Next Generation Sequencing-based Validation of the Revised International Staging System for Multiple Myeloma: An Analysis of the MMRF CoMMpass Study

Quantitative Integrative Survival Prediction in Multiple Myeloma Patients Treated With Bortezomib-Based Induction, High-Dose Therapy and Autologous Stem Cell Transplantation

PURPOSE

Given the high heterogeneity in survival for patients with multiple myeloma, it would be clinically useful to quantitatively predict the individual survival instead of attributing patients to two to four risk groups as in current models, for example, revised International Staging System (R-ISS), R2-ISS, or Mayo-2022-score.

PATIENTS AND METHODS

Our aim was to develop a quantitative prediction tool for individual patient's 3-/5-year overall survival (OS) probability. We integrated established clinical and molecular risk factors into a comprehensive prognostic model and evaluated and validated its risk discrimination capabilities versus R-ISS, R2-ISS, and Mayo-2022-score.

RESULTS

A nomogram for estimating OS probabilities was built on the basis of a Cox regression model. It allows one to translate the individual risk profile of a patient into 3-/5-year OS probabilities by attributing points to each prognostic factor and summing up all points. The nomogram was externally validated regarding discrimination and calibration. There was no obvious bias or overfitting of the prognostic index on the validation cohort. Resampling-based and external evaluation showed good calibration. The c-index of the model was similar on the training (0.76) and validation cohort (0.75) and significantly higher than for the R-ISS (P < .001) or R2-ISS (P < .01).

CONCLUSION

In summary, we developed and validated individual quantitative nomogram-based OS prediction. Continuous risk assessment integrating molecular prognostic factors is superior to R-ISS, R2-ISS, or Mayo-2022-score alone.

Recent Trends in Nanocarrier-Based Drug Delivery System for Prostate Cancer

Prostate cancer remains a significant global health concern, requiring innovative approaches for improved therapeutic outcomes. In recent years, nanoparticle-based drug delivery systems have emerged as promising strategies to address the limitations of conventional cancer chemotherapy. The key trends include utilizing nanoparticles for enhancing drug delivery to prostate cancer cells. Nanoparticles have some advantages such as improved drug solubility, prolonged circulation time, and targeted delivery of drugs. Encapsulation of chemotherapeutic agents within nanoparticles allows for controlled release kinetics, reducing systemic toxicity while maintaining therapeutic efficacy. Additionally, site-specific accumulation within the prostate tumor microenvironment is made possible by the functionalization of nanocarrier with targeted ligands, improving therapeutic effectiveness. This article highlights the basics of prostate cancer, statistics of prostate cancer, mechanism of multidrug resistance, targeting approach, and different types of nanocarrier used for the treatment of prostate cancer. It also includes the applications of nanocarriers for the treatment of prostate cancer and clinical trial studies to validate the safety and efficacy of the innovative drug delivery systems. The article focused on developing nanocarrier-based drug delivery systems, with the goal of translating these advancements into clinical applications in the future.

RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma

Background

Immunotherapeutic targets in multiple myeloma (MM) have variable expression height and are partly expressed in subfractions of patients only. With increasing numbers of available compounds, strategies for appropriate choice of targets (combinations) are warranted. Simultaneously, risk assessment is advisable as patient's life expectancy varies between months and decades.

Methods

We first assess feasibility of RNA-sequencing in a multicenter trial (GMMG-MM5, n=604 patients). Next, we use a clinical routine cohort of untreated symptomatic myeloma patients undergoing autologous stem cell transplantation (n=535, median follow-up (FU) 64 months) to perform RNA-sequencing, gene expression profiling (GEP), and iFISH by ten-probe panel on CD138-purified malignant plasma cells. We subsequently compare target expression to plasma cell precursors, MGUS (n=59), asymptomatic (n=142) and relapsed (n=69) myeloma patients, myeloma cell lines (n=26), and between longitudinal samples (MM vs. relapsed MM). Data are validated using the independent MMRF CoMMpass-cohort (n=767, FU 31 months).

