Time Frame For Action Research Articles

Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers

Children with rare, relapsed or refractory cancers often face limited treatment options, and few predictive biomarkers are available that can enable personalized treatment recommendations. The implementation of functional precision medicine (FPM), which combines genomic profiling with drug sensitivity testing (DST) of patient-derived tumor cells, has potential to identify treatment options when standard-of-care is exhausted. The goal of this prospective observational study was to generate FPM data for pediatric patients with relapsed or refractory cancer. The primary objective was to determine the feasibility of returning FPM-based treatment recommendations in real time to the FPM tumor board (FPMTB) within a clinically actionable timeframe (<4 weeks). The secondary objective was to assess clinical outcomes from patients enrolled in the study. Twenty-five patients with relapsed or refractory solid and hematological cancers were enrolled; 21 patients underwent DST and 20 also completed genomic profiling. Median turnaround times for DST and genomics were within 10 days and 27 days, respectively. Treatment recommendations were made for 19 patients (76%), of whom 14 received therapeutic interventions. Six patients received subsequent FPM-guided treatments. Among these patients, five (83%) experienced a greater than 1.3-fold improvement in progression-free survival associated with their FPM-guided therapy relative to their previous therapy, and demonstrated a significant increase in progression-free survival and objective response rate compared to those of eight non-guided patients. The findings from our proof-of-principle study illustrate the potential for FPM to positively impact clinical care for pediatric and adolescent patients with relapsed or refractory cancers and warrant further validation in large prospective studies. ClinicalTrials.gov registration: NCT03860376.

Abstract 937: Prioritizing treatment targets for an adolescent with metastatic processive malignancy using proteomics and personalized xenograft models within an actionable timeframe

Abstract Translation of precision oncology data into feasible precision therapies for hard-to-cure childhood, adolescent and young adult malignancies remains a significant challenge. Identifying therapeutic targets at the protein and pathway level and demonstrating treatment response in personalized models hold great promise, particularly for combination therapies, but may be considered complex and time consuming. Here, we present the case of an adolescent with metastatic, progressive spindle epithelial tumor with thymus-like differentiation (SETTLE) and evaluate how proteomics combined with rapid patient-derived models can identify treatment options not apparent at the genome or transcript level. Mass spectrometric proteome analysis of macro-dissected tumor and adjacent normal from formalin fixed paraffin embedded sections was completed within two weeks of biopsy and identified key proteins involved in one-carbon metabolism, including SHMT2 and DHFR as possible targets for single or combination therapy. Elevated SHMT2 levels were validated by immunohistochemistry and compared to levels across AYA tumors. Based on the suitability for an innovative therapy trial, we prioritized single-agent sertraline, a commercially available anti-depressant medication that inhibits SHMT2, and confirmed a positive drug response in both chicken chorioallantoic membrane (CAM) and larval zebrafish xenografts generated from the patient. Retrospective expansion in a murine xenograft enabled metabolic tracing on isolated SETTLE- patient-derived xenograft cells using 13C6-glucose confirming SHMT2 activity and response to in vitro treatment. Following failure of cytotoxic chemotherapy and second-line sorafenib treatment, a monotherapy trial of sertraline was initiated by the patient but stopped after 8 weeks after evidence of progressive disease. Possible combination therapies were evaluated further in the patient-derived models. Combining sertraline with the common antibiotic trimethoprim, resulted in enhanced growth inhibition of SETTLE cells in the larval zebrafish xenografts. Significance: Overall, we demonstrate that proteomics and personalized xenograft models may provide supportive pre-clinical data in a clinically meaningful timeframe to support medical decision-making and impact clinical practice. Citation Format: Georgina D. Barnabas, Tariq A. Bhat, Verena Goebeler, Pascal Leclair, Nadine Azzam, Nicole Melong, Jason N. Berman, Jennifer A. Chan, Donna L. Senger, Seth Parker, Christopher A. Maxwell, Gregor S. Reid, Jonathan Bush, Caron Strahlendorf, Rebecca Deyell, C James Lim, Philipp F. Lange. Prioritizing treatment targets for an adolescent with metastatic processive malignancy using proteomics and personalized xenograft models within an actionable timeframe [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 937.

