Application Of Precision Medicine Research Articles

Mapping the cell-membrane proteome of the SKBR3/HER2+ cell line to the cancer hallmarks.

The hallmarks of biological processes that underlie the development of cancer have been long recognized, yet, existing therapeutic treatments cannot prevent cancer from continuing to be one of the leading causes of death worldwide. This work was aimed at exploring the extent to which the cell-membrane proteins are implicated in triggering cancer hallmark processes, and assessing the ability to pinpoint tumor-specific therapeutic targets through a combined membrane proteome/cancer hallmark perspective. By using GO annotations, a database of human proteins associated broadly with ten cancer hallmarks was created. Cell-membrane cellular subfractions of SKBR3/HER2+ breast cancer cells, used as a model system, were analyzed by high resolution mass spectrometry, and high-quality proteins (FDR<3%) identified by at least two unique peptides were mapped to the cancer hallmark database. Over 1,400 experimentally detected cell-membrane or cell-membrane associated proteins, representing ~18% of the human cell-membrane proteome, could be matched to the hallmark database. Representative membrane constituents such as receptors, CDs, adhesion and transport proteins were distributed over the entire genome and present in every hallmark category. Sustained proliferative signaling/cell cycle, adhesion/tissue invasion, and evasion of immune destruction emerged as prevalent hallmarks represented by the membrane proteins. Construction of protein-protein interaction networks uncovered a high level of connectivity between the hallmark members, with some receptor (EGFR, ERBB2, FGFR, MTOR, CSF1R), antigen (CD44), and adhesion (MUC1) proteins being implicated in most hallmark categories. An illustrative subset of 138 hallmark proteins that included 42 oncogenes, 24 tumor suppressors, 9 oncogene/tumor suppressor, and 45 approved drug targets was subjected to a more in-depth analysis. The existing drug targets were implicated mainly in signaling processes. Network centrality analysis revealed that nodes with high degree, rather than betweenness, represent a good resource for informing the selection of putative novel drug targets. Through heavy involvement in supporting cancer hallmark processes, we show that the functionally diverse and networked landscape of cancer cell-membrane proteins fosters unique opportunities for guiding the development of novel therapeutic interventions, including multi-agent, immuno-oncology and precision medicine applications.

Recent Progress Toward Imaging Application of Multifunction Sonosensitizers in Sonodynamic Therapy.

Sonodynamic therapy (SDT) is a rapidly developing non-surgical therapy that initiates sensitizers’ catalytic reaction using ultrasound, showing great potential for cancer treatment due to its high safety and non-invasive nature. In addition, recent research has found that using different diagnostic and therapeutic methods in tandem can lead to better anticancer outcomes. Therefore, as essential components of SDT, sonosensitizers have been extensively explored to optimize their functions and integrate multiple medical fields. The review is based on five years of articles evaluating the combined use of SDT and imaging in treating cancer. By developing multifunctional sonosensitive particles that combine imaging and sonodynamic therapy, we have integrated diagnosis into the treatment of precision medicine applications, improving SDT cell uptake and antitumor efficacy utilizing different tumour models. This paper describes the imaging principle and the results of cellular and animal imaging of the multifunctional sonosensitizers. Efforts are made in this paper to provide data and design references for future SDT combined imaging research and clinical application development and to provide offer suggestions.

Is Monitoring of the Intracellular Active Metabolite Levels of Nucleobase and Nucleoside Analogs Ready for Precision Medicine Applications?

Nucleobase and nucleoside analogs (NAs) play important roles in cancer therapy. Although there are obvious individual differences in NA treatments, most NAs lack direct relationships between their plasma concentrationand efficacy or adverse effects. Accumulating evidence suggests that the intracellular active metabolite levels of NAs predict patient outcomes. This article reviewed the relationships between NA intracellular active metabolite levelsand their efficacy or adverse effects. The factors affecting the formation of intracellular active metabolites and combination regimens that elevate intracellular active metabolite levels were also reviewed. Given the mechanism of NA cytotoxicity, NA intracellular active metabolite levels may be predictive of clinical outcomes. Many clinical studies support this hypothesis. Therefore, the monitoring of intracellular active metabolite levels is beneficial for individualized NA treatment. However, to perform clinical monitoring in practice, well-designed studies are needed to explore the optimal threshold or range and the appropriate regimen adjustment strategies based on these parameters.

SRG-Vote: Predicting Mirna-Gene Relationships via Embedding and LSTM Ensemble.

