Biomarker Discovery Platform Research Articles

Abstract 4248: Multispectral immunofluorescence identifies pS6 as a biomarker of intrinsic resistance to ruxolitinib in patients with relapsed/refractory T-cell lymphomas

Abstract Introduction: Peripheral and cutaneous T cell lymphomas (TCL) are highly heterogeneous diseases that in the relapsed/refractory (R/R) stage have poor outcome. JAK/STAT, Pi3K/AKT and MAP kinase signaling are commonly involved in the pathogenesis of these lymphomas, and the efficacy of small molecule inhibitors targeting these pathways is being assessed in phase II trials. However, biomarkers to define patients most likely to benefit from specific inhibitors are lacking and mechanisms of acquired resistance in TCLs have not been reported. Methods: We are conducting two early phase trials in R/R TCL: 1) NCT02974647 targets JAK1/JAK2 with ruxolitinib (RUX, a JAK 1-2 inhibitor) and has enrolled 53 patients and 2) NCT02783625 targets Pi3K/AKT with duvelisib (DUV, a Pi3K gamma-delta inhibitor) in combination with either romidepsin or bortezomib and has enrolled 92 patients. We analyzed pre-, on and post- treatment biopsies with multiplex immunofluorescence (mIF) using the Vectra platform, and analyzed images for marker expression and cellular location using HALO software. We designed multiple mIF panels with 6 markers per slide, 3 identifying TCL cells based on disease-specific phenotypes (e.g. CD4+PD1+CD8- for angioimmunoblastic TCL), pSTAT3,5, pS6, and a macrophage marker (CD68). Areas of TCL involvement within biopsy slides were manually defined during image analysis based on mIF and comparison to clinical immunohistochemistry. Differences in marker expressions were analyzed with the Wilcoxon test using the program R. Results: We analyzed 53 biopsies from 39 patients with high-quality mIF images, of which 17 were from lymph nodes and 36 were from extranodal sites. Of 39 patients, 13 were treated with RUX (9 biopsies pre-, 7 on, 4 post- treatment) and 26 with DUV (20 biopsies pre-, 6 on, 10 post- treatment). Median number of total TCL cells in pre-treatment biopsies was 1041 (range, 97-6463) and median TCL cell fraction within involved areas was 27.86% (range, 5.12-88.02%). Median macrophage fraction within involved areas was 5.92% (range, 2.89-11.88%). For either agent, response to treatment was not associated with: 1) quantity of macrophages (p=0.1 for responders vs non-responders to DUV, p=0.53 for RUX), 2) average distance between TCL cells and closest macrophage, 3) TCL cell fraction within involved areas, or 4) fraction of TCL cells expressing pSTAT3/5 (Figure). In contrast, pS6 expression in <25% of TCL cells within pre-treatment biopsy completely distinguished responders from non-responders (Figure; p=0.02). Neither agent induced consistent changes in expression of pSTAT3/5 or pS6 at the on or post-treatment timepoints. Conclusions: mIF is a feasible platform for biomarker discovery and translation in TCL, a set of lymphomas with heterogeneous immunophenotypes. pS6 expression in <25% of TCL cells may predict response to ruxolitinib, suggesting that activation of S6 kinase downstream of PI3K or MAP kinase, may overcome dependence on JAK/STAT signaling. This is also the first data suggesting that ruxolitinib responses involve a TCL cell-intrinsic mechanism of action. Based on these data, we are assessing the use of pS6 IHC as a clinically-validated biomarker in patients with TCL prior to ruxolitinib. Citation Format: Paola Ghione, Alison Moskowitz, Priadarshini Kumar, Andrea Knezevic, Venkatraman Seshan, Eric Jacobsen, Jia Ruan, Johnathan Schatz, Sarah Noor, Patricia Myskowski, Helen Hancock, Theresa Davey, Obadi Obadi, Carlissa Onwasigwe, Nivetha Ganesan, Lauren Pomerantz, Christine Jarjies, Allison Sigler, Mark Geyer, Ariela Noy, David Straus, Natasha Galasso, Giorgio Inghirami, Steven Horwitz, David Weinstock, Travis Hollmann, Ahmet Dogan. Multispectral immunofluorescence identifies pS6 as a biomarker of intrinsic resistance to ruxolitinib in patients with relapsed/refractory T-cell lymphomas [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4248.