Results

RNA-sequencing is feasible in 90.8% of patients (GMMG-MM5). Actionable immune-oncological targets (n=19) can be divided in those expressed in all normal and >99% of MM-patients (CD38, SLAMF7, BCMA, GPRC5D, FCRH5, TACI, CD74, CD44, CD37, CD79B), those with expression loss in subfractions of MM-patients (BAFF-R [81.3%], CD19 [57.9%], CD20 [82.8%], CD22 [28.4%]), aberrantly expressed in MM (NY-ESO1/2 [12%], MUC1 [12.7%], CD30 [4.9%], mutated BRAF V600E/K [2.1%]), and resistance-conveying target-mutations e.g., against part but not all BCMA-directed treatments. Risk is assessable regarding proliferation, translated GEP- (UAMS70-, SKY92-, RS-score) and de novo (LfM-HRS) defined risk scores. LfM-HRS delineates three groups of 40%, 38%, and 22% of patients with 5-year and 12-year survival rates of 84% (49%), 67% (18%), and 32% (0%). R-ISS and RNA-sequencing identify partially overlapping patient populations, with R-ISS missing, e.g., 30% (22/72) of highly proliferative myeloma.

Conclusion

RNA-sequencing based assessment of risk and targets for first choice treatment is possible in clinical routine.

Current Main Topics in Multiple Myeloma

Multiple Myeloma (MM) remains a difficult to treat disease mainly due to its biological heterogeneity, of which we are more and more knowledgeable thanks to the development of increasingly sensitive molecular methods that allow us to build better prognostication models. The biological diversity translates into a wide range of clinical outcomes from long-lasting remission in some patients to very early relapse in others. In NDMM transplant eligible (TE) patients, the incorporation of mAb as daratumumab in the induction regimens, followed by autologous stem cell transplantation (ASCT) and consolidation/maintenance therapy, has led to a significant improvement of PFS and OS.; however, this outcome remains poor in ultra-high risk MM or in those who did not achieve a minimal residual disease (MRD) negativity. Several trials are exploring cytogenetic risk-adapted and MRD-driven therapies in these patients. Similarly, quadruplets-containing daratumumab, particularly when administered as continuous therapies, have improved outcome of patients not eligible for autologous transplant (NTE). Patients who become refractory to conventional therapies have noticeably poor outcomes, making their treatment a difficult challenge in need of novel strategies. In this review, we will focus on the main points regarding risk stratification, treatment and monitoring of MM, highlighting the most recent evidence that could modify the management of this still incurable disease.

Evaluation of the Simplified Score to Predict Early Relapse in Multiple Myeloma (S-ERMM) in the MMRF CoMMpass study

BACKGROUND

Zaccaria and colleagues recently proposed a new risk score to identify patients at high risk for relapse within 18 months of diagnosis (ER18), the Score for Early Relapse in Multiple Myeloma (S-ERMM). We performed external validation of the S-ERMM using data from the CoMMpass study.

PATIENTS AND METHODS

Clinical data was obtained from the CoMMpass study. Patients were assigned S-ERMM risk scores and risk categories by the three iterations of the International Staging System (ISS): ISS, R-ISS and R2-ISS. Patients with missing data or early mortality in remission were excluded. Our primary endpoint was the relative predictive ability of the S-ERMM versus other risk scores for ER18 as assessed by area-under-the-curve (AUC).

RESULTS

476 patients had adequate data to assign all four risk scores. 65%, 25% and 10% were low, intermediate and high risk by S-ERMM. 17% experienced ER18. All four risk scores stratified patients by risk for ER18. S-ERMM (AUC: 0.59 [95% CI 0.53-0.65]) was similar to R-ISS (0.63 [95% CI 0.58-0.69]) and statistically inferior to ISS (0.68 [95% CI 0.62-0.75]) and R2-ISS (0.66 [95% CI 0.61-0.72]) for prediction of ER18. Sensitivity analyses were performed and did not significantly impact results.

CONCLUSION

The S-ERMM risk score is not superior to existing risk stratification systems for predicting early relapse in NDMM and further studies are needed to identify the optimal approach.