Targeted therapy for one for a set of genes has made it possible to apply precision medicine for different patients due to the existence of tumor heterogeneity. However, how to regulate those genes are still problematic. One of the natural regulators of genes is microRNAs. Thus, a better understanding of the miRNA-gene interaction mechanism might contribute to future diagnosis, prevention, and cancer therapy. The interactions between microRNA and genes play an essential role in molecular genetics. The in-vivo experiments validating the relationships between them are time-consuming, money-costly, and labor-intensive. With the development of high-throughput technology, we dealt with tons of biological data. However, extracting features from tremendous raw data and making a mathematical model is still a challenging topic. Machine learning and deep learning algorithms have become powerful tools in dealing with biological data. Inspired by this, in this paper, we propose a model that combines features/embedding extraction methods, deep learning algorithms, and a voting system. We leverage doc2vec to generate sequential embedding from molecular sequences. The role2vec, GCN, and GMM for geometrical embedding were generated from the complex network from similarity and pair-wise datasets. For the deep learning algorithms, we leveraged LSTM and Bi-LSTM according to different embedding and features. Finally, we adopted a voting system to balance results from different data sources. The results have shown that our voting system could achieve a higher AUC than the existing benchmark. The case studies demonstrate that our model could reveal potential relationships between miRNAs and genes. The source code, features, and predictive results can be downloaded at https://github.com/Xshelton/SRG-vote.

Phenotypes based Classification of Blood-Brain-Barrier Drugs using Feature Selection Methods and Extreme Gradient Boosting.

In this work, an attempt has been made to discriminate drug with blood brain barrier (BBB) permeability using clinical phenotypes and extreme gradient boosting (XGBoost) methods. For this, the drug name and their clinical phenotypes namely side effects and indications are obtained from public available database. Prominent clinical phenotypes are selected using genetic algorithm (GA) and binary particle swarm optimization (BPSO). Four machine algorithms namely k-Nearest Neighbours, support vector machines, rotation forest and XGBoost are used for classification of BBB drugs. The result show that the proposed clinical phenotypes based features are able to distinguish drugs with BBB permeability. The maximum number of clinical phenotypes (69%) is reduced by BPSO and GA for classification. The XGBoost method is found to be most accurate [Formula: see text] is discriminating drugs with BBB permeability. The proposed approach are found to be capable of handling multi-parametric characteristics of the drugs. Particularly, the combination of XGBoost with combination of side effects and indications could be used for precision medicine applications. Clinical relevance- This establishes XGBoost approach for improved BBB permeability based drug classification with F1 =98.7% using exclusively clinical phenotypes.

DNA methylation biomarkers in colorectal cancer: Clinical applications for precision medicine.

Colorectal cancer (CRC) is the second leading cause of cancer death worldwide that is attributed to gradual long-term accumulation of both genetic and epigenetic changes. To reduce the mortality rate of CRC and to improve treatment efficacy, it will be important to develop accurate noninvasive diagnostic tests for screening, acute and personalized diagnosis. Epigenetic changes such as DNA methylation play an important role in the development and progression of CRC. Over the last decade, a panel of DNA methylation markers has been reported showing a high accuracy and reproducibility in various semi-invasive or noninvasive biosamples. Research to obtain comprehensive panels of markers allowing a highly sensitive and differentiating diagnosis of CRC is ongoing. Moreover, the epigenetic alterations for cancer therapy, as a precision medicine strategy will increase their therapeutic potential over time. Here, we discuss the current state of DNA methylation-based biomarkers and their impact on CRC diagnosis. We emphasize the need to further identify and stratify methylation-biomarkers and to develop robust and effective detection methods that are applicable for a routine clinical setting of CRC diagnostics particularly at the early stage of the disease.

Forces that control self-organization of chemically-propelled Janus tori

Control of the individual and collective behavior of self-propelled synthetic micro-objects has immediate application for nanotechnology, robotics, and precision medicine. Despite significant progress in the synthesis and characterization of self-propelled Janus (two-faced) particles, predictive understanding of their behavior remains challenging, especially if the particles have anisotropic forms. Here, by using molecular simulation, we describe the interactions of chemically-propelled microtori near a wall. The results show that a torus hovers at a certain distance from the wall due to a combination of gravity and hydrodynamic flows generated by the chemical activity. Moreover, electrostatic dipolar interactions between the torus and the wall result in a spontaneous tilt and horizontal translation, in a qualitative agreement with experiment. Simulations of the dynamics of two and four tori near a wall provide evidence for the formation of stable self-propelled bound states. Our results illustrate that self-organization at the microscale occurs due to a combination of multiple factors, including hydrodynamic, chemical, electrostatic and steric interactions.

