Conditional survival in multiple myeloma and impact of prognostic factors over time

A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches

Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behavior of M-protein and the transition across lines of therapy (LoT), which may be a consequence of disease progression, should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such as M-protein, and the competing risks of death and transition to the next LoT. Additionally, we explore two estimation approaches for our joint model: simultaneous estimation of all parameters (joint estimation) and sequential estimation of parameters using a corrected two-stage strategy aiming to reduce computational time. Our proposed model and estimation methods are applied to a retrospective cohort study from a real-world database of patients diagnosed with MM in the US from January 2015 to February 2022. We split the data into training and test sets in order to validate the joint model using both estimation approaches and make dynamic predictions of times until clinical events of interest, informed by longitudinally measured biomarkers and baseline variables available up to the time of prediction.

Patient-reported outcome domains in multiple myeloma randomized controlled trials and association with survival outcomes

Patient-reported outcomes (PROs) are crucial endpoints in multiple myeloma (MM) randomized controlled trials (RCTs), yet there is significant variability in their methodology and reporting. Our study aimed to (a) identify the most commonly pre-specified PRO domains in MM RCTs and those most responsive to modern therapies, and (b) examine the association between PROs and progression-free survival (PFS)/overall survival (OS). We performed a systematic review of MM RCTs that used EORTC QLQ-C30 and published between 01/2014-06/2023. The association between PFS/OS and PRO was explored using Fisher's exact test or Pearson's Chi-squared test. Thirty-five RCTs were identified, with PROs as secondary or exploratory endpoints in all studies. About one-third of RCTs (n=11, 31.4%) pre-specified at least one EORTC QLQ-C30 domain, with the most common domains being Global health status/Quality of life (GHS/QoL) (n = 10, 90.9%), Physical Functioning (n = 6, 54.5%), Fatigue (n = 6, 54.5%), and Pain (n = 6, 54.5%). A statistically significant and/or clinically meaningful difference in at least one EORTC QLQ-C30 domain between arms was seen in 23/35 trials (65.7%), with the most common domains showing improvement being GHS/QoL (12/23 trials), Pain (11/23 trials), Fatigue (9/23 trials), and Physical Functioning (9/23 trials). PRO was noted to be concordant with PFS in 19/33 (57.6%) trials (p = 0.398), and with OS in 22/31 (71%) trials (p = 0.018). Our study identified key PRO domains that can be potentially used as primary endpoint in MM RCTs. Additionally, significant association between PROs and OS highlight the importance of integrating PROs to better capture treatment efficacy.

Expression profile of Bcl-2 family proteins in newly diagnosed multiple myeloma patients

Antiapoptotic Bcl-2 family proteins are involved in myeloma cell survival. To date, their expression in multiple myeloma (MM) patients has mostly been analyzed at the RNA level. In the present study, we quantified for the first time the protein expression of the Bcl2-family members using a capillary electrophoresis immunoassay in 120 newly diagnosed MM patients, aged ≤65 years, treated in the context of the PETHEMA/GEM2012 study. We found that the pattern of expression of Bcl-2 family proteins was highly heterogeneous among patients. Although cases with t(11;14) had significantly higher levels of Bcl-2/Bcl-xL and Bcl-2+Bim+Bax/Bcl-xL ratios than those without t(11;14), the presence of this translocation was not synonymous with such high levels of expression. Conversely, some patients with other genetic alterations also showed higher levels of those ratios. Survival analysis revealed that the high expression of Bad and Puma proteins was associated with significantly longer overall survival (p = 0.001 and p < 0.001, respectively). Bcl-2 protein ratios predicting sensitivity to venetoclax in vitro were also able to distinguish patients with shorter time to progression after triplet-based induction therapy and ASCT. This is the first study to assess the expression of the most important Bcl-2 family proteins by a quantitative method in a large set of MM patients according to their cytogenetic abnormalities. We shed light on the impact of these proteins on MM prognosis, which could help to consider the levels of proteins involved in apoptosis in the development of new therapeutic strategies.

Cellular Therapies for Multiple Myeloma: Engineering Hope

Multiple myeloma (MM) treatment remains challenging due to its relapsed/refractory disease course as well as intra- and inter-patient heterogeneity. Cellular immunotherapies, especially chimeric antigen receptor (CAR)-T cells targeting B cell maturation antigen (BCMA), mark a major breakthrough, achieving long-lasting remissions and instilling hope for a potential cure. While ongoing clinical trials are increasingly driving approved cellular products towards earlier lines of therapy, novel targets as well as advanced approaches employing natural killer (NK) cells or dendritic cell (DC) vaccines are currently under investigation. Treatment resistance, driven by tumor-intrinsic factors such as antigen escape and the intricate dynamics of the tumor microenvironment (TME), along with emerging side effects such as movement and neurocognitive treatment-emergent adverse events (MNTs), are the major limitations of approved cellular therapies. To improve efficacy and overcome resistance, cutting-edge research is exploring strategies to target the microenvironment as well as synergistic combinatorial approaches. Recent advances in CAR-T cell production involve shortened manufacturing protocols and "off-the-shelf" CAR-T cells, aiming at decreasing socioeconomic barriers and thereby increasing patient access to this potential lifesaving therapy. In this review, we provide an extensive overview of the evolving field of cellular therapies for MM, underlining the potential to achieve long-lasting responses.

