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Brain Tumours: Rise in Glioblastoma Multiforme Incidence in England 1995–2015 Suggests an Adverse Environmental or Lifestyle Factor

Authors:
  • Children with Cancer UK
  • Powerwatch

Abstract and Figures

Objective To investigate detailed trends in malignant brain tumour incidence over a recent time period. Methods UK Office of National Statistics (ONS) data covering 81,135 ICD10 C71 brain tumours diagnosed in England (1995–2015) were used to calculate incidence rates (ASR) per 100k person–years, age–standardised to the European Standard Population (ESP–2013). Results We report a sustained and highly statistically significant ASR rise in glioblastoma multiforme (GBM) across all ages. The ASR for GBM more than doubled from 2.4 to 5.0, with annual case numbers rising from 983 to 2531. Overall, this rise is mostly hidden in the overall data by a reduced incidence of lower-grade tumours. Conclusions The rise is of importance for clinical resources and brain tumour aetiology. The rise cannot be fully accounted for by promotion of lower–grade tumours, random chance or improvement in diagnostic techniques as it affects specific areas of the brain and only one type of brain tumour. Despite the large variation in case numbers by age, the percentage rise is similar across the age groups, which suggests widespread environmental or lifestyle factors may be responsible. This article reports incidence data trends and does not provide additional evidence for the role of any particular risk factor.
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Research Article
Brain Tumours: Rise in Glioblastoma Multiforme Incidence in
England 1995–2015 Suggests an Adverse Environmental or
Lifestyle Factor
Alasdair Philips ,1,2 Denis L. Henshaw,1,3 Graham Lamburn,2 and Michael J. O’Carroll4
1 Children with Cancer UK, 51 Great Ormond Street, London, WC1N 3JQ, UK
2Powerwatch, Cambridgeshire, UK
3Professor Emeritus, University of Bristol, UK
4Professor Emeritus, Vice–Chancellors Office, University of Sunderland, UK
Correspondence should be addressed to Alasdair Philips; alasdair@brambling.info
Received 19 December 2017; Revised 14 March 2018; Accepted 21 March 2018; Published 24 June 2018
Academic Editor: Evelyn O. Talbott
Copyright © 2018 Alasdair Philips et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective. To investigate detailed trends in malignant brain tumour incidence over a recent time period. Methods. UK Oce of
National Statistics (ONS) data covering 81,135 ICD10 C71 brain tumours diagnosed in England (1995–2015) were used to calculate
incidence rates (ASR) per 100k person–years, age–standardised to the European Standard Population (ESP–2013). Results.We
report a sustained and highly statistically signicant ASR rise in glioblastoma multiforme (GBM) across all ages. e ASR for GBM
more than doubled from 2.4 to 5.0, with annual case numbers rising from 983 to 2531. Overall, this rise is mostly hidden in the
overall data by a reduced incidence of lower-grade tumours. Conclusions. e rise is of importance for clinical resources and brain
tumour aetiology. e rise cannot be fully accounted for by promotion of lower–grade tumours, random chance or improvement
in diagnostic techniques as it aects specic areas of the brain and only one type of brain tumour. Despite the large variation in
case numbers by age, the percentage rise is similar across the age groups, which suggests widespread environmental or lifestyle
factors may be responsible. is article reports incidence data trends and does not provide additional evidence for the role of any
particular risk factor.
1. Introduction
e causes of brain tumours in adults remain largely
unknown [1]. In 2011, the World Health Organisation (WHO)
prioritised the monitoring of detailed brain tumour incidence
trends through population–based cancer registries [2]. is
article reports recent changes in malignant brain tumour
incidence in England that include age, sex, morphology and
tumour location.
2. Materials and Methods
2.1. Data. e International Classication of Diseases for
Oncology (ICD–O) is a dual classication, with coding sys-
tems for both topography and morphology [3]. e relevant
topology codes are listed in Table 1, along with the number of
tumours diagnosed in 1995 and 2015.
ere are 102 dierent ICD–O–3.1 morphology codes
usedinthedataset,thoughmanyhavefewcases.e
morphology code describes the cell type and its biological
activity / tumour behaviour.
WHO last updated their classications in 2016, but their
changes have minimal impact on our analysis of the data [4,
5]. Malignant brain neoplasms without histology are recorded
as ICD–10 D43 (D43.0 & D43.2 supratentorial).
We used anonymised individual–level national cancer
registration case data from the UK Oce of National
Statistics (ONS) for all 81,135 ICD10–C71 category primary
malignant brain tumours diagnosed in England for the years
from 1995 to 2015, plus 8,008 ICD10–D43 supratentorial
malignant tumours without histology/morphology data from
1998–2015. e initial data is supplied by the National Cancer
Registration Service (NCRS). e ONS then apply further
Hindawi
Journal of Environmental and Public Health
Volume 2018, Article ID 7910754, 10 pages
https://doi.org/10.1155/2018/7910754
Journal of Environmental and Public Health
T : ONS WHO ICD brain tumour data for England.
 
C Malignant primary neoplasm of brain cases cases
C. Cerebrum except lobes & ventricles  
C. Frontal lobe  
C. Temporal lobe  
C. Parietal lobe  
C. Occipital lobe  
C. Cerebral ventricle  
C. Cerebellum  
C. Brain stem  
C. Overlapping lesion of brain  
C. Brain, unspecied site  
C All topology sites  
D Uncertain behaviour (no histology data)
 
D.-. Unspecied tumour details - cases  
validation checks and the UK Department of Health use the
ONS data to inform policy making. e ONS state their
cancer data are generally within % of the correct values [].
