Personalized Medicine Programs Research Articles

Abstract 1770: Right oncogene, wrong tumor - CBL mutations in pediatric CNS and solid tumors

Abstract The molecular analysis of individual patient tumors by personalized medicine programs like the Zero Childhood Cancer Program (ZERO) enables the detection of potentially targetable lesions, unlocking new therapeutic opportunities for patients. Somatic mutations in the E3-ubiquitin ligase, CBL, are known to activate receptor tyrosine kinases (RTKs) in cancer and have been exclusively characterized in hematological malignancies. This includes acute myeloid leukemia, where CBL mutations lead to FLT3 activation. Using molecular data from ZERO we have identified known and novel CBL variants in novel tumor contexts. Our findings raise the possibility that CBL mutation may be a marker of RTK activation in a range of paediatric cancer types and may represent a group of patients who could benefit from tyrosine kinase inhibitor (TKI) therapy. We analyzed whole genome and RNA sequencing data from the ZERO cohort and identified 26 somatic CBL variants in 22 individual patients, the majority of which were in CNS tumors (14 patients). In 8 patients with CNS tumors, we identified missense and splicing variants, which were either novel or not previously seen in this tumor type, that are predicted to have a functional impact on CBL E3 ligase activity due to their location in the linker region or RING finger domain of CBL. Interestingly, most of these patients did not fall into a pre-defined DNA methylation-based subtype classification of CNS tumors and had no significant upregulation of RTK genes. In addition to the novel variants, we identified an established oncogenic CBL deletion variant, CBL exon 8/9 deletion (CBL ex8/9Δ), in novel tumor types - neuroblastoma and a germ cell tumor. Functionally, we show that overexpression of CBL ex8/9Δ enhances cell proliferation and maintains EGFR signaling by blocking CBL mediated degradation of phosphorylated EGFR in neuroblastoma cells. In this study, we demonstrate that CBL is mutated in a range of pediatric cancer types beyond hematological malignancies and may represent a marker of RTK activation and a new target for high-risk patients with limited therapeutic options. Citation Format: Lauren M. Brown, Chelsea Mayoh, Pablo Acera Mateos, Antoine De Weck, Robert Salomon, Teresa Sadras, Neevika Manoharan, Marie Wong, Mark J. Cowley, Paul G. Ekert. Right oncogene, wrong tumor - CBL mutations in pediatric CNS and solid 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 1770.

Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms

Background Myeloid neoplasms (MN) present clinical and molecular heterogeneity and therefore a risk-adapted treatment strategy is mandatory. In MN, classification and prognostic tools based on clinical and morphologic criteria are being complemented by introducing genomic features. The clinical implementation of next-generation classifications and prognostic systems requires the availability of a robust methodological framework together with a solution to provide access to these technologies for clinicians. Aims Machine learning (ML) and Deep Learning (DL) approaches produce powerful predictive models and offer explainable solutions to assure full interpretability of a model when applied in clinical settings. Here we provided a comprehensive assessment of explainable ML/DL-based methods for classification and prognostic assessment of MN and we developed a solution to apply these methods across different clinical Centres through a Federated Learning (FL) approach. Methods We analysed two cohorts of patients from GenoMed4All consortium with myelodysplastic syndrome (MDS), n=2,043 and n=2,384, with available clinical and molecular features to train and validate the models. Methods were then applied to other MN, i.e. acute myeloid leukemia (AML, n=1154) and chronic myelomonocytic leukemia (CMML, n=1037). We stratified patients by two clustering approaches based on Hierarchical Dirichlet Process (HDP) and HDBSCAN combined with UMAP data reduction. We trained a Random Forest (RF) classifier to assign new patients to the existing clusters, considering Balanced Accuracy (BA) and Cohen's K (CK) as performance metrics. We then compared different survival prediction methods: CoxPH model (and its penalized version), Random Survival Forests, DeepCox, Gradient Boosting and XGboost survival methods. Models’ explainability was performed through SHapley Additive exPlanations approach (SHAP). C-index was used to evaluate the models performance. Finally, we developed a Federated Learning (FL) environment together with an imputation approach to handle missing values by a deep decoder model. Results In MDS training cohort, we identified 18 and 8 clusters by using HDBSCAN and HDP, respectively (Figure 1). We measured the average Silhouette Coefficient on the data space obtaining the following performance in terms of classification task: HDBSCAN (BA:92.7±1.3%, CK:92.1±1.4%) and HDP (BA:85.8±0.8%, CK:83.3±0.9%). Similar distributions were observed when focusing on the validation cohort. Model explainability analysis (SHAP) showed that in both populations similar features drive patients’ classification. Comparison of survival prediction for MDS is displayed in Figure 2, showing the models’ performance in the two cohorts considering demographics, clinical, cytogenetics and genomic features. Non-linear ML/DL-based methods outperformed classical CoxPH-based approaches without requiring huge data pre-processing. Moreover, all the models showed higher C-indices with respect to that of conventional IPSS-R score. SHAP analysis showed similar feature importance ranking for both training and validation cohorts. Models were then applied to AML and CMML cohorts, showing consistent results across different type of MN. Finally, we aimed to develop a federated learning (FL) solution (FedAvg, with a deep decoder model for missing data imputation) to favour a wide clinical implementation of the models. Data were collected to a single server and used to build and train a centralized model. Using global data training was expected to improve the model efficiency. This approach also ensured that the data in each node adhere to data privacy policies. We implemented CoxPH model in a setting of 3 nodes (Centers) respectively contributing to 60%, 30% and 10% of the training data. We observed that the poor node (i.e., node contributing to 10% of data) benefit from FedAvg with respect to working on an isolated setting (C-index 0.63 vs. 0.54). The centralized model trained on the whole dataset presented the highest efficiency (C-index 0.74). Conclusion Machine Learning/Deep Learning approach produces explainable and robust solutions to optimize classification and prognostic assessment in MN, as a basis for personalized medicine programs in these disorders. Federate learning algorithms allow a wide clinical implementation of the models by ensuring high performance and data protection. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Towards Symptom-Specific Intervention Recommendation Systems.

Mobile devices and their capabilities (e.g., device sensors and human-device interactions) are increasingly being considered for use in clinical assessments and disease monitoring due to their ability to provide objective, repeatable, and more accurate measures of neurocognitive performance. These mobile-based assessments also provide a foundation for the design of intervention recommendations. The purpose of this work was to assess the benefits of various physical intervention programs as they relate to Parkinson's disease (PD), its symptoms, and stages (Hoehn and Yahr (H&Y) Stages 1-5). Ninety-five participants (n = 70 PD; n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multi-function) and standardized health questionnaires. 208 symptom-specific digital features were normalized to assess the benefits of various physical intervention programs (e.g., aerobic activity, non-contact boxing, functional strength, and yoga) for individuals with PD. While previous studies have shown that physical interventions improve both motor and non-motor PD symptoms, this paper expands on previous works by mapping symptom-specific neurocognitive functionalities to specific physical intervention programs across stages of PD. For early-stage PD (e.g., H&Y Stages 1 & 2), functional strength activities provided the largest overall significant delta improvement (Δ= 0.1883; p = 0.0265), whereas aerobic activity provided the largest overall significant delta improvement (Δ= 0.2700; p = 0.0364) for advanced stages of PD (e.g., H&Y Stages 3-5). As mobile-based digital health technology allows for the collection of larger, labeled, objective datasets, new ways to analyze and interpret patterns in this data emerge which can ultimately lead to new personalized medicine programs.

Comparison of Survival and Clinical Profile of Adults with COVID-19 Hospitalized in Two Clinics in Medellín, Colombia.

