Patient-specific Therapeutic Strategies Research Articles

Alpha/beta values in pediatric medulloblastoma: implications for tailored approaches in radiation oncology

BackgroundMedulloblastoma is the most common malignant pediatric brain tumor, typically treated with normofractionated craniospinal irradiation (CSI) with an additional boost over about 6 weeks in children older than 3 years. This study investigates the sensitivity of pediatric medulloblastoma cell lines to different radiation fractionation schedules. While extensively studied in adult tumors, these ratios remain unknown in pediatric cases due to the rarity of the disease.Materials and methodsFive distinct medulloblastoma cell lines (ONS76, UW228-3, DAOY, D283, D425) were exposed to varying radiation doses and fractionation schemes. In addition, ONS76 and UW228-3 stably overexpressing MYC were analyzed. Alpha/beta values, representing fractionation sensitivity, were quantified using the linear-quadratic model of radiation survival.ResultsThe study unveiled elevated alpha/beta ratios across diverse medulloblastoma cell lines, with a weighted mean alpha/beta value of 11.01 Gy (CI: 5.23–16.79 Gy). Neither TP53 status nor the levels of MYC expression influenced fractionated radiosensitivity. Furthermore, differences in alpha/beta values cannot be correlated with molecular subgroups (p = 0.07) or radiosensitivity (SF2).ConclusionThese in vitro findings strongly recommend normofractionated or hyperfractionated radiotherapy for paediatric medulloblastoma cases due to consistently high alpha/beta values across subgroups. Conversely, hypofractionated radiotherapy is not advisable within a curative approach. This study presents significant potential by enabling the estimation of radiobiological fractionations and dose effects in young, vulnerable patients, highlighting its importance for advancing patient-specific therapeutic strategies.

Artificial Intelligence in Cancer: A SWOT Analysis

Cancer, a collection of maladies that has undergone extensive examination over centuries, remains a formidable challenge. Despite the array of available pharmacological and therapeutic interventions, the intricate molecular dynamics and heterogeneity of cancer continue to challenge the scientific community. Artificial Intelligence (AI) emerges as a promising avenue, offering the potential for expedited, precise diagnostics devoid of human expertise. Additionally, AI facilitates the tailoring of patient-specific therapeutic strategies targeting various facets of cancer, spanning macroscopic to microscopic levels. Nonetheless, it is imperative to scrutinize the potential benefits and limitations of AI technologies in this context. This review undertakes a comprehensive Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of AI's application in cancer. An extensive compilation of AI applications encompasses predictive modeling, diagnostic capabilities, prognostic assessments, and personalized therapeutic modalities, spanning genomic analyses to individualized treatment regimens. The synthesis of evidence suggests that the advantages of AI outweigh its drawbacks; nevertheless, obstacles to its widespread integration persist.

Microbial spectrum and resistance of odontogenic abscesses - microbiological analysis using next generation sequencing

ObjectivesThis study aimed to map the microbiome of odontogenic abscesses using next-generation sequencing (NGS) to identify bacterial and fungal species, as well as antibiotic resistances.Materials and methodsWound swabs were taken from patients treated for odontogenic abscesses at the Department of Oral and Maxillofacial Plastic Surgery, University Hospital Bonn. NGS was used to analyze the swabs, and bioinformatic analysis assigned the genetic material to microorganism profiles and identified antibiotic resistances.ResultsBacteria were detected in all samples from 51 patients. Anaerobes were found in 50 swabs, while aerobic bacteria were detected in 30. Four of the five most common bacterial genera were anaerobes (Fusobacterium, Prevotella, Parvimonas, Porphyromonas). A median of eight genera were identified per swab. Infections were mostly polymicrobial; only one case was a monoinfection with Streptococcus. Aerobic bacteria were less frequent in patients who had prior antibiotic therapy (p = 0.015). Fungi were present in 52.9% of cases, with Malassezia (33.3%), Aspergillus (9.8%), and Candida (3.9%) being the most common. Antibiotic resistance was detected in 66.7% of patients, mainly against lincosamides, macrolides and tetracyclines. Fusobacterium showed a 25.9% resistance rate to clindamycin.ConclusionsThe microbiome of odontogenic abscesses is polymicrobial, dominated by anaerobic bacteria, and more extensive than indicated by traditional cultural diagnostics. NGS provides detailed pathogen diagnostics, aiding in precise and individualized antibiotic therapy.Clinical relevanceImproved understanding of the bacterial and fungal spectrum, along with current resistance patterns of odontogenic abscesses, is crucial for optimizing treatment outcomes. NGS offers rapid, accurate and detailed microbiome analysis, enhancing patient-specific therapeutic strategies.