Until about , some cases in the oldest age–groups will
not have been recorded in the cancer registries. Since 
this error is likely to be small.
Glioblastoma Multiforme (GBM), the most common and
most malignant primary tumour of the brain, is associated
with one of the worst ve–year survival rates among all
humancancers,withanaveragesurvivalfromdiagnosis
of only about  year. is ensures that few cases will be
unrecorded in the ONS database and we show that their
number of GBM tumours is similar to NHS hospital inpatient
numbers. e data include the year of diagnosis, age at
diagnosis, sex of patient, primary site and morphology
code. National population estimates of age and gender
by calendar year were also obtained from ONS data []
and age–specic incidence rates per , person–years
and for a wide variety of tumour types were calculated
in -year age group bins for males and females sepa-
rately.
Some published incidence analyses have used dierent
criteria as to which glioma and astrocytoma should be
considered malignant. WHO considers Grades I to IV as
biologically malignant even if they have not been graded
histologically malignant. We have taken the WHO/IARC
morphology behaviour codes /, / and / as being histologi-
cally malignant which means that Grade I and II tumours are
classed as low–grade malignancies.
We are not aware of any specic bias in the ONS data.
ere is a slight data–lag in cancer registry data, which are
regularly checked and updated if necessary, but are generally
stable aer  to  years. Our ONS data extract is dated 4th July
.
Brodbelt et al. () [] reported an analysis of treatment
and survival for , GBM cases in England over the period
–, which had an overall median survival of only
. months, rising to . months with maximal treatment.
Brodbelt et.al.s GBM case total from English hospital data
is only .% higher that our ONS GBM total of , cases
for the same time period; this suggests that a very complete
UK cancer diagnosis and registration system is now in place.
In contrast, Ostrom et al. () [] reporting on USA SEER
brain tumour data provide a scatter–plot that shows a median
complete registration and histological conrmation level of
only about %, with the best examples returning less than
% full completion in .
2.2. Confounding. We h a d a l a r ge number of catego r i e s an d
sub–categories in the data. It was necessary to combine some
of these to increase the resolving power. We ran analyses
separately for each site (C. to C.), for each main type of
tumour, and for tumour grade (I to IV). It was immediately
obvious that the most signicant change was in the incidence
of GBM in frontal and temporal lobes. e obvious potential
confounders would be the C. (overlapping) and C.
(unspecied) categories due to better imaging techniques and
we discuss this later.
2.3. Standardisation. Incidence rates rise dramatically with
age and standardisation is necessary as population age pro-
les are changing with time. We calculated age–standardised
incidence rates (ASR) per k person–years to the current
recommended European Standard Population (ESP–), as
it best represents the reality of the case burden on society [].
Adjusting European cancer incidence to the World Standard
Population is not helpful as the age-spectra are so dierent.
Table  lists the morphology codes with the highest case
numbers, totalling  tumours. Included in our analyses
are an additional  cases in  other categories, each with
fewer than  cases over the  years. A full listing of all the
cases in the data set is provided in the Supplementary File
[S].
We needed to group data to improve resolution and
reduce random data noise. We examined infant and child
Journal of Environmental and Public Health
T  : ICD-O- morphology codes with more than  cases between - inclusive. (A full listing of all the morphology codes
and cases is present in the Supplementary le).
Morphology Grade All cases Group Sub-group WHO/IARC summary description
  NOS unclassied, malignant, blastoma, NOS
  carcinoma carcinoma, metastatic, NOS
  carcinoma epithelial tumour, carcinoma, malignant
  carcinoma carcinoma, metastatic, NOS
  sarcoma rhabdoid sarcoma
  germ cell neoplasia
  glioma NOS glioma, malignant, NOS, not neoplastic
  glioma astrocytic gliomatosis cerebri
  glioma astrocytic mixed glioma / oligoastrocytoma
  glioma ependymal ependymoma
  glioma ependymal anaplastic ependymoma
  glioma astrocytic astrocytoma, NOS, diuse
  glioma astrocytic anaplastic astrocytoma (high grade)
  glioma astrocytic germistocytic astrocytoma, diuse
  glioma astrocytic brillary astrocytoma, diuse
  glioma astrocytic pilocytic astrocytoma
  glioma astrocytic pleomorphic xantoastrocytoma
  glioma GBM-IV glioblastoma multiforme
  glioma GBM-IV giant cell glioblastoma
  glioma GBM-IV gliosarcoma
  glioma oligodendrial oligodendroglioma
  glioma oligodendrial anaplastic oligodendroglioma
  glioma embryonal medulloblastoma
  glioma embryonal desmoplastic medulloblastoma
  glioma embryonal primitive neuroectodermal tumour
neoplasms separately, but did not nd any statistically signif-
icant time–trends. ree age-groups seemed reasonable. We
chose a child, teenage and young-adult group (-), a main
middle-age group (-) and an older age group (over 
years of age). ese reasonably split the population into three
roughly equal (,  and  million) groups of people. e
case totals in the three groups were about .k, .k and k
respectively. We tested moving the cut-point boundaries by
 years in both directions and it made little dierence to the
overall results.
