Citations

Introduction: In many cases, treatment decisions for multiple myeloma patients must be made in the absence of high quality randomized controlled clinical trials. As a result, many clinicians lean on prognostic models to drive treatment selection. The most widely used of these models incorporate a limited number of patient related factors that reflect tumor burden, cytogenetic features, or gene expression profiling. These prediction scores are derived from regression models that incorporate these variables at the time of diagnosis. They do not account for variables that change dynamically over time, do not explore the relationship between treatment selection and progression, and imperfectly predict survival outcomes. We designed a prognostic model based on an ensemble machine learning platform to predict progression in NDMM using Extreme Gradient Boosting Machine (XGBoost) combined with accelerated failure time modeling for survival analysis. Methods: We utilized a large retrospective data set containing treatment and response information for 1127 patients with newly diagnosed multiple myeloma (NDMM) treated at the Cleveland Clinic Foundation between 2000 and 2023. Following data preprocessing based on data completeness, 953 patient records were included in our training, testing, and cross validation datasets. For the purposes of our analysis, the initial dataset was randomly split into training (70%), testing (15%) and validation (15%) subsets at the patient level. We also defined the lower and upper bounds for each subset, which is critical due to the right-censored nature of survival data. Hyperparameters for XGBoost were optimized using Bayesian search, minimizing the negative log-likelihood. A random forest method was applied for the imputation of missing data (MissForest). We then trained the XGBoost model using GPU acceleration for enhanced computational efficiency. Finally, a log-rank test on predicted survival times was used to test the model's performance on patients with and without known progression. Results: Our preprocessed data set had 953 patients with a mean age at disease onset of 65 years and a slight male predominance (55%). Approximately 27% of patients harbored high cytogenetic risk disease and 3.5% of the cohort presented with extramedullary disease a diagnosis. At a median follow up of 35 months, 47% of patients had experienced disease progression or death. Induction therapy included an immunomodulatory drug in 40% of patients and proteasome inhibitors in 22%. Frontline autologous stem cell transplantation followed induction in 28% of patients. Median progression free survival in the overall data set was 44 months. These features were consistent across the randomly assigned training, testing, and validation cohorts. Following data preprocessing, 34 independent clinical and genomic variables including selection of first line treatment were assessed as model inputs each available record. Under optimized parameters, the model was trained with a maximum tree depth = 7 and a learning rate ~ 0.22 for the desired output of progression free survival (PFS). We then queried PFS for both the training and validation sets and divided patients into those known to have progressed and those without a progression event. Survival analysis was undertaken using predicted PFS values for patients known to have progressed and those who remained progression free. For the validation data set, our model successfully discerned between progressed and non-progressed cases (log-rank test statistic = 22.07, p < 0.005) (Figure 1). Discussion: We present a machine learning approach based on regularized gradient boosting that accurately discerns between patients who experience progression and those who remain progression free at a median ~35 months of follow-up in a large retrospective data set in patients presenting with NDMM. Further elaboration of our model will allow for the incorporation of large amounts data to predict survival outcomes on the basis of dynamic variables such as depth of response and treatment selection. With the ability to parse outcomes among multiple myeloma patients at high resolution, future clinicians and clinical trialists may be able to overcome limitations in trial design and patient-level therapy selection for both newly diagnosed and relapsed patients.

