The addition of HER2-targeted agents to neoadjuvant chemotherapy has dramatically improved pathological complete response (pCR) rates in early-stage, HER2-positive breast cancer. Nonetheless, up to 50% of patients have residual disease after treatment, while others are likely overtreated. Here, we performed multiplex spatial proteomic characterization of 122 samples from 57 HER2-positive breast tumors from the neoadjuvant TRIO-US B07 clinical trial sampled pre-treatment, after 14–21 d of HER2-targeted therapy and at surgery. We demonstrated that proteomic changes after a single cycle of HER2-targeted therapy aids the identification of tumors that ultimately undergo pCR, outperforming pre-treatment measures or transcriptomic changes. We further developed and validated a classifier that robustly predicted pCR using a single marker, CD45, measured on treatment, and showed that CD45-positive cell counts measured via conventional immunohistochemistry perform comparably. These results demonstrate robust biomarkers that can be used to enable the stratification of sensitive tumors early during neoadjuvant HER2-targeted therapy, with implications for tailoring subsequent therapy.
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DSP protein counts, bulk RNA expression data, perimetric complexity data, CD45 IHC data and the clinical covariate data generated for this study are available through the Curtis Lab GitHub repository (https://github.com/cancersysbio/BreastCancerSpatialProteomics). Source data for Figs. 1–6 and Extended Data Figs. 1–10 are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.
Code associated with the manuscript is available at https://github.com/cancersysbio/BreastCancerSpatialProteomics. R v.3.6.0 was used with base/standard packages and the following additional packages: vioplot_0.3.2; lmerTest_3.1-0; dplyr_0.8.3; ggrepel_0.8.1; tidyr_0.8.3; reshape_0.8.8; ggplot2_3.2.1; limma_3.28.21. Python v.3.6.8 was used with base/standard packages and the following additional packages: pandas_0.25.1; numpy_1.17.2; matplotlib_3.1.1; seaborn_0.9.0; statsmodels_0.10.1; scipy_1.3.1; pystan_22.214.171.124; arviz_0.6.1; joblib_0.13.2; scikit-learn_0.21.3.
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We thank NanoString for technical support. We thank members of the Curtis lab, especially Z. Hu, J. A. Seoane, A. Khan and J. Brugge for input on the HER2 and Ki67 IHC. This project was supported by awards to C.C. from the National Institutes of Health (NIH)/National Cancer Institute (NCI) (no. R01CA182514) and the Breast Cancer Research Foundation. C.C. is a Susan G. Komen Scholar. K.L.M. is supported by grant no. F30CA239313-02 from the NIH/NCI. J.L.C. was a Damon Runyon Physician-Scientist supported by the Damon Runyon Cancer Research Foundation (no. PST 11-17) and a Susan G. Komen Postdoctoral Fellowship Award (no. PDR17481769). M.F.P. was supported by the Breast Cancer Research Foundation and Tower Cancer Research Foundation and grant no. R01CA182514. D.J.S. and S.S. were also supported in part by grant no. R01CA182514. The multiplex IHC staining performed at the Fred Hutchinson Cancer Research Center experimental histopathology shared resources was supported by the Fred Hutchinson/University of Washington Cancer Consortium (no. P30 CA015704).
M.F.P. received research funding from Cepheid, Eli Lilly and Company, Zymeworks and Novartis; performed consulting/advisory board work for Biocartis, Eli Lilly and Company, Zymeworks, Novartis, Puma Biotechnology, Merck Millipore, AstraZeneca, CME Outfitters and Clinical Care Options; and has private equity in TORL Biotherapeutics. D.J.S. received research funding from Pfizer, Novartis, Syndax, Millenium Pharmaceuticals, Aileron Therapeutics, Bayer and Genentech; owned stock in BioMarin, Amgen, Seattle Genetics and Pfizer; served on the board of directors for BioMarin; and performed consulting/advisory board work for Eli Lilly and Company, Novartis, Bayer and Pfizer. S.A.H. received contracted research and medical writing assistance from Ambrx, Amgen, Arvinas, Bayer, Daiichi Sankyo, Genentech/Roche, GlaxoSmithKline, Immunomedics, Eli Lilly and Company, Macrogenics, Novartis, Pfizer, OBI Pharma, Pieris, Puma Biotechnology, Radius Health, Sanofi, Seattle Genetics and Dignitana. R.J. is currently employed by Tempus. M.K., Z.Z., M.H. and J.B. are all employees of NanoString and declare that they have competing interests. C.C. performed advisory board/consulting work for Genentech, GRAIL, Illumina and NanoString and reports GRAIL stockholdings. C.C. and K.L.M. are coinventors on a patent application filed by Stanford University relating to this manuscript. J.L.C., Z.M., E.K., G.R.B. and J.Z. declare no conflicts of interest.