Cardiovascular Toxicity in Cancer Therapy: Protecting the Heart while Combating Cancer.

This review explores the cardiovascular toxicity associated with cancer therapies, emphasizing the significance of the growing field of cardio-oncology. It aims to elucidate the mechanisms of cardiotoxicity due to radiotherapy, chemotherapy, and targeted therapies, and to discuss the advancements in human induced pluripotent stem cell technology (hiPSC) for predictive disease modeling. Recent studies have identified several chemotherapeutic agents, including anthracyclines and kinase inhibitors, that significantly increase cardiovascular risks. Advances in hiPSC technology have enabled the differentiation of these cells into cardiovascular lineages, facilitating more accurate modeling of drug-induced cardiotoxicity. Moreover, integrating hiPSCs into clinical trials holds promise for personalized cardiotoxicity assessments, potentially enhancing patient-specific therapeutic strategies. Cardio-oncology bridges oncology and cardiology to mitigate the cardiovascular side-effects of cancer treatments. Despite advancements in predictive models using hiPSCs, challenges persist in accurately replicating adult heart tissue and ensuring reproducibility. Ongoing research is essential for developing personalized therapies that balance effective cancer treatment with minimal cardiovascular harm.

Supraglottic Laryngeal Maneuvers in Adductor Laryngeal Dystonia During Connected Speech

ObjectiveAdductor laryngeal dystonia (AdLD) disrupts fine motor movements of vocal folds during speech, resulting in a strained, broken, and strangled voice. Laryngeal high-speed videoendoscopy (HSV) in connected speech enables the direct visualization of detailed laryngeal dynamics, hence, it can be effectively used to study AdLD. The current study utilizes HSV to investigate supraglottic laryngeal tissue maneuvers obstructing the view of the vocal folds, in AdLD and normophonic speakers during connected speech. Characterizing the laryngeal maneuvers in these groups can facilitate a deeper understanding of the normophonic voice physiology and AdLD voice pathophysiology. MethodsHSV data were obtained from six normophonic speakers and six patients with AdLD during production of connected speech. Three experienced raters visually analyzed the data to determine laryngeal tissues leading to obstructions of vocal folds in HSV images. The raters recorded the duration of each obstruction and indicated the specific tissue(s) leading to the obstruction After the completion of their individual visual analysis, the raters came to consensus about their observations and measurements. ResultsStatistical analysis indicated that AdLD patients exhibited higher occurrences of vocal fold obstructions and longer durations of obstructions compared to the normophonic group. Similar obstruction types were found in both groups, with the epiglottis being the primary site of obstruction for both. Participants with AdLD displayed significantly elevated occurrences of sphincteric compression resulting in vocal fold obstruction. ConclusionHSV can be used to study the movements of laryngeal tissues in detail during connected speech. The analysis of supraglottic laryngeal tissue dynamics in speech can help us characterize the AdLD pathophysiology. The study's findings regarding the tissues implicated in obstructions may potentially inform the development of patient-specific therapeutic strategies targeting individual control over specific laryngeal muscles during phonation and speech production.

Revealing heterogeneity in chronic lymphocytic leukemia: AI-driven insights into aggressive and indolent disease subtypes.

