Basic immunology research over several decades has led to an improved understanding of tumour recognition by components of the immune system and mechanism of tumour evasion from immune detection. These findings have ultimately led to creating antitumour immunotherapies in patients with different kind of cancer including prostate cancer. The increasing number of reports confirms that immune-based therapies have clinical benefit in patients with prostate cancer with potentially less toxicity in comparison with traditional systemic treatments including surgical resection, chemotherapy, or radiotherapy in various forms. This review focuses on the possibility of modulation of the optimal immunotherapy based on vaccination strategy adopted to individual patients in order to increase quality and quantity of their life. Today, there are a number of immune-based cancer treatments in development for prostate cancer (PC). In papers , 2, it hThe paper is organized as follows. At first, we briefly present the mathematical model of prostate cancer immunotherapy which we use and then discuss possible phenotypes that arise from this numerical model. Finally, we analyse chosen vaccination strategy on the population of statistically generated patients.V) and prostate cancer cells (P) by the use of immune system, that is, antigen-presenting dermal dendritic cells (Dm), mature dendritic cells (DC), “exhausted” dendritic cells (DR), antigen specific effector cells (C), and regulatory/inhibitory cells (R). At the cellular level, it can be presented as .(1)d.(1)dV) a7 cells. Therefore, with V(0), we take 2.4 · 107 cells or the multiple of this. The initial populations of the immune system cells are taken to be equal to zero, so that Dm(0)=0, DC(0)=0, DR(0)=0, C(0)=0, R(0)=0. Initial population of tumour cells is proportional to the tumour size and average tumour in the initial stage of PC contains about 30 · 109 PC cells.Following  in TableIn order to determine which parameters contribute the most to output variability, we applied global quantitative sensibility analysis based on Sobol' (variance decomposition) approach , 13. SobTo make calculation, we have established time of the simulation, variation of the input parameters Δ, and the number of samples for the quasi-random Monte Carlo simulation. One has noticed that calculations of the sensitivity indices are independent of the time of the simulation and the number of samples for the simulation; therefore, we have performed calculations only for different variations of the input parameters Δ. We have calculated the parameters sensitivities of a PC immunotherapy model presented in equations –7)) bas) bas7)) r and ap out of 15 input parameters (see V(0)Dose of the vaccine P(0)Initial tumour size rTumour growth rate apMaximal PC cell killing efficacy According to ters see  which diThe simulations of the PC model were performed on a Matlab Simulink environment . We solved the model equations by implementing them in Simulink using graphical programming language, whereas strategy of calculation and parameters setting were implemented in the appropriate m-files. Meanwhile, to calculate the sensitivity indices , we used the Matlab software tool GSAT  .Our methodology of research is based on the mathematical model of prostate cancer described in We start numerical analysis of PC evolution lasting over a period of one month for the case of single vaccination. In this case, one can distinguish numerically only three possible phenotypes of PC developing. In the first phenotype, a statistical patient is clinically responding; he starts responding immediately after vaccination and maintains this state over some period of time. The example of such patient with personalized parameters given in In the second phenotype, a statistical patient is delayed nonresponder. It means that the population of PC cell counts initially increases and then after two or three days decreases but finally rebounds. The example of such patient with personalized parameters given in In the third phenotype, a patient is clinically nonresponding; the population of PC cell is increasing over the considered period of evolution and is independent from vaccination. The example of such patient with personalized parameters given in ap parameter, that is, aP= · (10−6) =65 · 109, r=10−3, and V(0)=2.4 · 107), are presented in ap parameters are chosen in such way to show different evolution scenarios of the PC for maximal value of tumour growth rate r and to catch the moment when PC cells population starts to grow in an uncontrolled way. In 4 . Seven coloured lines represent PC evolution for different patients, whereas changing of colour in each line is caused by vaccination. From the other side, we can choose patients (labelled by r parameter) with the same maximal PC cell killing efficiency ap parameter and different tumour growth rate r, according to the list r= · (10−3) =65 · 109, ap=0.25 · 10−6, and V(0)=2.4 · 107). Then one can also observe the situation when population of PC cells grows in an uncontrolled way  and the dose of vaccination V(0). Such complex analysis for virtual patients generated as points uniformly distributed in the whole space of accessible parameters is given in This simple approach shows that, in order to make full analysis of PC evolution, we should vary P01=30 · 109, P02=45 · 109, P03=60 · 109[cells]. A group of 10100 patients has been generated for each size of tumour based on the scope of variation of r and ap. Every patient in that group has unique parameters of tumour growth rate (r[h−1]) in the range from 10−5[h−1] to 10−3[h−1] and maximal PCa cell killing efficacy (ap[cell−1 · h−1]) ranging from 0 to 2 · 10−6[cell−1 · h−1].In this analysis, we restrict ourselves to the three sizes of tumour: 7[cells] (1.0 d), and, in addition, half of standard dose (0.5 d) and one and a half of the standard dose (1.5 d) are given to the groups. After simulating the treatment, it can be concluded that, in many cases, the plan used quickly dealt with cancer in a large number of cases.The treatment plan consisted of administering the dose of vaccine at weekly intervals for four weeks. The standard dose of the vaccine is 2.4 · 109 and for dosing of 0.5 d, vaccination has not completely cured the tumour for 1574 patients; additionally, for this group, vaccination did not help at all 472 patients, meaning that P > P0. Dependency ap(r) for given tumour size and dosing is presented in ap(r) for given tumour size and 1.5 d dosing is presented in For tumour size of 30 · 109 and dosing of 0.5 d, vaccination has not completely cured the tumour for 2294 patients, of whom 681 were not helped at all by vaccination. Correlation ap(r) for given tumour size and dosing is presented in For the tumour size of 45 · 109 and dosing of 0.5 d (For the tumour size of 60 · 10of 0.5 d , vaccinaof 0.5 d  did not of 0.5 d , vaccinaap values. For a maximum tumour growth rate of r=10−3, the 30 · 109 tumour disappears at ap=3.4 · 10−7[cell−1 · h−1] for dosing of 0.5 d and ap=2.4 · 10−7[cell−1 · h−1] for dosing of 1.0 d, and, for dosing of 1.5 d, tumour disappears at ap=2 · 10−7[cell−1 · h−1].With the assumed treatment model, the success of the treatment is visible already for very small 9 disappears at ap=5.2 · 10−7[cell−1 · h−1] and dosing of 0.5 d. For dosing of 1.0 d, tumour disappears at ap=3.6 · 10−7[cell−1 · h−1] and for dosing of 1.5 d tumour disappears at ap=3 · 10−7[cell−1 · h−1].The tumour of size 45 · 109 is cured at ap=6.8 · 10−7[cell−1 · h−1] and dosing of 0.5 d. For dosing of 1.0 d largest tumour disappears at ap=4.8 · 10−7[cell−1 · h−1] and for dosing of 1.5 d tumour disappears at ap=4 · 10−7[cell−1 · h−1]. It can be noted that a multitude of vaccine doses affect the curability but not much. It can also be noted that very small changes in ap cause very large changes in the number of cancer cells P. This trend can be observed in Figures 5The largest tumour of size of 60 · 10Modern therapies, commonly used against cancer, still do not cure patients with advanced cancers. However, in contrast to chemotherapy or radiotherapy, personalized therapy extends the time free from symptoms of the disease, increases the quality of life of patients, which often allows them to return to work and full physical capacity, usually has minor side effects, and, most importantly, lengthens the patients' life time. Today, there are a number of immune-based treatments in development for different kind of cancer . Immunotap values. Conducted analysis showed that immune responses against cancer are highly heterogeneous, not only between the level of cancer advancement but also within different patients with the same type of cancer, indicating that personalized immunotherapy should be employed, based on the immune status of the individual patient.The purpose of this study was to determine if it is possible to make and administer safely a “personalized” vaccine to treat patients that have been diagnosed with prostate cancer and are not candidates for curative therapy. We have taken into consideration three hypothetical models of patients. The simulations carried out consisted in comparing the severity of the patient's disease and its response to the standard vaccination schedule with its modifications. We have shown that in the considered model from the numerical point of view there are only three possible phenotypes concerning PC evolution. Moreover, taking into account the results of the treatment of statistical patients, the proposed strategy of therapy is a promising one. It can be noted that, for unhealed groups of patients, over half of the cases show tumour responsiveness. Lack of responsiveness can only be noticed at low


Briefly, a hydro-thermophoretic trap works by the competition between thermoplasmonic flows due to laser heating of a substrate and thermophoresis away from the hotspot of the laser. Here, we extend that work to demonstrate the controlled roll rotation of a microparticle in a hydro-thermophoretic trap using experiments and theory. We experimentally measure the roll angular velocity of the trapped particle. We predict this roll rotation from theoretical computation of the fluid flow. The expression for the angular velocity fits the experimental data. Our method has potential applications in microrheology by employing a different mode of rotation.In recent years, there has been a growing interest in controlling the motion of microparticles inside and outside a focused laser beam. A hydro-thermophoretic trap was recently reported [Nalupurackal  IThe control and manipulation of micro-/nano-objects is a key element in the field of nanophotonics ,2, materyaw  and 1.23 g of sodium citrate (Na3C6H5O7) are dissolved and stirred on a hotplate for 10 min. The aforementioned solution is then mixed with 0.38 g of ytterbium nitrate  and 0.037 g of erbium nitrate  that has been dissolved in 21 mL of the aqueous solution, resulting in a white solution. It is then converted into a clear solution by the addition of 1.411 g of sodium fluoride (NaF) in 67 mL deionized (DI) water. Magnetic stirring is kept alive for 1 h. The final solution is then transferred into a Teflon-lined autoclave and sealed tightly. The autoclave reactor is kept in a muffle furnace and heated at 200 °C for 12 h. The resulting white powder is washed with ethanol and water five times and dried at 100° to obtain the sample in pure form. We have reported the size and hexagonal geometry of the particles in our earlier works [The hydrothermal method is exploited to synthesize 5-μm-sized hexagonal upconverting particles (UCPs) or NaYFarticles ,44. Initer works .CThe sample of upconverting particles for the experiment is prepared by dispersing them in water, 20 μL of which is transferred to a glass slide  and mounted with a gold-coated coverslip (170 μm English glass + 30 nm gold) to constitute the sample chamber. It is then transferred to the sample stage of the optical tweezers setup such that the gold coating on the coverslip comes in direct contact with the sample, as shown in IIIIn this section, we describe our main experimental and theoretical results.Aroll sense confined in a hydro-thermophoretic