Background: Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and whole transcriptome sequencing on plasma and leukocytes with advanced machine learning techniques to satisfy all three clinical testing needs on one platform. Patients and Methods: Caris Assure for therapy selection was CLIA validated using 1,910 samples. 376,197 tissue profiles along with 7,061 paired blood and tissue profiles were used to engineer features for three machine learning models. The MCED model was trained on 1,013 patients and validated on an independent set of 2,675 patients. The tissue of origin for MCED model was trained on 1,166 samples and validated using 5-fold cross validation. The MRD & Monitoring model was trained on 3,439 patients and validated on two independent sets of 86 patients for MRD and 101 patients for monitoring. Results: For early detection, sensitivities for stages I-IV cancers (n= 284, 129, 90, 23 respectively) were 83.1%, 86.0%, 84.4%, and 95.7%, all at 99.6% specificity (n=2149). The diagnostic first-line procedure for tissue of origin was determined for 8 categories with a top-3 accuracy of 85% for stage I and II cancers. Detection of driver mutations for therapy selection from blood collected within 30 days of matched tumor tissue, demonstrated high concordance (PPA of 93.8%, PPV of 96.8%) using CHIP subtraction. For MRD and recurrence monitoring, the disease-free survival of patients whose cancers were predicted to have an event was significantly shorter than those predicted not to have an event using a tumor naive approach (HR=33.4, p<0.005, HR=4.39, p=.008, respectively). Conclusion(s): The data presented here demonstrate a unified liquid biopsy platform that uses blood-based whole-exome and transcriptome sequencing coupled with artificial intelligence to address the important clinical needs in multi-cancer early detection, monitoring of MRD and recurrent cancers, and precision selection of molecularly targeted therapies. ### Competing Interest Statement Disclosure of Potential Conflicts of Interest: JA, VD, NP, SK, SA, JX, DAS, SS, RH, AS, JS, DDH, MO, MR, GWS and DS are employees of Caris Life Sciences. GP is a member of the board of directors of CLS. JLM, GDD, and AB serve on the scientific advisory board of Caris Life Sciences. All of the above have equity and/or equity options in Caris Life Sciences. EH, EL, SVL, and WSE have unpaid consultant/advisory board relationships with Caris Life Sciences. AFS serves on the consultant/advisory board, is on the speakers bureau, and has received travel funding from Caris Life Sciences. SVL reports advisory role for Abbvie, Amgen, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Catalyst, Daiichi Sankyo, Eisai, Elevation Oncology, Genentech/Roche, Gilead, Guardant Health, Janssen, Jazz Pharmaceuticals, Merck, Merus, Mirati, Novartis, Pfizer, Regeneron, Sanofi, Takeda, and Turning Point Therapeutics; research grant (to institution) from Abbvie, Alkermes, Elevation Oncology, Ellipses, Genentech, Gilead, Merck, Merus, Nuvalent, RAPT, and Turning Point Therapeutics; and serving on a Data Safety Monitoring Board for Candel Therapeutics.YN reports advisory role from Guardant Health Pte Ltd., Natera,Inc., Roche Ltd., Seagen,Inc., Premo Partners, Inc., Daiichi Sankyo Co., Ltd., Takeda Pharmaceutical Co., Ltd., Exact Sciences Corporation, Gilead Sciences, Inc.; speakers bureau from Guardant Health Pte Ltd., MSD K.K., Eisai Co., Ltd., Zeria Pharmaceutical Co., Ltd., Miyarisan Pharmaceutical Co., Ltd., Merck Biopharma Co., Ltd., CareNet,Inc., Hisamitsu Pharmaceutical Co., Inc., Taiho Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Chugai Pharmaceutical Co., Ltd., Becton, Dickinson and Company, Guardant Health Japan Corp; research funding from Seagen,Inc., Genomedia Inc., Guardant Health AMEA, Inc., Guardant Health, Inc., Tempus Labs, Inc., Roche Diagnostics K.K., Daiichi Sankyo Co., Ltd., Chugai Pharmaceutical Co., Ltd. TY research funding from Amgen K.K., Chugai Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Eisai Co., Ltd., FALCO biosystems Ltd., Genomedia Inc., Molecular Health GmbH, MSD K.K., Nippon Boehringer Ingelheim Co., Ltd., Ono Pharmaceutical Co., Ltd., Pfizer Japan Inc., Roche Diagnostics K.K., Sanofi K.K., Sysmex Corp. and Taiho Pharmaceutical Co., Ltd.; honoraria for lectures from Chugai Pharmaceutical Co., Ltd., MSD K.K., Ono Pharmaceutical Co., Ltd., Bayer Yakuhin, Ltd., Merck Biopharma Co., Ltd. and Takeda Pharmaceutical Co., Ltd.; Consulting fees from Sumitomo Corp. GDD has received institutional support for oncology research studies to Dana-Farber Cancer Institute from Adaptimmune, Bayer, Novartis, PharmaMar, and Daiichi-Sankyo; he is also is a co-founder and consulting scientific advisory board member with minor equity holding in IDRx; a consultant/SAB member with minor equity holding in Erasca Pharmaceuticals, RELAY Therapeutics, Bessor Pharmaceuticals, CellCarta, Ikena Oncology, Kojin Therapeutics, Aadi Biosciences, Acrivon Therapeutics, Blueprint Medicines, Tessellate Bio, and Boundless Bio; he is also a scientific consultant for EMD-Serono/Merck KGaA, WCG/Arsenal Capital, and Minghui Pharmaceuticals. ### Funding Statement This work was supported by Caris Life Sciences. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was conducted in accordance with the guidelines of the Declaration of Helsinki, Belmont report, and U.S. Common rule. In keeping with 45 CFR 46.101(b)(4), this study utilized retrospective, de-identified clinical data. Waiver of patient consent and exempt status were determined by WCG Institutional Review Board. Information regarding consent and the source of all samples used in these studies can be found in Supplementary Table 1. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes De-identified data is available upon request for academic or non-profit research purposes. To request access, please contact Caris legal at legal{at}CarisLS.com. Access is subject to review by the Caris legal team and approval will only be granted following execution of a formal agreement with acceptable terms. Requests will generally be processed within 6 months.