Abstract 4200: A novel 3D culture platform, E-slice, retains intact tumor microenvironment and tumor infiltrating immune cells

Abstract Human cancer tissue is a complex ecosystem with heterogeneous cell types that communicate with each other dynamically to promote tumor progression and immune evasion. Hence, experimental systems that destroy or select for cancer cells in culture have often failed to accurately predict clinical responses of new drugs. Therefore, a technology that can retain the intact tumor microenvironment (TME) and accurately predict cancer patient’s response to anti-cancer treatments, including immunotherapies, will greatly benefit patients and dramatically reduce the cost of new drug development. E-slice is a proprietary 3D human tumor culture platform that maintains individual patient tumor’s unique ecosystem and the TME ex vivo for up to 30 days. It is generated by making thin sections of intact, fresh tumor tissues and culturing them in serum-free, defined media. As such, immune components and the TME in E-slices faithfully recapitulate human tumors ex vivo. We demonstrate that E-slices can be used to: 1) measure viability changes upon anti-cancer agent treatment longitudinally; 2) retain the native TME and tissue architecture since E-slices are never dissociated or artificially reconstituted; 3) culture any solid tumor types from patient needle biopsy cores or surgical samples, PDX, or mouse models; 4) perform high content drug screening on human tissues; 5) discover secreted biomarkers and to 6) discover or validate molecular mechanisms of action or therapy resistance; and 7) measure immunotherapy responses ex vivo from human tissues. Importantly, it has been shown to accurately predict individual patient treatment responses to chemotherapies and targeted therapies in 4-8 days, paving the way for evidence-based personalized treatment selections in a clinically actionable time frame. Furthermore, single cell RNA-sequencing analysis demonstrates that E-slices can retain the viability and molecular phenotypes of tumor infiltrating immune cells for up to 8 days ex vivo, providing a unique platform to study human tumor-immune interactions and evaluate efficacy of immune modulators on human tumor tissues ex vivo. Citation Format: Viridiana Leyva-Aranda, Thomas Gallup, Jose Maldonado, Corina Margain, Sang Yun, David Gallup, Min Kim, Kyuson Yun. A novel 3D culture platform, E-slice, retains intact tumor microenvironment and tumor infiltrating immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4200.

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a ‘predict and preempt’ paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2–3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

Implementation of routine genomic surveillance provided insights into a locally acquired outbreak caused by a rare clade of Salmonella enterica serovar Enteritidis in Queensland, Australia.

Salmonellosis is a significant public health problem globally. In Australia, Salmonella enterica serovar Enteritidis is one of the main causes of salmonellosis. This study reports how the implementation of routine genetic surveillance of isolates from human S. Enteritidis cases enabled identification of the likely source of an outbreak that occurred in a remote town in Far North Queensland, Australia. This study included patient, food and water samples collected during an outbreak investigation. S. Enteritidis of the novel sequence type 5438 was isolated from all seven patient samples and one bore water sample but not any of the food samples. Both whole-genome single nucleotide polymorphism (SNP) and core-genome multilocus sequence typing analysis revealed that S. Enteritidis isolated from outbreak-related patient samples and the bore water isolates clustered together with fewer than five SNP differences and ten allelic differences. This genetic relatedness informed the outbreak response team around public health interventions and no further cases were identified post-treatment of the bore water. This disease cluster was identified through the routine sequencing of S. Enteritidis performed by the state public health laboratory in an actionable time frame. Additionally, genomic surveillance captured a case with unknown epidemiological links to the affected community, ruled out a simultaneous outbreak in an adjacent state as the source and provided evidence for the likely source preventing further transmission. Therefore, this report provides compelling support for the implementation of whole-genome sequencing based genotyping methods in public health microbiology laboratories for better outbreak detection and management.