trap, repositioned just above the line joining two hotspots (H1 and H2) in the x-y plane. The laser powers at each hotspot (P1 and P2) are fixed at 5 mW, and the separation between them (d) is changed [thus to change l and sin θ in In x-axis of the experimental system [defined as roll rotation in our experiments, x and y) are generated by tracking its centroid as shown in x and y positional distributions of the particle to confirm its confinement and fit them with Gaussian curves [xk = 32.6 ±5.3 fN/μm and yk = 43.9 ±6.4 fN/μm. By taking account of the observation that the particle’s displacement within the trap is on the orders of micrometers, we are simultaneously applying and estimating the hydro-thermophoretic forces on the order of femto-newton here. Further, the average angular velocity values are determined by changing d and plotted as a function of a scaling variable ζ in The particle is confined in a side-on configuration in such a way that the rotation is observed about the n curves . The traF0) due to thermoplasmonic fluid flows. We observe that the force of confinement increases with an increase in laser power at both hotspots, not to mention the increase in angular velocity. These effects need to be studied qualitatively and will be pursued as a separate work in the future.To ascertain the laser power dependence of hydro-thermophoretic forces and the angular velocity of the particle, we perform the same experiment with elevated powers at the hotspots. The new power readings at H1 and H2 are made to be 8.5 mW. Again, the estimated angular velocity values are plotted as a function of ζ and fitted with Bl = 6.2 μm). The continuous roll rotational motion of the particle about the x-axis is also observed as shown in Here, we show that the location of the hydro-thermophoretic trap can be tuned exclusively by changing the laser power at both hotspots. Initially, the laser powers at each hotspot (H1 and H2) are kept at 4.5 mW. We observe that the particle is getting confined above the line joining hotspots (y axis) and gets confined exactly to the middle of the line joining two hotspots, bringing l = 0, sin θ = 0 (see l = 0).As we decrease the laser power at each hotspot to 2.5 mW, the particle traces a downward trajectory  that are shown in The CIn this section, we obtain an analytical expression of the angular velocity of the trapped microparticles by studying the equations of motion.1v(s) at a point s in the bulk fluid satisfies the Stokes equation [p is the fluid pressure, η is the viscosity, and ρ is the fluid density, β is the thermal expansion coefficient, and g is the acceleration along with δT = (T – T0). The fluid is incompressible, V · v = 0. In addition, the fluid flow vanishes at the plane walls of the experimental chamber. There is a reduction of mass density around the gold-coated substrate because of heating by the laser, which yields the upward convection of the fluid driven by the force density f, defined above. The fluid flow is driven by the plasmonic heating of the gold substrate through laser radiations of source densities iq [κ is the thermal conductivity, while q1 and q2 are the heat source density which serves as a source term for the computation of the temperature field T(s). The total heat power generated on the hotspot from the two lasers is Q1 and Q2, where iQ = ∫q(s) sd.We now study the fluid flows produced by plasmonic heating of the gold substrate through laser radiations ∇DT is the thermophoretic mobility [vT is away from the hotspot. In [Ω of the trapped particle can be controlled by tuning the location of the trap center.The laser heating of the gold-coated substrate results in a temperature gradient that is maintained even outside the laser beam because a gold-coated substrate has high thermal conductivity. Indeed, all our experiments are performed on a gold-coated substrate for this very reason. Thus, a microparticle in the vicinity of the substrate executes a thermophoretic motion ,54 even mobility . For ourspot. In , it was 2v is induced by thermoplasmonic heating of the gold-substrate, as per the Stokes equation given in f at a point s in the fluid is given as F0 is a constant strength of the point forces located at Ri, where i = 1, 2. The location Ri of the ith point force is chosen to be at the center of the ith hotspot, while both point forces are located at a height h from the substrate. This modeling of thermoplasmonic flows by a point force (or a Stokeslet) was first done in [F0 is expected to increase with the increasing power of the laser. The fluid velocity at a field point s can then be written as a sum of contributions from two-point forces at point R1 and R2 [v at a point r is then given as G is a Green’s function of Stokes flow, which is defined as [Δ · v = 0. In addition, it should ensure the no-slip boundary condition or the fact that the fluid flow must vanish at the substrate. Thus, we must have v = 0 at the substrate, which is chosen to be at z = 0. A Green’s function of the Stokes equation, which satisfies these properties, for a source at a height h from the wall, can be written as r* = R – R*, R* = 𝓜 · R′ is the image at a distance h from the wall at z = 0. Note, r = |r| = |R – R′|, and G* is the correction necessary to satisfy the boundary condition [The fluid flow  done in . We note1 and R2 . The expfined as  (6)∇βGαβn tensor ,60 with ondition . The expΩ of the particle follows from the vorticity ω in the fluid at the location of the particle. It is given as ri is the distance of the field point from the ith hotspot, where i = 1, 2, while h is the height of the particle and point force from the substrate. We use h = 1.4a, where a is the radius  of the particle. The above expression for the vorticity is plotted in x = y = 0, which is also the center of the line joining the hotspots. Moreover, for the plane x = 0, the vorticity vector points along the negative y > 0 and the positive y < 0. The vorticity vanishes identically at the point x = y = 0. Thus, we have qualitatively explained the experimental rotational dynamics.The angular velocity r1 = r2 = r and l = r sin θ; see F0 in θ = 0. On the other hand, the angular velocity is nonzero when the particle is pushed above or below the line joining the hotspots by increasing the laser power. From the expression Ω pointing along the negative y > 0 and with angular velocity Ω pointing along the positive y < 0. See roll rotation of the trapped microparticle observed in the experiments is captured by our model of the thermoplasmonic fluid flow, both qualitatively  performs roll rotations while confined in a quasi-3D hydro-thermophoretic trap. The roll rotational motion of the microparticle is due to the convection currents of thermoplasmonic fluid flows. We show that a simple model for thermoplasmonic fluid flows can predict the angular velocity of the microparticle. In our model, the fluid flow driven by the laser heating of each hotspot can be captured by a point force in the fluid near the substrate. The predicted angular velocity from this model is in excellent agreement with the experimental observations. Thus, using a combined theoretical and empirical analysis, we have demonstrated that the roll rotations of a particle trapped in a hydro-thermophoretic trap can be tuned to desirable values.To summarize, we have presented an experimental method to generate a controlled and continuous out-of-plane rotation of a microparticle in the Our results show that this approach could be used to develop new techniques for the manipulation and control of microparticles in fluidic environments. This could have important applications in fields such as microfluidics, biophysics, and materials science. We believe that this technique could have important implications for the development of new tools and techniques for the manipulation and control of microparticles in fluidic environments. Moreover, the controlled rotation of microparticles may be used to study nonequilibrium statistical mechanics in colloidal systems . The comMovie 1Movie 2


Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant brain tumours in adults. Because of their locally aggressive behaviour and the fact that they cannot be cured by current therapies, they represent one of the most devastating cancers. The present review summarises recent advances in our understanding of glioma development and progression by use of various in vitro and in vivo models, as well as more complex techniques including cultures of 3D organoids and organotypic slices. We discuss the progress that has been made in understanding glioma heterogeneity, alteration in gene expression and DNA methylation, as well as advances in various in silico models. Lastly current treatment options and future clinical trials, which aim to improve early diagnosis and disease monitoring, are also discussed. A brain tumour is formed when there is an abnormal clonal growth of cells within the brain. Brain tumours can be classified into malignant (cancerous) and benign (non-cancerous). Malignant tumours can be primary or secondary; the former originate from cells within the central nervous system (CNS), while the latter arise from cancer cells, which have metastasised from other tissues of the body to the brain via haematogenous spread. Two main cell types compose the CNS: neurons, which act as the communicators of the nervous system, and glia , which provide support and protection to neurons, maintain homeostasis and form myelin sheaths around them . In the Gliomas are the most frequent (75%) of all primary malignant brain tumours in adults ; becauseThe revised 2016 WHO Classification of Tumours of the CNS divides gliomas into low grade glioma  and high-grade glioma  based on integrated classic histological features and molecular biomarkers [Of all adult primary brain tumours, glioblastoma , has classified brain tumours based on cell type and grading (grades I–IV), using histopathological criteria which refer to similarities with putative cells of origin and presumed levels of differentiation . The pleIDH1 and 2), wildtype (non-mutant) versus mutant (IDH mutations are thought to occur early on in the genesis of a glioma [IDH1 Arg 132 (R132H). The mutated IDH enzyme reduces α-ketoglutarate (α-KG) to D-2-hydroxyglutarate (D-2HG) [In the WHO classification of 2016, “diffuse gliomas” (Grades II–IV) are classified in two groups based on a hallmark genetic feature, the mutational status of the isocitrate dehydrogenase genes (s mutant . IDH muta glioma  with the (D-2HG) . D-2HG i (D-2HG) ,16, poss (D-2HG) .IDH gene [TERT) can also be found within this group [90% of glioblastomas (grade IV), termed as primary or de novo, and roughly 30% of diffuse and anaplastic astrocytomas  contain the wildtype IDH gene ,19. Furtis group ,21, resuis group . After tis group . IDH-wilis group ,24.Seventy percent of diffuse and anaplastic astrocytomas (grades II-III), 100% of diffuse and anaplastic oligodendrogliomas, and 10% of glioblastomas ,19 are IATRX mutations to the tumour neurobiology has not been established to date, although there is some evidence to suggest it has a role in cell cycle regulation, histone regulation, and chromatin remodelling [The second group refers to the majority of grade II–III gliomas that carry mutations in the transcriptional regulators ATRX and TP53 but no 1p19q codeletion, encompassing the diffuse or anaplastic astrocytomas (II–III) and secondary glioblastomas (IV) . Decipheodelling .IDH mutations and 1p19q codeletion status) and phenotype as diagnostic criteria, most  tumours of this group fall into the category of oligodendroglioma or astrocytoma [IDH-mutant, ATRX-mutant, 1p/19q-intact) and oligodendroglial  mixed populations of tumour cells [A major achievement in the WHO classification of 2016 is the  omission of the previously ill-defined diagnoses of oligoastrocytoma and anaplastic oligoastrocytoma. By using both genotype , or how tumour cells overcome contact inhibition ,40. HoweThe use of cerebral organoids is becoming a more attractive approach when investigating human glioma biology. They can be derived from human pluripotent stem cells including human induced pluripotent stem cells (iPSC) and human