Abstract Effective clinical management of cancer patients requires an accurate and early diagnosis, highly sensitive monitoring of minimal residual disease (MRD), and precise therapy selection. There are multiple tests available that attempt to address each of these needs independently with varying degrees of clinical utility. Caris Assure is a proprietary circulating nucleic acid sequencing platform that couples whole exome and transcriptome (WES/WTS) sequencing on white blood cells and plasma with advanced machine learning techniques to satisfy all three testing needs on one platform. This test detects SNVs, INDELs, structural variants, copy number, gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), fragment length, and aneuploidy of both somatic (tumor and clonal hematopoiesis) and germline origin. Caris’ extensive database of over 350,000 tissue WES/WTS from solid malignancies was used to train deep learning neural networks to identify the molecular underpinnings of cancer. These networks were then deployed on WES/WTS data from plasma and buffy coat in pursuit of signals that can inform early detection, MRD and therapy selection. Validation studies were performed to characterize the analytic and clinical performance of Assure on over 3000 patient blood samples. These samples include ~1000 non-cancer patients (controls), ~1700 newly diagnosed patients where blood was collected at surgery (early detection), ~500 early-stage patients during adjuvant therapy at multiple time points (MRD), and ~200 locally advanced/metastatic patients where matched tissue testing was also performed (therapy selection). For early detection, stratification of blood samples from patients with stage I-IV cancer versus those with no reported cancer resulted in an AUC > 0.99 and included over 30 types of solid tumors. Notably, at 99.5% specificity, the sensitivities for stages I-IV (n= 119, 54, 50, 27) were 73%, 80%, 76%, and 89%. In the MRD setting for high-risk patients, the disease-free survival of patients whose cancers were predicted to recur was significantly shorter (39.6 mo) than those predicted not to recur (93.4 mo) (HR: 5.18, 95%CI: 2.94-9.09, p<.00001). This performance was observed across multiple lineages of cancer including but not limited to breast, colon, lung, and bladder. Lastly, for therapy selection, detection of driver mutations where blood was collected within 30 days of matched tissue demonstrated high concordance with a PPA of 93.8% and PPV of 96.8%. CHIP correction proved to be essential as ~35% percent of patients had CHIP mutations, including KRAS, BRAF, ATM, BRCA1/2, findings that could lead to improper therapy selection. Herein, we demonstrate for the first time a single liquid biopsy assay that addresses the entire continuum of care in clinical oncology with optimal diagnostic, prognostic, and predictive utility for patients and physicians. Citation Format: Jim Abraham, Valeriy Domenyuk, Maria Perdigones Borderias, Takayuki Yoshino, Elisabeth I. Heath, Emil Lou, Stephen Liu, John Marshall, Wafik S. El-Deiry, Anthony Shields, Martin Dietrich, Yoshiaki Nakamura, Takao Fujisawa, David D. Halbert, Dominic Sacchetti, Seth Stahl, Adam Stark, Sergey Klimov, Sourabh Antani, Chadi Nabhan, Jeffrey Swensen, George Poste, Matt Oberley, Milan Radovich, George W. Sledge, David Spetzler. AI enabled whole exome& transcriptome liquid biopsy addressing MCED, MRD, and therapy selection on a single platform [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 2300.

