramming of epigenomic alterations in progressed IDH1-mutant gliomas. Indeed, preliminary results indicate that the 6-month progression-free survival of IDH1-mutant glioma, chondrosarcoma, and cholangiocarcinoma is 25, 56, and 43%, respectively, suggesting that the potential of the IDH1 inhibitor may be weaker in IDH1-mutant gliomas than in other cancers [158].
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10.2 Other treatment strategies
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10.2.1 DNA demethylating agents
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In addition to IDH1 inhibitor treatments, other strategies to control IDH1-mutant tumor cells have been proposed. Because the IDH1 mutation promotes proliferation by blocking DNA demethylation, treatment with DNA demethylating agents reverses DNA methylation and inhibits proliferation in IDH1-mutant cells [71, 159]. Intriguingly, treatment with both the DNA demethylating agent 5-azacytidine (5-Aza) and TMZ demonstrated extensively prolonged survival in an IDH1-mutant orthotopic xenograft model [160].
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10.2.2 Bcl-2 family inhibitors
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Since 2-HG suppresses the activity of cytochrome c oxidase in mitochondrial complex IV, the mitochondrial threshold for apoptosis was decreased after BCL-2 inhibition in IDH1 and IDH2-mutant AML [161]. Similarly, another Bcl-2 family member, the Bcl-xL inhibitor, induced apoptosis in IDH-mutant cells, including endogenous IDH1-mutant glioma cells [162]. Together, inhibition of Bcl-2 family members may be targetable to control growth in IDH-mutant cells.
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10.2.3 DNA damaging agents
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Because PLK1 activation provokes a rapid bypass through the G2 checkpoint after TMZ treatment in IDH1-mutant tumors, combination treatments with TMZ and a PLK1 inhibitor significantly suppressed tumor growth in an IDH1-mutant in vivo model [138]. In tumors with ATRX mutation-associated alternative lengthening telomeres (ALT), ATR inhibitor is highly sensitive [163], implying that such inhibition may be useful for treatments of IDH1-mutant astrocytic tumors with positive ALT. IDH1 mutation blocked HR, so-called “BRCA ness” phenotype provided specific sensitivity for PARP inhibitor both in vitro and in vivo [144].
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10.2.4 DLL-3 targeting therapy
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Since Notch ligand DLL-3 is overexpressed in IDH-mutant gliomas, anti-DLL3 antibody-drug conjugate (ADC), rovalpituzumab tesirine (Rova-T), is a potent therapeutic agent for IDH-mutant gliomas [164].
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10.2.5 Vaccination therapy
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Schumacher et al. [165] reported an immunological approach to control IDH1-mutant cells. They showed that an epitope derived from the IDH1-mutant amino acid sequence is presented in HLA class II molecules of antigen-presenting cells, which elicit a strong immune response via CD4 + T cells. In addition, they showed that constitutive stimulation with synthetic peptides having the IDH1-mutation sequence developed an immune response that eradicated IDH1 mutated tumors in a mouse model with human HLA molecules. Thus, vaccine therapy targeting for IDH1-mutation is expected to develop for future clinical trial [165, 166]. Moreover, IDH1-mutation caused downregulation of leukocyte chemotaxis, resulting in repression of the tumor-associated immune system including immune cells, such as macrophages [167]. Additionally, tumor infiltrating lymphocytes (TILs) and programmed death ligand 1 (PD-L1) were expressed at low levels in IDH1-mutant gliomas [168]. In contrast, Kohanbash et al. [153] demonstrated reduced expression of cytotoxic T lymphocyte-associated genes and IFN-gamma inducible chemokines in IDH1-mutant cells; these results were reversed by specific IDH1 inhibitor. Therefore, combination treatments with vaccine immunotherapy and IDH1 inhibitor result in enhanced toxicity in IDH-mutant tumors.
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10.2.6 Target for altered metabolism
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IDH1 mutation induced altered metabolism is also expected as a novel therapeutic target. Based on the fact that the main carbon source for α-KG and 2-HG synthesis in IDH1-mutant cells is glutamine from glutaminolysis, a suitable target therapy would be the use of glutaminase (GLS) inhibitor or anti-diabetic drug metformin via the inhibition of mitochondrial complex I in the electron transport system [83, 169, 170, 171]. Since reduced glutamate blocks glutathione synthesis, inhibition of glutaminase specifically sensitizes IDH-mutant glioma cells to oxidative stress and radiation [86].