2.4. Analysis. e cases were analysed by morphology, topol-
ogy, sex, age, age–specic and age–standardised incidence.
e Annual Average Percentage Change (AAPC) and cor-
responding % CI and p–values were calculated using
Stata SE. (StataCorp). A linear model on the log of the
age–standardised rates, which tests for a constant rate of
change (e(ln(rate))), best tted the data. See Supplementary File
sections [S]and [S].
2.5. Background. In a major  review article, Hiroko
Ohgaki and Paul Kleihues [] wrote “Glioblastoma is the
most frequent and malignant brain tumor. e vast majority
of glioblastomas (%) develop rapidly de novo in elderly
patients, without clinical or histologic evidence of a less
malignant precursor lesion (primary glioblastomas). Sec-
ondary glioblastomas progress from low-grade diuse astro-
cytoma or anaplastic astrocytoma. ey manifest in younger
patients, have a lesser degree of necrosis, are preferentially
located in the frontal lobe, and carry a signicantly better
prognosis.”
Overall primary malignant brain tumour ASRs are only
rising slowly and are oen considered fairly static. Figure 
shows the age–standardised trends from  to . From
the s to about  there was a fairly steady rise in
recorded overall incidence, however since then the rise has
slowed, though clinicians have been reporting a rise in high-
grade, aggressive tumours.
Overall adult survival for all malignant brain tumours
aer diagnosis during – was about % for one
year and % for ve years, falling to about % for aggres-
sive grades–III and IV tumours. ONS data show age-
standardised death rates from malignant brain tumours (C)
have increased by % between  and , showing that
improvements in treatment alone are inadequate and that
there is a need to nd ways of preventing brain cancer [].
3. Results
Comparing new case numbers in  with  shows an
extra  aggressive GBM tumour cases annually. Figure 
Journal of Environmental and Public Health
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2013
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0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
ASR ESP2013 Rate / 100k people
ICD10 C71 Male
ICD10 C71 all
ICD10 C71 Female
ICD10 D43.0 & D43.2 (see text)
Primary brain tumour mortality
F : Age–standardised overall trends from  to  using
data in ONS MB series, including a smaller number of supratento-
rial neoplasms without histology or morphology data coded D.
& D.. e data table for this gure is in the SI le as [S].
0.0
1.0
2.0
3.0
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5.0
6.0
7.0
8.0
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2010
2011
2012
2013
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2015
ASR ESP2013 Rate / 100k people
(1) GBM (2) astrocytic non_GBM
(3) glioma 93803 (4) other glioma
(5) = (1)+(2)+(3)
F : Age–standardised incidence rates for all C glioma
cases diagnosed between  and  analysed by type and year
(Data in Table ). Grouping details: () = – () =
, – () =  () = , , –,
–.
and Table  show that up to about  the overall rise
in GBM incidence (Annual Average Percentage Change
(AAPC) .%, % CI .–., p <) could be mostly
compensated for by the fall in incidence of all lower grade
astrocytoma and “glioma, malignant, NOS, ICD–”.
is leaves a fairly steady rise in the GBM ASR from 
to  (AAPC .%, % CI .–., p <.).
Ohgaki and Kleihues [] reported that most secondary
GBMs are found in younger middle-age people and most
primary GBMs are in over s. We tested our (–) and
(>) age group data, splitting the total GBM into de novo
and promoted tumours. We estimated the maximum possible
number of promoted tumours using the change in the grades
II and III diuse and anaplastic astrocytomas. e results are
shown in Figures (a) and (b). ese are discussed later.
We found a large decrease of ASR over time for Grade–II
diuse astrocytoma, a slight rise in ASR for WHO Grade–III
anaplastic astrocytoma (;  cases). ere was little
change in rates of anaplastic oligodendroglioma (;
 cases), anaplastic ependymoma (;  cases)
Grade–II oligodendroglioma (; cases), embryonal,
or ependymal tumours.
Figure  shows the relative increase in age-specic GBM
incidence between the averaged periods (–) and
(–) for –year age–groups. is .-fold change
is remarkably similar across the age–groups, suggesting a
universal factor.
Figure  shows ASR GBM rates for frontal lobe, temporal
lobe, unspecied & overlapping (C. & C.) and ‘all other
brain regions. Most of the rise is in the frontal and temporal
lobes, and most of the cases are in people over  years of age,
with a highly statistically signicant overall AAPC of .%
(see Table ). ere was an extra rise in frontal and temporal
GBM incidence between  and , which coincided
with a slight reduction in the GBM ASR in overlapping and
unspecied regions and may be due to improved imaging.
4. Discussion
Using suciently high–quality data, we present a clearer
picture of the changing pattern in incidence of brain tumour
types than any previously published. We report a sustained
and highly statistically signicant ASR rise in GBM across
all ages and throughout the  years (–), which is
of importance both for clinical resources and brain tumour
aetiology.
Dobes et al. () [] reported a signicant increase
in malignant tumour incidence from  to  in the
–year age group. In a second article they noted an
increasing incidence of GBM (APC, .; % CI, .–.) in
patients in the same age group, especially in temporal and
frontal lobes []. De Vocht et al. () [] reported a rise in
temporal lobe tumour incidence in ONS data, but dismissed
its signicance. In a  paper he claimed no increase in
GBM incidence, but later published a major correction to the
paper that shows an increase [].
