Valeriy Domenyuk

A technology for developing synbodies with antibacterial activity.

The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and in vitro tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding Staphylococcus aureus to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block S. aureus induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.

Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach.

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 ~1011 ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 106 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.

Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes

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.Patients’ selection is particularly important in cancer treatment. Here the authors present a proof-of-principle methodology that could be potentially important in assisting therapeutic decisions in the treatment of breast cancer patients.

A Simple Platform for the Rapid Development of Antimicrobials

Recent infectious outbreaks highlight the need for platform technologies that can be quickly deployed to develop therapeutics needed to contain the outbreak. We present a simple concept for rapid development of new antimicrobials. The goal was to produce in as little as one week thousands of doses of an intervention for a new pathogen. We tested the feasibility of a system based on antimicrobial synbodies. The system involves creating an array of 100 peptides that have been selected for broad capability to bind and/or kill viruses and bacteria. The peptides are pre-screened for low cell toxicity prior to large scale synthesis. Any pathogen is then assayed on the chip to find peptides that bind or kill it. Peptides are combined in pairs as synbodies and further screened for activity and toxicity. The lead synbody can be quickly produced in large scale, with completion of the entire process in one week.

Abstract P4-12-08: Use of an aptamer library based next generation omics platform for the development of a novel trastuzumab predictive assay

Introduction: Previous attempts to use individual aptamers as diagnostic reagents have failed to consistently achieve performance comparable to antibodies. Here we report a novel systems biology approach using poly-ligand aptamer libraries to identify responders and non-responders to traztuzumab-based regimens in metastatic breast cancer. Methods: To overcome the fundamental limitation of the individual aptamer binding affinities, large libraries (106 species) were created so that potentially thousands of aptamers could bind to each of a multitude of targets related to the whole cellular changes in response to trastuzumab therapy. A set of breast cancer patients, which received trastuzumab mono- or combined therapy for at least 7 months were classified as “Responders” (R); cases with particular regimen discontinued in the period not exceeding 5 months were classified as “Non-Responders”(NR). A library of 2x1012 unique 90-mer ssDNA oligodeoxynucleotides (ssODN) was trained on FFPE tissue of both R and NR patients. Partitioning of aptamer libraries was done by microdissection of the tumor tissue, after incubation of aptamer library with the entire tissue section, to drive selection pressure toward cancer cells. A total of 10 cases of R and NR, 6 Her2+ cases each, were used to train separate aptamer libraries, with 1 positive and 2 counter selection cases per enrichment. Enriched libraries were screened on 20 R and 20 NR cases (11 Her2+ cases each) by adopting modified immunohistochemistry protocol. Each library was used as an independent reagent (similar to an antibody in IHC) across all 40 cases to evaluate the efficacy of the aptamer library to distinguish differences between the R and NR groups. Staining (DAB chromogen) profiles were scored from 0 to 3+ (nuclear and cytoplasmic staining) by a pathologist without any knowledge of the clinical outcomes. Initial validation was done by t-test using raw histological scores. Four libraries showed significant p-values between groups of responders and non-responders, a classification algorithm was constructed and evaluated using area under the receiver-operator characteristic curve (AUC). The datasets of two best-performing libraries were combined into one model using logistic regression to further improved the classifier performance. Results: Of seventeen trained libraries, eight were evaluated and four showed significant correlation to clinical benefit with a minimum accuracy of 75% for each library when evaluated independently. Furthermore, two libraries showed exceptional performance (ROC curve AUC of 0.86 and 0.77). Combination of the profiling data from these two libraries using logistic regression resulted in an AUC of 0.985. A prospective validation of aptamer histochemical theranostic testing has been initiated. Summary: Enriched aptamer libraries appear to distinguish trastuzumab responsiveness in metastatic breast cancer. This technology could be used as an additional technique beyond FISH testing to determine sensitivity to anti-HER2 agents. The demonstrated platform is applicable to virtually any disease where the safe and effective use of corresponding drug is yet to be improved. Citation Format: Spetzler D, Domenyuk V, Santhanam R, Wei X, Stark A, Wang J, Gatalica Z, Miglarese M, Vidal G, Schwartzberg LS. Use of an aptamer library based next generation omics platform for the development of a novel trastuzumab predictive assay [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-12-08.