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Mutant IDH1 alters steady state levels of NAD+ through inhibiting NAPRT1, one rate limiting enzyme for NAD+ biosynthesis. Therefore, inhibition of nicotinamide phosphoribosyltransferase (NAMPT), another rate limiting enzyme, induced high cytotoxicity in IDH1-mutant patient-derived glioma cells [141]. Since TMZ rapidly consumes NAD+ through PARP activation, combination treatments with TMZ and NAMPT inhibitor further enhanced NAD+ depletion-mediated cytotoxicity in IDH1-mutant cancers [142]. Similarly, Lu et al. [143] reported that the PARP-associated DNA repair pathway was extensively compromised in IDH1-mutant cells due to decreased NAD+ availability, thus sensitive to TMZ.
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Because of the relationships between IDH1 mutation and MYC activation [38, 40, 172], target therapy to regulate MYC, by using bromodomain and extra-terminal (BET) inhibitors, CDK7 or MYC-induced glycolysis may be used for IDH-mutant gliomas [40, 173, 174, 175]. Given the results of these studies, IDH1 mutation-specific biological alterations and metabolic feature may be expected as novel therapeutic targets.
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11. Conclusions
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In summary, investigations on IDH mutations enabled distinctive tumor classification and may allow the development of specific therapeutic strategies. Further preclinical and clinical studies are warranted to overcome the outcomes of cancer development in IDH-mutant glioma patients.
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\n\n',keywords:"IDH mutation, glioma, 2-hydroxyglutarate, tumor biology, cancer metabolism, target therapy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65823.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65823.xml",downloadPdfUrl:"/chapter/pdf-download/65823",previewPdfUrl:"/chapter/pdf-preview/65823",totalDownloads:1151,totalViews:0,totalCrossrefCites:2,dateSubmitted:"October 8th 2018",dateReviewed:"January 18th 2019",datePrePublished:"February 23rd 2019",datePublished:"January 29th 2020",dateFinished:"February 23rd 2019",readingETA:"0",abstract:"Isocitrate dehydrogenase (IDH) mutation is one of the most critical genomic alterations in lower grade and secondary glioblastoma patient. More than 90% of IDH mutation is located at codon R132 of IDH1 gene. IDH mutation produces oncometabolite “2-hydroxyglutarate” and induces epigenetic alteration, such as DNA global methylation and histone methylation. As a result, IDH mutation promotes early gliomagenesis. Since IDH mutation is the earliest genomic event and almost always retained during tumor progression, IDH mutation is expected as novel therapeutic target. Herein, we review the clinical characteristics of IDH-mutant gliomas, biological role of IDH mutation for gliomagenesis, and current and future therapeutic approach for IDH mutant tumors.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65823",risUrl:"/chapter/ris/65823",signatures:"Kensuke Tateishi and Tetsuya Yamamoto",book:{id:"7864",title:"Brain and Spinal Tumors",subtitle:"Primary and Secondary",fullTitle:"Brain and Spinal Tumors - Primary and Secondary",slug:"brain-and-spinal-tumors-primary-and-secondary",publishedDate:"January 29th 2020",bookSignature:"Lee Roy Morgan and Feyzi Birol Sarica",coverURL:"https://cdn.intechopen.com/books/images_new/7864.