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UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE
INSTITUTO METRÓPOLE DIGITAL
PROGRAMA DE PÓS-GRADUAÇÃO EM BIOINFORMÁTICA
Construção e Validação de um Modelo Prognóstico Integrando
Dados de Expressão Gênica e Metilação de DNA em
Meduloblastoma
GUSTAVO LOVATTO MICHAELSEN
Natal-RN
2023
GUSTAVO LOVATTO MICHAELSEN
Construção e Validação de um Modelo Prognóstico Integrando Dados
de Expressão Gênica e Metilação de DNA em Meduloblastoma
Dissertação de mestrado apresentada ao
Programa de Pós-Graduação em
Bioinformática da Universidade Federal do
Rio Grande do Norte.
Área de concentração: Bioinformática
Linha de Pesquisa: Biologia de Sistemas
Orientadora: Drª Marialva Sinigaglia
Coorientadora: Drª Mariane da Cunha
Jaeger
Natal-RN
2023
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GUSTAVO LOVATTO MICHAELSEN
Construção e Validação de um Modelo Prognóstico Integrando Dados
de Expressão Gênica e Metilação de DNA em Meduloblastoma
Dissertação de mestrado apresentada ao
Programa de Pós-Graduação em
Bioinformática da Universidade Federal do
Rio Grande do Norte.
Área de concentração: Bioinformática
BANCA EXAMINADORA
Aprovada em 14/09/2023
Profª.: Drª Marialva Sinigaglia (Orientadora)....…………………….……..…Avaliadora Interna
Instituto do Câncer Infantil
PPgBioINFO, IMD, UFRN
Profª.: Drª Beatriz Stransky Ferreira..………….…………….……………..Avaliadora Interna
Departamento de Engenharia Biomédica - DEB, UFRN
PPgBioINFO, IMD, UFRN
Profª.: Drª Carolina Nor..……………………...….…………...……………...Avaliadora Interna
Hospital for Sick Chidren Peter Gilgan Centre
for Research and Learning
Natal-RN
2023
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AGRADECIMENTOS
Ao Instituto do Câncer Infantil de Porto Alegre, seus funcionários, voluntários e
apoiadores por tornarem essa causa tão valorosa realidade. Tambeḿ agradeço por toda a
infraestrutura, equipamentos e pelo fomento a pesquisa durante o mestrado.
À minha orientadora Meg pelas conversas, discussões, conselhos e em especial por ter
me recebido ainda como aluno de iniciação e me guiado nesse universo da bioinformática e da
ciência como um todo. Ao Professor Rodrigo Dalmolin pelos preciosos conselhos ao longo da
minha formação. Aos alunos de iniciação científica do ICI Douglas Lima e Lívia dos Reis pelo
auxílio, espelho e conversas ao longo do trabalho.
A todos os colegas e amigos do BioME, em especial à Rafaela Ferraz pelas discussões,
risadas, conselhos e caronas e por toda a companhia durante o tempo que passei na cidade de
Natal e também ao Matheus Dalmolin, por ser esse grande amigo de todas as horas, sempre
muito companheiro e prestativo.
Por fim, agradeço a minha família que sempre me incentivaram, independente da minha
escolha profissional e proporcionaram todas as condições para que eu pudesse me dedicar aos
meus estudos.
O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001.
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“In the measurement world, you set a goal and strive for it.
In the universe of possibility, you set the context and let life unfold.”
Benjamin Zander
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RESUMO
O meduloblastoma (MB) é um dos tumores cerebrais pediátricos mais frequentes e
estima-se que um terço dos pacientes irão a óbito devido à doença. Visto que a falta de
biomarcadores prognósticos precisos é um grande desafio para a melhora clínica desses
pacientes, uma vez que parâmetros prognósticos convencionais da doença apresentam
correlações limitadas e pouco confiáveis com seu desfecho, o nosso objetivo foi construir uma
assinatura gênica e avaliar seu potencial como um novo modelo prognóstico para pacientes
com MB. A desregulação da metilação como a hipermetilação de genes supressores tumorais e
a hipometilação de oncogenes são cruciais para o processo de tumorigênese e para a
manutenção tumoral, inclusive em MB. Neste estudo nós utilizamos seis conjuntos de dados
totalizando 1679 amostras, incluindo dados de expressão gênica e metilação de DNA de MB
primário, bem como amostras controle de cerebelo saudável. Nós identificamos genes
regulados via metilação (MDGs) em MB, genes cuja expressão está correlacionada com sua
metilação e que também encontram-se diferencialmente metilados em relação ao tecido
saudável. Utilizamos a regressão LASSO, um método estatístico de machine learning
supervisionado, aos MDGs tendo como resultado final uma assinatura de dois genes (AG-2)
candidatos a biomarcadores prognósticos para o MB (CEMIP e NCBP3). Através de um
modelo de escore de risco, confirmamos o impacto na sobrevida global (OS) da AG-2 com a
análise de Kaplan-Meier (log-rank p < 0,01). Avaliamos sua robustez e acurácia por meio da
área sob a curva ROC prevendo a OS em 1, 3 e 5 anos em múltiplos conjuntos de dados
(conjunto de treino: 77,2%, 73,2% e 71,2%, média em três conjuntos de validação: 83,6%,
77,6%, 75,4% em 1, 3 e 5 anos respectivamente). Avaliamos a AG-2 como um biomarcador
prognóstico independente com a regressão de Cox multivariada a qual apresentou p-valor <
0,01 em todos os quatro conjuntos de dados avaliados. O modelo de escore de risco da AG-2
regulado via metilação pode efetivamente classificar pacientes com MB em alto e baixo risco,
reforçando a importância dessa modificação epigenética nessa doença. Tais genes destacam-se
como biomarcadores prognósticos promissores com potencial aplicação no tratamentos de MB.
Palavras-chave: meduloblastoma · biomarcador prognóstico · metilação de DNA · medicina
de precisão
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ABSTRACT
Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is
estimated that one-third of patients will die from the disease. The lack of accurate
prognostic biomarkers is a major challenge for the clinical improvement of those patients,
with conventional prognostic parameters having limited and unreliable correlations with the
disease outcome. Acknowledging this issue, our aim was to build a gene signature and
evaluate its potential as a new prognostic model for patients with the disease.
Hypermethylation of tumor suppressor genes and hypomethylation of oncogenes are
methylation dysregulations crucial for cancer tumorigenesis and tumor maintenance, and it
is no exception for MB. In this study, we used six datasets totaling 1679 MB samples,
including RNA gene expression and DNA methylation data from primary MB as well as
control samples from healthy cerebellum. We identified methylation-driven genes (MDGs)
in MB, genes whose expression is correlated with their methylation and which are also
differentially methylated in relation to healthy tissue. After, LASSO regression, a
supervised machine learning statistical method, was used with the MDGs as a parameter
resulting in a two-gene signature (GS-2) of candidate prognostic biomarkers for MB
(CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall
survival (OS) with Kaplan-Meier analysis (log-rank p < 0.01). We evaluated its robustness
and accuracy with receiver operating characteristic (ROC) curves predicting OS at 1, 3 and
5 years in multiple datasets (training set: 77.2%, 73.2% and 71.2%, mean in three validation
sets: 83.6%, 77.6%, 75.4% at 1, 3 and 5 years respectively). We evaluated the GS-2 as an
independent prognostic biomarker with multivariable Cox regression which showed p-value
< 0.01 in all four datasets evaluated. The methylation-regulated GS-2 risk score model can
effectively classify patients with MB into high and low-risk, reinforcing the importance of
this epigenetic modification in the disease. Such genes stand out as promising prognostic
biomarkers with potential application for MB treatment.
Keywords: medulloblastoma · prognostic biomarker · DNA methylation · precision
medicine
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LISTA DE FIGURAS
Figura 1: Série temporal das taxas de mortalidade………………………………….………...13
Figura 2: Atual modelo de manejo molecular de risco adaptativo…………………………….15
Figura 3: Processo de metilação de DNA………………………………………….………….19
Figura 4: Métodos utilizados no desenvolvimento de assinaturasgênicas
prognósticas…………………………………………………………………………………….21
Figura 5: Processo de validação cruzada……………………………………..……………….24
LISTA DE TABELAS
Tabela 1. Características clínicas e genéticas dos subgrupos de MB….…………...………….17
Tabela 2. Critérios baseados em evidências para uma assinatura gênica prognóstica desde o
laboratório até a prática clínica…………………………………………………………...……22
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LISTA DE ABREVIATURAS
INCA Instituto Nacional de Câncer
MB Meduloblastoma
WNT Wingless
SHH Sonic hedgehog
SNC Sistema nervoso central
TTRA Tumor Teratóide Rabdóide Atípico
LCA Large Cell/Anaplastic (células grandes e anaplásicas)
DE Desmoplásico/Nodular
OMS Organização Mundial da Saúde
ICS Irradiação Crânio-Espinhal
RT Ressecção Total
RST Ressecção Subtotal
RQT Ressecção Quase Total
AG-2 Assinatura Gênica de dois genes
LASSO Least Absolute Shrinkage and Selection Operator
HR Hazard Ratio (razão de risco)
RNAm RNA mensageiro
mDNA Metilação de DNA
OS Overall survival (sobrevida global)
RS Risk Score (escore de risco)
MDG Methylation Driven Gene (gene regulado via metilação)
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SUMÁRIO
1 · INTRODUÇÃO................................................................................................................................. 8
1.1 Câncer infantil...............................................................................................................................8
1.2 O Meduloblastoma......................................................................................................................10
1.3 Aspectos Moleculares do Meduloblastoma................................................................................ 13
1.4 Assinaturas Gênicas Como Marcadores Prognósticos................................................................16
1.5 Justificativa..................................................................................................................................21
2 · OBJETIVO......................................................................................................................................21
2.1 Objetivo Geral.............................................................................................................................21
2.2 Objetivos Específicos..................................................................................................................21
3 · CAPÍTULO I...................................................................................................................................22
A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma……...…….………..23
1 Introduction....................................................................................................................................24
2 Methods......................................................................................................................................... 25
2.1 Patient Cohorts and Data Acquisition....................................................................................25
2.2 Data Processing......................................................................................................................26
2.3 Screening for Methylation-Driven Genes.............................................................................. 26
2.4 Construction of Prognostic Signature.................................................................................... 26
2.5 Gene Signature Validation.................................................................................................... 27
2.6 Comparisons Between MB Subgroups or Chromosomal Configurations............................. 27
2.7 Differentially Expressed Gene Analysis and Enrichment Analysis.......................................27
3 Results............................................................................................................................................27
3.1 Methylation-Driven Genes in Medulloblastoma....................................................................27
3.2 Construction of MDG-Based Signature.................................................................................27
3.3 Validation of the GS-2........................................................................................................... 30
3.4 Prognostic Value of GS-2 as an Independent Marker............................................................30
3.5 Comparison of GS-2 Between MB Subgroups......................................................................31
3.6 Differentially Expressed Gene and Functional Analyses.......................................................32
4 Discussion......................................................................................................................................33
5 Conclusion..................................................................................................................................... 36
6 Statements and Declarations..........................................................................................................36
7 References......................................................................................................................................37
8 Supplementary Materials............................................................................................................... 42
4 · DISCUSSÃO....................................................................................................................................45
5 · CONCLUSÃO................................................................................................................................. 50
6 · REFERÊNCIAS..............................................................................................................................50
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1 · INTRODUÇÃO
1.1 Câncer infantil
O câncer em crianças e adolescentes (entre 0 e 19 anos) se refere a um grande conjunto
de doenças que apresentam características próprias, não somente de natureza histológica, mas
também relacionadas ao comportamento clínico (LITTLE 1999). Taxas de mortalidade eram
expressivamente maiores há meio século, contudo o sucesso no tratamento hoje chega a 80%
em países desenvolvidos, onde há acesso a abordagens modernas e cuidados à saúde robustos,
com mais de 500 mil sobreviventes de câncer pediátrico apenas nos Estados Unidos.