embryonic stem cells (ESC) . CerebraCerebral organoids are a more complex and physiological system to study brain cancer compared to other in vitro and in vivo models. Cell lines and patient-derived tumour xenografts (PDTXs) used to study tumorigenesis have their limitations: genetic changes acquired during the in vitro cultures and two dimensional (2D) grown might make the findings obtained by using these methods unreliable; PDTXs models are expensive and require long term studies . CerebraG12V-IRES-tdTomato construct into the TP53 locus by homologous recombination using CRISPR-Cas9 technology, Ogawa et al. were able to cause simultaneous disruption of TP53 and gain of function of HRas proteins, generating tumour formation. Organoids positive for tdTomato reporter were shown to exhibit a greater proportion of proliferative cells positive for the marker Ki67, when compared to controls represented by untransduced organoids. Flow cytometry showed that at 8 weeks post-electroporation, 5.7% of the organoid culture was composed of tumourigenic cells (tdTomato positive), which greatly increased to 86.8% at 16 weeks post-electroporation. Tumourigenic cerebral organoids also demonstrate the ability to proliferate and invade otherwise healthy organoid [Recently, cerebral organoids derived from human ES cells following the protocol first described by Lancaster  have beeorganoid . This stOther studies have employed similar methods to study the invasive behaviour of multiple patient-derived GBM samples. Krieger et al. describe their use of human iPSC-derived cerebral organoids as scaffolds for the study of growth and invasion of GBM samples . The fluGJA1) encoding connexin-43, which allows gap junctions to form between GBM microtubes; Collagen Type IV Alpha 5 Chain (COL4A5) which encodes for a component of the extracellular matrix, and Glypican 3 (GPC3), an activator of the Wnt signaling pathway previously associated with invasiveness. This data demonstrate how co-culture of patient-derived GBMs with iPSC-derived cerebral organoids provides the cellular interactions necessary to induce a change in the transcriptome towards invasion. This mixed-organoid model and scRNA-seq also describes the heterogeneity between different patients’ tumours and provides a way to test the individual tumour’s susceptibility to targeted drug therapies on a clinically relevant time scale.Recently, Krieger et al. have investigated differentially expressed genes in GBM cultures in combination with/without normal organoid cultures derived from iPSC. By using single cell RNA-sequencing (scRNA-seq), they identify a subset of genes that were differentially expressed in patient samples that had been co-cultured with healthy organoids compared to patient samples with no co-culture or healthy organoids cultures alone. This gene set was upregulated across all GBM samples that had been co-cultured and included: Gap Junction Protein Alpha 1  genetically engineered mouse models (GEMMs) which involve altering specific genes known to influence glioma formation and progression and (2) xenograft models which utilize patient-derived cells/tumour fragments. Xenograft models are generated using either glioma cell lines or patient tumour samples cultured in vitro and later inserted directly into the mouse brain to induce tumour formation, or by injecting small fragments of human gliomas directly into the brain, thus bypassing the need for culturing the samples, which could lead to the loss/acquisition of genetic alterations due to adaptation of tumour cells to culture conditions .In genetically engineered mouse models (GEMMs), specific genetic alterations identified in human tumours are introduced in the germline  or in the somatic cells  of a mouse model to generate a de novo tumour. In these models, the genetic alterations of interest are targeted to a specific cellular population within the mouse tissue/organ, and to a specific developmental stage of the animal . GEMMs represent an important experimental tool to investigate the cellular and molecular events responsible for tumour initiation and progression, and to determine the minimum genetic alterations necessary for malignant transformation. Another important advantage represented by GEMM is that they have been established in immunocompetent animals, highlighting the importance of the microenvironment in tumour biology. GEMMs have been used for testing biomarker expression, drug delivery methods and new therapeutic strategies, and many studies using GEMMs have been successfully translated into clinical trials.There are numerous glioma GEMMs some of which are reviewed here .+/− and Trp53+/− to produce a double heterozygous mouse. This model produced mostly low-grade diffuse astrocytoma (WHO grade II), which in some cases progressed to a grade IV glioblastoma [Ras, EGFR, and Akt among others [A model of astrocytoma was developed by Reilly et al. by crossing two mouse strains, Nf1blastoma . There hg others . Some mog others . GEMMs og others . These dAlthough GEMMs are useful tools in understanding how acquisition of mutation(s) cause tumour formation, they also present some limitations: the genetic alterations generated are limited to a number of genes, thus they cannot fully represent the genetic complexity and heterogeneity that is often seen in resected human glioma samples. Moreover, their development is normally costly and time consuming.In a patient derived xenograft (PDX) model, human gliomas in the form of tumour cell models or en bloc biopsies are injected or transplanted into an immunodeficient mouse to prevent rejection of the transplant from the host. Several strains of mice with different levels of inhibition of the immune system can be used in these studies: athymic nude mice, in which T-cells populations are greatly reduced; severely compromised immune deficient (SCID) mice, in which both B and T cells are lacking; NOD-SCID mice, in which there is an impaired development of B and T lymphocytes and natural killer (NK) cells; and recombination-activating gene 2 (Rag2)-knockout mice, in which both the maturation of both B- and T-cells is impaired. In these mice, the tumour sample can be implanted heterotopically, into an area of the mouse unrelated to the original tumour site  or orthotopically, into the site of origin of the tumour . Tumours generated in xenograft models grow relatively quickly and retain the cellular and molecular characteristics of the patient’s tumour. Preservation of the intra-tumoural genetic heterogeneity allows an accurate representation of the sample of origin. Furthermore, PDX have the advantage of developing an in vivo vascular supply. For these reasons, they represent a useful clinical model to test the response of tumour cells to therapy, albeit for a limited number of in vivo passages in the mouse. After which a selective pressure for propagation starts that causes specific genetic and epigenetic signature changes in the cells, making the generated tumours less representative of the original tumour from the patient .In a heterotopic xenograft model, glioma cells are injected in a tissue where they will not experience the same stimuli and cell-cell interactions present in the microenvironment of the tissue of origin. Brain tumour samples will also lack input from brain-specific immune factors, such as microglia and macrophages, which can both affect and be effected by the tumour cells ,74. To rRecently, a particular xenograft model using patient-derived glioblastoma cells was generated by implanting cells via intravitreal injection into the mouse eye . This moXenograft models using human tumour samples can provide an excellent insight into human glioma biology, however these models rely on immunodeficient murine hosts. Given the more recent interest in the role of the immune system in cancer progression, these xenograft models present some limitations, as they cannot fully recapitulate the ‘true’ microenvironment in which immune and cancer cells interact.In ‘humanized’ NOD/SCID mice models a functional human immune system is generated, through engraftment with human primary hematopoietic stem cells (HSCs) typically derived from human foetal tissues, umbilical cord blood, or peripheral blood mononuclear cells (PBMCs). To facilitate engraftment, the human leucocyte antigen (HLA) of HSCs and PBMCs should be matched to the tissue being xenotransplanted . Human gRecently, studies of glioma samples transplanted into mice with intact immune systems have started to emerge. Semenkow et al. provided evidence of an immunocompetent mouse model that was able to more faithfully resemble the pathophysiology seen clinically in patients with GBMs, compared to transplants grown in immunodeficient hosts . To faciIntra-tumour heterogeneity describes a phenomenon in which different cells within the same tumour acquire distinct epigenetic and genomic alterations. This allows development of dissimilar morphological and phenotypic features and consequent divergent development from the progenitor cancer cell population. Intra-tumour heterogeneity has been proposed to be one of the major obstacles for conventional and targeted therapies to maintain remission ,88,89,90EGFR, PDGFRA, and MET using fluorescent in situ hybridisation (FISH) [PDGFRA, MDM4, and AKT3 loci, and deletion of the genomic locus containing PTEN. Furthermore, each tumour copy number alternations (CNA), were classified as ‘common’, ‘shared’, and ‘unique’, i.e., all tumour fragments, more than one but not all fragments, and only one fragment, having CNA respectively. The vast majority of CNAs (31/38), showed ‘shared’ and ‘unique’ alterations. It was deduced that ‘common’ aberrations should have occurred earlier in development, as all tumour fragments (four fragments per tumour) were extracted from the same tumour mass. While ‘unique’ and ‘shared’ alternations found in a subset of tumour cells should have occurred later on in the development. Therefore, it was implied that taking samples from multiple sites within the tumour, enables both spatial and temporal interrogation of mutations. ‘Common’ CNAs were found in chromosomes 7 and 10, of which the most frequent putative glioblastoma drivers, such as EGFR, CDK6, MET, and PTEN, are located. Using microarrays and hierarchical clustering, it was also found that distinct glioblastoma subtypes are present within the same tumour. By measuring the mitotic distance between cells within a fragment, they also demonstrated that different subclones exist within each fragment, and also that distinct cell lineages are present, illustrating a complex and dynamic hierarchy. The authors concluded that, the founder clone initially displayed amplification/gain of EGFR, CDK6, and MET, and loss/deletion of CDKN2A/B, PTEN, and PARK2. Later clones displayed altered gene expression and loss of chromosomes. The variation in gene expression in tumour development suggests that a single therapy is highly unlikely to be effective for each subclone, meaning that while it might be effective for one clone, the rest of the tumour will be able to survive by either having inherent resistance [Previously, intra-tumour heterogeneity was demonstrated at the level of n (FISH) . Sottorin (FISH) . This wan (FISH) . Thirty-sistance  or acquiEGFR truncations and in frame deletions (EGFRvIII). Additionally, using hierarchical clustering and principle component analysis four meta signatures  were comprised of clusters, which vary amongst the whole set of cells, enriched by genes related to each meta-signature. Quiescence markers were found to be significantly higher in non-cycling tumour cells. Clustering of genes anti-correlated to the cell cycle signature, 9/12 were within the hypoxia meta-signature. Reordering of the cells by hypoxic module score, showed clear gradients for two clusters (MGH28/31), implying that hypoxia as a factor of tumour microenvironment can play a role in gene expression. This finding emphasised previous findings reporting tumours containing niches with phenotypic changes including quiescence and self-renewal [A recent study by Patel et al., used scRNAseq to measure intra-tumour heterogeneity in patients affected by primary