Abstract
Alzheimer’s disease (AD) is a heterogeneous disease and exhibits
diverse clinical presentations and disease progression. Some
pathological and anatomical subtypes have been proposed. However, these
subtypes provide a limited mechanistic understanding for AD. Leveraging
gene expression data of 222 AD patients from The Religious Orders Study
and Memory and Aging Project (ROSMAP) Study, we identified two AD
molecular subtypes (synaptic type and inflammatory type) using
consensus non-negative matrix factorization (NMF). Synaptic type is
characterized by disrupted synaptic vesicle priming and recycling and
synaptic plasticity. Inflammatory type is characterized by disrupted
IL2, interferon alpha and gamma pathways. The two AD molecular subtypes
were validated using independent data from Gene Expression Omnibus. We
further demonstrated that the two molecular subtypes are associated
with APOE genotypes, with synaptic type more prevalent in AD patients
with E3E4 genotype and inflammatory type more prevalent in AD patients
with E3E3 genotype (p = 0.031). In addition, two molecular subtypes are
differentially represented in male and female AD, with synaptic type
more prevalent in male and inflammatory type in female patients (p =
0.051). Identification of AD molecular subtypes has potential in
facilitating disease mechanism understanding, clinical trial design,
drug discovery, and precision medicine for AD.
Introduction
Alzheimer’s disease (AD) is the most common neurodegenerative disease
in elderly population, characterized by pathological extracellular
deposition of beta-amyloid (Aβ) peptides and intracellular tau protein
fibers in the brain [[26]1]. AD is a heterogenous and multifactorial
disease, with diverse clinical presentations in different affected
brain areas (left and right cerebral hemispheres as well as
anterior-posterior axis) [[27]2–[28]5], different phenotypes
(dysexecutive, amnesic and aphasic) [[29]6, [30]7], and different rates
of disease progression [[31]8]. Recent studies suggested that Aβ
aggregates in different biochemical composition [[32]9]. Defining
subtypes of AD is important for disease mechanism understanding,
clinical trial design, drug discovery, and personalized treatments.
Neuroimaging, beta amyloid and tau have been used for AD subtyping
[[33]9–[34]13], however, subtypes identified based on image analysis
and beta amyloid offer limited mechanistic understanding into AD
pathophysiology. High-throughput genomic data has greatly expanded our
understanding for disease mechanism of AD. Genome-wide association
studies (GWAS) have initially identified over 20 loci for late-onset AD
[[35]14, [36]15]. A recent approach called genome-wide
association-by-proxy (GWAX) using larger sample size has further
expanded the susceptibility loci of AD to 40 [[37]16–[38]18]. Several
pathways or molecular networks involved in AD were identified using
gene expression data [[39]19, [40]20]. In addition, advanced machine
learning and statistical methods have used genomic data to classify AD
from normal and mild cognitive impairment (MCI) or predicting MCI to AD
conversion [[41]21–[42]24]. However, genomic data have not been used
for AD subtyping.
The Religious Orders Study and Memory and Aging Project (ROSMAP) is a
longitudinal clinical-pathologic cohort study of aging and AD [[43]25].
Currently, around 2,500 individuals were involved in this study and
genomic data from 642 participants are available. In this study, we
leveraged these valuable data for AD molecular subtyping using
non-negative matrix factorization (NMF) clustering method. It has been
shown that NMF-based classification is accurate and robust for
clustering of genomic data as compared to other methods [[44]26]. NMF
has been used in cancer molecular subtyping [[45]27, [46]28]. In this
study, we applied NMF to identify molecular subtypes of AD using gene
expression data from ROSMAP and validated the AD molecular subtype in
independent datasets. We also investigated the association of AD
molecular subtype with patient demographic, clinical and APOE status
variables.