Abstract 2754: A novel liquid biopsy method for development of aptamer libraries that bind blood plasma exosomes from breast cancer patients

Improved technologies capable of characterizing system-wide changes associated with complex diseases will be required to be able to detect millions of proteins and their isoforms as well as multi-molecular complexes. We present a method for developing aptamer libraries using blood plasma exosomes that provides unprecedented system-wide coverage of native exosomal complexes. To train a naive aptamer library toward cancer samples (positive selection), the library (~1013 biotinylated ssODN species) was incubated with plasma from individual cancer patients and aptamer-bound exosomes were isolated using polymer-based precipitation. Negative selection was performed by contacting the aptamer library with exosomes from donors without breast cancer and recovering unbound aptamers from the supernatant. In all, 12 libraries trained toward 12 individual breast cancer patients were used to probe additional samples. Exosome-bound aptamers were identified and quantified by Next Generation Sequencing (NGS) to build highly accurate signatures for cancer/healthy donor classification. Using these signatures, cancer patients’ binding profiles were easily distinguishable from controls. Interestingly, cancer-trained libraries did not distinguish any of the negative control samples from each other, indicating that the selection pressure for cancer was high and noise due to inherent inter-healthy donor heterogeneity was minimal. Full validation studies are ongoing. Aptamer libraries may ultimately be deployed as a minimally-invasive diagnostic adjunct in breast and other cancers. Citation Format: Valeriy Domenyuk, Symon Levenberg, Adam Stark, Mark Miglarese, David Spetzler. A novel liquid biopsy method for development of aptamer libraries that bind blood plasma exosomes from breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2754. doi:10.1158/1538-7445.AM2017-2754

Abstract LB-135: Adaptive dynamic artificial poly-ligand targeting (ADAPT) enables plasma-based exosome profiling with potential diagnostic utility

Introduction: Single stranded DNA (ssDNA) libraries consisting of several trillion oligodeoxynucleotides (ODNs) can adopt a nearly infinite number of three-dimensional structures. These structures can potentially bind any biomolecule and can be screened for specificity toward important biomarkers by employing suitable enrichment schemes. Since no prior knowledge on the binding partner is required, massively parallel biomarker identification is possible even on complex matrices like biological fluids and across a wide range of biological conditions. Here we present Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT) as a platform for biomarker and target discovery. We employed ADAPT for the molecular profiling of exosome-associated proteins in small volume plasma samples from women with breast cancer and healthy donors. Results: Random ssDNA-libraries of 10 11 unique ODNs were subjected to a number of selection and counter-selection steps on pooled blood plasma of breast cancer and healthy women. Several positive and negative enrichment schemes were employed, and exosome isolation and ODNs library partitioning were performed by ultracentrifugation and/or PEG precipitation. After library enrichment reduction of complexity to 10 6 -10 7 ), ODN libraries were used to probe an independent set of individual plasma samples from women with or without breast cancer. Two thousand differentially-binding aptamers with significant p-values were re-synthesized and combined in equimolar amounts to create a profiling library (L2000). The L2000 library was used to probe plasma samples from 323 individuals (206 from breast cancer patients and 117 from healthy donors) in triplicate. Using Next Generation Sequencing, we quantitated bound ODN from each plasma sample. ANOVA revealed 350 aptamers with significant p-values in distinguishing plasma samples from cancer patients and healthy donors, far in excess of the number of ODNs that would have achieved statistical significance by random sampling of the 2000 ODNs. Generalized linear model showed an AUC in a ROC curve of 0.94 for the training set. Random forest modelling was used to assess classification performance and revealed an AUC of 0.73 (p Conclusions: We have demonstrated the feasibility of aptamer library enrichment directly on blood plasma and have identified a set of 2000 DNA aptamers that distinguish plasma from women with breast cancer from women without breast cancer. This liquid biopsy approach requires only 200 microliters of plasma and is amenable to high-throughput processing. By employing a number of statistical approaches including rigorous cross-validation, we consistently achieve ROC AUC values >0.6. Further optimization of the aptamer library and testing on additional samples is ongoing. Upon complete validation, an ADAPT TM - derived breast cancer test may serve as a vital diagnostic adjunct that can be easily incorporated into standard clinical practice. Citation Format: Valeriy Domenyuk, Zhenyu Zhong, Jie Wang, Adam Stark, Nianqing Xiao, Mark Miglarese, George Poste, Michael Famulok, Gunter Mayer, David Spetzler. Adaptive dynamic artificial poly-ligand targeting (ADAPT) enables plasma-based exosome profiling with potential diagnostic utility. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-135.

