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Purpose: Tumor microenvironment (TME) immune markers have been correlated with both response to neoadjuvant therapy and prognosis in patients with breast cancer. Here, immune-cell activity of breast cancer tumors was inferred by expression-based analysis to determine if it is prognostic and/or predictive of response to neoadjuvant paclitaxel-based therapy in the GeparSepto (G7) trial (NCT01583426). Experimental Design: Pre-study biopsies from 279 patients with HER2-negative breast cancer in the G7 trial underwent RNA-seq-based profiling of 104 immune-cell-specific genes to assess inferred Immune Cell Activity (iICA) of 23 immune-cell types. Hierarchical clustering was used to classify tumors as iICA “hot,” “warm,” or “cold” by comparison of iICA in the G7 cohort relative to that of 1,467 samples from a tumor database established by Nantomics LLC. Correlations between iICA cluster, pathology-assessed tumor-infiltrating lymphocytes (TIL), and hormone receptor (HR) status for pathologic complete response (pCR), disease-free survival (DFS), and overall survival (OS) were determined. Results: iICA cluster correlated with TIL levels. The highest pCR rates were observed in hot cluster tumors, and those with relatively higher TILs. Greater inferred activity of several T-cell types was significantly associated with pCR and survival. DFS and OS were prolonged in patients with hot or warm cluster tumors, the latter particularly for HR negative tumors, even if TILs were relatively low. Conclusions: Overall, TIL level better predicted pCR, but iICA cluster better predicted survival. Differences in associations between TILs, cluster, pCR, and survival were observed for HR-positive tumors versus HR-negative tumors, suggesting expanded study of the implication of these findings is warranted.

G7 trial design; demographics; tissue types; gene names; displayed data from additional analyses; statistical data

Purpose:Tumor microenvironment (TME) immune markers have been correlated with both response to neoadjuvant therapy and prognosis in patients with breast cancer. Here, immune-cell activity of breast cancer tumors was inferred by expression-based analysis to determine if it is prognostic and/or predictive of response to neoadjuvant paclitaxel-based therapy in the GeparSepto (G7) trial (NCT01583426).Experimental Design:Pre-study biopsies from 279 patients with HER2-negative breast cancer in the G7 trial underwent RNA-seq-based profiling of 104 immune-cell-specific genes to assess inferred Immune Cell Activity (iICA) of 23 immune-cell types. Hierarchical clustering was used to classify tumors as iICA “hot,” “warm,” or “cold” by comparison of iICA in the G7 cohort relative to that of 1,467 samples from a tumor database established by Nantomics LLC. Correlations between iICA cluster, pathology-assessed tumor-infiltrating lymphocytes (TIL), and hormone receptor (HR) status for pathologic complete response (pCR), disease-free survival (DFS), and overall survival (OS) were determined.Results:iICA cluster correlated with TIL levels. The highest pCR rates were observed in hot cluster tumors, and those with relatively higher TILs. Greater inferred activity of several T-cell types was significantly associated with pCR and survival. DFS and OS were prolonged in patients with hot or warm cluster tumors, the latter particularly for HR negative tumors, even if TILs were relatively low.Conclusions:Overall, TIL level better predicted pCR, but iICA cluster better predicted survival. Differences in associations between TILs, cluster, pCR, and survival were observed for HR-positive tumors versus HR-negative tumors, suggesting expanded study of the implication of these findings is warranted.

Purpose:Next-generation sequencing (NGS) can be used for comprehensive investigation of molecular events in breast cancer. We evaluated the relevance of genomic alterations for response to neoadjuvant chemotherapy (NACT) in the GeparSepto trial.Experimental Design:Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational (AKT1, BRAF, CDH1, EGFR, ERBB2, ESR1, FBXW7, FGFR2, HRAS, KRAS, NRAS, SF3B1, TP53, HNF1A, PIK3CA, and PTEN) and 8 genes for copy-number alteration analysis (CCND1, ERBB2, FGFR1, PAK1, PIK3CA, TOP2A, TP53, and ZNF703).Results:The most common genomic alterations were mutations of TP53 (38.4%) and PIK3CA (21.5%), and 8 different amplifications (TOP2A 34.9%; ERBB2 30.6%; ZNF703 30.1%; TP53 21.9%; PIK3CA 24.1%; CCND1 17.7%; PAK1 14.9%; FGFR 12.6%). All other alterations had a prevalence of less than 5%. The genetic heterogeneity in different breast cancer subtypes [lum/HER2neg vs. HER2pos vs. triple-negative breast cancer (TNBC)] was significantly linked to differences in NACT response. A significantly reduced pathologic complete response rate was observed in PIK3CA-mutated breast cancer [PIK3CAmut: 23.0% vs. wild-type (wt) 38.8%, P < 0.0001] in particular in the HER2pos subcohort [multivariate OR = 0.43 (95% CI, 0.24–0.79), P = 0.006]. An increased response to nab-paclitaxel was observed only in PIK3CAwt breast cancer, with univariate significance for the complete cohort (P = 0.009) and the TNBC (P = 0.013) and multivariate significance in the HER2pos subcohort (test for interaction P = 0.0074).Conclusions:High genetic heterogeneity was observed in different breast cancer subtypes. Our study shows that FFPE-based NGS can be used to identify markers of therapy resistance in clinical study cohorts. PIK3CA mutations could be a major mediator of therapy resistance in breast cancer.