SUMMARYUnderstanding the dynamic adaptive plasticity of microorganisms has been advanced by studying their responses to extreme environments. Spaceflight research platforms provide a unique opportunity to study microbial characteristics in new extreme adaptational modes, including sustained exposure to reduced forces of gravity and associated low fluid shear force conditions. Under these conditions, unexpected microbial responses occur, including alterations in virulence, antibiotic and stress resistance, biofilm formation, metabolism, motility, and gene expression, which are not observed using conventional experimental approaches. Here, we review biological and physical mechanisms that regulate microbial responses to spaceflight and spaceflight analog environments from both the microbe and host-microbe perspective that are relevant to human health and habitat sustainability. We highlight instrumentation and technology used in spaceflight microbiology experiments, their limitations, and advances necessary to enable next-generation research. As spaceflight experiments are relatively rare, we discuss ground-based analogs that mimic aspects of microbial responses to reduced gravity in spaceflight, including those that reduce mechanical forces of fluid flow over cell surfaces which also simulate conditions encountered by microorganisms during their terrestrial lifecycles. As spaceflight mission durations increase with traditional astronauts and commercial space programs send civilian crews with underlying health conditions, microorganisms will continue to play increasingly critical roles in health and habitat sustainability, thus defining a new dimension of occupational health. The ability of microorganisms to adapt, survive, and evolve in the spaceflight environment is important for future human space endeavors and provides opportunities for innovative biological and technological advances to benefit life on Earth.

naturemedicine and ethicists, is developing international, consensus-based guidelines for use by researchers and patient partners in preparing ethics submissions and for use by research ethics committees and institutional review boards in the assessment of PRO research.The guidelines will focus specifically on ethical considerations of PRO research and data collection in clinical practice, using methodological guideline development of the EQUATOR (Enhancing Quality and Transparency of Health Research) Network 10 .The development process will include a literature review, a modified Delphi exercise and an international consensus meeting involving members of research ethics committees, experts in research ethics, patient partners, trialists and PRO researchers.Given the dearth of guidance currently available, the authors plan to hold the Delphi exercise and consensus meeting with a view to publishing the guideline in 2021.

Microbiological research has made important discoveries about how life responds to non-terrestrial environments, such as those found aboard the International Space Station. As human space exploration transitions to longer, deep-space missions, microorganisms will continue to play an increasingly critical role in astronaut health, habitat sustainability and mission success.

![][1] In a time of pandemic distractions and anxieties, it is easy to succumb to the tyranny of the immediate. Then an event happens to jolt the senses and trigger a flood of memories and broader reflection on those rare individuals who truly make a difference in advancing science

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA HomeNew OnlineCurrent IssueFor Authors Podcasts Clinical Reviews Editors' Summary Medical News Author Interviews More Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA journal

John N. Weinstein, Evelyn Ralston, Lee D. Leserman, Richard D. Klausner, Paul Dragsten, Pierre Henkart, and Robert Blumenthal IV. Applications of Fluorescence Self-Quenching(FSQ)……………………….195 A. Release from Liposomes In Vitro…………………………………195.

Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737-43. ©2017 AACR.