Salienta-se que apenas 10% das crianças residem nesses países com alta renda (NOONE
2018). A medida do impacto global do câncer na infância e adolescência ainda permanece mal
quantificada. Crianças com câncer representam aproximadamente apenas 1% a 3% de todos os
cânceres diagnosticados em todo o mundo a cada ano. A criação de estimativas sólidas do
impacto global do câncer infantil é um grande desafio devido à escassez de registros de câncer
de alta qualidade e dados de estatísticas vitais nos países em desenvolvimento, bem como as
diferenças na etiologia, patogênese e apresentação das neoplasias mais comuns entre crianças e
adultos (Global Burden of Disease Cancer Collaboration et al. 2017).
No Brasil, o câncer infantil é a primeira causa de morte por doença em crianças de 1 a
19 anos, superando as doenças infecciosas. O Instituto Nacional do Câncer (INCA) estima
para cada ano do triênio de 2023 a 2025 a ocorrência de 7.930 novos casos de câncer
pediátrico no Brasil, porém com diferenças contrastantes entre as regiões do país (INCA
2023).
Já é bem estabelecido que os tumores infantis diferem dos de adultos quanto aos seus
aspectos morfológicos, origens histológicas, comportamento clínico, localizações primárias e
período de latência, assim, devem ser estudados e tratados separadamente (INCA 2016).
Crianças tratadas em centros de oncologia pediátrica possuem 80% de chances de cura, já
quando são tratadas em protocolos e centros de adultos a chance de cura cai para 40%. As
chances de cura efetiva no câncer infantil vão depender ainda do tipo de câncer e estão
intimamente relacionadas com:(i) a capacidade de diagnosticar precocemente e com precisão;
(ii) tratamento dentro de protocolos clínicos e equipe multidisciplinar especializada em
oncologia pediátrica e (iii) eficácia em detectar doença metastática ou recidiva. Ainda segundo
dados INCA de 2016, estima-se que 3% do total de casos de câncer ocorrem em crianças e
adolescentes (0-19 anos). A perspectiva da sobrevida para todas as neoplasias em crianças e
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adolescentes foi em torno de 64%, variando de 50% na Região Norte, 60% na Região
Nordeste, 65% na Região Centro-Oeste, 70% na Região Sudeste, e 75% na Região Sul. As
taxas de sobrevivência de cinco anos para alguns tipos de câncer continuam a melhorar nas
últimas décadas (INCA 2016). Para que o progresso seja sustentado e renovado, é importante
que pesquisas sejam conduzidas para identificar novas e inovadoras terapias que se baseiam em
uma maior compreensão das vias celulares que promovem o crescimento e a sobrevivência das
células tumorais em pacientes pediátricos (Smith et al. 2010).
Atualmente, os tumores do sistema nervoso central (SNC) são o principal desafio para
a oncologia pediátrica, com incidências de 19% a 27% dentre as neoplasias em países
desenvolvidos (Ferlay et al. 2015; NATIONAL CANCER INSTITUTE. 2000). As taxas de
sobrevida, em relação a outras neoplasias, apresentam uma melhora insatisfatória. Acredita-se
que 20% dos casos não são diagnosticados devido à baixa incorporação de novas tecnologias
na saúde e investimento em infraestrutura, contudo, há um aumento progressivo de sua
incidência (Desmeules et al. 1992; Papathoma et al. 2015). No Brasil, a estimativa de
sobrevida é de apenas 42% segundo o INCA. Entre 2009 e 2013, a taxa de mortalidade média
foi de 10,26 por milhão de habitantes. A região sul do Brasil apresentou a maior taxa (13 por
milhão), quanto a menor foi observada na região norte (7 por milhão) (Figura 1) (INCA 2016).
O INCA ainda destaca a grande variabilidade dos indicadores analisados, reflexo da
dificuldade de diagnóstico, tratamento e, consequentemente, da obtenção de informações.
Figura 1: Série temporal das taxas de mortalidade. Taxas de mortalidade
ajustadas por idade, por 1 milhão de crianças e adolescentes (0-19 anos), para
tumores do SNC no Brasil e suas regiões (INCA 2016).
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1.2 O Meduloblastoma
O meduloblastoma (MB) é um dos tumores cerebrais pediátricos mais frequentes, com
aproximadamente 5 casos a cada 1 milhão de indivíduos, correspondendo a 20% de todos os
tumores pediátricos do SNC (Ostrom et al. 2018). Raramente diagnosticado em adultos com
incidência de 0,5 casos a cada 1 milhão de indivíduos (Giordana et al. 1999). Não há nenhuma
diferença significativa da incidência de MB em diferentes etnias ou regiões geográficas,
contudo estudos são escassos nessa área (Makino et al. 2010). Características hereditárias são
o único fator de risco de incidência da doença, com nenhuma causa ambiental
comprovadamente ligada à doença. Diversas mutações germinativas em genes envolvidos em
vias de sinalização ligadas ao desenvolvimento e processos moleculares típicos da patogênese
do MB estão correlacionadas com sua incidência (Waszak et al. 2018).
Inicialmente, as manifestações clínicas da doença são pouco específicas como dores de
cabeça, perda de coordenação, náusea, vômito frequente pela manhã, fadiga e pior
desempenho escolar, o que pode estar ligado com um aumento de pressão intracraniana ou
efeito direto do tumor. Sintomas mais específicos incluem ataxia, dificuldades motoras e
problemas de visão como estrabismo (Wilne et al. 2007). O diagnóstico é realizado através de
uma biópsia do tumor juntamente com análises histopatológicas e moleculares. Essas análises
são essenciais para o diagnóstico visto que a fossa posterior é um local comum para outros
tumores que são radiograficamente similares ao MB, como astrocitoma pilocítico,
ependimoma, tumor teratóide rabdóide atípico (TTRA) e tumor embrionário com rosetas em
multicamadas. A biópsia é obtida durante a cirurgia de ressecção do tumor. Tradicionalmente o
diagnóstico é estabelecido de acordo com a avaliação histopatológica dividida em quatro
morfologias distintas: clássica, células grandes e anaplásicas (LCA), desmoplásico/nodular
(DE) e MB com extensiva nodularidade (Ellison 2010). A classificação atual da Organização
Mundial da Saúde (OMS) requer a combinação da definição histológica juntamente com
variantes geneticamente definidas em um diagnóstico integrado (Louis et al. 2021).
Terapias multimodais agressivas e inespecíficas (cirurgia, radioterapia e quimioterapia)
melhoraram significativamente a sobrevida de pacientes com MB (Crawford et al. 2007).
Regimes terapêuticos utilizados compreendem tipicamente a ressecção do tumor, irradiação
crânio-espinhal (ICS) em pacientes com mais de 3 anos e quimioterapia de combinação com
um subconjunto de agentes antineoplásicos, incluindo vincristina (antimicrotubular), cisplatina
(alquilante), ciclofosfamida (alquilante), carboplatina (baseado em platina) e/ou lomustina
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(alquilante) (Needle et al. 1997; Packer et al. 2006). Estima-se que um terço dos pacientes com
MB irão a óbito devido à doença (Polkinghorn & Tarbell 2007). A causa da maioria das mortes
relacionadas com MB são metástases leptomeníngeas e a recaída é um evento quase
uniformemente fatal, sem taxas de salvamento significativas (Ramaswamy & Taylor 2017).
Ressecção cirúrgica total seguido de ICS e quimioterapia adjuvante produzem os melhores
resultados na sobrevida de MB (Fig. 1) (Gajjar et al. 2006). As principais desvantagens deste
tratamento é sua alta toxicidade com sobreviventes apresentando efeitos colaterais
neurológicos e cognitivos graves de início tardio incluindo déficit neurocognitivo,
endocrinopatias e malignidades secundárias e é considerada muito debilitante
neurofisiologicamente especialmente para crianças menores de 3 anos (Packer & Vezina 2008;
Packer et al. 2013). Por consequência, estratégias terapêuticas buscaram evitar ou adiar a ICS
em pacientes mais jovens, contudo não apresentaram melhores resultados que o tratamento
convencional. Além disso, pacientes de diferentes subgrupos moleculares que receberam
terapias idênticas apresentam desfechos divergentes e imprevisíveis, o que abriu portas para
modelos de terapias de risco adaptativas específicas com o objetivo de reduzir a toxicidade
quando apropriado e manter ou melhorar o desfecho e a qualidade de vida dos pacientes
(Figura 2) (Gajjar & Finlay 2015).
Figura 2: Atual modelo de manejo molecular de risco adaptativo. Os ensaios clínicos recentes de MB
incorporaram uma estratificação de riscomolecular. No diagnóstico, pacientes passam pela ressecção da massa
tumoral, estadiamento com imagem de ressonância magnética cranial e espinhal e citologia do líquido
cefalorraquidiano lombar para avaliação e doença metastática. Em seguida, os pacientes são encaminhados a
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terapia pós-operatória estratificada com o modelo risco adaptativo. Pacientes WNT de risco intermediário
recebem taxas menores de irradiação crânio-espinhal. Pacientes SHH podem receber inibidores da via de SHH.
Quimioterapia convencional refere-se a agentes multimodais sistêmicos intravenosos. RT, ressecção total; RST,
ressecção subtotal; RQT, ressecção quase total; ICS, irradiação crânio-espinhal; Boost: irradiação focada no
sítio do tumor primário. Figura adaptada de (Northcott et al. 2019).
1.3 Aspectos Moleculares do Meduloblastoma
O surgimento dos estudos de sequenciamento genômico proporcionou um grande
aumento na compreensão das bases genéticas do câncer infantojuvenil e destacou as principais
diferenças entre tumores pediátricos e adultos. Enquanto a maioria dos tumores adultos é
caracterizada por um número elevado de mutações somáticas, os cânceres pediátricos
normalmente têm poucas mutações somáticas, e essas alterações ocorrem com alta frequência
em genes que codificam proteínas envolvidas na regulação epigenética (Huether et al. 2014).
Além disso, há uma heterogeneidade nos tipos de alterações genéticas que impulsionam o
crescimento dos tumores pediátricos, incluindo alterações nos números de cópia e fusões
gênicas (Sweet-Cordero & Biegel 2019).
O MB é uma classe heterogênea de tumores cerebelares embrionários que apresentam
aberrações em vias conhecidas por participarem de papéis críticos no desenvolvimento do
cerebelo (Taylor et al. 2012). Atualmente a classificação molecular de Tumores do SNC da
OMS reconhece quatro subgrupos de MB geneticamente distintos de MB: wingless (WNT),
sonic hedgehog (SHH), Grupo 3 e Grupo 4 (Louis et al. 2021). Esses subgrupos são
caracterizados por apresentarem diferenças transcricionais e genéticas específicas, bem como
implicações clínicas distintas (Tabela 1) (Kuzan-Fischer et al. 2017; Northcott et al. 2011). A
adição de terapias direcionadas estão atualmente sendo avaliadas em ensaios clínicos. Os
protocolos atuais tiveram um impacto nas taxas de sobrevida de até aproximadamente 70%,
embora os pacientes frequentemente sofram de sequelas graves que prejudicam muito sua
qualidade de vida adulta (Gajjar & Finlay 2015; Gajjar et al. 2006). Trabalhos recentes
avaliando dados de expressão e metilação identificaram uma certa heterogeneidade dentro dos
quatro subgrupos, assim, cada um dos subgrupos é ainda composto por outros subtipos, sendo
WNT com os subtipos alpha e beta, SHH por quatro subtipos de alpha a delta, e os subgrupos
3 e 4 com os subtipos alpha, beta e gamma cada. Esses subtipos também apresentam
características clínicas e moleculares específicas (Cavalli et al. 2017).
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Tabela 1. Características clínicas e genéticas dos subgrupos de MB.
