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Cited by 293

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy

Open access
Kato, Shumei, Kim, Ki Hwan, Lim, Hyo Jeong, Boichard, Amelie, Nikanjam, Mina, Weihe, Elizabeth, Kuo, Dennis J., Eskander, Ramez N., Goodman, Aaron, Galanina, Natalie, Fanta, Paul T., Schwab, Richard B., Shatsky, Rebecca, Plaxe, Steven C., Sharabi, Andrew, Stites, Edward, Adashek, Jacob J., Okamura, Ryosuke, Lee, Suzanna, Lippman, Scott M., Sicklick, Jason K., Kurzrock, Razelle·2020·Nature Communications
DOI 10.1038/s41467-020-18613-3
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Abstract

Next-generation sequencing (NGS) can identify novel cancer targets. However, interpreting the molecular findings and accessing drugs/clinical trials is challenging. Furthermore, many tumors show resistance to monotherapies. To implement a precision strategy, we initiated a multidisciplinary (basic/translational/clinical investigators, bioinformaticians, geneticists, and physicians from multiple specialties) molecular tumor board (MTB), which included a project manager to facilitate obtaining clinical-grade biomarkers (blood/tissue NGS, specific immunohistochemistry/RNA expression including for immune-biomarkers, per physician discretion) and medication-acquisition specialists/clinical trial coordinators/navigators to assist with medication access. The MTB comprehensively reviewed patient characteristics to develop N-of-One treatments implemented by the treating physician’s direction under the auspices of a master protocol. Overall, 265/429 therapy-evaluable patients (62%) were matched to ≥1 recommended drug. Eighty-six patients (20%) matched to all drugs recommended by MTB, including combinatorial approaches, while 38% received physician’s choice regimen, generally with unmatched approach/low degree of matching. Our results show that patients who receive MTB-recommended regimens (versus physician choice) have significantly longer progression-free (PFS) and overall survival (OS), and are better matched to therapy. High (≥50%) versus low (<50%) Matching Score therapy (roughly reflecting therapy matched to ≥50% versus <50% of alterations) independently correlates with longer PFS (hazard ratio [HR], 0.63; 95% confidence interval [CI], 0.50–0.80;P < 0.001) and OS (HR, 0.67; 95% CI, 0.50–0.90;P = 0.007) and higher stable disease ≥6 months/partial/complete remission rate (52.1% versus 30.4%P < 0.001) (all multivariate). In conclusion, patients who receive MTB-based therapy are better matched to their genomic alterations, and the degree of matching is an independent predictor of improved oncologic outcomes including survival.

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Metadata

Authors
Kato, S., Kim, K. H., Lim, H. J., Boichard, A., Nikanjam, M., Weihe, E., Kuo, D. J., Eskander, R. N., Goodman, A., Galanina, N., Fanta, P. T., Schwab, R. B., Shatsky, R., Plaxe, S. C., Sharabi, A., Stites, E., Adashek, J. J., Okamura, R., Lee, S., Lippman, S. M., Sicklick, J. K., & Kurzrock, R.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-10-02
Volume
11
Issue
1
Pages
4965-4965
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-18613-3
OpenAlex
W3089562193
Cited by
293
References
37
FWCI
12.80
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. https://app.cochat.ai/papers/10.1038/s41467-020-18613-3
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Kato, S., Kim, K. H., Lim, H. J., Boichard, A., Nikanjam, M., Weihe, E., Kuo, D. J., Eskander, R. N., Goodman, A., Galanina, N., Fanta, P. T., Schwab, R. B., Shatsky, R., Plaxe, S. C., Sharabi, A., Stites, E., Adashek, J. J., Okamura, R., Lee, S., . . . Kurzrock, R. (2020). Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nature Communications. https://doi.org/10.1038/s41467-020-18613-3

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.