Purpose of ReviewThis study compares the survival and clinical profile of hospitalized adults with COVID-19 in two clinics in the city of Medellín, Colombia, with a prospective study with 198 patients in clinic A and 201 in clinic B. Comparisons were made with chi-square and Mann–Whitney U, factors associated with survival were identified with a Cox regression.Recent FindingsThe proportion of deaths was 7.1% in clinic A with a mean survival of 51.9 days (95% CI = 45–59); in clinic B 13.9% of patients died with mean survival of 37.8 days (95% CI = 32–43). The most prevalent comorbidities were hypertension (41.6%), diabetes (23.8%), obesity (15.0%), hypothyroidism (13.0%), dyslipidemia (11.0%), and chronic lung disease (10.8%) with similar proportions in both clinics. There were also differences by the clinic in the most prevalent complications: bacterial pneumonia (18.8%), acute renal failure (14.3%), and encephalopathy (9.5%). There were no differences in the days of hospitalization, mechanical ventilation (clinic A 23.7% and clinic B 29.4%) and admission to the ICU (25.3% in A and 32.3% in B).SummaryWe evidence the heterogeneity of the survival and the clinical profile of the patients who are cared for by two institutions of the same city. These findings demonstrate the need to conduct unique studies for each institution, which poses a significant challenge for hospital epidemiology programs due to the impossibility of extrapolating evidence from other healthcare institutions and the need to implement personalized medicine programs given the clinical diversity of patients hospitalized for COVID-19.

Prevalence of ABCB1 3435C>T polymorphism in the Cuban population.

ABCB1 gene polymorphisms can modify P-glycoprotein function with clinical consequences. The 3435C>T polymorphism prevalence was analyzed using oligonucleotide probes and next-generation sequencing in 421 unrelated healthy individuals living in Cuba. Data were stratified by gender, ethnic background and residence. The genotype and allelic frequencies were determined. The genotype distribution met the Hardy-Weinberg equilibrium assumption. The allelic frequency was 63.5% for the 3435C variant. The genotype frequencies were 41.1% for CC, 44.9% for CT and 14.0% for TT. The allele and genotype distributions differed between individuals living in La Habana and Santiago de Cuba (p<0.05) when ethnic background was analyzed. The allelic distribution was similar among Admixed and Black subjects, and they differed from Caucasians. The CC genotype was equally distributed among Admixed and Black subjects, and they differed from Caucasians. The TT genotype frequency differed between Caucasians and Admixed. The CT genotype was distributed differently among the three groups. Similar distribution was obtained in Brazilians, whereas some similarities were observed in African, Spanish and Chinese populations, consistent with the mixed Cuban ethnic origin. This is the first report on allele and genotype frequencies of the 3435C>T polymorphism in Cuba, which may support personalized medicine programs.

Abstract 2814: Establishment and optimization of a drug-resistant PDX-based platform using in vivo and in vitro approaches

Abstract Induced resistance after anticancer agent treatment is the main reason for cancer relapse and short median survival of patients. Here we employed in vivo and in vitro approaches to establish and optimize a drug-resistant platform based on patient-derived xenografts (PDXs). PDXs derived from lung cancer patients who acquired drug resistance reproduced the insensitivity to standard target therapies in mice. Alternatively, resistance was induced in established PDX by continuously exposing with dosage increasing, which demonstrated the induced response declining after the treatment of one second-line therapy drug (Irinotecan). These drug resistant models were additionally confirmed by the in vitro 3D cell based assay, which mimicked the precise histologic structure and cellular composition of the corresponding patient samples. These results from in vivo and in vitro studies displayed good correlation, suggesting that this platform of PDXs and 3D cell based assay can be applied to preclinical development, mechanism research and new drug screen to overcome treatment resistance and have the potential for application to personalized medicine programs. Citation Format: Xiaoying Zhuang, Nan Xie, Lei Jin, Wengang Zeng, Yuhui Zheng, Chunling Zhang, Liping Cai, Hanmei Sun, Jie Xu, Wenjie Gao, Qunsheng Ji. Establishment and optimization of a drug-resistant PDX-based platform using in vivo and in vitro approaches [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2814.