glioblastomas. Six thousand genes were analysed from 430 cells isolated from each tumour; this constitutes adequate coverage to characterise heterogeneous gene expression and delineate correlation between transcriptional and functional expression . Extensi-renewal  and adap-renewal . Finally-renewal . They id-renewal . MoreoveFailure of clinical trials to show survival benefit for “diffuse glioma” is well known ,98. The In a recent study, the critical differences in gene expression between IDH mutant astrocytomas (A) versus oligodendrogliomas (O) were deciphered, by combining 9879 single cell RNA-seq profiles from 10 IDH-A tumours, 4347 single cell RNA-seq profiles from 6 IDH-O tumours, and 165 TCGA bulk RNA profiles . DiffereTP53 mutated in astrocytomas has been shown to influence the nuclear factor kappa beta NF-kβ immunoregulatory pathway [A greater macrophage-like expression was found in tumours of higher histopathological grades (II vs. III vs. IV) and this was correlated with increased endothelial cell signature. TME composition might be partly influenced by tumour cell gene expression, i.e.,  pathway . In contThe TME plays an important role in gliomagenesis and may influence the tumour evolutionary process. Unsupervised transcriptome analysis of the TCGA data revealed four clusters of glioblastoma subtypes: classical (CL), mesenchymal (MES), neural (NE), and proneural (PN), each linked to specific genomic alternations . MES is Tumour-associated macrophages (TAM) and microglia are innate immune cells that can make up to half of the tumour mass in glioblastoma. Brain TAM and microglia retain plasticity and exist in a spectrum of different cellular states classified according to their environment: M1 proinflammatory and M2 cytoprotective . During + T cells [+ T cells were found to be significantly increased at recurrence in comparison with their primary tumours [The type of oncological treatment can also be affected by the TME. It has been postulated that TMZ treatment can induce aberrant mutations ,118. The T cells . CD8+ T  tumours . Hypermu tumours . Further tumours . Therefo tumours ,123.Glioblastoma metastasis outside the brain are rare , while tInvasion of glioma cells into normal tissue is a complex process, which is determined by complex interactions between glioma cells and the extracellular microenvironment (ECM); this involves expression and activity of proteolytic enzymes, such as matrix-degrading proteases (MMPs) and cathepsins to degrade ECM components, as well as interactions of tumour cells with normal astrocytes . By usinThe permeability of the blood–brain barrier (BBB) presents one of the biggest barriers in the design and testing of novel brain cancer therapies. The BBB is comprised of endothelial cells that are held together by tight junctions which is what gives the capillaries of the brain such low permeability . This laWang et al., made use of molecular dynamics (MD) simulations to simulate the bilayer of human brain microvascular endothelial cells (hBMECs), which forms the outer layer of the BBB . This moDisruption of the BBB in order to deliver chemotherapeutic agents to the brain is a potential avenue of study when considering how best to get drugs into the brain. The tight junctions of the BBB make the process of drug delivery difficult and so Goliaei et al. presented a molecular dynamics simulation using ultrasound and nanobubbles to disrupt the integrity of the BBB . To do tPrabhakar and Banerjee, have since gone on to demonstrate the biological potential of these nanobubbles in delivering drugs to cells in vitro. By combining nanobubbles, liposomes and the chemotherapy drug, paclitaxel (PTX) they show that bursting nanobubbles with ultrasound lead to a much higher percentage of PTX being released compared to the use of nanobubbles without ultrasound. They also demonstrated that the combined use of PTX-containing nanobubbles and ultrasound caused a much higher cytotoxicity in a variety of cell lines than those without ultrasound . The useClonal evolution is one of the main processes that drives tumour progression. The emergence of heterogeneous clones within a tumour is caused by the genetic, epigenetic, and microenvironmental selective pressure that tumour cells experience during cancer progression. Clonal evolution is also one of the major causes of therapeutic resistance to anticancer agents. Understanding the evolutionary course of an individual patient’s cancer is likely to be essential in deciding their optimal treatment plan and diagnosis and subsequent points of progression or recurrence.In recent years, multi-region sequencing has allowed researchers to discover the temporal sequence of some genomic changes within a tumour, however the robust identification of repeated evolution across patients remains a challenge. Recently, Caravagna and co-authors have developed a new analysis tool, named REVOLVER (Repeated EVOLution in cancER), that utilises a machine learning approach named ‘transfer learning’ to jointly analyse multi-region sequencing data . This noDrug resistance and tumour recurrence can also be caused by clonal evolution induced by the selective pressure during therapy. Small populations of genetically distinct subclones present in a tumour can evolve under the pressure of drug treatment, resulting in the selection of the more ‘fit’ subclones. These subclones may persist after treatment and repopulate the tumour over time, leading to disease recurrence. Resistant subclones are likely to be generated in response to therapy in two different ways: (1) drug therapies induce genetic changes in a small number of cells, generating mutant subclones, which are preferentially augmented during subsequent treatment; (2) therapy resistant subclones generated during tumour progression, are present pre-treatment and continue to thrive despite further therapy. In some cases, such as metastatic breast cancer, leukaemia, non-small cell lung cancer as well as glioblastoma , it has Mathematical modelling of the evolution of drug resistance is of great clinical benefit as it could identify resistant subclones within a population that have resistance against certain therapies and therefore inform different clinical decisions. Evolutionary herding is the use of drugs that select for specific phenotypes, this can present differential therapy courses depending on each patient’s tumour heterogeneity. Each drug will produce ‘fitness peaks’, where cell populations unaffected by that drug will cluster, and ‘fitness valleys’ where affected cell populations will cluster. Combination therapy that selects for overlapping fitness peaks will select for multi-drug resistant cell populations. The idea behind evolutionary herding is for one drug to select for a cell population that falls into one fitness peak that forms a fitness valley under the influence of a second drug—thus leading to the elimination of the cancer cell population ,142.IDH1 or IDH2 mutations combined with the loss of both 1p/19q chromosomal arms. It is a relatively slow growing tumour that mostly affects younger patients, has a longer median survival time and has a better response to alkylating agents in comparison to the other diffuse gliomas [Oligodendroglioma is characterised by either  gliomas . Despite gliomas . Data frThe use of computational modelling for drug design can provide more precise insight into the structure and chemistry required from a new compound. This can help to eliminate time spent in the lab optimising hundreds of potential compounds. Virtual drug screens can be conducted using quantitative structure-activity relationships (QSAR) and ligand-based virtual screens (LBVS) .QSAR models have been previously utilised in the design and virtual testing of new EGFR inhibitors, which could provide future treatments for EGFR-amplified glioma. The study conducted by Zhao et al. employed 2D-QSAR to first ascertain whether or not a series of potential drugs would be classed as EGFR inhibitors . The 2D-DNA methylation occurs as a covalent transfer of a methyl group onto one of the DNA nucleotide bases, either as a targeted modification via DNA methyltransferases or as an effect of DNA damaging agents . MethylaGliomas exhibit several oncogenic pathways involved in tumour onset, progression and therapeutic resistance, and like other tumours their initiation and progression is associated with epigenetic abnormalities . Hence tIDH mutations occurs most commonly at position R132 and the most common mutation in glioma results in a change to R132H. The result of this mutation is neomorphic enzyme activity that instead of producing α-ketoglutarate produces 2-hydroxyglutarate (2-HG) [e (2-HG) . 2-HG ace (2-HG) .IDH mutant brain tumours have focused on understanding the pathways and mechanisms involved in IDH-related gliomagenesis, and in particular the role of the epigenetic modifications and effects on IDH mutant gliomas. A review by Park and Turcan [Multiple studies conducted on d Turcan , discussmut cancers all showed hypermethylation when compared to their IDHWT counterparts. IDHmut glioma also demonstrate the highest amount of genome hypermethylation in comparison to the other cancer types tested, with 19% of CpG sites methylated compared to 2–3% for the other cancers [WT and IDHmut gliomas (~4200 genes) in comparison to the other cancers tested (~135–420 genes). IDHmut gliomas were also enriched for sets of genes involved in the tissue development pathway as well as the immune response, which may explain why IDHmut gliomas are often poorly differentiated. The effect of IDH mutations in glioma methylation changes may be influenced by the differentiation state of the cell that the mutation occurs within. Introducing IDH1R132H into both differentiated human astrocytes and neural progenitor cells showed that undifferentiated NPCs were more susceptible to hypermethylation than differentiated astrocytes [mut may not produce reproducible gains across all IDHmut cancer types.Mapping of CpG sites across multiple cancer types including glioma, melanoma, cholangiocarcinoma, and acute myeloid leukaemia revealed that IDH cancers . These ttrocytes . AlthougWT primary GBMs. De Souza et al. proposed that a small population of cells within IDHmut primary gliomas may already have a G-CIMP low phenotype and thus contribute to the tumour’s recurrence as the more aggressive and therapy resistant phenotype [IDH mutations in glioma produces a unique methylation pattern termed Glioma CpG Island Methylator Phenotype (G-CIMP) . LGGs cahenotype .The methylation shift data presented  indicate6-mA). The N6-mA modification has been demonstrated in bacteria and mammals but was only first identified in the human genome very recently [6-mA specific antibody the existence of the modification in primary human glioblastoma as well as in validated patient-derived glioblastoma cancer stem cell models was proven [6-mA modification and when this enzyme is inhibited, tumour formation decreases, along with the self-renewing capacity of the glioma stem cells. Knockdown of ALKBH1 led to a downregulation of genes involved in the hypoxia response indicating that targeting of ALKBH1 could have clinical benefit. Like the N6-mA modification itself, ALKBH1 was highly expressed in human glioblastoma when compared to the healthy brain tissue indicating that therapeutic targeting of this enzyme could more specifically target the glioma cells over the healthy cells. The presence of high ALKBH1 levels was correlated to higher glioma grades as well as poorer survival [Whilst the methylation of cytosine bases is most commonly associated with epigenetics, another form of DNA modification exists, the methylation of adenine bases at the secondary amine  or computer tomography (CT) scans. All these techniques however have their limitations; overall the diagnostic process requires time, a lot of expensive equipment and stress and in case of tissue biopsies, it could also confer a risk of brain swelling and haemorrhages to the patient. Liquid biopsies in gliomas are a minimally invasive technique, which have great potential in all aspects of glioma management. From detection to monitoring tumour response