Medicine Platform Research Articles

Intraindividual variability in lipoprotein(a): the value of a repeat measure for reclassifying individuals at intermediate-risk

Abstract Background Lipoprotein(a) [Lp(a)] is an independent risk factor for cardiovascular disease and outcomes. Levels are predominantly genetically determined and repeat measurements are traditionally considered unlikely to be useful. However, the temporal variation of Lp(a) is not well characterized and it is unclear whether a single measure of Lp(a) is sufficient to assess cardiovascular risk in all individuals. Purpose To determine the intraindividual variability of Lp(a) and whether a repeated measure reclassified Lp(a)-specific cardiovascular risk using the European Atherosclerosis Society (EAS) consensus statement risk categories. Methods This retrospective cohort study analyzed initial and repeated Lp(a) measures obtained using the same laboratory assay from 609 individuals in the Nashville Biosciences database accessed through the American Heart Association Precision Medicine Platform. Categorical variables are presented as counts (%) and compared using Fisher’s exact test. Continuous variables are presented as mean ± standard deviation or median (Q1-Q3), tested for normality using the Shapiro-Wilk test, and compared using parametric and nonparametric tests where appropriate. The Spearman’s Rank correlation coefficient was used to assess the relationship between two variables. The significance level was set at p<0.05. Results Median age was 52.5 (37.1-62.8) years, 45% were women, 80.3% were White patients, 13.3% were Black patients, 1.6% were Asian patients, and 4.8% were other/unknown patients. In terms of relevant comorbidities, 41% had ischemic heart disease, 24.1% had chronic kidney disease, and 13.5% had liver disease. The median time between the two Lp(a) measures was 1.07 (0.5-2.1) years. Our analysis shows a significant difference between the paired Lp(a) values (p<0.05), with 38.1% [95% CI 34.2%-42%] changing by ≥10mg/dL, and 40.5% [95%CI 36.6%-44.3%] changing by >25%. There was a positive correlation between the baseline Lp(a) levels and the absolute changes in Lp(a), (r=0.59, p<0.01). The proportions of individuals with variability are similar in those who were never on the potentially Lp(a)-altering agents (statin, niacin, or PCSK9 inhibitor drugs) (n=91), with 32% [95%CI 22.4%-41.6%] changing by ≥10mg/dL, and 34% [95%CI 24.3%-43.7%] changing by >25%. While over 90% of those whose values were initially in the EAS low-risk and high-risk categories remained in those categories, 53% of those in the intermediate "grey-zone" category transitioned to either the low (20%)- or the high (33%)- risk category (Figure). Conclusion(s) Temporal-related changes in Lp(a) variability were present in many individuals and should be included in calculating the expected effect sizes in future studies targeting Lp(a). A repeat Lp(a) measure may allow more precise Lp(a)-specific cardiovascular risk prediction for individuals whose initial value is in the EAS-defined intermediate "grey-zone" category.

Advancing KidneyIntelX Precision Medicine Platform in CKD

Advancing KidneyIntelX Precision Medicine Platform in CKD

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine.

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

Evolution of treatment for unspecific back pain: From past to future.

Unspecific back pain (UBP) has long puzzled medical professionals. Historically, back pain (BP) was often attributed to mystical causes, treated with incantations or herbal concoctions. The Middle Ages shifted towards empirical practices, though still intertwined with superstition, using methods like leeches and bloodletting. The Renaissance introduced systematic healthcare approaches, laying the foundation for modern medicine. The 20th century saw significant advancements with diagnostic imaging, pharmacotherapy, physical therapy, and surgical interventions, though UBP remained elusive. Recent decades have seen a paradigm shift towards multidisciplinary approaches, addressing BP's multifactorial nature through holistic methods considering biomechanical, psychosocial, and lifestyle factors. This shift integrates quantitative research with hermeneutic interpretation, emphasizing evidence-based guidelines. Non-pharmacological interventions such as exercise therapy, electrotherapy, cognitive behavioral therapy, and mindfulness-based stress reduction have gained prominence, empowering individuals in their recovery. Technological innovations like virtual reality and artificial intelligence offer personalized treatment plans, optimizing outcomes. The future of BP treatment holds promise with advancements in regenerative medicine, neuromodulation, telemedicine, and remote monitoring platforms, enhancing accessibility and continuity of care, especially in underserved communities. However, challenges such as the opioid epidemic and healthcare disparities remain, necessitating judicious prescribing practices and equitable resource distribution. The evolving treatment landscape for UBP reflects the dynamic interplay between scientific progress, clinical innovation, and societal needs, aiming to alleviate the burden of back pain and improve quality of life.

Empowering Research With the American Heart Association Get With The Guidelines Registries Through Integration of a Database and Research Tools.