Means and methods of world traditional systems in personalized medicine programs: legal framework and world experience

The article is devoted to the analysis of the role of knowledge and experience of traditional medical systems of the world in the rapidly emerging new paradigm of the academic school-personalized preventive medicine. It shows the huge opportunities that open up to the world and national medical community when combining traditions and innovations into a single system, based on long-term experience of personalized traditional approaches to prevention, treatment and rehabilitation. The article focuses on the legal issues of this synergistic approach.

Functional Precision Medicine in AML: Technical Performance Evaluation for in Vitro Diagnostics Using High-Throughput Image-Based Screening of Primary Patient Cells

Introduction: Identification of the right drug, for the right patient, at the right time is the defining goal for precision and personalized medicine programs. Many tools are currently being pursued for this including next-gen sequencing and other -omics approaches to determine target availability (Moscow et al, 2018 Nat Rev Clin Oncol.), functional drug response profiling in viable patient samples (Snijder et al, 2017 Lancet Haem), or a combination of both (Schmidl et al, 2019 Nat Chem Bio). Of those, functional precision medicine approaches are only just beginning to gain clinical interest, and because of this, nearly all programs lack systematic testing in prospective clinical studies (Letai, 2017 Nat Rev Med). One of the first functional precision medicine approaches to be tested in a small prospective clinical study was the use of high-throughput image-based screening and single cell analysis of drug response in primary tissues ("Pharmacoscopy") as a tool to prospectively rank drug options for patients with late-stage hematological indications (Snijder et al 2017, Lancet Haem, NCT03096821). In this study, taking Pharmacoscopy data into account for treatment selection led to a higher overall response rate (88% vs. 24%) and longer progression free survival (22.6 vs. 5.7 weeks) compared to the previous line of therapy. In order for functional precision medicine programs to reach widespread routine use, ultimately the development of fully-certified in vitro diagnostic test products will be required. This requires amongst others a systematic understanding of i) the technical robustness of functional drug testing approaches and ii) a detailed understanding of how pre-analytical sample handling influences assay results. Here, we set out to investigate two crucial questions that arose in the course of systematic assay development for AML: a) do CD34+ cells derived from peripheral blood and bone marrow AML patients respond differently to short term ex vivo treatment with small molecule drugs and b) does the overall response of these cells change upon cryopreservation? These issues are of high relevance even outside the setting of functional precision medicine development programs given worldwide biobanking efforts, and the lack of robust systematic model comparison. Methods: Fresh patient blood and bone marrow samples of newly diagnosed as well as relapsed and refractory patients with AML were collected at Medical University of Graz under appropriate ethics approval. Samples were divided into two parts and one part used immediately, the other cryopreserved as viable cells. The response of CD34+ cells against 140 different small molecule drugs (two concentrations and three technical replicates) was evaluated using single-cell high content microscopy (Allcyte's "Pharmacoscopy" platform) in both peripheral blood and bone marrow as well as freshly used and previously viably frozen samples. Drug response was determined by fitting to generalised linear and mixed models. Further, RNA is isolated from additional patient samples before and after biobanking for Nanostring analysis. Results: Overall, across all tested patient samples, we observed a high correlation between drug response tested in blood and bone marrow samples from this group of patient samples, indicating, at least for AML, no strong niche dependence for the drugs tested. Additionally, we also observed a high correlation between drug response tested in fresh and frozen tissues (Figure 1). The differences that did appear were, in particular, in drugs targeting metabolic and cell-growth dependent functions, amongst others. Conclusions: Understanding how biobanking affects the ex vivo drug response of primary human tissues is paramount to being able to understand how far translational efforts can go using tissues and functional drug response assays. We provide systematic data supporting the use of cryopreserved samples for the functional analysis of drug response in AML. This is a fundamental question associated with the use of biobanks for investigations of primary patient material. Figure 1 Disclosures Krall: Allcyte: Employment, Equity Ownership. Meszaros:Allcyte: Employment. Vilagos:Allcyte: Employment. Sill:Astellas: Other: Advisory board; Novartis: Other: Advisory board; Astex: Other: Advisory board; AbbVie: Other: Advisory board. Vladimer:Allcyte: Employment, Equity Ownership, Patents &amp; Royalties: EP3198276A1.

