Multiple Myeloma
Conditions
Brief summary
This will be a 2 year study to evaluate and improve cancer sequencing as applied to the characterization of tumor molecular make-up and the identification of novel therapeutics (total n=100; approximately 50/year). Participants who will undergo tumor biopsy for management of multiple myeloma (MM) will self-refer to the study or be referred by their treating physician. Participants will initially meet with a clinician to review study consents and provide medical, medication, and family history information. After informed consent, biospecimen samples from peripheral blood, cheek swab, and tumor samples from bone marrow (aspirate and biopsy), peripheral blood, or any mass/fluid containing tumor cells will be obtained (from procedures indicated as part of their standard oncology care) for cancer sequencing (CS) (whole exome sequencing of germline and tumor genomes, RNA sequencing of tumor transcriptome, single cell, and CyTOF analysis). CS data will be interpreted via somatic variation identification, network modeling, and cancer transcriptome profiling to facilitate mapping activity levels of genes to networks and for identifying genes activated or dysregulated in cancer cells. Technologies and methodologies are developing rapidly, varying on a near daily basis which pre-empts our ability to define analysis and interpretation techniques in detail. Sequencing and analysis will be performed at the Genomics Core Facility at the Icahn School of Medicine at Mount Sinai. In instances where internal sequencing capabilities do not allow for certain types of analysis (e.g., a technology that is not yet available at Mount Sinai), de-identified samples or data may be sent out to third parties for additional analysis.. All external genetic tests will be performed in a CLIA certified lab and all tests will be FDA or NYS approved. The RNA Sequencing test will receive NYS Department of Health (Wadsworth Center) approval before results are provided to physicians . Samples will be de-identified and processed by the Mount Sinai Human Immune Monitoring Core (HIMC) before being sent to an external CLIA-certified lab for sequencing and analysis. Interpretation will be performed by a multidisciplinary team that includes genomicists, pathologists, and clinicians familiar with the particular cancer diagnosed in the participant. Once results are available, they will be shared with the study team. This study is not intended to implement the findings on CS, only to report the results obtained to the study team.
Interventions
None listed
Sponsors
Icahn School of Medicine at Mount Sinai
National Cancer Institute (NCI)
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Total number of somatic Single-nucleotide variants (SNVs) per patient | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Somatic variants identified as targets of FDA-approved drugs (pharmacogenomics variant data) | End of study at 30 months | 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. |
| Network-informed key driver variants identified | 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. |
| Transcriptome variations identified | 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. |
| Germline mutations identified in cancer predisposition genes | 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. |
| FDA approved drugs available that block enzymes produced in those pathways identified | 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. |
| Treatment recommended by computational pipeline based on patient's clinical and genetic | End of study at 30 months | A listing of recommended treatments as determined by sequencing, analysis of the tumor microenvironment, and computational analysis. |
| Germline whole exome sequencing profile | End of study at 30 months | The results of whole exome sequencing of the germline genome. |
| Tumor genome whole exome sequencing profile | End of study at 30 months | The results of whole exome sequencing of the tumor genome. |
| Tumor transcriptome profile | End of study at 30 months | The results of RNA sequencing of the tumor transcriptome. |
| Single-cell sequencing profile | End of study at 30 months | The results of single cell sequencing analysis. |
| Cytometric profile | End of study at 30 months | The results of the cytometry by time of flight (CyTOF) analysis. |
| Signaling Pathways associated | End of study at 30 months | 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. |
| Enzymes associated with each signaling pathway identified | End of study at 30 months | Enzymes associated with each signaling pathway identified as determined by sequencing and computational analysis. |
| Improvement of cancer sequencing-guided treatment recommendations by machine learning | End of study at 30 months | 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. |
| Total number of somatic insertions (INS) per patient | 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. |
| Total number of somatic deletions (DEL) per patient | 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. |
| Number of SNVs per megabase of the MM genome | 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. |
| Number of INS per megabase of the MM genome | 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. |
| Number of DEL per megabase of the MM genome | End of study at 30 months | 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. |
| Number of mutations per megabase among MM subgroups | End of study at 30 months | 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. |
| Number of mutations per megabase among genomic regions for all MM and mutational subgroups | End of study at 30 months | 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. |
| Gene mutations identified | End of study at 30 months | 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. |
| Chromosomal abnormalities identified | 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. |
| Established Prognostic markers identified | End of study at 30 months | 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. |
| Molecular signatures identified | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORCesar Rodriguez Valdes, MD, PhD
Icahn School of Medicine at Mount Sinai
Outcome results
None listed