Research ArticleCancer

Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state

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Science Translational Medicine  17 Apr 2019:
Vol. 11, Issue 488, eaav0936
DOI: 10.1126/scitranslmed.aav0936
  • Fig. 1 PDX models of treatment-naïve TNBC exhibit diverse responses to AC.

    One or two cycles of AC were administered to PDX models of treatment-naïve TNBC starting on day 0 (arrows). All PDXs were derived from primary tumors with the exception of PIM001-M, which was derived from the dermal metastasis to the chest wall of the same patient from whom PIM001-P (primary tumor) was derived. Data are means ± SEM (n = 3 per group).

  • Fig. 2 Residual and regrown tumors cannot be eliminated by continued chemotherapy treatment.

    (A) To model the schedule of AC treatments administered to patients, we administered AC to mice in regular 21-day intervals (arrows). To enable prolonged dosing without toxic side effects, we used NRG mice for these long-term treatment studies. The horizontal dotted line denotes 100% of the starting tumor volume (measured on day 0). Data are means ± SEM (n = 4 per group). (B) NOD/SCID mice were treated with AC on day 0 and were only redosed with AC when tumors regrew to the starting tumor size (arrows). Data are means ± SEM. (C) NRG mice bearing PIM001-P tumors were treated with a total of five cycles of AC, and each subsequent dose was administered only when tumors regrew to the starting tumor size (arrows). Data are means ± SEM.

  • Fig. 3 Residual tumors adopt a distinct histologic state that is reverted in regrown tumors.

    (A) Replicate formalin-fixed paraffin-embedded (FFPE) tumor samples were assembled into tissue microarrays (triplicate 1-mm punches) and stained with hematoxylin and eosin (H&E). (B) FFPE primary tumor samples obtained from a TNBC patient (ART-57) before, during, and after completion of NACT were stained with H&E and imaged. An image of her metastatic relapse to the chest wall is shown in the bottom panel. Chemotherapy effects on fibrosis and tumor cell morphology are shown with arrows. PaCT, panitumumab + carboplatin + paclitaxel (Taxol); RCB, residual cancer burden assessed by examination of the surgical biopsy; No tx., no treatment was administered between surgery and metastatic relapse. Volumetric reduction after AC treatment was assessed by ultrasound. Scale bars, 200 μm.

  • Fig. 4 Shifts in the transcriptome of residual tumors are reversible.

    Vehicle (blue; day 0), residual (green; AC-treated day 21), and regrown (purple; AC-treated day 50) tumors were subjected to RNA-seq. (A) RNA-seq data were analyzed by principal components analysis, and the first two principal components (PC1 and PC2) are plotted for each PDX model. Principal components were calculated using log2-transformed transcripts per million (TPM) values for the 500 genes with the highest variance between samples, considering only genes with at least 20 reads in at least one sample. The mean was set as zero. (B) Within each PDX model, genes significantly altered (log2FC ≥ 0.5, false discovery rate (FDR) < 0.05, Benjamini-Hochberg test, sum of TPMs across all samples ≥ 100) in any pairwise comparison [vehicle (veh.) versus regrown (regr.), residual (res.) versus vehicle, and residual versus regrown] are displayed in a heat map organized by hierarchical clustering. The color scale refers to TPMs. (C) Genes significantly differentially expressed, as defined in (B), in residual tumors compared to vehicle-treated tumors were compared between three PDX models. The list includes significantly altered process networks (GeneGo Metacore) regulated by the 54 genes significantly differentially expressed in residual tumors compared to vehicle-treated tumors across all three PDX models. NADPH, reduced form of nicotinamide adenine dinucleotide phosphate; ROS, reactive oxygen species.

  • Fig. 5 Residual tumors maintain the clonal architecture and genomic complexity of pretreatment tumors.

    (A) Lentiviral barcodes were introduced into freshly dissociated tumor cells from three PDX models and then, after brief ex vivo culture, engrafted into the MFPs of NOD/SCID mice. DNA extracted from tumors was subjected to high-throughput barcode sequencing. (B) Density plots show the overall distribution of the top 95% most frequent barcodes in each sample. CPM, counts per million. (C) The top 95% most abundant barcodes were quantified in each sample, thus excluding barcodes detected at extremely low frequencies (two-tailed t tests comparing residual to regrown). Data are means ± SEM. (D) Line plots of estimated cellular prevalence of mutation clusters in PIM001-P as modeled by PyClone analysis of whole-exome sequencing (WES) data. Each line represents a mutation cluster, and the thickness of the line is proportional to the number of mutations within that cluster. The number of mutations comprising each cluster is shown in parentheses.

  • Fig. 6 Subclone analysis of serially biopsied human TNBCs reveals lack of subclone enrichment after AC.

    (A) Serial biopsies from two TNBC patients were analyzed by WES. The tumors’ volumetric changes in response to four cycles of AC treatment are indicated. PDT, atezolizumab + Abraxane. PaCT, panitumumab + carboplatin + paclitaxel (Taxol). (B) Line plots of estimated cellular prevalence of mutation clusters modeled by PyClone. Each line represents a mutation cluster, and the thickness of the line is proportional to the number of mutations within the cluster. The number of mutations comprising each cluster is shown in parentheses. (C) These plots display the prevalence of subclones throughout treatment. Subclonal architecture was reconstructed on the basis of PyClone results.

  • Fig. 7 The residual tumor state is targetable by inhibition of oxidative phosphorylation.