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"158053",title:"Dr.",name:"Lee Roy",middleName:null,surname:"Morgan",slug:"lee-roy-morgan",fullName:"Lee Roy Morgan"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"280036",title:"Dr.",name:"Kensuke",middleName:null,surname:"Tateishi",fullName:"Kensuke Tateishi",slug:"kensuke-tateishi",email:"ktate12@yokohama-cu.ac.jp",position:null,institution:null},{id:"291758",title:"Dr.",name:"Tetsuya",middleName:null,surname:"Yamamoto",fullName:"Tetsuya Yamamoto",slug:"tetsuya-yamamoto",email:"y_neuros@yokohama-cu.ac.jp",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Discovery of IDH mutations in cancers",level:"1"},{id:"sec_3",title:"3. Tumorigenesis of IDH-mutant gliomas",level:"1"},{id:"sec_3_2",title:"3.1 Genomic characteristics of IDH-mutant glioma",level:"2"},{id:"sec_4_2",title:"3.2 Developmental hierarchy in IDH-mutant gliomas",level:"2"},{id:"sec_5_2",title:"3.3 IDH-mutant xenograft model",level:"2"},{id:"sec_7",title:"4. The 2016 WHO classification",level:"1"},{id:"sec_8",title:"5. Epidemiology of IDH-mutant gliomas",level:"1"},{id:"sec_8_2",title:"5.1 Age distribution of IDH-mutant gliomas",level:"2"},{id:"sec_9_2",title:"5.2 Prognosis of IDH-mutant gliomas",level:"2"},{id:"sec_10_2",title:"5.3 Prognostic classification for gliomas",level:"2"},{id:"sec_12",title:"6. The mechanism of tumorigenesis in IDH1-mutant gliomas",level:"1"},{id:"sec_12_2",title:"6.1 IDH mutation drives production of oncometabolite D-2-hydroxyglutarate",level:"2"},{id:"sec_13_2",title:"6.2 IDH-mutation induced epigenetic alterations",level:"2"},{id:"sec_13_3",title:"6.2.1 IDH-mutation inducible DNA hypermethylator phenotype",level:"3"},{id:"sec_14_3",title:"6.2.2 IDH mutation promotes global histone methylation",level:"3"},{id:"sec_16_2",title:"6.3 IDH mutation inducible metabolic alterations",level:"2"},{id:"sec_18",title:"7. Role of extensive resection in IDH1-mutant gliomas",level:"1"},{id:"sec_19",title:"8. Prediction of IDH status",level:"1"},{id:"sec_20",title:"9. Treatment vulnerability in IDH-mutant gliomas",level:"1"},{id:"sec_20_2",title:"9.1 Radiotherapy for IDH-mutant gliomas",level:"2"},{id:"sec_21_2",title:"9.2 Chemotherapeutic evidence for IDH-mutant gliomas",level:"2"},{id:"sec_21_3",title:"9.2.1 Temozolomide",level:"3"},{id:"sec_22_3",title:"9.2.2 Other chemotherapeutic agents",level:"3"},{id:"sec_25",title:"10. Novel therapeutic target in IDH1-mutant tumors",level:"1"},{id:"sec_25_2",title:"10.1 Specific IDH inhibitor",level:"2"},{id:"sec_26_2",title:"10.2 Other treatment strategies",level:"2"},{id:"sec_26_3",title:"10.2.1 DNA demethylating agents",level:"3"},{id:"sec_27_3",title:"10.2.2 Bcl-2 family inhibitors",level:"3"},{id:"sec_28_3",title:"10.2.3 DNA damaging agents",level:"3"},{id:"sec_29_3",title:"10.2.4 DLL-3 targeting therapy",level:"3"},{id:"sec_30_3",title:"10.2.5 Vaccination therapy",level:"3"},{id:"sec_31_3",title:"10.2.6 Target for altered metabolism",level:"3"},{id:"sec_34",title:"11. 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Detection of oncogenic IDH1 mutations using magnetic resonance spectroscopy of 2-hydroxyglutarate. The Journal of Clinical Investigation. 2013;123(9):3659-3663'},{id:"B105",body:'de la Fuente MI, Young RJ, Rubel J, Rosenblum M, Tisnado J, Briggs S, et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro-Oncology. 2016;18(2):283-290'},{id:"B106",body:'Emir UE, Larkin SJ, de Pennington N, Voets N, Plaha P, Stacey R, et al. Noninvasive quantification of 2-hydroxyglutarate in human gliomas with IDH1 and IDH2 mutations. Cancer Research. 2016;76(1):43-49'},{id:"B107",body:'Nagashima H, Tanaka K, Sasayama T, Irino Y, Sato N, Takeuchi Y, et al. Diagnostic value of glutamate with 2-hydroxyglutarate in magnetic resonance spectroscopy for IDH1 mutant glioma. Neuro-Oncology. 