Multigene Germline Panel Testing in Gastric Cancer Patients in Portugal
Conditions: Gastric Cancer (Diagnosis), Stomach Cancer
Sponsor: Unidade Local De Saúde Do Norte Alentejano

Study Protocol 
 
Title: Multigene Panel Test in Gastric Cancer Patients in Portugal 
 
NCT:  
Date: 01 September 2024. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
Authors: 
1. Mourato B., MD  
a. 
Affiliation:  Unidade Local de Saúde do Norte Aletenjano;  
b. Contacts: email: beatriz.mourato@ulsna.min-saude.pt; Address: Hospital Doutor José 
Maria Grande, Avenida de Santo António, 7300-853, Portalegre; 
c. 
Contributions: Main author; Study designer.  
2. Areia M., PhD  
a. 
Affiliation 
i. Instituto Português de Oncologia de Coimbra; 
ii. RISE@CI-IPO (Health Research Network), Portuguese Oncology Institute of 
Porto (IPO Porto) 
b. Contacts: email:  miguel.areia75@gmail.com ; Address: Avenida Bissaya Barreto 98, 
3000-075 Coimbra. 
c. 
Contributions: Intellectual contributor; Reviewer. 
3. Fronteira, I., PhD 
a. 
Affiliation:  
b. Contacts: ines.fronteira@ensp.unl.pt; Address: Avenida Padre Cruz, 1600-560 Lisboa.  
c. 
Contributions: Study designer; Intellectual contributor; Reviewer. 
4. Dinis, R., PhD 
a. 
Affiliation: Hospital do Espírito Santo de Évora,  
b. Contacts: email: rui.p.dinis@ulsna.min-saude.pt; Address: Largo do Sr. Da Probreza, 
7000-811 Évora; 
c. 
Contributions: Study designer; Study collaborator; Reviewer.  
 
 
 
 
 
 
 

Abstract 
 
Abstract: Gastric cancer (GC) is the fifth with the highest incidence and mortality worldwide (1). It 
is a multifactorial and heterogeneous disease, where both environmental and genetic factors are 
involved. Three hereditary syndromes primarily affect the stomach: Hereditary Diffuse Gastric 
Cancer (HDGC), Gastric Adenocarcinoma and Stomach Proximal Polyposis (GAPPS), and Familial 
Intestinal Gastric Cancer (FIGC) (2), but in addition to these, many other syndromes and genetic 
mutations are associated with an increased risk of GC (3–5). As GC is a neoplasm with numerous 
somatic and hereditary mutations involved in its carcinogenesis, the use of the Multigene Germline 
Panel Test (MGPT) may be crucial, as it allows the analysis of hereditary mutations in a significant 
number of genes simultaneously (6,7). This observational, retrospective study aims to quantify 
and characterise the pathogenic variants (PVs) found in the MGPT of GC patients in Alto Alentejo 
(Portugal).  
 
 
Introduction 
Background/ 
Rationale 
According to data from the Global Cancer Observatory, gastric cancer (GC) ranks 
fifth in terms of incidence and mortality worldwide (1). 
It is a multifactorial and heterogeneous disease involving both environmental 
and genetic factors. Although the majority of cases are sporadic, caused by 
environmental exposures, up to 20% of patients have a familial aggregation and 
of these, 3 to 5% are classified as hereditary with an identified mutation (8,9). 
Three hereditary syndromes primarily affect the stomach: Hereditary Diffuse 
Gastric Cancer (HDGC); Gastric Adenocarcinoma and Stomach Proximal 
Polyposis (GAPPS); and Familial Intestinal Gastric Cancer (FIGC) (2). HDGC is an 
autosomal dominant syndrome, predominantly caused by germline mutations in 
the CDH1 gene, which encodes the e-cadherin protein with important functions 
in cell aggregation, and is characterised by a high risk of early-onset diffuse GC 
and invasive lobular breast cancer (10). GAPPS is caused by germline mutations 
in APC promoter 1B and is characterised by the presence of fundic gland 
polyposis with focal dysplasia and intestinal or mixed adenocarcinoma at the 
level of the gastric fundus and sparing the antrum (4,11). Finally, FIGC is an 
autosomal dominant syndrome, still poorly characterized genetically, which is 
associated with an increased risk of intestinal GC (10).  
Other genetic syndromes are also associated with an increased risk of GC. Some 
of the genes involved include MLH1, MSH2, MSH6, and PMS2 (Lynch Syndrome - 
LS), TP53 (Li-Fraumeni Syndrome), APC (PAF and ADC / proximal polyposis of 
the stomach) and MUTYH (Polyposis Associated MUTYH), BMPR1A and SMAD4 
(Juvenile Polyposis Syndrome), STK11 (Peutz-Jegher Syndrome) and PTEN 
(Cowden Syndrome) (3–5).  
As GC is a neoplasm with numerous somatic and hereditary mutations involved 
in its carcinogenesis, the use of MGPT can be crucial, as it performs the analysis 
of hereditary mutations in a significant number of genes simultaneously 
(depending on the genes included in the panel) (6,7). In addition to the described 
mutations, MGPT has allowed the identification of new genetic mutations 
associated with GC risk, including in the BRCA 1/2, PALB2, ATM and RAD51C 
genes (typically associated with hereditary breast and ovarian cancer), MAP3K6, 