Impact of Polypharmacy on Candidate Biomarker miRNomes for the Diagnosis of Fibromyalgia and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Striking Back on Treatments

Fibromyalgia (FM) and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are diseases of unknown etiology presenting complex and often overlapping symptomatology. Despite promising advances on the study of miRNomes of these diseases, no validated molecular diagnostic biomarker yet exists. Since FM and ME/CFS patient treatments commonly include polypharmacy, it is of concern that biomarker miRNAs are masked by drug interactions. Aiming at discriminating between drug-effects and true disease-associated differential miRNA expression, we evaluated the potential impact of commonly prescribed drugs on disease miRNomes, as reported by the literature. By using the web search tools SM2miR, Pharmaco-miR, and repoDB, we found a list of commonly prescribed drugs that impact FM and ME/CFS miRNomes and therefore could be interfering in the process of biomarker discovery. On another end, disease-associated miRNomes may incline a patient’s response to treatment and toxicity. Here, we explored treatments for diseases in general that could be affected by FM and ME/CFS miRNomes, finding a long list of them, including treatments for lymphoma, a type of cancer affecting ME/CFS patients at a higher rate than healthy population. We conclude that FM and ME/CFS miRNomes could help refine pharmacogenomic/pharmacoepigenomic analysis to elevate future personalized medicine and precision medicine programs in the clinic.

Current landscape of personalized medicine adoption and implementation in Southeast Asia

BackgroundThe emergence of personalized medicine (PM) has raised some tensions in healthcare systems. PM is expensive and health budgets are constrained - efficient healthcare delivery is therefore critical. Notwithstanding the cost, many countries have started to adopt this novel technology, including resource-limited Southeast Asia (SEA) countries. This study aimed to describe the status of PM adoption in SEA, highlight the challenges and to propose strategies for future development.MethodsThe study included scoping review and key stakeholder interviews in four focus countries – Indonesia, Malaysia, Singapore, and Thailand. The current landscape of PM adoption was evaluated based on an assessment framework of six key themes – healthcare system, governance, access, awareness, implementation, and data. Six PM programs were evaluated for their financing and implementation mechanisms.ResultsThe findings revealed SEA has progressed in adopting PM especially Singapore and Thailand. A regional pharmacogenomics research network has been established. However, PM policies and programs vary significantly. As most PM programs are champion-driven and the available funding is limited, the current PM distribution has the potential to widen existing health disparities. Low PM awareness in the society and the absence of political support with financial investment are fundamental barriers. There is a clear need to broaden opportunities for critical discourse about PM especially for policymakers. Multi-stakeholder, multi-country strategies need to be prioritized in order to leverage resources and expertise.ConclusionsAdopting PM remains in its infancy in SEA. To achieve an effective PM adoption, it is imperative to balance equity issues across diverse populations while improving efficiency in healthcare.

Clinical exome sequencing in France and Quebec: what are the challenges? What does the future hold?

BackgroundThe decreasing cost of next-generation sequencing technologies (NGS) has resulted in their increased use in research, and in the clinic. However, France and Quebec have not yet implemented nation-wide personalized medicine programs using NGS. To produce policies on the large-scale implementation of NGS, decision makers could benefit from a detailed understanding of how these technologies are currently used, their limitations, and the benefits they could bring to patients.ObjectivesWe aimed at answering two research questions: How are patients’ NGS data currently managed in healthcare institutions in Quebec and in France? What issues do technology users identify which should be solved in order to implement clinical genomics at the national level?MethodThrough a multiple case study method, we analysed interviews and documentation from four teams that use whole-exome sequencing in hybrid clinical research projects focusing on cancer and rare diseases.ResultsInterviewees detailed numerous challenges linked with managing the complexity of the process of collecting and interpreting data in a relevant manner for patients, and described how obtaining buy-in from multiple stakeholders was necessary.ConclusionA strong political will is essential for personalized medicine to be implemented efficiently in France and Quebec.