Abstract LB-401: A proteomics-based biomarker discovery platform for predicting clinical response to immune checkpoint inhibitor therapy in non-small cell lung cancer

Abstract Immune checkpoint inhibitors (ICIs) have changed the treatment paradigm for non-small cell lung cancer (NSCLC) due to their unprecedented rates of durable response. However, their therapeutic efficacy is limited to a minority of NSCLC patients. Thus, there is a great need for reliable biomarkers to predict outcome. Our previous studies have identified pro-tumorigenic, host-mediated responses to cancer treatment modalities including chemotherapy, radiation and targeted drugs. In this study, we characterized the host-mediated response to ICI therapy, and investigated its potential to predict clinical outcome in NSCLC. In preclinical experiments, Lewis lung carcinoma (LLC) cells exhibited enhanced migratory and invasive properties in vitro upon exposure to plasma from tumor-free, anti-PD-1-treated mice. Additionally, in comparison to control arms, an increased mortality rate was observed in mice intravenously injected with LLC cells that had been pre-conditioned with plasma from anti-PD-1-treated mice. These findings suggest that anti-PD-1 treatment induces a systemic host response that potentially promotes tumor aggressiveness. To identify host response-based biomarkers that predict clinical outcome of ICI therapy, plasma samples were obtained from NSCLC patients at baseline and 4 weeks after a single dose of anti-PD-1 therapy. Proteomic profiling of plasma samples was performed using ELISA-based protein arrays. We then used support vector machine (SVM) algorithm to identify a proteomic signature that is predictive of clinical response to treatment. The cohort consisted of 87 NSCLC patients, who were divided into a training set (n=33) and an independent validation set (n=54). Our classifier identified a three-protein signature that accurately distinguishes between responders and non-responders, with an area under the curve (AUC) of 0.89 [confidence interval (CI): 0.76 - 1.0; p-value: 4.11E-05] in the training set and 0.72 [CI: 0.55 - 0.89; p-value: 0.01] in the validation set. Pathway enrichment analysis of proteomic profiles of non-responders revealed multiple pathways directly related to cancer and lung cancer specifically, as well as pathways associated with immunosuppression. Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Citation Format: Michal Harel, Irena Khononov, Eran Issler, Coren Lahav, Ella Fremder, Eyal Jacob, Nili Dahan, Ziv Raviv, Avishag Shkedy, Reema Jacob, Adam Dicker, Haim Bar, Ofer Sharon, Yuval Shaked. A proteomics-based biomarker discovery platform for predicting clinical response to immune checkpoint inhibitor therapy in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-401.

Chick chorioallantoic membrane (CAM) assays as a model of xenografts derived from circulating cancer stem cells from breast cancer patients.

e15596 Background: Circulating cancer stem cells (cCSCs) are a rare fraction of circulating tumor cells with a profile of stemness, resistance to chemotherapy and the capacity to generate metastases. Patient-derived xenografts (PDX) are an increasingly noticed tool in oncology, providing biologically meaningful models of many cancer types, and potential platforms for the development of precision oncology approaches. Commonly, mouse models are used for the in vivo assessment of potential new therapeutic targets in cancers. However, given the high cost, time investment, and legal obligations of such animal models CAM assays are an attractive alternative. Methods: In this study, primary cultures from circulating cancer stem cells were established using sphere-forming assays. Subsequently, tumorspheres were transplanted onto the CAM membrane of fertilized chicken eggs to form secondary microtumors. Results: We have developed an innovative in vitro platform for cultivation of CSCs from peripheral blood of breast cancer patients. The number of tumorspheres increased significantly with tumor progression. Patients with metastatic disease had statistically more tumorspheres as compared to patients without metastasis (30 vs 10/100µl blood, p < 0.05). Patients with multiple metastases had more tumorspheres compared to patients with single metastases (30 vs 60/100µl blood, p < 0.05). The number of tumorspheres was positively correlated with Ki-67, Her2 status and grade score in primary breast tumors. Tumorspheres showed self-renewal, growth potentials, invasion and differentiation in vivo. Their transplantation on CAM membranes resulted in the rapid formation of microtumors in many cases. These tumors pathologically closely resembled the primary tumor. The success rate of PDX was positively correlated with aggressiveness and proliferation capacity of the primary tumor. Conclusions: The number of tumorspheres cultured from peripheral blood of cancer patients and the success rate of establishing PDX directly reflect the aggressiveness and proliferation capacity of the primary tumor. A CAM-based PDX model using circulating cancer stem cells provides a fast, low-cost, easy to handle and powerful preclinical platform for drug screening, therapy optimization, and biomarker discovery.