WNT SHH Group 3 Group 4
% de Casos 10% 30% 25% 35%
Faixa Etária >4 anos <3 anos, >16 anos
<4 anos, <16 anos,
>16 anos
Todas idades
Propensão a
metástase
Baixa Baixa Alta Alta
Prognóstico Excelente Intermediário Ruim Intermediário
Marcadores
Sem alteração
recorrente
MYCN (12%), GLI1
(8%)
MYC (17%), PVT
(12%), OTX2 (8%)
SNCAIP (10%), MYCN
(6%), CDK6 (5%)
Histologia
Clássica, raramente
LCA
Desmoplásica, Clássica,
LCA
Clássica, LCA Clássica, LCA
Mutações
CTNNB1 (91%),
DDX3X (50%),
SMARCA4 (26%),
MLL2 (13%), TPS3
(13%)
TERT (60%), PTCH1
(46%), SUFU (24%),
MLL2 (16%), SMQ
(14%), TPS3 (13%)
SMARCA4 (11%),
MLL2 (4%)
KDM6A (13%), MLL2
(5%)
Aberrações
cromossômicas
Perda do 6
Ganho do 3q, perda do
9q, 10q e 14q
Ganho do 1q, 7, 17q
e 18q, perda do 8,
10q, 11, 16p, 17p
Ganho do 7, 17q e 18q,
perda do 8, 11p e X
Expressão Sinalização de WNT
Sinalização de SHH e
P13K
Assinatura de
MYC/retinal
Assinatura de
MYC/neuronal
Adaptado de (Rawal et al. 2020)
O subgrupo WNT representa aproximadamente 10% de todos os MBs sendo
caracterizado pela ativação da via de sinalização WNT. Apresenta o melhor prognóstico entre
todos os subgrupos, raramente é metastático ao diagnóstico (menos de 10% dos casos), com
taxa de sobrevida de cinco anos em pacientes pediátricos superior a 95% (Taylor et al. 2012).
Já o subgrupo SHH é definido pela ativação da via SHH e corresponde a aproximadamente
30% dos casos de MB. Mutações somáticas de PTCH1, SMO e SUFU e amplificações de GLI2
e MYCN são características desse subgrupo (Kool et al. 2014). Semelhante ao WNT, SHH é
igualmente distribuído entre os sexos (Taylor et al. 2012). O prognóstico para os pacientes
desse subgrupo é intermediário, com taxa de sobrevivência em cinco anos de 75% (Northcott
et al. 2012a). O MB de Grupo 3 representa um quarto de todos os casos de MBs e se
caracteriza por ter o pior prognóstico entre todos os subgrupos, com sobrevida inferior a 60%
em cinco anos. A metástase é identificada no diagnóstico de cerca de 45% de todos os
pacientes e raramente ocorre em adultos (Taylor et al. 2012). O Grupo 3 possui uma assinatura
de expressão gênica gabaminérgica (Northcott et al. 2012b) e foi proposto que esse tumor pode
surgir de células-tronco neurais multipotentes na zona ventricular do cerebelo embrionário que
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geram neurônios gabaminérgicos cerebelares. Amplificações de MYC (C-MYC) parecem ser
quase exclusivas para o MB de Grupo 3, com cerca de 17% de incidência (Shih et al. 2014). O
MB de Grupo 4 possui uma assinatura molecular glutamatérgica e alterações citogenéticas
recorrentes incluem amplificação de MYCN, amplificação de CDK6, perda do cromossomo 11
e duplicação do cromossomo cinco onde o gene SNCAIP está localizado. O Grupo 4 constitui
cerca de 35% dos casos, sendo o subgrupo mais frequente (Northcott et al. 2012b) com
sobrevida em cinco anos de 75% (Northcott et al. 2012a).
O padrão ouro para a classificação dos subgrupos moleculares do MB atualmente é
através do perfil de metilação do tumor, apesar de análises de expressão gênica também
apresentarem bons resultados. Ensaios baseados em imuno-histoquímica podem ser usados
para diferenciar MB do subgrupo WNT ou SHH de subgrupos não WNT/SHH (Hovestadt et
al. 2014). Alguns marcadores moleculares também podem auxiliar no diagnóstico diferencial
de MB de outros tumores do SNC com histologias similares, como a perda de função com
genes SMARCB1 e SMARCA4 que podeser averiguada via imunohistoquímica para
diferenciação de TTRA ou a averiguação da imunorreatividade de GFAP (marcador de células
da glia) para diferenciação de glioblastoma (Chiang & Ellison 2017; Panwalkar et al. 2017).
Comparado aos tumores adultos os tumores pediátricos apresentam menos mutações
somáticas sendo as alterações epigenéticas um fator preponderante nesses pacientes (Huether
et al. 2014). Na última década, a caracterização molecular de vários grupos de tumores
pediátricos têm contribuído para a classificação e o prognóstico de grupos de risco. Estas
informações revelaram alterações genéticas e epigenéticas importantes que desempenham um
papel relevante na iniciação, manutenção e/ou progressão tumoral (La Madrid & Kieran 2018).
As alterações herdáveis que não envolvem perturbações nas sequências de nucleotídeos são
chamadas coletivamente de epigenéticas. A regulação epigenética é mediada pela metilação do
DNA, modificações de histonas, remodelação da cromatina, microRNAs e LncRNAs (Sharma
et al. 2010). Um dos fenômenos epigenéticos mais bem caracterizados é a metilação do DNA,
que normalmente ocorre em ilhas CpGs na região promotora dos genes, resultando na
repressão transcricional (Esteller 2008), entretanto a metilação de apenas um sítio CpG
também pode ter um grande impacto na expressão de um gene, dependendo de sua localização
(Watt & Molloy 1988). A metilação do DNA é um mecanismo epigenético catalisado pela
família de DNA metiltransferases que transferem um grupo metil da S-adenil metionina
(SAM) para o quinto carbono de um resíduo de citosina para formar a 5mC (Figura 3) (Moore
et al. 2013). Este tipo de metilação regula a expressão gênica recrutando proteínas envolvidas
na repressão gênica ou inibindo a ligação de fatores de transcrição ao DNA. Arranjos de
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metilação de DNA de todo o genoma podem verificar 850.000 locais CpG em regiões gênicas
e também regiões não traduzidas. Esses arranjos não apenas ajudaram a confirmar os quatro
subgrupos principais de MB, mas também identificaram vários subtipos dentro deles
(Schwalbe et al. 2013). Essas análises sugerem que a metilação do DNA desempenha um papel
importante na patogênese do MB, reprimindo genes para evitar a diferenciação celular e a
morte celular, por exemplo (Dubuc et al. 2013).
Figura 3: Processo de metilação de DNA. As DNA metiltransferases 3a e 3b são responsáveis pela adição de
novo do grupo metil (vermelho) ao quinto carbono na dupla fita de DNA. Figura adaptada de (Moore et al. 2013).
Estudos iniciais usando conversão de bissulfito de DNA genômico seguido de
amplificação por reação em cadeia da polimerase revelaram vários genes supressores tumorais
silenciados por hipermetilação de promotores CpG-densos, incluindo CDKN2A, HIC1 e
RASSF1 em MB (Shahi et al. 2010, 2011). Outras abordagens identificaram genes reprimidos,
incluindo PTCH1, o regulador negativo da sinalização de SHH, a família SFRP, inibidores da
via de sinalização WNT e o repressor transcricional ZIC2, entre outros (Pfister et al. 2007).
Os achados relacionados à metilação aberrante de genes participantes da tumorigênese
e de vias relacionadas ao câncer, impulsionaram a utilização da metilação de DNA como
estratégia terapêutica no combate ao tumor. Algumas das primeiras drogas epigenéticas
propostas como terapia anticâncer foram inibidores da metilação do DNA, incluindo
5-azacitidina (5-aza-CR) e 5-aza-2′-desoxicitidina (5-aza-CdR). Esses agentes citotóxicos se
incorporam ao DNA e prendem as metiltransferases, moléculas responsáveis pela metilação do
DNA, prevenindo a atividade e restaurando a expressão de genes supressores de tumor
(Esteller 2008).
1.4 Assinaturas Gênicas Como Marcadores Prognósticos
Análises de sobrevida são um ramo da estatística que estuda o tempo em que um evento
ocorre, como a morte de um paciente ou sua recaída, e que leva em consideração eventos
faltantes (dado censurado). O tempo em que um evento clínico ocorre pode estar
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correlacionado com algumas variáveis explicativas também chamadas de covariáveis e quando
elas são identificadas e validadas, elas podem ser classificadas como um marcador prognóstico
caso o evento em questão esteja ligado a sobrevida do paciente. Historicamente alguns
métodos são comumente empregados em análises que utilizam dados de sobrevida. Destaca-se
o método de Kaplan-Meier que estima curvas de sobrevida para cada grupo de pacientes de
interesse, como tratado contra não-tratado ou caso contra controle. Se a variável de interesse
for quantitativa, como a expressão de um gene, os grupos de pacientes podem ser separados de
diferentes formas, seja pela média, mediana, quartis da expressão ou com formas mais
rebuscadas como o método de classificação maximamente selecionado, também conhecido
como Kaplan-Scan (Kaplan & Meier 1958; Lausen & Schumacher 1992). Tradicionalmente, o
teste log-rank é utilizado para comparar as curvas definidas (Mantel 1966). O modelo de Cox
também é amplamente utilizado em estudos de medicina, por exemplo, na avaliação da eficácia
de tratamentos (Figura 4). Esse modelo não é sempre realístico ou intuitivo. Ele é
relativamente difícil de se interpretar quando há mais de uma covariável, pois covariáveis
atuam multiplicativamente na função de risco. A função de risco representa a taxa de risco de
um grupo em função do tempo e pode ser comparada com o risco de um segundo grupo, assim
sendo obtida da razão de risco (HR), que representa quantas vezes o risco de um grupo é maior
ou menor que o risco do segundo grupo de pacientes. O coeficiente de um preditor nesse tipo
de regressão é estimado através da máxima verossimilhança parcial, que busca encontrar um
valor para o coeficiente que maximiza a chance de se observar o dado de sobrevida associado.
Na regressão de Cox multivariada, o modelo ajusta o efeito de múltiplas variáveis preditoras
simultaneamente. Os coeficientes associados a cada preditor indicam a proporção da alteração
na razão de risco para cada mudança em uma unidade do preditor correspondente, enquanto
mantém os outros preditores constantes. Esse ajuste permite acessar o efeito de cada preditor
no desfecho de forma independente, controlando a influência dos outros preditores, assim
permitindo uma avaliação mais acurada das relações entre as variáveis preditoras e o evento
(Cox 1972).
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Figura 4: Métodos utilizados no desenvolvimento de assinaturas gênicas prognósticas. O método de
Kaplan-Scanbusca a separação ótima ao considerar múltiplas possibilidades e escolhendo a separação mais
significativa entre os grupos. A separação por quartis utiliza um corte arbitrário para a separação dos grupos,
como separações com a mediana ou cortes em 25% e 75% da distribuição. A regressão de Cox é a abordagem
mais flexível, pois não faz nenhuma suposição de como a expressão gênica e o tempo de sobrevida segregam.
Figura adaptada de (Raman et al. 2019).
Predizer o prognóstico do paciente é crucial para o manejo do câncer e ainda é um
desafio devido a limitações de parâmetros clinicopatológicos. Em pacientes com MB, sua
estratificação é tradicionalmente dividida entre risco intermediário ou alto de acordo com a
apresentação clínica do paciente, junto a parâmetros como a quantidade de doença residual
pós-cirurgia definitiva, categorização histopatológica ou características moleculares do tumor
(Massimino et al. 2016). Por exemplo, os grupos histológicos clássico e de células
grandes/neoplásicas são associadas com um pior prognóstico, enquanto desmoplásico/nodular
e MB com extensiva nodularidade com melhor prognóstico. Os parâmetros mais
significativamente associados com a sobrevida de pacientes com MB dentre dezenas de
características clínicas, características demográficas e dados sobre o tratamento são: o grupo
histológico do tumor, subgrupos moleculares, ressecção total do tumor e radioterapia (Liu et
al. 2022). Contudo, ainda há divergência entre diferentes estudos que apontam quimioterapia e
MB metastático como também associados a sobrevida (Kocakaya et al. 2016; Rutkowski et al.
