Precision Medicine Study
Cancer Sequencing Guided Personalized and Precision Medicine Platform in Multiple Myeloma
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Katherine Vandris
- Email: katherine.vandris@mssm.edu
Study Contact Backup
- Name: Cesar Rodriguez Valdes, MD, PhD
- Phone Number: (212) 241-7873
- Email: Cesar.Rodriguez@mssm.edu
Study Locations
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New York
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New York, New York, United States, 10029
- Mount Sinai Health System
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients must be 18 years of age at the time of registration.
- Participant must have an established diagnosis of relapsed Multiple Myeloma based on IMWG criteria, be willing to participate, and able to consent
- Participant must have a treating physician who agrees to participate in the study
- Participant will be undergoing a bone marrow biopsy or tumor biopsy as part of their standard of care.
- Patients must be willing to participate in this study and able to sign informed consent.
- Participants are not participating in any interventional clinical trial using systemic therapy directed towards control of MM.
Exclusion Criteria
- Known diagnosis of AL amyloidosis, Waldenstrom Macroglobulinemia, POEMS, or Castleman´s disease.
- Diagnosis of cancer other than myeloma or skin cancer (squamous cell or basal cell) that is ongoing or treated within the last 2 years.
- Tumor sample inadequate or unavailable for analysis (e.g., due to insufficient number of tumor cells).
- Patient will not be receiving systemic MM-directed chemotherapy/immunotherapy in the following 2 months from the time tumor biopsy is performed.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
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Participants with Multiple Myeloma
Participants who will undergo tumor biopsy for management of multiple myeloma (MM)
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Total number of somatic Single-nucleotide variants (SNVs) per patient
Time Frame: End of study at 30 months
|
The number of genetic alterations found in the genome through genetic sequencing and comparison to the most common genetic sequence.
A given variant may describe an alteration that is benign, pathogenic, or of unknown significance.
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End of study at 30 months
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Total number of somatic insertions (INS) per patient
Time Frame: End of study at 30 months
|
Total number of somatic insertions (INS) per patient.
The number of instances where nucleotides have been erroneously added to the genome, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Total number of somatic deletions (DEL) per patient
Time Frame: End of study at 30 months
|
The number of instances where nucleotides that have been erroneously omitted from the genome, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Number of SNVs per megabase of the MM genome
Time Frame: End of study at 30 months
|
The number of genetic alterations detected in MM tumor cells through sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Number of INS per megabase of the MM genome
Time Frame: End of study at 30 months
|
The number of instances where nucleotides have been erroneously added to the MM tumor genome, per length of DNA, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Number of DEL per megabase of the MM genome
Time Frame: End of study at 30 months
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The number of instances where nucleotides that have been erroneously omitted from the MM tumor genome, per length of DNA, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Number of mutations per megabase among MM subgroups
Time Frame: End of study at 30 months
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The number of genetic alterations detectable in >1 % or <1 % of the population, per length of DNA, among multiple myeloma (MM) subgroups, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Number of mutations per megabase among genomic regions for all MM and mutational subgroups
Time Frame: End of study at 30 months
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The number of genetic alterations detectable in >1 % or <1 % of the population, per length of DNA, by genetic region (i.e., promoter, coding region, and termination sequence), for all MM and mutational subgroups, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
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Gene mutations identified
Time Frame: End of study at 30 months
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The number and type of genetic alterations detectable in >1 % or <1 % of the population identified, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Chromosomal abnormalities identified
Time Frame: End of study at 30 months
|
. The numbers and types of chromosomal abnormalities identified, as determined by genetic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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Molecular signatures identified
Time Frame: End of study at 30 months
|
Number and type of sets of biomolecular features identified that could be useful in predicting the course of disease or response to therapeutic intervention among patients with MM and other cancers, as determined by sequencing, and gene set variation and targeted drug analysis.
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End of study at 30 months
|
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Established Prognostic markers identified
Time Frame: End of study at 30 months
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The number and type of established prognostic markers identified.
Evaluation of biological characteristics known to be useful in predicting the course of disease or response to therapeutic intervention among patients with MM and other cancers, as determined by sequencing and comparison to databases of known prognostic markers.
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End of study at 30 months
|
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Somatic variants identified as targets of FDA-approved drugs (pharmacogenomics variant data)
Time Frame: End of study at 30 months
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The number and type of genetic alterations found in the genome that could be treated with FDA-approved therapies, as determined by sequencing and comparison to databases of known targets and associated FDA-approved drugs.