A proteomic biomarker discovery platform for predicting clinical benefit of immunotherapy in advanced melanoma.

10037 Background: Immune checkpoint inhibitor-based immunotherapies that target CTLA-4 and the PD-1/PD-L1 axis have revolutionized the treatment of advanced melanoma due to their remarkable clinical benefit. However, only a limited number of patients respond to treatment. Therefore, biomarkers to identify appropriate candidates who will benefit from such therapy are needed. Our previous studies have identified therapy-induced, host-mediated mechanisms that drive resistance to a variety of cancer treatment modalities. Here, we explored whether assessing the systemic host-mediated response to immunotherapy can serve as a basis for predicting clinical outcome in melanoma patients. Methods: The cohort consisted of 34 advanced melanoma patients receiving anti-PD-1 monotherapy or anti-PD-1 and anti-CTLA-4 combination therapy. Clinical benefit was assessed. Plasma samples were obtained from patients at baseline and 2-4 weeks after a single treatment. Proteomic profiling of plasma samples was performed using ELISA-based protein arrays. A generalized linear model (GLM) was applied to a subset of the cohort (n = 13) to identify a proteomic signature that can predict clinical response to treatment. The predictive signature was then tested on the entire cohort (n = 33), excluding one patient with stable disease. Results: We identified a 10-protein signature that accurately distinguishes between responders and non-responders with an area under the curve (AUC) of 0.84 (confidence interval: 0.69-0.99, p-value 5.56E-04), and sensitivity and specificity of 0.94 and 0.79, respectively. These results are currently being validated in a larger cohort in an ongoing prospective study (PROPHETIC trial, NCT04056247). To explore the biological basis of resistance to immunotherapy, we performed a pathway enrichment analysis. Multiple mechanisms of resistance were identified in the non-responder group, including signaling pathways associated with immunosuppression and inflammation. Comparison between the two treatment modalities revealed pathways unique to each treatment, implying important differences between the two regimens. Conclusions: Our study provides insights into mechanisms of resistance to immunotherapy and paves the way towards the discovery of novel predictive biomarkers for patient stratification in melanoma.

Profiling of Endogenously Intact N-Linked and O-Linked Glycopeptides from Human Serum Using an Integrated Platform

Endogenous glycopeptides in serum are an invaluable resource for biomarker discovery. Due to the low abundance and the poor fragmentation in tandem mass spectrometry, the identification of endogenously intact glycopeptides still faces many challenges. Herein, an integrated platform is fabricated for the identification of N-linked and O-linked endogenously intact glycopeptides. In this platform, the high-temperature acid denaturation, ultrafiltration, and hydrophilic interaction chromatography steps are combined together for the highly efficient extraction of the endogenously intact glycopeptides from a small amount of serum. Additionally, the twin-spectra scheme and in silico deglycosylation strategy were applied for the identification of N-linked and O-linked endogenous glycopeptides, respectively. In total, 223 intact N-glycopeptides and 51 intact O-glycopeptides are identified from only 40 μL of the human serum sample. This is the first study reporting the identification of endogenously intact N-linked and O-linked glycopeptide and is also the largest data set of endogenously intact glycopeptides reported so far. The distributions of glycans among peptides and proteins and cleavage sites on peptides are further analyzed to seek the regulation of endogenous glycosylation for disease mechanism. The developed strategy provides a novel platform for the disease biomarker discovery.