Abstract Introduction: Deconvolution of multi-nodal perturbations in cancer network architecture demands highly multiplexed profiling assays. We demonstrate the value of polyligand profiling of tumor systems states using libraries of single stranded oligodeoxynucleotides (ssODN) to distinguish between tumor tissue from breast cancer patients who did or did not derive benefit from treatment regimens containing trastuzumab. Methods: This study included cases from women with invasive breast cancer who received chemotherapy+ trastuzumab (C+T) or trastuzumab monotherapy with available retrospective data on the time to next treatment (TTNT). A library of 2x1012 unique ssODN was exposed to FFPE tissues from patients who benefited (B) or not (NB) from trastuzumab-based regimens in several rounds of positive and negative selection. Two enriched libraries were screened on independent set of 42 B and 19 NB cases using a modified IHC protocol for detection of bound ssODNs. Poly-Ligand Profiles (PLP) were scored by a blinded pathologist. Two libraries, EL-NB and EL-B, showed significant p-values between groups of responders and non-responders. A Cox-PH model was fitted using either tumors' HER2 status or PLP test results as the independent variable. Median survival time was calculated from the Kaplan-Meier estimate. A separate group of 63 cases with TTNT data from chemotherapy without trastuzumab was used as a control to distinguish prognostic from predictive performance. Results: The PLP scores of EL-NB and EL-B were assessed by receiver operating characteristic (ROC) curves and resulted in a combined AUC value of 0.81. EL-NB and EL-B were able to effectively classify B and NB patients with either HER2-negative/equivocal (AUC = 0.73) or HER2-positive cancers (AUC = 0.84). In contrast, HER2 status alone yielded an AUC value of 0.47. The combined PLP scores for the independent set of 63 patients treated with C excluding trastuzumab resulted in an AUC value of 0.53, indicating that the assay was predictive and not simply prognostic. Kaplan-Meier curves analysis shows that PLP+ cases have 429 days median TTNT, while PLP- cases have 129 days (HR = 0.38, log-rank p = 0.001). Analysis based on HER2 status showed no significant difference in TTNT between patients that were HER2+ (280 days) or HER2-negative/equivocal (336 days, HR = 1.27, log-rank p =0.45). Summary: Performance of the PLP assay in differentiating patients who did or did not benefit from trastuzumab therapy outperforms the standard IHC assay for HER2 status. These results represent a promising step towards the development of a CDx to identify the 50-70% of HER2+ patients who will not benefit from trastuzumab. In addition, PLP also has the potential to identify the HER2-negative/equivocal patients who may benefit from trastuzumab-containing regimens. Citation Format: Domenyuk V, Gatalica Z, Santhanam R, Wei X, Stark A, Kennedy P, Toussaint B, Levenberg S, Wang R, Xiao N, Greil R, Rinnerthaler G, Gampenrieder S, Heimberger AB, Berry DJ, Barker A, Demetri GD, Quackenbush J, Marshall JL, Poste G, Vacirca JL, Vidal GA, Schwartzberg LS, Halbert DD, Voss A, Miglarese MR, Famulok M, Mayer G, Spetzler D. Polyligand profiling differentiates cancer patients according to their benefit of treatment [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-09-09.

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan-Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

12067 Background: The MAESTRO trial randomized 693 locally advanced or metastatic pancreatic cancer patients to gemcitabine (G) + placebo vs G + evofosfamide (GE). OS hazard ratio (HR) was 0.84; p = 0.059. We developed a PLP assay that identifies patients most likely to benefit from GE vs G alone. By capitalizing on ssDNA aptamer binding properties, PLP measures network changes in tumors, including those that predict drug response. Methods: FFPE tissues of pancreatic cancer patients from the MAESTRO trial with good (OS > 13 mos) or poor (OS < 7 mos) outcome from GE were used for PLP assay development. Assay cut-points were determined using a training set (n = 12) and performance metrics were then determined using an independent blinded test set (n = 172). The study population enrolled in MAESTRO was divided into four cohorts based on treatment and benefit (cut-point = 240 days). The assay performance from the blinded test set was used to generate 1000 different possible patient subsets. Each simulation represents one possible trial outcome if the PLP assay had been used to enroll patients. We used the average value of the simulated median increase in OS from the 1000 random selections to estimate the impact the PLP assay would have had on MAESTRO. Results: 97% of the simulations yielded log-rank p < 0.05. Compared to MAESTRO, the average median OS increase for GE improved by 116±38% (simulation s.d.) with an average HR of 0.72±0.04. Moreover, the sample size in the simulations was 49% smaller compared to the original study, reflecting the percentage of test positive patients in the blinded test set. When only data from primary tumors were used, 100% of the permutations yielded log-rank p < 0.05 with an average median OS increase of 216±36% compared to MAESTRO and an average HR of 0.63±0.03. Conclusions: This retrospective study demonstrates that PLP, if prospectively applied, likely would have resulted in a successful study comparing GE to G pancreatic cancer patients. PLP is a powerful, flexible and facile platform warrants further study of additional therapies and in prospective trials.

