Cited by 2,770Cancer Cells and MetastasisDrug Transport and Resistance MechanismsCancer Genomics and Diagnostics

A view on drug resistance in cancer

Open access
Vasan, Neil, Baselga, José, Hyman, David M.·2019·Nature

Abstract

The problem of resistance to therapy in cancer is multifaceted. Here we take a reductionist approach to define and separate the key determinants of drug resistance, which include tumour burden and growth kinetics; tumour heterogeneity; physical barriers; the immune system and the microenvironment; undruggable cancer drivers; and the many consequences of applying therapeutic pressures. We propose four general solutions to drug resistance that are based on earlier detection of tumours permitting cancer interception; adaptive monitoring during therapy; the addition of novel drugs and improved pharmacological principles that result in deeper responses; and the identification of cancer cell dependencies by high-throughput synthetic lethality screens, integration of clinico-genomic data and computational modelling. These different approaches could eventually be synthesized for each tumour at any decision point and used to inform the choice of therapy.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41586-019-1730-1

Cites

184 of 184 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Low dependency on K-Ras signaling could confer intrinsic resistance to K-RasG12C inhibitors.

    “Mechanistically, low dependency on K-Ras signaling could confer intrinsic resistance to K-RasG12C inhibitors.12,58,61”

    From Overcoming Resistance to Drugs Targeting KRAS Mutation (2020) · cites this paper as [61]

  • In the clinic, intrinsic and acquired resistance may co-exist and intertwine in the same patient treated with K-RasG12C-targeted therapies.

    “In the clinic, intrinsic and acquired resistance may co-exist and intertwine in the same patient treated with K-RasG12C-targeted therapies.57,61”

    From Overcoming Resistance to Drugs Targeting KRAS Mutation (2020) · cites this paper as [61]

  • Complete and durable responses to targeted cancer therapies are rare in patients with advanced-stage cancers.

    “Complete and durable responses to targeted cancer therapies are rare in patients with advanced-stage cancers.61”

    From Overcoming Resistance to Drugs Targeting KRAS Mutation (2020) · cites this paper as [61]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

4 of the 188 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. [19]Goldie JH & Coldman AJ A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat. Rep 63, 1727– 1733 (1979). 526911

Cited by

Sorted by citations

Authors
Vasan, N., Baselga, J., & Hyman, D. M.
Affiliations
Memorial Sloan Kettering Cancer Center (US)
Corresponding author
Hyman, David M.
Venue
Nature
Published
2019-11-13
Volume
575
Issue
7782
Pages
299-309
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
OpenAlex
W2988773550
Cited by
2,770
References
188
Citations by year
2026: 344 · 2025: 533 · 2024: 524 · 2023: 467 · 2022: 397 · 2021: 346 · 2020: 150 · 2019: 4
FWCI
21.44
Type
journal article
Language
en
Open access
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Eli Lilly and Company
  • National Cancer Institute (R01CA207244, R01CA190642, P30CA008748, T32CA009207)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
  • Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
chemoresistance, tumor heterogeneity, tumor microenvironment, tumor immune evasion, tumor growth kinetics, tumor burden, early cancer detection, adaptive monitoring, synthetic lethality screen, precision oncology
MeSH terms
Antineoplastic Agents, Humans, Models, Biological, Neoplasms, Drug Resistance, Neoplasm, Tumor Microenvironment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review