Comprehensive profiling of Small RNAs in human embryo-conditioned culture media by improved sequencing and quantitative PCR methods

ABSTRACTEmbryo implantation depends on two primary factors: the quality of the embryo and endometrial receptivity. Small RNAs have been shown to be potent epigenetic regulators influencing cell proliferation, differentiation, and communication even in the context of early embryonic development. However, previous reports are limited to miRNAs and lack sensitivity. Here, we describe a platform for non-invasive small RNA biomarker discovery and validation from embryo-conditioned culture media (ECCM). We hypothesize that small non-coding RNAs (sncRNAs) are secreted by the embryo into the ECCM and test the limit of detection for profiling sncRNA by deep sequencing and quantitative PCR. In the first set of experiments, we evaluated sequencing sensitivity by comparing sncRNA profiles from pools of 10, 5, 3, and single ECCM drops. Next, we performed a similar test for TaqMan qPCR sensitivity by measuring select sncRNAs in 5, 3 and single drop ECCM pools. Finally, we compared the expression of an sncRNA panel by qPCR in single ECCM vs no-embryo control media . We report the first comprehensive sequencing of sncRNAs in ECCM with a sequencing sensitivity of 3 single embryo drops, capturing ~150 miRNAs and an abundance of tRNA-derived small RNAs (tsRNAs). We then profiled 15 sncRNAs by qPCR and determined that the assay maintains sensitivity in single ECCM drops. Finally, we found significant differences in these sncRNA expression between control and ECCM drops. Improving embryo selection is crucial for reducing time to pregnancy. Here we describe a sensitive technique for biomarker discovery by sequencing and qPCR validation in ECCM, demonstrating that the majority of sncRNAs are embryo derived. We also report an abundance of tsRNAs which suggests these sncRNAs may have functions in endometrial-maternal communication beyond the microRNAs which have been described previously.Abbreviations: PGT-A: Preimplantation genetic testing for aneuploidies; ECCM: Embryo-conditioned culture media; sncRNAs: Small non-coding RNAs; miRNAs: microRNAs; EVs: Extracellular vesicles; PCA: Principal component analysis.

Differential Dependency of Human Pancreatic Cancer Cells on Targeting PTEN via PLK 1 Expression.

Even though the tumour suppressive role of PTEN is well-known, its prognostic implications are ambiguous. The objective of this study was to further explore the function of PTEN expression in human pancreatic cancer. The expression of PTEN has been dominant in various human cancers including pancreatic cancer when compared with their matched normal tissues. The pancreatic cancer cells have been divided into PTEN blockade-susceptible and PTEN blockade-impassible groups dependent on targeting PTEN by altering intracellular signaling. The expression of PTEN has led to varying clinical outcomes of pancreatic cancer based on GEO Series (GSE) data analysis and Liptak’s z analysis. Differential dependency to PTEN blockade has been ascertained based on the expression of polo-like kinase1 PLK1 in pancreatic cancer cells. The prognostic value of PTEN also depends on PLK1 expression in pancreatic cancer. Collectively, the present study provides a rationale for targeting PTEN as a promising therapeutic strategy dependent on PLK1 expressions using a companion biomarker discovery platform.

Gel-Based Proteomics of Clinical Samples Identifies Potential Serological Biomarkers for Early Detection of Colorectal Cancer.