The American Heart Association's Get With The Guidelines (GWTG) has emerged as a vital resource in advancing the standards and practices of inpatient care across stroke, heart failure, coronary artery disease, atrial fibrillation, and resuscitation focus areas. The GWTG registry data have also created new opportunities for secondary use of real-world clinical data in biomedical research. Our goal was to implement a scalable database with an integrated user interface (UI) to improve GWTG data management and accessibility. The curation of registry data begins by going through a data processing and quality control pipeline programmed in Python. This pipeline includes data cleaning and record exclusion, variable derivation and unit harmonization, limited data set preparation, and documentation generation of the registry data. The database was built using PostgreSQL, and integrations between the database and the UI were built using the Django Web Framework in Python. Smaller subsets of data were created using SQLite database files for distribution purposes. Use cases of these tools are provided in the article. We implemented an automated data curation pipeline, centralized database, and UI application for the American Heart Association GWTG registry data. The database and the UI are accessible through a Precision Medicine Platform workspace. As of March 2024, the database contains over 13.2 million cleaned GWTG patient records. The SQLite subsets benefit researchers by optimizing data extraction and manipulation using Structured Query Language. The UI improves accessibility for nontechnical researchers by presenting data in a user-friendly tabular format with intuitive filtering options. With the implementation of the GWTG database and UI application, we addressed data management and accessibility concerns despite its growing scale. We have launched tools to provide streamlined access and accessibility of GWTG registry data to all researchers, regardless of familiarity or experience in coding.

Efficacy assessment of a novel pan-RAS inhibitor in KRAS-mutant and wild type colorectal 3D bioprinted organoid tumor tissue.

91 Background: Colorectal cancer (CRC) is the third leading type of cancer worldwide, with ~150,000 new cases in the US annually and a grim 14% 5-year survival for patients diagnosed at a late stage. A lack of treatment options leads to persistently poor prognosis for patients with advanced stage disease. KRAS mutations are well known drivers of CRC and other GI cancers. Multiple KRAS mutations occur in CRC, including G12D (34%), G12V (21%), G13D (20%), G12C (8%), and others (18%). Existing KRAS-targeted therapies have limited use in CRC, underscoring the need for pan-RAS inhibitors in treating CRC and other RAS driven cancers. Objective: Assess activity of ADT-007, our pan-RAS inhibitor, on wild-type (WT) and KRAS-mutant 3D bioprinted organoid tumor (BOT) tissue using our high-throughput ex vivo platform. Methods: Using previously established bioprinting protocols, WT and mutant BOTs were printed with HT29 and HCT116 cells, respectively. HT29 is an established human WT CRC cell line with known sensitivity to proteosome and survivin inhibitors. HCT116 is a KRASG13D mutant human CRC cell line. 3 sets of BOTs were generated and acclimated for 24h. One set was treated for 72h with proteosome inhibitor Bortezomib, another with survivin inhibitor YM155, and the third with our novel pan-RAS inhibitor ADT-007. Dose response curves were generated from both conventional ATP luminescence readouts and high-content imaging. Results: BOT tissue microarchitecture was validated and >200 µm diffusion in BOTs was confirmed using high-content imaging. Differential response was quantified using Cell TiterGlo endpoint assay as well as advanced image processing of high-content live/dead nuclear stained images captured at multiple z-plains. ADT-007 IC50 was found to be substantially lower for mutant HCT116 compared to that for WT HT29 cell line BOTs, which was consistent with separately conducted in vitro and in vivo studies. Conclusions: A pan-RAS inhibitor, such as ADT-007 with high selectivity for cancer cells with activated RAS that is not limited to a specific KRAS mutant allele or RAS isozyme, could have broader use for CRC and other RAS-driven cancers. Further, due to their potential to replicate biophysical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine platforms can provide more accurate drug efficacy readout compared to in vitro cancer models.

Self-assembling Peptides Based Fibers: A Pioneering Innovation for Regenerative Medicine

Self-assembling peptides (SAPs) have emerged as a groundbreaking platform in regenerative medicine, offering innovative strategies for tissue engineering and repair. These biomolecules, designed to autonomously organize into nanofibers, mimic the natural extracellular matrix, providing essential structural support and biological cues for tissue regeneration. This review highlights the versatile applications of SAPs in regenerating diverse tissues, including bone, cartilage, cardiac, neural, and dermal structures, showcasing their potential to address a broad spectrum of medical challenges. The unique properties of SAPs, such as biocompatibility, biodegradability and specific cell receptor interaction capabilities, make them ideal for developing advanced therapeutic strategies. We discuss here the molecular design principles that allow for precise control over the nanofibers physical and chemical characteristics, facilitating the creation of scaffolds that promote cell attachment, proliferation, and differentiation. Additionally, the review covers the use of SAPs in drug delivery systems, emphasizing their ability to control the release of therapeutic agents, a critical aspect in enhancing tissue repair processes. Moreover, we address key considerations regarding the safety, biodegradability and potential immunogenicity of SAPs, underscoring the importance of thorough preclinical and clinical evaluations. The future directions and challenges in optimizing SAPs design and functionalization for maximizing regenerative outcomes are also explored.

