Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05512169 |
| Other study ID # |
MPGx-INDALL |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
December 1, 2022 |
| Est. completion date |
March 30, 2026 |
Study information
| Verified date |
February 2024 |
| Source |
University of Geneva, Switzerland |
| Contact |
Chakradhara Rao S Uppugudnuri |
| Phone |
+41223794685 |
| Email |
chakradhara.uppugunduri[@]unige.ch |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
A five-year prospective observational cohort study. The study is focused on observing the
relation between static germline variants and therapeutic response in Indian children with
acute lymphoblastic leukemia (ALL). The project is an International multicenter setup. This
collaborative research project between Switzerland and India includes one main center in
Geneva that has conceptualized, designed, received grants for the study and two investigating
centers in India (Puducherry and New-Delhi) involved in study design, patient care and
recruitment for this specific study. All the participants for the study will be recruited
form these two centers in India, and no patient recruitment is planned at main center i.e.
Geneva.
The study will be conducted in two phases. The first aims to investigate genetic
predisposition (static germline variants) to early chemotherapy treatment related toxicities
(TRTs). The second aims to investigate somatic genetic markers associated with the efficacy
of steroid treatment among patients undergoing the standardized IciCLe-ALL-14 treatment
protocol. A total of 500 children with ALL will be recruited to investigate primary objective
of the study i.e. TRT, and a subset of 250 patients will be included to investigate another
research question i.e. response to steroid therapy.
Description:
Primary objectives:
1. To study the associations of static germline variants with early chemotherapy-related
toxicities (treatment related toxicities) in children with ALL undergoing the Icicle
treatment protocol.
2. To investigate the somatic and germline genetic markers associated with the efficacy and
toxicity of glucocorticoid response, respectively.
3. To biobank biological samples and clinical data for future association analyses in order
to develop biomarkers of the treatment protocol's efficacy and toxicity.
Secondary Objectives:
1. To study the impact of the occurrence of early toxicities on quality of life during
active ALL treatment (physical and emotional quality of life using the PedsQL tool).
2. To evaluate genetic associations (somatic and germline) with overall survival,
non-relapse mortality, relapse free survival, and event-free survival.
Clinical outcomes:
All data will be recorded directly into an electronic Case Report Form (CRF) or onto initial
paper-based forms by the center's clinical research assistant or data manager with the help
of research nurse and senior research fellow employed within the project. Each center's
clinical investigator will check, date, and sign the forms electronically, with planned
inter-center and third-party data monitoring. All patient data will be saved under a
pseudonym. Clinical data relevant to the study's objectives will be shared (e.g.,
demographics, disease classification, minimal residual disease (MRD) testing, cytogenetics,
follow-up details, toxicity data, survival data, relapse data, liver function tests, early
treatment-related toxicities). This will also include any drug-related toxicity ≥ grade 3
occurring from the first day of induction until the middle of maintenance therapy.
TRTs during maintenance therapy are mainly hematological and hepatic toxicities occurring
within the first 100 days of the initiation of maintenance or continuation treatment, and
whichever occurs first will be used for incidence analysis. Toxicities will be graded using
the Common Terminology Criteria for Adverse Events (CTCAE-version 5.0 -
https://ctep.cancer.gov/protocolDevelopment/electronic_applications/ctc.htm#ctc_50. Multiple
events for the same patient will also be recorded for analysis. Data on MRD, disease response
at one week (prophase), and disease response at the end of steroid therapy will be assessed
in relation to somatic genetic variants.
Other clinical outcomes to be assessed in relation to genetic markers are incidence of
relapse of disease (i.e., the duration between the day of complete remission and the day of
occurrence of relapse), non-relapse mortality (death due to any cause other than a relapse of
the disease), and overall survival (OS). There will be a minimum of one year's follow-up
(However, all the patients would continue to be followed and will be analyzed again at 5
years post treatment for survival outcomes) to evaluate outcomes such as OS and RFS. EFS is
defined as the time from treatment initiation to the first induction failure, non-response,
or progression of the disease, death from any cause, or is censored at the date of last
follow up. OS is defined as the time from therapy initiation to death from any cause.
Patients from Jawaharlal Institute of Post-graduate Medical Education and Research (JIPMER)
and All India Institute of Medical Sciences (AIIMS) will respond to a quality of life
questionnaire at the time of diagnosis and at around day 100 (+/-30 days) of the maintenance
phase (other time points during maintenance therapy would be evaluated but not under the
purview of the current research proposal), using the generic core scales of the pediatric
quality of life inventory, PedsQL (https://www.pedsql.org/). Cancer module of pedSQl
questionnaire will be implemented if it is ready by the time of the recruitment of the first
patient as per the guidelines by Mapi research trust who is supplying these questionnaires
upon obtaining license (https://eprovide.mapi-trust.org/). Otherwise generic module will be
implemented. Other data, such as socio-economic and nutritional status data will be collected
(Uploaded on the web portal along with" CRF"), and the nutritional recommendations
implemented as part of the routine standard treatment protocol will be extracted and used at
the time of analysis if they have an impact as confounders on any of the outcomes under
investigation
Sampling procedure:
Saliva samples at the time of recruitment, whole-blood sample (3-5 ml) is collected at
complete remission, collected in EDTA tubes will be keep it frozen at -80°C until extraction.
