RNAseq-Based Machine Learning Models for Prognostication of Multiple Myeloma
Shah, K. U.; Millan, K. A.; Pula, A. E.; Kubicki, T. F.; Cannova, J.; Wu, S.; Bhagwat, M.; Guenther, Q. C.; Cooperrider, J.; Roloff, G.; Venkat, A.; Derman, B. A.; Jakubowiak, A. J.; Drazer, M. W.
Show abstract
BackgroundMultiple myeloma (MM) is characterized by abnormal plasma cell proliferation in the bone marrow, leading to symptoms like osteolytic lesions, anemia, hypercalcemia, and elevated serum creatinine. RNA-sequencing-based prognostic indicators for MM have shown promise in stratifying risk and assessing first-line treatment options. This study uses machine learning techniques and leverages RNA-sequencing, clinical, and biochemical data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass cohort to predict patient prognosis. MethodsRNAseq data of 60,623 genes from bone marrow samples of 708 MM patients were pre-processed for batch effect correction and split into training (70%) and testing (30%) sets. Feature selection involved MAD, mRMR, and iterative permutation importance filtering for predicting PFS and OS. Machine learning survival models like Random Survival Forest (RSF), Gradient Boosted (GB), and Component-wise Gradient Boosted (CGB) were developed and optimized. Performance was evaluated using C-index and integrated Brier score (IBS). ResultsThe RSF and GB models showed the highest performance for predicting progression-free survival (PFS) and overall survival (OS) on the testing dataset. Significant features for PFS included stem cell transplant status, serum {beta}2-microglobulin levels, germline mutational status, and expression of C12orf75 and ENSG00000256006. For OS, stem cell transplant status, age, serum {beta}2-microglobulin levels, germline mutational status, and expression of NUTM2B-AS1 and ENSG00000287022 were prominent. Gene ontology analyses confirmed the biological relevance of enriched pathways related to cell division, protein localization, and cancer. ConclusionIntegrating RNAseq and clinical data with advanced machine learning models presents a robust approach for predicting MM prognosis, highlighting gene expression programs, germline mutational status, and clinical markers as significant features. Future research should focus on independent validation to confirm findings and explore additional genomic data for enhanced prognostication.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Use of high-plex data reveals novel insights into the tumour microenvironment of clear cell renal cell carcinoma 93%
- Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data 93%
- Topological Structures in the Space of Treatment-Naive Patients With Chronic Lymphocytic Leukemia 92%
Similar papers in this journal
- Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation 92%
- Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics 91%
- Methylome analysis for prediction of long and short-term survival in glioblastoma patients from the Nordic trial 91%
Similar papers in this journal
- A Comprehensive Targeted Panel of 295 Genes: Unveiling Key Disease Initiating and Transformative Biomarkers in MultipleMyeloma 94%
- Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches 92%
- Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines 92%
Similar papers in this journal
- Predicting GD2 expression across cancer types by the integration of pathway topology and transcriptome data 92%
- Machine-learning-based determination of sex-related bladder cancer biomarkers 90%
- An algorithm for drug discovery based on deep learning with an example of developing a drug for the treatment of lung cancer 90%
Similar papers in this journal
- Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning 93%
- IOBR: Multi-omics Immuno-Oncology Biological Research to decode tumor microenvironment and signatures 91%
- Machine Learning Identifies Complicated Sepsis Trajectory and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.