On April 4, 2022 Bruker Corporation (Nasdaq: BRKR) reported expanded capabilities for deeper proteomic and epiproteomic coverage, including enhanced phosphopeptide analysis using the innovative TIMScore algorithm, which is now a part of the new PaSER 2022 GPU-based platform (Press release, Bruker, APR 4, 2022, View Source [SID1234611414]). The novel TIMScore algorithm takes advantage of machine learning (ML) to predict CCS values of tryptic and phosphorylated peptides. Experimentally measured CCS values are referenced against the predicted CCS value to call the most probable assignment, thereby increasing peptide confidence and coverage in high sensitivity applications. The TIMScore algorithm is especially adept in identifying phosphopeptides even at the strictest false localization rate (1% FLR) using LuciPHOr1, where it typically identifies 10%-25% more phosphopeptides.

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The human kinome comprises over 500 kinases and is essential for catalyzing protein phosphorylation, which during dysregulation is a known contributor to oncogenesis.2 In a previous human cancer cell line study by Mann et. al., at least three-fourths of the detected proteome (7832 out of 10,801 proteins) were found to be phosphorylated3. In a recent study performed in the Tenzer Lab at the University Medical Center of the Johannes Gutenberg University Mainz, some 27,768 phosphopeptides contained 4,672 isobaric phosphopeptide pairs of which 51% were chromatographically coeluting. TIMScore could separate 19% of the coeluting isomer pairs, refining the view of the phosphoproteome of a human osteosarcoma cell line.

Dr. Stefan Tenzer is Professor of Quantitative Proteomics and the Head of the Mass Spectrometry Core Facility at the University Medical Center of the Johannes Gutenberg University Mainz. Dr. Tenzer commented: "In our lab we use four timsTOF systems to understand posttranslational modifications and signaling pathways. TIMScore in PaSER uses machine learning to predict CCS values of phosphopeptides to decrease peptide ambiguity. The integration of the TIMScore model in PaSER allows for the seamless search of our data and provides an impressive increase of over 25% in the number of identified unique phosphopeptides, enabling deeper phosphoproteome coverage."

TIMScore complements the capabilities of TIMS DIA-NN. Both have been integrated in PaSER 2022 for "Run & Done" dda-PASEF and dia-PASEF workflows. Using TIMScore and dda-PASEF acquisitions of K562 and MOLT-4 cell lines, 40 fractions run at short (35 min) and long (120 min) gradients and filtered to a 1% FDR4 resulted in the identification of more than 513,000 precursors and 13,114 protein groups. With access to this experimentally derived and statistically filtered ultra-deep library, TIMS DIA-NN and short, 35-minute gradient dia-PASEF runs routinely identify >8000 protein groups and >100,000 precursors, setting the stage for high-throughput translational proteomics.

Dr. Rohan Thakur, President of the Bruker Life-Science Mass Spectrometry division, added: "It is exciting to see this greater depth in the biologically relevant phosphoproteome using mass spectrometry. With ultra-deep protein libraries of over 13k protein groups (PGs), our customers are now able to quantify over 8000 PGs with dia-PASEF, using TIMS DIA-NN, short-gradients and small sample amounts for human cell-line studies in single runs. Since mass spectrometry is a direct high-specificity readout of actual peptides and their modifications, and not a surrogate epitope-binding measurement with unknown FDRs, it has become the high-specificity method of choice for the study of the phosphoproteome. With the release of TIMScore and TIMS DIA-NN, large-scale, high-throughput, label free quantitative phosphoproteomic studies essential for cell signaling pathway analysis are now feasible. This will further enhance biomarker discovery in kinase signaling processes for understanding pathobiology, including in cancer."

The PaSER 2022 software includes TIMScore and TIMS DIA-NN for processing dda-PASEF and dia-PASEF workflows on timsTOF Pro 2, timsTOF SCP and timsTOF fleX systems. PaSER utilizes data streaming for dia-PASEF workflows in real-time, supporting ‘run & done’ for high-throughput 4D proteomics and 4D epiproteomics workflows.

References:

Fermin D., Walmsley SJ, Gringras AC., Choi, H., Nesvizhskii AI (2013). LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol. Cell Proteomics. Nov 12(11): 3409-19. Doi 10.1074/mcp/. PMID: 23918812.
Human Kinome study, Savage SR, Zhang B. Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources. Clin Proteomics. 2020 Jul 11;17:27. doi: 10.1186/s12014-020-09290-x. PMID: 32676006; PMCID: PMC7353784.
Sharma K, D’Souza RC, Tyanova S, Schaab C, Wiśniewski JR, Cox J, Mann M. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 2014 Sep 11;8(5):1583-94. doi: 10.1016/j.celrep.2014.07.036. Epub 2014 Aug 21. PMID: 25159151.
Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007 Mar;4(3):207-14. doi: 10.1038/nmeth1019. PMID: 17327847.

