High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response

Journal name:
Nature Medicine
Year published:
Published online


Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ~1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

At a glance


  1. The Novartis Institutes for Biomedical Research patient-derived tumor xenograft encyclopedia (NIBR PDXE).
    Figure 1: The Novartis Institutes for Biomedical Research patient-derived tumor xenograft encyclopedia (NIBR PDXE).

    (a) Distribution of cancer types in the PDXE by lineage (n = 1,075). (b) Similarity of PDXs between passages and lineages using Affymetrix mRNA expression data (MAS5 normalized). x axis, Pearson correlation coefficient (bar, median; box, first and third quartile; whiskers, data within 1.5*IQR of lower or upper quartile; circles: data outside whisker range). y axis, passage distance (defined in Supplementary Fig. 1); numbers in parentheses, number of PDX pairs in each passage distance. (c) Somatic mutation frequencies in PDXs. Points, individual PDX models; parenthesis, number of models per indication. Tumor types are ordered by median somatic mutation frequency, and colored by chromosomal instability (CIN) score. Lower panel, relative proportions of the six different possible base-pair substitutions. SS, soft tissue sarcoma; PDAC, pancreatic ductal carcinoma; EC, esophageal cancer; OVC, ovarian carcinoma; RCC, renal cell carcinoma; BRCA, breast carcinoma; CRC, colorectal cancer; NSCLC, non-small cell lung carcinoma; CM, cutaneous melanoma. (d) Genomic landscape analysis of melanoma across PDXE, TCGA and CCLE data sets. Parenthesis, number of models per indication; blue, homozygous deletions; salmon, amplification >5 copies; red, amplification > 8 copies; light green, known COSMIC (Catalog of Somatic Mutations in Cancer) gain-of-function mutations; dark green, truncating mutations/frameshift or known COSMIC loss-of-function; mustard, novel mutation; purple, pathway altered in at least one gene; gene names colored black or gray to indicate inclusion in same-colored pathway listed above; percentages indicate percentage of samples altered for the given gene or pathway.

  2. Systematic approach for in vivo compound profiling using PDXs (PCT), and its reproducibility.
    Figure 2: Systematic approach for in vivo compound profiling using PDXs (PCT), and its reproducibility.

    (a) Feasibility assessment of 1 × 1 × 1 PCT approach by Pearson correlation analysis. x axis, number of majority response from each response category; y axis, fraction of individual animal response relative to the majority (average ± s.e.m.). A total of 2,138 single-animal response data were collected and categorized from 440 unique treatment models (Online Methods). CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease. (b) Summary of compound sensitivity in the PCTs. The BestAvgResponse was used to make response calls (Online Methods), and each square represents a PDX. A total of 62 treatment groups were tested in 277 PDXs across six indications (BRCA (breast cancer, n = 43), CM (cutaneous melanoma, n = 33), CRC (colorectal carcinoma, n = 59), GC (gastric cancer, n = 64), NSCLC (non-small cell lung carcinoma, n = 36) and PDAC (pancreatic ductal adenocarcinoma, n = 42)). Arrow (CRright arrowPD, PRright arrowPD, SDright arrowPD, and CR>PD, PR>PD, SD>PD) indicates disease progression; > indicates progression seen after 64 d; > pindicates progression in <64 d. (c) Waterfall plot of responses to the PI3K inhibitors CLR457 (n = 205) and BKM120 (n = 213) across all indications; each bar represents an individual PDX. (d) Kaplan-Meier progression-free survival curve of PDXs treated with CLR457 (n = 205) and BKM120 (n = 213) across all indications.

  3. PCT predicts targeted therapy response and validates predictive gene signature.
    Figure 3: PCT predicts targeted therapy response and validates predictive gene signature.