Pooled RNA-extraction-free testing of saliva for the detection of SARS-CoV-2

The key to limiting SARS-CoV-2 spread is to identify virus-infected individuals (both symptomatic and asymptomatic) and isolate them from the general population. Hence, routine weekly testing for SARS-CoV-2 in all asymptomatic (capturing both infected and non-infected) individuals is considered critical in situations where a large number of individuals co-congregate such as schools, prisons, aged care facilities and industrial workplaces. Such testing is hampered by operational issues such as cost, test availability, access to healthcare workers and throughput. We developed the SalivaDirect RT-qPCR assay to increase access to SARS-CoV-2 testing via a low-cost, streamlined protocol using self-collected saliva. To expand the single sample testing protocol, we explored multiple extraction-free pooled saliva testing workflows prior to testing with the SalivaDirect RT-qPCR assay. A pool size of five, with or without heat inactivation at 65 °C for 15 min prior to testing resulted in a positive agreement of 98% and 89%, respectively, and an increased Ct value shift of 1.37 and 1.99 as compared to individual testing of the positive clinical saliva specimens. Applying this shift in Ct value to 316 individual, sequentially collected, SARS-CoV-2 positive saliva specimen results reported from six clinical laboratories using the original SalivaDirect assay, 100% of the samples would have been detected (Ct value < 45) had they been tested in the 1:5 pool strategy. The availability of multiple pooled testing workflows for laboratories can increase test turnaround time, permitting results in a more actionable time frame while minimizing testing costs and changes to laboratory operational flow.

Abstract 4670: True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention

Abstract There has been dramatic improvement in treatment outcomes for many pediatric cancers over the last three decades. However, for the 20% of young people with relapsed or refractory cancer, the prognosis remains grim. Canada’s PRecision Oncology For Young peopLE (PROFYLE) and Australia’s Zero Childhood Cancer (ZERO) programs, leverage nation-wide scientific and clinical oncology expertise to provide personalized precision medicine to children, adolescent and young adult (CAYA) cancer patients who lack treatment options. Beyond improving the lives of young cancer patients nationally, PROFYLE and ZERO have partnered to create an international pipeline for knowledge sharing and sample acquisition. A key component of both programs is the use of mice for patient-derived xenografts (PDXs). However, long lead times for mouse PDX generation make the timely return of preclinical drug response data challenging. PROFYLE has uniquely incorporated zebrafish larval xenografts, which have the potential to provide comparable information in a clinically actionable timeframe. In a pilot study, we compared retrospective matched ZERO patient and mouse PDX therapeutic response data with prospective zebrafish larval PDX data as a proof-of-principle that drug efficacy signals were maintained across model systems. Three ZERO avatars: high-risk neuroblastoma, Ewing’s sarcoma, and anaplastic large cell lymphoma were shipped from Australia to Canada and transplanted into 48h casper zebrafish. Following dose optimization, zebrafish PDXs were treated with targeted single and combination drug treatments by immersion therapy. Strikingly, in as little time as a week, cell proliferation rates and drug responses to single agents and combinatorial therapy in zebrafish PDXs recapitulated mouse and patient data. To expand on this pilot project, we tested additional patient samples, including multiple subtypes of sarcomas, T-cell acute lymphoblastic leukemia and an embryonal tumor with multilayered rosettes. Results further validated the practical utility of the zebrafish larval PDX model and in fact provided drug response data when mouse PDX data were unavailable. This study demonstrates the robustness and feasibility of the zebrafish larval PDX model as a preclinical tool for personalized precision therapeutic decision-making and highlights the value of international collaboration in improving outcomes of rare childhood cancers. Citation Format: Nadine Azzam, Nicole Melong, Lissandra Tuzi, Lisa Pinto, Jamie I. Fletcher, Alvin Kamili, Biljana Dumevska, Loretta Lau, Jennifer A. Chan, Donna L. Senger, Stephanie A. Grover, Michelle Haber, David Malkin, Jason N. Berman. True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4670.