Purpose: Improved predictive tools are desired in classic Hodgkin lymphoma (cHL) to improve prognostication and to better risk stratify patients (pts) for clinical trials. In younger pts who tolerate intensive therapies, focus has been on limiting late treatment toxicity through risk-adapted, PET-guided treatment strategy. In older pts, acute treatment toxicities are limiting factors for optimal disease control. Due to these fundamental differences, predictive tools may perform differently in younger and older pts. In particular, tools developed in younger pts may be suboptimal in older populations and vice versa. The primary objective of this study was to assess the performance of the A-HIPI (development was limited to 18-65 years of age) against the international prognostic score (IPS) for modelling overall survival (OS) and progression-free survival (PFS) in younger and older pts with cHL. Methods: This study utilized data from the population-based, nationwide Danish National Lymphoma registry. Pts with advanced-stage cHL (Ann Arbor stage III-IV or IIB with extra nodal disease) were included and categorized by age in two groups: ages ≤65 years vs. >65 years. In the primary analysis, all pts regardless of therapy were included. A sensitivity analysis restricting to pts starting on BEACOPP or ABVD was performed (Only ABVD in the >65 cohort). Concordance index (C-index) and calibration were used to assess model performance and generalizability. Calibration was visualized using calibration plots and quantified by calibration in the large (CITL) and calibration slope (CS) at 5-years post diagnosis. Results: A total of 893 advanced-stage HL pts fulfilled the inclusion criteria (634 in the ≤65 cohort and 259 in the >65 cohort). The 5-year OS and PFS in the >65-year cohort were 44% (Confidence intervals (CI); 37-51%) and 35% (CI; 30-42%), respectively. Fifty-six pts in the >65 cohort were classified as low-intermediate-risk (IPS; 1-2), 128 intermediate-risk (IPS; 3-4), and 75 high-risk (IPS; 5-7). For the ≤65 cohort, the 5-year OS and PFS was 88% (CI; 85-90%) and 76% (CI; 73-80%), respectively. Twenty-three pts were classified as low-risk (IPS; 0), 244 low-intermediate-risk, 286 intermediate-risk, and 81 high-risk. The A-HIPI had a C-index of 0.74 and 0.63 for OS and PFS, respectively in the ≤65 years cohort vs. 0.65 and 0.64 for OS and PFS, respectively, in the >65 cohort. The IPS had a C-index in the ≤65 cohort of 0.66 and 0.60 for OS and PFS, respectively, vs. 0.58 and 0.59 for OS and PFS, respectively, in the >65 cohort (i.e., the IPS performed better in the ≤65 cohort in terms of OS and PFS but was inferior to the A-HIPI). When restricting to pts treated with ABVD or BEACOPP, the A-HIPI C-index in the >65 cohort (116 pts) were reduced to 0.55 for OS and 0.54 for PFS. For the ≤65 cohort (595 pts), the A-HIPI C-index was stable at 0.75 and 0.62 for OS and PFS, respectively. In terms of A-HIPI calibration in the >65 cohort, CITL and CS were -0.78 and 0.39 for OS vs. -0.98 and 0.80 for PFS. In the ≤65 cohort, CITL and CS were -0.06 and 0.99 for OS vs. 0.21 and 1.18 for PFS. Visual inspection of the calibration plot (Figure 1) for the >65 cohort showed that the A-HIPI underestimated the observed proportion for both OS and PFS (i.e., the observed events were higher than the predicted). Calibration plot for the ≤65 cohort (Figure 2) indicated that the A-HIPI accurately modelled the observed proportion for both OS and PFS (i.e., the predicted probability was consistent with the observed proportion). Conclusion: The newly developed A-HIPI was validated in a large population-based cohort of Danish pts with advanced-stage cHL. Performance of the A-HIPI was high in the ≤65 cohort and superior to the IPS for OS prediction. Calibration of A-HIPI was excellent in pts ages ≤65 years. However, A-HIPI performed significantly worse among pts >65 years and attained poor performance in pts receiving ABVD or BEACOPP, which is not surprising since patients older than 65 were not included in the development. This study emphasizes the importance of further efforts to develop or update models that apply specifically in older individuals. The inclusion of additional variables reflecting comorbidities and pts fitness into a prognostic model may be necessary to effectively deliver accurate prognoses and risk assessments for the older pt.