MYD88, among others  (9,12–17). However, the use of MGPT, especially in 
unselected GC patients, is not without risks, such as the unexpected detection of 
PV in genes not strictly associated with the phenotype (secondary findings) or, 
more commonly, the high rate of variants of unknown significance (VUS), which 
can raise important questions regarding the uncertainty of how to approach 
these patients (18). Therefore, some studies advocate that MGPT or single gene 
testing (SGT) should be offered only to selected patients, especially those with 
suspected HDCG (5,19–21). On the other hand, the evidence of many other 
germline mutations associated with GC and the lack of genetic characterisation 
of FIGC justifies the growing interest in performing MGPT in unselected patients 
with GC (10,18,22,23). Although these studies suggest abenefit in the use of 
MGPT in unselected patients with GC, this test is not yet widely used.  
In Portugal, the incidence and mortality rates of GC have been increasing over 
the last few decades. It is therefore important to better understand the genetic 
characteristics of GC in this country in order to develop better strategies for 
managing this disease. 
Objectives 
The main objective of this research is to quantify and characterize the PV found 
in MGPT of GC patients in Alto Alentejo (Portugal).  
 
The specific objectives are: 
- Determine the global rate of PVs in GC patients of Alto Alentejo; 
- Determine which PVs are most common in our GC population;  
- Correlate the PVs found with clinical factors: Age at diagnosis; Sex; Weight; 
Height; Presence of other diseases (Diabetes; Arterial Hypertension; 
Dyslipidaemia; Stroke; Heart attack); Date of diagnosis; Stage at diagnosis; 
Histologic subtype; Surgery for GC; Personal history of other cancers; Familial 
history of GC and other cancers;   
- Correlate the PVs found with prognostic factors: 12-month survival rate; 
mortality.   
 
Methods 
 
Study design 
Observational, retrospective study 
Despite this design, there was uniformity in the data recorded for each 
participant who agreed to be submitted to MGPT, minimizing the variability 
between records or the missing clinical data. 
 
Setting 
This study takes place at the Unidade Local de Saúde do Alto Alentejo (ULSAALE).  
The MGPT was always performed in the same laboratory (Germano de Sousa – 
Centro de Genética Laboratorial) using peripheral blood samples from patients 
with GC. 
Two types of MGPT were used: 
• 
MGPT 4005 (15 genes): APC, ATM, BLM, BMPR1A, CDH1, CTNNA1, 
EPCAM, MLH1, MSH2, MSH6, PMS2, PTEN, SMAD4, STK11TP53. 
 
• 
MGPT 4013 (30 genes): ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, 
CDH1, CHEK2, EPCAM, FANCC, MLH1, MRE11A, MSH2, MSH6, MUTYH, 

NBN, NF1, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, RECQL4, 
RINT1, SLX4, SMARCA4, STK11, TP53, XRCC2.  
 
The majority of patients with GC were tested with MGPT 4005. Patients with 
neoplasms other than GC or with a family history of other neoplasms were tested 
with MGPT 4013. The decision to perform MGPT and the choice of panel type 
were made in multidisciplinary consultation. 
 