Anomaly Detection in Blockchain Networks: A Comprehensive Survey

Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field.

Molecular prognostication in Ph-negative MPNs in 2022.

The application of genomic techniques, including cytogenetics and DNA sequencing, to decipher the molecular landscape of patients with myeloproliferative neoplasms (MPNs) has radically modified diagnostic approach and management through improved risk stratification. Three driver mutated genes (JAK2, MPL, CALR) are variably harbored by >80% of patients and associated with clinical characteristics, as well as major disease-related complications and different survival outcomes. Therefore, JAK2 V617F mutation is included in the revised International Prognosis Score of Thrombosis for Essential Thrombocythemia score for prediction of thrombosis in patients with essential thrombocythemia and prefibrotic primary myelofibrosis, while a CALR type 1 mutated genotype constitutes a favorable variable for survival in patients with myelofibrosis (MF). Novel, integrated clinical and cytogenetic/mutation scores (Mutation-Enhanced International Prognostic Score System for Transplantation-Age Patients with Primary Myelofibrosis [MIPSS70/v2], genetically inspired prognostic scoring system [GIPSS], Myelofibrosis Secondary to PV and ET- Prognostic Model [MYSEC-PM]) have been devised that guide selection of stem cell transplantation candidates with MF or help predict the risk associated with the transplant procedure (Myelofibrosis Transplant Scoring System), with greater performance compared with conventional scores based on hematologic and clinical variables only. On the other hand, several clinical needs remain unmet despite the great amount of molecular information available nowadays. These include the prediction of evolution to acute leukemia in a clinically actionable time frame, the identification of patients most likely to derive durable benefits from target agents, in primis JAK inhibitors, and, conversely, the significance of molecular responses that develop in patients receiving interferon or some novel agents. Here, we discuss briefly the significance and the role of genomic analysis for prognostication in patients with MPNs from a clinician's point of view, with the intent to provide how-to-use hints.

Development and Assessment of a Novel Whole-Gene-Based Targeted Next-Generation Sequencing Assay for Detecting the Susceptibility of Mycobacterium tuberculosis to 14 Drugs.

Targeted next-generation sequencing (tNGS) has emerged as an alternative method for detecting drug-resistant tuberculosis (DR-TB). To provide comprehensive drug susceptibility information and to address mutations missed by available commercial molecular diagnostics, we developed and evaluated a tNGS panel with 22 whole-gene targets using the Ion Torrent platform to predict drug resistance to 14 drugs, namely, rifampicin (RIF), isoniazid (INH), ethambutol (EMB), pyrazinamide (PZA), moxifloxacin (MFX), levofloxacin (LFX), amikacin (AMK), capreomycin (CM), kanamycin (KM), streptomycin (SM), bedaquiline (BDQ), clofazimine (CFZ), linezolid (LZD), and delamanid (DLM). We selected 50 and 35 Mycobacterium tuberculosis isolates with various DR profiles as the training set and the challenge set, respectively. Comparative variant analyses of the DR genes were performed using Sanger sequencing and whole-genome sequencing (WGS). Phenotypic drug susceptibility testing (pDST) results were used as gold standards. Regarding the limit of detection, the tNGS assay detected 2.9 to 3.8% minority variants in 4% mutant mixtures. The sensitivity and specificity of tNGS were 97.0% (95% confidence interval [CI] = 93.1 to 98.7%) and 99.1% (95% CI = 97.7 to 99.7%), respectively. The concordance of tNGS with pDST was 98.5% (95% CI = 97.2 to 99.2%), which was comparable to that of WGS (98.7%, 95% CI = 97.4 to 99.3%) and better than that of Sanger sequencing (96.9%, 95% CI = 95.3 to 98.0%). The agreement between tNGS and pDST was almost perfect for RIF, INH, EMB, MFX, LFX, AMK, CM, KM, SM, BDQ, and LZD (kappa value = 0.807 to 1.000) and substantial for PZA (kappa value = 0.791). Our customized novel whole-gene-based tNGS panel is highly consistent with pDST and WGS for comprehensive and accurate prediction of drug resistance in a strengthened and streamlined DR-TB laboratory program. IMPORTANCE We developed and validated a tNGS assay that was the first to target 22 whole genes instead of regions of drug resistance genes and comprehensively detected susceptibility to 14 anti-TB drugs, with great flexibility to include new or repurposed drugs. Notably, we demonstrated that our custom-designed Ion AmpliSeq TB research panel platform had high concordance with pDST and could significantly reduce turnaround time (by approximately 70%) to meet a clinically actionable time frame. Our tNGS assay is a promising DST solution for providing needed clinical information for precision medicine-guided therapies for DR-TB and allows the rollout of active pharmacovigilance.

