Project description:Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. Instead of discretizing the problem with a discrete Markov Decision Process, we apply the Partially-Observed Monte-Carlo Planning algorithm to solve the continuous-time, continuous-state problem, leveraging the near-deterministic nature of cancer progression. Our approach, tested on multiple myeloma patient data, outperforms exact solutions of the discrete model and allows greater flexibility in cost function modeling, enabling patient-specific follow-ups. This method can also be adapted to other diseases.

| S-EPMC11658637 | biostudies-literature

Multiple Myeloma follow-up sequencing study

Project description:A total of 40 Multiple Myeloma (MM) patients at clinical relapse who progressed during Proteasome Inhibitors (PIs) or Immunomodulating Drugs (IMiDs)-based therapies and who are assigned to antiCD38-based salvage treatments, will be enrolled. We will collect bone marrow (BM) and peripheral blood (PB) samples from patients at specific timepoints: baseline (BM, PB and buccal swab) every 3 month (PB) achievement of response (Ã¢Â‰Â¥ Very Good Partial Response (VGPR)) (BM and PB) relapse or refractory status to antiCD38-based treatments (BM and PB) Samples will be processed and stored in the "Hematological Laboratory" located in the University of Turin (Italy) for various proposed analyses: at specific time-points CD138+ (Plasma Cells-PCs) awill be immunomagnetically enriched from the BM mononuclear cells and frozen as viable cells in dimethyl sulfoxide (DMSO); PB mononuclear cells (PBMCs) will be isolated from whole blood by density-gradient centrifugation, and frozen as above; plasma fraction from PB and BM will be obtained by centrifugation and stored frozen; Neutrophils will be obtained at the time of enrollment as a source of control germline DNA and stored frozen.

| EGAS00001007092 | EGA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018662 | ENA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018664 | ENA

Data Access Commitee for single-cell analysis of multiple myeloma and precursors

Project description:Data Access Committee EGAC00001002920

| EGAC00001002920 | EGA

Dexamethasone-resistant multiple myeloma cell line OPM1 treated with Dexamethasone and/or DAC

Project description:To identify genes responsible for the synergistic effect of DAC with Dex, we performed cDNA microarray analyses using cDNA prepared from Dex-resistant OPM1 cells treated with/without Dex, DAC or DAC+Dex.

2009-01-03 | GSE14261 | GEO

Multiple Myeloma: EHA-ESMO Clinical Practice Guidelines for Diagnosis, Treatment and Follow-up.

Project description: Not available

| S-EPMC7861652 | biostudies-literature