to therapies and early recurrence. Potential biomarkers may be identified to measure disease progression. Importantly, liquid biopsies can be obtained regularly over time and can provide up to date information about the molecular heterogeneity, and longitudinal genomic evolution of the tumour from diagnosis through progression/recurrence.Many different biological materials can be sampled from liquid biopsy of human body fluids including: circulating tumour cells (CTCs); circulating cell-free DNA (cfDNA) which in cancer patients contains circulating tumourDNA (ctDNA); circulating cell-free tumour RNA (ctRNA) containing mRNAs and mainly small RNAs; extracellular vesicles (EVs); proteins, metabolites, and tumour-educated platelets . In gliWT patients having more cfDNA than IDHmut, which they explained as being down to the more aggressive nature of IDHWT tumours and their associated increase in BBB breakdown. Noushmehr H. et al., found that there was a genome wide change in methylation status of glioma patients compared to healthy tissues. This led them to test whether other CpG methylation sites could be detected in cfDNA. They found that not only was there a detectable serum methylome profile but that it was distinct from that of other neoplasms. The analysis of cfDNA methylome also showed that it was possible to decipher the IDH status of the tumour from the serum methylome. A simple blood test capable of identifying a glioma over other neoplasms and also being able to identify the IDH status may provide a non-intrusive way by which patients can be stratified early on in the treatment process without having to undergo invasive biopsies or time-consuming scans.Recently Noushmehr H. et al., provide evidence of how a simple blood biopsy, capable of detecting cell-free DNA (cfDNA), is important for patient stratification . They obRecently, increasing interest in developing tests from cerebrospinal fluid (CSF) has been developed, since this fluid is produced in the choroid plexus of the brain and would have direct access to any waste products such as cfDNA . The useGlioma surgery has been revolutionised over the last decade, due to the combination of significant advances in brain tumour imaging, intraoperative technologies and neurosurgical techniques. The philosophy of treatment, that safe, maximal glioma resection improves symptom management, quality of life, and both progression free and overall survival has also become less nihilistic. Currently, the relationship between the extent of resection (EOR) of both LGG and HGG, and clinical outcomes remain incompletely understood. There is no validated metric to quantify EOR and randomized clinical trials are impractical, which hinders achievement of level-one evidence for EOR. However, a growing body of clinical data supports the prognostic importance of gross total resection in LGG and HGG , and is Numerous intraoperative tools have been developed and evaluated, aiming to enhance the neurosurgeon’s ability to identify tumour boundaries and augment resection, while simultaneously preserving eloquent brain function.Fluorescence guided surgery allows real-time intraoperative identification of residual tumour. The best studied intraoperative fluorescence imaging technology is 5-aminolevulinic acid (5-ALA), which is used in HGG . Oral adCortical functional organization varies considerably between patients, tumour mass effect may distort anatomic relationships and cortical plasticity may result in reorganization of neural networks. Intraoperative neurophysiological/electrical stimulation mapping and monitoring either during awake craniotomy or under general anaesthesia, aims to reliably and reproducibly identify and localize the cortical areas and subcortical pathways involved in language, motor, sensory, and cognitive function and preserve these functions during tumour resection . MeRecent advances in intra-operative neurosurgical imaging including intraoperative neuronavigation, intraoperative MRI (iMRI), and intraoperative ultrasound (iUS) have significantly enhanced the potential to achieve maximal and even supra-maximal glioma resection ,178. IntFollowing surgery, diffuse astrocytoma and oligodendroglioma patients are stratified into low risk and high risk. It is worth noting that a definition for high-risk and low-risk low grade gliomas is highly variable . PatientThe standard of post-operative care for high risk high-grade gliomas is based on results from three phase 3 trials (20–22) and interim result from the CATNON trial. This includes radiotherapy (60 Gray) with either six cycles of adjuvant PCV or concurrent plus adjuvant temozolamide (TMZ). The interim analysis of CATNON trial showed that non 1p19q codeleted anaplastic astrocytomas can benefit from 12 cycles of TMZ as well. The CODEL trial is currently investigating whether TMZ can replace PCV in the treatment of 1p19q codeleted oligodendrogliomas. Further, the POLCA trial is currently investigating whether radiotherapy for 1p19q codeleted oligodendrogliomas can be replaced by PCV only. For glioblastoma, radiotherapy of 60 Gray for six weeks with concurrent daily TMZ followed by six cycles of TMZ has been the standard of care since 2005 . AdditioFor glioblastoma patients older than 70 years of age the Nordic  and the Current systemic delivery of chemotherapy in glioma patients does not reach a high concentration at target within the glioma or the CNS, and leads to systemic side effects such as myelosuppression. Novel drug delivery systems aimed at improved delivery both spatially and temporally within the brain are actively being developed. These including injectable nanoparticles, implantable controlled-release polymer systems, convection enhanced delivery using a catheter-based approach to an agent directly into the brain tumour or parenchyma and using positive pressure infusion and BBB disruption by pulsed ultra-sound (reviewed in ).Carmustine (BCNU) loaded wafers are FDA- approved and are placed along the surface of the glioma surgical resection cavity to deliver local chemotherapy for the following days to weeks. The hypothesis was based on the fact that local delivery of a chemotherapeutic agent will enable bypassing of the blood–brain barrier. Brem et al., has showed that usage of BCNU showed improvement in median survival (31 vs. 23 weeks compared to the placebo ). While MGMT promoter and with no baseline corticosteroid use. This suggests a potential clinical benefit from a therapy with immune checkpoint inhibitors for this subgroup of patients [In the past, the brain has been considered an immune-privileged organ, meaning the effects or at least the full effects of the immune system are not expected to occur. This has been based on the fact that no lymphatics exist and the blood–brain-barrier (BBB) prevents influx of immune regulators and cells. Though it has been increasingly recognised that this might be not entirely true. Importantly, it is appreciated that brain tumours are actively evading and suppress the immune system, by minimising expression of major histocompatibility complex (MHC) proteins, reduced cell activation (T-cell in particular), and expressing pro-apoptotic factors. Understanding the immune evading mechanisms has been an emerging and exciting new research field. Three main methods for overcoming immunosuppression have been in the forefront of research: (i) cytokine therapy, (ii) active immunotherapy, and (iii) passive immunotherapy. Cytokine therapy has mainly been focused on activating the immune system, hoping it will fight the tumour. Active immunotherapy has primarily been focused on priming the immune response against known tumour antigens by vaccination and adoptive T-cell therapies . Passivepatients . FurtherAnti-angiogenic therapies have also failed the initial expectations to enhance survival and quality of life of GBM patients. Only one agent, the anti-VEGF antibody bevacizumab, has shown some efficacy in controlled clinical trials so far. This efficacy is, however, limited to improve progression-free survival (PFS) but not the overall survival in patients affected by primary and recurrent glioblastoma . TargetiEGFR-amplified GBMs have shown limited efficacy. The INTELLANCE-2 is a randomised controlled phase II trial investigating, the efficacy and safety of depatuxizumab mafodotin  in patients with recurrent EGFR-amplified glioblastoma. Depatux-M is an antibody-drug conjugate, that targets cancer cells by linking the toxin monomethyl auristatin F (MMAF) with an antibody directed against the epidermal growth factor receptor (EGFR) or mutant EGFRvIII. In this trial the efficacy of Depatux-M was studied in combination with TMZ or as a single agent, versus TMZ or lomustine alone (NCT02343406). Data obtained from the study showed a clinical benefit of Depatux-M in combination with TMZ in recurrent EGFR-amplified glioblastoma, suggesting a possible role for the use of Depatux-M in combination with TMZ, especially in patients relapsing well after the end of first-line adjuvant TMZ treatment [For instance recent studies designed to target the EGFR pathway in patients with reatment . HoweverIDHs, huge efforts have been spent pursuing the identification of novel direct inhibitors of the mutant IDH. AGI-5198 has been reported as the first novel, synthetic, direct enzyme inhibitor of the IDH mutant enzyme [IDH mutant gliomas, impairing xenograft progression in vivo [https://www.fda.gov/drugs/informationondrugs/approveddrugs/ucm569482.htm). AG-221 was tested in a clinical trial for gliomas and other IDH mutant tumours in 2014, showing inhibitory effects for these tumours. However, appropriate dosing was an issue. Also, a number of clinical trials are underway, currently evaluating the efficacy and safety profile of AG-120 and AG-881 (NCT03343197). Since the discovery that IDH1 R132H is the most common mutation in ‘diffuse’ gliomas, more clinical trials have emerged . Recently, Agios Pharmaceuticals has conducted a multicentre clinical study on recurrent LGG with IDH mutation using an AG-120 and AG-881 (NCT03343197). The primary outcome was to compare the D-2-HG concentrations in surgically removed tumours, which were treated versus not treated with the drug inhibitors. Clinical safety, dosage, tolerance, and pharmacokinetics will also be studied. This safety and feasibility trial will provide appropriate dosing for future studies. It is worth noting that the new inhibitors exhibit a good CSF-plasma ratio [Since a huge percentage of low grade gliomas and secondary glioblastomas bear mutations in t enzyme . This dr in vivo . As repo in vivo  . Initially, a spontaneous immune response to the IDH mutation has been documented . The reswww.birmingham.ac.uk/brainmatrix) is a national platform study striving to optimise this strategy. The aim of BRAIN MATRIX is to test the hypothesis that comprehensive whole-genome sequencing and epigenomic profiling of gliomas is feasible in a timely manner in the UK, and that the results improve the stratification of patients for next generation (targeted) therapies, to ultimately improve clinical outcomes and quality of life. Furthermore, optimised fresh tumour sampling pipelines can be used to provide good quality tissue in order to create autologous vaccines and potential immunotherapies. Advanced trial techniques, such as adaptive designs, can make clinical trials more flexible by using results accumulating in the trial to modify the trial’s course in accordance with pre-specified rules. These techniques can shorten trials duration, obtain more precise conclusions however are more complex and have had success in other cancers notably lung cancer .The ability to deliver precision/stratified medicine represents a challenge in glioma patients. Future treatments will benefit from ‘fresh tumour tissue’ resected at surgery that is surplus to diagnostic requirement. The tissue must be suitable for whole genome analysis, epigenomics, transcriptomics, methylomics, and proteomics to allow identification of specific tumour targets, drug sensitivities, and immune modulatory factors. The British feasibility study of molecular stratification and targeted therapy to optimize the clinical management of patients with glioma by enhancing clinical outcomes, reducing avoidable toxicity, improving management of post-operative residual and recurrent disease and improving survivorship (The Tessa Jowell BRAIN MATRIX trial Future advancements in glioma treatments require a more integrated approach and close collaboration between patients, clinicians, scientists, and clinical trial units in order to incorporate translational research focusing on precision medicine and adaptive clinical trials. The use of single cell RNA-seq methods and a wider range of in vitro and in vivo models will allow the molecular events propagating glioma to be further elucidated. The role of the tumour microenvironment as well as that of normal brain cells requires further attention in order to understand how these brain-specific components influence the development, progression, and recurrence of glioma so that more comprehensive therapies can be developed. Aiding this, in silico drug design and testing allows us to quickly understand if a certain drug will pass the blood–brain barrier, a major hurdle in the development of systemically administered drugs, and whether a new drug will provide any therapeutic benefit. Our understanding of the molecular events during glioma onset and progression have greatly improved in recent years, sadly the clinical aspects of patient stratification care have not matched this. Greater co-operation between clinicians, scientists and clinical trials will, in the future, lead to more therapeutic options to combat this invariably lethal disease.


