Evidence map›Paper›PMID 30706044›Full record

ArticleJCO precision oncology2018

Precision Medicine for Relapsed Multiple Myeloma on the Basis of an Integrative Multiomics Approach.

Alessandro Laganà, Itai Beno, David Melnekoff, Violetta Leshchenko, Deepu Madduri, Dennis Ramdas, Larysa Sanchez, Scot Niglio, Deepak Perumal, Brian A Kidd and 8 more

Abstract read
In one paragraph

Article in JCO precision oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
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

20 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022
    Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
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  11. Review
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  13. Article
  14. Article
  15. Review
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  17. Article
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  20. Review
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

18 authors.

Alessandro Laganà *, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Itai Beno *, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
David Melnekoff, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Violetta Leshchenko, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Deepu Madduri, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Dennis Ramdas, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Larysa Sanchez, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Scot Niglio, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Deepak Perumal, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Brian A Kidd, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Riccardo Miotto, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Rita Shaknovich, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Ajai Chari, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Hearn Jay Cho, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Bart Barlogie, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Sundar Jagannath, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Joel T Dudley *, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.
Samir Parekh *, and , Icahn School of Medicine at Mount Sinai, New York, NY; and , Cancer Genetics, Rutherford, NJ.

Funding

THE TISCH CANCER INSTITUTE - CANCER CENTER SUPPORT GRANTP30CA196521 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Deanna L Benson · 2015 to 2026
$35.4M
Development of a multi-omic clinical decision platform to guide personalized therapyR01CA244899 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PAREKH, SAMIR · 2020 to 2024
$2.8M
Transforming Genomics with 5 PB Big Omics Data Engine Cray CS300-AC SupercomputerS10OD018522 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2014 to 2014
$1.9M
A network-based approach for personalized treatment of Multiple MyelomaR21CA209875 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PAREKH, SAMIR · 2017 to 2018
$406k
NCI NIH HHS P30 CA196521NCI NIH HHS R01 CA244899NCI NIH HHS R21 CA209875NIH HHS S10 OD018522
6 · The paper itself

Abstract

purposeMultiple myeloma (MM) is a malignancy of plasma cells, with a median survival of 6 years. Despite recent therapeutic advancements, relapse remains mostly inevitable, and the disease is fatal in the majority of patients. A major challenge in the treatment of patients with relapsed MM is the timely identification of treatment options in a personalized manner. Current approaches in precision oncology aim at matching specific DNA mutations to drugs, but incorporation of genome-wide RNA profiles has not yet been clinically assessed.

methodsWe have developed a novel computational platform for precision medicine of relapsed and/or refractory MM on the basis of DNA and RNA sequencing. Our approach expands on the traditional DNA-based approaches by integrating somatic mutations and copy number alterations with RNA-based drug repurposing and pathway analysis. We tested our approach in a pilot precision medicine clinical trial with 64 patients with relapsed and/or refractory MM.

resultsWe generated treatment recommendations in 63 of 64 patients. Twenty-six patients had treatment implemented, and 21 were assessable. Of these, 11 received a drug that was based on RNA findings, eight received a drug that was based on DNA, and two received a drug that was based on both RNA and DNA. Sixteen of the 21 evaluable patients had a clinical response (ie, reduction of disease marker ≥ 25%), giving a clinical benefit rate of 76% and an overall response rate of 66%, with five patients having ongoing responses at the end of the trial. The median duration of response was 131 days.

conclusionOur results show that a comprehensive sequencing approach can identify viable options in patients with relapsed and/or refractory myeloma, and they represent proof of principle of how RNA sequencing can contribute beyond DNA mutation analysis to the development of a reliable drug recommendation tool.

Identifiers

PMID30706044
PMCPMC6350920

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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.