Introduction: The accumulation of a multitude of subtle molecular aberrations during tumor progression limit the efficacy of anti-cancer drugs. A vast array of these variations can be assessed with Poly-Ligand Profiling (PLP), which is utilizing libraries of trillion unique ssDNA with aptamer binding properties. The aims of this study were to develop a PLP library that differentiate pancreatic cancer patients who can benefit from gemcitabine+evofosfamide (GE) or gemcitabine+placebo (G) and identify its molecular targets. Methods: Patients: locally advanced or metastatic pancreatic cancer patients randomized to G vs GE in the unsuccessful phase III MAESTRO trial (Threshold Pharmaceuticals, Merck KgaA). FFPE tissues of patients with good (OS > 13 mos) or poor (OS < 7 mos) outcome from GE were used for PLP library development. Affinity maturation and testing of library for binding FFPE tissue is done with IHC-like protocol. Assay conditions and algorithm were locked based on the training set (n = 12) and used for testing assay performance in the blinded set (n = 172, primary and metastatic sites). PLP-assay performance metrics from blinded test set served to estimate the impact on the MAESTRO study (n = 693) by performing 1000 simulations. For target ID, FFPE tissue of patients with poor outcome, stained with enriched library, was recovered, lysed, underwent affinity-based pull-downs, purified with PAGE gel and subjected to high resolution mass-spectrometry (MS). Results: 1,000 simulations of projected PLP-positive patients from MAESTRO study revealed a median OS increase of 37.6% (mean) in G+E cohort, compared to G (17.4% OS increase in MAESTRO) with mean Hazard Ratio (HR) 0.72 (0.84 in MAESTRO). 96.9% of simulated trials achieved statistical significance. For primary tumor samples the median OS increase for G+E patients was 53.4% with mean HRs of 0.64 with 100% of trials exhibiting log-rank p < 0.05. MS reliably detected 20 proteins, 11 of which have reported associations with pancreatic cancer and 6 have been associated with resistance to gemcitabine: vimentin (VIM), pyruvate kinase (PKM), endoplasmic reticulum chaperone BiP (HSPA5), heat shock protein HSP 90-alpha (HSP90AA1), Histone H3-1 (HIST1H3A), heat shock protein beta-1 (HSPB1). Vimentin is a mesenchymal marker whose expression increases during epithelial–to-mesenchymal transition (EMT) and tumor progression. EMT results in the suppression of human equilibrative/concentrative nucleoside transporter and protects tumor cells from gemcitabine. GRP78 overexpression confers resistance to gemcitabine and its knockdown sensitizes tumor cells to drug treatment. Alternative splicing of PKM promotes gemcitabine resistance in pancreatic cancer cells most likely by boosting glycolysis-fueled proliferation. Heat shock proteins regulate multiple tumor survival and progression pathways and their inhibition attenuates resistance of cancer cells to gemcitabine. Pancreatic tumors demonstrate increased histones acetylation, which was correlating with increased protection against gemcitabine. Further characterization of these candidate targets is ongoing. Conclusion: PLP is a novel platform for classifying pancreatic cancer patients according to their benefiting from GE treatment. MS of the PLP library pull-downs reveals targets associated with gemcitabine resistance. In principle, the novel PLP platform could be applied to different therapeutic regimen for the development of urgently needed companion diagnostic tests in cancer and other diseases.

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~10 11 ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10 6 enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.

Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.

e23058 Background: Understanding individual biomarkers and multi-molecular complexes in their native states represents a major hurdle in the development of systems biology platforms and requires multiplexing capabilities many orders of magnitude greater than what is currently available. This knowledge is especially important in diseases like cancer, where perturbations in signaling pathways lead to the initiation and propagation of a vast range of molecularly heterogeneous phenotypes. Ideally, this information would be gathered non-invasively from peripheral compartments. We report ADAPT as an unbiased discovery method to identify native proteins and multi-molecular complexes in plasma. Methods: ssDNA libraries of 2x10^11 unique sequences were enriched towards association with exosomes of plasma pools from breast cancer patients (n=60) or from a control cohort of women without breast cancer (n=60). Two thousand ODNs were selected from the enriched library and used to profile 500 plasma samples: 206 breast cancer biopsy positive (BC+), 177 biopsy negative (BC-) and 117 self-declared healthy (H). Results: Random Forest Modeling (RFM) was used to build classifiers for BC+ vs. both BC- and H, BC+ vs. BC- and BC+ vs. H, with AUC values of 0.64, 0.64, and 0.73, respectively. Out-of-bag validation was used for this analysis. Notably, 10-fold cross-validation, repeated 20 times, resulted in ROC AUC values of 0.58 for BC+ vs. both controls, 0.59 for BC+ vs. BC-, 0.62 for BC+ vs. H. This difference between the out-of-bag and cross-validation result suggests that the study is underpowered to define an algorithm capable of handling the heterogeneity found in this set of breast cancer patients. Conclusions: The ADAPT platform uses ultra-high complexity libraries to improve the probability of identifying ODNs which recognize biomolecules in their native state. This unbiased approach quantifies differences between plasma from women with breast cancer and women without breast cancer, likely due to perturbations in molecular pathways. An ADAPT derived breast cancer test may find utility as a diagnostic tool in clinical practice.