Abstract 5350: Single cell RNA-seq reveals transcriptionally distinct subpopulations within patient-derived pancreatic cancer organoids

Abstract Advances in 3D cell culture over the last decade have enabled the establishment of organoid models from a multitude of organs in varying stages of development and disease. Organoids recapitulate the tissue-specific cell types and exhibit similar morphology and spatial organization as the organ it is derived from. However, to the best of our knowledge, tumor organoids have never been profiled at a single cell level. In this study, we applied Drop-seq, a high throughput single cell RNA-seq platform, to characterize the different cell types present in pancreatic cancer organoids. We sequenced the transcriptomes of 5494 single cells from organoids derived from a patient undergoing a Whipple procedure for pancreatic ductal adenocarcinoma (PDAC). We then developed a novel clustering approach based on a class of probabilistic generative models called topic models, leading to the identification of two subpopulations of cells. One subgroup expressed marker genes known to play important roles in PDAC biology, such as MUC1, CLDN4 and GAS5. Interestingly, this subgroup also bore markers for cancer stem cells (HES1 and SQSTM1) that have been found by multiple studies to mediate chemoresistance in PDAC. The other subpopulation differentially expressed AGR2 and TMBIM6, which are associated with endoplasmic reticulum (ER) stress and correlates with invasiveness of PDAC cell lines. Functional studies are underway to determine the exact role of these subgroups, but our preliminary findings are suggestive of a subpopulation of cells that maybe involved in resistance to chemotherapy. This is consistent with the clinical progression of this patient's cancer, suggesting that rapid, inexpensive single cell transcriptomics might provide more precise molecular information to personalized medicine programs than traditional RNA-seq alone. Citation Format: Mohana Ray, Rajib K. Nandi, Isabel Romero Calvo, Kori Kirby, Christopher Weber, Hedy L. Kindler, Mary Buschmann, Kevin Roggin, Kevin P. White. Single cell RNA-seq reveals transcriptionally distinct subpopulations within patient-derived pancreatic cancer organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5350.

Abstract 2784: Genetic analysis of breast cancer in admixed populations of central Colombia