e19029 Background: Chronic lymphocytic leukemia (CLL) exhibits a broad spectrum of clinical behaviors, from indolent courses requiring minimal intervention to aggressive forms demanding immediate treatment. The underlying heterogeneity of CLL poses significant challenges in predicting disease progression and tailoring patient-specific therapeutic strategies. Leveraging advanced and novel artificial intelligence (AI) and machine learning (ML) methodologies, our study aims to dissect the complexity of CLL, focusing on the identification and characterization of biomarkers that differentiate aggressive and indolent disease forms. Methods: The NetraAI is a priorietaryAI/ML system that is also being utilized in a research collaboration at a leading American oncology institute (not disclosed) that integrates machine intelligence, augmented intelligence, and an agent that interacts with AI-derived patient representations. This system was designed to learn from datasets with a small number of samples and many independent variables. This system operates by generating hypotheses in the form of interactive representations of patient populations that reveal heterogeneity along with statistically significant driving factors. Finally, an automated agent called AutoPlay interacts with these representations and generates an accounting of all variables, subpopulations of samples, and it also generates a human readable perspective on what was discovered by the data set. Results: We applied the NetraAI to a CLL dataset and used it to refine the analysis and interpretation of the corresponding transcriptomic data (GSE39411). Significantly lower levels of FADS3, GSDME, LPL, IMMT, NMB, and AEBP1 and higher expressions of COBLL1, P2RY1R, PDE8A, SYNE2, and FCRL3 were identified as hallmarks of indolent CLL, suggesting these markers could inform less aggressive disease management strategies. Furthermore, within a total of 104 CLL samples, we uncovered two distinct subpopulations within aggressive CLL, delineated by unique genetic markers. Group A, encompassing 31 aggressive CLL samples is predominantly characterized by the expression of lipoprotein lipase (LPL), while Group B, comprising 22/23 aggressive samples, is characterized by ZBTB20 and SYNE2. Our poster will also provide glimpses into a more refined heterogeneity that emerged from the analysis. Our results not only underscore the heterogeneity within aggressive CLL, but also highlights the potential for these markers to guide prognostic assessments and therapeutic decisions. Conclusions: This study not only advances our understanding of CLL heterogeneity, but also sets the stage for the development of more personalized and effective treatment modalities. By identifying drivers of disease aggression and indolence, we pave the way for targeted therapies that could significantly improve patient outcomes in CLL.

Smart Capsule for Targeted Detection of Inflammation Levels Inside the GI Tract.

Effective management of Inflammatory Bowel Disease (IBD) is contingent upon frequent monitoring of inflammation levels at targeted locations within the gastrointestinal (GI) tract. This is crucial for assessing disease progression and detecting potential relapses. To address this need, a novel single-use capsule technology has been devised that enables region-specific inflammation measurement, thereby facilitating repeatable monitoring within the GI tract. The capsule integrates a pH-responsive coating for location-specific activation, a chemiluminescent paper-based myeloperoxidase (MPO) sensor for inflammation detection, and a miniaturized photodetector, complemented by embedded electronics for real-time wireless data transmission. Demonstrating linear sensitivity within the physiological MPO concentration range, the sensor is capable of effectively identifying inflammation risk in the GI fluid. Luminescence emitted by the sensor, proportional to MPO concentration, is converted into an electrical signal by the photodetector, generating a quantifiable energy output with a sensitivity of 6.14 µJ/U.ml-1. The capsule was also tested with GI fluids collected from pig models simulating various inflammation states. Despite the physiological complexities, the capsule consistently activated in the intended region and accurately detected MPO levels with less than a 5% variation between readings in GI fluid and a PBS solution. This study heralds a significant step towards minimally invasive, in situ GI inflammation monitoring, potentially revolutionizing personalized IBD management and patient-specific therapeutic strategies.

Towards Precision Ophthalmology: The Role of 3D Printing and Bioprinting in Oculoplastic Surgery, Retinal, Corneal, and Glaucoma Treatment.

In the forefront of ophthalmic innovation, biomimetic 3D printing and bioprinting technologies are redefining patient-specific therapeutic strategies. This critical review systematically evaluates their application spectrum, spanning oculoplastic reconstruction, retinal tissue engineering, corneal transplantation, and targeted glaucoma treatments. It highlights the intricacies of these technologies, including the fundamental principles, advanced materials, and bioinks that facilitate the replication of ocular tissue architecture. The synthesis of primary studies from 2014 to 2023 provides a rigorous analysis of their evolution and current clinical implications. This review is unique in its holistic approach, juxtaposing the scientific underpinnings with clinical realities, thereby delineating the advantages over conventional modalities, and identifying translational barriers. It elucidates persistent knowledge deficits and outlines future research directions. It ultimately accentuates the imperative for multidisciplinary collaboration to enhance the clinical integration of these biotechnologies, culminating in a paradigm shift towards individualized ophthalmic care.

Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the efficient selection of features, resulting in the underutilization of the temporal richness of EEG and the spatial specificity of fNIRS data.To effectively address this challenge, this study proposed a deep learning architecture called the multimodal DenseNet fusion (MDNF) model that was trained on two-dimensional (2D) EEG data images, leveraging advanced feature extraction techniques. The model transformed EEG data into 2D images using a short-time Fourier transform, applied transfer learning to extract discriminative features, and consequently integrated them with fNIRS-derived spectral entropy features. This approach aimed to bridge existing gaps in EEG-fNIRS-based BCI research by enhancing classification accuracy and versatility across various cognitive and motor imagery tasks.Experimental results on two public datasets demonstrated the superiority of our model over existing state-of-the-art methods.Thus, the high accuracy and precise feature utilization of the MDNF model demonstrates the potential in clinical applications for neurodiagnostics and rehabilitation, thereby paving the method for patient-specific therapeutic strategies.