Materials and methods
The overall methods were illustrated in [47]Fig 1. The Religious Orders
Study and Memory and Aging Project (ROSMAP) was used as the discovery
dataset. First, we applied consensus matrix-based NMF into ROSMAP to
identify AD molecular subtypes. Second, subtype analysis was performed
to identify signature genes and enriched pathways for each molecular
subtype. Third, we validated these molecular subtypes in independent
datasets (GEO). Finally, we investigated the association of AD
molecular subtype with available demographic and clinical variables,
and APOE genotype.
Fig 1. Overview of the methods.
[48]Fig 1
[49]Open in a new tab
NMF: non-negative matrix factorization.
ROSMAP data
ROSMAP contains 222 participants with clinical consensus diagnosis of
AD at time of death. Raw gene expression data from frontal cortex and
corresponding clinical data were downloaded from synapse.org
(syn3219045). Raw count data were normalized and processed according to
commonly used procedure described in edgeR (version: 3.28.0) [[50]29,
[51]30]. Data were first normalized by sequencing library size.
Non-expressed genes, defined as count per million less than 5 in 80% of
samples, were then filtered out, resulting in 12281 genes. To obtain a
robust classifier and also reduce the number of genes for NMF-based
clustering, we experimented with the different cutoffs ranging from top
10% to 40% (1228 to 4912 genes) based on their interquartile range
(IQR) for clustering. While the obtained results were very similar, we
presented the clustering result using the top 20% cutoff (2456 genes).
Consensus NMF for AD molecular subtyping
Non-negative matrix factorization
Among different variants of NMF, we employed divergence-based algorithm
proposed by Lee and Seung [[52]31] due to its simplicity and robustness
[[53]26, [54]31]. Briefly, given a gene expression matrix A of size n ×
m (n genes and m samples) and desired number of clusters k, the NMF
decomposes A into two non-negative matrices W (n × k) and H (k × m)
([55]Fig 2).
Fig 2. Non-negative matrix factorization procedure.
[56]Fig 2
[57]Open in a new tab
W and H matrix are computed using iterative method to minimize the
following cost function.
[MATH: D=∑ij(
AijlogAij(
WH)ij<
mo>-Aij+WHij) :MATH]
In each iteration, W and H are updated using following multiplicative
updating rules,
[MATH: Hau←Hau∑iWiaAiu/<
mrow>(WH)iu∑kWka
:MATH]
[MATH: Wia←Wia∑uHauAiu/<
mrow>(WH)iu∑vWav
:MATH]
Cluster membership of each sample is assigned based on the row index of
maximal number in the column of H matrix.
Consensus-matrix based model selection
We used consensus matrix-base model selection strategy to select best
number of clusters [[58]26]. For a given number of clusters K, NMF
groups the samples into K clusters. A total of 40 NMF runs were
employed to construct the consensus matrix C (n × n). Each element of
consensus matrix represents the probability that two samples cluster
together. Then, the cophenetic correlation coefficient ρ[k] was
computed as the Pearson correlation of the distance matrix between
samples induced by the consensus matrix, i.e., I − C, and the distance
matrix induced by the hierarchical clustering of I − C. ρ[k] measures
how faithfully a dendrogram preserves the pairwise distances in the
consensus matrix and was calculated using the cophenet function in the
scikit-learn library [[59]32]. The best clustering is based on the
value of ρ[k].
Identification of molecular subtype-specific signatures
To identify molecular subtype-specific signatures, we first computed
the silhouette for each sample using following equation.
[MATH:
s(i)=<
mrow>b(i)-a(i)max{a(i),b(i)}
:MATH]
Where a(i) and b(i) were computed as following,
[MATH:
a(i)=<
mrow>1|Ci|-1∑j∋C
i,i≠
jd(i,j) :MATH]
[MATH:
b(i)=mink≠i1|Ck<
/msub>|∑j∋C
kd(i,j)
:MATH]
a(i) is the mean distance of a sample to all other samples in the same
cluster. It measures how well a sample is assigned to its own cluster.