On April 4, 2022 ATCC, the world’s premier biological materials management and standards organization, reported that it has entered into an agreement with QIAGEN, a recognized leader in bioinformatics solutions, to provide them with sequencing data from its collection of cell lines, and animal and human biological materials (Press release, Qiagen, APR 4, 2022, View Source [SID1234611413]). QIAGEN Digital Insights, the bioinformatics unit of QIAGEN, will establish a database from this information to develop and deliver high-value digital biology content for the biotechnology and pharmaceutical industries, enabling the use of authenticated biological data sets to uncover new disease pathways and discover novel therapeutic targets.

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"This first-of-its-kind arrangement will allow ATCC to provide data provenance that is traceable, standardized and authenticated to its original source," said Raymond H. Cypess, D.V.M., Ph.D., chairman and CEO of ATCC. "We are embracing this digital biotechnology to be able to share our expertise in advancing authentication to the research community."

"Our expertise in bioinformatics software and services, combined with ATCC’s sequencing data, will further expand our genomics knowledge and OmicSoft Land databases, thus empowering scientists from around the world with actionable insights into the discovery and development of precision therapeutics," said Jonathan Sheldon, Senior Vice President and Head of QIAGEN Digital Insights. "We are honored to be collaborating with ATCC on this project."

Cell lines form the cornerstone of cell-based experimentation studies that help researchers understand the underlying mechanisms of normal and disease biology, including cancer. However, it is commonly acknowledged that inaccurate public datasets and contamination may adversely affect biomedical research and development and the effective use of bioinformatics platforms.

ATCC will initially produce fully authenticated transcriptome (RNAseq) and whole exome sequencing (WES) datasets from the most highly utilized human and animal cell lines found in ATCC’s collection. These datasets will include multiple biological and technical replicates that will help establish a baseline for a wide range of cell lines under typical cell culture conditions. Users will also be able to request datasets to be included in the database in the future.

"This agreement will enable a data-driven drug discovery process to take shape – where the data representing biological materials can be searched, analyzed and incorporated into existing research pipelines," said Ruth Cheng, Ph.D., Chief Innovation and Strategy Officer & Vice President of Corporate Development. "This will, in turn, enable researchers to discover new cell lines in our collection to include in their research that previously may have remained largely hidden from view."

QIAGEN Digital Insights provides solutions that enable its customers within healthcare, forensics, academia, and the pharmaceutical and biotechnology industries to gain valuable molecular insights from samples containing the building blocks of life. Their sample and assay technologies isolate and process DNA, RNA and proteins from blood, tissue and other materials and make these biomolecules visible and ready for analysis. Bioinformatics software and knowledge bases interpret data to report relevant, actionable insights, tied together in seamless and cost-effective workflows through automation solutions.

On April 4, 2022 Genialis, a leader in applied data science for the development of precision medicines, reported it will leverage its proprietary ResponderID AI platform technology to provide retrospective analysis to customers and collaborators to help classify patients for targeted therapy using the OncXerna Xerna TME Panel, an RNA-based pan-tumor biomarker shown to be predictive of responses to multiple immune-targeted cancer therapies (Press release, Genialis, APR 4, 2022, View Source [SID1234611412]).

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Targeted therapies today provide benefit only to a fraction of the patients for a given disease indication because current laboratory models do not faithfully represent patient biology. ResponderID starts at the end—with the patients. The platform’s computational models of disease biology embrace the complexity that defines each patient. The TME Panel measures approximately 100 genes by RNA sequencing and applies machine learning to decipher the therapeutically relevant patterns in those data. The TME Panel can enrich patient response to different classes of therapy (e.g. immune checkpoint inhibitors and anti-angiogenics) in several cancer types, including gastric and ovarian.

"Inclusion of the TME Panel algorithm within ResponderID will create a valuable starting point for numerous potential research and commercial partnerships," said Rafael Rosengarten, Ph.D., CEO of Genialis. "By offering our customers and collaborators the opportunity to quickly and comprehensively analyze their retrospective data, together we will be able to define and implement the most effective precision medicine strategy."

Collaborators that wish to use the Xerna TME Panel for future clinical studies and/or prospective research will be referred to one of OncXerna’s commercial diagnostic partners.

