Kenneth Kinzler
Johns Hopkins University
H-index: 248
North America-United States
Description
Kenneth Kinzler, With an exceptional h-index of 248 and a recent h-index of 136 (since 2020), a distinguished researcher at Johns Hopkins University, specializes in the field of Cancer, Genetics, Genomics.
His recent articles reflect a diverse array of research interests and contributions to the field:
TRBC1-targeting antibody–drug conjugates for the treatment of T cell cancers
Machine learning to detect the SINEs of cancer
Circulating tumor DNA analysis informing adjuvant chemotherapy in locally advanced rectal cancer: The randomized AGITG DYNAMIC-Rectal study.
Safe sequencing system
Methods and materials for treating t cell cancers
Methods of detecting high risk barrett's esophagus with dysplasia, and esophageal adenocarcinoma
Circulating mutant dna to assess tumor dynamics
Signal
Professor Information
University | Johns Hopkins University |
---|---|
Position | ___ |
Citations(all) | 348487 |
Citations(since 2020) | 83067 |
Cited By | 304837 |
hIndex(all) | 248 |
hIndex(since 2020) | 136 |
i10Index(all) | 583 |
i10Index(since 2020) | 466 |
University Profile Page | Johns Hopkins University |
Research & Interests List
Cancer
Genetics
Genomics
Top articles of Kenneth Kinzler
TRBC1-targeting antibody–drug conjugates for the treatment of T cell cancers
Antibody and chimeric antigen receptor (CAR) T cell-mediated targeted therapies have improved survival in patients with solid and haematologic malignancies 1, 2, 3, 4, 5, 6, 7, 8, 9. Adults with T cell leukaemias and lymphomas, collectively called T cell cancers, have short survival 10, 11 and lack such targeted therapies. Thus, T cell cancers particularly warrant the development of CAR T cells and antibodies to improve patient outcomes. Preclinical studies showed that targeting T cell receptor β-chain constant region 1 (TRBC1) can kill cancerous T cells while preserving sufficient healthy T cells to maintain immunity 12, making TRBC1 an attractive target to treat T cell cancers. However, the first-in-human clinical trial of anti-TRBC1 CAR T cells reported a low response rate and unexplained loss of anti-TRBC1 CAR T cells 13, 14. Here we demonstrate that CAR T cells are lost due to killing by the patient’s normal T …
Authors
Tushar D Nichakawade,Jiaxin Ge,Brian J Mog,Bum Seok Lee,Alexander H Pearlman,Michael S Hwang,Sarah R DiNapoli,Nicolas Wyhs,Nikita Marcou,Stephanie Glavaris,Maximilian F Konig,Sandra B Gabelli,Evangeline Watson,Cole Sterling,Nina Wagner-Johnston,Sima Rozati,Lode Swinnen,Ephraim Fuchs,Drew M Pardoll,Kathy Gabrielson,Nickolas Papadopoulos,Chetan Bettegowda,Kenneth W Kinzler,Shibin Zhou,Surojit Sur,Bert Vogelstein,Suman Paul
Journal
Nature
Published Date
2024/4
Machine learning to detect the SINEs of cancer
We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the …
Authors
Christopher Douville,Kamel Lahouel,Albert Kuo,Haley Grant,Bracha Erlanger Avigdor,Samuel D Curtis,Mahmoud Summers,Joshua D Cohen,Yuxuan Wang,Austin Mattox,Jonathan Dudley,Lisa Dobbyn,Maria Popoli,Janine Ptak,Nadine Nehme,Natalie Silliman,Cherie Blair,Katharine Romans,Christopher Thoburn,Jennifer Gizzi,Robert E Schoen,Jeanne Tie,Peter Gibbs,Lan T Ho-Pham,Bich NH Tran,Thach S Tran,Tuan V Nguyen,Michael Goggins,Christopher L Wolfgang,Tian-Li Wang,Ie-Ming Shih,Anne Marie Lennon,Ralph H Hruban,Chetan Bettegowda,Kenneth W Kinzler,Nickolas Papadopoulos,Bert Vogelstein,Cristian Tomasetti
Journal
Science Translational Medicine
Published Date
2024/1/24
Circulating tumor DNA analysis informing adjuvant chemotherapy in locally advanced rectal cancer: The randomized AGITG DYNAMIC-Rectal study.