The smaller the value is, the better the assignment is. b(i) is the
smallest mean distance of a sample to all samples in any other cluster.
|C[i]| is the number of samples in its own cluster, |C[k]| is the
number of samples in any other cluster, and d(i, j) is the distance of
two samples computed with Euclidean distance.
The silhouette is a measure of how similar a sample is to its own
cluster compared to other clusters. After removing outlier samples with
negative silhouette width from each subtype, we applied statistical
package edgeR (version: 3.28.0) to obtain pairwise differentially
expressed genes (DEGs) between molecular subtypes. To facilitate
downstream analysis of molecular subtypes, we used fold change of 1.5
and false discover rate (FDR) of 0.05 as cutoffs. We define the gene
signature of each subtype as DEGs that have the highest value in each
molecular subtype.
Pathway enrichment analysis
A Bioconductor package clusterProfiler (Version 3.14.3) [[60]33] was
used to perform pathway enrichment analysis for each identified
molecular subtype. ClusterProfile is a statistical package that
integrates several ontologies, including Gene Ontology, Disease
Ontology, and KEGG pathway, to perform over-representation analysis and
gene set enrichment analysis.
Validation of AD molecular subtype in independent datasets
Two independent datasets from Gene Expression Omnibus ([61]GSE44770,
[62]GSE118553) were used for validation of AD molecular subtypes.
[63]GSE44770 includes gene expression data from frontal cortex of 230
subjects, 128 of which are late-onset Alzheimer´s disease (LOAD)
patients. [64]GSE118553 includes gene expression data from frontal
cortex of 112 subjects, including 52 AD patients. We used normalized
data from [65]GSE44770 and [66]GSE118553 to validate molecular subtypes
identified based on ROSMAP data.
Since ground truth of clusters in a dataset is unknown, there are no
quantitative method to formally validate clusters in an independent
dataset. Therefore, visualization is suggested as a valid approach
[[67]34]. A discovery by signature gene strategy proposed by other
studies was used for this validation [[68]27, [69]28]. The basic idea
of this strategy is that using the signature gene from the discovery
dataset to cluster a new dataset to see if the signature gene
expression shows similar patterns with the discovery dataset. It
includes three steps. First, signature genes were projected onto
normalized independent dataset and consensus NMF clustering was used to
identify number of clusters. Second, molecular subtype identity was
assigned using signature genes. Third, a heatmap of signature gene
expression was then generated to visualize the molecular subtype. In
addition, we performed pathway enrichment analysis to further confirm
the molecular subtypes in independent datasets.
Correlation of AD molecular subtype with patient demographics,
clinicopathology, and APOE genotype
We examined the demographic distributions of AD molecular subtype,
including age, sex, race and education, and assessed the associations
of AD molecular subtype with APOE genotype and clinical variables,
including Braak stage, The Consortium to Establish a Registry for
Alzheimer’s Disease (CERAD) diagnosis, and Mini-Mental State
Examination (MMSE) score. The Braak stage is a semiquantitative measure
of severity of neurofibrillary tangle (NFT) pathology [[70]35, [71]36].
Braak stages I and II indicate NFTs confined mainly to the entorhinal
region of the brain. Braak stages III and IV indicate involvement of
limbic regions such as the hippocampus. Braak stages V and VI indicate
moderate to severe neocortical involvement. CERAD score is a
semiquantitative measure of neurotic plaques [[72]37]. Based on
semiquantitative estimates of neurotic plaque density, a
neuropathologic diagnosis was made of no AD, possible AD, probable AD,
or definite AD. MMSE test is a 30-point questionnaire that is used
extensively in clinical and research settings to measure cognitive
impairment.
For categorical variables, including Braak stage, CERAD, and APOE,
Fisher’s exact test was used to assess their associations with AD
molecular subtype. For continuous variables, such as MMSE and
education, student’s t-test was used. All statistical analysis was
performed using R (version: 3.6.2). Significance level was defined as p
value less than 0.05.