2016;18(11):1559-1568'},{id:"B108",body:'Choi C, Raisanen JM, Ganji SK, Zhang S, McNeil SS, An Z, et al. Prospective longitudinal analysis of 2-hydroxyglutarate magnetic resonance spectroscopy identifies broad clinical utility for the management of patients with IDH-mutant glioma. Journal of Clinical Oncology. 2016;34(33):4030-4039'},{id:"B109",body:'Tan WL, Huang WY, Yin B, Xiong J, Wu JS, Geng DY. Can diffusion tensor imaging noninvasively detect IDH1 gene mutations in astrogliomas? A retrospective study of 112 cases. AJNR. American Journal of Neuroradiology. 2014;35(5):920-927'},{id:"B110",body:'Kickingereder P, Sahm F, Radbruch A, Wick W, Heiland S, Deimling A, et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Scientific Reports. 2015;5:16238'},{id:"B111",body:'Yamashita K, Hiwatashi A, Togao O, Kikuchi K, Hatae R, Yoshimoto K, et al. MR imaging-based analysis of glioblastoma multiforme: Estimation of IDH1 mutation status. AJNR. 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Increased sensitivity to radiochemotherapy in IDH1 mutant glioblastoma as demonstrated by serial quantitative MR volumetry. Neuro-Oncology. 2014;16(3):414-420'},{id:"B120",body:'Li S, Chou AP, Chen W, Chen R, Deng Y, Phillips HS, et al. Overexpression of isocitrate dehydrogenase mutant proteins renders glioma cells more sensitive to radiation. Neuro-Oncology. 2013;15(1):57-68'},{id:"B121",body:'Wang XW, Labussiere M, Valable S, Peres EA, Guillamo JS, Bernaudin M, et al. IDH1(R132H) mutation increases U87 glioma cell sensitivity to radiation therapy in hypoxia. BioMed Research International. 2014;2014:198697'},{id:"B122",body:'Kessler J, Guttler A, Wichmann H, Rot S, Kappler M, Bache M, et al. IDH1(R132H) mutation causes a less aggressive phenotype and radiosensitizes human malignant glioma cells independent of the oxygenation status. Radiotherapy and Oncology. 2015;116(3):381-387'},{id:"B123",body:'Bleeker FE, Atai NA, Lamba S, Jonker A, Rijkeboer D, Bosch KS, et al. 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Cancer Research. 2005;65(14):6394-6400'},{id:"B140",body:'Yoshimoto K, Mizoguchi M, Hata N, Murata H, Hatae R, Amano T, et al. Complex DNA repair pathways as possible therapeutic targets to overcome temozolomide resistance in glioblastoma. Frontiers in Oncology. 2012;2:186'},{id:"B141",body:'Tateishi K, Wakimoto H, Iafrate AJ, Tanaka S, Loebel F, Lelic N, et al. Extreme Vulnerability of IDH1 Mutant Cancers to NAD+ Depletion. Cancer Cell. 2015;28(6):773-784'},{id:"B142",body:'Tateishi K, Higuchi F, Miller J, Koerner MVA, Lelic N, Shankar GM, et al. The alkylating chemotherapeutic temozolomide induces metabolic stress in IDH1-mutant cancers and potentiates NAD+ depletion-mediated cytotoxicity. Cancer Research. 2017;77(15):4102-4115'},{id:"B143",body:'Lu Y, Kwintkiewicz J, Liu Y, Tech K, Frady LN, Su YT, et al. Chemosensitivity of IDH1 mutant gliomas due to an impairment in PARP1-mediated DNA repair. Cancer Research. 2017;77(7):1709-1718'},{id:"B144",body:'Sulkowski PL, Corso CD, Robinson ND, Scanlon SE, Purshouse KR, Bai H, et al. 2-Hydroxyglutarate produced by neomorphic IDH mutations suppresses homologous recombination and induces PARP inhibitor sensitivity. Science Translational Medicine. 2017;9(375)'},{id:"B145",body:'Inoue S, Li WY, Tseng A, Beerman I, Elia AJ, Bendall SC, et al. Mutant IDH1 downregulates ATM and alters DNA repair and sensitivity to DNA damage independent of TET2. Cancer Cell. 2016;30(2):337-348'},{id:"B146",body:'Wang F, Travins J, DeLaBarre B, Penard-Lacronique V, Schalm S, Hansen E, et al. Targeted inhibition of mutant IDH2 in leukemia cells induces cellular differentiation. Science. 