Peer review information Nature Cancer thanks Jennifer Giltnane and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a. Summary of the clinical characteristics of the TRIO-US B07 DSP discovery cohort, including treatment arm, pathologic complete response (pCR), estrogen receptor (ER) status, and PAM50 status inferred based on pre-treatment bulk expression data. Two-way contingency tables compare the distribution of ER status, pCR status, and treatment arm. b. Pathology-estimated cellularity pre-treatment and on-treatment for the discovery cohort. Samples with green shading indicate those used for subsequent analysis. For the pCR column, 0=non-pCR, 1=pCR. For ER status column, 0=ER-negative, 1=ER-positive. c. An example region from case 30 sampled on-treatment. While cellularity was estimated to be 0 based on pathology review of a distinct tissue section, tumor regions were identified upon imaging the tissue section used in this analysis. d,e. CD45 values and CD56 values from the Digital Spatial Profiling (DSP) protein data on-treatment (d, n = 24 tumors with on-treatment data) and pre-treatment (e, n = 27 tumors with pre-treatment data) in the pCR cases versus the non-pCR cases. Each point represents the average probe values for all panCK-enriched ROIs for that case On-treatment. The p-value was derived using a linear mixed-effect model (two-sided test) over the multi-region data with blocking by patient. Adjustments were not made for multiple comparisons. For each violin plot, the white box represents the interquartile range and the black lines extending from the white box represent 1.5X the interquartile range. Analyses based on the discovery cohort. f. Volcano plot demonstrating treatment-associated changes from pre-treatment to surgery in tumors that did not undergo pCR using DSP protein expression levels in pancytokeratin-enriched (PanCK-E) regions from n = 28 tumors. Significance, -log10(FDR adjusted p-value), is indicated along the y-axis. The p-value is determined based on a linear mixed-effects model (two sided test) with blocking by patient and is derived from the t-value (a measure of the size of the difference relative to the variation in the sample data).
Extended Data Fig. 2 Digital Spatial Profiling (DSP) is used for multiplex protein quantification within tumor regions.
a Overview of NanoString DSP workflow adapted with permission from NanoString, Inc. b. Location of spatially separated ROIs within tissue specimens for a representative pCR (69) and non-pCR case (58). An average of 4 ROIs were profiled per tissue (range: 1–7). c. Correlation plot comparing Ki67 percent positive (evaluated using IHC) with normalized DSP Ki67 expression (averaged across ROIs) across n = 42 tumor biopsies (24 pre-treatment, 18 on-treatment). Pearson correlation coefficient and p-value are noted. Grey shading indicates 95% confidence interval (CI) of the correlation coefficient. d. Boxplot comparing normalized DSP Her2 expression (averaged across all ROIs in a given sample) between cases that exhibited strong (3+) IHC Her2 staining (using a distinct tissue slice from the same case and timepoint) or weaker (0–2) IHC Her2 staining. A total of n = 44 biologically independent tumor biopsies (23 pre-treatment, 21 on-treatment) with paired Her2 IHC and DSP data were utilized in this analysis. A two-sided Wilcoxon test was used to assess significance. For each boxplot, the center is the median, the bounds of the box indicate the 25th and 75th percentile, the bounds of the whiskers extend to the most extreme data points that are no more than 1.5x the interquartile range from the bounds of the box; all individual data points, including maxima and minima are overlaid. e. Correlation plot comparing normalized on-treatment CD45 DSP protein values with on-treatment stromal tumor infiltrating lymphocyte (TILs) score for all cases (n = 31) with both data types available. Grey shading indicates the 95% CI of the correlation coefficient. P value is assessed via a one-sided t-test (correlation coefficient not equal to 0). f. Markers with a signal to noise ratio (SNR) < 3 (Methods) in the discovery cohort indicated by a caret (^) and those with SNR < 3 in the validation cohort indicated with an asterisk (*).
