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1. Atovaquone is active against AML by upregulating the integrated stress pathway and suppressing oxidative phosphorylation

   https://doi.org/10.1182/bloodadvances.2019000499  

   Stevens, Alexandra M.; Xiang, Michael; Heppler, Lisa N.; Tošić, Isidora; Jiang, Kevin; Munoz, Jaime O.; Gaikwad, Amos S.; Horton, Terzah M.; Long, Xin; Narayanan, Padmini; et al (December 2019, Blood Advances)

   Key Points Atovaquone induces AML blast apoptosis and prolongs survival in AML xenografts. Atovaquone induces proapoptotic signaling and inhibits the mTOR pathway through upregulation of ATF4 and also suppresses OXPHOS. 

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2. An In Silico Glioblastoma Microenvironment Model Dissects the Immunological Mechanisms of Resistance to PD‐1 Checkpoint Blockade Immunotherapy

   https://doi.org/10.1002/smtd.202100197  

   Zhang, Zhuoyu; Liu, Lunan; Ma, Chao; Cui, Xin; Lam, Raymond H. W.; Chen, Weiqiang (April 2021, Small Methods)

   Abstract The PD‐1 immune checkpoint‐based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials, mainly due to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti‐PD‐1 resistance, an in silico model is established to quantitatively describe the biological rationale of critical GBM‐immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor‐associated macrophage (TAM) mediated immunosuppression. Such an in silico experimentation and predictive model, based on the in vitro microfluidic chip‐measured end‐point data and patient‐specific immunological characteristics, allows for a comprehensive and dynamic analysis of multiple TAM‐associated immunosuppression mechanisms against the anti‐PD‐1 immunotherapy. The computational model demonstrates that the TAM‐associated immunosuppression varies in severity across different GBM subtypes, which results in distinct tumor responses. The prediction results indicate that a combination therapy by co‐targeting of PD‐1 checkpoint and TAM‐associated CSF‐1R signaling can enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti‐PD‐1 therapy. The development of a patient‐specific in silico–in vitro GBM model will help navigate and personalize immunotherapies for GBM patients. 

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3. Alemtuzumab induction is associated with decreased hospitalization rates in pediatric kidney transplant: A UNOS data review for safety and outcomes with common induction regimens

   https://doi.org/10.1111/petr.14783  

   Aydin‐Ghormoz, Emmanuel; Ortiz, Jorge; Koizumi, Naoru; Li, Meng‐Hao; Faddoul, Geovani (June 2024, Pediatric Transplantation)

   Abstract BackgroundWe hypothesized that alemtuzumab use is safe in pediatric kidney transplant recipients (KTRs) with equivalent long‐term outcomes compared to other induction agents. MethodsUsing pediatric kidney transplant recipient data in the UNOS database between January 1, 2000, and June 30, 2022, multivariate logistic regression, multivariable Cox regression, and survival analyses were utilized to estimate the likelihoods of 1st‐year and all‐time hospitalizations, acute rejection, CMV infection, delayed graft function (DGF), graft loss, and patient mortality among recipients of three common induction regimens (ATG, alemtuzumab, and basiliximab). ResultsThere were no differences in acute rejection or graft failure among induction or maintenance regimens. Basiliximab was associated with lower odds of DGF in deceased donor recipients (OR 0.77 [0.60–0.99],p = .04). Mortality was increased in patients treated with steroid‐containing maintenance (HR 1.3 [1.005–1.7]p = .045). Alemtuzumab induction correlated with less risk of CMV infection than ATG (OR 0.76 [0.59–0.99],p = .039). Steroid‐containing maintenance conferred lower rate of PTLD compared to steroid‐free maintenance (HR 0.59 [0.4–0.8]p = .001). Alemtuzumab was associated with less risk of hospitalization within 1 year (OR 0.79 [0.67–0.95]p = .012) and 5 years (HR 0.54 [0.46–0.65]p < .001) of transplantation. Steroid maintenance also decreased 5 years hospitalization risk (HR 0.78 [0.69–0.89]p < .001). ConclusionsPediatric KTRs may be safely treated with alemtuzumab induction without increased risk of acute rejection, DGF, graft loss, or patient mortality. The decreased risk of CMV infections and lower hospitalization rates compared to other agents make alemtuzumab an attractive choice for induction in pediatric KTRs, especially in those who cannot tolerate ATG. 

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4. Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit

   https://doi.org/10.3389/fped.2021.711104  

   Bose, Sanjukta N.; Greenstein, Joseph L.; Fackler, James C.; Sarma, Sridevi V.; Winslow, Raimond L.; Bembea, Melania M. (August 2021, Frontiers in Pediatrics)

   null (Ed.)