The T Cell Epitope Landscape of SARS-CoV-2 Variants of Concern

During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present a critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and revealed insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated using a unique AI prediction platform, a significant conservation of presentable T cell epitopes across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.

Abstract 4103: Feasibility and efficacy of a functional precision medicine approach in the management of relapsed/refractory childhood cancers

Abstract Pediatric cancers are fundamentally different from those in adults, with lower frequency of genetic mutations and fewer options for targeted therapies. The implementation of functional precision medicine (FPM) - the integration of ex vivo drug screening and mutation profiling- can, therefore, provide better treatment options for pediatric tumor patients. In this study, we investigated the feasibility and clinical utility of FPM in the management of pediatric patients with recurrent and/or refractory cancers. We use a functional ex vivo drug screening test (DST) panel encompassed 40 formulary drugs frequently used at Nicklaus Children’s hospital and 47 non-formulary drugs approved by FDA for cancer treatment, as well as drugs from phase III and IV clinical trials. Drug sensitivity scores (DSS) were calculated for each drug based on cancer cells’ responses. DST results were then combined with results from targeted mutation profiles to match actionable mutations with selective targeted therapies. We have recruited a total of 21 patients into this ongoing clinical trial (number NCT03860376) and were able to perform drug testing and mutation profiling on 17 patients. We optimized and successfully performed DST on at least 13 different tumor types including acute myeloid leukemia, chronic lymphoblastic leukemia, ependymoma, osteosarcoma, Ewing’s sarcoma, rhabdomyosarcoma, glioblastoma, medulloblastoma, astrocytoma, neuroblastoma, rhabdoid, lung, and liver tumors. Our feasibility study, so far, has demonstrated that ex vivo DST can be performed within a clinically actionable time frame (median: 7 days). Ex vivo DST returned between 10 and 30 treatment options for each patient. These patients showed different responses to the 103 FDA-approved compounds used in the screen. More than half of the evaluated compounds were not active in any of the patients. Remarkably, DST provided valuable information to the oncologists on drug dosing and treatments that may not be effective and should be avoided. DSS synergizes with genomic data to further refine treatment recommendations. FPM-guided treatment regimens resulted in encouraging partial and complete responses as compared to progressive disease in prior regimens and physician choice regimens. Thus, our study shows technical feasibility of integrating functional precision medicine approaches for patients with refractory/relapsed pediatric cancers. Routine clinical integration of FPM for treatment selection is technically feasible and has led to improved treatment of pediatric cancer patients with refractory malignancies in an initial patient cohort, warranting further investigation. Citation Format: Arlet Maria Acanda de la Rocha, Maggie Fader, Ebony Coats, Joseph Dunn, Leat Perez, Cima Saghira, Ileana Sotto, Ziad Khatib, Ossama Maher, Haneen Abdella, Cristina M. Andrade-Feraud, Alexa Jacome, Lilliam Rimblas, Paula S. Espinal, Tomás R. Guilarte, Jennifer McCafferty-Fernandez, Daria Salyakina, Diana J. Azzam. Feasibility and efficacy of a functional precision medicine approach in the management of relapsed/refractory childhood cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4103.