a. Volcano plot demonstrating treatment-associated changes based on comparison of pre-treatment versus on-treatment bulk RNA expression levels. RNA transcripts with corresponding Digital Spatial Profiling (DSP) protein markers were used in this analysis. Significance, -log10(FDR adjusted p-value), is indicated along the y-axis. Analyses based on the discovery cohort (n = 28 tumors). b. Pairing of protein antibodies and gene names used in comparative analyses between DSP and bulk expression data. Genes listed here were used to generate the bulk expression volcano plot shown in panel a. For direct comparisons yielding the correlation plots shown in panel c, the marker names indicated by the asterisk were used; specifically, for proteins with multiple form (for example AKT/pAKT, CD45/CD45RO), the unmodified form of the protein was used when available and the breast-cancer associated keratin gene with the highest mean expression level was used. c. Spearman correlation between DSP protein probes (averaged across all ROIs per case) and bulk RNA transcripts corresponding to these markers pre-treatment. Significantly correlated probes (with p-value < 0.05, correlation coefficient not equal to 0, assessed via a one-sided t-test) are indicated by an asterisk. Two exemplary correlation plots are shown, where each dot represents a single case. The grey shading indicates the 95% confidence interval of the correlation coefficient. Analyses based on the discovery cohort cases with paired DSP and bulk RNA data (n = 23 tumors).
Extended Data Fig. 4 Treatment-associated changes and model performance assessed in cases treated with trastuzumab.
a. Volcano plot demonstrating treatment-associated changes based on comparison of pre-treatment versus on-treatment protein marker expression levels in pancytokeratin-enriched (PanCK-E) regions in the trastuzumab-treated cases (arms 1 and 3, n = 23 biologically independent tumors). Significance, -log10(FDR adjusted p-value), is indicated along the y-axis. Analyses based on the discovery cohort. b. Receiver operating characteristic (ROC) curves for On- plus Pre-treatment DSP protein L2-regularized classifier in the discovery cohort using the subset of cases with data available at both timepoints that were treated with trastuzumab or trastuzumab+lapatinib (n = 19 biologically independent tumors). Model performance was assessed via cross-validation using the 40 DSP protein markers profiled in both cohorts c. Correlation plot comparing the marker coefficients for the On- plus Pre-treatment DSP protein trained using all cases in the discovery cohort (n = 23 tumors with both timepoints versus using only those cases treated with trastuzumab (arms 1 and 3, n = 19 tumors with both timepoints). The grey shading indicates the 95% confidence interval of the correlation coefficient. P value is assessed via a one-sided t-test (correlation coefficient not equal to 0). d. Coefficients for each marker in the L2-regularized On- plus Pre-treatment DSP protein model, trained using only those cases treated with trastuzumab (arms 1 and 3, n = 19 tumors with both timepoints). e. OC curves for classifiers trained using either DSP on-treatment CD45 levels or IHC CD45 % positive. These models were evaluated on the subset of cases on which CD45 IHC was performed that were treated with trastuzumab or trastuzumab+lapatinib (n = 20 biologically independent tumors).
Extended Data Fig. 5 Waterfall plots illustrating treatment-associated changes in various data subsets.