2010).
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Assinaturas prognósticas de expressão gênica são uma ferramenta adicional para a
classificação de pacientes em diferentes grupos de risco, favorecendo uma decisão de
tratamento personalizada. Seu primeiro exemplo em câncer foi desenvolvido em 2002, com
uma assinatura de 70 genes capazes de classificar pacientes entre bom prognóstico ou ruim
(van de Vijver et al. 2002). Essa primeira assinatura foi futuramente validada em diversos
estudos retrospectivos e por fim em um estudo clínico prospectivo de fase III, o ensaio
MINDACT. Atualmente é utilizada como uma prática rotineira para guiar decisões do uso de
quimioterapia adjuvante em câncer de mama precoce (Cardoso et al. 2016). Isso só foi possível
com o advento da tecnologia de microarranjo em 1995, capaz de avaliar a expressão de
milhares de genes em uma única rodada (Schena et al. 1995). Desde então, essa tecnologia
revolucionou a pesquisa genética com diversas assinaturas gênicas sendo desenvolvidas e
usadas de diversas maneiras, tanto na classificação de tipos tumorais, como também no
reconhecimento de seus diferentes estados e predizendo seu prognóstico. Apesar de seu grande
número, poucas assinaturas gênicas foram introduzidas em práticas clínicas e menos ainda
foram minuciosamente validadas. Transladar perfis moleculares com sua potencial aplicação
clínica e integrá-la nas tomadas de decisão do tratamento para enfim aperfeiçoar a terapia e a
sobrevida dos pacientes permanece o principal desafio. Para melhor entender esse problema,
um modelo prognóstico molecular pode ser avaliado em sete diferentes níveis, desde a prova
de conceito até a relação custo-efetividade de sua aplicação (Tabela 2).
Tabela 2. Critérios baseados em evidências para uma assinatura gênica prognóstica desde o
laboratório até a prática clínica.
Níveis Conceito Elaboração
1 Prova de conceito
Os níveis da assinatura diferem substancialmente entre pacientes com diferentes
desfechos?
2 Validade Analítica A acurácia e a confiabilidade da medição do dado de interesse entre e dentro dos
laboratórios
3 Validade Clínica A assinatura prediz o desfecho em múltiplas coortes independentes ou em estudos
aninhados caso-controle/caso-coorte?
4 Valor incremental
A assinatura adiciona informações suficientes aos marcadores prognósticos
clínico-patológicos estabelecidos ou fornece uma medida mais reprodutível de um
deles?
5 Impacto Clínico A assinatura prediz o risco suficientemente para alterar a terapia recomendada?
6 Utilidade Clínica
O uso da assinatura melhora o resultado clínico, especialmente quando usado
prospectivamente para decisões de tratamento em um estudo randomizado
controlado?
7 Custo-efetividade O uso da assinatura melhora o resultado clínico o suficiente para justificar os custos
adicionais de testes e tratamento?
Adaptado de (Michiels et al. 2016).
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Nesta dissertação, focamos no desenvolvimento e validação de um modelo prognóstico
abordando os quatro primeiros níveis conceituais e seus desafios. Um dos principais problemas
do ponto de vista estatístico é a alta taxa de falso positivo, visto que o número de potenciais
preditores testados (genes) tende a ser extremamente maior que o número de pacientes
acompanhados. Um método para abordar esse problema de redução de variáveis e regularizar a
solução é a regressão de LASSO (Least Absolute Shrinkage and Selection Operator),
comumente utilizada visto que covariáveis (genes) com pouca ou sem nenhuma ligação com o
evento em questão terão seus coeficientes estimados para zero e serão excluídos do modelo. O
método possui um parâmetro de regularização/ajuste chamado lambda (λ), que quanto maior,
menos covariáveis serão selecionadas e é definido comumente através do processo de validação
cruzada (Figura 5) (Tibshirani 1997; van Houwelingen et al. 2006). Diversos estudos já
mostraram o potencial do método de LASSO na construção de modelos prognósticos em
diversos tipos de câncer como adenocarcinoma de pulmão (Li et al. 2019), câncer gástrico
(Jiang et al. 2018), câncer pancreático (Wu et al. 2019), carcinoma hepatocelular (Liu et al.
2020) e também em MB (Li et al. 2020; Wang et al. 2020c; Zhu et al. 2020b). O estudo de Li e
colaboradores publicado em maio de 2020 foi o primeiro a começar a investigar esse tipo de
assinatura em MB, desenvolvendo um modelo prognóstico de 13 genes com um grande
conjunto de dados para construção da assinatura e um segundo para validá-la, ambos conjuntos
também utilizados no nosso trabalho. Posteriormente em setembro de 2020, Zhu e
colaboradores publicaram uma assinatura de 12 genes metodologicamente muito semelhante
ao modelo construído no estudo anterior por Li. Também em maio de 2020, Wang e
colaboradores publicaram um modelo prognóstico de dois genes, dessa vez utilizando dois
pequenos conjuntos de dados (69 e 28 amostras), contudo não utilizaram o método de LASSO
durante o desenvolvimento (Wang et al. 2020c).
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Figura 5: Processo de validação cruzada. Nesse processo os dados são divididos em K grupos e iterado K
vezes. Para cada grupo, os restantes K - 1 grupos são utilizados para seleção dos marcadores mais preditivos que
compõema assinatura e seus coeficientes. Então, o grupo excluído inicialmente é utilizado para testar o modelo.
Em cada iteração um conjunto de valores do parâmetro de ajuste (λ no caso da regressão de LASSO) são testados.
O parâmetro de ajuste de melhor performance nos K conjuntos de validação será o utilizado para gerar o modelo
final, contudo diversas estratégias podem ser empregadas para a seleção do modelo final a depender do contexto
do problema. Figura adaptada de (Michiels et al. 2016).
1.5 Justificativa
Estima-se que um terço dos pacientes com MB irão a óbito devido à doença, em
especial os pacientes dos subgrupos SHH, Grupo 3 e Grupo 4 (Polkinghorn & Tarbell 2007).
Marcadores prognósticos convencionais da doença apresentam correlações limitadas e pouco
confiáveis com o prognóstico de MB, constituindo um dos principais desafios para a melhora
clínica desses pacientes (Kocakaya et al. 2016; Liu et al. 2022; Rutkowski et al. 2010). O
desenvolvimento de modelos prognósticos moleculares capazes de predizer o desfecho dos
pacientes, tempo de sobrevida e caracterizar molecularmente a doença é um passo essencial
para tratamentos mais precisos que objetivam aperfeiçoar o tratamento e a melhora clínica dos
pacientes. Assim, essa pesquisa tem o intuito de construir e validar uma assinatura gênica
como um novo modelo prognóstico para pacientes com MB.
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2 · OBJETIVO
2.1 Objetivo Geral
● Desenvolver e validar uma assinatura gênica de valor prognóstico em
meduloblastoma.
2.2 Objetivos Específicos
● Identificar genes regulados via metilação em MB;
● Identificar quais genes regulados via metilação em MB possuem relação com o
prognóstico da doença;
● Construir uma assinatura gênica de valor prognóstico composta por genes
regulados via metilação em MB;
● Avaliar a capacidade prognóstica da assinatura proposta em conjuntos quatro de
dados independentes de MB;
● Avaliar a capacidade da assinatura dentre cada subgrupo molecular de MB;
● Avaliar o capacidade da assinatura como um marcador prognóstico
independente;
● Caracterizar pacientes em alto e baixo risco utilizando a assinatura;
● Identificar genes diferencialmente expressos entre pacientes de alto e baixo
risco;
● Analisar funcionalmente as vias e processos biológicos ligados aos genes
diferencialmente expressos entre pacientes de alto e baixo risco.
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3 · CAPÍTULO I
CAPÍTULO I
Artigo: A Prognostic Methylation-Driven Two-Gene
Signature in Medulloblastoma
Escrito por: Gustavo Lovatto Michaelsen, Lívia dos Reis Edinger da Silva, Douglas Silva de
Lima, Mariane da Cunha Jaeger, André Tesainer Brunetto, Rodrigo Juliani Siqueira Dalmolin,
Marialva Sinigaglia.
Artigo Submetido ao Periódico Journal of Cancer Research and Clinical Oncology
A Prognostic Methylation-Driven Two-Gene Signature in
Medulloblastoma
26
A Prognostic Methylation-Driven Two-Gene
Signature in Medulloblastoma
Gustavo Lovatto Michaelsen1,3,7, Lívia dos Reis Edinger da Silva1,5, Douglas Silva de Lima1,6,
Mariane da Cunha Jaeger1,7, André Tesainer Brunetto1,2,7, Rodrigo Juliani Siqueira Dalmolin3,4,
Marialva Sinigaglia1,3,7*
1Children's Cancer Institute, Porto Alegre, RS, 90620-110, Brazil;
2Cancer and Neurobiology Laboratory, Experimental Research Center,
Clinical Hospital (CPE-HCPA), Federal University of Rio Grande do Sul, Porto Alegre, RS, 90035-003, Brazil;
3Bioinformatics Multidisciplinary Environment-BioME, Digital Metropole Institute, Federal University of Rio Grande do Norte,
Natal, RN, 59076-550, Brazil;
4Department of Biochemistry, Federal University of Rio Grande do Norte, Natal, RN, 59064-741, Brazil;
5Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, 90050-170, Brazil;
6Institute of Basic Health Sciences, Federal University of Rio Grande do Sul, Porto Alegre, RS, 90035-003, Brazil;
7National Science and Technology Institute for Children’s Cancer Biology and Pediatric Oncology – INCT BioOncoPed, Porto
Alegre, RS, 90035-003, Brazil.
*Corresponding author. E-mail: msinigaglia@ici.ong. ORCID: 0000-0002-8324-6860.
Contributing authors. ORCID: GLM, 0000-0002-9433-0822; MCJ, 0000-0002-3465-4083; ATB,
0000-0002-7958-1279; RJSR, 0000-0002-1688-6155.
Abstract
Purpose Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that
one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and
unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement.
Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic
model for patients with the disease.
Methods In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA
methylation data from primary MB as well as control samples from healthy cerebellum. We identified
methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We
employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model.
Results Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for
MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with
Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves
predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as
an independent prognostic biomarker compared to traditional MB markers.
Conclusion The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high
and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as
promising prognostic biomarkers with potential application for MB treatment.
Keywords: medulloblastoma · prognostic biomarker · DNA methylation · precision medicine
Acknowledgments
The authors deeply acknowledge and thank the High-Performance Computing Center (NPAD/UFRN)
for the assistance and infrastructure on data processing and analyses.
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1 Introduction
Medulloblastoma (MB) is a heterogeneous class of embryonic cerebellar tumors that exhibit aberrations
in pathways known to play critical roles in cerebellar development (Taylor et al. 2012). Currently, the World
Health Organization molecular classification of central nervous system tumors recognizes four genetically distinct
MB subgroups: wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 (Louis et al. 2021). These
subgroups are characterized by having specific transcriptional and genetic differences as well as distinct clinical
implications (Northcott et al. 2011; Kuzan-Fischer et al. 2017).
MB is one of the most frequent pediatric brain tumors, corresponding to 8.3% of primary brain and other
central nervous system tumors in children (age 0-14 years) (Ostrom et al. 2018). Total surgical resection followed
by cranial-spinal irradiation and adjuvant chemotherapy yields the best results in MB survival (Gajjar et al. 2006).