Ethics statement
This is a secondary research use for ROSMAP data and patient
information is not identifiable. The IRB at Case Western Reserve
University determined that the proposed activity is not research
involving human subjects and IRB review and approval is not required
(STUDY20190935). Therefore, patient consent is not applicable or not
required.
Results
AD consists of two molecular subtypes
We used consensus NMF to cluster gene expression data of 222 AD
patients from ROSMAP. Compared with three and four clusters, consensus
matrix from two clusters are more stable ([73]Fig 3A–3C). In addition,
cophenetic correlation coefficient drops when we assign the data into
three subtypes ([74]Fig 3D). These evidences indicate that patient data
can be best represented by two distinct subtypes. We obtained 403
differentially expressed genes between these two molecular subtypes as
signature genes using 197 core samples with positive silhouette score
([75]Fig 4A). We can see the distinct pattern of signature gene
expression in these two subtypes ([76]Fig 4B).
Fig 3. NMF-based clustering of gene expression data from 222 AD patients in
ROSMAP.
[77]Fig 3
[78]Open in a new tab
(A-C) Consensus matrices for 2, 3 and 4 clusters respectively. (D) Plot
of cophenetic correlation coefficient against the number of clusters.
Fig 4. Signature genes in each molecular subtype.
Fig 4
[79]Open in a new tab
(A) Silhouette score for each sample (B) Heatmap for signature gene
expression in each molecular subtype. Gene expression is represented as
normalized value.
We named the molecular subtypes according to signature genes
up-regulated in each cluster. For synaptic type, highly expressed genes
are associated with synapse function, such as SNAP25, RAB3A, VAMP1,
SYNJ1, and STXBP1. A total of 37 pathways were significantly enriched
and 23 of 37 (62.2%) are related to synapse function ([80]S1 Table).
The top 10 enriched pathways of this subtype are shown in [81]Table 1.
We can see that synaptic type is characterized by dysfunction of
synapse, including synaptic vesicle priming and recycling, and
neurotransmitter secretion ([82]Table 1).
Table 1. Top 10 enriched pathways in the synaptic type of AD.
PATHWAY P value (adjusted) Fold enriched
Synaptic vesicle cycle 4.1E-04 5.63
Vesicle-mediated transport in synapse 4.1E-04 5.36
Synaptic vesicle priming 1.4E-03 21.32
Synaptic vesicle recycling 1.4E-03 8.98
Calcium ion regulated exocytosis 1.4E-03 5.81
Synaptic vesicle endocytosis 3.0E-03 9.33
Presynaptic endocytosis 3.0E-03 9.33
Neurotransmitter secretion 3.3E-03 5.03
Signal release from synapse 3.2E-03 5.03
Signal release 1.1E-02 2.92
[83]Open in a new tab
For inflammatory type, highly expressed genes are related to
inflammatory pathways, such as BST2, GBP4, IFI44L, IFITM2, IFITM3,
IL4R, IRF, MT2A, PSMB9, and TXNIP. A total of 3 pathways were
significantly enriched using the signature genes. This subtype is
characterized with dysfunction of inflammatory responses, including
interferon alpha (IFN-α), interferon gamma (INF-γ) and IL2 pathways
([84]Table 2).
Table 2. Enriched pathways in the inflammatory type of AD.
PATHWAY P value (adjusted) Fold enriched
Interferon alpha response 4.3E-05 7.83
Interferon gamma response 1.4E-03 4.22
IL2-Stat5 signaling 2.1E-02 3.37
[85]Open in a new tab
AD molecular subtypes were validated in independent datasets
We validated the two AD molecular subtypes using two independent
datasets from GEO ([86]GSE44770, n = 128 and [87]GSE118553, n = 40).
Using consensus NMF, we identified clusters based on these two
independent datasets from GEO (Figs [88]5 and [89]6). Majority of
samples have positive silhouette scores (Figs [90]5E and [91]6E),
indicating that samples are well classified using signature genes we
obtained from ROSMAP. We can see distinct patterns for signature gene
expression in these two clusters, indicating that these two clusters
represent the same molecular subtypes from ROSMAP (Figs [92]5E and
[93]6F).