Causal relationship between GLP-1 agonists and depressive symptomatology in patients with type 2 Diabetes: A systematic review

Background: Glucagon-like peptide-1 (GLP-1) receptor agonists are drugs used for the treatment of type 2 diabetes and some (Liraglutide and Semaglutide) for weight loss with significant effects on visceral fat. Adverse effects include gastrointestinal symptoms such as nausea, vomiting or diarrhea and is associated with an increased frequency of acute pancreatitis. Material and methods: This systematic review has used search engines such as PubMed, Google Scholar, Cochrane and the Acsess Medicine platform to search for literature investigating the effects of GLP-1 receptor agonists versus other antidiabetic therapies on depressive symptoms including suicidal ideation, finding a total of 07 potential articles related to the use of GLP-1 agonists and their neuropsychological effects. Results: Of the 7 articles analyzed, the type of study design, the period, the number of patients included, the number of patients diagnosed with diabetes, and the use of GLP-1 agonist versus other control treatment therapies are detailed. Conclusion: The studies reviewed indicate that GLP-1 agonists were significantly superior to control treatments in relation to the effect of antidepressant symptomatology, the mechanism by which this effect occurs remains unclear. We found no available verifiable evidence showing a causal association of GLP-1 agonists and depressive symptomatology in patients with type 2 Diabetes.

MDB-61. STARVING TUMORS OF POLYAMINES: A NOVEL APPROACH IN TREATING AGGRESSIVE PEDIATRIC MEDULLOBLASTOMA

Abstract BACKGROUND Polyamines, essential intracellular polycations, govern fundamental cell processes and drive oncogenesis. Our research unveiled the dependency of pediatric Diffuse Midline Gliomas (DMG) on the polyamine pathway (Khan et al.,2021). Disruption of this pathway with the synthesis inhibitor DFMO, and the transport inhibitor AMXT1501, potently inhibited DMG tumors, yielding unprecedented survival in-vivo. We hypothesized that dual polyamine pathway targeting could similarly suppress growth in other aggressive pediatric brain tumors. METHODS/RESULTS We analyzed the expression of polyamine pathway regulators in high-risk pediatric brain cancers ZERO precision medicine platform and found elevated expression in medulloblastoma. ODC1, a polyamine synthesis driver, was found to have significant correlation in high-grade gliomas with four key functional tumor states—stemness, cell cycle, hypoxia, and angiogenesis. Medulloblastoma cells were sensitive to treatment with DFMO but showed a compensatory increase in polyamine transporter expression. Conversely, treatment with AMXT1501 increased ODC1 expression. Dual targeting of the polyamine pathway potently and synergistically inhibited cell proliferation and clonogenic potential in Group-3 medulloblastoma cells. Flow cytometric analysis of Annexin V-stained cells showed that treatment with DFMO and AMXT1501 in combination led to induction of apoptosis. Critically, dual polyamine inhibition potently sensitized medulloblastoma cells to the topoisomerase-I inhibitor, SN-38, leading to complete eradication of medulloblastoma cell proliferation and colony formation at nanomolar chemotherapeutic concentrations, with the effect most pronounced in Group-3 medulloblastoma. In triple combination-treated cells, over-representation analysis revealed heightened mitotic DNA damage, macroautophagy, and apoptosis, coupled with diminished neuron/synapse development, glycosylation, and cellular signaling pathways. We are currently testing this treatment strategy in aggressive orthotopic medulloblastoma models. CONCLUSION These findings suggest targeting the polyamine pathway as a promising strategy to sensitize aggressive pediatric medulloblastoma tumors to chemotherapy. DFMO/AMXT1501 is in Phase I evaluation for adults (NCT05500508) and advancing to a Phase I/II trial for children through the CONNECT consortium.

American Heart Association Cardiogenic Shock Registry: Design and Implementation.

Cardiogenic shock is a morbid complication of heart disease that claims the lives of more than 1 in 3 patients presenting with this syndrome. Supporting a unique collaboration across clinical specialties, federal regulators, payors, and industry, the American Heart Association volunteers and staff have launched a quality improvement registry to better understand the clinical manifestations of shock phenotypes, and to benchmark the management patterns, and outcomes of patients presenting with cardiogenic shock to hospitals across the United States. Participating hospitals will enroll consecutive hospitalized patients with cardiogenic shock, regardless of etiology or severity. Data are collected through individual reviews of medical records of sequential adult patients with cardiogenic shock. The electronic case record form was collaboratively designed with a core minimum data structure and aligned with Shock Academic Research Consortium definitions. This registry will allow participating health systems to evaluate patient-level data including diagnostic approaches, therapeutics, use of advanced monitoring and circulatory support, processes of care, complications, and in-hospital survival. Participating sites can leverage these data for onsite monitoring of outcomes and benchmarking versus other institutions. The registry was concomitantly designed to provide a high-quality longitudinal research infrastructure for pragmatic randomized trials as well as translational, clinical, and implementation research. An aggregate deidentified data set will be made available to the research community on the American Heart Association's Precision Medicine Platform. On March 31, 2022, the American Heart Association Cardiogenic Shock Registry received its first clinical records. At the time of this submission, 100 centers are participating. The American Heart Association Cardiogenic Shock Registry will serve as a resource using consistent data structure and definitions for the medical and research community to accelerate scientific advancement through shared learning and research resulting in improved quality of care and outcomes of shock patients.