Waterfall plots show changes (pre-treatment to on-treatment) based on in pancytokeratin-enriched (PanCK-E) regions from DSP protein expression data. Analyses based on the discovery cohort (n = 28 tumors). a. Input data was stratified both by estrogen receptor (ER) status and pathologic complete response (pCR) outcome. b. Samples were stratified both by PAM50 status (Her2-Enriched or other) and pCR. c. Waterfall plots illustrating treatment-associated changes (pre-treatment to on-treatment) in the Normal-like cases in the discovery cohort. PAM50 status was determined using bulk expression data from the pre-treatment biopsies. d. Waterfall plots when only one region is used to profile each sample (averaged across 100 iterations of random samples of a single region per timepoint), rather than the 2–7 regions from each sample used in other analyses. The leftmost plot is for all patients, and the plots on the right are stratified by pCR status.
Analyses based on the discovery cohort (n = 28 tumors). Heterogeneity was calculated as the mean squared error within patients based on analysis of variance. P-values are based on a two-sided Wilcoxon matched-pair signed rank test. a. Comparison of DSP CD45 protein levels pre-treatment and on-treatment for all regions profiled per case per timepoint. Also shown is a comparison of the mean squared error in DSP CD45 protein expression pre-treatment versus on-treatment within and between patients. b. Pre-, on-, and post-treatment heterogeneity for each DSP protein marker in non-pCR cases (patients with tumor cells present at surgery). c, Pre-treatment heterogeneity in DSP protein marker expression in pCR and non-pCR cases.
a. Digital Spatial Profiling (DSP) was performed on multiple regions of interest (ROIs) per tissue sample. Protein counts were measured within phenotypic regions corresponding to the PanCK-enriched (tumor-enriched) masks that include tumor cells and co-localized immune cells and separately for the inverted mask corresponding to panCK-negative (tumor microenvironment, TME) regions. b–d. Waterfall plots, generated using the DSP protein data, comparing immune marker expression between the panCK-enriched regions and the surrounding panCK-negative regions. Analyses based on the discovery cohort (n = 28 tumors). b. A comparison of all data pre-treatment on-treatment, and post-treatment. c. A comparison of pre-treatment and on-treatment timepoints, in pCR (n = 14) and non-pCR cases (n = 14). Pre-treatment, the correlation between immune marker fold-change values in the pCR and non-pCR cases was 0.98 indicating similar immune distribution across the panCK-enriched regions and surrounding microenvironment regardless of pCR outcome and this correlation remained high on-treatment (0.95). d. A comparison of pre-treatment and on-treatment timepoints, in ER-positive cases (n = 14), and ER-negative cases (n = 14).
a. Multiplex immunohistochemistry (mIHC) images showing the distribution of HER2, CD45, and CD8 signal in representative tissue stamps pre-treatment and on-treatment. The panCK mIHC channel (not shown) was used to generate the panCK mask and the tissue mask (outlined in yellow). Due to the limiting nature of the biopsy specimens which derive from independent donors, we did not perform technical replicates of the mIHC pilot experiments. b. IHC marker expression levels for HER2, CD45, and CD8 were quantified for the whole tissue section (across all digitized sub-images) and within the panCK-enriched tumor regions (across all digitized sub-images). c. Illustration of panCK-enriched binary masks and perimetric complexity-based quantification of the tumor-microenvironment border. d. Comparison of perimetric complexity values pre-treatment between pCR cases and non-pCR cases. The p-value was derived using a linear mixed-effect model (two-sided test) over the multi-region data with blocking by patient (n = 28 patients). Adjustments were not made for multiple comparisons. For each violin plot, the white box represents the interquartile range and the black lines extending from the white box represent 1.5X the interquartile range. e. Comparison of pre-treatment versus on-treatment perimetric complexity values. PanCK-enriched ROIs were used to quantify perimetric complexity. P-values computed with a linear model, blocked by patient (n = 28 patients). Adjustments were not made for multiple comparisons. For each violin plot, the white box represents the interquartile range and the black lines extending from the white box represent 1.5X the interquartile range. f. Spearman correlation between the DSP protein expression values and perimetric complexity per region of interest (ROI) in the pre-treatment and on-treatment tissue specimens from the discovery cohort. Significantly correlated probes: p-value < .05 are denoted by an asterisk. P value is assessed via a one-sided t-test (correlation coefficient not equal to 0). Correlation plot for Ki-67, the marker with the highest correlation with perimetric complexity, where each dot represents an individual ROI. The grey shading indicates the 95% confidence interval of the correlation coefficient. Analyses are based on the discovery cohort (n = 28 patients).
