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Characterization of hypoxia-associated molecular features to aid hypoxia-targeted therapy


Tumour hypoxia is a major contributor to resistance to anticancer therapies. Given that the results of hypoxia-targeted therapy trials have been disappointing, a more personalized approach may be needed. Here, we characterize multi-omic molecular features associated with tumour hypoxia and identify molecular alterations that correlate with both drug-resistant and drug-sensitive responses to anticancer drugs. Based on a well-established hypoxia gene expression signature, we classify about 10,000 tumour samples into hypoxia score-high and score-low groups across different cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Then, we identify various types of molecular features associated with hypoxia status that correlate with drug resistance but, in some cases, also with drug sensitivity, contrasting the conventional view that hypoxia confers drug resistance. We further show that 110 out of 121 (90.9%) clinically actionable genes can be affected by hypoxia status and experimentally validate the predicted effects of hypoxia on the response to several drugs in cultured cells. Our study provides a comprehensive molecular-level understanding of tumour hypoxia and may have practical implications for clinical cancer therapy.

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Codes were implemented in R and have been deposited in GitHub:

Data availability

All data supporting the findings of the current study are listed in Supplementary Tables 13 and Supplementary Data 13.


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This work was supported by the Cancer Prevention & Research Institute of Texas (grant no. RR150085 to L.H., grant no. RP140462 to H.L., grant nos. RP150094 and RP180259 to C.L. and grant no. R1218 to L.Y.); the National Institutes of Health (grant nos. CA168394, CA098258 and CA143883 to G.B.M., grant no. CA175486 to H.L., grant no. CA209851 to H.L. and G.B.M., grant no. R00DK094981, 1R01CA218025 and 1R01CA231011 to C.L., grant no. R00CA166527 and 1R01CA218036 to L.Y. and grant no. R01 HL137990 and 1R01HL136969 to Y.X.). Department of Defense Breakthrough Awards were granted to C.L. and L.Y. (award no. BC180196 to C.L. and award no. BC151465 to L.Y.). The American Association for Cancer Research–Bayer Innovation and Discovery Grant (no. 18-80-44) and Andrew Sabin Family Foundation Fellows Award were awarded to L.Y., J.G. was awarded an MD Anderson Physician Scientist Award, a Khalifa Physician Scientist Award, an Andrew Sabin Family Foundation Fellows Award, an MD Anderson Faculty Scholar Award and a Doris Duke Charitable Foundation Career Development Award (award no. 2018097). The National Natural Science Foundation of China supported S.Z. with grant nos. 81822034 and 81773119. We gratefully acknowledge contributions from the TCGA Research Network. We thank L.-A. Chastain for editorial assistance.

Author information

L.H. conceived and supervised the project. Y.Ye and L.H. designed and performed the research. Y.Ye, H.C., Y.Yuan, Y.Xiang, H.R., Z.Z., A.S., H.Z., L.L. and L.D. performed the data analysis. Y.Ye, Q.H. K.L., C.L., L.Y. and L.H. performed the drug tests. Y.Ye, Y.L., B.Z., S.Z., J.G., E.J., S.H.L., L.W., Y.Xia, L.Y., C.L., G.B.M., H.L. and L.H. interpreted the results. Y.Ye, Q.H., G.B.M., H.L. and L.H. wrote the manuscript with input from all other authors.

Correspondence to Liuqing Yang or Gordon B. Mills or Han Liang or Leng Han.

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Competing interests

G.B.M. has sponsored research support from AstraZeneca, Critical Outcomes Technologies, Karus Therapeutics, Illumina, Immunomet, NanoString, Tarveda Therapeutics and Immunomet. He is on the Scientific Advisory Boards of AstraZeneca, Critical Outcomes Technologies, Immunomet, Ionis Pharmaceuticals, Nuevolution, Symphogen and Tarveda Therapeutics. H.L. is a shareholder and scientific advisor of Precision Scientific and Eagle Nebula. J.G. serves as a consultant for ARMO Biosciences, AstraZeneca, Jounce Therapeutics, Nektar and Pfizer.

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Supplementary information

Supplementary Information

Supplementary Figures 1–10 and Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Hypoxia-associated features across 21 cancer types

Supplementary Data 2

Spearman correlation of hypoxia-associated genes and drug sensitivity of drugs in GDSC

Supplementary Data 3

Spearman correlation of hypoxia score and imputed drug response across cancer types

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Further reading

Fig. 1: Validation of a 15-gene expression signature for hypoxia status.
Fig. 2: Classification of hypoxia status across different cancer types.
Fig. 3: Overview of the propensity score algorithm and the hypoxia-associated molecular patterns across cancer types.
Fig. 4: Hypoxia-associated miRNA and protein signatures.
Fig. 5: Effects of multidimensional hypoxia-associated signatures on drug response.
Fig. 6: Hypoxia-associated somatic mutation and SCNA signatures.
Fig. 7: Hypoxia-associated molecular signatures in clinically actionable genes and effects on the response to individual drugs.