Feasibility study utilizing a combinatorial functional precision medicine platform, Optim.AI, to help guide treatment for pancreatic cancer.

e13614 Background: Functional screening methods are increasingly applied to guide patient treatment and could be beneficial in indications with poor survival and response rates, such as pancreatic cancer. However, challenges faced when implementing ex vivo testing include complexities in obtaining sufficient tumor cells for different cancer types and maximizing the number of treatments tested with limited sample material. The latter is especially significant in informing clinical decision making as combination therapies are common with cancer, exponentially increasing the number of regimens to select from. To address this hurdle, we developed Optim.AI, a combinatorial functional precision medicine platform, which utilizes efficiently designed experiments and small data AI to expand to over 530,000 phenotypic response data points and predictively rank all actionable treatments within a 12-drug panel. In this feasibility study, we explored the application of Optim.AI on patient-derived tumor cells in pancreatic cancer. Methods: We established a procedure to run Optim.AI with isolated tumor cells from pancreatic cancer patients. After dissociation, these cells were then incubated to allow for 3D organoid formation within the following week. Upon organoid formation, the cells were treated with an FDA-approved panel of chemotherapy and targeted drugs. Cell viability was quantified post-drug treatment for analysis with Optim.AI software to rank all possible single to 4-drug combinatorial therapies for report generation. Results: The viability of the cells post-processing was high (mean = 97.4% live cells), passing the quality check of having at least 60% live cells before proceeding with Optim.AI testing. The success rate of organoid formation was 100% within 1 week of sample processing. The turnaround time from the receipt of samples to dissemination of the reports to clinicians was approximately a week (mean = 7.67 days). Z’ factor, a measurement of statistical effect size for high throughput screening, was demonstrated to be more than 0.5 for the reports generated, indicative of very good assays. The top ranked drug combinations were sensitive, with their normalized cell viability reported to be under 0.2, while the prior line of treatment was shown to be resistant, 100% concordant with previously observed clinical outcomes. Notably, Gemcitabine with either a HDAC or PI3K inhibitor was ranked high amongst the reports generated. Conclusions: Optim.AI had a high success rate in generating reports within a very short timeframe for pancreatic cancer. Comparing the top ranked therapies against the prior lines of treatment allows the clinicians to better evaluate the alternative treatments. Based on this initial feasibility study, Optim.AI could potentially be viable in guiding clinicians in managing treatment options for pancreatic cancer patients.

Biological subgroups in melanoma: A correlation of tumor genomic and transcriptomic analysis with spatial immunophenotyping.

e14667 Background: A prominent challenge advancing immunotherapy is the heterogeneity of resistance mechanisms leading to drug development failure despite efficacy in subgroup of patients. To address these shortcomings, precision medicine platforms aimed at prospectively stratifying patients into treatment groups based on tumor mutational burden (TMB) and tumor inflammation scores by gene expression profiling (GEP) are now being explored. Whether these gene-based variables reflect the true immunologic state of the tumor remains an open question. Here, we sought to evaluate TMB and GEP as markers of intratumoral inflammatory state by single-cell spatial analysis. Methods: Under an IRB approved protocol through the Huntsman Cancer Institute, 25 melanoma tumors with available clinical, WES, and RNAseq data were stained with a multiplex immunofluorescence-based protocol using Opal dye and Leica Bond Rx (CD8, CD68, PD1, PD-L1, Ki67, SOX10, DAPI) and scanned with Akoya Polaris whole slide scanner. Images were analyzed using HALO. Results: Melanoma tumors were stratified into 4 subgroups based on TMB (<10 vs ≥10 m/Mb) by WES, and inflammatory (Inflm) gene signature expression (GSEA >0 vs ≤0) by RNAseq, denoted as S1 (TMBhi/Inflmhi; n=4), S2 (TMBhi/Inflmlo; n=4), S3 (TMBlo/Inflmhi; n=8), and S4 (TMBlo/Inflmlo; n=9). The rate of disease progression was 0% (S1), 25% (S2), 25% (S3), and 56% (S4). Infiltration analysis of CD8 T cells showed elevated intratumoral CD8 density in S1 (249/mm2) and S3 (157/mm2) vs S2 (65/mm2) and S4 (65/mm2; p=0.0002), consistent with GEP findings. Spatial analysis showed a similar pattern of CD8 distribution across subgroups with peak CD8 density in the tumor periphery. Proximity analyses showed average distance of CD8 T cell to the nearest tumor cell was not different across subgroups. Furthermore, we found Inflmhi tumors had a higher frequency of PD1+ CD8 T cells relative to Inflmlo tumors (12.9 vs 2.8 cells/HPF; p=0.0002), suggesting GEP also reflects CD8 activation and functional status. Increased frequency of PD1+ CD8 T cells was correlated with a much improved overall survival (HR=3.3; p=0.017). A similar analysis performed on CD68+ macrophages showed significantly increased peri-tumoral macrophage recruitment in S4 (445/mm2) vs S1 (209/mm2; p<0.0001), S2 (91/mm2; p<0.0001), and S3 (267/mm2; p=0.0003) subgroups. Interestingly, Inflmhi tumors were associated with an increased proportion of PDL1+ macrophages regardless of TMB status (43% vs 3%; p<0.0001), suggestive of an upregulated adaptive immune response in tumors with active inflammation. Conclusions: Our findings illustrate that biological groupings based on TMB and GEP biomarkers correlate with CD8 spatial location and activation state. Each subgroup has unique tumor immune features that can be related to immune resistance and warrant further investigation.