The burden of colorectal cancer (CRC) is considerable—approximately 1.8 million people are diagnosed each year with CRC and of these about half will succumb to the disease. In the case of CRC, there is strong evidence that an early diagnosis leads to a better prognosis, with metastatic CRC having a 5-year survival that is only slightly greater than 10% compared with up to 90% for stage I CRC. Clearly, biomarkers for the early detection of CRC would have a major clinical impact. We implemented a coherent gel-based proteomics biomarker discovery platform for the identification of clinically useful biomarkers for the early detection of CRC. Potential protein biomarkers were identified by a 2D gel-based analysis of a cohort composed of 128 CRC and site-matched normal tissue biopsies. Potential biomarkers were prioritized and assays to quantitatively measure plasma expression of the candidate biomarkers were developed. Those biomarkers that fulfilled the preset criteria for technical validity were validated in a case-control set of plasma samples, including 70 patients with CRC, adenomas, or non-cancer diseases and healthy individuals in each group. We identified 63 consistently upregulated polypeptides (factor of four-fold or more) in our proteomics analysis. We selected 10 out of these 63 upregulated polypeptides, and established assays to measure the concentration of each one of the ten biomarkers in plasma samples. Biomarker levels were analyzed in plasma samples from healthy individuals, individuals with adenomas, CRC patients, and patients with non-cancer diseases and we identified one protein, tropomyosin 3 (Tpm3) that could discriminate CRC at a significant level (p = 0.0146). Our results suggest that at least one of the identified proteins, Tpm3, could be used as a biomarker in the early detection of CRC, and further studies should provide unequivocal evidence for the real-life clinical validity and usefulness of Tpm3.

Abstract A044: A precision medicine platform to predict the clinical response to chemo- and immunotherapy for epithelial ovarian cancer

Abstract Introduction: Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. First-line therapy in advanced EOC is surgery in combination with platinum-based chemotherapy and paclitaxel. 15-20% of patients do not respond and in 80% of advanced cases, the disease recurs within three years. PARP inhibitors synergize with platinum therapy and have been approved for platinum sensitive EOC. Clinical trials with immunotherapies, such as PD-1/PD-L1 blockade, have so far not been successful. Currently, the only approved companion diagnostic is BRCA gene mutations for PARP inhibitors. More diagnostic assays to predict the clinical response to chemo- or immunotherapies are needed. We have developed a biomarker discovery platform using ascites of ovarian cancer patients. Experimental procedures Ascites was gathered from patients by punction or during debulking surgery. Cells were collected by centrifugation and characterized by flow cytometry using specific antibodies. Genomic DNA was sequenced using Illumina cancer gene panels. Gene expression was analyzed by quantitative PCR (qPCR). Cellular activity of the tryptophan metabolizing enzymes IDO1 and TDO was measured with NFK Green [1]. Levels of L-tryptophan and its metabolite L-kynurenine in ascites fluid and blood were determined with LC-MS/MS. In vitro data were related to tumor histopathology and clinical response data. Results: Low passage cell samples from twenty patients were profiled for sensitivity to various cytotoxic agents and targeted anti-cancer therapies in cell proliferation assays. In parallel the mutation status of fifty cancer genes including BRCA1 and 2 was assessed by DNA sequencing. The expression of genes implicated in resistance to chemotherapy (CCNE1, ABCB1) or immunotherapy (PD-L1, IDO1, TDO) was determined with qPCR. The immune status of ascites was analyzed by measuring the relative proportion of different immune cell populations, i.e., cytotoxic and regulatory T cells, monocytes, dendritic and natural killer cells. The expression of the immune suppressive markers PD-L1, IDO1 and TDO was related to the immune cell composition of the ascites, kynurenine-tryptophan ratio, and clinical response data. A tumor cell sample derived from a patient with low grade serous ovarian cancer (LGSOC) was heterozygous for an oncogenic NRAS mutation and was much more sensitive to MEK inhibitors than other samples not harboring the mutation. The cells also expressed IDO1 and high levels of PD-L1 at the cell surface. Two other samples derived from high grade serous ovarian cancer (HGSOC) expressed high PD-L1 and one also IDO1. Several HGSOC samples expressed TDO. One HGSOC sample showed high expression of the ABCB1 gene, encoding the multidrug transporter P-glycoprotein. The sample was relatively resistant to paclitaxel, a known substrate of P-glycoprotein Conclusion: Our study shows that in vitro drug sensitivity assays with primary patient samples can be used to confirm or identify predictive drug response biomarkers. In an ongoing study, in which hundred patients with HGSOC will be included, the in vitro drug response of tumor cells from ascites to first-line cytotoxic anti-cancer agents will be determined and compared to the clinical response of patients bearing specific genomic biomarkers. [1] Seegers et al. (2014) J Biomol Screen 19, 1266-1272 Citation Format: Guido J.R. Zaman, Judith E. den Ouden, Jelle Dylus, Antoon M. van Doornmalen, Winfried R. Mulder, Diep Vu-Pham, Suzanne J.C. van Gerwen, Joost C.M. Uitdehaag, Rogier C. Buijsman, Leon F. Massuger, Anne M. van Altena. A precision medicine platform to predict the clinical response to chemo- and immunotherapy for epithelial ovarian cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A044. doi:10.1158/1535-7163.TARG-19-A044