    (a,b) Waterfall plot of response to the BRAF inhibitor encorafenib (n = 33) (a) and encorafenib in combination with the MEK inhibitor binimetinib (n = 33) (b) among melanoma PDXs. GOF, gain of function; WT, wild type. (c) Waterfall plot of response to DR5 agonist TAS266 among DR5 signature–positive and DR5 signature–negative melanoma PDXs (n = 33). (d) Kaplan-Meier PFS curve with TAS266 treatment in melanoma, stratified by DR5 predictive signature (n = 33). (e) Waterfall plot of response to PI3Kα inhibitor BYL719 among PIK3CA- and PTEN- mutated PDXs across five indications (n = 205). LOF, loss of function; blue, PIK3CA GOF/PTEN WT (n = 21); green, PIK3CA GOF/PTEN LOF (n = 6); brown, PIK3CA WT/PTEN LOF (n = 9); gray, PIK3CA WT/PTEN WT (n = 169).

  4. Combination therapies increase the overall response rate and progression-free survival.
    Figure 4: Combination therapies increase the overall response rate and progression-free survival.

    (a) Box plot of anti-tumor activity of single agents (36) and combinations (26) across six indications (n = 277) (BRCA, CM, CRC, GC, NSCLC and PDAC) in PCTs. Middle bar, median; box, first and third quartile; whiskers, data within 1.5 = IQR of lower or upper quartile. (b) Kaplan-Meier PFS curve of the single agents (36) and combinations (26) across six indications in the PCTs (n = 277). The y axis is the percentage of animals on study as calculated by tumor doubling (when a tumor volume has doubled compared to its baseline tumor volume), after which a model is considered to have progressed on treatment. (c) Waterfall plot of response to LEE011 among melanoma PDXs (n = 33). (d) Waterfall plot of response to the encorafenib-LEE011 combination among melanoma PDXs (n = 33). (e) Kaplan-Meier PFS curve of encorafenib and LEE011 single agents and encorafenib-LEE011 combination among BRAF-mutated melanoma PDXs (n = 15).

  5. IGF1R inhibitor does not potentiate anti-tumor activities of targeted therapy in vivo.
    Figure 5: IGF1R inhibitor does not potentiate anti-tumor activities of targeted therapy in vivo.

    (a) Waterfall plot of response to 500 combinations among 45 CRC cell lines. A hit is defined as a combination with a synergy score above 2 and a maximum growth inhibition above 0.7 in individual cell line. (b) Score plots for the combination of LFW527-binimetinib. Circle, hit; square, miss. Symbol size translates the increase of effect between the combination and either agent. (c) Waterfall plot of response to binimetinib among CRC PDXs (n = 43). Binimetinib was dosed at 10 mg/kg twice daily. (d) Waterfall plot of response to LFW527-binimetinib combination among CRC PDXs (n = 33). LFW527 was dosed at 12.5 mg/kg daily, and binimetinib was dosed at 10 mg/kg twice daily in the combination. (e) Kaplan-Meier PFS curve of binimetinib single-agent (n = 43) and LFW527-binimetinib combination (n = 33) among CRC PDXs.


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Author information

  1. Present addresses: Patronus Therapeutics, Inc., San Francisco, California, USA (M.S.); Janssen China R&D and Scientific Affairs, Shanghai, China (C.Z.); BioMarin Pharmaceutical, Inc., Novato, California, USA (N.K.P.); and Duke University, Durham, North Carolina, USA (A.R.).

    • Mallika Singh,
    • Chao Zhang,
    • Anupama Reddy &
    • Nancy K Pryer
  2. These authors contributed equally to this work.

    • Hui Gao,
    • Joshua M Korn &
    • Stéphane Ferretti
  3. These authors jointly directed this work.

    • Juliet A Williams &
    • William R Sellers


  1. Oncology Disease Area, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.

    • Hui Gao,
    • Joshua M Korn,
    • Guizhi Yang,
    • O Alejandro Balbin,
    • Hongbo Cai,
    • Derek Y Chiang,
    • Shawn M Cogan,
    • Scott D Collins,
    • John Green,
    • Colleen Kowal,
    • Rebecca J Leary,
    • Alice Loo,
    • E Robert McDonald III,
    • Jason Merkin,
    • Angad P Singh,
    • Roberto Velazquez,
    • Kavitha Venkatesan,
    • Hans Bitter,
    • Nicholas Keen,
    • Juliet A Williams &
    • William R Sellers
  2. Oncology Disease Area, Novartis Institutes for Biomedical Research, Basel, Switzerland.