Prediction of TKI response in EGFR-mutant lung cancer patients-derived organoids using malignant pleural effusion

Patient-derived organoids (PDOs) are valuable in predicting response to cancer therapy. PDOs are ideal models for precision oncologists. However, their practical application in guiding timely clinical decisions remains challenging. This study focused on patients with advanced EGFR-mutated non-small cell lung cancer and employed a cancer organoid-based diagnosis reactivity prediction (CODRP)-based precision oncology platform to assess the efficacy of EGFR inhibitor treatments. CODRP was employed to evaluate EGFR-tyrosine kinase inhibitors (TKI) drug sensitivity. The results were compared to those obtained using area under the curve index. This study validated this index by testing lung cancer-derived organoids in 14 patients with lung cancer. The CODRP index-based drug sensitivity test reliably classified patient responses to EGFR-TKI treatment within a clinically suitable 10-day timeline, which aligned with clinical drug treatment responses. This approach is promising for predicting and analyzing the efficacy of anticancer, ultimately contributing to the development of a precision medicine platform.

Cutaneous targets for topical pain medications in patients with neuropathic pain: individual differential expression of biomarkers supports the need for personalized medicine.

Numerous potential cutaneous targets exist for treating chronic pain with topically applied active pharmaceutical ingredients. This preliminary human skin tissue investigation was undertaken to characterize several key biomarkers in keratinocytes and provide proof-of-principle data to support clinical development of topical compounded formulations for peripheral neuropathic pain syndromes, such as postherpetic neuralgia (PHN). The study intended to identify objective biomarkers in PHN skin on a patient-by-patient personalized medicine platform. The totality of biopsy biomarker data can provide a tissue basis for directing individualized compounded topical preparations to optimize treatment efficacy. Referencing 5 of the most common actives used in topical pain relief formulations (ketamine, gabapentin, clonidine, baclofen, and lidocaine), and 3 well-established cutaneous mediators (ie, neuropeptides, cannabinoids, and vanilloids), comprehensive immunolabeling was used to quantify receptor biomarkers in skin biopsy samples taken from ipsilateral (pain) and contralateral (nonpain) dermatomes of patients with PHN. Epidermal keratinocyte labeling patterns were significantly different among the cohort for each biomarker, consistent with potential mechanisms of action among keratinocytes. Importantly, the total biomarker panel indicates that the enriched PHN cohort contains distinct subgroups. The heterogeneity of the cohort differences may explain studies that have not shown statistical group benefit from topically administered compounded therapies. Rather, the essential need for individual tissue biomarker evaluations is evident, particularly as a means to direct a more accurately targeted topical personalized medicine approach and generate positive clinical results.

Abstract 5460: Discovery and validation of effective combination therapies targeting cell state-specific master regulator vulnerabilities by network-based protein activity inference in diffuse midline glioma

Abstract Diffuse Midline Glioma (DMG) are fatal pediatric brain tumors with no effective systemic therapies. We leveraged network-based methodologies to dissect tumor heterogeneity, discover Master Regulators (MR) representing pharmacologically accessible vulnerabilities of distinct DMG cell states, and validated candidate MR-reversing drugs predicted by the NYS CLIA-certified OncoTarget and OncoTreat algorithms (Cancer Discov 2023) in vivo. We first interrogated single cell DMG regulatory networks generated by ARACNe (Nat Genet 2005) with publicly available gene expression signatures from 3,039 tumor cells across 6 patients using VIPER (Nat Genet 2016) to infer single cell regulatory protein activity. Clustering of cells by protein activity defined 7 patient-independent cell states with distinct MR profiles reflecting known glial lineage markers (OPC-like-S1, OPC-like-S2, OC-like-S1, OC-like-S2, Cycling, AC-like, and AC/OPC-like). We then generated drug-induced differential protein activity from RNAseq profiles following perturbation with 372 oncology drugs in two DMG cell lines that together recapitulate the MR signatures of the cell states and used this to identify drugs that invert tumor MR activity profiles using OncoTreat. Candidate drugs predicted by OncoTarget (inhibitors of individual MRs) and OncoTreat were distinct across the cell states, and we selected five drugs targeting the OPC/cycling-like cells (Trametinib, Dinaciclib, Avapritinib, Mocetinostat, and Etoposide), and four drugs targeting the AC-like cells (Ruxolitinib, Venetoclax, Napabucasin, Larotrectonib) for further validation as these states comprised most tumor cells across patients. We generated single-cell RNAseq for 95,687 cells after 5 days of treatment with either vehicle control (n = 4) or candidate drug (n = 2-3/drug) in subcutaneous SU-DIPG-17 mouse models. We show this model recapitulates cell states seen in patients and confirm reduction in tumor growth and significant depletion of either OPC/cycling-like cells or AC-like cells in line with our drug predictions for 8/9 candidate drugs. We then treated a syngeneic (DIPG4423) orthotopic DMG model with each drug and demonstrate significant differences in survival with Avapritinib, Dinaciclib, and Trametinib. Notably, the combination of drugs targeting OPC/cycling-like and AC-like cells (i.e. Trametinib+Ruxolitinib and Avapritinib+Venetoclax) showed significantly lower tumor volumes after 2 weeks of treatment as compared to vehicles or each drug alone, and significant survival differences for some combinations. This work provides a precision medicine platform to nominate much-needed novel drug combinations addressing DMG tumor heterogeneity for further study to improve outcomes in this devastating disease. Citation Format: Ester Calvo Fernandez, Lorenzo Tomassoni, Xu Zhang, Aleksandar Obradovic, Pasquale Laise, Aaron T. Griffin, Lukas Vlahos, Junqiang Wang, Hanna E. Minns, Diana V. Morales, Christian Simmons, Matthew Gallito, Hong-Jian Wei, Zhiguo Zhang, Robyn Gartrell, Luca Szalontay, Stergios Zacharoulis, Cheng-Chia Wu, Andrea Califano, Jovana Pavisic. Discovery and validation of effective combination therapies targeting cell state-specific master regulator vulnerabilities by network-based protein activity inference in diffuse midline glioma [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 5460.