Self-Assembled Nanoscale Materials for Neuronal Regeneration: A Focus on BDNF Protein and Nucleic Acid Biotherapeutic Delivery.

Enabling challenging applications of nanomedicine and precision medicine in the treatment of neurodegenerative disorders requires deeper investigations of nanocarrier-mediated biomolecular delivery for neuronal targeting and recovery. The successful use of macromolecular biotherapeutics (recombinant growth factors, antibodies, enzymes, synthetic peptides, cell-penetrating peptide–drug conjugates, and RNAi sequences) in clinical developments for neuronal regeneration should benefit from the recent strategies for enhancement of their bioavailability. We highlight the advances in the development of nanoscale materials for drug delivery in neurodegenerative disorders. The emphasis is placed on nanoformulations for the delivery of brain-derived neurotrophic factor (BDNF) using different types of lipidic nanocarriers (liposomes, liquid crystalline or solid lipid nanoparticles) and polymer-based scaffolds, nanofibers and hydrogels. Self-assembled soft-matter nanoscale materials show favorable neuroprotective characteristics, safety, and efficacy profiles in drug delivery to the central and peripheral nervous systems. The advances summarized here indicate that neuroprotective biomolecule-loaded nanoparticles and injectable hydrogels can improve neuronal survival and reduce tissue injury. Certain recently reported neuronal dysfunctions in long-COVID-19 survivors represent early manifestations of neurodegenerative pathologies. Therefore, BDNF delivery systems may also help in prospective studies on recovery from long-term COVID-19 neurological complications and be considered as promising systems for personalized treatment of neuronal dysfunctions and prevention or retarding of neurodegenerative disorders.

Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes

In many important applications of precision medicine, the outcome of interest is time to an event (e.g., death, relapse of disease) and the primary goal is to identify the optimal individualized decision rule (IDR) to prolong survival time. Existing work in this area have been mostly focused on estimating the optimal IDR to maximize the restricted mean survival time in the population. We propose a new robust framework for estimating an optimal static or dynamic IDR with time-to-event outcomes based on an easy-to-interpret quantile criterion. The new method does not need to specify an outcome regression model and is robust for heavy-tailed distribution. The estimation problem corresponds to a nonregular M-estimation problem with both finite and infinite-dimensional nuisance parameters. Employing advanced empirical process techniques, we establish the statistical theory of the estimated parameter indexing the optimal IDR. Furthermore, we prove a novel result that the proposed approach can consistently estimate the optimal value function under mild conditions even when the optimal IDR is nonunique, which happens in the challenging setting of exceptional laws. We also propose a smoothed resampling procedure for inference. The proposed methods are implemented in the R-package QTOCen. We demonstrate the performance of the proposed new methods via extensive Monte Carlo studies and a real data application. Supplementary materials for this article are available online.

The SKBR3 cell-membrane proteome reveals telltales of aberrant cancer cell proliferation and targets for precision medicine applications

The plasma membrane proteome resides at the interface between the extra- and intra-cellular environment and through its various roles in signal transduction, immune recognition, nutrient transport, and cell–cell/cell–matrix interactions plays an absolutely critical role in determining the fate of a cell. Our work was aimed at exploring the cell-membrane proteome of a HER2+ breast-cancer cell line (SKBR3) to identify triggers responsible for uncontrolled cell proliferation and intrinsic resources that enable detection and therapeutic interventions. To mimic environmental conditions that enable cancer cells to evolve adaptation/survival traits, cell culture was performed under serum-rich and serum-deprived conditions. Proteomic analysis enabled the identification of ~ 2000 cell-membrane proteins. Classification into proteins with receptor/enzymatic activity, CD antigens, transporters, and cell adhesion/junction proteins uncovered overlapping roles in processes that drive cell growth, apoptosis, differentiation, immune response, adhesion and migration, as well as alternate pathways for proliferation. The large number of tumor markers (> 50) and putative drug targets (> 100) exposed a vast potential for yet unexplored detection and targeting opportunities, whereas the presence of 15 antigen immunological markers enabled an assessment of epithelial, mesenchymal or stemness characteristics. Serum-starved cells displayed altered processes related to mitochondrial OXPHOS/ATP synthesis, protein folding and localization, while serum-treated cells exhibited attributes that support tissue invasion and metastasis. Altogether, our findings advance the understanding of the biological triggers that sustain aberrant cancer cell proliferation, survival and development of resistance to therapeutic drugs, and reveal vast innate opportunities for guiding immunological profiling and precision medicine applications aimed at target selection or drug discovery.