Abstract 694: E-slice: A novel 3D culture platform for precision medicine

Abstract The need for personalized medicine in oncology is widely accepted but translating this important concept into clinical practice has been challenging. Currently, the dominant platform for precision medicine utilizes genomics/sequencing-based assays to measure the expression and/or mutational profiles and then infer patient responses to therapies based on previous knowledge; however, this approach benefits less than 10-15% of patients with profiled tumors. Recognizing the inherent limitations of these inference-based methods, functional assays (e.g., organoids and PDX models) have been developed; however, these approaches also have significant limitations including high cost and time required to establish the models, low “take rates”, and destruction of the native tumor microenvironment (TME). To overcome these challenges, EMPIRI uses a novel 3D ex vivo tumor slice culture method (E-slices) that enables rapid, personalized drug sensitivity testing in intact patient tumor tissues. Major differentiators of the E-slice platform from other ex vivo methods include the use of chemically defined, serum-free medium, longitudinal viability measurements from the same tissue, tracking of dynamic responses to treatment over 2-3 weeks, and retention of the native TME and tissue architecture, unlike other approaches. In addition, E-slices can be generated from any solid tumor tested thus far (breast, lung, colorectal, pancreas, brain, head 7 neck, and others) from patient tumors directly and PDX and genetically engineered mouse models. In addition, because E-slices retain tumor-infiltrating immune cells in their native microenvironment and spatial topography of all cell types in the endogenous configuration, it sustains immune cell survival and proliferation and measures immunotherapy responses ex vivo. The E-slice method is compatible with biopsies as well as surgical samples. Importantly, it has been shown to accurately predict individual patient treatment responses to chemotherapies and targeted therapies in 4-12 days, paving the way for evidence-based personalized treatment selections in a clinically actionable time frame. In summary, we present a novel ex vivo 3D human tumor tissue drug sensitivity platform that faithfully replicates the patient tumor tissues and provides personalized treatment responses in a clinically actionable time frame. Citation Format: Archana Gopalan, Thomas D. Gallup, Stephanie Wood, Jose Maldonado, Corina Margain, Nestor F. Esnaola, Min P. Kim, E. Scott Kopetz, Kyuson Yun. E-slice: A novel 3D culture platform for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 694.

MEDIA FORMATION OF DESIGN ELEMENTS OF LVIV ARCHITECTURAL ENVIRONMENT

The modern rhythm of life necessitates the creation of new principles of organization of design elements in the architectural information and communication environment. Its media formation is a reflection of the spiritual and cultural values of modern society which are transformed into dynamic and interactive elements. The set of media visual elements of architectural space is a complex hierarchical system of different types. Although design objects play an important role in the perception of the architectural environment, they are not sufficiently studied in details today. To characterize the informative essence of visual elements of the modern architectural environment, their classification by function, time frame of action and location is proposed. The research was conducted on the example of media elements of the urban architectural environment of Lviv. Historical and modern visual elements of urban space design are analyzed. The signs of harmonious use of media design elements in the existing architectural environment are highlighted. The role of informative saturation of the city on the intuitive form of human perception is determined. The evaluation of the characteristic features of media design elements in Lviv was carried out. These features include content, functionality, subordination of composition and ergonomics.

CalmBelt: Rapid SARS-CoV-2 Genome Characterization for Outbreak Tracking.