Abstract 1454: Expanding the therapeutic application of PARP inhibitor: AI evaluation of real-word clinico-genomic data from spontaneous canine tumors

Abstract The widespread application of Poly-ADP ribose polymerase (PARP) inhibitors in anti-cancer therapy is constantly increasing their role as a treatment strategy across various neoplasms, the majority of which are linked with BRCA deficiency. While the most significant advantages are observed in cancers with BRCA1/2 mutations, it is evident that the benefits extend beyond this specific group. Preclinical evidence encourages the exploration of PARP inhibitors in neoplasms demonstrating BRCAness or homologous recombination deficiency (HRD), both as monotherapy and in combination with chemotherapy. This study employs spontaneous canine tumors as a complementary model for cancer research, aiming to seamlessly bridge the transition from preclinical investigations to clinical application. Leveraging the FidoCure® Precision Medicine Platform, we identified biomarkers associated with prognosis and treatment prediction, specifically with the PARP inhibitor olaparib, facilitating a smoother translation from laboratory findings to practical clinical use. Analyzing real-world clinico-genomic data from 1278 dogs with cancer revealed intriguing insights of specific targeted treatments developed for humans showing a positive prognosis when applied to canine tumors with specific genomic alterations. Notably, olaparib exhibited efficacy in TP53 and BRCA1-mutated cases (OS HR 0.34, P < 0.001; HR 0.39, P = 0.004), while rapamycin (mTOR inhibitor) demonstrated promising outcomes in TP53 and RB1-mutated canine tumors (OS HR 0.73, P = 0.028; HR 0.32, P = 0.024). Further stratification by tumor types unveiled noteworthy correlations. TP53 mutant osteosarcomas exhibited improved prognosis with olaparib treatment (OS HR 0.11, P < 0.001), and soft tissue sarcomas with TP53 mutations demonstrated favorable responses to rapamycin and olaparib therapy (OS HR 0.11, P = 0.012; HR 0.07, P = 0.011). These findings underscore the potential of leveraging canine tumor models and clinico-genomic analyses to inform targeted treatment strategies, offering valuable insights for advancing precision medicine in both veterinary and human oncology. Further investigation into the association between TP53 mutation and olaparib response is warranted. The intriguing aspect lies in the role TP53 mutation has been linked in previous studies to heightened chromosomal instability and elevated HRD scores. Utilizing real-world evidence and data tools within the FidoCure dataset, we have effectively pinpointed correlations between gene mutations and survival, particularly in relation to responses to targeted therapy treatment. Canine models, mirroring human diseases with intact immune systems and comparable tumor genomic profiles, as verified by the FidoCure database, expedite clinical studies of novel treatments that face significant scalability challenges. Citation Format: Lucas Rodrigues, Kevin Wu, Garrett Harvey, Gerald Post, Abigail Hull, Aubrey Miller, Lindsay Lambert, Christina Lopes, James Zou. Expanding the therapeutic application of PARP inhibitor: AI evaluation of real-word clinico-genomic data from spontaneous canine tumors [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 1454.

Abstract 2548: Retrospective prediction of clinical response to standard-of-care therapies in acute myeloid leukemia by an ex vivo drug sensitivity assay using cryopreserved primary samples