The HIF‐1 and β‐catenin protective pathways represent the two most significant cellular responses that are activated in response to acute kidney injury. We previously reported that murine Mucin 1 (Muc1) protects kidney function and morphology in a mouse model of ischemia‐reperfusion injury (IRI) by stabilizing HIF‐1α, enhancing HIF‐1 downstream signaling and thereby preventing metabolic stress (Pastor‐Soler et al. 2015 Am. J. Physiol. Renal Physiol. 308, F1452‐F1462). We asked if Muc1 regulates the β‐catenin protective pathway during IRI as published data indicate that (i) β‐catenin is found in co‐immunoprecipitates with human MUC1 in extracts of both cultured cells and tissues, (ii) β‐catenin nuclear targeting is MUC1‐dependent in cultured human cells, and (iii) MUC1 prevents β‐catenin phosphorylation by glycogen synthase kinase 3β (GSK3b) and thereby β‐catenin degradation. Using our same mouse model of IRI, and by using immunohistochemistry and immunoblotting of kidney tissue, we found that levels of active GSK3β were significantly lower in kidneys of control mice compared to Muc1 KO mice. Consequently, β‐catenin was significantly increased at 24 and 72 h recovery and appeared in the nuclear fraction at 72 h in control mouse kidneys. Both β‐catenin induction and nuclear targeting were absent in Muc1 KO mice. In accord with these data, we also found downstream induction of β‐catenin pro‐survival factors (activated Akt, survivin, transcription factor TCF4 and its downstream target cyclin D1) and repression of pro‐apoptotic factors (p53, active Bax and cleaved caspase 3) in control mouse kidneys that were absent or aberrant in kidneys of Muc1 KO mice. Altogether, the data clearly indicate that Muc1 protection during AKI proceeds by enhancing both the HIF‐1 and β‐catenin protective pathways. Support or Funding Information This work was supported by National Institutes of Health (NIH) Grants DK‐099345 (to T. A. Sutton), DK‐084184 (to N. M. Pastor‐Soler), CA‐64389 (to S. J. Gendler), DK‐62277, DK‐100287, and DK‐095498 (to S. P. Monga), DK‐097889 (to M. M. Al‐bataineh), DK107632, DK‐079307 (Pilot Project) and Samuel and Emma Winters Foundation grant (to R. P. Hughey), and P30‐DK079307 (to the Microscopy Core N. M. Pastor‐Soler and S. Bastacky).

Background Inconsistent nomenclature and anatomical descriptions of regional anesthetic techniques hinder scientific communication and engender confusion; this in turn has implications for research, education and clinical implementation of regional anesthesia. Having produced standardized nomenclature for abdominal wall, paraspinal and chest wall regional anesthetic techniques, we aimed to similarly do so for upper and lower limb peripheral nerve blocks. Methods We performed a three-round Delphi international consensus study to generate standardized names and anatomical descriptions of upper and lower limb regional anesthetic techniques. A long list of names and anatomical description of blocks of upper and lower extremities was produced by the members of the steering committee. Subsequently, two rounds of anonymized voting and commenting were followed by a third virtual round table to secure consensus for items that remained outstanding after the first and second rounds. As with previous methodology, strong consensus was defined as ≥75% agreement and weak consensus as 50%–74% agreement. Results A total of 94, 91 and 65 collaborators participated in the first, second and third rounds, respectively. We achieved strong consensus for 38 names and 33 anatomical descriptions, and weak consensus for five anatomical descriptions. We agreed on a template for naming peripheral nerve blocks based on the name of the nerve and the anatomical location of the blockade and identified several areas for future research. Conclusions We achieved consensus on nomenclature and anatomical descriptions of regional anesthetic techniques for upper and lower limb nerve blocks, and recommend using this framework in clinical and academic practice. This should improve research, teaching and learning of regional anesthesia to eventually improve patient care.

Abstract During aging, muscle gradually undergoes sarcopenia, the loss of function associated with loss of mass, strength, endurance, and oxidative capacity. However, the 3D structural alterations of mitochondria associated with aging in skeletal muscle and cardiac tissues are not well described. Although mitochondrial aging is associated with decreased mitochondrial capacity, the genes responsible for the morphological changes in mitochondria during aging are poorly characterized. We measured changes in mitochondrial morphology in aged murine gastrocnemius, soleus, and cardiac tissues using serial block‐face scanning electron microscopy and 3D reconstructions. We also used reverse transcriptase‐quantitative PCR, transmission electron microscopy quantification, Seahorse analysis, and metabolomics and lipidomics to measure changes in mitochondrial morphology and function after loss of mitochondria contact site and cristae organizing system (MICOS) complex genes, Chchd3 , Chchd6 , and Mitofilin . We identified significant changes in mitochondrial size in aged murine gastrocnemius, soleus, and cardiac tissues. We found that both age‐related loss of the MICOS complex and knockouts of MICOS genes in mice altered mitochondrial morphology. Given the critical role of mitochondria in maintaining cellular metabolism, we characterized the metabolomes and lipidomes of young and aged mouse tissues, which showed profound alterations consistent with changes in membrane integrity, supporting our observations of age‐related changes in muscle tissues. We found a relationship between changes in the MICOS complex and aging. Thus, it is important to understand the mechanisms that underlie the tissue‐dependent 3D mitochondrial phenotypic changes that occur in aging and the evolutionary conservation of these mechanisms between Drosophila and mammals.