Bone marrow samples will also be collected in EDTA tubes and kept frozen at -80°C until
extraction, as will samples left over after MRD testing. Plasma samples will also be
collected in EDTA blood collection tubes, and plasma separation will be performed (4 ml
whole-blood sample) using a swinging bucket centrifuge spinning for 10 minutes at a speed of
1300g of relative centrifugal force. Aliquots of 250 µL of plasma will be stored at -80°C in
pre-labeled cryotubes.
DNA extraction will be performed in batches, and an aliquot will be shared with the Geneva
team for sequencing analyses. DNA concentrations will be determined using Qubit or another
SYBR Green-based method, and integrity will be measured using Tapestation, with GQN values
greater than 7.5 being used for the genetic analyses. Purification and quantification
processes have already been established at both the JIPMER and AIIMS centers. Remaining
aliquots will be stored in their respective biobanks. No hand-written labels will be
accepted; all labels will be duly printed with an anonymized patient ID, sample type, and
time of collection. Each center will ensure sample anonymization before sending out the
samples for shared analyses. Two aliquots,each with approximately 1.0 µg of DNA in 50 µl 10
mM of Tris (8.0), will be used for sequencing (germline 70X, somatic 150X) in phase 1
analysis in Geneva and for real-time PCR genotyping or open array genotyping in phase 2
analysis in India. Other aliquots would be stored in biobanks at respective centers for
future investigations.
Phase 1 genetic variant analysis using Whole-exome sequencing:
Whole-exome sequencing will be performed at Campus Biotech's Genomics Platform at the
University of Geneva Using 100 germline DNA samples and 100 DNA samples from leukemic cells
(same patients), whole-exome sequencing will use the following workflow according to
manufacturers' protocols: (i) library preparation; (ii) sequencing using a HiSeq4000 system
with a mean coverage of 70X for germline DNA samples and 150X for somatic DNA samples
(Illumina); (iii) raw data integration and storage in the Laboratory Information Management
System (LIMS) at the Pediatric Onco-Hematology Research Platform at the University of
Geneva's Department of Pediatrics, Gynecology, and Obstetrics. Data will be shared with the
Swiss Institute of Bioinformatics via the LIMS, for extra input into the analysis (analyses
will mainly be performed by a postdoctoral fellow), and with the JIPMER and AIIMS for future
investigations.
Genetic data analysis:
For genetic data analysis, whole genomes will be aligned with the hg19 reference genome using
a Burrows-Wheeler aligner, PCR duplicates will be removed using PICARD tools, and base
quality-score recalibrations will be performed using the Genome Analysis Toolkit (GATK).
Cleaned BAM files will be used to create pile-up files using the SAM tool. Germline variants
will be called using a GATK Haplotype Caller, and variant quality scores will be recalibrated
based on public data sets using the Variant Recalibrate tool and annotated using ANNOVAR.
Non-leukemic variants will then be simply subtracted from leukemic variants to obtain a
leukemic specific variant list. Leukemic and non-leukemic sequences will also be analyzed
simultaneously to detect mutations using the heuristic methods available in the Varscan and
Mu Tect tools. Variants which might arise due to sequence errors will be detected by the SAVI
algorithm. We will perform analysis via (i) a candidate gene approach with filtered
variants/mutations to the selected genes based on a hypothesis, and (ii) hypothesis-free,
exome-wide, association analysis, the focus of which will be on the variants located in
exonic regions, missense or nonsense variants with predicted functional effect, as well as
variations in splicing sites. The predicted effects of missense variants on protein function
will be assessed in silico using SIFT and PolyPhen2 incorporated into VEP tools. Variant
filtering will be performed based on the 1000 Genomes and the NHLBI GO Exome Sequencing
projects. Fisher's exact test (allelic association) and the Cochran- Armitage test for trends
will be implemented in PLINK to search for associations between clinical outcomes and genetic
variants.
Association analysis for quantitative and binary data will be analyzed using general
linearized models in PLINK, with a significance threshold of 0.05 per number of variants for
the candidate and exome-wide approaches. Alternative analyses with a p-value of 0.05 will be
set up, and adjustments for multiple testing will be performed using the Benjamin-Hochberg
false discovery rate method: variants are considered as significantly associated if they have
a false discovery rate lower than 5% (for EWAS) or 10% (for candidate gene analysis).