Reducing healthcare disparities using multiple multiethnic data distributions with fine-tuning of transfer learning

Healthcare disparities in multiethnic medical data is a major challenge; the main reason lies in the unequal data distribution of ethnic groups among data cohorts. Biomedical data collected from different cancer genome research projects may consist of mainly one ethnic group, such as people with European ancestry. In contrast, the data distribution of other ethnic races such as African, Asian, Hispanic, and Native Americans can be less visible than the counterpart. Data inequality in the biomedical field is an important research problem, resulting in the diverse performance of machine learning models while creating healthcare disparities. Previous researches have reduced the healthcare disparities only using limited data distributions. In our study, we work on fine-tuning of deep learning and transfer learning models with different multiethnic data distributions for the prognosis of 33 cancer types. In previous studies, to reduce the healthcare disparities, only a single ethnic cohort was used as the target domain with one major source domain. In contrast, we focused on multiple ethnic cohorts as the target domain in transfer learning using the TCGA and MMRF CoMMpass study datasets. After performance comparison for experiments with new data distributions, our proposed model shows promising performance for transfer learning schemes compared to the baseline approach for old and new data distributation experiments.

A simple additive staging system for newly diagnosed multiple myeloma

Risk stratification in multiple myeloma is important for prognostication, patient selection for clinical trials, and comparison of treatment approaches. We developed and validated a staging system that incorporates additional FISH abnormalities not included in the R-ISS and reflects the additive effects of co-occurring high-risk disease features. We first evaluated the prognostic value of predefined cytogenetic and laboratory abnormalities in 2556 Mayo Clinic patients diagnosed between February 2004 and June 2019. We then used data from 1327 patients to develop a risk stratification model and validated this in 502 patients enrolled in the MMRF CoMMpass study. On multivariate analysis, high-risk IgH translocations [risk ratio (RR): 1.7], 1q gain/amplification (RR: 1.4), chromosome17 abnormalities (RR: 1.6), ISS III (RR: 1.7), and elevated LDH (RR: 1.3) were independently associated with decreased overall survival (OS). Among 1327 evaluable patients, OS was 11.0 (95% CI: 9.2–12.6), 7.0 (95% CI: 6.3–9.2), and 4.5 (95% CI: 3.7–5.2) years in patients with 0 (stage I), 1 (stage II), and ≥2 (stage III) high-risk factors, respectively. In the MMRF cohort, median OS was 7.8 (95% CI: NR-NR), 6.0 (95% CI: 5.7-NR), and 4.3 (95% CI: 2.7-NR) years in the 3 groups, respectively (P < 0.001). This 5-factor, 3-tier system is easy to implement in practice and improves upon the current R-ISS.

Prognostic and predictive performance of R-ISS with SKY92 in older patients with multiple myeloma: the HOVON-87/NMSG-18 trial

The standard prognostic marker for multiple myeloma (MM) patients is the revised International Staging System (R-ISS). However, there is room for improvement in guiding treatment. This applies particularly to older patients, in whom the benefit/risk ratio is reduced because of comorbidities and subsequent side effects. We hypothesized that adding gene-expression data to R-ISS would generate a stronger marker. This was tested by combining R-ISS with the SKY92 classifier (SKY-RISS). The HOVON-87/NMSG-18 trial (EudraCT: 2007-004007-34) compared melphalan-prednisone-thalidomide followed by thalidomide maintenance (MPT-T) with melphalan-prednisone-lenalidomide followed by lenalidomide maintenance (MPR-R). From this trial, 168 patients with available R-ISS status and gene-expression profiles were analyzed. R-ISS stages I, II, and III were assigned to 8%, 75%, and 7% of patients, respectively (3-year overall survival [OS] rates: 80%, 65%, 33%, P = 8 × 10-3). Using the SKY92 classifier, 13% of patients were high risk (HR) (3-year OS rates: standard risk [SR], 70%; HR, 28%; P < .001). Combining SKY92 with R-ISS resulted in 3 risk groups: SKY-RISS I (SKY-SR + R-ISS-I; 15%), SKY-RISS III (SKY-HR + R-ISS-II/III; 11%), and SKY-RISS II (all other patients; 74%). The 3-year OS rates for SKY-RISS I, II, and III are 88%, 66%, and 26%, respectively (P = 6 × 10-7). The SKY-RISS model was validated in older patients from the CoMMpass dataset. Moreover, SKY-RISS demonstrated predictive potential: HR patients appeared to benefit from MPR-R over MPT-T (median OS, 55 and 14 months, respectively). Combined, SKY92 and R-ISS classify patients more accurately. Additionally, benefit was observed for MPR-R over MPT-T in SKY92-RISS HR patients only.