   Objective: The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients. Design: The design of the study is a retrospective observational cohort study. Setting: The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD. Patients: The patients included in the study were <18 years of age admitted to the PICU between July 2014 and October 2015. Measurements and main results: Organ dysfunction labels were generated every minute from preceding 24-h time windows using the International Pediatric Sepsis Consensus Conference (IPSCC) and Proulx et al. MOD criteria. Early MOD prediction models were built using four machine learning methods: random forest, XGBoost, GLMBoost, and Lasso-GLM. An optimal threshold learned from training data was used to detect high-risk alert events (HRAs). The early prediction models from all methods achieved an area under the receiver operating characteristics curve ≥0.91 for both IPSCC and Proulx criteria. The best performance in terms of maximum F1-score was achieved with random forest (sensitivity: 0.72, positive predictive value: 0.70, F1-score: 0.71) and XGBoost (sensitivity: 0.8, positive predictive value: 0.81, F1-score: 0.81) for IPSCC and Proulx criteria, respectively. The median early warning time was 22.7 h for random forest and 37 h for XGBoost models for IPSCC and Proulx criteria, respectively. Applying spectral clustering on risk-score trajectories over 24 h following early warning provided a high-risk group with ≥0.93 positive predictive value. Conclusions: Early predictions from risk-based patient monitoring could provide more than 22 h of lead time for MOD onset, with ≥0.93 positive predictive value for a high-risk group identified pre-MOD. 

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5. Abstract 4567: Transcriptomic analysis of a 3D engineered cancer model recapitulating stage-dependent heterogeneity in colorectal PDX tumors

   https://doi.org/10.1158/1538-7445.AM2023-4567  

   Tian, Yuan; Hassani, Iman; Anbiah, Benjamin; Ahmed, Bulbul; Van_Der_Pol, William; Lefkowitz, Elliot J; Kuhlers, Peyton C; Habbit, Nicole L; Heslin, Martin J; Lipke, Elizabeth A; et al (April 2023, Cancer Research)

   Colorectal cancer (CRC) is the third-most leading cause of cancer-related deaths in the United States. To advance the understanding of CRC tumor progression, models which mimic the tumor microenvironment (TME) and have translatable study outcomes are urgently needed. CRC patient-derived xenografts (PDXs) are promising tools for their ability to recapitulate tumor heterogeneity and key patient tumor characteristics, such as molecular characteristics. However, as in vivo models, CRC PDXs are costly and low-throughput, which leads to a need for equivalent in vitro models. To address this need, we previously established an in vitro model using a tissue engineering toolset with CRC PDX cells. However, it is unclear whether tissue engineering has the capacity to maintain patient- and/or cancer stage-specific tumor heterogeneity. To address this gap, we employed three PDX tumor lines, originated from stage II, III-B, and IV CRC tumors, in the formation of 3D engineered CRC PDX (3D-eCRC-PDX) tissues and performed an in-depth comparison between the 3D-eCRC-PDX tissues and the original CRC-PDX tumors. To form the tissues, CRC-PDX tumors were expanded in vivo and dissociated. The isolated cells were encapsulated within poly(ethylene glycol)-fibrinogen hydrogels and remained viable and proliferative post encapsulation over the course of 29 days in culture. To gain molecular insight into the maintenance of PDX line stage heterogeneity, we performed a transcriptomic analysis using RNA seq to determine the extent to which there were similarities and differences between the CRC-PDX tumors and the 3D-eCRC-PDX tissues. We observed the greatest correspondence in overlapping differentially expressed human genes, gene ontology, and Hallmark gene set enrichment between the 3D-eCRC-PDX tissues and CRC-PDX tumors in the stage II PDX line, while the least correspondence was observed in the stage IV PDX line. The Hallmark gene set enrichment from murine mapped RNA seq transcripts was PDX line-specific which suggested that the stromal component of the 3D-eCRC-PDX tissues was maintained in a PDX line-dependent manner. Consistent with our transcriptomic analysis, we observed that tumor cell subpopulations, including human proliferative (B2M+Ki67+) and CK20+ cells, remained constant for up to 15 days in culture even though the number of cells in the 3D-eCRC-PDX tissues from all three CRC stages increased over time. Yet, tumor cell subpopulation differences in the stage IV 3D-eCRC-PDX tissues were observed starting at 22 days in culture. Overall, our results demonstrate a strong correlation between our in vitro 3D-eCRC-PDX models and the originating in vivo CRC-PDX tumors, providing evidence that these engineered tissues may be capable of mimicking patient- and/or cancer stage-specific heterogeneity. 

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