Abstract Background: Assessing drug efficacy in primary acute myeloid leukemia (AML) patient cells is crucial for pinpointing drug targets and rationally selecting therapies for patients. We have previously shown that our flow cytometry-based predictive precision medicine platform (PPMP) predicts patient outcomes for standard-of-care (SOC) AML treatments using fresh blood or bone marrow samples with high precision. Here, we wanted to test the feasibility and efficacy of the PPMP on cryopreserved patient-derived leukemic samples. However, cryopreserved leukemic samples are known for exhibiting low viability and cell recovery, rendering their practical utilization often challenging. In this study, we adapt an existing methodology for cryopreservation for patient-derived leukemic cells to investigate the potential of cryopreserved samples as an alternative to fresh samples for ex vivo prediction of treatment outcomes on our PPMP. Methods: We screened freshly isolated white blood cells from AML patients on the PPMP to assess their response to multiple SOC single agents and combinations. The assay was also run without drug treatments immediately after receiving the fresh sample (≤ 2 days from draw) to establish a phenotype baseline. The cryopreserved samples were processed using a similar protocol, with post-thaw culture times of either 24 or 48 hours in two types of cytokine-containing media. A comparative analysis between untreated fresh and cryopreserved samples determined the optimal post-thaw culture time for cryopreserved samples. Once culture time was optimized, we screened cryopreserved samples on the PPMP with identical SOC drug conditions as the fresh samples and compared the response of leukemic blasts to drug treatment as well as impacts on other phenotypic markers. Results: Based on our preliminary data, we found that culturing cryopreserved cells for 24 hours post-thaw was sufficient to maintain a minimum cell viability of 80% while closely maintaining a phenotype that recapitulated that of the corresponding fresh samples. In response to drug treatments within the ex vivo assays, preliminary data support a correlation (Decitabine single agent, R2=0.81) between the response profiles of cryopreserved and fresh patient samples as gauged from AUC calculations. Conclusion: While the preferred method for screening is to use fresh patient samples, many practical constraints make it challenging or unfeasible. The ability of Notable Lab’s high-throughput platform to use cryopreserved samples opens the doors to enhanced retrospective studies leveraging clinically annotated data from biobanks or clinical trials. These retrospective studies provide a substantial reduction in the time needed to conclude such investigations. Citation Format: Lokesh Patil, Christine J. Gu, Wade Anderson, Nicola Long, Kara Johnson, Cristina Tognon, Markus D. Lacher. Retrospective prediction of clinical response to standard-of-care therapies in acute myeloid leukemia by an ex vivo drug sensitivity assay using cryopreserved primary samples [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 2548.

Abstract 6566: Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas

Abstract Purpose: Soft tissue sarcomas (STS) are a collection of heterogenous cancers with limited range of treatment options and poor outcomes. First line treatment for most STS is anthracycline-based, with gemcitabine-based combinations, pazopanib or ifosfamide as treatment options in refractory/relapsed STS. There is a clinical unmet need to improve treatment options for STS as median time to progression following second- and third-line treatments is less than 4 months. We have previously demonstrated in a 71-patient relapsed/refractory non-Hodgkin’s lymphoma clinical study that a functional precision medicine platform can effectively guide treatment, with several exceptional responders (Science Translational Medicine, 2022). This ex vivo drug sensitivity platform, quadratic phenotypic optimization platform (QPOP), analyses a predesigned array of 155 test combinations performed on a primary patient sample to rank and compare all possible therapeutic combinations from a 12-drug set. In this study, we evaluated clinical concordance of QPOP to predict treatment outcomes in an STS cohort. We also explored QPOP’s combination therapy ranking function to identify novel combinations that may benefit larger groups of STS patients when appropriately guided. Experimental Detail: A 12-drug set comprised of STS standard of care, FDA approved drugs and promising investigational drugs was evaluated in QPOP analysis of primary STS tumor samples. Ex vivo cultures of primary STS samples were treated with a predesigned array of 155 test combinations to provide a quantitative phenotypic output for each test combination. This dataset was analyzed by QPOP to rank all possible drug combinations towards a predicted treatment outcome. Concordance analysis was then performed comparing patient sample results with either parallel treatment initiated at time of sample collection or physician choice to use QPOP-guided treatment. Validation of QPOP-derived top-ranked drug combinations was also performed. Results: 12-drug, 3-level QPOP was successfully performed on 45 of 51 primary patient samples. Clinical concordance analysis showed QPOP had a total predictive value (TPV) of 76.9% and an AUCROC of 85.7%. Exceptional responders to guided treatment with pazopanib or eribulin were observed. Additionally, top-ranked epigenetic-based combinations were identified as effective across multiple STS subtypes and validated preclinically. Conclusion: The results show the potential for QPOP to predict clinical response in solid cancers, such as STS. Furthermore, QPOP can improve drug development pipelines through patient samples-based design of effective combinations with investigational drugs or underutilized drugs that may benefit larger populations of patients or specific subgroups of patients. Citation Format: Edward Kai-Hua Chow, Sharon P. Chan, Tan Boon Toh, Masturah Rashid, Valerie S. Yang. Clinical evaluation of functional combinatorial precision medicine platform to predict treatment outcomes and enhance combination therapy design in soft tissue sarcomas [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 6566.

