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Tissue Microarray of Hematological Malignancies

Tissue Microarray of Hematological Malignancies: Search for Novel Regulators of Disease Pathology Across Disease Entities

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04142372
Acronym
HemaTMA
Enrollment
5000
Registered
2019-10-29
Start date
2020-03-10
Completion date
2026-12-31
Last updated
2024-04-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hematological Malignancies

Keywords

leukemia, lymphoma, biomarker, prognosis, myeloma, myelodysplasia, myeloproliferative disorder, diagnostics, tissue microarray, proteomics, genomics, clinical outcome, biobanking, digitalization, data mining, hematology, oncology, pathology, bioinformatics, real world data, hematological malignancy, survival, treatment response

Brief summary

The aim of the study is to create new tools for improving management of patients with hematological malignancies by combining extensive clinical data from patients newly diagnosed with hematological malignancies and innovative laboratory analyses made on available tissue samples in regional biobanks from these patients.

Detailed description

Firstly, clinical information is collected on all hematological malignancies diagnosed in our hospital district area retrospectively between the years 2000 and 2019. Clinical outcomes, laboratory results, clinically relevant diagnoses, characteristics defining clinical stage and established prognostic parameters are gathered. Simultaneously a tissue microarray (TMA) of diagnostic samples is compiled using representative annotated tissue areas. This TMA is used in combination with additional control material to identify prognostic and predictive biomarkers. A combined microarray dataset of hematological malignancies (Hemap) is utilized to point out genes of possible drug targets, disease specific markers, prognostic markers, or predictive markers. The clinical datasets and Hemap dataset is ultimately utilized to gain knowledge, new tools for prognostication and diagnostics, and targets for treatment. Artificial intelligence -assisted differential diagnostics will be tested.

Interventions

None listed

Sponsors

Tampere University
CollaboratorOTHER
University of Eastern Finland
CollaboratorOTHER
Fimlab Oy
CollaboratorUNKNOWN
Helsinki University Central Hospital
CollaboratorOTHER
Tampere University Hospital
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

Inclusion criteria

* hematological malignancy/neoplasm

Exclusion criteria

* Non-sufficient data available

Design outcomes

Primary

MeasureTime frameDescription
Progression-free survival (PFS)From the first line treatment up to the end of the study period (April 2019).Time from the first line treatment for hematological malignancy until the date of first documented relapse or transformation or death of any cause, whichever came first, assessed up to the end of the study period (April 2019).
Overall survival (OS)From the diagnosis up to the end of the study period (April 2019).Survival time from the diagnosis of hematological malignancy until the date of death of any cause, assessed up to the end of the study period (April 2019).
Response to treatmentFrom the first line treatment up to the end of the study period (April 2019).Best response to the first line treatment for the hematological malignancy, according to malignancy in question, e.g. complete response (CR), stringent complete response (sCR), partial response (PR), very good partial response (VGPR), stable disease (SD), progressive disease (PD), treatment failure, clinical response, hematological response etc.
Event-free survival (EFS)From the first line treatment up to the end of the study period (April 2019).Survival time from the first line treatment for hematological malignancy until any primary event (death, relapse, disease progression/transformation, secondary malignancy, resistant disease etc.), whichever came first, assessed up to the end of the study period (April 2019).

Secondary

MeasureTime frameDescription
ICU admissionFrom the first line treatment up to the end of the study period (April 2019).Admission to intensive care unit
Adverse effectsFrom the first line treatment up to the end of the study period (April 2019).Treatment-related adverse effects/events
Secondary malignancyFrom the first line treatment up to the end of the study period (April 2019).Secondary malignancy after the diagnosis of hematological malignancy
RelapseFrom the first line treatment up to the end of the study period (April 2019).Relapse after or during the treatment.
Sepsis or other life-threatening infectionFrom the first line treatment up to the end of the study period (April 2019).Fulminant infection after diagnosis
Time to complete remissionFrom the first line treatment up to the end of the study period (April 2019).Time to complete remission
Best responseFrom the first line treatment up to the end of the study period (April 2019).Best response e.g. hematological remission, molecular remission, radiological remission
Time to best responseFrom the first line treatment up to the end of the study period (April 2019).Time to best response e.g. hematological remission, molecular remission, radiological remission
Complete remissionFrom the first line treatment up to the end of the study period (April 2019).Complete remission after the treatment
Multiple organ failureFrom the first line treatment up to the end of the study period (April 2019).Altered organ function in acutely ill patient
Thrombo-embolismFrom the first line treatment up to the end of the study period (April 2019).Venous thromboembolism
Disease transformationFrom the diagnosis up to the end of the study period (April 2019).Hematological malignancy transforms into another malignancy

Countries

Finland

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026