The recruitment process takes place between January 2023 and December 2024.  
All data will be collected and entered in a database until September 2025  
The manuscript for publication will be written between October and November 
2025. 
Participants 
Eligibility criteria:  
- 
Adult patients (over 18 years old) with a histologically proven diagnosis 
of gastric adenocarcinoma that have done MGPT between 2023 and 
2024;   
- 
Ability to understand and speak Portuguese and/or English language; 
- 
Ability to reliably provide informed consent for inclusion in the study. 
There are no matched or randomized groups. 
Variables / 
Measurement 
Table 2 
Data sources 
The variables related to the patient's clinical history and personal data will be 
collected through a questionnaire administered during a personal interview and 
supplemented by consultation of each patient's digital medical record in order 
to obtain more detailed information on the variables characterising the disease 
and the results of the MGPT. 
Bias 
Efforts to address potential sources of bias:  
Clear Inclusion and Exclusion Criteria: The inclusion criteria are clear to select 
patients that are representative of the target population (patients with gastric 
adenocarcinoma) and that there is no bias in participant selection. 
Control of Confounding Factors: There are some confounding factors that may 
influence the study results, such as age, sex, Weight; Height; Body mass index; 
Presence of other diseases: Diabetes; Arterial Hypertension; Dyslipidaemia; 
Stroke; Heart attack). This will be achieved through appropriate statistical 
techniques or data stratification during analysis. 
Uniformity in data records: There was uniformity in the data recorded for each 
participant who agreed to be submitted to MGPT, minimizing the variability 
between records or the missing clinical data. 
Consistent and Accurate Data Collection: This will be achieved through adequate 
training of interviewers or data collection personnel and the use of standardized 
data collection protocols. 

Blind Data Assessment: After collection, the data will be pseudo-anonymized so 
that the researcher who analyses the data will not have access to the patient's 
identifying data. 
Time-Sensitive Analysis: Data analysis takes into account the timing of data 
collection relative to relevant clinical events. 
Sensitivity Analysis: It will be performed a sensitivity analyses to assess the 
robustness of the results across different results of MGPT and clinical factors.  
 
Study size 
The sample calculation takes into account the number of annual cases of GC 
recorded in Alto Alentejo (65 cases in 2020, data from National Oncologic 
Registry). Therefore, the sample calculation suggests that it should include 56 
patients with GC, with a confidence level of 95% and a margin of error of 5%. 
Quantitative 
variables 
For quantitative variables we will do the descriptive statistics analyses with 
mean, mode, median, standard deviation, interquartile variation, maximum and 
minimum.  
Subgroup analysis will be carried out, for each quantitative and qualitative 
variable, dividing patients who have positive or negative MGPT. We will analyse 
the effect measures of the difference between the two groups, with confidence 
interval (IC) 95%, and if possible, inferential statistical analysis will be 
conducted. 
 
Statistical 
methods 
a) Statistical analysis will be carried out using SPSS software. 
 
b) In inferential statistical analysis, the normality of the distribution of 
quantitative variables will be determined by the Kolmogorov-Smironov 
test. For variables with normal distribution, the T-sudent test will be 
used and for those that do not have normal distribution, the Mann-
Whitney test will be performed, with the purpose of analysing the 
statistical significance of the difference between the groups with IC 95% 
(positive MGPT and negative MGPT). For qualitative variables, the 
statistical significance of the difference between groups, with 95% CI, 
will be calculated using the Chi-square test (if more than 10% of the cells 
in the 2x2 tables have frequencies 5) or the Exact Fisher Test.  
We will later perform a multivariate analysis with logistic regression to 
control confounding variables. 
 
c) Missing data: We addressed missing data by employing multiple 
strategies. Firstly, we conducted a thorough examination of patterns of 
missingness to discern if they followed a missing completely at random, 
missing at random, or missing not at random pattern. Subsequently, for 
cases with missing data, we utilized multiple imputation techniques to 
impute missing values based on observed data and variables that were 
predictive of missingness. Additionally, we performed sensitivity 
analyses to assess the robustness of our findings to different 
assumptions regarding the missing data mechanism. Overall, these 

approaches allowed us to minimize bias and maintain the integrity of 
our analyses in the presence of missing data. 
 
d) To conduct sensitivity analysis for this study, we will systematically vary 
key parameters or assumptions within our analytical framework to 
assess their impact on the study outcomes. This may involve testing 
different statistical models, criteria for participant inclusion/exclusion, 
handling of missing data, or adjustments for potential confounding 
variables. By exploring a range of scenarios, we aim to evaluate the 
robustness of our findings and assess the degree to which they are 
influenced by specific methodological choices or assumptions. 
Sensitivity analysis allows us to identify the potential sources of 
uncertainty and to provide a more comprehensive understanding of the 
reliability and generalizability of our study results. 
 