2013;340(6132):622-626'},{id:"B147",body:'Kernytsky A, Wang F, Hansen E, Schalm S, Straley K, Gliser C, et al. IDH2 mutation-induced histone and DNA hypermethylation is progressively reversed by small-molecule inhibition. Blood. 2015;125(2):296-303'},{id:"B148",body:'Okoye-Okafor UC, Bartholdy B, Cartier J, Gao EN, Pietrak B, Rendina AR, et al. New IDH1 mutant inhibitors for treatment of acute myeloid leukemia. Nature Chemical Biology. 2015;11(11):878-886'},{id:"B149",body:'Kats LM, Reschke M, Taulli R, Pozdnyakova O, Burgess K, Bhargava P, et al. Proto-oncogenic role of mutant IDH2 in leukemia initiation and maintenance. Cell Stem Cell. 2014;14(3):329-341'},{id:"B150",body:'DiNardo CD, Stein EM, de Botton S, Roboz GJ, Altman JK, Mims AS, et al. Durable remissions with ivosidenib in IDH1-mutated relapsed or refractory AML. The New England Journal of Medicine. 2018;378(25):2386-2398'},{id:"B151",body:'Stein EM, DiNardo CD, Pollyea DA, Fathi AT, Roboz GJ, Altman JK, et al. Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood. 2017;130(6):722-731'},{id:"B152",body:'Pusch S, Krausert S, Fischer V, Balss J, Ott M, Schrimpf D, et al. Pan-mutant IDH1 inhibitor BAY 1436032 for effective treatment of IDH1 mutant astrocytoma in vivo. Acta Neuropathologica. 2017;133(4):629-644'},{id:"B153",body:'Kohanbash G, Carrera DA, Shrivastav S, Ahn BJ, Jahan N, Mazor T, et al. Isocitrate dehydrogenase mutations suppress STAT1 and CD8+ T cell accumulation in gliomas. The Journal of Clinical Investigation. 2017;127(4):1425-1437'},{id:"B154",body:'Davis MI, Gross S, Shen M, Straley KS, Pragani R, Lea WA, et al. Biochemical, cellular, and biophysical characterization of a potent inhibitor of mutant isocitrate dehydrogenase IDH1. The Journal of Biological Chemistry. 2014;289(20):13717-13725'},{id:"B155",body:'Suijker J, Oosting J, Koornneef A, Struys EA, Salomons GS, Schaap FG, et al. Inhibition of mutant IDH1 decreases D-2-HG levels without affecting tumorigenic properties of chondrosarcoma cell lines. Oncotarget. 2015;6(14):12505-12519'},{id:"B156",body:'Turcan S, Makarov V, Taranda J, Wang Y, Fabius AWM, Wu W, et al. Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence. Nature Genetics. 2018;50(1):62-72'},{id:"B157",body:'Johannessen TA, Mukherjee J, Viswanath P, Ohba S, Ronen SM, Bjerkvig R, et al. Rapid conversion of mutant IDH1 from driver to passenger in a model of human gliomagenesis. Molecular Cancer Research. 2016;14(10):976-983'},{id:"B158",body:'Fujii T, Khawaja MR, DiNardo CD, Atkins JT, Janku F. Targeting isocitrate dehydrogenase (IDH) in cancer. Discovery Medicine. 2016;21(117):373-380'},{id:"B159",body:'Borodovsky A, Salmasi V, Turcan S, Fabius AW, Baia GS, Eberhart CG, et al. 5-azacytidine reduces methylation, promotes differentiation and induces tumor regression in a patient-derived IDH1 mutant glioma xenograft. Oncotarget. 2013;4(10):1737-1747'},{id:"B160",body:'Yamashita AS, da Costa Rosa M, Borodovsky A, Festuccia WT, Chan T, Riggins GJ. Demethylation and epigenetic modification with 5-azacytidine reduces IDH1 mutant glioma growth in combination with temozolomide. Neuro-Oncology. 2018'},{id:"B161",body:'Chan SM, Thomas D, Corces-Zimmerman MR, Xavy S, Rastogi S, Hong WJ, et al. Isocitrate dehydrogenase 1 and 2 mutations induce BCL-2 dependence in acute myeloid leukemia. Nature Medicine. 2015;21(2):178-184'},{id:"B162",body:'Karpel-Massler G, Ishida CT, Bianchetti E, Zhang Y, Shu C, Tsujiuchi T, et al. Induction of synthetic lethality in IDH1-mutated gliomas through inhibition of Bcl-xL. Nature Communications. 