Abstract A026: An automated biomarker discovery platform based on in vitro pharmacology raw data

Abstract In vitro pharmacology studies using cultures of biological models, such as cancer cell lines or lately organoids in the oncology field, are commonly used to assess drug efficacy. The baseline genetic or epigenetic features, including those reflective of the original patient tissue, can be characterized by genomic profiling of the respective models, and correlated to the efficacy observed during pharmacology studies to discover potential predictive biomarkers. On the other hand, the changes of these features following specific treatment (pharmacodynamic changes) can be used to explore the drug mechanism of action (MOA) or potential drug targets. Discovery and/or validation of relevant biomarkers using these in vitro preclinical pharmacology studies are obviously critical in drug discovery/development. Although important, the biomarker discovery and validation process can be complex and labor intensive, and currently there are few robust tools that can be used readily to take on the task of automated computation discovery. To this end, we have established an automated biomarker discovery platform for in vitro preclinical pharmacology studies by implementing a variety of machine learning algorithms and statistical methods, combining them with Shiny package in R, an integrated suite of software facilities for data manipulation, calculation and graphical display. This platform is user-friendly and efficiently manages raw data uploads, data conversions, drug efficacy overview, biomarker discovery analysis as well as data report generation. In conclusion, our platform provides a new solution for precision biomarker discovery in preclinical, especially immune-oncology studies. Citation Format: Jia Xue, Henry QX Li, Sheng Guo. An automated biomarker discovery platform based on in vitro pharmacology raw data [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A026. doi:10.1158/1535-7163.TARG-19-A026

Communication between EMT and PD-L1 signaling: New insights into tumor immune evasion.

Immune checkpoint blockage has been considered a breakthrough in cancer treatment, achieving encouraging anti-tumor effects in some advanced solid malignancies. However, low response rate and therapeutic resistance represent significant challenges in this field. In addition to its typical role in embryonic development and tissue fibrosis, epithelial-mesenchymal transition (EMT) plays a pivotal role in tumor immunosuppression and immune evasion. Previous studies revealed that EMT is associated with activation of different immune checkpoint molecules, including PD-L1. EMT-induced immune escape promotes cancer progression and may also provide a platform for discovery of novel therapeutic approaches and predictive biomarkers for checkpoint inhibitor therapeutic response. Here, we summarize recent findings focused on EMT-induced immune suppression and evasion in the tumor microenvironment (TME). EMT transcription factors (EMT-TFs), immune cells, cell plasticity and their regulatory role in the immune response are thoroughly reviewed. Bidirectional regulation between EMT and PD-L1 signaling is discussed in terms of cancer immune escape and possible combined therapies. Additionally, we investigated the value of preclinical or clinical trials using EMT targeted therapy combined with PD-L1 inhibitors. This review may help to further understand the role of EMT and PD-L1 signaling in cancer immune evasion. Meanwhile, additional molecular mechanistic studies and clinical trials are urgently needed.

Decellularized Human Gut as a Natural 3D Platform for Research in Intestinal Fibrosis.