Characterization of Immune-Based Molecular Subtypes and Prognostic Model in Prostate Adenocarcinoma.

Prostate adenocarcinoma (PRAD), also named prostate cancer, the most common visceral malignancy, is diagnosed in male individuals. Herein, in order to obtain immune-based subtypes, we performed an integrative analysis to characterize molecular subtypes based on immune-related genes, and further discuss the potential features and differences between identified subtypes. Simultaneously, we also construct an immune-based risk model to assess cancer prognosis. Our findings showed that the two subtypes, C1 and C2, could be characterized, and the two subtypes showed different characteristics that could clearly describe the heterogeneity of immune microenvironments. The C2 subtype presented a better survival rate than that in the C1 subtype. Further, we constructed an immune-based prognostic model based on four screened abnormally expressed genes, and they were selected as predictors of the robust prognostic model (AUC = 0.968). Our studies provide reference for characterization of molecular subtypes and immunotherapeutic agents against prostate cancer, and the developed robust and useful immune-based prognostic model can contribute to cancer prognosis and provide reference for the individualized treatment plan and health resource utilization. These findings further promote the development and application of precision medicine in prostate cancer.

Biofabrication approaches and regulatory framework of metastatic tumor-on-a-chip models for precision oncology.

The complexity of the tumor microenvironment (TME) together with the development of the metastatic process are the main reasons for the failure of conventional anticancer treatment. In recent years, there is an increasing need to advance toward advanced in vitro models of cancer mimicking TME and simulating metastasis to understand the associated mechanisms that are still unknown, and to be able to develop personalized therapy. In this review, the commonly used alternatives and latest advances in biofabrication of tumor‐on‐chips, which allow the generation of the most sophisticated and optimized models for recapitulating the tumor process, are presented. In addition, the advances that have allowed these new models in the area of metastasis, cancer stem cells, and angiogenesis are summarized, as well as the recent integration of multiorgan‐on‐a‐chip systems to recapitulate natural metastasis and pharmacological screening against it. We also analyze, for the first time in the literature, the normative and regulatory framework in which these models could potentially be found, as well as the requirements and processes that must be fulfilled to be commercially implemented as in vitro study model. Moreover, we are focused on the possible regulatory pathways for their clinical application in precision medicine and decision making through the generation of personalized models with patient samples. In conclusion, this review highlights the synergistic combination of three‐dimensional bioprinting systems with the novel tumor/metastasis/multiorgan‐on‐a‐chip systems to generate models for both basic research and clinical applications to have devices useful for personalized oncology.

Abstract 5737: Precision oncology driven real world clinical genomics data mining of MET alterations, TMB, and PDL-1 to empower indication agnostic patient enrollment and combination strategies