11521 Background: The role of genomic and protein biomarkers in the selection of treatments of advanced cancer is limited by the availability of outcome data following biomarker selected treatment. TNT has been used as a clinical endpoint by the FDA and reflects the clinical decision making process integrating efficacy and toxicity components. We hypothesized that TNT is a meaningful surrogate endpoint correlating to overall survival (OS) for biomarker selected treatments. Methods: We studied 4729 unselected patients (2009-2015), heterogeneous in cancer type, stage, and line of therapy, who were referred for panomic testing utilizing IHC, PCR, ISH, NGS and RNAseq technologies. Treatment data were retrospectively obtained from the International Oncology Network database. TNT was defined as the interval between start of first treatment after tissue collection and next line of treatment. An unselected subset of 952 patients OS data was used to validate TNT as a meaningful clinical surrogate endpoint. Patients were considered “matched” (M) when the biomarker and treatment were consistent and “unmatched” (U) when they were not. The first regimen of treatment was often delivered without knowledge of biomarker results, minimizing selection bias between groups. Results: A significant improvement was observed between M (n=3011) and U (n=1718) cohorts [HR 0.85 (CI:(0.78,0.93), p<0.001)]. The median TNT was 15% longer in the M v U cohorts (248 v 215 days). Improved OS (HR of 0.69 (CI: (0.56,0.84), p<0.001)) was observed between M (n= 505) and U (n=447), with a median increase of more than 1 year (M = 1069 and U = 686 days). Conclusions: Concordance of TNT and OS for patients with biomarker-associated therapies validates the clinical utility of TNT as a surrogate endpoint that can be assessed using EMR extracted data. Additionally, this analysis demonstrates that patients receiving therapies that match their biomarker profile achieve a longer time until subsequent therapy is used. TNT reflects results of a therapeutic medical decision and thus should be further evaluated against PFS or DFI to determine the best surrogate for OS.

5040 Background: Patients with PC have limited treatment options after failure of hormonal and taxane therapy. Androgen receptor (AR) signaling may exert therapeutic effects on the DNA repair pathway in PC. We have assessed the proteomic/genomic DNA repair aberrations in primary (P) and metastatic (M) PC and explored the therapeutic implications of these mutations using panomic next generation sequencing (NGS). We hypothesized that there is a differential in gene expression and mutation between P and M tumors. Methods: Molecular profiles of 437 PC tumor samples were defined. Protein expression (IHC), gene amplification (ISH) and sequencing (NGS) were performed. A panel of 30 DNA repair genes was used to define DNA repair intact (DRI) and DNA repair deficient (DRD) subgroups. Unclassified variants were included for analysis. Pearson’s chi-squared test was used to test for significant differences. Results: Biopsies from 437 PCs (median age 67) were studied. Specimens submitted for profiling included 158 P PCs (36%) and 279 M PCs (64% [18% bone; 37% visceral; 24% lymph nodes; 21% other sites]). The most frequently mutated DNA repair genes included TP53 (31%), ERCC5 (19%), FANCG (16%), MSH6 (13%), PMS1 (13%), POLE (10%) PTEN (9%) and BRCA2 (6%). Functional protein loss as measured by IHC was seen in ERCC1 (44%), MGMT (39%), and PTEN (43%). In a limited cohort of patients tested using a 592-gene hybrid-capture NGS, 26/31 (84%) had alterations in at least 1 DNA repair gene. DRD PC exhibited higher expression rates of AR (57% vs. 20%; p = .048) and TOPO1 (88% vs. 40%; p = .02) than DRI PC. An optimal taxane therapeutic response profile was observed in 20% of DRD tumors. Significant differences between P and M tumors were seen in ERCC1, AR, ATM and TP53. M tumors had significantly increased expression of TOP2A, TS and TUBB3. Conclusions: DNA repair defects are common in PC with a difference in gene expression and mutation between P and M tumors. Differential expression between African American and Caucasian patients and further classification of variants are currently being assessed. Taxane-platinum combination chemotherapy should be tested specifically in DRD PC.