Extended Data Fig. 9 Comparison of alternative models including non-DSP measures and model validation cohort.
a. AUROC (Area Under the Receiver Operating Characteristics) performance (using nested cross-validation with Holm-Bonferroni correction for multiple hypotheses) comparing DSP protein on- plus pre-treatment L2-regularized classifiers trained using marker means versus marker standard error of the mean (SEM) for tumor markers and immune markers (n = 23 patients with paired data across both timepoints). b–d. Receiver operating characteristic (ROC) curves and AUROC quantification for the On- plus Pre-treatment DSP protein L2-regularized classifier using all 40 markers compared to other models: (b) model trained using estrogen receptor (ER) status, PAM50 status, and HER2 IHC (3 + vs other) (n = 19 patient) and model incorporating these three measures as well as On- plus Pre-treatment DSP; (c) model trained using ER status, PAM50 status, and pre-treatment HER2 FISH ratio (n = 21 patients) and model incorporating these three measures as well as On- plus Pre-treatment DSP; (d) model trained using on-treatment stromal tumor infiltrating lymphocytes (TILs) (n = 16 patients) and model incorporating TILs as well as On- plus Pre-treatment DSP. Model comparisons were performed in the discovery cohort. e. Summary of the clinical characteristics for the TRIO-US B07 clinical trial Digital Spatial Profiling (DSP) validation cohort used for model testing. Treatment arm, pathologic complete response (pCR), ER status, and PAM50 status inferred based on pre-treatment bulk expression data are included. Two-way contingency tables compare the distribution of ER status, pCR status, and treatment arm. f. Volcano plot demonstrating treatment-associated changes based on comparison of pre-treatment versus on-treatment protein marker expression levels in pancytokeratin-enriched (PanCK-E) regions in the validation cohort (n = 29 patients). g. Volcano plots demonstrating treatment-associated changes in pCR versus non-pCR cases in the PanCK-E regions in the validation cohort (n = 29 patients). Significance, -log10(FDR adjusted p-value), is indicated along the y-axis. The p-value was derived using a linear mixed-effect model (two-sided test) over the multi-region data with blocking by patient.
Extended Data Fig. 10 On-treatment immunohistochemistry for CD45 predicts pathologic complete response.
a. Summary of the clinical characteristics of cohort used to evaluate IHC CD45 percent positive cells within tumor enriched regions as a biomarker. These features include pathologic complete response (pCR), estrogen receptor (ER) status, and PAM50 status. b. Correlation plot comparing the normalized on-treatment CD45 DSP protein values with the paired on-treatment CD45 % positive derived from IHC. The grey shading indicates the 95% confidence interval of the correlation coefficient. P value is assessed via a one-sided t-test (correlation coefficient not equal to 0) using n = 28 tumors with paired data. c. Boxplots showing DSP on-treatment CD45 levels (n = 53 biologically independent tumors), on-treatment stromal tumor infiltrating lymphocyte (TILs) score (n = 31 biologically independent tumors), and on-treatment tumor cellularity (n = 47 biologically independent tumors) stratified by pCR utilizing all available data where these features were measured across the discovery and validation cohorts. P-values were derived using the two-sided Wilcoxon rank-sum test. For each boxplot, the colored box represents the interquartile range and the black lines extending from the box represent 1.5X the interquartile range. d. For both the CD45 on-treatment IHC data (n = 28 patients) and the normalized CD45 on-treatment DSP data (n = 24 patients in the discovery cohort, n = 29 patients in the validation cohort), thresholds were defined to predict pCR vs non-pCR cases. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are shown for such thresholds.
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McNamara, K.L., Caswell-Jin, J.L., Joshi, R. et al. Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response. Nat Cancer 2, 400–413 (2021). https://doi.org/10.1038/s43018-021-00190-z
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