Evidence map›Paper›PMID 38167262›Full record

ArticleNature communications2024

Integrative genotyping of cancer and immune phenotypes by long-read sequencing.

Livius Penter, Mehdi Borji, Adi Nagler, Haoxiang Lyu, Wesley S Lu, Nicoletta Cieri, Katie Maurer, Giacomo Oliveira, Aziz M Al'Khafaji, Kiran V Garimella and 7 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
6.8field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 30 citations in OpenAlex.

  1. Review
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  6. HSCs/MPPs as cells of origin with altered differentiation hierarchy impairing immunomicroenvironment inProceedings of the National Academy of Sciences of the United States of America · 2026
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  18. Long and Accurate: How HiFi Sequencing is Transforming Genomics.Genomics, proteomics & bioinformatics · 2025
    Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 4 institutions in 2 countries.

Livius Penter *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0002-9060-0207
Mehdi Borji *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Adi Nagler *Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Haoxiang LyuTranslational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0003-0165-9934
Wesley S LuTranslational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA, USA.
Nicoletta CieriDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0003-1340-6272
Katie MaurerDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0002-4160-181X
Giacomo OliveiraDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0001-7435-5603
Aziz M Al'KhafajiBroad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.ORCID 0000-0002-4677-5667
Kiran V GarimellaBroad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
Shuqiang LiDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0001-9106-6141
Donna S NeubergDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0003-2566-3145
Jerome RitzDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0001-5526-4669
Robert J SoifferDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Jacqueline S GarciaDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Kenneth J LivakDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.ORCID 0000-0001-9105-5856
Catherine J WuDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. catherine_wu@dfci.harvard.edu.
Broad Institute · USDana-Farber Cancer Institute · USBrigham and Women's Hospital · USHarvard University · US

Funding

Treating the P13-Kinase/AKTP50CA101942 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI David McDermott · 2003 to 2026
$53.4M
TYROSINE KINASE ONCOGENESIS IN MYELOID LEUKEMIAP01CA066996 · NCI · DANA-FARBER CANCER INSTITUTE · PI SCOTT A ARMSTRONG · 1996 to 2026
$52.5M
XPO1 inhibitors Selinexor and Eltanexor in Combination with Venetoclax and Decitabine (ASTX727) in AMLUM1CA186709 · NCI · DANA-FARBER CANCER INST · PI KEITH T FLAHERTY, DONALD W. KUFE · 2014 to 2026
$25.9M
High-Dimensional Immune Monitoring of NCI-Supported Immunotherapy TrialsU24CA224319 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Sacha Gnjatic, Seunghee Kim-Schulze · 2017 to 2026
$21.5M
Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)U24CA224309 · NCI · STANFORD UNIVERSITY · PI Sean Curtis Bendall, Holden T. Maecker · 2017 to 2026
$19.9M
Cancer Immune Monitoring and Analysis CenterU24CA224331 · NCI · DANA-FARBER CANCER INST · PI FRANK S HODI, Catherine Ju-Ying Wu · 2017 to 2026
$18.4M
Translational Cancer Immune Monitoring and Analysis Center (TCIMAC)U24CA224285 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Gheath Al-Atrash, Cara L Haymaker · 2017 to 2026
$16.7M
Single Cell Analysis and ImmunogeneticsP01HL158505 · NHLBI · DANA-FARBER CANCER INST · PI Corey S Cutler · 2022 to 2026
$15.3M
Single Cell and ImmunogenomicsP01CA229092 · NCI · DANA-FARBER CANCER INST · PI KIM, HAESOOK T · 2019 to 2023
$13.8M
Personal tumor neoantigens for immunity against chronic lymphocytic leukemiaR01CA155010 · NCI · DANA-FARBER CANCER INST · PI Catherine Ju-Ying Wu · 2011 to 2026
$9.6M
Cancer Immunologic Data Commons (CIDC) Supplement RequestU24CA224316 · NCI · DANA-FARBER CANCER INST · PI CERAMI, ETHAN, MICHOR, FRANZISKA · 2017 to 2022
$6.8M
Single cell investigation of co-evolution in cancer cells and host cell immune microenvironmentR50CA251956 · NCI · DANA-FARBER CANCER INST · PI Shuqiang Li · 2020 to 2026
$1.4M
NCI NIH HHS K08 CA245209NCI NIH HHS P01 CA066996NCI NIH HHS P01 CA229092NCI NIH HHS P50 CA101942NCI NIH HHS R01 CA155010NCI NIH HHS R50 CA251956NCI NIH HHS U24 CA224285NCI NIH HHS U24 CA224309NCI NIH HHS U24 CA224316NCI NIH HHS U24 CA224319NCI NIH HHS U24 CA224331NCI NIH HHS UM1 CA186709NHLBI NIH HHS P01 HL158505
6 · The paper itself

Abstract

Single-cell transcriptomics has become the definitive method for classifying cell types and states, and can be augmented with genotype information to improve cell lineage identification. Due to constraints of short-read sequencing, current methods to detect natural genetic barcodes often require cumbersome primer panels and early commitment to targets. Here we devise a flexible long-read sequencing workflow and analysis pipeline, termed nanoranger, that starts from intermediate single-cell cDNA libraries to detect cell lineage-defining features, including single-nucleotide variants, fusion genes, isoforms, sequences of chimeric antigen and TCRs. Through systematic analysis of these classes of natural 'barcodes', we define the optimal targets for nanoranger, namely those loci close to the 5' end of highly expressed genes with transcript lengths shorter than 4 kB. As proof-of-concept, we apply nanoranger to longitudinal tracking of subclones of acute myeloid leukemia (AML) and describe the heterogeneous isoform landscape of thousands of marrow-infiltrating immune cells. We propose that enhanced cellular genotyping using nanoranger can improve the tracking of single-cell tumor and immune cell co-evolution.

Indexed as

High-Throughput Nucleotide SequencingLeukemia, Myeloid, AcuteGene Expression ProfilingGenotypeHumansPhenotype

Identifiers

PMID38167262
PMCPMC10762175
OpenAlexW4390500426

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.