Abstract P2-01-08: Adaptive dynamic artificial poly-ligand targeting: Aptamer-based profiling of liquid biopsies to improve the accuracy of breast cancer diagnoses in women with dense breast tissue

Introduction: Breast cancer screening relies upon mammography, but for women with dense breast tissue this method is often uninformative. Routine screening identifies suspicious breast lesions in some women, but the pain and risk associated with follow-up biopsies along with the poor accuracy of traditional histopathology urgently call for improved approaches to breast cancer screening. This is especially important for those high-risk patients for whom mammography is of limited value. We describe a non-invasive liquid biopsy method of profiling plasma exosome preps designed to improve the accuracy and safety of breast cancer screening for women with dense breast tissue. Results: We incubated plasma samples (300 microliters per sample) from breast cancer patients (n=60) and a control cohort (n=60) with a high-complexity DNA aptamer library using a modified SELEX scheme, termed “adaptive dynamic artificial poly-ligand targeting (ADAPTTM)”. Differentially bound (cancer vs. non-cancer) aptamers were recovered from precipitated exosomes and were identified by deep sequencing. Two thousand aptamer sequences were resynthesized and used to probe a larger set of 500 plasma samples from a patient cohort (n=206) and a control cohort comprised of self-reported healthy volunteers (n=117) and patients whose biopsies led to a diagnosis of non-cancer (n=177). We employed several statistical models to build a cancer/non-cancer predictor, including a Random Generalized Linear Model (RGLM) and a Random Forest Model (RFM). Both models yielded an equivalent classification performance with areas under the receiver-operator characteristic curve (ROC AUC) of 0.7. Testing the prediction performance by 100 Out-of-Bag permutations or by pre-filtered (read cutoff and estimated sample size) cross-validation (CV) resulted in ROC AUC values of 0.66 and 0.62, respectively. When samples were randomly assigned to groups, the aptamers were no longer able to distinguish the groups (ROC AUC = 0.54), indicating that the underlying information driving the model is truly specific to cancer. Importantly, incorporation of BIRAD results as a clinical covariate did not influence model performance, signifying that predictions by ADAPTTM were independent of breast tissue density. Conclusions: We have identified a set of 2000 DNA aptamers that distinguish women with breast cancer from women without breast cancer. Our liquid biopsy approach requires only 300 microliters of plasma and is amenable to high-throughput processing. By employing a number of statistical approaches including rigorous cross-validation, we consistently achieve cross validation ROC AUC values approaching 0.7. The performance of the predictor was not affected by BIRAD scores, supporting its potential utility in difficult cases where imaging is insufficient, such as in women with dense breast tissue. Further optimization of the aptamer library and testing on additional samples should improve performance. Upon complete validation, an ADAPTTM – derived breast cancer test may serve as a vital diagnostic adjunct that can be easily incorporated into standard clinical practice. Citation Format: Domenyuk V, Zhong Z, Wang J, Stark A, Chen W, Xiao N, Miglarese MR, Famulok M, Mayer G, Spetzler DB. Adaptive dynamic artificial poly-ligand targeting: Aptamer-based profiling of liquid biopsies to improve the accuracy of breast cancer diagnoses in women with dense breast tissue. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-01-08.