12Background: Adjuvant chemotherapy (CT) following neoadjuvant chemoradiation and surgery for locally advanced rectal cancer (LARC) is widely adopted, despite uncertain survival benefit. Circulating tumor DNA (ctDNA) detection after surgery has been shown to be a strong prognostic marker in localized colorectal cancer and potentially could inform adjuvant treatment decision making. Methods: AGITG DYNAMIC-Rectal is a multi-centre randomized controlled phase II trial. Eligible patients (pts) had LARC (cT3-4 and/or cN+) treated with neoadjuvant chemoradiation, total mesorectal excision, and were fit for adjuvant CT. Pts were randomly assigned 2:1 to ctDNA-guided management or standard management (clinician decision). A tumor-informed personalized ctDNA assay was used. For the ctDNA-guided group, a positive result at 4 and/or 7 weeks after surgery prompted 4 months of oxaliplatin-based or …
Authors
Jeanne Tie,Joshua D Cohen,Yuxuan Wang,Pablo Gonzalez Ginestet,Rachel Wong,Jeremy David Shapiro,Rob Campbell,Fiona Day,Theresa M Hayes,Morteza Aghmesheh,Christos Stelios Karapetis,Maria Popoli,Lisa Dobbyn,Janine Ptak,Natalie Silliman,Christopher B Douville,Nickolas Papadopoulos,Kenneth W Kinzler,Bert Vogelstein,Peter Gibbs
Published Date
2024/1/20
Safe sequencing system
The identification of mutations that are present in a small fraction of DNA templates is essential for progress in several areas of biomedical research. Though massively parallel sequencing instruments are in principle well-suited to this task, the error rates in such instruments are generally too high to allow confident identification of rare variants. We here describe an approach that can substantially increase the sensitivity of massively parallel sequencing instruments for this purpose. One example of this approach, called “Safe-SeqS” for (Safe-Sequencing System) includes (i) assignment of a unique identifier (UID) to each template molecule;(ii) amplification of each uniquely tagged template molecule to create UID-families; and (iii) redundant sequencing of the amplification products. PCR fragments with the same UID are truly mutant (“super-mutants”) if≥ 95% of them contain the identical mutation. We illustrate the …
Published Date
2023/10/3
Methods and materials for treating t cell cancers
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Published Date
2024/1/4
Methods of detecting high risk barrett's esophagus with dysplasia, and esophageal adenocarcinoma
AOJJSUZBOXZQNB-VTZDEGQISA-N 4'-epidoxorubicin Chemical compound O ([C@ H] 1C [C@@](O)(CC= 2C (O)= C3C (= O) C= 4C= CC= C (C= 4C (= O) C3= C (O) C= 21) OC) C (= O) CO)[C@ H] 1C [C@ H](N)[C@@ H](O)[C@ H](C) O1 AOJJSUZBOXZQNB-VTZDEGQISA-N 0.000 claims description 6GAGWJHPBXLXJQN-UORFTKCHSA-N Capecitabine Chemical compound C1= C (F) C (NC (= O) OCCCCC)= NC (= O) N1 [C@ H] 1 [C@ H](O)[C@ H](O)[C@@ H](C) O1 GAGWJHPBXLXJQN-UORFTKCHSA-N 0.000 claims description 6
Published Date
2024/2/29
Circulating mutant dna to assess tumor dynamics
DNA containing somatic mutations is highly tumor specific and thus, in theory, can provide optimum markers. However, the number of circulating mutant gene fragments is small compared to the number of normal circulating DNA fragments, making it difficult to detect and quantify them with the sensitivity required for meaningful clinical use. We apply a highly sensitive approach to quantify circulating tumor DNA (ctDNA) in body samples of patients. Measurements of ctDNA can be used to reliably monitor tumor dynamics in subjects with cancer, especially those who are undergoing surgery or chemotherapy. This personalized genetic approach can be generally applied.
Published Date
2024/1/4
Signal
A method for classifying data using non-negative matrix factorization can include receiving a population of sample data, generating a first matrix of the amplicon counts per sample data, dividing the first matrix into a product of a second matrix and a third matrix, in the second matrix, determining whether each signature is a long or short fragment per each amplicon count, in the third matrix, determining intensities of each signature per the sample data, and classifying the sample data based on the intensities of each signature. The population can include amplicon counts per sample data. The second matrix can include signatures of short and long DNA fragments and the third matrix can include intensities of each signature of the short and long DNA fragments.
Published Date
2024/2/8
Professor FAQs
What is Kenneth Kinzler's h-index at Johns Hopkins University?
The h-index of Kenneth Kinzler has been 136 since 2020 and 248 in total.
What are Kenneth Kinzler's top articles?
The articles with the titles of
TRBC1-targeting antibody–drug conjugates for the treatment of T cell cancers
Machine learning to detect the SINEs of cancer
Circulating tumor DNA analysis informing adjuvant chemotherapy in locally advanced rectal cancer: The randomized AGITG DYNAMIC-Rectal study.
Safe sequencing system
Methods and materials for treating t cell cancers
Methods of detecting high risk barrett's esophagus with dysplasia, and esophageal adenocarcinoma
Circulating mutant dna to assess tumor dynamics
Signal
...
are the top articles of Kenneth Kinzler at Johns Hopkins University.
What are Kenneth Kinzler's research interests?
The research interests of Kenneth Kinzler are: Cancer, Genetics, Genomics
What is Kenneth Kinzler's total number of citations?
Kenneth Kinzler has 348,487 citations in total.