Artificial Intelligence's Use in the Diagnosis of Mouth Ulcers: A Systematic Review.

Artificial intelligence (AI) has been cited as being helpful in the diagnosis of diseases, the prediction of prognoses, and the development of patient-specific therapeutic strategies. AI can help dentists, in particular, when they need to make important judgments quickly. It can eliminate human mistakes in making decisions, resulting in superior and consistent medical treatment while lowering the workload on dentists.The existing studies relevant to the study and application of AI in the diagnosis of various forms of mouth ulcers are reviewed in this work. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were followed in the preparation of the review. There were no rule violations, with the significant exception of the use of a better search method that led to more accurate findings. Using search terms mainly such as AI, oral health, oral ulcers, oral herpes simplex, oral lichen planus, pemphigus vulgaris, recurrent aphthous ulcer (RAU), oral cancer, premalignant and malignant disorders, etc., a comprehensive search was carried out in the reliable sources of literature, namely PubMed, Scopus, Embase, Web of Science, Ovid, Global Health, and PsycINFO. For all papers, exhaustive searches were done using inclusion criteria as well as exclusion criteria between June 28, 2018, and June 28, 2023. An AI framework for the automatic categorization of oral ulcers from oral clinical photographs was developed by the authors, and it performed satisfactorily. The newly designed AI model works better than the current convolutional neural network image categorization techniques and shows a fair level of precision in the classification of oral ulcers. However, despite being useful for identifying oral ulcers, the suggested technique needs a broader set of data for validation and training purposes before being used in clinical settings. Automated OCSCC identification using a deep learning-based technique is a quick, harmless, affordable, and practical approach to evaluating the effectiveness of cancer treatment. The categorization and identification of RAU lesions through the use of non-intrusive oral pictures using the previously developed ResNet50 and YOLOV algorithms demonstrated better accuracy as well as adequate potential for the future, which could be helpful in clinical practice. Moreover, the most reliable projections for the likelihood of the presence or absence of RAU were made by the optimized neural network. The authors also discovered variables associated with RAU that might be used as input information to build artificial neural networks that anticipate RAU.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

BackgroundPharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). MethodsA total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. ResultsThree reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. ConclusionWe provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

The developmental origin of cancers defines basic principles of cisplatin resistance.

Cisplatin-based chemotherapy has been used for more than four decades as a standard therapeutic option in several tumor entities. However, being a multifaceted and heterogeneous phenomenon, inherent or acquired resistance to cisplatin remains a major obstacle during the treatment of several solid malignancies and inevitably results in disease progression. Hence, we felt there was an urgent need to evaluate common mechanisms between multifarious cancer entities to identify patient-specific therapeutic strategies. We found joint molecular and (epi)genetic resistance mechanisms and specific cisplatin-induced mutational signatures that depended on the developmental origin (endo-, meso-, ectoderm) of the tumor tissue. Based on the findings of thirteen tumor entities, we identified three resistance groups, where Group 1 (endodermal origin) prominently indicates NRF2-pathway activation, Group 2 (mesodermal origin, primordial germ cells) shares elevated DNA repair mechanisms and decreased apoptosis induction, and Group 3 (ectodermal and paraxial mesodermal origin) commonly presents deregulated apoptosis induction and alternating pathways as the main cisplatin-induced resistance mechanisms. This review further proposes potential and novel therapeutic strategies to improve the outcome of cisplatin-based chemotherapy.

Potential clinical application of free-circulating DNA from blood in renal cancer