Abstract Methylation profiling of brain tumors has revealed novel clusters whose biological and clinical significance is unknown. These include three entities termed by the 12.5 version of the Heidelberg methylation classifier as adult-type diffuse high grade glioma, IDH-wildtype, subtype B (HGG_B), subtype E (HGG_E), and subtype F (HGG_F). We have identified 75 cases belonging to these groups: HGG_B (25 cases, 32% female), HGG_E (40 cases, 42% female), and HGG_F (18 cases, 33% female). Although originally characterized as “adult-type,” we find that 70% of HGG_E cases are pediatric, with a median age at diagnosis of 11 years. HGG_B cases are predominantly seen in young adults, and HGG_F in older adults. The histology varies among methylation classes, with HGG_B cases demonstrating high-grade, predominantly astrocytic, morphology, while HGG_E cases demonstrate high-grade glial histology with distinct nuclear features, and HGG_F cases show highly infiltrative but lower grade glial morphology. Patient outcome data obtained to date shows a collective median survival exceeding 5 years, suggesting re-consideration of the proposed grading for these tumors. MGMT promoter methylation differs across the HGG_B (0%), HGG_E (12%), and HGG_F (0%) subtypes. Mutational analyses show enrichment of TERT promoter mutations in the HGG_F group, while HGG_E samples are enriched for tumors with POLE mutations and elevated tumor mutation burden. HGG_E tumors show epigenetic similarity to pediatric high-grade diffuse gliomas (pedHGG_RTK1A); we therefore propose that HGG_E be considered a pediatric-type HGG. HGG_B tumors show demographic and genomic similarities to high-grade astrocytomas with piloid features, including a subset of cases with CDKN2A/2B loss and ATRX mutation. HGG_F separate into two groups dictated by brain location, and despite the presence of TERT promoter mutations, appear distinct from GBMs. Overall, our work represents an initial effort to understand the morphologic, genomic and clinical characteristics of these previously uncharacterized subtypes of gliomas.

AbstractCancer and its treatment produce deleterious symptoms across the phases of care. Poorly controlled symptoms negatively affect quality of life and result in increased health-care needs and hospitalization. The Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium was created to develop 3 large-scale, systematic symptom management systems, deployed through electronic health record platforms, and to test them in pragmatic, randomized, hybrid effectiveness and implementation trials. Here, we describe the IMPACT Consortium’s conceptual framework, its organizational components, and plans for evaluation. The study designs and lessons learned are highlighted in the context of disruptions related to the COVID-19 pandemic.

Abstract Background Electronic health record–linked portals may improve health-care quality for patients with cancer. Barriers to portal access and use undermine interventions that rely on portals to reduce cancer care disparities. This study examined portal access and persistence of portal use and associations with patient and structural factors before the implementation of 3 portal-based interventions within the Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium. Methods Portal use data were extracted from electronic health records for the 12 months preceding intervention implementation. Sociodemographic factors, mode of accessing portals (web vs mobile), and number of clinical encounters before intervention implementation were also extracted. Rurality was derived using rural-urban commuting area codes. Broadband access was estimated using the 2015-2019 American Community Survey. Multiple logistic regression models tested the associations of these factors with portal access (ever accessed or never accessed) and persistence of portal use (accessed the portal ≤20 weeks vs ≥21 weeks in the 35-week study period). Results Of 28 942 eligible patients, 10 061 (35%) never accessed the portal. Male sex, membership in a racial and ethnic minority group, rural dwelling, not working, and limited broadband access were associated with lower odds of portal access. Younger age and more clinical encounters were associated with higher odds of portal access. Of those with portal access, 25% were persistent users. Using multiple modalities for portal access, being middle-aged, and having more clinical encounters were associated with persistent portal use. Conclusion Patient and structural factors affect portal access and use and may exacerbate disparities in electronic health record–based cancer symptom surveillance and management.