Biological collections have been historically regarded as fundamental sources of scientific information on biodiversity. They are commonly associated with a variety of biases, which must be characterized and mitigated before data can be consumed. In this work, we are motivated by taxonomic and collector biases, which can be understood as the effect of particular recording preferences of key collectors on shaping the overall taxonomic composition of biological collections they contribute to. In this context, we propose two network models as the first steps towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors’ interests and activities. Building upon the defined network models, we present a case study in which we use our models to explore the community of collectors and the taxonomic composition of the University of Brasília herbarium. We describe topological features of the networks and point out some of the most relevant collectors in the biological collection as well as their taxonomic groups of interest. We also investigate their collaborative behaviour while recording specimens. Finally, we discuss future perspectives for incorporating temporal and geographical dimensions to the models. Moreover, we indicate some possible investigation directions that could benefit from our approach based on social network analytics to model and analyse biological collections. Indeed, biological collections stand as invaluable sources of primary biodiversity information, physically storing biological materials that testify to the existence of living organisms over time and geographic space. Regarded as important natural history repositories, biological collections have been increasingly used for a multitude of ecological and conservationist investigations7. As many of these initiatives require intensive use of biodiversity data, typically covering wide geographic areas and long periods of time, they would become impracticable without biological collections, due to the high costs associated with collecting new data in the field on demand. Besides, more species have been recently discovered by taxonomists by inspecting unidentified materials at biological collections than by exploring and collecting at new locations6. One important limitation, however, is that biological collections provide only a sampled partial view of the actual biological diversity within their actuation regions. Furthermore, applications aiming at investigating wider ecological and biogeographic processes should be able to combine data from multiple biological collections. In this work, we are particularly motivated by the challenge of characterizing sampling biases in data, defined as systematic errors that are introduced in data as an effect of not using random sampling designs8. Sampling biases are typically introduced in biodiversity datasets when collectors record specimens in the field in an opportunistic fashion, deploying uneven sampling efforts throughout the studied area and recording preferentially organisms with particular characteristics over others. In addition, collectors consider the accessibility of potential sampling sites while selecting them, and thus locations such as roadsides and the proximities of urban centers are often oversampled8, while others that are more remote remain poorly represented. Indeed, what is collected is typically a product of both targeted collecting (based on academic interest) and opportunistic collecting . Sampling biases are in fact one of the main limitations of biological collections, and have been observed to strongly impact the overall quality of models in case they fail to account for them11. Characterizing bias in such datasets would therefore require a systematic analysis of how the complex arrangements of the perceptions, interests, and interactions of collectors shape the overall composition of the collections. Not surprisingly, a number of recent efforts has been made to quantify the extent of biases, in particular sampling completeness13.How data is classified in information infrastructures directly impacts our potential knowledge about different domainssocial process, resulting from the collecting activities of collectors and their collaborative interactions. Networks have been used in a wide range of domains for the investigation of complex systems of interacting entities, from studies of the World Wide Web14 to ecological interaction webs15. However, to the best of our knowledge, social network analysis has not yet been applied in biodiversity informatics for investigating the assemblage of biological collections. One recent study uses network analytics to investigate the connectivity and roles of many organizations in the biodiversity informatics landscape, in terms of how they exchange information16. Grounded on recent advances in network science theory19 and social network analytics21, in this work, we introduce two classes of network models: (i) Species-Collector Networks (SCNs) for analysing interest relations involving collectors and species; and (ii) Collector coWorking Networks (CWNs) for structuring collaborative relations involving pairs of collectors.Within this scope, we propose two network models as the first steps towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors’ interests and activities. This paper thus contributes towards a novel modeling approach, based on social network analysis, for investigating the assemblage of biological collections as a We demonstrate the practical use of our network models by carrying out a case study, using the species occurrence dataset from the University of Brasília Herbarium (UB), downloaded through the GBIF platform. Once the network models are built, we explore their basic topological features and investigate the formation of communities (interest communities in SCNs and coworking communities in CWNs). We also investigate the relative relevance of collectors in the herbarium, regarding both their taxonomic contributions and their social positions.Finally, we believe our network models open new perspectives for research in biodiversity informatics, specifically for applications that rely on data from biological collections. With further developments from our work, we expect to provide a mechanism for systematically classifying collectors according to their expertises, their behaviours, and their social roles in the collections they contribute to. Another perspective is to enrich species occurrence datasets with contextual information, inferred by observing the composition of collectors associated with each record (and their respective profiles). Moreover, although we have not yet incorporated the temporal and geographical dimensions to the structure of our networks in this work, we believe this would be a fundamental advance, allowing to investigate how collectors interact and which species they record through time and geographic space.19. Such relational systems are naturally represented as networks, in which interactions are represented as pairwise connections (links) between entities (nodes) and assume particular semantics depending on the nature of the modeled phenomenon. Next, we introduce and formally describe two classes of network models that were developed during this study.Network science refers to a relatively new domain of scientific investigation, which aims at describing emergent properties and patterns from complex systems of interacting entitiesinterest networks describing relationships of type “collector samples species” or, conversely, “species is sampled by collector”. The network is thus composed of collectors holding links to every single species they have ever recorded or, alternatively, species holding links to every collector who has ever recorded them. An important semantic aspect of this model worth emphasizing is that here we model collectors recording species rather than specimens. Each occurrence record that is used to build the network includes a single specimen, which is a representative of a species. Thus, while collectors are represented in the network at the individual level (each collector is a person), species are instead represented as entities comprising groups of individuals. Nevertheless, each species is uniquely represented as a node in the network.SCNs are a particular type of SCN = ,  where Scol = {u1, u2, . . . , un} is the node set representing the collectors group; Ssp = {v1, v2, . . . , vm} is the node set representing the species group; and E is the set of undirected edges between members of Scol and Ssp. The bipartite graph can also be represented as a rectangular biadjacency matrix An×m for which aij ≠ 0 iff  ∈ E.As collectors and species refer to distinct entities in our system, we represent them in our network as two disjoint node sets. This matches the description of a bipartite network n collectors, n links connecting each individual collector to the recorded species are created . In the end of the process, the strength of each link is equivalent to the number of times each species-collector association appears in the original dataset. Link strength is graphically represented by the edge thickness. The strengh of each edge is stored in the biadjacency matrix A, and thus the value of element aij is the number of times the edge  occurs.A SCN model is built from a species occurrence dataset by using basically two fields. The first is the collectors field, containing the names of all collectors that were responsible for the record; and the second one is the species field, storing the species identity assigned to the specimen in the record. The network is built up from the dataset in an iterative process, in which weighted edges linking collectors to species are structured from rows in the dataset. For each new record containing species bag. This attribute is therefore exclusively derivable for collector nodes and this is a convenient structure for comparing collectors in terms of the composition of their records, i.e. their respective species bags. For that task, a high variety of well-known distance algorithms for vectors in literature can be readily applied. The species bag ui is thus defined as m is the length of the species set and each aij is the total number of records of species vj by collector ui. The sum of all elements in a collector’s species bag corresponds to the total number of records for that collector ui.The entire set and counts of species a collector has recorded in a dataset, which can be thought as a collector’s species signature, composes his/her quorum, an exclusive attribute of species nodes. This concept can be thought as the