Development and validation of a novel risk stratification algorithm for relapsed multiple myeloma

The prognostic utility of dynamic risk stratification at disease progression in patients with multiple myeloma

OBJECTIVES

There may be a shift in risk stratification at progression compared to that at diagnosis in patients with multiple myeloma (MM). We aimed to evaluate whether re-staging and stage migration is of prognostic impact.

METHODS

Real-world data from the National Longitudinal Cohort of Hematologic Diseases-multiple myeloma were collected; 263 consecutive patients demonstrating disease progression were finally included. Staging at diagnosis and re-staging at progression were performed using the International Staging System (ISS) and Revised International Staging System (RISS).

RESULTS

Based on ISS re-staging, the median post-progression survival (mPPS) of patients with stage I, II, and III was 44.2, 21.7, and 11.6 months, respectively (P < 0.0001). Based on RISS re-staging, the mPPS of patients with stage I, II, and III was 50.3, 22.2, and 11.4 months, respectively (P < 0.0001). The mPPS in patients with improved, maintained, and deteriorated ISS stage migration from diagnosis was 33.6, 20.9, and 16 months, respectively (P = 0.0051) and that with improved, maintained, and deteriorated RISS stage migration was 48.4, 23.1, and 13.9 months, respectively (P < 0.001). Compared to patients with maintained or improved disease stage, those with deteriorated ISS/RISS migration showed significantly higher incidence of Del(17P) at progression and worse PPS. Multivariate analyses indicated both re-staging and stage migration by ISS/RISS at progression were independent predictors for PPS.

CONCLUSIONS

We demonstrated that ISS/RISS re-staging showed superior prognostic utility over ISS/RISS staging in predicting PPS. Patients with deteriorated stage migration or maintained advanced stage at progression may need more individualized treatment.

Elderly patients with stage II gastric cancer do not benefit from adjuvant chemotherapy

BACKGROUND

With the aging of the population, the burden of elderly gastric cancer (EGC) increases worldwide. However, there is no consensus on the definition of EGC and the efficacy of adjuvant chemotherapy in patients with stage II EGC. Here, we investigated the effectiveness of adjuvant chemotherapy in defined EGC patients.

METHODS

We enrolled 5762 gastric cancer patients of three independent cohorts from the Sixth Affiliated Hospital of Sun Yat-sen University (local), the Surveillance, Epidemiology, and End Results (SEER), and the Asian Cancer Research Group (ACRG). The optimal age cutoff for EGC was determined using the K-adaptive partitioning algorithm. The defined EGC group and the efficacy of adjuvant chemotherapy for them were confirmed by Cox regression and Kaplan-Meier survival analyses. Furthermore, gene set variation analyses (GSVA) were performed to reveal pathway enrichment between groups.

RESULTS

The optimal age partition value for EGC patients was 75. In the local, SEER, and ACRG cohorts, the EGC group exhibited significantly worse overall survival and cancer-specific survival than the non-EGC group (P < 0.05) and was an independent risk factor. Stratified analyses based on chemotherapy showed that EGC patients derived little benefit from adjuvant chemotherapy. Furthermore, GSVA analysis revealed the activation of DNA repair-related pathways and downregulation of the p53 pathway, which may partially contribute to the observed findings.

CONCLUSION

In this retrospective, international multi-center study, 75 years old was identified as the optimal age cutoff for EGC definition, and adjuvant chemotherapy proved to be unbeneficial for stage II EGC patients.