Abstract 5178: Guided by a predictive ex vivo test: Bringing the PLK1 inhibitor volasertib back into the clinic for venetoclax-HMA relapsed/refractory acute myeloid leukemia patients

Abstract INTRODUCTION: Volasertib, a polo-like kinase 1 (PLK1) inhibitor, has a roughly 30% CR/CRi response rate in de novo acute myeloid leukemia (AML) patients when combined with low-dose cytarabine. However, its development was halted due in part to toxicity in a Phase 3 study. We have built a flow cytometry-based, high-throughput predictive precision medicine platform to forecast clinical responses to various treatments. Here, we present our design plan for a Phase 2a clinical trial with volasertib in relapsed/refractory (R/R) AML using a modified protocol to reduce toxicity and the development of a companion diagnostic to predict responders in a follow-up Phase 2b study. METHODS: We will initiate an open-label clinical trial in AML with volasertib in venetoclax-hypomethylating agent (HMA) R/R patients while co-developing a companion diagnostic to selectively enroll predicted responders in a follow-up, Phase 2b, study. Primary and secondary objectives will focus on efficacy/safety of intravenous volasertib and ex vivo responses, respectively. To reduce the toxicity seen in the Phase 3 study, specific strategies like tailored dosing will be implemented. To develop the clinical trial assay, peripheral blood or bone marrow samples were collected from AML patients and treated ex vivo with volasertib for 3 days. Surviving blast cells were enumerated by flow cytometry. Using a dynamic separation model restricted to the 35% most sensitive samples, we estimated optimal blast cutoffs for volasertib sensitivity. RESULTS: In single-agent experiments with 41 primary AML samples (31 de novo, 9 R/R, 1 with disease status unclear), we observed dose-response profiles consistent with volasertib’s role as a cell cycle inhibitor. Irrespective of the disease status, median and mean EC50 values were about 10 nM (1st-3rd quartile from 7-12 nM). To identify an optimal concentration of volasertib for patient stratification, we compared the ex vivo resistant blast fractions at each concentration with the areas under the dose-response curves (AUCs), assuming that in our case, AUCs most accurately reflect clinical responses. At 31.6 and 100 nM, the Pearson correlation coefficients were highest (r > 0.8), suggesting that these are optimal stratifying doses. With these concentrations and corresponding blast cutoff values, 32-33% of the de novo and 25% of the R/R patients would have been predicted to be responders were they given volasertib, assuming a positive predictive value of 100%. CONCLUSION: Volasertib’s EC50 values clustered within a narrow range, suggesting that EC50 may not be a suitable metric to stratify patients into responders and nonresponders, in contrast to AUCs and residual blast fractions at 31.6 and 100 nM of volasertib, which displayed more heterogeneous distributions. Limitations include small sample sizes, especially for the R/R group. Citation Format: Markus Daniel Lacher, Glenn Michelson, Allen Ling, Wade Anderson, Jeaneille Vinas, Christine J. Gu, Chris Leonardi, Joseph Wagner, Gabriel N. Mannis. Guided by a predictive ex vivo test: Bringing the PLK1 inhibitor volasertib back into the clinic for venetoclax-HMA relapsed/refractory acute myeloid leukemia patients [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 5178.

Abstract 934: RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients

Abstract Sensitivity to therapeutic agents is impacted both by genetic heterogeneity between patients or among clones within a tumor and by heterogeneity of epigenetic cell states. Prior work demonstrates that human pancreatic tumors comprise malignant cells in a mixture of multiple cell states with distinct genetic dependencies, leading to a reservoir of chemoresistance across the tumor; targeting just one or two malignant cell states is insufficient to achieve durable therapeutic responses. One approach to overcome this challenge is to identify agents with efficacy against malignant cells in multiple cell states. However, in the absence of genetic distinctions between cell states, identifying therapeutic vulnerabilities for each malignant cell state present in a patient’s tumor in real time remains a challenge. OncoTreat is a CLIA certified algorithm that matches drugs to patients based on tumor gene expression profiles. The approach utilizes regulatory network analysis, a systems biology framework that transforms gene expression data into regulatory protein activity profiles, in a manner that facilitates the identification of master transcriptional regulators of cell state. We carried out a Phase 1 clinical trial of the OncoTreat framework to examine feasibility for implementing RNA-based precision medicine in patients with metastatic pancreatic ductal adenocarcinoma (2nd line and beyond). Patient-derived xenografts were established from subjects prior to 1st line therapy, and selected agents matched by OncoTreat were assessed co-clinically while subjects received standard of care regimens, with the potential for subsequent treatment with successful agents upon progression. Two subjects in the study matched to selenexor, an XPO1 inhibitor that is FDA approved for multiple myeloma. Co-clinical studies of selinexor in patient-derived xenograft models generated from the matched subjects showed prolonged survival compared to control regimens. Single-cell RNA sequencing analysis of these tumors found that the activity of master regulators confirmed that the activity of selinexor-responsive master regulators were inverted in response to treatment in vivo, reducing the fraction of malignant cells in the tumors. Strikingly, selenexor treatment was associated with significant inhibition of RAS/MAPK signaling, suggesting a potential novel role for selinexor targeting Ras. Together, these data indicate a potential therapeutic vulnerability for a subset of PDAC patients that can be predicted by a CLIA-certified RNA-based precision medicine platform. Citation Format: Alvaro Curiel-Garcia, Lorenzo Tomassoni, Tanner C. Dalton, Amanda R. Decker-Farrell, Carmine F. Palermo, Daniel R. Ross, Stephen A. Sastra, Urszula N. Wasko, Isabel M. Goncalves, Prabhot Mundi, Basil S. Bakir, Rachael A. Safyan, Hanina Hibshoosh, Gulam A. Manji, Andrea Califano, Kenneth P. Olive. RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients [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 934.