Galectin-9 - ligand axis: an emerging therapeutic target for multiple myeloma

Galectin-9 (Gal-9) is a tandem-repeat galectin with diverse roles in immune homeostasis, inflammation, malignancy, and autoimmune diseases. In cancer, Gal-9 displays variable expression patterns across different tumor types. Its interactions with multiple binding partners, both intracellularly and extracellularly, influence key cellular processes, including immune cell modulation and tumor microenvironment dynamics. Notably, Gal-9 binding to cell-specific glycoconjugate ligands has been implicated in both promoting and suppressing tumor progression. Here, we provide insights into Gal-9 and its involvement in immune homeostasis and cancer biology with an emphasis on multiple myeloma (MM) pathophysiology, highlighting its complex and context-dependent dual functions as a pro- and anti-tumorigenic molecule and its potential implications for therapy in MM patients.

Conditional survival and changing risk profile in patients with gliosarcoma

Background

Conditional survival (CS) considers the duration since the initial diagnosis and can provide supplementary informative insights. Our objective was to evaluate CS among gliosarcoma (GSM) patients and develop a CS-incorporated nomogram to predict the conditional probability of survival.

Methods

This retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database included patients with GSM between 2000 and 2017. The CS was defined as the probability of surviving additional y years after already surviving for x years. The formula utilized for CS was: CS(y|x) = S(y + x)/S(x), where S(x) denotes the overall survival at x years. Univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were used for significant prognostic factors screening. Following this, backward stepwise multivariable Cox regression was utilized to refine predictor selection. Finally, a novel CS-integrated nomogram model was developed and we also employed diverse evaluation methods to assess its performance.

Results

This study included a total of 1,015 GSM patients, comprising 710 patients in training cohort and 305 patients in validation cohort. CS analysis indicated a gradual increase in the probability of achieving a 5-year survival, ascending from 5% at diagnosis to 13, 31, 56, and 74% with each subsequent year survived after 1, 2, 3, and 4 years post-diagnosis, respectively. Following variable screening through univariate Cox regression, BSR, and LASSO analysis, five factors-age, tumor stage, tumor size, radiotherapy, and chemotherapy-were ultimately identified for constructing the CS-nomogram model. The performance of the nomogram model was validated through discrimination and calibration assessments in both the training and validation cohorts. Furthermore, we confirmed that the effectiveness of the CS-nomogram in stratifying GSM patient risk status.

Conclusion

This nationwide study delineated the CS of patients diagnosed with GSM. Utilizing national data, a CS-nomogram could provide valuable guidance for patient counseling during follow-up and risk stratification.

Trends in allogeneic hematopoietic cell transplantation survival using population-based descriptive epidemiology method: analysis of national transplant registry data

Prognosis for patients undergoing hematopoietic cell transplantation (HCT) has been improving. Short-term survival information, such as crude survival rates that consider deaths immediately after the transplantation, may not be sufficiently useful for assessing long-term survival. Using the data of the Japanese HCT registry, the net survival rate of patients who survived for a given period was determined according to age, disease, and type of transplant. We included a total of 41,716 patients who received their first allogeneic hematopoietic cell transplantation between 1991 and 2015. For each disease, age group, graft source subcategory, net survival was calculated using the Pohar-Perme method, and 5-year conditional net survival (CS) was calculated. Ten-year net survivals of total patient cohort were 41.5% and 47.4% for males and females, respectively. Except for myelodysplastic syndrome, multiple myeloma, and adult T-cell leukemia/lymphoma, 5-year CS for 5-year transplant survivors exceeded 90%. CS was especially high for aplastic anemia, of which was over 100% for children and younger adults receiving cord blood, suggesting that these patients have similar longevity to an equivalent group from the general population. These findings provide useful information for long-term survival, and can serve as benchmark for comparisons among registries, including other cancers.

The risk and survival of multiple myeloma as the second primary malignancy in a single Chinese center

Background

As the overall survival (OS) of patients with multiple myeloma (MM) improves, the incidence of second primary malignancy (SPM) in long-term complications increases. However, there are limited data regarding MM as a SPM. Therefore, this study aimed to determine the time trends in the incidence of MM, as well as the incidence and survival of patients with MM as the SPM.

Methods

Kaplan-Meier survival analysis was performed to determine the survival curve, while a log-rank test was used to determine OS.