    (A) NOD/SCID mice bearing PIM001-P tumors were treated with an inhibitor of oxidative phosphorylation (IACS-010759, orally, once daily) or vehicle in the treatment-naïve or in the residual setting after AC treatment (“>” in the figure indicates sequential treatments). Days of IACS-010759 treatment are indicated by brackets. Days of AC treatment are indicated by arrows. ***P < 0.001 (day 21), ****P < 0.0001 (day 61), analysis of variance (ANOVA). Data are means ± SEM (n = 4 to 6 per group). The right panel is a Km curve of the time for each mouse’s tumor to reach 200% of the starting tumor volume (measured on day 0), and the log-rank P value is shown. Testing for interaction of treatment effects using a hazards model (data file S9) shows synergy in the AC + IACS-010759 sequential combination (****P < 0.0001). (B) As above, mice bearing PIM001-M tumors were treated with the indicated agents. ****P < 0.0001 (days 31 and 66), ANOVA. Data are means ± SEM (n = 3 to 9 per group). Testing for interaction of treatment effects using a hazards model (data file S9) shows synergy in the AC + IACS-010759 sequential combination (****P < 0.0001). (C) As above, mice bearing PIM005 tumors were treated with the indicated agents. ****P < 0.0001 (days 21 and 48), ANOVA. Data are means ± SEM (n = 4 to 8 per group).

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/11/488/eaav0936/DC1

    Materials and Methods

    Fig. S1. Establishing a tolerated NACT regimen for administration to immunocompromised mice.

    Fig. S2. Histologic characterization of the desmoplastic response in residual tumors.

    Fig. S3. Maintenance of cycling cell subpopulations in residual tumors.

    Fig. S4. Depletion of mouse cells from freshly dissociated PDX tumors.

    Fig. S5. No enrichment for cancer stem–like cell properties in residual tumors.

    Fig. S6. Assessment of EMT status of the residual tumor state.

    Fig. S7. Histologic features in pre- and post-AC–treated TNBC patient biopsies.

    Fig. S8. RNA sequencing of three PDX models throughout chemotherapy treatment.

    Fig. S9. GSEA of residual tumor signatures across three PDX models.

    Fig. S10. Mining available gene expression data from post-NACT residual breast tumors from patients.

    Fig. S11. Reversible shifts in the proteome of residual tumors.

    Fig. S12. Barcoding to monitor clonal dynamics during AC treatment in PDXs.

    Fig. S13. WES to monitor genomic evolution during AC treatment in PIM001-P.

    Fig. S14. Modeling of genomic subclonal architecture in PIM001-P.

    Fig. S15. Genomic analysis of serially biopsied human TNBCs.

    Fig. S16. Assessment of drug target engagement of PIM001-P tumors treated with the oxidative phosphorylation inhibitor.

    Data file S1. PDX characteristics.

    Data file S2. RNA-seq data from PDXs.

    Data file S3. RPPA data from PDXs.

    Data file S4. WES sample summary.

    Data file S5. PDX tumor mutation data.

    Data file S6. PDX tumor copy number data.

    Data file S7. Patient tumor mutation data.

    Data file S8. Patient tumor copy number data.

    Data file S9. IACS-010759 synergy calculations.

    Data file S10. Prediction of altered epigenetic regulator activity in residual tumors.

    Data file S11. Individual data points.

    References (5056)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Establishing a tolerated NACT regimen for administration to immunocompromised mice.
    • Fig. S2. Histologic characterization of the desmoplastic response in residual tumors.
    • Fig. S3. Maintenance of cycling cell subpopulations in residual tumors.
    • Fig. S4. Depletion of mouse cells from freshly dissociated PDX tumors.
    • Fig. S5. No enrichment for cancer stem–like cell properties in residual tumors.
    • Fig. S6. Assessment of EMT status of the residual tumor state.
    • Fig. S7. Histologic features in pre- and post-AC–treated TNBC patient biopsies.
    • Fig. S8. RNA sequencing of three PDX models throughout chemotherapy treatment.
    • Fig. S9. GSEA of residual tumor signatures across three PDX models.
    • Fig. S10. Mining available gene expression data from post-NACT residual breast tumors from patients.
    • Fig. S11. Reversible shifts in the proteome of residual tumors.
    • Fig. S12. Barcoding to monitor clonal dynamics during AC treatment in PDXs.
    • Fig. S13. WES to monitor genomic evolution during AC treatment in PIM001-P.
    • Fig. S14. Modeling of genomic subclonal architecture in PIM001-P.
    • Fig. S15. Genomic analysis of serially biopsied human TNBCs.
    • Fig. S16. Assessment of drug target engagement of PIM001-P tumors treated with the oxidative phosphorylation inhibitor.
    • Legends for data files S1 to S11
    • References (5056)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). PDX characteristics.
    • Data file S2 (Microsoft Excel format). RNA-seq data from PDXs.
    • Data file S3 (Microsoft Excel format). RPPA data from PDXs.
    • Data file S4 (Microsoft Excel format). WES sample summary.
    • Data file S5 (Microsoft Excel format). PDX tumor mutation data.
    • Data file S6 (Microsoft Excel format). PDX tumor copy number data.
    • Data file S7 (Microsoft Excel format). Patient tumor mutation data.
    • Data file S8 (Microsoft Excel format). Patient tumor copy number data.
    • Data file S9 (Microsoft Excel format). IACS-010759 synergy calculations.
    • Data file S10 (Microsoft Excel format). Prediction of altered epigenetic regulator activity in residual tumors.
    • Data file S11 (Microsoft Excel format). Individual data points.

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