However, it is estimated that still, one-third of MB patients will die from the disease (Polkinghorn and Tarbell
2007). Furthermore, patients from different molecular subgroups who received identical therapies have divergent
and unpredictable outcomes, therefore it is crucial the development of methods capable of stratifying patients' risk
and adaptive risk therapies aimed at reducing toxicity when appropriate thus improving patients' outcome and
quality of life (Gajjar and Finlay 2015). Traditionally, MB patients are categorized into risk groups based on their
clinical presentation and conventional prognostic markers. However, manyof those markers show limited and
unreliable association with disease outcome (Kocakaya et al. 2016; Liu et al. 2022). Prognostic gene expression
signatures are an additional tool for classifying patients into different risk groups, favoring a personalized
treatment decision. Therefore, the construction of an accurate prognostic model that is highly effective and widely
applicable would support the prognostic prediction of patients diagnosed with MB.
Epigenetics, especially DNA methylation, is now known as a universal feature of tumorigenesis (Shen
and Laird 2013) and can act as an early diagnostic and prognostic biomarker (Church et al. 2014; Long et al.
2019). In MB, the methylome is the gold standard for subgroup classification and it is even capable of identifying
subtypes within each subgroup (Schwalbe et al. 2013; Hovestadt et al. 2013). Also, several tumor suppressor
genes silenced by hypermethylation of CpG-dense promoters have been identified in MB, including CDKN2A,
HIC1, RASSF1, and the negative regulator of SHH signaling PTCH1 (Shahi et al. 2010, 2011).
In this study, we integrated DNA methylation and gene expression data to identify MDGs in MB and
developed a methylation-driven 2-gene signature (GS-2) that can consistently and accurately stratify MB patients
into low and high-risk groups. The results were validated in three independent data sets and its biological
functions investigated. The GS-2 genes stand out as promising prognostic biomarkers with potential application
for MB treatment.
2 Methods
2.1 Patient Cohorts and Data Acquisition
All datasets were acquired from Gene Expression Omnibus (GEO) (Edgar et al. 2002). MB datasets
GSE85212 (Illumina HumanMethylation450 BeadChip, GPL13534) and GSE85217 (Affymetrix Human Gene
1.1 ST Array, GPL22286) contain 763 paired samples of DNA methylation data and gene expression data
respectively. Gene expression datasets GSE202043 (Affymetrix HT Human Genome U133A Array, GPL3921),
GSE37418 (Affymetrix Human Genome U133 Plus 2.0 Array, GPL570) and GSE201583 (Affymetrix HT Human
Genome U133A Array, GPL3921), were also acquired for validation purposes. Ten samples of normal pediatric
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cerebellum from GSE148388 (Infinium MethylationEPIC, GPL21145) were used as DNA methylation controls
(Table 1). Any additional data were retrieved from their respectives GEO pages or from corresponding
publications (Tamayo et al. 2011; Robinson et al. 2012; Cavalli et al. 2017).
Table 1 Summary of study cohorts.
Training Set Validation Set 1 Validation Set 2 Validation Set 3 DNAm Tumor DNAm Control
Data Type mRNA mRNA mRNA mRNA DNAm DNAm
GEO ID GSE85217 GSE202043 GSE37418 GSE201583 GSE85212 GSE148388
Platform ID GPL22286 GPL3921 GPL570 GPL3921 GPL13534 GPL21145
Included Samples 607 134 67 105 756 10
Overall Survival
Mean, SD (y) 4.98, 3.79 4.31, 2.91 3.66, 1.89 4.53, 2.70 -- --
Alive/Dead 449/158 99/35 55/12 77/28 -- --
Histology
Classic 337 90 45 71 -- --
DE 85 22 5 15 -- --
LCA 62 16 15 16 -- --
MBEN 14 0 2 0 -- --
Subgroup
WNT 62 NA 8 NA -- --
SHH 171 NA 9 NA -- --
Group 3 112 NA 16 NA -- --
Group 4 262 NA 32 NA -- --
Metastasis
(Yes/No) 160/373 19/105 NA 16/83 -- --
DNAm, DNA methylation; mRNA, messenger RNA; GEO, Gene Expression Omnibus; SD, Standard Deviation; y, years; DE,
Desmoplasic/nodular; LCA, Large-Cell Anaplastic; MBEN, Medulloblastoma with Extensive Nodularity; SHH, Sonic Hedgehog; WNT,
Wingless, NA; Not Available; --, not applicable. All included cases in the training and validation sets have overall survival information, but
not necessarily for other clinical characteristics. All patients samples included in the training set are also present in the methylation tumor set,
but of different data types (RNA expression and DNA methylation).
2.2 Data Processing
All outlier cases from RNA expression datasets detected with the quality control R/Bioconductor
package "arrayQualityMetrics" were excluded (Kauffmann et al. 2009). Afterward, normalization and processing
were conducted with the "oligo" R/Bioconductor package (Carvalho and Irizarry 2010). For genes detected with
multiple probe sets, the set with the highest standard deviation was selected for analysis. Gene symbols from all
platforms were transformed into official gene symbols with the "alias2Symbol" function from the R/Bioconductor
package "limma" (Ritchie et al. 2015). Only probe sets mapping to genes present on all analyzed platforms were
included.
DNA methylation data quality control, probe filteringand normalization were accomplished with the
R/Bioconductor package "minfi" (Aryee et al. 2014). For normalization, "preprocessFunnorm" were used. Probes
with detection p > 0.01, located at sexual chromosomes, with high polymorphism rate, or high
cross-hybridization were filtered. Only probes mapped to gene promoter regions (TSS1500, TSS200, 5’UTR, and
1st exon) were kept. Since GSE85212 and GSE148388 are from different platforms, the function
"combineArrays" was utilized to merge them together.
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2.3 Screening for Methylation-Driven Genes
Identification of MDGs was achieved by integrating DNAm and mRNA sequencing data from
GSE85217 and GSE85212 paired samples and GSE148388 DNAm control samples using the "MethylMix"
R/Bioconductor package (Gevaert 2015). MethylMix integrates DNA methylation from normal and tumorous
samples and matched mRNA expression data via a probe clustering step followed by a three-step algorithm. The
methylation data produced by the Illumina 450k methylation array consists of multiple CpG probes for each gene.
Since the probes are highly correlated, they are clustered prior to a learning mixture model. A complete linkage
hierarchical clustering algorithm for all probes of a single gene clusters the probes into CpG clusters. A Pearson
correlation threshold of 0.7 was used as a cut-off of the hierarchical tree. With this method, a single gene can have
up to dozens of clusters, each cluster consisting of one up to a few of dozen of methylation probes. After
clustering, MethylMix's first step requires that a CpG cluster possesses a significant effect on its corresponding
gene mRNA expression in order for that gene to be considered a methylation-driven gene. Next, the methylation
states of a cluster are identified using a univariate beta mixture modeling approach to identify subgroups of
patients with similar DNA methylation levels (Ji et al. 2005). Lastly, hyper and hypomethylated genes are defined
relative to control by comparing the methylation levels of each methylationstate to the mean of the DNA
methylation levels of normal tissue samples using a Wilcoxon rank sum test (p-value < 0.05).
2.4 Construction of Prognostic Signature
The prognostic signature was constructed with the training set (Table 1). First, MDG were subjected to
univariate Cox regression analysis and only genes with (p < 0.05) were kept for the next step. We then used the
least absolute shrinkage and selection operator (LASSO) regression analysis to construct the prognostic gene
signature with the "glmnet" R package (Tibshirani 1997; Simon et al. 2011). LASSO was conducted through a
1000 iteration 5-fold cross-validation. The partial likelihood deviance was used to evaluate the models' error. For
each iteration, a thousand lambdas (λ) evenly spaced between 0 and 1000 were checked and the λ 1se was
selected (largest value of λ such that error is within 1 standard error of the minimum error). Duplicated λ 1se were
removed. Each non-duplicated λ 1se selected had its accuracy checked in all validation datasets by the area under
the curve (AUC) mean from time-dependent receiver operator characteristic (ROCs) curves for 1, 3, and 5 years
(Kamarudin et al. 2017). The gene signature that appeared most times between the non-duplicated λ 1se was the
final signature selected and the final λ used belongs to the highest accuracy one of these models. A risk score (RS)
for each patient was determined using the coefficients (β) from the LASSO regression final model and the mRNA
expression of all genes present in the signature of that model as shown in the formula: RS = βgene (1) × mRNA
expression of gene (1) + βgene (2) × mRNA expression of gene (2) +⋯+ βgene (n) × mRNA expression of gene
(n). High-risk and low-risk patient groups were divided with the “maximally selected rank statistics method” for
each dataset (Lausen and Schumacher 1992).
2.5 Gene Signature Validation
To validate the RS, we used Kaplan-Meier followed by a Log-rank test and ROCs in the training and
validation datasets (Table 1). To identify if the signature depends on other clinicopathological factors, such as
tumor histology, molecular subgroups, metastasis, and age in predicting overall survival (OS), we performed
multivariable Cox regression analyses using the R package "survival".
2.6 Comparisons Between MB Subgroups or Chromosomal Configurations
Comparisons between MB subgroups or chromosomal configurations utilizing the RS or mRNA data
were made by pairwise comparison with Student's t-test. Adjusted p-values were acquired with
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Benjamine-Hochberg (BH) correction (Benjamini and Hochberg 1995). Log-rank test was used to compare
Kaplan-Meier curves. Only the training set, GSE85217, was analyzed in these steps.
2.7 Differentially Expressed Gene Analysis and Enrichment Analysis
We used the "limma" R/Bioconductor package (Ritchie et al. 2015) for differently expressed genes
identification between high and low-risk patients according to the RS proposed. Genes with ( | log2 fold change |
> 0.8 and BH adjusted p-value < 0.05) were considered differentially expressed. Kyoto Encyclopedia of Genes
and Genomes (KEGG) analyses, Gene Ontology (GO), and MSigDB oncogenic signatures gene sets enrichment
analyses were performed with the "Genekitr" and "clusterProfiler" R/Bioconductor packages (Yu et al. 2012).
Significantly enriched categories were determined by BH p-value < 0.05. "RedeR" Bioconductor/R package was
used for GO enriched process visualization (Castro et al. 2012).
3 Results
3.1 Methylation-Driven Genes in Medulloblastoma
Employing 756 MB samples of mRNA expression and DNA methylation together with 10 normal
pediatric cerebellum samples as control, we found a total of 2691 hyper or hypomethylated DNA methylation
clusters with transcriptionally predictive potential, mapping to 2213 genes called methylation-driven genes
(MDGs) (Table S1) of a total of 11133 genes evaluated. Function enrichment analysis of MDGs (p < 0.01)
revealed biological pathways related to glutamatergic and GABAergic synapses, morphine and alcoholism
addictions, and TGF-beta signaling (Fig. S1). Biological processes were related to the development and
morphogenesis of sensorial systems, but mainly the visual system. Also relations to neuronal processes such as
axogenesis, neural projection extension, and axon guidance (Fig. S2).
3.2 Construction of MDG-Based Signature
To assess which 2213 MDGs had a relationship with OS, univariate Cox regression analyses were
performed resulting in 474 MDGs significantly associated with OS (p < 0.05) (Table S2). Both the identification
of MDGs and their association with OS are filtration steps needed previous to machine learning. LASSO
regression, a supervised statistical machine learning method, was performed with these 474 MDGs genes to
construct the prognostic gene signature. Through a 1000 iteration 5-fold cross-validation with the training dataset
(GSE85217), the regularization parameter λ 1se was selected from each iteration (Fig. 1A). Only 57
non-duplicated λ 1se were kept. The gene signature that appeared most times between non-duplicated λ 1se (22
times) was the final signature selected, composed by CEMIP and NCBP3 (Table S3). The final λ used (λ =
0.1121, Fig. 1B) belongs to the highest accuracy one of these 22 models. CEMIP had two methylation clusters
with the capability to predict its mRNA expression, one hypermethylated and one hypomethylated (Figs. 1 C-D),
while NCBP3 had one hypomethylated (Fig. 1E). Both genes had a negative correlation between their mRNA
expression and methylation levels (Fig. 1 F-H). All clusters had only one methylation probe (Table 2). The RS of
each patient was defined as the sum of the products of the RNAm expression of both MDGs and their
corresponding coefficients from the LASSO regression model as follows: RS = ( -0.0687 * CEMIP expression ) +
( -0.0643 * NCBP3 expression ). Negative coefficients of these genes implied their upregulation represented a
good OS, therefore being protective genes. Indeed, patients classified as high-risk according to the RS had worse
OS compared with low-risk (Fig. 2A) and the model was capable to predict with high accuracy their survival
outcome with ROCs for 1, 3, and 5 years of 0.772, 0.732, 0.712 AUCs respectively (Fig. 2E).