Results

A total of 794 patients were diagnosed with MM among 7,921 patients with hematologic malignancy between 2009 and 2017. The incidence of MM showed an annual upward trend, increasing from 9.3% [2009-2011] to 10.8% [2015-2017]. Of the 794 patients with MM, 16 were diagnosed as the SPM commonly secondary to cancers of the lung (n=4), colon (n=3), breast (n=3), and other (n=6). The median survival of patients with MM as the SPM was 24.5 months (range, 1-95 months). The patients with MM without multiple malignancies had significantly longer survival (median, 46.5 months; range, 17-132 months; P=0.04).

Conclusions

This retrospective study suggests that the incidence of MM may be increasing annually and that the survival of patients with MM as the second primary malignant was significantly shorter than that of those without multiple malignancies.

Development of a cell adhesion-based prognostic model for multiple myeloma: Insights into chemotherapy response and potential reversal of adhesion effects

Multiple myeloma (MM) is a hematologic malignancy notorious for its high relapse rate and development of drug resistance, in which cell adhesion-mediated drug resistance plays a critical role. This study integrated four RNA sequencing datasets (CoMMpass, GSE136337, GSE9782, and GSE2658) and focused on analyzing 1706 adhesion-related genes. Rigorous univariate Cox regression analysis identified 18 key prognosis-related genes, including KIF14, TROAP, FLNA, MSN, LGALS1, PECAM1, and ALCAM, which demonstrated the strongest associations with poor overall survival (OS) in MM patients. To comprehensively evaluate the impact of cell adhesion on MM prognosis, an adhesion-related risk score (ARRS) model was constructed using Lasso Cox regression analysis. The ARRS model emerged as an independent prognostic factor for predicting OS. Furthermore, our findings revealed that a heightened cell adhesion effect correlated with tumor resistance to DNA-damaging drugs, protein kinase inhibitors, and drugs targeting the PI3K/Akt/mTOR signaling pathway. Nevertheless, we identified promising drug candidates, such as tirofiban, pirenzepine, erlotinib, and bosutinib, which exhibit potential in reversing this resistance. In vitro, experiments employing NCIH929, RPMI8226, and AMO1 cell lines confirmed that MM cell lines with high ARRS exhibited poor sensitivity to the aforementioned candidate drugs. By employing siRNA-mediated knockdown of the key ARRS model gene KIF14, we observed suppressed proliferation of NCIH929 cells, along with decreased adhesion to BMSCs and fibronectin. This study presents compelling evidence establishing cell adhesion as a significant prognostic factor in MM. Additionally, potential molecular mechanisms underlying adhesion-related resistance are proposed, along with viable strategies to overcome such resistance. These findings provide a solid scientific foundation for facilitating clinically stratified treatment of MM.

Microarray-Based CD38 Peptide Probe Screening for Multiple Myeloma Imaging

Assessing CD38 expression in vivo has become a significant element in multiple myeloma (MM) therapy, as it can be used to detect lesions and forecast the effectiveness of treatment. Accurate diagnosis requires a multifunctional, high-throughput probe screening platform to develop molecular probes for tumor-targeted multimodal imaging and treatment. Here, we investigated a microarray chip-based strategy for high-throughput screening of peptide probes for CD38. We obtained two new target peptides, CA-1 and CA-2, from a 105 peptide library with a dissociation constant (KD) of 10-7 M. The specificity and affinity of the target peptides were confirmed at the molecular and cellular levels. Peptide probes were labeled with indocyanine green (ICG) dye and 68Ga-DOTA, which were injected into a CD38-positive Ramos tumor-bearing mouse via its tail vein, and small animal fluorescence and positron emission tomography (PET) imaging showed that the peptide probes could show specific enrichment in the tumor tissue. Our study shows that a microchip-based screening of peptide probes can be used as a promising imaging tool for MM diagnosis.

Survival trends and conditional survival in primary non-metastatic esophageal cancer: a SEER population-based study and external validation

Background

The dynamic survival trend of patients with primary non-metastatic esophageal cancer (nMEC) is unknown. We conducted a conditional survival (CS) analysis and developed a novel nomogram to predict it.

Methods

Patients with primary nMEC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors of cancer-specific survival (CSS) were identified. The log-rank test and Cox analysis were used to calculate probabilities of CS. We constructed nomograms to predict survival trends and CS probabilities based on the prognostic factors. Calibration curves and C-indexes were used for internal and external validation.

Results

A total of 9,008 patients were identified from the SEER database and 37 patients were recruited as an external validation cohort. The 1- and 3-year CS rates were 69.6% and 43.1% at diagnosis, rising to 95.2% and 86.2% at the fifth conditional year. CS probabilities by different variables continuously improved over time. The calibration curves of the CS nomograms fit well. The C-indexes were 0.700 (95% CI: 0.693-0.709) in the training cohort, 0.693 (95% CI: 0.669-0.717) in the internal validation cohort, and 0.683 (95% CI: 0.556-0.810) in the external validation cohort.

Conclusions

CS rates are more dynamic than traditional survival rates for patients surviving for a relatively longer period. The CS rates of patients with nMEC improved over time and became stable after surviving for a few years. We developed and validated nomograms to predict CS probabilities.