Fig 5. Molecular subtype validation in GEO dataset ([94]GSE44770).
[95]Fig 5
[96]Open in a new tab
(A-C) Consensus matrices for 2, 3 and 4 clusters respectively. (D) Plot
of cophenetic correlation coefficient against the number of clusters.
(E) Silhouette distance for each sample. (F) Heatmap for signature gene
expression.
Fig 6. Molecular subtype validation in GEO dataset ([97]GSE118553).
[98]Fig 6
[99]Open in a new tab
(A-C) Consensus matrices for 2, 3 and 4 clusters respectively. (D) Plot
of cophenetic correlation coefficient against the number of clusters.
(E) Silhouette distance for each sample. (F) Heatmap for signature gene
expression.
To further validate the AD molecular subtypes in these two datasets, we
performed pathway enrichment for each cluster in each dataset. For
[100]GSE44770 dataset, a total of 30 pathways were significantly
enriched in first cluster ([101]S2 Table). Seven of them exactly occur
in enriched pathways of ROSMAP-based synaptic type AD and ten
additional pathways are related to synaptic function, indicating that
this cluster is a synaptic type. Ten pathways were enriched in second
cluster and all three enriched pathways in ROSMAP-based inflammatory AD
occur in this cluster, indicating that this cluster is an inflammatory
type. Similar results were obtained in [102]GSE118553 dataset. A total
of ten pathways and two pathways were significantly enriched in each
cluster respectively ([103]S3 Table). In the first cluster, four of ten
pathways are overlapped with the enriched pathways of ROSMAP-based
synaptic type AD and five other pathways are related to synaptic
function. In the second cluster, two enriched pathways are overlapped
with the enriched pathways in ROSMAP-based inflammatory subtype.
Association analyses of AD molecular subtype with patient demographics,
clinicopathology, and APOE genotype
We investigated whether AD subtypes are associated with demographic and
clinical variables using the core samples from ROSMAP dataset (197
patients). The distributions of AD molecular subtype in demographic
variables, including age, race and education, show no significant
difference ([104]Table 3). Interestingly, we noticed that synaptic type
AD is more prevalent than inflammatory type in male patients (p =
0.051). Several measurements for AD severity are available in ROSMAP,
including AD Braak stage, CREAD score and MMSE score. We didn’t see
significant associations of AD molecular subtype with these variables
([105]Table 3). This result suggests that AD molecular subtype might be
not related to AD severity, but caution should be taken when explaining
this result due to small sample size. ROSMAP also includes APOE
genotype, the most important genetic risk factor for late-onset AD. A
significant association of AD molecular subtype with APOE was observed
(p = 0.031). We can see that synaptic type AD is more prevalent in
patients with E3E4 genotype and inflammatory type AD is more prevalent
in patients with E3E3 genotype ([106]Table 3).
Table 3. Association of AD molecular subtype with demographic, clinical
variables and APOE genotype in the ROSMAP dataset.
Synaptic type (Num. of Patients) Inflammatory type (Num. of Patients) p
[107]^a
Age
< 65 0 0 1.0
65–80 5 4
> 80 101 87
Sex
Female 65 68 0.051
Male 41 23
Race
White 104 90 1.0
Black 2 1
Education 16.70 16.21 0.98
Braak stage
I 4 2 0.88
II 4 2
III 20 17
IV 35 29
V 41 37
VI 2 4
CREAD score
Definite 48 50 0.47
Probable 44 28
Possible 5 5
No AD 9 8
MMSE 13.84 12.23 0.19
APOE
E2E2 0 1 0.031
E2E3 12 9
E2E4 5 2
E3E3 46 55
E3E4 43 22
E4E4 0 2
[108]Open in a new tab
^a For categorical variables, including Braak stage, CREAD score and
APOE, p value was computed using Fisher’s exact test. For continuous
variables, including Education and MMSE, the p value was computed using
student’s t-test.