inverse of a species bag, being the collector signature of a species. The quorum vector vj is directly obtained from the graph’s biadjacency matrix as the jth column-vector, i.e., n is the length of the set of collectors and each element aij is the total number of times collector ui has recorded species vj. The total number of occurrences of species vj in the entire dataset is the sum of all elements in its quorum vector.The entire set and counts of collectors who have recorded a particular species in a dataset comprise its genus or a family. This process is defined as a taxonomic aggregation, and is performed by (i) obtaining a grouping of species using some taxonomic rank; (ii) obtaining quorum vectors for each species; (iii) summing up quorum vectors for all species in each group; and (iv) building a new SCN model, aggregated on taxonomic rank T. The taxonomic resolution of a SCN is thus the taxonomic rank at which species are aggregated in the model. For the sake of model interpretability, all nodes in Ssp must necessarily be taxa belonging to the same taxonomic rank as the taxonomic resolution adopted for the model.In some contexts, it might be desired to simplify SCNs by grouping species nodes into higher taxonomic ranks (or levels), such as a collaboration networks describing co-authoring relationships between collectors from species occurrence records. We consider two collectors to be co-authors in a given record if they are both included in the collector field for that record. The collector field holds a list of the names of the collectors who have authored each record in the dataset. We refer to each distinct list of collectors in this context as a team. As opposed to SCNs, which basically describe the interests of collectors towards species, relationships in CWNs are directly formed between collectors who have effectively worked collaboratively in the field, i.e. the coworking (team) behaviour around the registering of one species record is reflected as a group of fully connected collectors in a CWN. These collaborative relationships are semantically described as “collector records specimen with collector”. Each individual species occurrence record with at least two collectors  is thus considered a distinct collaboration act, originating new pairwise connections between all the involved collectors. For records with team size equal to 1, which we refer to as non-collaborative records, no connections are created. Differently from SCNs, where two classes of entities are represented in the graph as disjoint node sets with the bipartite constraint, CWNs exclusively model direct relationships between entities from a single class (collectors), with the only connectivity restriction that a collector should not hold collaborative ties to itself. The model is thus formally described as an undirected graph CWN = ,  where S = {u1, u2, . . . , un} is the graph’s node set of collectors and E is the set of undirected edges linking members of S.CWNs are a particular instance of 22, in reality not all collaboration acts should contribute in the same way for a collector’s network. Collectors tend to hold weaker collaborative ties with each other when they collaborate in larger teams than when they collaborate in smaller teams. Therefore, we use a hyperbolic weighting rule to account for this fact, while also considering the total number of collaborations between two collectors as a factor contributing to the strength of their common link. According to this rule, not every new occurrence of the link increases the edge weight equally. The contribution of each new link depends on the number of the collectors nk) ∈ E. Values of non-zero elements depend on the weighting method adopted for representing link strength, e.g. simple full count or hyperbolic weighting for fractional counting. Additionally, the model’s connectivity constraint states that all diagonal elements in A are necessarily equal to 0, thus ensuring that no self-loops are formed.Relationships in the CWN graph can be represented in a symmetric adjacency matrix Although structurally distinct, SCNs and CWNs provide complementary perspectives on the recording behaviour of collectors from a given species occurrence dataset. From SCNs, we retrieve information on which collectors have recorded which species and, conversely, which species were recorded by which collectors. On the other hand, CWNs allow us to investigate which collectors team up with whom during fieldwork.In this section, we use the network models proposed in the Methods section to understand aspects regarding the taxonomic preferences and the collecting behaviour of collectors who have contributed to the University of Brasília (UB) Herbarium with specimens records.23, which is publicly available for download through the Global Biodiversity Information Facility (GBIF) data portal24. We have used the Python v.3.6 language loaded with packages Pandas, Numpy, and Matplotlib for exploring the occurrence dataset; and the Caryocar package  for programatically constructing the SCN and CWN models from occurrence data. At the time of this study, the entire occurrences dataset from the UB herbarium had a total of 185,311 records and 235 fields, covering records from 1800 to 2017, although approximatelly half of the records are from the last 30 years (1988–2017). For our application, however, only a small subset of those fields were considered to be relevant and were thus included in our analysis: recordedBy, eventDate, stateProvince, countryCode, decimalLatitude, decimalLongitude, issue, scientificName, and taxonRank. Most of these fields  follow Darwin Core terms standards25.In this case study, we have used the entire digitized collection of records from the UB Herbariumi) selecting occurrence records from which relevant social ties could be derived for both network models; (ii) extracting atomized collector names from the recordedBy field, which originally contains a string of names; (iii) normalizing the extracted collector names to obtain their id’s; (iv) resolving inconsistencies on collector names and mapping name variants to entities; and (v) filtering out inadequate collector names. All this data pre-processing, data cleaning routines, and data transformation is done by the code available in the Caryocar package. All the presented analyses used the dataset from Munhoz et al.23 and our package (examples on how to do it are provided with the package as well). The result is the SCN and CWN models we further discuss in the remainder of this section. In the presented figures, for a richer visualisation experience26, we also add links to browsable interfaces that reveal the scale and complexity of the represented networks.Before we could use the UB Herbarium dataset for actually building the network models, we submitted the tabular dataset to some data filtering and transformation routines. The data preparation process consisted of  contains the majority of nodes in the network (93.6% of the collectors and 95.0% of the species). From a collector’s perspective, the requirement for it to belong to the giant component is that it must have collected at least one species in common with another collector who is already included in the giant component. The same reasoning applies to species nodes, by observing the inverse relationship. Apart from the giant component, most other connected components contain as few as two or three nodes, representing collectors who have never recorded a species in common with any collector from c1; and conversely, species that have never been collected by any of those collectors that belong to c1. One of those 350 remaining connected components, however, is considerably larger than the others, with a total of 3 collectors and 141 species. We refer to it as the second largest component (c2). Further, c1 is mostly composed of species from phylum Tracheophyta , followed by phylum Bryophyta (mosses), comprising 8% of the records. Component c2, on the other hand, is represented by algae , bacteria (phylum Cyanobacteria (4.3%)), and other microscopic eukariotic organisms ), which are taxonomically distinct from the vast majority of species in the herbarium. The remaining components  include a total of 431 distinct collectors and 446 distinct species.The UB SCN is composed of a total 351 connected components, the largest of which  from 15,344 to 474, although the number of edges  did not decrease in the same proportion. This incurred in a 10 ×  increase in network density, from 1.37 × 10−3 to 1.36 × 10−2. In the second step of the summarization routine, we removed collector-family ties that occurred less than 20 times throughout the entire dataset. As this edge filtering routine resulted in many isolated collectors, most of which novice collectors with low absolute recording counts, we also omitted them as to improve the figure readability. Three communities are visually distinguishable from the central region of the network, which we refer to as the network core  are placed more centrally in the network core by the graph layout algorithm, whereas those who are more specialists are consequently pushed towards the borders of the network core, as near as possible to their most recorded taxa.As the size of the UB SCN is relatively large for it to be informative in a static figure, we summarized the network in two steps. First we aggregated the SCN onto the Howard S. Irwin  is the collector with most records in the network, having intensively collected organisms from many distinct families, especially from the most central ones , it was relatively overlooked by ‘irwin,hs’, having himself contributed with only 399 Myrtaceae records. The main Myrtaceae collector in the herbarium is Jair E. Q. Faria , who apparently has a preference towards this family (it comprises 31.0% of his entire set of records). Carolyn E. B. Proença  is another key Myrtaceae collector, although she also seems interested, to the same extent, in families Fabaceae and Asteraceae. Moreover, Fig. Vanessa G. Staggmeier  for Myrtaceae and Regina C. Oliveira  for Poaceae, for instance.Represented as the biggest hub in the network, i) in Fig. Cryptogams Lab, together with the taxa they are typically most interested in. This lab is part of the University of Brasília Department of Botany, having Paulo Eduardo A. S. Câmara , Micheline C. Silva , and Maria das Graças M. de Souza  as the principal investigators. The first two researchers, included in community (i), are mostly interested in bryophytes (mosses and liverworts), mainly those from families Sematophyllaceae, Hypnaceae, and Dicranaceae. Micheline C. Silva also shows interest towards Piperaceae, a family of flowering plants that is also fairly recorded by some collectors from the network core. Therefore, Piperaceae is an important node connecting community (i) to the network core, as it intermediates many paths between collectors from both network regions. Although she is one of the principal investigators of the Cryptogams Lab, ‘souza,mgm’ was placed in community (iii), instead of (i), due to her taxonomic interest towards algae, a taxonomic group that is overlooked by the vast majority of collectors in the UB Herbarium, including bryophytes collectors. She is mostly interested in families Eunotiaceae, Naviculaceae, and Pinnulariaceae, which compose a group of algae known as diatoms.Community  contains 46% of all nodes in the network. Such a relatively low percentage of nodes in the giant component contrasts to most empirical scientific paper-publishing collaboration networks studied by Newman29, with giant components containing as much as 80% to 90% of all nodes. Moreover, only 318 of the connected components in the UB CWN  are composed of collectors with at least one collaborative tie. The remaining 2,673 components  are all disconnected nodes (i.e. nodes with degree k = 0), which we refer to as individualist collectors. Individualist collectors are those who have never recorded specimens collaboratively—or at least they have not included the names of their collaborators in the records as authors—, and thus are considered to have no structural role in the collaboration network of collectors. They