Zada et al. () [] using USA SEER data for –
reported a rising trend in frontal and temporal lobe tumours,
the majority of which were GBM, with a decreased incidence
of tumours across all other anatomical sub–sites. Ho et
al. () [] reported a .–fold increase in glioblastoma
incidence in the Netherlands over the period –  (APC
., p<.).
ere were no material classication changes over the
analysis period that might explain our ndings [], though
multidisciplinary team working was strengthened (
onwards) and better imaging has resulted in improved
diagnosis along with a more complete registration of brain
tumours in the elderly. We analysed our data in -year age
group categories to look for evidence of improved diagnosis;
the data do suggest diagnosis and registration have improved
Journal of Environmental and Public Health
T : ICD-C and (D. + D.) cases and age-standardised (ESP-) incidence rates.
Typ e ->GBM astro-c
non GBM
glioma

Other
glioma
other
C
D.
+D. GBM astro-c
non GBM
glioma

Other
glioma
other
C
all
C
D.
+D.
Ye a r Case numbers Age-standardised (ESP-) incidence rates
      n/a . . . . . . n/a
      n/a . . . . . . n/a
      n/a . . . . . . n/a
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
       . . . . . . .
      . . . . . . .
       . . . . . . .
       . . . . . . .
      . . . . . . .
      . . . . . . .
Journal of Environmental and Public Health
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Age-standardised GBM rates (30-54)
promoted
de novo
0
1
2
3
4
Incidence rate per 100,000 people
(a)
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
Age-standardised GBM rates ( >54)
promoted
de novo
0
2
4
6
8
10
12
14
Incidence rate per 100,000 people
(b)
F : Age–standardised ratesfor two age groups. e possiblesplit between de novo and se condary promoted GB Ms is based on inci dence
change of Grades II and III diuse and anaplastic astrocytoma.
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
GBM ASpR (2011-2015) relative to that in (1995-1999)
Male Female
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Relative change in Age Specic Rate
F : Relative change in GBM age–specic incidence rates
(ASpR) averaged over two ve-year periods - and -
in -year age bands and gender.
in people aged over . However, at earlier ages the incidence
rate of ‘all’ glioma (and all C) registrations have remained
almost constant, whereas the rates for lower–grade tumours
fell until about  and have since remained fairly static as
the rate for GBM has risen steadily.
Most GBM cases seem to originate without any known
genetic predisposition. GBMs from promoted lower–grade
gliomas usually have dierent molecular genetic markers
from de novo GBMs []. e  revision of the WHO
classication of CNS tumours [, ] highlights the need for
recording molecular genetic markers and divides glioblas-
tomas into two main groups. e IDH–wildtype mostly
corresponds to clinically dened primary or de novo glioblas-
toma and accounts for about % of cases. e remaining
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Frontal & temporal lobes ASR
All other brain sites ASR
Frontal lobe ASR
Temporal lobe ASR
C71.8 + C71.9 ASR
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ASR ESP2013 Rate / 100k people
F : Frontal and temporal lobe GBM age–standardised inci-
dence rates by tumour site and year (data table in the SI as [S]).
% are IDH–mutant cases, which usually arise in younger
patients and mostly correspond to secondary or promoted
lower–grade diuse glioma [, ]. Figures (a) and (b)
support the conclusion of Ohgaki and Kleihues [] that
promoted (secondary) tumours mainly occur in younger
people and that de novo GBMs dominate in the over- age
group. It is important that this pattern is monitored using
modern genetic techniques.
GBM tumours are almost always fatal and are not likely
to have been undiagnosed in the time-frame of our data. It
ispossiblethatsomeelderlycaseswerenotfullyclassied,
Journal of Environmental and Public Health
T : Age standardised incidence rates to ESP- (/k people).
Ye a r G BMallbrainsites all ages all ages GBM frontal and temporal lobes all ages all ages
age->< - + all ages M F < - + all ages M F
AAPC . . . . . . . . . . . .
CI . . . . . . . . . . . . . . . . . . . . . . . .
p. <. <. <.<. <.<. <.<. <.<. <.
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . . .
 . . . . . . . . . . .
 . . . . . . . . . . . .
Journal of Environmental and Public Health
but then they should have been recorded as ICD–D.
However, as D rates have remained very constant over this
time period (see Figure ), this is unlikely to have been a
signicant confounder.
4.1. Possible Causal Factors. We cite examples of some
possible causal factors that have been discussed in the
literature that could contribute changes in GBM incidence.
In an important  “state of science” review of glioma
epidemiology, Ostrom et al. [] list and discuss a number
of potential factors that have been associated with glioma
incidence, some of which we list below.
Ionising radiation, especially from X-rays used in CT
scans, has the most supportive evidence as a causal factor.
Due to the easy availability of CT imaging and relative lack
and higher cost of MRI imaging in UK NHS hospitals, CT
scans are oen used, especially for initial investigations. eir
use over the period - is shown in the Supplementary
File [S]. Given the time-frame of the trend that we have
identied, we suggest that CT imaging X-ray exposures
should be further investigated for both the promotion and
initiation of the rising incidence of GBM tumours that we
have identied.