Abstract Background: Following MET inhibitor and checkpoint inhibitor approvals, it is essential to identify cancer indications that harbor molecular alterations of interest and a thorough real-world molecular genomics based characterization of those tumors would allow to expand cancer specific indications that might benefit from MET and checkpoint inhibitors and shed light on exploring clinically efficacious combo strategies. Importantly, such an approach provides the end-to-end solutions that shall significantly contribute to clinical translation and/or back translation strategies for innovative cancer therapies. Methods: We systemically interrogated FoundationInsights®, a dataset of real-world molecular genomic tumor profiling from North American patients, and investigated the prevalence of MET amplifications, METex14 skipping and MET fusions prevalence in 62110 unique patient samples across five indications (lung adenocarcinoma [LUAD], esophageal/gastroesophageal-junction/stomach adenocarcinoma [Eso/GEJ/Sto], papillary renal cell carcinoma, hepatocellular carcinoma [HCC] and glioblastoma). MET alteration prevalence was further categorized based on tumor cell PD-L1 IHC status (22C3 pharmDx) and TMB status. Additionally, comprehensive text mining from clinicaltrials.gov and landscape overview were conducted. Results: MET amplification was the most frequent MET alteration examined with a prevalence ranging from 1.4% in HCC to 4.8% in Eso/GEJ/Sto. In HCC and LUAD, MET amplification prevalence was higher in tumors with high TMB (≥10 mutations/megabase, P&amp;lt;0.05). In Eso/GEJ/Sto and LUAD, MET amplification occurrence was higher in PD-L1-high tumor vs. PD-L1-low or PD-L1-neg tumors (P&amp;lt;0.05). METex14 skipping mutations were observed in LUAD (2.3%), rarely in the other five tumor types analyzed (≤0.1%). Lastly in LUAD, METex14 skipping mutations were more frequent in PD-L1 high tumors but not those with high TMB (P&amp;lt;0.05). Text mining results indicate that- while most trials use MET amplification for patient stratification- there is a distinct tendency for MET inhibitor trials to increasingly include METex14 skipping as a major selection criterion as well. Conclusions: Real world genomics corroborated MET amplification as a predominant MET alteration across various indication. The potential clinical benefit to combine MET inhibitors with PD-1/PD-L1 blockades in specific indications and specific subsets of patients is further recognized. Collectively, the comprehensive molecular profiling &amp; text mining approaches will continue to guide application of precision medicine in clinical trial design based on appropriate biomarker selection. Citation Format: Zheng Feng, Jean-Christophe Pignon, Radwa Sharaf, Natalie Romanov, Julien Doudement, Lee A. Albacker, Noriaki Kurata, Neil R. Smith, Nobutoshi Matsushita, Juergen Scheuenpflug. Precision oncology driven real world clinical genomics data mining of MET alterations, TMB, and PDL-1 to empower indication agnostic patient enrollment and combination strategies [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 5737.

Abstract 3108: Preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities

Abstract Gastric cancer (GC) is one of the most common malignant tumors of the digestive system, and its prognosis remains poor because most tumors were detected in late stages and the 5-year survival is less than 5%. The Cancer Genome Atlas (TCGA) study found that 70% of all GCs have mutations that can be targeted with existing drugs, yet only four molecularly-guided therapies have been approved. GCs have strong disparities in incidence, mortality and survival that disproportionately affect different populations of color. No large-scale cancer genomic research has been carried out in U.S. minority populations. GC molecular data from U.S. minority residents is crucial to improve outcomes, as genomic phenotypes have been associated with differential prognosis and response to treatments. To our knowledge, minority patient-derived models have not been used to test GC therapies. In this study, several organoids lines from minority patients were established and used as an in vitro platform for screening effective therapies. After genomic data analyses, six minority patient-derived organoids with or without cell cycle regulator or PI3K/AKT/mTOR pathway gene alterations were tested for responses to first-line therapies, targeted drugs and combination treatments. Among the 24 PDX models established, the efficacy study of PDX model PDX015 which has amplifications of CDK and AKT genes showed the targeting therapy (a CDKi) can effectively prolong overall survival of mice bearing xenograft treated with the CDKi (60 days), compared with those mice in control group (22 days). In addition, an AKT inhibitor was found significantly inhibited tumor growth in CDKi-resistant mice treated with the CDKi/AKTi combination. In conclusion, investigating the efficacy of targeted therapies in these U.S. minorities will empower precision medicine applications in GC patients and improve GC treatment outcomes. Minority-patient derived tumor models have great potential in preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities. Citation Format: Hongyong Zhang, Ana Patricia Estrada-Florez, Paul Lott, Ted William Toal, Alexa Morales-Arana, Alma Poceros-Coba, UCaMP Consortium, Ai-Hong Ma, Nicole Britney Halmai, Luis Carvajal-Carmona. Preclinical studies of chemotherapies and targeted therapies to address gastric cancer disparities [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 3108.

Whole-exome sequencing analysis of NSCLC reveals the pathogenic missense variants from cancer-associated genes

BackgroundNon-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC. ObjectivesThe primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC. MethodsWe examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC. ResultsOur findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene. ConclusionThis study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC.

Application of precision medicine in clinical routine in haematology-Challenges and opportunities.