Early diagnosis of renal cancer carcinoma is a key determinant of patient survival. The asymptomatic disease course and lack of reliable diagnostic markers lead to the fact that more than 30 % renal cancer cases discovered at an advanced stage, when the prognosis is poor because kidney tumors are resistant to standard chemotherapy and radiation. More than 30 % of renal cancer carcinoma recur or metastasize after surgical treatment. Despite the implementation of novel targeted drugs and immune point inhibitors, the 5-year survival rate for metastatic renal cancer carcinoma remains dismal. Unsatisfactory result of renal cancer treatment may be caused by high inter- and intra-tumor heterogeneity and tumor evolution during therapy, as well as the lack of predictive and on-treatment monitoring biomarkers. Liquid biopsy test that utilizes free-circulating DNA (cfDNA) in the blood of patients, opens up new opportunities for managing patients with renal cancer. The diagnostic and predictive potential of these minimally invasive biomarkers has been demonstrated for various types of cancer. The use of highly sensitive methods of cfDNA analysis may allow early cancer detection and prediction of postoperative disease recurrence before dinical and radiographic progression. Serial cfDNA samples, that were collected before and during course of treatment, can provide information about the dynamic mutational changes in the volume of the entire tumor and metastases in real time, and the emergence of drug resistance during treatment. This information may be promising toolfor optimizing patient-specific therapeutic strategies. This review is focusing on the potential clinical application of cfDNA from blood in renal cancer.

Leukemia Cutis: A Systematic Review

Leukemia cutis (LC) is a rare cutaneous infiltration of extramedullary neoplastic hematopoietic cells with a paucity of data on their management, given that most cases are from individual case reports or case series, which further impact outcomes. This review aims to investigate the clinical characteristics of LC and highlight our cytogenetic findings that could contribute to our growing understanding and help reshape the prognosis of this rare but deadly condition. Patients and methods PubMed, Medline, ScienceDirect, and Scopus databases searched for "Leukemia Cutis" case reports from January 2000 to July 2020 pooled with a case from our institution. Results We included 184 biopsy-proven LC cases. Male: female ratio was 1.49:1. Mean age at diagnosis (± SD) was 48.9 (±25.9) years. Regarding age groups, 17 (9.2%), 16 (8.7%), 14 (7.6%), 12 (6.5%), 16 (8.7%), 29 (15.8%), and 80 (43.5%) patients were noted in the age groups of <1, 1-19, 19-30, 31-40, 41-50, 51-60, and >60 years, respectively. The demographics and clinical features of LC are summarized in Table 1. The presenting finding was LC in 66 (35.8%) cases, with an average time-to-leukemia diagnosis of 8.1 months (range, five days-72 months), and aleukemic LC (ALC) in 17 (9.2%) cases. Mean leukemia-to-LC diagnosis interval was 25.6 (range, 0-180 months). Acute myeloblastic leukemia (AML) was the most common type, found in 82 (44.5%) cases, out of which, M5, M4, and M2 variants were predominant at 21.9%, 9.7%, and 9.7%, respectively. Sixteen (8.7%) cases were secondary to acute lymphocytic leukemia (ALL), out of which 56.2% were B-cell lineage. Eleven (5.9%) cases were secondary to chronic lymphocytic leukemia (CLL). Other less common types were chronic myelomonocytic leukemia (CMML), myelodysplastic syndromes (MDS), chronic myeloid leukemia (CML), and acute promyelocytic leukemia (APL) in 9 (4.9%), 7 (3.8%), 5 (2.7%), and 5 (2.7%), respectively. The most common treatment modality was chemotherapy in 109/133 (81.9%) cases with the available data, out of which, 80 (73.4%) had chemotherapy alone, 16 (14.7%) had chemotherapy plus stem cell transplantation (SCT), 8 (7.3%) had chemotherapy plus radiotherapy, 3 (2.8%) had chemotherapy plus surgery and radiotherapy, 1 (0.9%) had chemotherapy plus radiation and SCT, and 1 (0.9%) had chemotherapy plus surgery. Mean duration of follow-up was 11 months (range, 1 day-100 months). In terms of outcomes, 61 (33.15%) patients were alive upon follow-up, out of which, 19 (31.1%) in remission, 18 (29.5%) disease-free, 17 (27.9%) with persistent disease, 2 (3.3%) had a recurrence, and 5 (8.2%) outcome not mentioned. Moreover, 91 (49.46%) patients died from disease. For the reported data, the average interval from leukemia diagnosis to death was 4.4 months (range, 3 days-15 months) for AML and 7.6 months (range, four days-25 months) for ALL (Table 2). Interestingly, at the time of our patient's LC diagnosis in our institution, cytogenic analysis revealed a novel inv(11)(p15q23) chromosomal aberration that herald MDS-to-AML transformation. Discussion In this review, several findings are noteworthy. First, males were more commonly affected. Second, 109 (59.2%) patients were older than 50 years of age. Third, LC noted as the initial presentation of systemic disease in more than a third of patients, whereas ALC was the initial presentation in only around 9% of patients. Fourth, multiple cutaneous lesions were more prevalent, with nodules being the most common presentation. Fifth, AML was the most predominant type overall, found in almost 45% of cases, followed by ALL, CLL, CMML, and other less common types. Sixth, chemotherapy was the most common treatment modality overall in more than 80% of patients with reported data. Seventh, almost half of patients died from the disease or complications related to it. Distinctly, very few studies in the literature reported this unique AML association with chromosome 11 aberrations. Conclusion LC is relatively rare and has a dismal prognosis. It most likely presents as the initial manifestation of leukemia, and physicians could easily misdiagnose this condition if managed without a biopsy. In this study, we intend to promote early recognition among physicians and highlight our unique cytogenetic findings. This could support future endeavors and develop novel patient-specific therapeutic strategies that exploit chromosomal aberrations amidst possible leukemogenic mechanisms. Disclosures No relevant conflicts of interest to declare.

Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

Global Expression Changes of Malignant Plasma Cells over Time Reveals the Evolutionary Development of Signatures of Aggressive Clinical Behavior

Introduction: Clustering of gene expression signatures at diagnosis has identified a number of distinct disease groups that correlate with outcome in multiple myeloma (MM). Some of these are defined by an etiologic genetic event whereas others, such as the proliferation cluster (PR) and GEP70 risk relate to acquired clinical behaviors regardless of the underlying background. The PR cluster has a number of important features, including markers of proliferation, and has been associated with an adverse outcome. This logic led us to study how gene expression patterns change over time with the aim of gaining insight into acquired features that could be targeted therapeutically or be used to predict outcome. Methods: We followed 784 newly diagnosed MM patients from the Total Therapy trials over a median of 9.5 years for whom repeated GEP of CD138+ plasma cells using Affymetrix U133 Plus 2.0 plus arrays were obtained. Raw data were MAS5 normalized and GEP70-based high-risk (HR) scores, translocation classification (TC) and molecular cluster classification were derived, as previously reported. Results: At diagnosis, 85.9% percent of patients (666/784) were identified as low-risk (LR). Among them, 23.1% (154/666) went on to develop HR status (defined by a GEP70 score > 0.66) at least once after initial diagnosis. Among the non-PR cases, 28.5% (193/677) were seen to develop a PR phenotype at some point during follow-up. Similarly, among the PR patients (n=107), we observed that 43.1% (25/58) identified as LR by GEP70 at presentation eventually develop HR status at least once during follow-up. We further analyzed 147 patients with paired diagnosis and relapse samples. Seventeen percent of patients (25/147) were PR at diagnosis. Most patients were from favorable TC prognostic groups [80% D1-D2, 8% t(11;14), 8% t(4;14) and 4% t(14;20)]. Seventy-six percent of PR patients remained PR at relapse (19/25) whereas 23% switched cluster in accordance to their translocation group. Fifteen percent of patients (22/147) became PR at relapse. They originated from four clusters and three TC groups [77% from the D1-D2, 14% t(4;14) and 9% from the t(11;14)]. Overall-survival from the time of relapse was inferior for patients categorized as PR at relapse compared to other subgroups (p Discussion: Following the introduction of therapeutic regimens aimed at maximizing response, long term survival in MM has improved. This also led to an apparent increase in the development of more aggressive disease patterns at relapse including extra-medullary disease and plasma cell leukemia. Here we show, that HR features both in terms of PR and GEP70 risk status, develop as a variable over time. At relapse, most acquired HR cases originate from standard-risk presentation cases, suggesting selective pressure for HR features. Moreover, we show that the detection of such behaviors is associated with an adverse outcome from the time of relapse. These data also suggest that repeating GEP during follow-up adds precision to better comprehend individual risk and may help identify patient specific therapeutic strategies. Indeed, understanding how these patterns develop, which genes are implicated, and their impact on the immune microenvironment should allow us to effectively utilize a wide array of treatment approaches ranging from immune-therapies to novel cell-cycle targeting agents to specifically address this type of aggressive behavior. Conclusion: The acquisition of high risk patterns captured by GEP70 risk and PR status is an ongoing process from initial diagnosis. Such high risk prognostic features have an adverse outcome from the time of development. Repeating GEP during follow-up may therefore help better predict outcome and identify patient specific therapeutic strategies. Disclosures Boyle:Janssen: Honoraria, Other: travel grants; Takeda: Consultancy, Honoraria; Gilead: Honoraria, Other: travel grants; Abbvie: Honoraria; Celgene: Honoraria, Other: travel grants; La Fondation de Frace: Research Funding; Amgen: Honoraria, Other: travel grants. Dumontet:Janssen: Honoraria; Roche: Research Funding; Merck: Consultancy, Membership on an entity9s Board of Directors or advisory committees; Sanofi: Honoraria. Facon:Celgene: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Barlogie:Celgene: Consultancy, Research Funding; Multiple Myeloma Research Foundation: Other: travel stipend; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; Dana Farber Cancer Institute: Other: travel stipend; Millenium: Consultancy, Research Funding; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC; International Workshop on Waldenstrom9s Macroglobulinemia: Other: travel stipend. Davies:TRM Oncology: Honoraria; Janssen: Consultancy, Honoraria; ASH: Honoraria; Amgen: Consultancy, Membership on an entity9s Board of Directors or advisory committees; Abbvie: Consultancy; Takeda: Consultancy, Membership on an entity9s Board of Directors or advisory committees; MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees. Morgan:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria.