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End of study at 30 months
|
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Network-informed key driver variants identified
Time Frame: End of study at 30 months
|
Number and type of mutations known to lead to cancer cell transformation, growth, and spread in the body, as determined by genetic sequencing and comparison to the most common genetic sequence, and to databases of known cancer driver mutations.
These mutations will be categorized as follows: those that are known targets of FDA-approved drugs, those that may be targets of drugs under development that are not yet FDA-approved, and those that may serve as targets for novel therapies.
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End of study at 30 months
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Transcriptome variations identified
Time Frame: End of study at 30 months
|
Number and type of transcriptome variations identified with potential for the development of novel therapeutics (cell-surface expressed proteins that appear amenable to vaccine development), as determined by sequencing, network modeling, and cancer transcriptome profiling.
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End of study at 30 months
|
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Germline mutations identified in cancer predisposition genes
Time Frame: End of study at 30 months
|
Number and type of germline mutations identified in cancer predisposition genes, as determined by genomic sequencing and comparison to the most common genetic sequence.
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End of study at 30 months
|
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FDA approved drugs available that block enzymes produced in those pathways identified
Time Frame: End of study at 30 months
|
The number and type of FDA-approved drugs available that block enzymes produced in those pathways identified by comparison of genomic and transcriptomic findings to databases of known FDA-approved drugs and associated targets.
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End of study at 30 months
|
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Treatment recommended by computational pipeline based on patient's clinical and genetic
Time Frame: End of study at 30 months
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A listing of recommended treatments as determined by sequencing, analysis of the tumor microenvironment, and computational analysis.
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End of study at 30 months
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Germline whole exome sequencing profile
Time Frame: End of study at 30 months
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The results of whole exome sequencing of the germline genome.
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End of study at 30 months
|
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Tumor genome whole exome sequencing profile
Time Frame: End of study at 30 months
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The results of whole exome sequencing of the tumor genome.
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End of study at 30 months
|
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Tumor transcriptome profile
Time Frame: End of study at 30 months
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The results of RNA sequencing of the tumor transcriptome.
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End of study at 30 months
|
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Single-cell sequencing profile
Time Frame: End of study at 30 months
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The results of single cell sequencing analysis.
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End of study at 30 months
|
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Cytometric profile
Time Frame: End of study at 30 months
|
The results of the cytometry by time of flight (CyTOF) analysis.
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End of study at 30 months
|
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Signaling Pathways associated
Time Frame: End of study at 30 months
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Signaling Pathways associated with each gene mutation, chromosomal abnormality and molecular signature, i.e. aging, defective DNA repair, and apolipoprotein B editing complex (APOBEC)/activation-induced deaminase activity, identified in Aim 1, as determined by sequencing and computational analysis.
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End of study at 30 months
|
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Enzymes associated with each signaling pathway identified
Time Frame: End of study at 30 months
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Enzymes associated with each signaling pathway identified as determined by sequencing and computational analysis.
|
End of study at 30 months
|
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Improvement of cancer sequencing-guided treatment recommendations by machine learning
Time Frame: End of study at 30 months
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Use of artificial intelligence computing to implement cancer sequencing-based recommended therapies and improve accuracy of treatment prediction, to allow better interpretation of cancer sequencing data and advancement of the development of personalized and precision cancer therapies.
Improvement will be measured by tracking the precision and accuracy of machine learning and evaluating the resulting data using statistical analysis.
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End of study at 30 months
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Cesar Rodriguez Valdes, MD, PhD, Icahn School of Medicine at Mount Sinai
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Vascular Diseases
- Cardiovascular Diseases
- Neoplasms
- Immune System Diseases
- Neoplasms by Histologic Type
- Hematologic Diseases
- Lymphoproliferative Disorders
- Immunoproliferative Disorders
- Neoplasms, Plasma Cell
- Hemostatic Disorders
- Paraproteinemias
- Blood Protein Disorders
- Hemorrhagic Disorders
- Hemic and Lymphatic Diseases
- Multiple Myeloma
Other Study ID Numbers
Other Study ID Numbers
- STUDY-23-00503
- GCO 19-0175 (Other Grant/Funding Number: Icahn School of Medicine at Mount Sinai)
- 5R01CA244899 (U.S. NIH Grant/Contract)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Time Frame
IPD Sharing Access Criteria
IPD Sharing Supporting Information Type
- SAP
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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