 
Table 2 - Variables / Measurements 
 
Type of Variable 
Name of Variable 
Measurements 
MGPT Outcomes 
variables 
MGPT 
positive (1) or negative (0) 
PV 
integer number 
Likely pathogenic variables (LP) 
integer number 
Name of genes with PV 
descriptive 
PV in each gene 
integer number 
VUS 
integer number 
Potential 
confounders 
variables 
Age at diagnosis 
Years 
Sex 
Female(1)/Male(0) 
Weight 
Kg 
Height 
cm 
Body mass index 
Kg/m2 

Presence of other diseases: Diabetes; Arterial 
Hypertension; Dyslipidaemia; Stroke; Heart attack); 
Yes(1)/No(0) for each disease 
Stage at diagnosis (AJCC staging system); 
0, I, IIa, IIb, III, IVa, IVb 
Histologic subtype; 
Intestinal (1); Diffuse (2); Mixed (3) 
“Exposures” 
variables 
Personal history of other cancers 
Yes (1)/No (0) 
Types of other cancer in GC patient 
descriptive 
Familial history of GC 
Yes (1)/No (0) 
Age of onset of each familial case of GC 
integer number 
Familial history of other cancers (specifying) 
descriptive 
Age of onset of each familial case of other cancers 
Years 
Prognostic 
related variables 
12-months survival 
Yes (1)/No(0) 
Death due to GC 
Yes (1)/No(0) 
Date of death 
mm/yyyy 
Other variables: 
Date of diagnosis 
mm/yyyy 
Localization of tumor 
Cardia / Fundos / Body / Antrum/pyloro 
Time since diagnosis until the current moment 
months 
Surgery for GC 
Yes (1)/No (0); 
Type of surgery 
Total 
gastrectomy 
(1); 
Subtotal 
gastrectomy (0) 
Date of Surgery 
mm/yyyy 
Histologic staging after surgery 
0, I, IIa, IIb, III, IVa, IVb 
Underwent chemotherapy 
Yes (1) / No (0) 
Type of chemotherapy 
descriptive 

Cycles of chemotherapy 
integer number 
Positive MGPT is considered when it is found at least one PV in one gene of teste 
 
A comparative analysis will be made between the individual characteristics and tumor characteristics of positive MGPT 
cases with those with negative MGPT. 
Ethical Issues  
This research has already been approved by the Ethics Committee and the Board of Directors 
of ULSAALE (Ata 06/2024, document 60 / 202400991) and it will be submitted to Ethics 
Committee of the Nova Medical School.  
Participation in the study is voluntary, with informed consent and participants will be 
informed of the results before publication.  
 
Risks and Benefits 
The main benefits and expected outcomes of the study are the contribution to a better 
understanding of GC carcinogenesis, which could improve the diagnostic and therapeutic 
approach of GC patients and their relatives. In addition, this research may help to clarify 
whether the prevalence of hereditary GC is higher than that described in the literature (3-
5%) (4,5), and may justify screening the population for this disease. 
However, the use of MGPT is not without important risks, namely the identification of 
variants of undetermined significance (VUS), which requires careful integration of all clinical 
and genetic information for correct personal and family counselling. Whenever necessary, 
VUS carriers (as well as patients with identified PVs) will be referred to the Portuguese 
Institute of Oncology in Lisbon (IPOLFG) for family risk and genetic counselling. Other 
potential risks of this study include the psychological stress and anxiety caused by the survey 
and MGPT results, the need to travel to participate in the study, and the fact that the study is 
being conducted by non-geneticist researchers, which limits the immediate counselling that 
can be provided. In this way, we will have the collaboration of the psychologists of the Day 
Hospital, and our close collaboration with the Germano de Sousa Genetics Laboratory and the 
IPOLFG teams will allow an adequate response to technical questions about the MGPT. 
Finally, it should be noted that this observational study will only analyse the MGPT results of 
CG patients and will not propose any additional intervention for patients or their families. 
 

 
 
 
 
 
 
 
 
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