Characterization of Genomic Landscape and Risk Stratification of De Novo Cytogenetically Normal Acute Myeloid Leukaemia

Background. Acute myeloid leukaemia (AML) is a group of heterogeneous diseases with distinct clinicopathologic, cytogenetic and genetic features, sharing in common an abnormal increase in myeloblasts in blood or bone marrow (BM). Induction and consolidation chemotherapy as well as allogeneic haematopoietic stem cell transplantation (HSCT) are the mainstays of treatment. However, treatment outcome is unsatisfactory and only 30-40% patients achieve durable remission. Disease heterogeneity in AML may account for different treatment responses. In this study, we examined the mutation spectrum of cytogenetically normal AML (CN-AML) patients in Hong Kong, where AML treatment and indications for allogeneic HSCT are uniform, and evaluated their clinical outcome with respect to the specific gene mutations.Patients. Young adult patients (18-60 years old) with AML diagnosed between August 2003 to September 2017 in 8 regional hospitals in Hong Kong were included. Their clinicopathologic and cytogenetic features as well as treatment outcome were examined.Treatment and endpoints. Treatment comprised induction (daunorubicin 60-90 mg/m2 for 3 days; cytarabine 100 mg/m2 for 7 days) and consolidation (cytarabine 3 gram/m2 for 4-6 doses) for 3-4 courses. BM examination was performed on day 28 or until white cell and platelet counts recovered. Non-remission or relapsed cases were treated with salvage chemotherapy comprising combinations of cytarabine with idarubicin/etoposide, fludarabine, mitoxantrone or clofarabine. Complete remission (CR) was defined as circulating and BM blasts of ≤5% with neutrophil (≥1x109/L) and platelet count (≥100x109/L) recovery. CR with incomplete haematological recovery (CRi) was defined as circulating and BM blasts of ≤5% without complete neutrophil or platelet recovery.Mutation profiling. Next generation sequencing (NGS) for 36 recurrently mutated genes in AML was performed in diagnostic BM samples. Sequencing data were analyzed by in-house bioinformatics pipeline.Statistical evaluation. Patient survivals were calculated by Kaplan-Meier analysis and compared by log-rank test. P-values of <0.05 were considered statistically significant.Results. One hundred and seventy five patients were studied, with CR achieved in 150 cases after 1 (N=105, 70%), 2 (N=27, 18%) or ≥ 3 (N=18, 12%) courses of induction chemotherapy; and not achieved in 25 cases (primary refractory). Allogeneic HSCT was performed in 64 patients (at CR1,N=35; CR2, N=21; ≥ CR3, N=2; relapsed / persistent leukaemia, N=6). Relapse-free-survivals (RFS) at 1, 2 and 5 years were 41.3%, 27.3% and 9.3%; with median at 0.84 year. Overall survival (OS) at 1, 2 and 5 years were 69.1%, 38.3% and 15.4%, with median at 1.51 years. Mutations of ≥ 1 genes occurred in 169 cases (96%); involving with highest frequencies NPM1 (44%), FLT3-ITD (35.3%), DNMT3A (31%), IDH2 R140Q + R172K (14.3%) and CEBPA (bi-allelic, 13.7%). Individually these gene mutations had no impact on CR. However, FLT3-ITD + wildtype NPM1 and FLT3-ITD + mutated DNMT3A portended extremely poor RFS and OS. Bi-allelic CEBPA mutations portended significantly better OS but not RFS, suggesting that these cases were susceptible to relapse but were salvageable with re-induction and allogeneic HSCT. Co-existing mutations of NPM1, DNMT3A and FLT3 were common. Other mutations were mostly solitary, including bi-allelic CEBPA and IDH2 R172K mutations. NRAS and FLT3 mutations were mutually exclusive.Conclusion. CN-AMLs were intrinsically heterogeneous. Mutational profile might be useful for personalized treatment. The cooperativity and mutual exclusivity of gene mutations may be of pathogenetic significance and can inform patient-specific therapeutic strategies. DisclosuresKwong:Bayer: Consultancy, Honoraria; Beigene: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Merck: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Roche: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Amgen: Consultancy, Honoraria. Leung:Daiichi: Research Funding; Novartis: Speakers Bureau.