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Fig. 1 A Representation of one iteration of the cross validation step. The partial likelihood deviance was used to evaluate each model in
function of λ. The left dashed line is at the λ min (λ with the lowest partial likelihood deviance mean). The right dashed line is the selected λ
1se (largest value of λ such that error is within 1 standard error of the λ min). B LASSO coefficient profiles during regularization. With the
selected lambda of 0.11, only two genes present coefficients different than zero (CEMIP and NCBP3). Distribution of DNAm for C CEMIP
(methylation cluster A), D CEMIP (methylation cluster B), and E NCBP3 methylation cluster. Correlation of mRNA expression and DNAm
levels of F CEMIP (methylation cluster A), G CEMIP (methylation cluster A), and H NCBP3 methylation cluster.
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Table 2 Signatures MDGs general information.
CEMIP Cluster A CEMIP Cluster B NCBP3 Cluster
DNAm Probe ID cg11028052 cg23895729 cg10380345
mRNA Probe Set ID 7985317 7985317 8011462
DNAm Probe Location 5'UTR 5'UTR 1stExon
Correlation mRNA-DNAm
(Rho)
-0.437 -0.477 -0.326
DNAm Probe Location is relativeto its mapped gene; Rho, Pearson correlation coefficient.
3.3 Validation of the GS-2
The predictive value and robustness of the GS-2 were investigated in the validation sets 1, 2, and 3.
Survival analysis showed that the RS model proposed can significantly predict the OS of the patients in all
validation sets. AUCs for 1, 3, and 5 years of OS prediction were all higher than 0.65 in all datasets (Fig. 3 A-D),
with a mean between them all of 0.789. The GS-2 had better performance predicting the OS at 1 year and lost
performance at the higher time points with a mean accuracy of 0.836 in 1 year down to 0.756 at 5 years.
Kaplan-Meier curves showed that patients classified in the high-risk group by the GS-2 had the worst OS
compared with patients classified in the low-risk group in all validation sets (Fig. 3 E-H).
Fig. 2 Time-dependent ROC curves at 1, 3, and 5-years for A training set, B validation set 1, C validation set 2, and D validation set 3.
Kaplan-Meier curves between high and low-risk patients classified by the GS-2 for E training set, F validation set 1, G validation set 2, and
H validation set 3.
3.4 Prognostic Value of GS-2 as an Independent Marker
To assess the independent prognostic value of the GS-2, we performed multivariable Cox regression
analysis using the RS, molecular subgroups and traditional clinical prognostic markers such as metastasis and
histological groups with the training set and validation sets 1 and 3 (Fig. 3). The validation dataset 2 was not used
in this analysis due to information unavailability on metastasis status and its low number of events (n = 12). In all
datasets analyzed, the RS was strongly a positive risk factor for OS of MB patients. The RS presented very
33
significant p-values (p < 0.005) in all datasets with hazard ratios (HRs) and corresponding 95% confidence
intervals (95% CIs) of 2.33 (1.45 - 3.7) and 2.15 (1.31 - 3.5) in the validation sets. Only the histological group
LCA in the training set showed significant results compared to the classical histological group. Regarding
continuous variables, the HR is expressed as relative hazard per standard deviation unit change in the predictor
variable. As with the RS, an HR of 2.33 would indicate that if a patient had an RS one standard deviation higher
than another patient, he would have 2.33 times the risk of the event occurring.
Fig. 3Multivariable Cox Regression Analyses for A training set, B validation set 1 and C validation set 3.
3.5 Comparison of GS-2 Between MB Subgroups
Given the increasing application of MB subgroups into clinical practices, we investigated the RS and the
expression of GS-2 genes between MB subgroups as well as how the RS performs within each subgroup (Fig. 4).
The subgroups' RS distributions were statistically different in all six pairwise comparisons. Particularly among the
subgroups, the WNT subgroup exhibited the most distinct distribution with the lowest RS mean as expected due
to its historically more favorable prognosis. Consequently, CEMIP and NCBP3 expression was higher in the
WNT subgroup. Group 3 MB displayed the highest RS mean within the subgroups caused by its particularly low
expression of CEMIP, being the only subgroup with more patients characterized as high-risk than low-risk.
Additionally, Kaplan-Meier curves showed poor separations between high and low-risk Group 3 patients, while
SHH and Group 4 subgroups exhibited very good patient separations (Fig. 4 D-F). The WNT subgroup had only
one patient classified as high-risk, making it not suitable for this analysis. Since NCBP3 is located at chromosome
17p, the chromosome arm with the highest rate of abnormalities in MB (Biegel et al. 1997), we also evaluated its
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expression in different 17p chromosomal configurations (Fig. S3). Its mRNA expression was different across all
chromosomal configurations and increased accordingly to arm 17p copy number.
Fig. 4 Pairwise comparisons between the MB subgroups with the training set using A the RS developed, B CEMIP mRNA expression,
and C NCBP3 mRNA expression. Triangles are alive patients, (X) are dead patients, blue are high-risk patients and red are low-risk
patients. Bars shown: significant. Kaplan-Meier curves between high and low-risk patients for D SHH, E Group 3, and F Group 4
subgroups.
3.6 Differentially Expressed Gene and Functional Analyses
To evaluate the biological differences between patients classified as high and low-risk by the GS-2, we
performed differentially expressed gene analyses comparing these two groups utilizing the training set. A total of
167 DEGs were encountered (Table S4). KEGG enrichment analysis revealed eight biological pathways
over-represented, primarily synapse-related pathways such as GABAergic synapse, dopaminergic synapse, and
retrograde endocannabinoid signaling (Fig. 4A). Two oncologic signature gene sets from the Molecular
Signatures Database (MSigDB) also enriched (Fig. 4B). GO biological processes were related to neurological
functions, visual detection/perception, ion transport/development and morphogenesis of sensorial systems, mainly
the visual system (Fig. 4C).
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Fig. 5 Function analyses of DEGs between high and low-risk patients. A KEGG enrichment analysis. BMSigDB oncologic signatures
enrichment analysis. C GO biological processes enrichment analysis.
4 Discussion
Several conventional prognostic markers exhibit limited and unreliable association with MB prognosis,
including metastatic disease and histological groups. While some studies suggest they are associated with
prognostic parameters (Zeltzer et al. 1999; Rutkowski et al. 2010), others point to the opposite (Kocakaya et al.
2016; Liu et al. 2022), indicating that these markers may not be reliable predictors of MB prognosis. Due to the
limitations of clinicopathological parameters, prognostic gene signatures can help improve patients' therapy by
enhancing risk stratification, thus guiding a personalized treatment decision. Epigenetics, especially DNA
methylation, is now known as a universal feature of tumorigenesis (Shen and Laird 2013). The identification of
MDGs brought a new horizon in the exploration of the biological effects of this modification and it has been
widely used to discover potential biomarkers in cancer, such as renal clear cell carcinoma, breast, pancreatic and
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lung cancers (Wang et al. 2019a; Zhu et al. 2020a; Kuang et al. 2020; Xiao et al. 2022). In this study, we
developed a gene signature based on two MDGs and averred its outstanding prognostic value in MB.
We used a total of four independent MB datasets with different purposes during model development. The
largest dataset, GSE85212, was used as training set to fit the prognostic model. The other three datasets were used
as validation sets, specifically for hyperparameter tuning and model selection. Although we did not use any
dataset exclusively to test the model's performance, we evaluated it in all four independent sets with promising
results. Previous studies in MB that constructed prognostic gene signatures have used only one independent
validation dataset or subsetting a main set into two smaller ones for training and validation (Wang et al. 2020c; Li
et al. 2020; Zhu et al. 2020b). Evaluation of a prognostic model in multiple different cohorts is a crucial step given
its potential intrinsic biases and the heterogeneity of the disease (Cooney et al. 2023). Ensuring the model's ability
is generic enough to perform well in different population samplings without a meaningful performance loss is
decisive to establish a strong prognosticmarker. While testing in four different datasets cannot ensure good results
in any given scenario, it still represents a step forward in the development of robust prognostic models. We further
confirmed it as an independent predictor when a variety of conventional prognostic factors were considered
simultaneously. Also opting for a smaller number of genes to form the signature makes it less likely to overfit in
the training set, and easier to interpret and apply in clinical practices. On the other hand, a large number of genes
would provide a more comprehensive picture of the underlying biology of the disease; however, our focus was on
the applicability and predictive capabilities of the model. To our knowledge, this is the first study integrating
mRNA and DNAm data for the construction and validation of a gene signature-based prognostic model for MB.
When looking at the model capabilities within the MB subgroups, the GS-2 did not classify patients from
Group 3 nearly as well as Group 4 and SHH. Given the small number of events per molecular subgroup in the
datasets we analyzed, especially in validation sets, we opt to develop a model disregarding subgroup information.
As the availability of high-quality molecular MB data grows, subgroup-specific prognostic models will be feasible
to develop, ensuring good performances across all these scenarios. Since WNT patients have an excellent
prognosis compared to the other subgroups with an OS above 95% (Clifford et al. 2006), our model classified all
but one patient as low-risk in the training set. The major clinical challenge for patients of this subgroup is still
treatment long term side effects (Moxon-Emre et al. 2016), which was not addressed in our study given the
absence of available data in this regard.
The gene signature consists of two genes, CEMIP (cell migration-inducing and hyaluronan-binding
protein) and NCBP3 (nuclear cap binding protein 3). CEMIP was identified as a good prognosis gene. Its major
recognized role was in regulating hyaluronan metabolism and cell migration, as its name suggests (Yoshida et al.
2013), but it's now clear that it has many other functions, especially in gene expression regulation and signal
transduction (Birkenkamp-Demtroder et al. 2011; Duong et al. 2018; Tang et al. 2019; Wang et al. 2020a).
Divergently to our study and previous research focused on MB, CEMIP exhibits a wide association with poor
prognosis and metastasis in other types of cancer (Koga et al. 2017; Jia et al. 2017; Jiao et al. 2019; Rodrigues et
al. 2019; Huang et al. 2020; Wang et al. 2020b; Xue et al. 2022), whereas an opposite pattern was observed in MB
(Li et al. 2020; Zhou and Zhao 2023). Despite the existence of various proposed mechanisms, the understanding
of how CEMIP contributes to tumor progression and metastasis at the molecular level remains limited. Multiple
observations suggest that CEMIP plays a positive role in regulating Wnt signaling (Birkenkamp-Demtroder et al.
2011; Wu et al. 2021; Hua et al. 2021). In various cell types including gastric cancer cell lines, vascular smooth
muscle cells, osteoblastic stem cells, and chondrocytes, altering CEMIP expression either through overexpression
37
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or silencing leads to activation or attenuation of Wnt/β-catenin signaling, respectively (Jia et al. 2017; Chen et al.