Efficacy and safety profile of deep responders to carfilzomib-based therapy: a subgroup analysis from ASPIRE and ENDEAVOR

To understand the profile of best responders (complete response or better [≥CR]) to carfilzomib, we described the characteristics, progression-free survival (PFS), overall survival (OS) data, and the safety of patients who achieved ≥CR to carfilzomib-based treatment in ASPIRE and ENDEAVOR. In post hoc analyses from ASPIRE and ENDEAVOR, median PFS and OS were longer for ≥CR patients versus those who achieved a very good partial response or partial response (VGPR/PR). In the carfilzomib arm of ASPIRE, median PFS was 50.4 months for ≥CR versus 22.1 months for VGPR/PR; median OS was 67.0 versus 44.2 months, respectively. In the carfilzomib arm of ENDEAVOR, median PFS was 34.0 for ≥CR versus 20.4 months for VGPR/PR; median OS was non-estimable. Despite the longer treatment duration, fewer patients with ≥CR versus VGPR/PR experienced treatment-emergent adverse events that led to discontinuation of carfilzomib-based treatment in ASPIRE or ENDEAVOR. Low serum lactate dehydrogenase was the only factor associated with achieving ≥CR vs patients not achieving CR in ASPIRE in multivariate regression analyses. No association was found between cytogenetic risk status and reaching ≥CR. Carfilzomib treatment may lead to rapid and deep responses, irrespective of most patient characteristics.

Methodology of a Novel Risk Stratification Algorithm for Patients with Multiple Myeloma in the Relapsed Setting

INTRODUCTION

Risk stratification tools provide valuable information to inform treatment decisions. Existing algorithms for patients with multiple myeloma (MM) were based on patients with newly diagnosed disease, and these have not been validated in the relapsed setting or in routine clinical practice. We developed a risk stratification algorithm (RSA) for patients with MM at initiation of second-line (2L) treatment, based on data from the Czech Registry of Monoclonal Gammopathies.

METHODS

Predictors of overall survival (OS) at 2L treatment were identified using Cox proportional hazards models and backward selection. Risk scores were obtained by multiplying the hazard ratios for each predictor. The K-adaptive partitioning for survival (KAPS) algorithm defined four groups of stratification based on individual risk scores.

RESULTS

Performance of the RSA was assessed using Nagelkerke's R² test and Harrell's concordance index through Kaplan-Meier analysis of OS data. Prognostic groups were successfully defined based on real-world data. Use of a multiplicative score based on Cox modeling and KAPS to define cut-off values was effective.

CONCLUSION

Through innovative methods of risk assessment and collaboration between physicians and statisticians, the RSA was capable of stratifying patients at 2L treatment by survival expectations. This approach can be used to develop clinical decision-making tools in other disease areas to improve patient management.

FUNDING

Amgen Europe GmbH.

Novel risk stratification algorithm for estimating the risk of death in patients with relapsed multiple myeloma: external validation in a retrospective chart review

OBJECTIVES AND DESIGN

A novel risk stratification algorithm estimating risk of death in patients with relapsed multiple myeloma starting second-line treatment was recently developed using multivariable Cox regression of data from a Czech registry. It uses 16 parameters routinely collected in medical practice to stratify patients into four distinct risk groups in terms of survival expectation. To provide insight into generalisability of the risk stratification algorithm, the study aimed to validate the risk stratification algorithm using real-world data from specifically designed retrospective chart audits from three European countries.

PARTICIPANTS AND SETTING

Physicians collected data from 998 patients (France, 386; Germany, 344; UK, 268) and applied the risk stratification algorithm.

METHODS

The performance of the Cox regression model for predicting risk of death was assessed by Nagelkerke's R2, goodness of fit and the C-index. The risk stratification algorithm's ability to discriminate overall survival across four risk groups was evaluated using Kaplan-Meier curves and HRs.

RESULTS

Consistent with the Czech registry, the stratification performance of the risk stratification algorithm demonstrated clear differentiation in risk of death between the four groups. As risk groups increased, risk of death doubled. The C-index was 0.715 (95% CI 0.690 to 0.734).

CONCLUSIONS

Validation of the novel risk stratification algorithm in an independent 'real-world' dataset demonstrated that it stratifies patients in four subgroups according to survival expectation.

New Markers of Renal Failure in Multiple Myeloma and Monoclonal Gammopathies

* Correspondence: kasiajanda@op [...].