Preston et al. () [] concluded that radiation–
associated cancer persists throughout life regardless of age
at exposure and that glioma incidence shows a statistically
signicant dose response. Our oldest age group also expe-
rienced atmospheric atomic bomb testing fallout and some
association with ingested and inhaled radionuclides should
not be dismissed as a possible factor. England was in one of
the highest exposed regions for atmospheric testing fallout
as determined by the United Nations Scientic Committee
on the Eects of Atomic Radiation, UNSCEAR  Report
[]. Further information is given in Supplementary File S.
If only some of the population were susceptible and received
a signicant dose, any resulting extra cancers would show up
in the ONS data.
e European Study of Cohorts for Air Pollution Eects
by Andersen et al. () [] found suggestive evidence
of an association between trac-related air pollution and
malignant brain tumours.
ere is increasing evidence literature that many cancers
including glioma have a metabolic driver due to mitochon-
drial dysfunction resulting in downstream genetic changes in
the nucleus [–].
e International Agency for Research on Cancer
(IARC) judged both power–frequency ELF () [] and
radio–frequency RF () [] electromagnetic elds as
Group B ‘possible human carcinogens. Villeneuve et al.
() [] concluded that occupational (ELF) magnetic eld
exposure increases the risk of GBM with an OR = .
(% CI: . – .). Hardell and Carlberg () [] have
reported an increase in high–grade glioma associated with
mobilephoneuse.emulti-countryInterphonestudy[]
collected data from  to  and included few people
over  years of age and would have been unable to resolve
any association involving older–aged people. Volkow et al.
() [] found that, in healthy participants and compared
with no exposure, a -minute cell phone exposure produced
a statistically signicant increase in brain glucose metabolism
in the orbitofrontal cortex and temporal pole regions closest
to the handset.
5. Conclusions
() We show a linear, large and highly statistically signi-
cant increase in primary GBM tumours over  years
from –, especially in frontal and temporal
lobes of the brain. is has aetiological and resource
implications.
() Although most of the cases are in the group over
 years of age, the age–standardised AAPC rise is
strongly statistically signicant in all our three main
analysis age groups.
() e rise in age–standardised incidence cannot be fully
accounted for by improved diagnosis, as it aects
specic areas of the brain and just one type of
brain tumour that is generally fatal. We suggest that
widespread environmental or lifestyle factors may be
responsible, although these results do not provide
additional evidence for the role of any particular risk
factor.
() Our results highlight an urgent need for funding more
research into the initiation and promotion of GBM
tumours. is should include the use of CT imaging
for diagnosis and also modern lifestyle factors that
may aect tumour metabolism.
Data Availability
e data were obtained from the UK Oce for National
Statistics (ONS), who are the legal owners of the data. Some
data are publicly available in the ONS annual MB data series,
which are freely downloadable from the ONS website, but this
article uses the latest updated data, plus ICD–O– morphol-
ogy codes, extracted under personal researcher contract from
the ONS database in July . ONS Data Guardian approval
was required for the supply, control and use of the data. A
nominal charge is made by the ONS for such data extraction.
We are not permitted to supply the raw ONS extracted data
to anyone else. Other researchers can obtain the latest data
directly from the ONS in a similar manner. e authors
provide some extra tables and gures in the Supplementary
File downloadable from the journal website. Supplementary
data can be obtained from the corresponding author upon
request.
Conflicts of Interest
Alasdair Philips: Independent Engineer and Scientist. (a)
Trustee of Children with Cancer UK (unpaid); (b) On a
voluntary unpaid basis, has run Powerwatch for  years (a
small UK NGO providing free information on possible health
associations with EMF/RF exposure); (c) Technical Director
Journal of Environmental and Public Health
and shareholder of EMFields Solutions Ltd., who design
and sell EMF/RF measuring instruments and protective
shielding items; (d) Shareholder of Sensory Perspective Ltd.;
(e) Occasional voluntary advisor to the Radiation Research
Trust (Registered Charity). Denis L. Henshaw: (a) Scientic
Director of Children with Cancer UK (honorarium basis);
(b) Shareholder of Track Analysis Systems Ltd., a company
oering radon measurement services; (c) Voluntary scien-
tic advisor for Electrosensitivity UK (Registered Charity).
Michael J. O’Carroll: (a) Chairman of Rural England against
Overhead Line Transmission group; (b) Occasional advisor
to the Radiation Research Trust. Graham Lamburn: (a) Acts
as voluntary unpaid ‘Technical Manager’ for Powerwatch.
Authors’ Contributions
Alasdair Philips and Graham Lamburn conceived the study
and rst–draed most of the manuscript with signicant
input from Denis L. Henshaw and Michael J. O’Carroll.
Graham Lamburn organised the data obtained from the UK
ONS and wrote the database analysis scripts. All authors had
full access to the results of all analyses and have provided
strategic input over several years of following the ONS brain
tumour data. All authors have approved the nal manuscript.
Alasdair Philips is the guarantor for the ONS data.
Funding
is research received no funding from any external agency
or body. e ONS data extracts were paid for personally
by Alasdair Philips. Administration costs were paid for
personally by the authors.
Acknowledgments
We are very grateful to Professor Georey Pilkington and
Professor Annie Sasco for their invaluable comments on early
dras of this paper. We thank the ONS for providing the data
and Michael Carlberg, MSc for advice regarding statistical
analysis.