    • Stéphane Ferretti,
    • Christian Schnell,
    • Stéphanie Barbe,
    • Ernesta Dammassa,
    • Nicolas Ebel,
    • Audrey Kauffmann,
    • Claudia Röelli,
    • Francesca Santacroce,
    • Walter Tinetto,
    • Sonja Tobler,
    • Fabian Von Arx,
    • Marion Wiesmann,
    • Daniel Wyss,
    • Francesco Hofmann,
    • Robert Cozens &
    • Michael Rugaard Jensen
  3. Department of Oncology Translational Medicine, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.

    • John E Monahan,
    • Joseph Lehar,
    • Margaret E McLaughlin,
    • Ronald Meyer,
    • Tara L Naylor,
    • Anupama Reddy,
    • David A Ruddy &
    • Hui Qin Wang
  4. China Novartis Institutes for Biomedical Research, Shanghai, China.

    • Youzhen Wang,
    • Chao Zhang,
    • Yun Zhang,
    • Shannon Chuai,
    • Ying Liang,
    • Zongyao Wang,
    • Fiona Xu,
    • Peter Atadja &
    • En Li
  5. Oncology Disease Area, Novartis Institutes for Biomedical Research, Emeryville, California, USA.

    • Mallika Singh,
    • Fergal Casey,
    • Susmita Chatterjee,
    • Millicent Embry,
    • Edward Lorenzana,
    • Montesa Patawaran,
    • Fernando Salangsang,
    • Yan Tang,
    • Emma Lees &
    • Nancy K Pryer


H.G., S.F., Y.W., M.S., C.Z., C.S., G.Y., S.B., H.C., S. Chatterjee, S.M.C., S.D.C., N.E., M.E., C.K., E. Lorenzana, M.P., C.R., F. Salangsang, F. Santacroce, Y.T., W.T., S.T., R.V., F.V.A., Z.W., D.W. and F.X. performed the PCT trials; H.G., G.Y., Y.Z., S.M.C., J.G., C.K., A.L., R.V., Z.W. and F.X. performed PDX model development; E.D., Y.L., M.E.M. and R.M. performed histopathologic analysis; J.M.K. and E.R.M. led the genomic landscape analysis; J.M.K., F.C., S. Chuai, A.K., J.M., J.L., A.R. and K.V. performed computational biology and bioinformatics analysis; O.A.B., D.Y.C., R.J.L. and A.P.S. performed pan-cancer panel analysis for melanoma resistance; J.E.M., J.G., T.L.N. and D.A.R. performed or directed nuclear acid extraction, quality control and genomic data generation; H.Q.W. performed the PK analysis of the encorafenib and LEE011 combination; M.W. led the in vitro combination screens; H.G., J.M.K., J.M., A.R., O.A.B., D.Y.C. prepared figures and tables for the main text and supplementary information; H.G., J.M.K., J.E.M., S.F., M.S., C.S., O.A.B., A.P.S., D.Y.C., M.W., H.B., J.A.W. and W.R.S. wrote and edited the main text and supplementary information; P.A., R.C., M.R.J., N.K.P., J.A.W., E. Li, E. Lees, F.H., N.K. and W.R.S. contributed to project oversight and advisory roles; J.A.W. and W.R.S. provided overall project leadership.

Competing financial interests

This research was funded by Novartis, Inc. and all authors were employees thereof at the time the study was performed. The authors declare no other competing financial interests.

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Supplementary information

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  1. Supplementary Text and Figures (1,486 KB)

    Supplementary Figures 1–13

Excel files

  1. Supplementary Table 1 (122 KB)

    Genomic profiling of PDXs and raw response and curve metrics of PCTs.

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