Background: The ongoing COVID-19 pandemic is a global health crisis caused by the spread of SARS-CoV-2. Establishing links between known cases is crucial for the containment of COVID-19. In the healthcare setting, the ability to rapidly identify potential healthcare-associated COVID-19 clusters is critical for healthcare worker and patient safety. Increasing sequencing technology accessibility has allowed routine clinical diagnostic laboratories to sequence SARS-CoV-2 in clinical samples. However, these laboratories often lack specialized informatics skills required for sequence analysis. Therefore, an on-site, intuitive sequence analysis tool that enables clinical laboratory users to analyze multiple genomes and derive clinically relevant information within an actionable timeframe is needed.Results: We propose CalmBelt, an integrated framework for on-site whole genome characterization and outbreak tracking. Nanopore sequencing technology enables on-site sequencing and construction of draft genomes for multiple SARS-CoV-2 samples within 12 h. CalmBelt's interactive interface allows users to analyse multiple SARS-CoV-2 genomes by utilizing whole genome information, collection date, and additional information such as predefined potential clusters from epidemiological investigations. CalmBelt also integrates established SARS-CoV-2 nomenclature assignments, GISAID clades and PANGO lineages, allowing users to visualize relatedness between samples together with the nomenclatures. We demonstrated multiple use cases including investigation of potential hospital transmission, mining transmission patterns in a large outbreak, and monitoring possible diagnostic-escape.Conclusions: This paper presents an on-site rapid framework for SARS-CoV-2 whole genome characterization. CalmBelt interactive web application allows non-technical users, such as routine clinical laboratory users in hospitals to determine SARS-CoV-2 variants of concern, as well as investigate the presence of potential transmission clusters. The framework is designed to be compatible with routine usage in clinical laboratories as it only requires readily available sample data, and generates information that impacts immediate infection control mitigations.

3D Tissue-Engineered Bone Marrow Culture Predicts Patient Response to Drugs in Multiple Myeloma

Introduction:Multiple myeloma (MM) is the cancer of plasma cells within the bone marrow (BM) and represents the second most common hematologic malignancy. Although therapeutic options have broadened over the years, the disease is challenged by frequent relapses. Relapsed/refractory MM (RRMM) often becomes non-responsive to previous lines of treatment and has significantly poorer survival outcome. Physicians are faced with a difficult task to choose a right treatment regimen. Thus, a precision medicine tool that predicts the clinical response of individual patients to therapy is greatly desired. We have previously developed a novel 3D tissue-engineered BM (3DTEBM) culture model, which is patient derived, closely recapitulates the pathophysiological conditions in the BM, and allows ex vivo proliferation of primary cells of various hematologic malignancies. In this study, we conducted a retrospective study that tests the ability of the ex vivo 3DTEBM platform to predict the clinical response in individual MM patients, to help decision-making process for RRMM. We hypothesized that the 3DTEBM will be able to predict clinical responses of RRMM patients.Methods:We first performed a literature search to examine the clinical efficacious concentrations (Css) for 10 MM drugs. We then experimentally determined the in vitro efficacious concentrations (IC50) of these drugs in MM cell lines, in both 2D and 3DTEBM cultures. The IC50 values were then correlated with their respective clinical Css values to evaluate how well each culture system reproduce drug efficacy. For the retrospective trial, we used viably frozen whole BM samples from 19 RRMM patients with known clinical responsiveness to the regimen they received. 3DTEBM cultures were developed for each patient with BM biopsies obtained prior to the start of clinical regimen. Cultures were treated ex vivo with the same treatment regimen each patient received clinically, at increasing concentrations (0X, 3X and 10X of Css of individual drugs). After 4 days, cultures were digested and cells were retrieved for flow cytometry analysis. Primary cells were stained Leukocyte-/CD38+ and counted against counting beads. Survival was determined as % of untreated control, and ex vivo responsiveness was analyzed by ANOVA. Finally, the clinical team correlated the ex vivo response with the clinical response for each patient.Results:To demonstrate this discrepancy between drug efficacy in laboratory settings and clinical outcomes, we compared the in vitro IC50 to the clinical Css of 10 drugs used for the treatment of MM. We found that there was no correlation between the IC50 in classic 2D culture systems and the clinical Css (R 2=0.019) (Figure 1A). In contrast, the IC50 in the 3DTEBM directly correlated with the clinical Css (R 2=0.993) (Figure 1B). We then conducted a retrospective clinical trial to determine if the 3DTEBM platform is able to predict each patient's clinical response by recreating the same treatment regimen ex vivo (Figure 1C). The 3DTEBM was able to predict the response in 89% of the MM patient cohort across multiple treatment regimens, with no false positives (Figure 1D).Conclusions:Our retrospective clinical trial demonstrated that the 3DTEBM technology is a feasible platform for predicting therapeutic responses in MM with a high predictive accuracy within a clinically actionable time frame. Such platforms can provide precise clinical insight about the efficacy of different treatment plans and assist physicians to propose the best choice of therapy for their individual patients. Future prospective studies are needed to validate these significant findings by testing prospective prediction ability of 3DTEBM to improve therapy response in hematologic malignancies. [Display omitted] DisclosuresDe La Puente: Cellatrix LLC: Other: Co-founder. Azab: Cellatrix LLC: Current Employment. Vij: BMS: Research Funding; Takeda: Honoraria, Research Funding; Sanofi: Honoraria, Research Funding; BMS: Honoraria; GSK: Honoraria; Oncopeptides: Honoraria; Karyopharm: Honoraria; CareDx: Honoraria; Legend: Honoraria; Biegene: Honoraria; Adaptive: Honoraria; Harpoon: Honoraria. Azab: Cellatrix, LLC: Current Employment, Current holder of individual stocks in a privately-held company.