comprise 39.5% of all nodes in the network, and the fact that they lack connections impacts on the overall network density, making it relatively low. In fact, if we instead compute network density by only considering nodes from the giant component c1, we observe an increase in density from 4.5 × 10−4 to 1.95 × 10−3.The UB CWN is composed of a a total 2,991 connected components, the largest of which , many of them hold very few collaborative ties. In fact, almost 40% of them are individualist collectors, having never co-authored a single record.By inspecting the team sizes of all records in the dataset, we find that the average team size for records in the UB dataset is 1.73, as a consequence of the fact that a large part of all records are non-collaborative, betweenness centrality metric is also frequently used in social network analytics for ranking nodes that act as “bridges”, intermediating a considerable fraction of shortest paths between pairs of nodes in the network. We compute betweenness centrality of a node v by making V is the node set; σ is the number of shortest paths between nodes s, t ∈ V (both s and v different from v); and σ is the number of shortest paths between s and v that are pass through v30. The 4 collectors with the highest betweenness centrality scores in the UB CWN are ‘proenca,ceb’ (0.38), ‘faria,jeq’ (0.18), ‘mendes,vc’ (0.14), and ‘ratter,ja’ (0.13). By inspecting Fig. Carolyn E. Proença  is located at the center of the network, while ‘mendes,vc’ and ‘faria,jeq’ also interconnect nodes from many distinct communities. James A. Ratter  is a very important node bridging a very relevant group  to the remainder of the network. Through the diversity of connections they had with other collectors, this top collectors from the betweenness perspective act as “bridges” connecting different communities of collectors.The vertex entclass1pt{minimaLouvain heuristic method for community detection31. The Louvain algorithm maximizes modularity scores in the network in successive steps, whereas the adopted modularity index is the one proposed by Newman32. The software we used for that is the python-louvain module for the NetworkX packagea.camara,peas’, ‘carvalhosilva,m’, and ‘soares,aer’. Collectors from this coworking group  and citizen scientists.In this work, we introduced two network models that contribute towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors’ interests and activities. To this end, such network models represent the conceptual basis of a new approach for describing the assemblage of biological collections as a social process, driven by the taxonomic interests of contributor collectors as well as their social interactions. In this context, we provided methods for structuring species occurrence data from biological collections into two main classes of network models, each giving distinct perspectives on the recording behaviour of collectors. Species-Collector Networks (SCNs) model interest relations between collectors and taxa they record, whilst Collector coWorking Networks (CWNs) represent collaborative ties between collectors co-authoring records of specimens. As a case study, we demonstrated the use of our network models by exploring the species occurrence dataset of the University of Brasília (UB) Herbarium. Using the social network analytics framework33, network-based approaches allow analysts (i) to investigate the effects of interactions between individuals on shaping their own behaviours, rather than simply comparing static attributes of individuals within a population; and (ii) to investigate the formation of non-homogeneous communities, composed of individuals interacting with their groups at varying levels of commitment. We consider that these two aspects are particularly relevant in the context of biological collections.We believe our network models provide the structural basis for a more realistic understanding on how collector and taxonomic biases arise in biological collections. As stated by Marin and WellmanFirst, collectors often start their careers being supervised by one or more experienced collectors. As it naturally happens in many social systems, the behaviour of individuals can be strongly influenced by others at more privileged positions, and this is likely to be the case for collector communities as well. A network-based approach would allow us to investigate, for instance, how the collecting behaviour and taxonomic interests of novice collectors are shaped by their association with more experienced ones. Moreover, depending on its position on the network, a collector can interact with multiple groups of collectors at different extents, thus assuming the role of influencer in some cases while being influenced in others. The influential power of a collector depends not only on the absolute number of connections it holds with others, but also on how strongly it intermediates other connections, how close it is to every other collector in the network, and how influential are its own connections. All these aspects can be assessed using well-known network centrality metrics , and could be used for investigating which collectors are the most relevant for shaping the taxonomic composition of a biological collection from different perspectives.Second, although collectors often define their own taxonomic interests and expertises in terms of natural or functional groups of organisms, those are not necessarily the groups that best split the interests of collectors in the dataset of a given collection. In addition, collectors  are not restricted towards exclusively recording organisms of their expertises, nor they have uniform interest towards all of those organisms. In this context, SCNs provide the structure for discovering groupings of taxa that are better for characterizing and differentiating collectors based on their taxonomic interests (communities in the species projection); and for investigating associations of collectors with groups of taxa in a non-discrete manner, allowing collectors to be linked to taxa at multiple groups and at different intensities. In fact, while some groups of taxa are more specifically recorded by distinctive communities of specialized collectors, others are recorded by a wider range of collectors, with diverse taxonomic interests.34In this context, we believe the approach we introduce in this paper brings new perspectives on analysing data from biological collections based on social network analytics, including a broad range of potential further applications, given adequate and enough quality data is available as input. For instance, the knowledge extracted from the investigated networks can be used to plan better sampling campaigns. The studied methods can also be used to identify previously unrecognized biases or temporal trends in the culture of biological collecting. Some results may also be combined with new metrics to quantify, for instance, levels of “ignorance” in biological collections.recordedBy, in TDWG standards) and the taxonomic identity of the specimen  of each record. We decided to only use the dataset from the UB in our case study because of its relatively high quality, specifically for the two aforementioned fields. In all other herbaria we examined, the collectors field (recordedBy) was particularly problematic. Our hypothesis is that the low quality of this field is associated with its low value for most uses of species occurrence data. While imprecise taxonomic determinations in the scientificName field would also lead to low quality networks, this field is critical for many other applications of occurrence data, and thus improving its quality has been extensively pursued by the biodiversity informatics community. The most common and impacting issues associated with the collectors field were: (i) using inconsistent delimiter characters for separating the names of each collector in a record, leading to many non-atomized names and consequently to the existence of nodes in the network that represent more than one collector; (ii) registering collectors names using inconsistent naming conventions, which makes it hard to systematically interpret what are the component parts of a name; (iii) using multiple name variations for a collector, leading to collectors being represented by more than one node in the resulting network; and (iv) only including the name of the first collector in records , which is interpreted as an absence of collaborative ties and thus does not contribute for the formation of edges in CWNs. Constructing the network models from a low-quality dataset can therefore introduce several semantic imprecisions. We expect works as ours, which build upon the information about the collectors to enable new network-based perspectives on analysing data from biological collections, can motivate improved data curation to ease the construction of network models as those we investigate.The quality of our network models, however, strongly depends on the quality of the species occurrence dataset that is used to build them, more specifically on the fields containing the names of the collectors , and limited to representing relationships of a single type each. This implies that relationships modeled in both networks are assumed to occur irrespective of temporal and geographic dimensions, which is clearly limiting for the phenomena they model. As the careers of collectors have limited lifespans, they can only possibly collaborate with others if their activity periods overlap in time. In addition, both coworking (between collectors) and interest (involving a collector and a taxon) relationships derive from collecting events—each happening at a determined geographic location and at some point in time—, being thus temporally and spatially constrained. Indeed, cultural shifts among collectors and the possible existence of temporal trends in collector behaviour are interesting points to investigate. Therefore, incorporating these two dimensions to our models is also central for capturing network evolution in their structure. As in many other social systems, relationships in SCNs and CWNs change in time, as new ties are constantly formed while older ones are broken. It is reasonable to consider that collectors interact with distinct groups of people throughout their careers, assuming distinct roles in each relationship. For instance, we hypothesize that earlier in their careers, collectors are more likely to assume relationships and have their interests influenced by their academic supervisors or other collectors who are more experienced. On the other hand, relationships assumed by collectors later in their careers tend to be the opposite, as they assume the role of the more experienced collector . Further, depending on the stage of a collector’s career and the groups of collectors he/she interacts at that moment, we might observe substantial shifts in his/her taxonomic interests . Other factors can also influence the patterns of recording activity of a collector, including oscillations in the availability of financial resources for field expeditions, changes in his/her residence location, and even phenological aspects of plant species of his/her interest.Our network models, as proposed in this work, also have a set of limitations, which should be addressed in the future. One important limitation of our network models is that they are static and non-spatialized 37 or stream graphs and link streams38. Other alternatives for higher-order models of complex networked systems are also discussed in a recent paper by Lambiotte et al.39.As many applications using our network models would need to combine the perspectives of both SCNs and CWNs, we also recognize the importance of adopting an unifying model for seamlessly integrating the two networks into a single structure. The requirement for such a model is that it represents two types of connections (interest and coworking) and two distinct sets of nodes (collectors and species), besides incorporating the temporal and geographical dimensions into its structure. Although the concept of Python package we developed during this study, Caryocar, is publicly available: https://github.com/pedrosiracusa/caryocar.The