We then examined whether these associations can also be observed in the
two validation datasets. Although we didn’t see a significant
association of sex with molecular subtype, we observed that the
synaptic type is more prevalent in male patients than in females in
both datasets. In the [109]GSE44770, 37 of 60 (61.7%) are synaptic type
in male patients, while it is 33 of 66 (50.0%) in female patients. In
the [110]GSE118553, the prevalence of synaptic type in male and female
patients are 10 of 14 (71.4%) and 17 of 25 (68.0%) respectively. Due to
the lack of APOE genotype in these two datasets, we were unable to
investigate the association of APOE with molecular subtype ([111]Table
4).
Table 4. Association of AD molecular subtype with age and sex in the two
validation datasets.
Synaptic type (Num. of Patients) Inflammatory type (Num. of Patients) p
[112]GSE44770 Age
< 65 6 4 0.963
65–80 30 23
> 80 34 29
Sex
Female 33 33 0.212
Male 37 23
[113]GSE118553 Age
< 65 0 1 0.495
65–80 9 4
> 80 18 7
Sex
Female 17 8 1
Male 10 4
[114]Open in a new tab
Discussion
In this study, we applied non-negative matrix factorization combined
with consensus matrix-based cluster selection and identified two
molecular subtypes based on gene expression data of AD. Synaptic type
is characterized by dysfunction of synaptic pathways. Substantial loss
of neurons and synapses is a hallmark in late stage AD. Recent studies
also show synaptic dysfunction was observed in mild cognitive
impairment patients [[115]38–[116]40], suggesting that synaptic
dysfunction is a fundamental mechanism of AD. On the other hand,
inflammatory type is enriched with over-activation of IL-2, IFN-α, and
IFN-γ pathways. The central role of inflammation in AD development is
recently established [[117]41–[118]43]. A sustained inflammatory
response, mediated by over-activation of microglia and other immune
cells, has been demonstrated to exacerbate both amyloid and tau
pathology [[119]42]. Roy ER et al reported that IFN-α response drives
neuroinflammation and grossly upregulated in AD [[120]44]. A recent
study links IL-2 pathway to amyloid pathology of AD [[121]45]. All
these evidences demonstrated that inflammation represents another
mechanism of AD. Therefore, the two AD molecular subtypes we identified
reflect inherent molecular mechanism of AD. Interestingly, two studies
reported that microglia are involved in synaptic pruning and plays a
role in pathological remodeling of neuronal circuits [[122]46,
[123]47], indicating that two molecular processes may be related.
GWAS has identified more than 40 genes/loci as the genetic risk factors
of AD, which greatly expands our mechanistic understanding of the
etiology of AD. While some of these genes/loci have been mapped to Aβ
pathology, including amyloid precursor protein (APP) metabolism, Aβ
aggregation, clearance, toxicity, and Tau pathology, a large amount of
these genes is related to non-Aβ and -Tau pathways [[124]48]. Lambert
et al suggested that a common mechanism, i.e., focal adhesion pathway,
may link Aβ and tau pathology and ultimately lead to synapse
dysfunction. A shift from Aβ-centered hypothesis to synapse-centered
hypothesis has emerged [[125]48, [126]49]. Here, we used gene
expression data to define two molecular subtypes of AD and enriched
pathways high-lighten synapse dysfunction, which supports this
synapse-centered hypothesis. Furthermore, our study implies two
mechanisms for synaptic dysfunction. One is the aberrant synaptic
pathways themselves, such as synaptic vesicle endocytosis and
exocytosis. Another is the indirect mechanism through immune system
dysfunction, which may affect Aβ clearance and synaptic pruning.
Using available patient clinical information, we evaluated their
associations with molecular subtypes. We didn’t find significant
correlation of molecular subtype with severity of cognitive impairment.