Precision medicine is revolutionising patient care in cancer. As more knowledge is gained about the impact of specific genetic lesions on diagnosis, prognosis and treatment response, diagnostic precision and the possibility for optimal individual treatment choice have improved. Identification of hallmark genetic aberrations such as the BCR::ABL1 gene fusion in chronic myeloid leukaemia (CML) led to the rapid development of efficient targeted therapy and molecular follow‐up, vastly improving survival for patients with CML during recent decades. The assessment of translocations, copy number changes and point mutations are crucial for the diagnosis and risk stratification of acute myeloid leukaemia and myelodysplastic syndromes. Still, the often heterogeneous and complex genetic landscape of haematological malignancies presents several challenges for the implementation of precision medicine to guide diagnosis, prognosis and treatment choice. This review provides an introduction and overview of the important molecular characteristics and methods currently applied in clinical practice to guide clinical decision making in haematological malignancies of myeloid and lymphoid origin. Further, experimental ways to guide the choice of targeted therapy for refractory patients are reviewed, such as functional precision medicine using drug profiling. An example of the use of pipeline studies where the treatment is chosen according to the molecular characteristics in rare solid malignancies is also provided. Finally, the future opportunities and remaining challenges of precision medicine in the real world are discussed.

Developing the educational molecular tumor board in Ireland: Pilot to national initiative.

e13533 Background: The accelerated use of next generation sequencing (NGS) in oncology requires the integration of cancer genomics and precision oncology education to facilitate appropriate clinical decisions. The Molecular Tumour Board (MTB) in Ireland was established in November 2020 as a cross-institutional, educational and multi-disciplinary pilot and expanded nationally under Cancer Trials Ireland as a resource for clinicians and other oncology groups. Methods: The Irish MTB program was co-designed with Roche and local Health Care Providers (HCPs) over the pillars of co-creation, co-benefitting and co-education. Individual anonymized patient cases are discussed at each monthly meeting, supported by four multidisciplinary experts that discuss genomic findings, characterized mutations and their actionability, in addition to available therapies. The standardized end-to-end process to ensure preparation and proper handling of sensitive data is managed by an independent partner. A survey questionnaire was regularly administered to the MTB attendees to evaluate progress since the MTB initiation. Results: From November 2020 until December 2021, the program has grown to a monthly meeting where 30 patient cases have been discussed and 157 cumulative participants have been part of the sessions from 16 institutions. Survey findings revealed all of participants admitted that MTBs they attended met, exceeded, or significantly exceeded their expectations and 90% of presenters confirmed that experts communicated effectively. The majority of participants (87%) considered the discussions on clinical implications of targeted therapies or immunotherapies associated with genomic alterations as the most valuable component, 80% of presenters confirmed that the MTB discussions helped them to confirm, modify or change the treatment plan for at least one of their patients, and 65% of participants were very satisfied with the insights shared around molecular profiling and biological pathways. The pilot MTB has now been incorporated by the national trials group, Cancer Trials Ireland. Conclusions: The development of a national MTB in Ireland utilized pharmaceutical sponsorship and support to gather impetus for an independent national MTB managed by Cancer Trials Ireland. It offers a national forum for the discussion of cancers with specific molecular alterations in addition to complex clinical cases. Furthermore, it has created academic opportunities for the clinical application of precision medicine. Future directions will include leveraging the MTB infrastructure for research and the creation of a national Molecular Cancer Registry. Continued integration of experts will be key to secure the continuation of the national Irish MTB and to ultimately improving precision oncology delivery in patient care and trial enrolment through meaningful insights based on the collected data.

Clinical application of precision medicine among oncologists: A case study in RET-targeted therapy.