Supplementary Materials
S. Table of data morphology coding and the case numbers
usedinthestudy.S.GBMcasenumbersandage-specic
incidence rate data used in the study. S. Sample STATA data
and DO script. S. Data table for Figure . S. Data table for
Figure . S. CT and MRI use in the UK NHS. S. Some notes
on atomic bomb testing and other nuclear fallout in England.
(Supplementary Materials)
References
[] M. L. Bondy, M. E. Scheurer, B. Malmer et al., “Brain tumor
epidemiology: Consensus from the Brain Tumor Epidemiology
Consortium,Cancer,vol.,no.,pp.,.
[]E.VanDeventer,E.VanRongen,andR.Saunders,“WHO
research agenda for radiofrequency elds,Bioelectromagnetics,
vol.,no.,pp.,.
[] “IARC – International Classication of Diseases of Oncology
ICD-O-,” http://codes.iarc.fr/abouticdo.php.
[] D.N.Louis,A.Perry,G.Reifenbergeretal.,“eWorld
Health Organization Classication of Tumors of the Central
Nervous System: a summary,Acta Neuropathologica,vol.,
no. , pp. –, .
[] D. N. Louis, H. Ohgaki, O. D. Wiestler et al., “WHO Classica-
tion of Tumours of the Central Nervous System. th (rev),” in
IARC, ISBN–10 9283244923,.
[] UK Oce for National Statistics, “Cancer Statistics: Registrations
Series MB,” , https://www.ons.gov.uk/peoplepopulation-
andcommunity/healthandsocialcare/conditionsanddiseases/
bulletins/cancerregistrationstatisticsengland/data-quality.
[] UK Oce for National Statistics, “PopulationEstimates for UK,
England and Wales, Scotland and Northern Irel and,” , https:/ /
www.ons.gov.uk/peoplepopulationandcommunity/population-
andmigration/populationestimates.
[] A.Brodbelt,D.Greenberg,T.Winters,M.Williams,S.Vernon,
and V. P. Collins, “Glioblastoma in England: –,” Euro-
pean Journal of Cancer,vol.,no.,pp.,.
[] Q. T. Ostrom, H. Gittleman, J. Fulop et al., “CBTRUS statistical
report: primary brain and central nervous system tumors
diagnosed in the united states in -,Neuro-Oncology,
vol. , Supplement , pp. iv–iv, .
[] European Union, “European Standard Population,” http://ec
.europa.eu/eurostat/en/web/products-manuals-and-guidelines/
-/KS-RA--.
[] H. Ohgaki and P. Kleihues, “e denition of primary and
secondary glioblastoma,Clinical Cancer Research,vol.,no.
, pp. –, .
[] UK Oce for National Statistics, “<–  tcm–
.xls>,” downloaded from the ONS, th September, and
for – data, Table  inhttps://ww w.ons.gov.uk/le?uri=/
peoplepopulationandcommunity/healthandsocialcare/condi-
tionsanddiseases/datasets/cancerregistrationstatisticscancerreg-
istrationstatisticsengland//cancerregistrationsnal-
...xls downloaded from the ONS, th July .
[] M. Dobes, V. G. Khurana, and B. Shadbolt, “Increasing
incidence of glioblastoma multiforme and meningioma, and
decreasing incidence of Schwannoma (–): ndings
of a multicenter Australian study,Surgical Neurology Interna-
tional,vol.,no.,pp.,.
[] M.Dobes,B.Shadbolt,V.G.Khuranaetal.,“Amulticenterstudy
of primary brain tumor incidence in Australia (-),
Neuro-Oncology,vol.,no.,pp.,.
[] F. De Vocht, “Inferring the – impact of mobile phone
use on selected brain cancer subtypes using Bayesian structural
time series and synthetic controls,Environment International,
vol. , pp. –, .
[] F. De Vocht, “Corrigendum to “Inferring the – impact
of mobile phone use on selected brain cancer subtypes using
Bayesian struc tural time series and synthetic c ontrols. [Environ.
Int. (), , -],”Environment International,vol.,pp.
-, , http://www.sciencedirect.com/science/article/pii/
S.
[] G. Zada, A. E. Bond, Y.-P. Wang, S. L. Giannotta, and D.
Deapen, “Incidence trends in the anatomic location of primary
malignant brain tumors in the United States: –,Worl d
Neurosurgery,vol.,no.-,pp.,.
[] V. K. Y. Ho, J. C. Reijneveld, R. H. Enting et al., “Changing
incidence and improved survival of gliomas,European Journal
of Cancer,vol.,no.,pp.,.
 Journal of Environmental and Public Health
[] Clinical Coding toolbox, “UK Health and Social Care Informa-
tion Centre,” , https://web.archive.org/web//
http://systems.hscic.gov.uk:/data/clinicalcoding/codingad-
vice/toolbox.
[] G. P. Dunn, M. L. Rinne, and J. Wykosky, “Emerging insights
into the molecular and cellular basis of glioblastoma,Genes &
Development,vol.,pp.,.
[] H. Ohgaki and P. Kleihues, “Genetic alterations and signaling
pathways in the evolution of gliomas,Cancer Science,vol.,
no.,pp.,.
[] Q.T.Ostrom,L.Bauchet,F.G.Davisetal.,“eepidemiologyof
glioma in adults: A state of the science revie w,Neuro-Oncology,
vol.,no.,pp.,.
[] D. L. Preston, E. Ron, S. Tokuoka et al., “Solid cancer incidence
in atomic bomb survivors:–,Radiation Research,vol.