2019; Wang et al. 2019b; Ding et al. 2019; Xue et al. 2020). Presumably, this occurs through CEMIP's influence
on the expression and stability of β-catenin. Notably, our study revealed CEMIP overexpressed in the WNT
subgroup, which is categorized by a constitutively active Wnt signaling and associated with a significantly better
prognosis compared to other subgroups (Thompson et al. 2006). This finding potentially indicates the antagonistic
role of CEMIP in MB compared to its role in other cancer types. In Group 3, a contrasting pattern was observed
with CEMIP being down-regulated in comparison to all other subgroups. The RS also had a particularly poor
performance in this subgroup. The regulation of CEMIP expression comprises multiple processes, such as DNA
region methylation, histone H3k4me3 modifications, involvement of numerous microRNAs, lncRNAs, and the
transcriptional factors AP-1, Twist-1, GLI, ATF3, and NF-κB (Kuscu et al. 2012; Evensen et al. 2015;
Kwapiszewska et al. 2018; Wang et al. 2019c, 2020a; Zhou and Mu 2021; Xie et al. 2021; Dong et al. 2021).
Within these various processes, we identified that DNA promoter methylation plays a significant role in CEMIP
expression regulation in MB. However, further investigations are required to comprehensively understand its part
in the disease.
The other gene belonging to the GS-2 NCBP3 is much less studied in cancer. It's a non-canonical cap
bonding protein directly implicated in various steps of gene expression, likely to regulate pre-mRNA splicing,
pre-mRNA 3'-end processing, mRNA nuclear export, and mRNA translation (Baltz et al. 2012; Gebhardt et al.
2015; Guiro and Murphy 2017; Li et al. 2017; Dou et al. 2020). Current literature indicates that NCBP3 is a
non-essential protein under basal conditions and appears functionally critical under pathological conditions that
include viral infections and cancer (Gebhardt et al. 2015, 2019; Zhang et al. 2019). To our knowledge, it's the first
time NCBP3 is linked to a good prognosis in MB. We also find his expression differs according to chromosome
17p configurations, the most common chromosome with abnormalities in MB, which was already identified with
prognostic value (Biegel et al. 1997; McCabe et al. 2011).
To better understand the biological mechanisms underlying the GS-2, we performed a functional
enrichment analysis comparing low and high-risk patients. GO enrichment revealed many processes associated
with axonogenesis, axon guidance, neuron projection guidance, and regulation. These are common processes
linked to cerebellar granule neuron precursor (Luo et al. 2021), the cellular type thought to be the origin of SHH
MB (Schüller et al. 2008), and also frequently reported in malignant tumors (Miao et al. 2009; Tavora et al. 2020;
Yan et al. 2021). Many visual perception processes also appeared. Group 3 MB is recognized for exhibiting a
retinal expression signature commonly with the overexpression of OTX2, an essential gene for brain and sensory
organ development (Martinez-Morales et al. 2001). Expression of retinal genes was reported in MB as early as
1987, but their role in the disease or in cerebellum development remains unclear (Bunt et al. 2012).Recently, two
photoreceptor genes, NRL and CRX, were identified as required for Group 3 MB maintenance (Garancher et al.
2018). Following KEGG analyses, genes were found associated with retrograde endocannabinoid signaling,
morphine addiction, nicotine addiction, and GABAergic synapses. Consistent with previous studies, morphine,
nicotine, and cannabinoids are associated with the progression of brain tumors and also appear when comparing
MB to healthy cerebellum (Ellert-Miklaszewska et al. 2007; Sardi et al. 2011; Sepsova et al. 2014; Yang et al.
2019). Group 3 MB was proposed as the GABAergic group, due GABA receptor subunit GABRA5 being
statistically better at predicting Group 3 as a whole than NRL, CRX, or MYC biomarkers. Further, GABAA
receptors targeted with benzodiazepine derivatives enhance apoptosis in Group 3, pointing out the importance of
this process in the disease (Kallay et al. 2019; Vriend and Rastegar 2020). Two MsigDB oncogenic gene
38
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signatures were enriched. Mel18_DN.V1_DN consists of a set of genes down-regulated in DAOY cells
(medulloblastoma cell line) upon knockdown of Mel-18 by RNAi. Mel-18 belongs to the Polycomb group of
transcriptional regulators, which are believed to maintain gene repression by altering the chromatin state at
specific promoters (Gil et al. 2005). Its knockdown in DAOY cells results in the inhibition of proliferation, loss of
clonogenic survival, anchorage-independent growth, and suppression of tumor formation in nude mice. Several
cancer-relevant pathways were revealed to be altered upon the knockdown that may be controlled by Mel-18
(Wiederschain et al. 2007). The other set, KRAS.600_UP.V1_UP is a set of genes up-regulated in epithelial cell
lines over-expressing an oncogenic mutant form of KRAS. Although KRAS is the highest mutation rate oncogene
among all cancers, oncogenic mutations affecting it in MB development are rare events (Gilbertson et al. 2006;
Huang et al. 2021).
There are certain constraints within our study that are important to consider. First, training and validation
sets are from different platforms, making mRNA data less compatible, leading to risk-score cut-offs between
datasets not uniform. Second, samples of patients used were based on availability and convenience but who are
heterogeneous with regard to treatment and stage. Third, although we investigated the biological roles linked to
the GS-2, we did not unveil potential mechanisms of action responsible for their protective capabilities. Finally,
it's a developmental study, we did not evaluate the clinical utility and robustness of the model.
5 Conclusion
We identified MDGs in MB and developed a novel model composed of two MDGs. It was validated as a
strong predictor of MB prognosis across different cohorts. Both genes, CEMIP and NCBP3, stand out as
promising prognostic biomarkers with potential application for MB treatment. Further multicenter prospective
clinical studies are needed to verify the prognostic model developed in this study.
6 Statements and Declarations
Funding This study was supported by the Children's Cancer Institute from Brazil, CNPq, grant number
312305/2021-4, and the governmental Brazilian Agency Coordination for the Improvement of Higher Education
Personnel (CAPES).
Data availability The data on which the study is based is open available as described in the methods section.
Conflict of interest The authors declare that there is no conflict of interest that could be perceived as prejudicing
the impartiality of the research reported.
Author contributions GLM: carried out the research design, original manuscript writing, data acquisition,
processing, and analyses. DS: contributed to data acquisition and processing. LRES: contributed data acquisition,
processing, and analyses. RJSD, MCJ, ATB, and MS: provided suggestions. MS: supervised the project. All
authors have contributed to the article reviewing, data interpretation, and approved the submitted version.
Ethics approval All patient data used in this study are from identifiable public databases, so it does not require
the approval and informed consent of the agency review committee.
Consent to publish All authors consent to publish this article.
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8 Supplementary MaterialsSupplementary Table S1 - List of the 2213 Methylation-Driven Genes.
https://docs.google.com/spreadsheets/d/e/2PACX-1vS33cxiPzXf8uOeH9zhxzDUCvGWV3iHD0oXtaaF1AifKyf0
abzCzSFTy5Ooj7ruinyj9K_W4Y_OYHdu/pub?output=xlsx
Supplementary Table S2 - List of the 474 significant Methylation-Driven Genes in the
univariate Cox regression analysis.
https://docs.google.com/spreadsheets/d/e/2PACX-1vTXcwUEU2JXA9AN9ALsp2-j_TzB93gmknJqPgBKYAF7E
nHR1d7br82k1R-SKG8ZiHDoIkEjCnRcLbt1/pub?output=xlsx
Supplementary Table S3 - List of all gene signature models from LASSO regression during
lambda selection.
https://docs.google.com/spreadsheets/d/e/2PACX-1vRHSo8k9bfqtIvCXF-nNwRhI3ud75JfT_CcTZC8eNbNP9Rb
J84PmtljN6idiBg4ZQ/pub?output=xlsx
Supplementary Table S4 - List of 167 different expressed genes between high and low-risk
patients.
https://docs.google.com/spreadsheets/d/e/2PACX-1vQCHzN1KxrhEHyi4k0SnpoKvUlgInm_UN1rq9WTBtK3qv
eexShk4MSP2rEBuKCfksroRWUiIzHerhsC/pub?output=xlsx
Supplementary Figure S1 - Representation of the significant KEGG biological pathways of
the 2213 methylation-driven genes.
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Supplementary Figure S2 - Representation of the significant GO biological processes of the
2213 methylation-driven genes.
47
Supplementary Figure S3 - NCBP3 expression in different chromosome 17p configurations
in MB and in SHH, Group 3 and Group 4 subgroups.
48
4 · DISCUSSÃO
Vários marcadores prognósticos convencionais exibem associações limitadas e pouco
confiáveis com o prognóstico de MB, como doença metastática e grupos histológicos.
Enquanto alguns estudos sugerem que eles são associados a parâmetros prognósticos
(Rutkowski et al. 2010; Zeltzer et al. 1999), outros apontam para o contrário (Kocakaya et al.
2016; Liu et al. 2022), indicando que esses marcadores podem não ser preditores confiáveis
para o prognóstico da doença. Devido às limitações de parâmetros clínico-patológicos, as
assinaturas gênicas para fins prognósticos podem auxiliar na terapia dos pacientes ao oferecer
uma melhor estratificação de risco, orientando uma decisão de tratamento personalizada. A
epigenética, especialmente a metilação do DNA, é hoje conhecida como uma característica
universal da tumorigênese (Shen & Laird 2013). A identificação de GRMs trouxe um novo
horizonte na exploração dos efeitos biológicos dessa modificação e tem sido amplamente
utilizada para descobrir biomarcadores candidatos no câncer, como carcinoma renal de células
claras, câncer de mama, pâncreas e pulmão (Kuang et al. 2020; Wang et al. 2019a; Xiao et al.
2022; Zhu et al. 2020a). Neste estudo, desenvolvemos uma assinatura gênica com base em dois
GRMs e confirmamos seu excelente valor prognóstico em MB.
Usamos um total de quatro conjuntos de dados MB independentes com finalidades
diferentes durante o desenvolvimento do modelo. O maior deles, GSE85212, foi usado como
treinamento para ajustar o modelo prognóstico. Os outros três conjuntos de dados foram
usados para a validação, especificamente para ajuste de hiperparâmetros e seleção do modelo.
Embora nós não usamos nenhum conjunto de dados exclusivamente para testar o desempenho
do modelo proposto, nós o avaliamos em todos os quatro conjuntos independentes com
resultados promissores. Estudos anteriores em MB que construíram assinaturas gênicas
prognósticas usaram apenas um conjunto de dados de validação independente ou dividiram um
conjunto principal em dois menores para treinamento e validação (Li et al. 2020; Wang et al.
2020c; Zhu et al. 2020b). A avaliação de um modelo prognóstico em diversas coortes
independentes é uma etapa crucial para o desenvolvimento de um modelo robusto devido à alta
heterogeneidade da doença e potenciais vieses do modelo (Cooney et al. 2023). Garantir que o
modelo seja genérico para desempenhar em diferentes amostragens populacionais sem uma
perda significativa de acurácia é decisivo para estabelecer um forte marcador prognóstico.
Embora testar em quatro conjuntos de dados distintos não garanta bons resultados em qualquer
cenário, ainda representa um passo à frente no desenvolvimento de modelos prognósticos
robustos. Nós ainda o confirmamos como um preditor independente quando uma variedade de
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fatores prognósticos convencionais de MB foram considerados simultaneamente. Também
optamos por um número menor de genes para formar a assinatura, tornando-a menos provável
o sobreajuste no conjunto de treinamento, mais fácil interpretação e aplicabilidade clínica. Por
outro lado, um maior número de genes forneceria uma visão mais abrangente da biologia
subjacente da doença, entretanto focamos na aplicabilidade e na capacidade preditiva do
modelo. Ao nosso conhecimento, este é o primeiro estudo integrando dados de RNAm e
mDNA para construção e validação de uma assinatura gênica como modelo prognóstico em
MB.
