Improving predictive accuracy in multiple myeloma using a plasma cell profile derived from single-cell RNA sequencing

Multiple myeloma (MM) shows inherent clinical and biological heterogeneity, leading to variable treatment responses and outcomes. The complex molecular landscape of MM makes precise risk stratification through clinical genetic testing difficult. Thus, identifying better biomarkers is essential to enhance existing stratification methods and guide personalized therapy decisions. Here, we systematically analyzed the intratumor heterogeneity of tumor cells from 12 newly diagnosed MM patients with different outcomes at single-cell resolution, especially those with an overall survival of less than two years, considered extremely high-risk in the real world. Among the eight heterogeneous tumor cell subclusters in these patients' myeloma cells, a particularly aggressive subset was discovered, characterized by severe chromosomal instability, high-level drug resistance, and high-risk genes. Survival analysis indicated that a high rate of this aggressive cell subset was associated with patients’ poor outcomes. We revealed a seven genes signature (LILRB4, CD74, TUBA1B, CCND2, HIST1H4C, ITGB7, and CRIP1) extremely highly expression within this aggressive myeloma cell subset. Multivariate Cox analysis showed that the 7-gene signature score was a worst factor for patients' outcome independent with cytogenetic aberrant and the International Staging System (ISS) stages. We then established an integrated risk stratification model combined with the 7-gene signature score. This model could significantly improve the risk discrimination capabilities, especially to distinguish the ultra-high-risk myeloma patients with the worst outcome in our cohort and validated in five independent datasets of MM patients. We further devised a simply digital PCR method for feasible quantifying the 7-gene signature, which still significantly differentiated the survival of MM patients and possesses considerable clinical application value. Overall, this integrated risk-scoring model derived from scRNA-seq data was significantly associated with a more advanced stage of myeloma patients, facilitating guided risk-adapted treatment strategies for such ultra-high-risk patients.