The current methodologies for the identification of therapeutic targets for inflammatory bowel disease (IBD) are limited to conventional 2-dimensional (2D) cell cultures and animal models. The use of 3D decellularized human intestinal scaffolds obtained from surgically resected intestine and engineered with human intestinal cells may provide a major advancement in the development of innovative intestinal disease models. The aim of the present study was to design and validate a decellularization protocol for the production of acellular 3D extracellular matrix (ECM) scaffolds from the human duodenum. Scaffolds were characterized by verifying the preservation of the ECM protein composition and 3D architecture of the native intestine and were employed for tissue engineering with primary human intestinal myofibroblasts for up to 14 days. Engrafted cells showed the ability to grow and remodel the surrounding ECM. mRNA expression of key genes involved in ECM turnover was significantly different when comparing primary human intestinal myofibroblasts cultured in 3D scaffolds with those cultured in standard 2D cultures on plastic dishes. Moreover, incubation with key profibrogenic growth factors such as TGFβ1 and PDGF-BB resulted in markedly different effects in standard 2D vs 3D cultures, further emphasizing the importance of using 3D cell cultures. These results confirm the feasibility of 3D culture of human intestinal myofibroblasts in intestinal ECM scaffolds as an innovative platform for disease modeling, biomarker discovery, and drug testing in intestinal fibrosis.

5th Annual Immuno-Oncology 360° Conference: Spanning Science and Business to Bring New Therapies to Patients

5th Annual Immuno-Oncology 360° Conference: Spanning Science and Business to Bring New Therapies to Patients

A large collection of integrated genomically characterized patient-derived xenografts highlighting the heterogeneity of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) represents 10% of all breast cancers and is a very heterogeneous disease. Globally, women with TNBC have a poor prognosis, and the development of effective targeted therapies remains a real challenge. Patient-derived xenografts (PDX) are clinically relevant models that have emerged as important tools for the analysis of drug activity and predictive biomarker discovery. The purpose of this work was to analyze the molecular heterogeneity of a large panel of TNBC PDX (n = 61) in order to test targeted therapies and identify biomarkers of response. At the gene expression level, TNBC PDX represent all of the various TNBC subtypes identified by the Lehmann classification except for immunomodulatory subtype, which is underrepresented in PDX. NGS and copy number data showed a similar diversity of significantly mutated gene and somatic copy number alteration in PDX and the Cancer Genome Atlas TNBC patients. The genes most commonly altered were TP53 and oncogenes and tumor suppressors of the PI3K/AKT/mTOR and MAPK pathways. PDX showed similar morphology and immunohistochemistry markers to those of the original tumors. Efficacy experiments with PI3K and MAPK inhibitor monotherapy or combination therapy showed an antitumor activity in PDX carrying genomic mutations of PIK3CA and NRAS genes. TNBC PDX reproduce the molecular heterogeneity of TNBC patients. This large collection of PDX is a clinically relevant platform for drug testing, biomarker discovery and translational research.

Cancer protein biomarker discovery based on nucleic acid aptamers.

Identification of biomarkers is essential for diagnosis, targeted therapy and prognosis evaluation of diseases, especially cancers. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Nucleic acid aptamers are artificial single-stranded DNA or RNA sequences that can selectively bind to various targets with high specificity and affinity. Moreover, aptamers possess desirable advantages, including easy synthesis, convenient modification, relative chemical stability and low immunogenicity. Recently, different aptamer-based strategies have been developed to facilitate the discovery of biomarkers. Based on cell-SELEX technology, the selected aptamers can be used to identify cell-surface protein biomarkers of different cancer cells. SOMAscan can analyze thousands of proteins of different biological samples, which becomes a multiplexed protein biomarker discovery platform. Additionally, secreted protein biomarkers can be discovered by aptamers screened through secretome SELEX. In order to facilitate the identification of target proteins, several covalent cross-linking strategies have been developed, such as aptamer-based affinity labeling (ABAL), DNA-templated aptamer and protein-aptamer template (PAT). In this review, we mainly highlight the emerging nucleic acid aptamer-based biomarker discovery strategies and demonstrate their unique technological advantages in discovering cancer biomarkers. The challenges and perspectives of aptamer-based methods are also discussed.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases.

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

Discovering Protein Biomarkers from Clinical Peripheral Blood Mononuclear Cells Using Data-Independent Acquisition Mass Spectrometry.