Concepts of Double Hit and Triple Hit Disease in Multiple Myeloma, Entity and Prognostic Significance

Risk assessment in newly diagnosed multiple myeloma patients (NDMM) is the first and the most crucial determinant of treatment. With the utilization of FISH analysis as a part of routine practice, high risk Multiple Myeloma (MM) is defined as having at least one of the mutations related with poor prognosis including; t(4;14) t(14;16), t(14;20), del 17p, p53 mutation, gain 1q and del 1p. M-Smart MM risk stratification guideline by Mayo Clinic has proposed a concept similar to high grade lymphomas. Having two of the high risk genetic abnormalities were defined as double hit MM and having any three as triple hit MM. Based on these definitions which may bring a much more clinically relatable understanding in MM prognosis, we aimed to assess our database regarding these two concepts and their probable significance in terms of outcome and prognosis. We retrospectively evaluated 159 newly diagnosed multiple myeloma patients and their clinical course. Among these patients; twenty-four patients have one high risk determinant and also seven and two patients were classified as double hit MM and triple hit MM respectively. Overall survival (OS) of the patients with double hit MM was 6 months, 32.0 months for patients with single high risk abnormality and 57.0 months for patients with no high risk abnormality. Univariate analysis showed that Double Hit and Triple Hit MM is a predictive of low OS. Hazard Ratio of patients with one high risk abnormality was 1.42, double-hit MM patients was 5.55, and triple-hit MM patients was 7.3. Despite the development of novel drugs and their effects of prolonging survival, the treatment has not been individualized. Understanding the biology of each patient as a unique process will be the success of the treatment. As it is known that some MM patients harbor high risk genetic abnormalities according to FISH analysis, we can continue the argument that some patients bring an even higher risk and that can be defined as double or triple hit MM.

Mate pair sequencing outperforms fluorescence in situ hybridization in the genomic characterization of multiple myeloma

Fluorescence in situ hybridization (FISH) is currently the gold-standard assay to detect recurrent genomic abnormalities of prognostic significance in multiple myeloma (MM). Since most translocations in MM involve a position effect with heterogeneous breakpoints, we hypothesize that FISH has the potential to miss translocations involving these regions. We evaluated 70 bone marrow samples from patients with plasma cell dyscrasia by FISH and whole-genome mate-pair sequencing (MPseq). Thirty cases (42.9%) displayed at least one instance of discordance between FISH and MPseq for each primary and secondary abnormality evaluated. Nine cases had abnormalities detected by FISH that went undetected by MPseq including 6 tetraploid clones and three cases with missed copy number abnormalities. In contrast, 19 cases had abnormalities detected by MPseq that went undetected by FISH. Seventeen were MYC rearrangements and two were 17p deletions. MPseq identified 36 MYC abnormalities and 17 (50.0% of MYC abnormal group with FISH results) displayed a false negative FISH result. MPseq identified 10 cases (14.3%) with IgL rearrangements, a recent marker of poor outcome, and 10% with abnormalities in genes associated with lenalidomide response or resistance. In summary, MPseq was superior in the characterization of rearrangement complexity and identification of secondary abnormalities demonstrating increased clinical value compared to FISH.

Systems Biology of Cancer Metastasis

Cancer metastasis is no longer viewed as a linear cascade of events but rather as a series of concurrent, partially overlapping processes, as successfully metastasizing cells assume new phenotypes while jettisoning older behaviors. The lack of a systemic understanding of this complex phenomenon has limited progress in developing treatments for metastatic disease. Because metastasis has traditionally been investigated in distinct physiological compartments, the integration of these complex and interlinked aspects remains a challenge for both systems-level experimental and computational modeling of metastasis. Here, we present some of the current perspectives on the complexity of cancer metastasis, the multiscale nature of its progression, and a systems-level view of the processes underlying the invasive spread of cancer cells. We also highlight the gaps in our current understanding of cancer metastasis as well as insights emerging from interdisciplinary systems biology approaches to understand this complex phenomenon.