Ao observar a capacidade do modelo dentre os subgrupos moleculares MB, a assinatura
gênica (AG-2) não classificou os pacientes do Grupo 3 tão bem quanto comparado aos Grupo 4
e SHH. Dado o pequeno número de eventos por subgrupo nos conjuntos de dados que
analisamos, especialmente nos conjuntos de validação, optamos por desenvolver um modelo
desconsiderando as informações de subgrupo. À medida que a disponibilidade de dados
moleculares de MB com alta qualidade cresce, o desenvolvimento de modelos prognósticos
específicos para cada um tornar-se-á viável de modo a garantir bom desempenho dentre todos
os subgrupos. Visto que os pacientes WNT têm um excelente prognóstico em comparação com
os outros subgrupos, com uma OS acima de 95% (Clifford et al. 2006), nosso modelo
classificou todos os pacientes, exceto um, como de baixo risco no conjunto de treinamento. O
maior desafio clínico para os pacientes desse subgrupo ainda são os efeitos colaterais do
tratamento a longo prazo (Moxon-Emre et al. 2016), que não foram abordados em nosso
estudo devido à ausência de dados disponíveis a esse respeito.
Dois genes compõem a assinatura gênica, CEMIP (cell migration-inducing and
hyaluronan-binding protein) e NCBP3 (nuclearcap binding protein 3). CEMIP foi identificado
como um gene de bom prognóstico. Seu principal papel identificado foi na regulação do
metabolismo do ácido hialurônico e na migração celular, como seu próprio nome sugere
(Yoshida et al. 2013), mas hoje é evidente que ele possui diversas outras funções,
especialmente na regulação da expressão gênica e na transdução de sinal
(Birkenkamp-Demtroder et al. 2011; Duong et al. 2018; Tang et al. 2019; Wang et al. 2020a).
Diferentemente do nosso estudo e de pesquisas anteriores focadas em MB, CEMIP
tradicionalmente exibe uma ampla associação com mau prognóstico e metástase em outros
tipos de câncer (Jiao et al. 2019; Jia et al. 2017; Koga et al. 2017; Rodrigues et al. 2019; Wang
et al. 2020b; Xue et al. 2022), enquanto um padrão oposto é observado em MB (Li et al. 2020;
Zhou & Zhao 2023). Apesar da existência de vários mecanismos propostos, a compreensão de
como o CEMIP contribui para a progressão tumoral e metástase em nível molecular permanece
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limitada. Múltiplas observações sugerem que CEMIP desempenha um papel positivo na
regulação da via de sinalização de Wnt (Birkenkamp-Demtroder et al. 2011; Hua et al. 2021;
Wu et al. 2021). Em vários tipos celulares, incluindo linhagens celulares de câncer gástrico,
células musculares lisas vasculares, células-tronco osteoblásticas e condrócitos, a alteração da
expressão de CEMIP por superexpressão ou silenciamento leva à ativação, ou atenuação da
sinalização de Wnt/β-catenina, respectivamente (Chen et al. 2019; Ding et al. 2019; Jia et al.
2017; Wang et al. 2019b; Xue et al. 2020). Presumivelmente, isso ocorre pela influência de
CEMIP na expressão e estabilidade da β-catenina. Notavelmente, nosso estudo revelou CEMIP
superexpresso no subgrupo WNT, que é categorizado por pela via sinalização de Wnt
constitutivamente ativa (Thompson et al. 2006). Esse achado indica potencialmente o papel
antagônico de CEMIP em MB em comparação com seu papel em outros tipos de câncer.
Muitos pacientes deste subgrupo apresentam um prognóstico excelente, potencialmente devido
a alterações na vascularização cerebral periférica ao tumor. Presumidamente, a sobre expressão
de antagonistas da via de WNT, via mecanismo de feedback negativo, atuam de modo
parácrino em células endoteliais de vasos sanguíneos do microambiente tumoral, impedindo
sua especificação para um fenótipo de barreira hematoencefálico. Assim supostamente
aumentando a acessibilidade da quimioterapia sistêmica ao tumor (Phoenix et al. 2016). A
compreensão de como CEMIP influência na via de sinalização de Wnt e de seu papel na
composição dos componentes da matriz extra celular do microambiente tumoral permanecem a
ser esclarecida na doença. No Grupo 3, um padrão contrastante foi observado com CEMIP,
estando sub expresso em comparação com todos os outros subgrupos. O RS também teve um
desempenho particularmente ruim neste subgrupo. A regulação da expressão de CEMIP
compreende múltiplos processos, como metilação de DNA, modificações da histona H3k4me3,
envolvimento de inúmeros microRNAs, lncRNAs e os fatores de transcrição AP-1, Twist-1,
GLI, ATF3 e NF-κB (Dong et al. 2021; Evensen et al. 2015; Kuscu et al. 2012; Kwapiszewska
et al. 2018; Wang et al. 2019c, 2020a; Xie et al. 2021; Zhou & Mu 2021). Dentre esses
diversos processos, identificamos que a metilação de DNA desempenha um papel significativo
na regulação da expressão de CEMIP em MB. No entanto, mais investigações são necessárias
para compreender de forma abrangente o seu papel na doença.
O outro gene pertencente à AG-2, NCBP3, é bem menos estudado em câncer. Ele
traduz para uma proteína não canônica de ligação ao cap 5' do RNAm, diretamente implicado
em várias etapas da expressão gênica, como na regulação de splicing do pré-RNAm, no
processamento da extremidade 3', na exportação nuclear do RNAm e na tradução do RNAm
(Baltz et al. 2012; Dou et al. 2020; Gebhardt et al. 2015; Guiro & Murphy 2017; Li et al.
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2017). A literatura atual indica que NCBP3 é uma proteína não essencial em condições basais
e parece funcionalmente crítica em condições patológicas, o que inclui infecções virais e
câncer (Gebhardt et al. 2015; Zhang et al. 2019). Pelo que sabemos, esta é a primeira vez que
NCBP3 está associado a um bom prognóstico em MB. Também descobrimos que sua
expressão difere conforme as configurações do cromossomo 17p, onde ele está localizado. O
cromossomo 17 pode apresentar diversas aberrações em MB, incluindo a deleção do braço
curto, ganho do braço longo ou mais comumente ambos, configurando o isocromossomo do
17q, um hallmark citogenético de MB de alto risco (Biegel et al. 1997; McCabe et al. 2011).
Alguns driver genes afetados por essa alteração começaram a ser identificados. O knockdown
de LASP1, gene localizado no cromossomo 17q, reduz drasticamente a migração celular in
vitro, e é fortemente correlacionado com metástase e pior sobrevida (Traenka et al. 2010).
Outro estudo recente identificou o gene WIP1, também localizado no cromossomo 17q, sobre
expresso em MB de Grupo 3 e 4. Esse gene reprime a fosforilação e internalização de CXCR4.
O aumento da concentração de CXCR4 na superfície celular ativa a via de sinalização de
PI3K/AKT, promovendo proliferação e invasão (Buss et al. 2015). Aqui, nós identificamos o
gene NCBP3 no cromossomo 17p associado à melhor sobrevida e com sua expressão
correlacionada com essa alteração cromossômica fortemente vinculada ao risco dos pacientes.
Para entender melhor os mecanismos biológicos ligados à AG-2, realizamos uma
análise de enriquecimento funcional comparando pacientes de baixo e alto risco. O
enriquecimento utilizando o GO revelou diversos processos associados à gênese do axônio,
orientação de axônios, regulação e orientação da projeção neural. Estes são processos comuns
ligados ao precursor do neurônio grânulo cerebelar (Luo et al. 2021), o tipo celular que se
acredita ser a origem do MB SHH (Schüller et al. 2008), e também frequentemente relatadoem tumores malignos (Miao et al. 2009; Tavora et al. 2020; Yan et al. 2021). Muitos processos
de percepção visual também apareceram. O Grupo 3 de MB é reconhecido por exibir uma
assinatura de expressão retinal comumente com a superexpressão de OTX2, um gene essencial
para o desenvolvimento do cérebro e de órgãos sensoriais (Martinez-Morales et al. 2001). A
expressão de genes da retina foi relatada em MB já em 1987, mas seu papel na doença ou no
desenvolvimento do cerebelo permanece obscuro (Bunt et al. 2012). Recentemente, dois genes
fotorreceptores, NRL e CRX, foram identificados como necessários para a manutenção do
Grupo 3 MB (Garancher et al. 2018). Dentre as vias biológicas do KEGG, foram encontrados
genes associados à sinalização endocanabinóide retrógrada, dependência de morfina,
dependência de nicotina e sinapses GABAérgicas. Consistente com estudos anteriores,
morfina, nicotina e canabinóides estão associados à progressão de tumores cerebrais e também
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estão presentes ao comparar MB com cerebelo saudável (Ellert-Miklaszewska et al. 2007;
Sardi et al. 2011; Sepsova et al. 2014; Yang et al. 2019). O Grupo 3 de MB foi proposto como
o grupo GABAérgico, devido à subunidade de receptor GABA, GABRA5 ser estatisticamente
melhor em prever este subgrupo como um todo do que os biomarcadores NRL, CRX ou MYC.
Além disso, os receptores GABAA direcionados aos derivados de benzodiazepínicos aumentam
a apoptose no Grupo 3, apontando a importância desse processo na doença (Kallay et al. 2019;
Vriend & Rastegar 2020). Duas assinaturas oncológicas do MsigDB foram enriquecidas.
Mel18_DN.V1_DN consiste em um conjunto de genes sub-regulados em células DAOY (linha
celular de meduloblastoma) após knockdown de Mel-18 por RNAi. Mel-18 pertence ao grupo
Polycomb de reguladores transcricionais, que se acredita manter a repressão gênica alterando o
estado da cromatina em promotores específicos (Gil et al. 2005). Seu knockdown em células
DAOY resulta na inibição da proliferação, perda de sobrevivência clonogênica, crescimento
independente de ancoragem e supressão da formação de tumor em camundongos
imunoincompetentes. Vários vias relevantes para o câncer foram alteradas após este
knockdown, que potencialmente são reguladas por Mel-18 (Wiederschain et al. 2007). O outro
conjunto do MsigDB, KRAS.600_UP.V1_UP, é um conjunto de genes regulados positivamente
em linhas de células epiteliais que superexpressam uma forma mutante oncogênica de KRAS.
Embora o KRAS seja o oncogene com maior taxa de mutação entre todos os tipos de câncer,
sua associação com a tumorigênese de MB é escassa. As mutações oncogênicas nesse gene
que o afetam o desenvolvimento de MB são eventos significativamente raros (Gilbertson et al.
2006; Huang et al. 2021).
Existem certas restrições em nosso estudo que são importantes a serem consideradas.
Primeiro, os conjuntos de treinamento e validação são de plataformas diferentes, tornando os
dados de RNAm menos compatíveis, o que leva a cortes do RS entre conjuntos de dados não
uniformes. Em segundo lugar, as amostras de pacientes utilizadas foram baseadas em
disponibilidade e conveniência, mas que são heterogêneas em relação ao tratamento e estágio.
Em terceiro lugar, embora tenhamos investigado os papéis biológicos ligados da AG-2, não
revelamos os possíveis mecanismos de ação responsáveis por suas capacidades protetoras.
Enfim, é um estudo de desenvolvimento, não avaliamos a utilidade clínica e a robustez do
modelo.
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5 · CONCLUSÃO
Nós identificamos GRMs em MB e desenvolvemos um novo modelo prognóstico
composto por dois GRMs. Nós o avaliamos como um forte preditor prognóstico na doença em
diferentes coortes independentes, o qual apresentou alta capacidade preditiva do desfecho dos
pacientes. O modelo também apresentou potencial prognóstico de maneira independente de
marcadores convencionais da doença. Ambos os genes, CEMIP e NCBP3, destacam-se como
promissores biomarcadores prognósticos com potencial aplicação para o tratamento de MB.
Mais estudos são necessários para avaliar o modelo prognóstico desenvolvido neste estudo,
especificamente seu potencial impacto e utilidade clínica, idealmente em estudos clínicos
prospectivos.
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