HLA genotyping in Japanese patients with multiple myeloma receiving bortezomib: An exploratory biomarker study of JCOG1105 (JCOG1105A1)

Serum indicators in functional high-risk multiple myeloma patients undertaking proteasome inhibitors therapy: a retrospective study

OBJECTIVES

Multiple myeloma (MM) is a class of malignant plasma cell diseases. An increasing application of autologous stem cell transplantation (ASCT) and anti-myeloma agents represented by proteasome inhibitors (PIs) has improved the response rates and survival of MM patients. Patients progressing within 12 months were recently categorized with functional high-risk (FHR), which could not be clarified by existing genetic risk factors, with poor outcomes. Our study aimed to investigate clinical indices related to FHR and seek prognostic roles in transplant-eligible MM patients.

METHODS

Demographic and individual baseline clinical characteristics were compared by using the Pearson's chi-square and Mann-Whitney U test. Progression-free survival (PFS) and overall survival (OS) were described by Kaplan-Meier estimates and compared using the log-rank test. Logistic regression analysis was used to assess the association of baseline characteristics at MM diagnosis with FHR status.

RESULTS

From 18th January 2010 to 1st December 2022, 216 patients were included and divided into two groups according to the FHR status. There was no difference in baseline data between the two groups. Renal impairment (RI, Scr > 2 mg/dL) was common in MM patients and made sense in FHR status. AST levels were validated as independent predictors for FHR status (p = 0.019).

DISCUSSION

Patients with RI or higher AST levels (AST > 40 U/L) tended to have worse outcomes. However, transplants had apparently improved prognoses.

CONCLUSION

Therefore, in the PIs era, transplantations are still effective therapies for transplant-eligible MM patients.

Immunodiagnosis - the promise of personalized immunotherapy

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumor heterogeneity and immune status before treatment is key to identifying patients that are more likely to respond to immunotherapy. Demographic characteristics (such as sex, age, and race), immune status, and specific biomarkers all contribute to response to immunotherapy. A comprehensive immunodiagnostic model integrating all these three dimensions by artificial intelligence would provide valuable information for predicting treatment response. Here, we coined the term "immunodiagnosis" to describe the blueprint of the immunodiagnostic model. We illustrated the features that should be included in immunodiagnostic model and the strategy of constructing the immunodiagnostic model. Lastly, we discussed the incorporation of this immunodiagnosis model in clinical practice in hopes of improving the prognosis of tumor immunotherapy.