e18705 Background: Precision medicine has revolutionized cancer care across multiple tumor types and new actionable biomarkers and targeted therapies are emerging at an unprecedented pace, creating myriad opportunities to optimize care and mitigate the often-dire sequelae of traditional cancer therapy. Many oncology healthcare professionals (HCPs) in practice are not employing optimal testing methodologies to detect biomarkers in patients who could benefit from novel targeted therapies. In this study, we analyze HCP awareness and application of RET alteration testing and integration of recently approved, new-generation selective RET inhibitors into practice for appropriate patients with NSCLC and thyroid cancer. Methods: In August 2020 HCPs were surveyed on RET alteration testing and use of RET-targeted therapeutics in their current practice. Study eligibility criteria included active HCPs in an oncology, pulmonology, or pathology practice. A curriculum of live and online educational activities was then developed for any interested oncologists and pathologists on RET alteration testing and/or targeted therapy for RET-altered lung and thyroid cancers. These activities included case studies, polling, and evaluations that provided additional insight on self-identified practice trends. In June 2021, at the completion of the educational program, eligible HCPs were surveyed again on RET alteration testing and selection of RET inhibitor therapy for appropriate patients. Results: In August 2020, 123 practicing HCPs completed the initial survey and 33% were testing patients for RET gene alterations and 18% were aware of the most sensitive testing assay for detection of RET fusions. 25% and 7%, respectively, were aware of the current indications for RET inhibitors in RET fusion–positive NSCLC and RET-altered thyroid cancer. Self-identified practice trends identified among the unselected cohort of 12,537 individual HPCs participating in the educational activities also demonstrated similar lack of appropriate testing for RET alterations and use of RET inhibitors. In June 2021, 60 practicing HCPs completed the follow-up survey and 40% were testing patients for RET gene alterations and 25% were aware of the most sensitive testing assay for detection of RET fusions. 52% and 22%, respectively, were aware of the current indications for RET inhibitors in RET fusion–positive NSCLC and RET-altered thyroid cancer. Conclusions: The rate of broad testing for RET alterations across patients with NSCLC and thyroid cancer remains low and many HCPs lack understanding of when to consider treating with a RET inhibitor. These results underscore the lag in adoption of optimal precision medicine approaches in oncology and the need for expert guidance and educational activities to optimize individualized, biomarker-driven treatment approaches for patients with cancer.

Characterizing node-negative non-small cell lung cancer patients with similarity networks: A CancerLinQ Discovery analysis.

e20585 Background: Patients with node-negative non-small cell lung cancer (NSCLC) whose tumors are completely resected account for approximately 17% of all patients with lung cancer, and disease recurrence occurs in approximately 1 in 5 of these patients. The lack of consensus on factors associated with risk of disease recurrence among node-negative NSCLC patients is a significant barrier to applying precision medicine strategies in this patient population. Clustering similar patients based on distances between various features of data is an emerging topic in precision medicine. Patient similarity networks represent a new model for clustering patients based on heterogeneous data, whereby any data type is converted into a similarity network by defining a similarity measure. The objective of this study was to examine the utility of patient similarity networks to identify NSCLC patients at higher risk of adverse outcomes. Methods: We conducted a retrospective, observational study of 6,020 node-negative NSCLC patients with an initial diagnosis in 2011-2014 in the CancerLinQ Discovery database. A patient similarity network was assembled based on comorbidities and network communities of patients with similar comorbidities at diagnosis were identified. Using Cox proportional-hazards modeling, we examined the extent to which patient age, sex, race, ethnicity, and network community predicted 2-year disease recurrence and 2-year mortality. Results: In the adjusted analyses, patients in the network community enriched for renal disease and congestive heart failure had an 83% increased risk of mortality (95% CI = 1.39-2.41). Patients in the network community enriched for pulmonary disease had a 37% increased risk of mortality (95% CI = 1.06-1.74) yet a lower risk of recurrence (HR = 0.5, 95% CI = 0.34-0.75). After adjusting for comorbidity network community, male patients had a 14% increased risk of mortality (95%CI = 1.02-1.28) and a 21% increased risk of recurrence (95% CI = 1.05-1.40) and black patients had a lower mortality risk (HR = 0.71, 95% CI = 0.58-0.86). Conclusions: Future studies applying patient similar networks to integrate additional diverse and high dimensional data types may afford more clarity in assigning risk of adverse outcomes.

Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach.

Transcriptome profiling techniques, such as microarrays and RNA sequencing (RNA-seq), are valuable tools for deciphering the regulatory network underlying psoriasis and have revealed large number of differentially expressed genes in lesional and non-lesional skin. Such approaches provide a more precise measurement of transcript levels and their isoforms than any other methods. Large cohort transcriptomic analyses have greatly improved our understanding of the physiological and molecular mechanisms underlying disease pathogenesis and progression. Here, we mostly review the findings of some important large scale psoriatic transcriptomic studies, and the benefits of such studies in elucidating potential therapeutic targets and biomarkers for psoriasis treatment. We also emphasised the importance of looking into the alternatively spliced RNA isoforms/transcripts in psoriasis, rather than focussing only on the gene-level annotation. The neutrophil and blood transcriptome signature in psoriasis is also briefly reviewed, as it provides the immune status information of patients and is a less invasive platform. The application of precision medicine in current management of psoriasis, by combining transcriptomic data, improves the clinical response outcome in individual patients. Drugs tailored to individual patient’s genetic profile will greatly improve patient outcome and cost savings for the healthcare system.