,no.,pp.,.
[] United Nations Scientic Committee on the Eects of Atomic
Radiation, UNSCEAR 2000 Report to the General Assembly,
United Nations, New York, NY, USA, .
[] Z. J. Andersen, M. Pedersen, G. Weinmayr et al., “Long-
term exposure to ambient air pollution and incidence of brain
tumor: the European Study of Cohorts for Air Pollution Eects
(ESCAPE),Neuro-Oncology,vol.,no.,pp.,.
[] T. N. Seyfried, “Cancer as a mitochondrial metabolic disease,
Frontiers in Cell and Developmental Biology,vol.,pages,
.
[]M.G.Abdelwahab,K.E.Fenton,M.C.Preuletal.,“e
ketogenic diet is an eective adjuvant to radiation therapy for
the treatment of malignant glioma,PLoS ONE,vol.,no.,
Article ID e, .
[]T.N.Seyfried,R.E.Flores,A.M.Po,andD.P.DAgostino,
“Cancer as a metabolic disease: implications for novel therapeu-
tics,Carcinogenesis, vol. , no. , pp. –, .
[] IARC, Monographs on the Evaluation of Carcinogenic Risks to
Humans, Non–Ionizing Radiation, Part 1: Static and Extremely
Low–Frequency (ELF) Electric and Magnetic Fields,vol.,.
[] IARC, Monographs on the Evaluation of Carcinogenic Risks
to Humans, NonIonizing Radiation, Part 2: Radiofrequency
Electromagnetic Fields,vol.,.
[] P. J. Villeneuve, D. A. Agnew, K. C. Johnson et al., “Brain
cancer and occupational exposure to magnetic elds among
men: Results from a Canadian population-based case-control
study,International Journal of Epidemiology,vol.,no.,pp.
–, .
[] L. Hardell and M. Carlberg, “Mobile phone and cordless phone
use and the risk for glioma—analysis of pooled case-control
studies in Sweden, – and –,Pathophysiology,
vol.,no.,pp.,.
[] C. Wild, IARC Report to the Union for International Cancer
Control (UICC) on the Interphone Study,WHO,IARC,Lyon,
France,  October .
[] N. D. Volkow, D. Tomasi, G.-J. Wang et al., “Eects of cell phone
radiofrequency signal exposure on brain glucose metabolism,”
e Journal of the American Medical Association,vol.,no.,
pp. –, .
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Despite substantial drug discovery investments, the lack of any significant therapeutic advancement in the treatment of glioblastoma (GBM) over the past two decades calls for more innovation in the identification of effective treatments. The inter- and intra-patient heterogeneity of GBM presents significant obstacles to effective clinical progression of novel treatments by contributing to tumour plasticity and rapid drug resistance that confounds contemporary target directed drug discovery strategies. Phenotypic drug screening is ideally suited to heterogeneous diseases, where targeting specific oncogenic drivers have been broadly ineffective. Our hypothesis is that a modern phenotypic led approach using disease relevant patient derived GBM stem cell systems will be the most productive approach to identifying new therapeutic targets, drug classes and future drug combinations that target the heterogeneity of GBM. In this study we incorporate a panel of patient derived GBM stem cell lines into an automated and unbiased Cell Painting assay to quantify multiple GBM stem cell phenotypes. By screening several compound libraries at multiple concentrations across a panel of patient-derived GBM stem cells we provide the first comprehensive survey of distinct pharmacological classes and known druggable targets, including all clinically approved drug classes and oncology drug candidates upon multiple GBM stem cell phenotypes linked to cell proliferation, survival and differentiation. Our data set representing, 3866 compounds, 2.2million images and 64000 datapoints is the largest phenotypic screen carried out to date on a panel of patient-derived GBM stem cell models that we are aware off. We seek to identify agents and targets classes which engender potent activity across heterogenous GBM genotypes and phenotypes, in this study we further characterize two validated target classes, histone deacetylase inhibitors and cyclin dependent kinases that exert broad and potent effects on the phenotypic and transcriptomic profiles of GBM stem cells. Here we present all validated hit compounds and their target assignments for the GBM community to explore.