Candidate gene selection :
The pharmacogenetic variants are hypothesized to affect the kinetics and dynamics of
chemotherapeutic drugs in the treatment protocol (mainly obtained from PharmGKB). Other
pharmacogenes which might have an impact on the physiological functions contributing to the
pathophysiology of toxicities would also be included. Minor allele frequency above 15%,
functionality as a criterion of variant filtering for analysis would be implemented. A list
of potential candidates is given in annex "Germline_Genes.txt".
Candidate-gene selection for somatic variant testing: A set of candidate genes was compiled
following an extensive literature review. These genes were selected because they: i) were
reported by individual association studies; ii) participate in the pharmacodynamic pathways
of steroid therapies and other chemotherapeutic agents; iii) are key actors in disease
pathogenesis or the immunological pathways of disease control; iv) participate in the repair
of damage caused by chemotherapy and radiation; v) were up- or down-regulated in
lymphoblastoid cells sensitive or resistant to prednisolone in the experiments performed in
our collaborator and mentor Prof. Maja Krajinovic's laboratory; vi) can be obtained via
Ingenuity Pathway Analysis® specific to steroids, chemotherapy, and other supportive care
therapies. Only variants of these genes will be fed into the statistical analysis to find
associations. Unique population datasets from UK biobank (subset Indian population) will also
be utilized for specific phenotype to select some of the candidates. For somatic variants
public data repositories such as DepMap and TARGET data would be considered. The set of
candidate genes will thus enable the creation of a leukemia sequencing database of a single
ethnicity, along with its clinical and follow-up data, all obtained from a single study. This
will serve as a valuable resource for researchers investigating questions related to ALL
treatment in children.
The candidate somatic and germline variants detected by whole exome sequencing will be
validated using direct Sanger sequencing following PCR amplification. When present in a major
clonal population, Sanger sequencing enables the detection of mutations in heterozygous
states. Hence, mutations reported in < 25% of the reads will not be included in this
validation phase.
Phase 2 genotyping for selected candidates from the phase 1 analysis:
Phase 2 analyses will include screening 400 germline DNA samples from the top 120 candidates
identified in phase1's germline sequencing association analysis with TRT. Somatic DNA
analysis includes the screening of 150 samples from the top 100 candidates from phase 1
analysis. An open-array methodology (ThermoScientific) or custom-made microarrays (Axiom)
could be used as a cost-effective strategy. We plan to develop a cost-effective screening
methodology that is easy to implement for routine monitoring of the variants most associated
with the clinical outcomes investigated, e.g., allele-specific PCR or simple probe-based
allele discrimination assays or high-resolution meltcurve analysis methods. Data generated
will be stored locally in JIPMER and AIIMS' permanent data repositories and will also be
shared with Geneva's Pediatric Onco-Hematology Research Platform. An agreement will be signed
with the Clinical Pharmacogenomics Implementation Consortium®(CPIC) steering committee on
using the pharmacogenetic data generated in this study to assess which genetic tests would be
more appropriate for the Indian patient population, thus enabling the development of
population-specific panels in future
Measurement of drug levels in plasma or whole blood/red blood cells (Optional):
The analytical data obtained from routine therapeutic drug monitoring services will also be
collected and analyzed in relation to genetic variants and clinical outcomes. Plasma samples
or whole-blood samples will be routinely collected during induction and maintenance to
monitoring drug levels and activity using ELISA or LC-MS/MS-based analytical methods. This
will also allow us to evaluate genetic predispositions independent of drug levels. The
activity level of L-asparaginase will be measured (using ELISA) from plasma collected at day
3 after the first infusion. For 6-mercaptopurine, whole-blood samples will be collected
during the eight weeks of maintenance therapy (i.e., between days 49 and 56), and
6-mercaptopurine, 6-thioguanine, and 6-methyl mercaptopurine levels will be measured in RBCs.
During methotrexate induction therapy, whole-blood samples will be taken 48 hours after the
end of infusion. During methotrexate maintenance therapy, samples will be collected during
week 7, and its metabolites analyses. For vincristine during induction samples were collected
24 hours after the end of infusion.
Plasma protein-marker analysis and biological material repository for future studies:
This part is not planned to take place under the current research grant application, however,
sample banking will be performed. In a future separate project or as the continuation of the
current research project, the Proximity Extension Assay (PEA) developed by Olink Proteomics
AB (spun out of Uppsala University, Sweden) will be used to simultaneously quantify 184
oncology, cell growth, and immunology-related human protein biomarkers (to be chosen based on
the input from experts in the field and their roles in the pathophysiology). This assay
requires a sample of less than 10 µL of plasma or blood and can measure a panel of 92 protein
biomarkers and three internal control samples at once. This assay will be performed using a
96-well plate for each panel, selected separately, and it requires no washing steps. The
multi-marker score's ability to accurately predict outcomes will be detected using receiver
operating characteristic (ROC) curves, along with sensitivity and specificity analyses.