However, we were unable to control potential confounders due to very
limited information available in the dataset. We show that AD molecular
subtype is significantly associated with APOE genotype. APOE has three
alleles, including E2, E3 and E4. APOE4 is the main genetic determinant
for late-onset AD and individual with APOE4 significantly increases the
risk of AD [[127]50, [128]51]. While some studies show APOE4 promotes
AD by interaction with Aβ, especially it hinders Aβ clearance
[[129]52], other studies link APOE4 with synaptic function, such as
synapse recycling [[130]53]. In this study, we observed that synaptic
type of AD is more common in patients with E3E4 genotype. Although APOE
is not in the list of signature genes, it may regulate synaptic
function by interacting with downstream molecules including APOE
receptor in the brain. This observation further supports synaptic
mechanism of APOE4 in AD development.
We observed that inflammatory type of AD is more prevalent in women. On
the other hand, synaptic type of AD is more prevalent in men. Sex
differences in both synaptic plasticity and inflammatory response have
been observed [[131]54, [132]55]. Females often have strong both innate
and adaptive immune responses [[133]55]. This results in faster
clearance of pathogens in females than males, but also contributes to
increased susceptibility to inflammatory diseases in females, such as
systemic lupus erythematosus and multiple sclerosis [[134]56]. Since
inflammation plays a central role in AD development, females are more
likely to develop inflammatory type AD than males. Sex difference in
dendrite spine density (DSD) in the hippocampus has been observed in
animal models decades ago, which is regulated by steroid hormones and
environmental stress. The female rats have double of DSD than males and
DSD experienced dramatic changes during the estrous cycle [[135]57,
[136]58]. This structural change in the hippocampus was also observed
in human women during the menstrual cycle [[137]59]. Many animal
studies showed that increased spine density is associated with memory
enhancement [[138]60]. Compared to females, males have lower DSD in the
hippocampus. Besides, no periodic fluctuation of hormone in males may
lead to less synapse plasticity of hippocampal neurons due to lack of
“practicing”. We hypothesize that lower DSD and possibly less synapse
plasticity may make males more vulnerable to hippocampus damage, which
may explain why synaptic type AD is more common in males.
Identification of AD molecular subtype has an implication for better
design in clinical trials. Currently, clinical trials for AD are based
on different cognitive groups from mild, moderate, and severe AD.
However, most of this symptom-based clinical trials for AD fails,
reflecting a lack of mechanistic understanding of AD. A recent clinical
trial about a monoclonal antibody solanezumab failed the phase III
trials for mild to moderate AD [[139]61], but later it was found that
it has benefits for a subgroup of patients with mild symptoms
[[140]62], supporting that patient subgrouping is important. Molecular
subtyping of AD patients provides an attracting strategy for patient
stratification in clinical trials. We prospect that including molecular
subtype in clinical trial may contribute to discover personalized
treatments for AD.
One limitation of this study is that molecular subtyping is based on
gene expression data from post-mortem brain tissue, which limits its
clinical usage. Nevertheless, identified molecular subtypes will help
to understand the mechanism of AD. In the future, developing a
practical molecular subtyping system for AD is demanded. Proteomic data
from cerebrospinal fluid and genotype data from blood could be useful
for such purpose.
Conclusions
In this study, we reported the first gene expression-based molecular
subtyping of AD. Using consensus NMF, we identified two robust
molecular subtypes-synaptic type and inflammatory type-that represent
two fundamental mechanisms of AD. These molecular subtypes are
associated with APOE genotype and exhibit sex difference in
distribution. Identification of molecular subtypes may have an
implication in better clinical trial design and personalized medicine
for AD.
Supporting information
S1 Table. Pathways enriched in each cluster from ROSMAP.
(XLSX)
[141]Click here for additional data file.^ (12KB, xlsx)
S2 Table. Pathways enriched in in each cluster from [142]GSE44770.
(XLSX)
[143]Click here for additional data file.^ (12KB, xlsx)
S3 Table. Pathways enriched in in each cluster from [144]GSE118553.
(XLSX)
[145]Click here for additional data file.^ (10.9KB, xlsx)
Acknowledgments