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Введение. Опухоли головного мозга, несмотря на успехи в области диагностики и лечения новообразований в целом, являются актуальной проблемой в связи с низкой выживаемостью пациентов и высокой инвалидизацией. Цель. Цель данного исследования: изучение связи/зависимости исхода хирургического лечения нейроонкологических больных от особенностей клинических проявлений заболевания и некоторых организационных аспектов. Материал и методы. Исследование – обсервационное, описательно-аналитическое, проведено в 2017-2018 гг. Методом сплошного наблюдения изучено 636 медицинских карт стационарного больного (в г.Семей и в г. Усть-Каменогорск) за период 2012-2017 гг. Зависимость общесоматического статуса и исхода заболевания от особенностей клинических проявлений заболевания и некоторых организационных аспектов изучалась по материалам форм 003/у (медицинская карта стационарного больного) Университетского госпиталя Государственного Медицинского Университета г.Семей и Усть-Каменогорской городской больницы №1. Оценка связи/зависимости проводилась с помощью корреляционного анализа (коэффициент ранговой корреляции Спирмена). Результаты. Полученные результаты свидетельствуют о связи/зависимости состояния при поступлении нейроонкологических больных и исхода заболевания от возраста и социального статуса, места проживания, наличия неврологического дефицита и локализации опухоли, вида госпитализации, кем направлен больной и через какое время от начала заболевания госпитализирован больной, объема операции, осложнения операции и нахождение в реанимации. Выводы. Состояние при поступлении нейроонкологических больных и исход заболевания зависят от личностных данных пациентов, характеристики опухоли, неврологического дефицита и организационных аспектов медицинской помощи. Introduction. Brain tumors, despite advances in the diagnosis and treatment of tumors in general, are an urgent problem due to low patient survival and high disability. Purpose of the study. The purpose of this study: to study the relationship / dependence of the outcome of surgical treatment of neurooncological patients on the characteristics of the clinical manifestations of the disease and some organizational aspects. Methods. The study is an observational, descriptive and analytical, conducted in 2017-2018. 636 medical records of an inpatient (in Semey and in Ust-Kamenogorsk) for the period 2012-2017 were studied using the method of continuous observation. The dependence of the general somatic status and outcome of the disease on the characteristics of the clinical manifestations of the disease and some organizational aspects was studied based on the materials of forms 003 / y (inpatient medical record) of the University Hospital of the State Medical University of Semey and Ust-Kamenogorsk city hospital №1. Communication / dependency assessment was performed using correlation analysis (Spearman's rank correlation coefficient). Results. The results indicate the relationship / dependence of the state upon admission of neuro-oncological patients and the outcome of the disease on age and social status, place of residence, presence of neurological deficit and tumor localization, type of hospitalization, who referred the patient and after what time from the onset of the disease the patient was hospitalized, complications of the operation and being in intensive care. Findings. The condition of admission of neuro-oncological patients and the outcome of the disease depend on the personal data of the patients, tumor characteristics, neurological deficit and organizational aspects of medical care. Кіріспе. Ми ісіктері, жалпы ісіктерді диагностикалау мен емдеудегі жетістіктерге қарамастан, емделушілердің өмір сүру деңгейінің төмендігі мен жоғары мүгедектікке байланысты шұғыл мәселе болып табылады. Мақсаты. Зерттеудің мақсаты: аурудың клиникалық көріністері мен кейбір ұйымдастырушылық аспектілерінің сипаттамалары бойынша нейро-онкологиялық науқастарды хирургиялық емдеудің нәтижесінің өзара байланысын / тәуелділігін зерттеу. Материалы мен әдістері. Негізгі әдіс деректерді үнемі көшіру болып табылады (636 стационарлық медициналық жазбалар). Зерттеудің тереңдігі 2012-2017 ж.ж.. Өткізілген байқаушы, сипаттамалы және аналитикалық болып табылады. Үзіліссіз бақылау әдісі бойынша стационарда (Семей және Өскемен қалаларында) 2012-2017 жылдар аралығында 636 медициналық анықтама зерделенді. Семей қаласының Мемлекеттік медицина университетінің Университеттік госпиталі және №1 Өскемен қалалық ауруханасының 003 / y (стационарлық медициналық жазбалар) материалдарының негізінде аурудың клиникалық көріністері мен кейбір ұйымдастырушылық аспектілерінің сипаттамаларына байланысты жалпы соматикалық мәртебесі мен нәтижесінің тәуелділігі зерттелді. Байланыс / тәуелділікті бағалау корреляциялық талдауды (Spearman ранг корреляция коэффициенті) қолдану арқылы жүргізілді. Нәтижелері. Нәтижесінде неврологиялық онкологиялық науқастарды қабылдаған кезде мемлекеттің қарым-қатынасы / аурудың нəтижесі, жасына жəне əлеуметтік мəртебесіне, тұрғылықты жеріне, неврологиялық тапшылығына жəне ісіктердің локализациясына, ауруханаға жатқызу түріне байланысты жəне науқастың аурудың басталған кезінен бастап науқасқа жатқызылған кезде, операцияның асқынуы және реаниматологияда болу. Қорытынды.Нейро-онкологиялық науқастарды қабылдау және аурудың нәтижесі науқастардың жеке деректеріне, ісіктер сипаттамаларына, неврологиялық тапшылығына және медициналық көмек көрсетудің ұйымдастырушылық аспектілеріне байланысты.
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The fourth edition of the World Health Organization (WHO) classification of tumours of the central nervous system, published in 2007, lists several new entities, including angiocentric glioma, papillary glioneuronal tumour, rosette-forming glioneuronal tumour of the fourth ventricle, papillary tumour of the pineal region, pituicytoma and spindle cell oncocytoma of the adenohypophysis. Histological variants were added if there was evidence of a different age distribution, location, genetic profile or clinical behaviour; these included pilomyxoid astrocytoma, anaplastic medulloblastoma and medulloblastoma with extensive nodularity. The WHO grading scheme and the sections on genetic profiles were updated and the rhabdoid tumour predisposition syndrome was added to the list of familial tumour syndromes typically involving the nervous system. As in the previous, 2000 edition of the WHO ‘Blue Book', the classification is accompanied by a concise commentary on clinico-pathological characteristics of each tumour type. The 2007 WHO classification is based on the consensus of an international Working Group of 25 pathologists and geneticists, as well as contributions from more than 70 international experts overall, and is presented as the standard for the definition of brain tumours to the clinical oncology and cancer research communities world-wide