The immune microenvironment in hormone receptor-positive breast cancer before and after preoperative chemotherapy

Supplementary Material

Figure S1. REMARK diagram

Figure S2. NanoString Tumor Inflammation Signature by breast cancer intrinsic subtype

Figure S3. NanoString individual gene expression changes by cohort

Table S1. Pre-treatment immune microenvironment and response to preoperative therapy

Table S2. Tissue-based immune biomarkers in patients with pathologic complete response

Table S3. References for classification of genes as M1-like or M2-like

Registered on trial (n=104)

Excluded from efficacy population (n=5)  Did not receive study drug (n=1)  Withdrew consent (n=1)  Ineligible due to HER2+ (n=1)  No pathology to evaluate response (n=2)

Efficacy population (n=99)

Excluded for triple-negative status (n=21)

Hormone receptor-positive (n=78)

Excluded because tissue exhausted (n=20)  Unable to perform retrospective immune

biomarker analyses

Tissue available (n=58)

Tissue immune biomarker analyzed (n=55)

Excluded because assays failed (n=3)

Figure S1. REMARK diagram. Derivation of the analyzed cohort from the original cohort registered on trial DFCI #07-130.

Figure S2. NanoString Tumor Inflammation Signature by breast cancer intrinsic subtype. In the DFCI cohort, formalin-fixed paraffin-embedded sections from pre-treatment primary tumor underwent expression profiling via the NanoString PanCancer Immune Profiling Panel. A modified version of the Tumor Inflammation Signature (TIS; a gene expression profile denoting interferon gamma-related signaling) was calculated (N=33 patients) and compared across intrinsic subtypes.

Figure S3. NanoString individual gene expression changes by cohort. Changes in expression of individual genes from before to after neoadjuvant chemotherapy are shown in the Dana-Farber cohort (A) and the MD Anderson cohort (B). Genes were ranked by false discovery rate p-value (shown on the y-axis) and the top 10 most significant genes are labeled on each plot.

A.

B.

Table S1. Pre-treatment immune microenvironment and response to preoperative therapy

Biomarker (N=pts assessed) Value Patients Unadjusted RCB

correlation (rho; p-value)

Adjusted RCB correlation

(R2; p-value)(# (%))sTIL (N=50) 0-5% 19 (38%) -0.50; p<0.001 0.30; p=0.01

>5-10% 22 (44%)>10% 9 (18%)

tPD-L1 (N=51) <1% 40 (78%) -0.46; p<0.001 0.26; p=0.01>1-5% 6 (12%)>5-10% 3 (6%)>10% 2 (4%)

sPD-L1 (N=51) <1% 45 (88%) -0.56; p<0.001 0.35; p=0.001>1-5% 3 (6%)>5-10% 3 (6%)>10% 0 (0%)

CD8 (N=48) <200 cells/mm2 17% -0.46; p=0.001 0.30; p=0.02>200-800 52%>800 31%

CD68 (N=50) <200 cells/mm2 30% -0.43; p=0.002 0.24; p=0.02>200-800 60%>800 10%

Legend: Unadjusted correlations were calculated using Pearson correlation coefficient. Adjusted correlations (for tumor size and grade) were calculated using linear regression. Abbreviations: RCB, Residual Cancer Burden; sTIL,stromal tumor-infiltrating lymphocytes.

Table S2. Tissue-based immune biomarkers of patients with pathologic complete response

Patient TILs (%)

CD8 (quartile, Q)

CD68 (quartile, Q)

tPD-L1 (%)

sPD-L1 (%)

ER/PR expression Intrinsic

subtype

pCR(1) 10 Q4 Q1 1.9 0.3 <1%/5% BasalpCR(2) 1 Q4 Q4 12 5.8 Positive/rare Luminal BpCR(3) 30 Q4 Q4 60 6 <10%/0% BasalpCR(4) 20 Q4 Q4 4.9 9.3 10%/0% Basal

Abbreviations: ER, estrogen receptor; pCR, pathologic complete response; sPD-L1, PD-L1 expression on stromal cells; tPD-L1, PD-L1 expression on tumor cells; PR, progesterone receptor; TILs, tumor-infiltrating lymphocytes.

Table S3. References for classification of genes as M1-like or M2-like. Complete citations are included below the Table.

M1-like CXCL9 (Weagel E, 2015, Movahedi et al., 2010)

M1-like TNF (Beyer et al., 2012, Martinez et al., 2006)

M1-like NOTCH1 (Wang et al., 2010)

M1-like IL15 (Martinez et al., 2006)

M1-like TLR4 (Beyer et al., 2012)

M1-like CD40 (Liu et al., 2013)

M1-like FAS (Martinez et al., 2006)

M1-like NK-kB1 (Weagel E, 2015)

M2-like TGFB1 (Martinez et al., 2006, Movahedi et al., 2010)

M2-like CSF1R (Tarique et al., 2015)

M2-like STAT3 (Yan et al., 2016)

M2-like IL10 (Weagel E, 2015, Beyer et al., 2012)

M2-like CD163 (Movahedi et al., 2010)

M2-like CCL2 (Movahedi et al., 2010)

M2-like CD209 (Tarique et al., 2015)

M2-like PPARG (Stoger et al., 2012)

M2-like CCL13 (Martinez et al., 2006)

M2-like CXCR4 (Martinez et al., 2006)

M2-like MRC1 (Martinez et al., 2006, Movahedi et al., 2010)

M2-like CD36 (Martinez et al., 2006)

M2-like CCL14 (Martinez et al., 2006)

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LIU, C., LI, Y., YU, J., FENG, L., HOU, S., LIU, Y., GUO, M., XIE, Y., MENG, J., ZHANG, H., XIAO, B. & MA, C. 2013. Targeting the Shift from M1 to M2 Macrophages in Experimental Autoimmune Encephalomyelitis Mice Treated with Fasudil. PLOS ONE, 8, e54841.

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TARIQUE, A. A., LOGAN, J., THOMAS, E., HOLT, P. G., SLY, P. D. & FANTINO, E. 2015. Phenotypic, functional, and plasticity features of classical and alternatively activated human macrophages. Am J Respir Cell Mol Biol, 53, 676-88.

WANG, Y. C., HE, F., FENG, F., LIU, X. W., DONG, G. Y., QIN, H. Y., HU, X. B., ZHENG, M. H., LIANG, L., FENG, L., LIANG, Y. M. & HAN, H. 2010. Notch signaling determines the M1 versus M2 polarization of macrophages in antitumor immune responses. Cancer Res, 70, 4840-9.

WEAGEL E, S. C., LIU PG, ROBISON R, O’NEILL K 2015. Macrophage Polarization and Its Role in Cancer. Journal of Clinical & Cellular Immunology, 6.

YAN, D., WANG, H. W., BOWMAN, R. L. & JOYCE, J. A. 2016. STAT3 and STAT6 Signaling Pathways Synergize to Promote Cathepsin Secretion from Macrophages via IRE1alpha Activation. Cell Rep, 16, 2914-2927.