Abstract B14: Precision functional genomics for glioblastoma: Identifying molecular therapeutic targets using CRISPR-Cas9 and RNAi technologies in patient isolates

Abstract Glioblastoma (GBM) is the most aggressive and common form of adult brain cancer and is among the deadliest cancers, with a median survival of 15 months using standard-of-care therapies. Thus, improved treatments for GBM are desperately needed. To identify new GBM molecular therapeutic targets, our group has performed multiple functional genetic screens in patient-derived GBM stem-like cells (GSCs) and non-transformed human neural stem and progenitor cells (NPCs), which represent non-neoplastic controls. These screens, which have used both RNAi and CRISPR-Cas9 platforms, have led to the identification of several key molecular vulnerabilities in GSCs, including GBM-specific defects in: 3' splice site recognition, kinetochore function, and loss of redundancy between the kinase activities of PKMYT1 and WEE1. At this meeting we will present an overview of these studies, as well as our current efforts to: comprehensively retest all GBM-specific vulnerabilities scoring in these screens; address whether vulnerabilities arise from specific genetic alterations in patient samples (e.g. NF1 loss or PTEN loss); determine whether inhibition of specific molecular targets blocks tumor growth and/or maintenance; and demonstrate the mode of GBM-specific death for particular targets (e.g., cell cycle arrest, apoptosis, etc). In addition, we will highlight both strengths and limitations of applications of CRISPR-Cas9 technologies in patient samples. Collectively, our work illustrates the power of combining functional genetic technologies with the use of patient isolates to identify novel, patient-specific therapeutic strategies for GBM. Citation Format: Pia Hoellerbauer, Heather Feldman, Sonali Arora, Lucas Carter, Emily J. Girard, Philip Corrin, James M. Olson, Eric C. Holland, Patrick J. Paddison. Precision functional genomics for glioblastoma: Identifying molecular therapeutic targets using CRISPR-Cas9 and RNAi technologies in patient isolates [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr B14.

A new era of disease modeling and drug discovery using induced pluripotent stem cells.

In 2006, Shinya Yamanaka first reported that in vitro reprogramming of somatic cells toward pluripotency was achieved by simple induction of specific transcription factors. Induced pluripotent stem cell (iPSC) technology has since revolutionized the ways in which we explore the mechanisms of human diseases and develop therapeutics. Here, I describe the recent advances in human iPSC-based disease modeling and drug discovery and discuss the current challenges. Additionally, I outline potential future applications of human iPSCs in classifying patients based on their response to drugs in clinical trials and elucidating optimal patient-specific therapeutic strategies, which will contribute to reduced attrition rates and the development of precision medicine.

Self-assembly of vascularized tissue to support tumor explants in vitro.

Testing the efficacy of cancer drugs requires functional assays that recapitulate the cell populations, anatomy and biological responses of human tumors. Although current animal models and in vitro cell culture platforms are informative, they have significant shortcomings. Mouse models can reproduce tissue-level and systemic responses to tumor growth and treatments observed in humans, but xenografts from patients often do not grow, or require months to develop. On the other hand, current in vitro assays are useful for studying the molecular bases of tumorigenesis or drug activity, but often lack the appropriate in vivo cell heterogeneity and natural microenvironment. Therefore, there is a need for novel tools that allow rapid analysis of patient-derived tumors in a robust and representative microenvironment. We have developed methodology for maintaining harvested tumor tissue in vitro by placing them in a support bed with self-assembled stroma and vasculature. The harvested biopsy or tumor explant integrates with the stromal bed and vasculature, providing the correct extracellular matrix (collagen I, IV, fibronectin), associated stromal cells, and a lumenized vessel network. Our system provides a new tool that will allow ex vivo drug-screening and can be adapted for the guidance of patient-specific therapeutic strategies.