2017;8(1):1067'},{id:"B163",body:'Flynn RL, Cox KE, Jeitany M, Wakimoto H, Bryll AR, Ganem NJ, et al. Alternative lengthening of telomeres renders cancer cells hypersensitive to ATR inhibitors. Science. 2015;347(6219):273-277'},{id:"B164",body:'Spino M, Kurz SC, Chiriboga L, Serrano J, Zeck B, Sen N, et al. Cell surface Notch ligand DLL3 is a therapeutic target in isocitrate dehydrogenase mutant glioma. Clinical Cancer Research. 2019;25(4):1261-1271'},{id:"B165",body:'Schumacher T, Bunse L, Pusch S, Sahm F, Wiestler B, Quandt J, et al. A vaccine targeting mutant IDH1 induces antitumour immunity. Nature. 2014;512(7514):324-327'},{id:"B166",body:'Pellegatta S, Valletta L, Corbetta C, Patane M, Zucca I, Riccardi Sirtori F, et al. Effective immuno-targeting of the IDH1 mutation R132H in a murine model of intracranial glioma. Acta Neuropathologica Communications. 2015;3:4'},{id:"B167",body:'Amankulor NM, Kim Y, Arora S, Kargl J, Szulzewsky F, Hanke M, et al. Mutant IDH1 regulates the tumor-associated immune system in gliomas. Genes & Development. 2017;31(8):774-786'},{id:"B168",body:'Berghoff AS, Kiesel B, Widhalm G, Wilhelm D, Rajky O, Kurscheid S, et al. Correlation of immune phenotype with IDH mutation in diffuse glioma. Neuro-Oncology. 2017;19(11):1460-1468'},{id:"B169",body:'Seltzer MJ, Bennett BD, Joshi AD, Gao P, Thomas AG, Ferraris DV, et al. Inhibition of glutaminase preferentially slows growth of glioma cells with mutant IDH1. Cancer Research. 2010;70(22):8981-8987'},{id:"B170",body:'Emadi A, Jun SA, Tsukamoto T, Fathi AT, Minden MD, Dang CV. Inhibition of glutaminase selectively suppresses the growth of primary acute myeloid leukemia cells with IDH mutations. Experimental Hematology. 2014;42(4):247-251'},{id:"B171",body:'Cuyas E, Fernandez-Arroyo S, Corominas-Faja B, Rodriguez-Gallego E, Bosch-Barrera J, Martin-Castillo B, et al. Oncometabolic mutation IDH1 R132H confers a metformin-hypersensitive phenotype. Oncotarget. 2015;6(14):12279-12296'},{id:"B172",body:'Odia Y, Orr BA, Bell WR, Eberhart CG, Rodriguez FJ. cMYC expression in infiltrating gliomas: Associations with IDH1 mutations, clinicopathologic features and outcome. Journal of Neuro-Oncology. 2013;115(2):249-259'},{id:"B173",body:'Chipumuro E, Marco E, Christensen CL, Kwiatkowski N, Zhang T, Hatheway CM, et al. CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer. Cell. 2014;159(5):1126-1139'},{id:"B174",body:'Christensen CL, Kwiatkowski N, Abraham BJ, Carretero J, Al-Shahrour F, Zhang T, et al. Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor. Cancer Cell. 2014;26(6):909-922'},{id:"B175",body:'Tateishi K, Iafrate AJ, Ho Q , Curry WT, Batchelor TT, Flaherty KT, et al. Myc-driven glycolysis is a therapeutic target in glioblastoma. Clinical Cancer Research. 2016;22(17):4452-4465'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kensuke Tateishi",address:"ktate12@yokohama-cu.ac.jp",affiliation:'
Department of Neurosurgery, Yokohama City University, Yokohama, Japan
Department of Neurosurgery, Yokohama City University, Yokohama, Japan
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Pazos Rangel",slug:"rodolfo-a.