"We have a mounting body of evidence that suggests the Xerna TME Panel may be applicable to virtually any solid tumor type, and can be helpful to find the right patients for drugs that target the immune system," commented OncXerna CEO Laura Benjamin. "Genialis has the opportunity to evaluate the TME Panel across many of these disease types and for different therapeutic mechanisms."

ResponderID defines, models and validates disease models for drug development and discovery programs. The platform is a technology suite for clinical and translational research, built from years of experience working with partners across the industry and advanced internal R&D. ResponderID incorporates technologies and proprietary tools for feature selection, data harmonization, machine learning modeling and interpretation of the models. ResponderID is already in use to improve the efficiency, effectiveness and success rate of drug development and discovery across the oncology space, with more therapeutic areas in the works. Through numerous collaborations, ResponderID is building computational models to help treat cancers like gastric, colorectal, ovarian, lung, melanoma, acute myeloid leukemia, mesothelioma, and others.

OncXerna and Genialis co-authored a poster to be presented at AACR (Free AACR Whitepaper) 2022 that describes application of the TME Panel across various cancer types and drug modalities. For more information on Genialis and how it uses data science to advance precision medicine, please visit www.genialis.com.

On April 4, 2022 Checkpoint Therapeutics, Inc. ("Checkpoint") (NASDAQ: CKPT), a clinical-stage immunotherapy and targeted oncology company, reported that members of Checkpoint’s management team will participate in the two-day Fortress Biotech ("Fortress") Virtual R&D Summit taking place on Tuesday, April 5, 2022 and Wednesday, April 6, 2022 (Press release, Checkpoint Therapeutics, APR 4, 2022, View Source [SID1234611411]). The Summit will be hosted by the B. Riley Securities’ Healthcare Equity Research team and will feature multiple programs from Fortress’ diversified pipeline.

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Checkpoint will present a corporate overview and participate in a panel discussion on Wednesday, April 6, 2022 at 2:15 p.m. ET. Registration for the event is available here.

Following the meeting, the webcast will be available on the IR Calendar page under News & Events, located within the Investors section of Checkpoint’s website, View Source, for approximately 30 days following the meeting.

On April 4, 2022 Precigen, Inc. (Nasdaq: PGEN), a biopharmaceutical company specializing in the development of innovative gene and cell therapies to improve the lives of patients, reported that the FDA has granted Fast Track designation for PRGN-3006 UltraCAR-T in patients with relapsed or refractory (r/r) AML (clinical trial identifier: NCT03927261) (Press release, Precigen, APR 4, 2022, View Source [SID1234611410]). PRGN-3006 was previously granted FDA Orphan Drug Designation.

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PRGN-3006 UltraCAR-T is a multigenic autologous chimeric antigen receptor (CAR)-T cell treatment utilizing Precigen’s non-viral Sleeping Beauty system to simultaneously express a CAR specifically targeting CD33, which is over expressed on AML blasts; membrane bound IL-15 for enhanced in vivo expansion and persistence; and a kill switch to conditionally eliminate CAR-T cells for an improved safety profile.

Precigen’s UltraCAR-T platform is designed to overcome limitations of currently available CAR-T therapies by utilizing an advanced overnight non-viral gene delivery manufacturing process at a medical center’s cGMP facility without the need for ex vivo expansion. Current CAR-T cell therapies are limited due to, inter alia, the prolonged interval between apheresis to product infusion and an exhausted phenotype of T cells resulting from lengthy ex vivo expansion. UltraCAR-T cells for the PRGN-3006 study are manufactured overnight using Precigen’s proprietary UltraPorator system.

"We are very pleased to receive the FDA’s Fast Track designation, which facilitates development and expedites the review process of drugs that address serious conditions and high unmet medical needs," said Helen Sabzevari, PhD, President and CEO of Precigen. "AML is a rapidly progressing disease with a very poor prognosis. The Fast Track designation will help facilitate the timely development of this program and we look forward to working more closely with the FDA to potentially bring this new and highly differentiated overnight UltraCAR-T therapy to patients."

About AML
AML is a cancer that starts in the bone marrow, but most often moves into the blood.1 Though considered rare, AML is among the most common types of leukemia in adults.2 In 2019, it was estimated that 21,450 new cases of AML would be diagnosed in the US.2 AML is uncommon before the age of 45 and the average age of diagnosis is about 68.2 The prognosis for patients with AML is poor with an average 5‐year survival rate of approximately 25 percent overall, and less than a 5 percent 5‐year survival rate for patients older than 65.3 Amongst elderly AML patients (≥ 65 years of age), median survival is short, ranging from 3.5 months for patients 65 to 74 years of age to 1.4 months for patients ≥ 85 years of age.3