Abstract Breast cancer (BC) is the most frequently diagnosed cancer among women with an estimated 1,676,633 new cases resulting in ∼521,817 deaths each year globally. Thus, it is no surprise BC research has become one of the hottest cancer research areas in the past three decades. Past studies examining and identifying BC risk loci have focused on three major populations (European, African, and Asian). Past research has led to greater understanding of the etiology (genetic and environmental) and treatment of BC; resulting in a ∼30% decrease in mortality rate in wealthy countries. However, when looking at BC incidence and mortality in low to middle income nations similar gains are not seen; in fact, in these countries mortality has increased in the past 20 years. This trend holds true for the Hispanic community. While Hispanic women have a ∼27% lower rate of BC than in non-Hispanic white women, Hispanic women are more likely to be diagnosed with larger tumors and later stage cancer, leading to ∼20% increase in mortality for Hispanic women compared to their non-Hispanic white counterpart with similar diagnosis stage and age. Differences in incidence and mortality have been attributed to environmental and genetic factors. This case-control association study aims to address a research disparity by examining the role of ethnicity and genetics on development of BC risk in central Colombian Hispanic populations. It provides one of the first assessments of known BC risk factors in the South American Hispanic community. The study was conducted in central Colombia using ∼600 breast cancer cases from COLUMBUS initiative &amp; ∼700 matched cancer-free controls from the CHIBCHA consortium. Cases were genotyped using Affymetrix Axiom UK Biobank arrays and controls were genotyped using both Affymetrix Axiom LAT and Axiom Custom arrays followed by imputation using 1000 Genomes data. We analyzed previously discovered BC loci and under independent analysis found 6 significant loci: rs11780156 (P = 0.005, OR: 1.34), rs2981582 (P = 0.007, OR: 1.25), rs4784227 (P = 0.016, OR: 1.22), rs2981579 (P = 0.017, OR 1.21), rs1011970 (P = 0.0274, OR 1.20), rs1219648 (P = 0.042, OR 1.18) and several loci showing borderline significant associations involving the same risk allele. Interestingly, we identified additional novel BC risk loci that were genome-wide significant and are undergoing replication in populations from Central Mexico. In summary, we performed an assessment of BC risk loci in central Colombian Hispanic population and found significant overlap with BC risk loci previously identified in European and East Asian populations as well as potentially novel loci that are unique to Hispanic populations. These results are integral to the establishment of personalized medicine programs and will form the basis for future BC prevention and treatment program in Latin American and US Hispanics. Citation Format: Paul C. Lott, Angel Criollo, Anna Marie Tuazon, Carolina Ramirez Alfonso, Ruta Sahasrabudhe, Ana Estrada, Elisha Garcia, Rodrigo Prieto, John Williamson, Javier Torres, Mabel Bohorquez, Maria Magdalena Echeverry de Polanco, CHIBCHA Consortium, COLUBMUS Consortium, Luis G. Carvajal-Carmona. Genetic analysis of breast cancer in admixed populations of central Colombia. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2784. doi:10.1158/1538-7445.AM2015-2784

Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology

Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context. The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively. Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method. The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.

Where cancer genomics should go next: a clinician's perspective.

Large-scale, genomic studies of specific tumors such as The Cancer Genome Atlas have provided a better understanding of the alterations of pathways involved in the development of solid tumors including glioblastoma, breast cancer, ovarian and endometrial cancers, colon cancer and lung squamous cell carcinoma. This tremendous effort of the scientific community has confirmed the view that cancer actually represents a wide variety of diseases originating from different organs. These studies showed that TP53 and PI3KCA are the two most mutated genes in all types of cancers and that 30-70% of all solid tumors harbor potentially 'actionable' mutations that can be exploited for patient stratification or treatment optimization. Translation of this huge oncogenomic data set to clinical application in personalized medicine programs is now the main challenge for the future. The gap between our basic knowledge and clinical application is still wide. Closing the gap will require translational personalized trials, which may initiate a radical change in our routine clinical practice in oncology.

The Impact of Online Networks and Big Data in Life Sciences

Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.

University of Florida Clinical and Translational Science Institute: Transformation and Translation in Personalized Medicine

We are constantly reflecting on whether and how our efforts are transformational as we work to achieve the ambitious goals articulated for the University of Florida’s Clinical and Translational Science Institute (UF CTSI). As the only Clinical and Translational Science Award (CTSA) recipient in the fourth most populous state, the UF CTSI must aim to support research and improve health care for 19 million Floridians, 6% of the US population. The strong commitment of our university leadership and a team-science approach has enabled us to move swiftly in the context of a very large comprehensive public research university. The UF CTSI involves two major health systems, agricultural extension offices in all 67 counties in Florida, and numerous partners throughout the state. All of UF’s 16 colleges* participate in the CTSI, which supports 140 faculty and staff; 10 programs; 13 clinical research units; five core labs; and six educational programs. As we enter year three of our grant, the UF CTSI is launching a Personalized Medicine Program – a feat made possible by the transformational changes championed by the National Institutes of Health (NIH), its National Center for Research Resources (NCRR) and the CTSA program. We share this as one of many examples unfolding across the country in which CTSA institutions are adding new capacity for translational research, which in turn is leading to new approaches for how we translate scientific discoveries into medical practice – a need around which the NIH is vigorously seeking to catalyze new breakthroughs.1