Global proteomics analyses are traditionally performed in data-dependent acquisition (DDA) mode, which results in inadequate reproducibility across large sample cohorts due to the under-sampling inherent to shotgun proteomics. Recently, data-independent acquisition (DIA) strategies were introduced to allow reproducible detection and quantification of thousands of proteins with consistent sensitivity across samples. Here, we present an approach to analyze changes to the protein network in human peripheral blood mononuclear cells (PBMCs) from clinical blood samples, using DIA as a unique platform for biomarker discovery. We describe how to generate spectral PBMC proteome libraries by applying peptide fractionation followed by DDA analysis, and then how to apply DIA methods to PBMC samples from individual patients using a high-resolution Orbitrap Fusion mass spectrometer.

In Aptamers They Trust: The Caveats of the SOMAscan Biomarker Discovery Platform from SomaLogic.

In Aptamers They Trust: The Caveats of the SOMAscan Biomarker Discovery Platform from SomaLogic.

Increased levels of circulating MMP3 correlate with severe rejection in face transplantation

Face transplantation is a viable treatment option for carefully selected patients with devastating injuries to the face. However, acute rejection episodes occur in more than 80% of recipients in the first postoperative year. Unfortunately, neither a correlation between histological grades of rejection and anti-rejection treatment nor systemic surrogate markers of rejection in face transplantation are established in clinical routine. Therefore, we utilized next generation aptamer-based SOMAscan proteomics platform for non-invasive rejection biomarker discovery. Longitudinal serum samples from face transplant recipients with long-term follow-up were included in this study. From the 1,310 proteins analyzed by SOMAscan, a 5-protein signature (MMP3, ACY1, IL1R2, SERPINA4, CPB2) was able to discriminate severe rejection from both no-rejection and nonsevere rejection samples. Technical validation on ELISA platform showed high correlation with the SOMAscan data for the MMP3 protein (rs = 0.99). Additionally, MMP3 levels were significantly increased during severe rejection as compared to no-rejection (p = 0.0009) and nonsevere rejection (p = 0.0173) episodes. Pathway analyses revealed significant activation of the metallopeptidase activity during severe face transplant rejection. This pilot study demonstrates the feasibility of SOMAscan to identify non-invasive candidate biomarkers of rejection in face transplantation. Further validation in a larger independent patient cohort is needed.

Cohort study protocol: Bioresource in Adult Infectious Diseases (BioAID)

Introduction: Infectious diseases have a major impact on morbidity and mortality in hospital. Microbial diagnosis remains elusive for most cases of suspected infection which impacts on the use of antibiotics. Rapid advances in genomic technologies combined with high-quality phenotypic data have great potential to improve the diagnosis, management and clinical outcomes of infectious diseases. The aim of the Bioresource in Adult Infectious Diseases (BioAID) is to provide a platform for biomarker discovery, trials and clinical service developments in the field of infectious diseases, by establishing a registry linking clinical phenotype to microbial and biological samples in adult patients who attend hospital with suspected infection. Methods and analysis: BioAID is a cohort study which employs deferred consent to obtain an additional 2.5mL RNA blood sample from patients who attend the Emergency Department (ED) with suspected infection when they undergo peripheral blood culture sampling. Clinical data and additional biological samples including DNA, serum and microbial isolates are obtained from BioAID participants during hospital admission. Participants are also asked to consent to be recalled for future studies. BioAID aims to recruit 10,000 patients from 5-8 sites across England. Since February 2014 >4000 individuals have been recruited to the study. The final cohort will be characterised using descriptive statistics including information on the number of cases that can be linked to biological and microbial samples to support future research studies. Ethical approval and section 251 exemption have been obtained for BioAID researchers to seek deferred consent from patients from whom a RNA specimen has been collected. Samples and meta-data obtained through BioAID will be made available to researchers worldwide following submission of an application form and research protocol. Conclusions: BioAID will support a range of study designs spanning discovery science, biomarker validation, disease pathogenesis and epidemiological analyses of clinical infection syndromes.