A pilot study of 3D tissue-engineered bone marrow culture as a tool to predict patient response to therapy in multiple myeloma

Cancer patients undergo detrimental toxicities and ineffective treatments especially in the relapsed setting, due to failed treatment attempts. The development of a tool that predicts the clinical response of individual patients to therapy is greatly desired. We have developed a novel patient-derived 3D tissue engineered bone marrow (3DTEBM) technology that closely recapitulate the pathophysiological conditions in the bone marrow and allows ex vivo proliferation of tumor cells of hematologic malignancies. In this study, we used the 3DTEBM to predict the clinical response of individual multiple myeloma (MM) patients to different therapeutic regimens. We found that while no correlation was observed between in vitro efficacy in classic 2D culture systems of drugs used for MM with their clinical efficacious concentration, the efficacious concentration in the 3DTEBM were directly correlated. Furthermore, the 3DTEBM model retrospectively predicted the clinical response to different treatment regimens in 89% of the MM patient cohort. These results demonstrated that the 3DTEBM is a feasible platform which can predict MM clinical responses with high accuracy and within a clinically actionable time frame. Utilization of this technology to predict drug efficacy and the likelihood of treatment failure could significantly improve patient care and treatment in many ways, particularly in the relapsed and refractory setting. Future studies are needed to validate the 3DTEBM model as a tool for predicting clinical efficacy.

Artificial intelligence sepsis prediction algorithm learns to say \u201cI don\u2019t know\u201d

Sepsis is a leading cause of morbidity and mortality worldwide. Early identification of sepsis is important as it allows timely administration of potentially life-saving resuscitation and antimicrobial therapy. We present COMPOSER (COnformal Multidimensional Prediction Of SEpsis Risk), a deep learning model for the early prediction of sepsis, specifically designed to reduce false alarms by detecting unfamiliar patients/situations arising from erroneous data, missingness, distributional shift and data drifts. COMPOSER flags these unfamiliar cases as indeterminate rather than making spurious predictions. Six patient cohorts (515,720 patients) curated from two healthcare systems in the United States across intensive care units (ICU) and emergency departments (ED) were used to train and externally and temporally validate this model. In a sequential prediction setting, COMPOSER achieved a consistently high area under the curve (AUC) (ICU: 0.925–0.953; ED: 0.938–0.945). Out of over 6 million prediction windows roughly 20% and 8% were identified as indeterminate amongst non-septic and septic patients, respectively. COMPOSER provided early warning within a clinically actionable timeframe (ICU: 12.2 [3.2 22.8] and ED: 2.1 [0.8 4.5] hours prior to first antibiotics order) across all six cohorts, thus allowing for identification and prioritization of patients at high risk for sepsis.

Future Role of the Clinical Lab in Population Health.

Future Role of the Clinical Lab in Population Health.

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.

BackgroundPrior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.ObjectiveOur goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.MethodsWe developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.ResultsForecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.ConclusionsReal-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.