-pazos-rangel",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Instituto Tecnológico de Ciudad Madero",institutionURL:null,country:{name:"Mexico"}}},booksEdited:[],chaptersAuthored:[{title:"The K-Means Algorithm Evolution",slug:"the-em-k-em-means-algorithm-evolution",abstract:"Clustering is one of the main methods for getting insight on the underlying nature and structure of data. The purpose of clustering is organizing a set of data into clusters, such that the elements in each cluster are similar and different from those in other clusters. One of the most used clustering algorithms presently is K-means, because of its easiness for interpreting its results and implementation. The solution to the K-means clustering problem is NP-hard, which justifies the use of heuristic methods for its solution. To date, a large number of improvements to the algorithm have been proposed, of which the most relevant were selected using systematic review methodology. As a result, 1125 documents on improvements were retrieved, and 79 were left after applying inclusion and exclusion criteria. The improvements selected were classified and summarized according to the algorithm steps: initialization, classification, centroid calculation, and convergence. It is remarkable that some of the most successful algorithm variants were found. Some articles on trends in recent years were included, concerning K-means improvements and its use in other areas. Finally, it is considered that the main improvements may inspire the development of new heuristics for K-means or other clustering algorithms.",signatures:"Joaquín Pérez-Ortega, Nelva Nely Almanza-Ortega, Andrea Vega-Villalobos, Rodolfo Pazos-Rangel, Crispín Zavala-Díaz and Alicia Martínez-Rebollar",authors:[{id:"120775",title:"Prof.",name:"Rodolfo A.",surname:"Pazos Rangel",fullName:"Rodolfo A. Pazos Rangel",slug:"rodolfo-a.-pazos-rangel",email:"r_pazos_r@yahoo.com.mx"},{id:"136948",title:"Prof.",name:"Joaquín",surname:"Pérez-Ortega",fullName:"Joaquín Pérez-Ortega",slug:"joaquin-perez-ortega",email:"jpo_cenidet@yahoo.com.mx"},{id:"295815",title:"Ph.D.",name:"Nelva Nely",surname:"Almanza-Ortega",fullName:"Nelva Nely Almanza-Ortega",slug:"nelva-nely-almanza-ortega",email:"avlenylen@hotmail.com"},{id:"295816",title:"Ph.D. Student",name:"Andrea",surname:"Vega-Villalobos",fullName:"Andrea Vega-Villalobos",slug:"andrea-vega-villalobos",email:"vegaandrea92@gmail.com"},{id:"295817",title:"Dr.",name:"Crispín",surname:"Zavala-Díaz",fullName:"Crispín Zavala-Díaz",slug:"crispin-zavala-diaz",email:"crispin_zavala@uaem.mx"},{id:"295820",title:"Dr.",name:"Alicia",surname:"Martínez-Rebollar",fullName:"Alicia Martínez-Rebollar",slug:"alicia-martinez-rebollar",email:"amartinez@cenidet.edu.mx"}],book:{title:"Introduction to Data Science and Machine Learning",slug:"introduction-to-data-science-and-machine-learning",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"136948",title:"Prof.",name:"Joaquín",surname:"Pérez-Ortega",slug:"joaquin-perez-ortega",fullName:"Joaquín Pérez-Ortega",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:null,institutionString:null,institution:{name:"Centro Nacional de Investigación y Desarrollo Tecnológico",institutionURL:null,country:{name:"Mexico"}}},{id:"151757",title:"Dr.",name:"Laura M.",surname:"Castro",slug:"laura-m.-castro",fullName:"Laura M. Castro",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/151757/images/system/151757.png",biography:"Dr. Castro graduated in Computer Engineering in 2003. Ph.D. Cum Laude in Computer Science in 2010. She has a masters degree in Insurance and Business Risk Management. She is currently an associate professor at the University of A Coruña (Spain) and a coordinator of the CS undergraduate studies programme.",institutionString:"University of A Coruña",institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}},{id:"265237",title:"Prof.",name:"Igor",surname:"Sheremet",slug:"igor-sheremet",fullName:"Igor Sheremet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265237/images/system/265237.jpg",biography:"Dr. Igor Sheremet was born 23/03/1956 in Minsk region, Belarus. 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