Supplementary Figure from Predictive Markers of Response to Neoadjuvant Durvalumab with Nab-Paclitaxel and Dose-Dense Doxorubicin/Cyclophosphamide in Basal-Like Triple-Negative Breast Cancer

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Supplementary Figures

(A) Paired scatter plot demonstrating the correlation between the immune-associated gene expression and PD-L1. (B) PD-L1 immunohistochemistry and mRNA expression in pre-treatment core biopsies. The definition of >= 1 % of positively stained tumor cells is associated with CD274 (=PD-L1) mRNA expression (t-test P-value and area under the curve).

Supplementary materials and tables

Differentially expressed genes according to treatment response

8 Supplementary Figures and 2 Supplementary Tables

The presence of tumor-infiltrating lymphocytes (TIL) is a favorable prognostic factor in breast cancer, but what drives immune infiltration remains unknown. Here we examine if clonal heterogeneity, total mutation load, neoantigen load, copy number variations (CNV), gene- or pathway-level somatic mutations, or germline polymorphisms (SNP) are associated with immune metagene expression in breast cancer subtypes. Thirteen published immune metagenes correlated separately with genomic metrics in the three major breast cancer subtypes. We analyzed RNA-Seq, DNA copy number, mutation and germline SNP data of 627 ER+, 207 HER2+, and 191 triple-negative (TNBC) cancers from The Cancer Genome Atlas. P-values were adjusted for multiple comparisons, and permutation testing was used to assess false discovery rates. Increased immune metagene expression associated significantly with lower clonal heterogeneity estimated by MATH score in all subtypes and with a trend for lower overall mutation, neoantigen, and CNV loads in TNBC and HER2+ cancers. In ER+ cancers, mutation load, neoantigen load, and CNV load weakly but positively associated with immune infiltration, which reached significance for overall mutation load only. No highly recurrent single gene or pathway level mutations associated with immune infiltration. High immune gene expression and lower clonal heterogeneity in TNBC and HER2+ cancers suggest an immune pruning effect and equilibrium between immune surveillance and clonal expansion. Thus, immune checkpoint inhibitors may tip the balance in favor of immune surveillance in these cancers. Cancer Res; 77(12); 3317–24. ©2017 AACR.

Candidate gene list of biological pathways related to antigen presentation, cytokines, chemokines, angiogenesis related signaling, the MAPK pathway, cell adhesion, and epithelial to mesenchymal transition.

Supplemental Material and Methods

Supplementary Table 1. Microarray dataset accessions

Supplementary Figure 1. Effects of EGFR antagonists, FAK or PYK2/FAK inhibition/depletion on basal-like TNBC growth either alone or in combination.

Correlation of Affymetrix microarray expression data and IHC scoring of three genes in independent TNBC dataset

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Supplementary Figure S1. The expression distribution of each of the 13 immune metagenes is shown for the (A) ER positive, (B) TNBC and (C) HER2 positive cancers. Supplementary Figure S2. Correlation between the LCK metagene expression and infiltrating lymphocyte (TIL) count. Supplementary Figure S3. The distribution of genomic features and LCK metagene expression within breast cancer subtypes in the TCGA. Supplementary Figure S4. Correlations between 13 immune metagene expressions and 5 exome-wide genomic features including all breast cancers combined in TCGA. Supplementary Figure S5. Correlations between the LCK immune metagene expression and five different genomic features within each breast cancer subtype in the TCGA. Supplementary Figure S6. Distribution of genomic features and their correlation with an immune metagene in the breast cancer subtypes from TCGA data.

Table S1: Clinico-pathological features for samples in the PROGNOSTIC collection; Table S2: Clinico-pathological features for samples in the TAM collection; Table S3: Clinico-pathological features for samples in the CHEMO collection; Table S4: Output of immune cluster refinement in ER-HER2- samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S5: Output of immune cluster refinement in HER2+ samples. Five years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S6: Output of proliferation cluster refinement in 508 ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S7: Output of ER-related cluster refinement in 394 high proliferation ER+HER2- samples. Five-years DMFS for high, intermediate and low expression groups and log-rank test p-values (average and standard deviation of 100 10-fold cross-validations) are reported as a function of the number of genes; Table S8: Univariable Cox regression analysis for the CTM in the combined dataset or in each dataset for ER- or HER2+ cases or separately for ER-HER2- and HER2+ subgroups; Table S9: Univariable and multivariable Cox regression analysis in 205 ER-HER2- and HER2+ samples from the CHEMO collection (all biomarkers were used as continous variables); Table S10: Univariable Cox regression analysis for the combined proliferation and ER-related metagenes in ER+HER2- cases from the combined dataset or from each dataset

CCR Translation for This Article from A Comprehensive Analysis of Human Gene Expression Profiles Identifies Stromal Immunoglobulin κ C as a Compatible Prognostic Marker in Human Solid Tumors

Purpose:We evaluated mRNA signatures to predict response to neoadjuvant PD-L1 inhibition in combination with chemotherapy in early triple-negative breast cancer.Experimental Design:Targeted mRNA sequencing of 2,559 transcripts was performed in formalin-fixed, paraffin-embedded samples from 162 patients of the GeparNuevo trial. We focused on validation of four predefined gene signatures and differential gene expression analyses for new predictive markers.Results:Two signatures [GeparSixto signature (G6-Sig) and IFN signature (IFN-Sig)] were predictive for treatment response in a multivariate model including treatment arm [G6-Sig: OR, 1.558; 95% confidence interval (CI), 1.130–2.182; P = 0.008 and IFN-Sig: OR, 1.695; 95% CI, 1.234–2.376; P = 0.002), while the CYT metric predicted pathologic complete response (pCR) in the durvalumab arm, and the proliferation-associated gene signature in the placebo arm. Expression of PD-L1 mRNA was associated with better response in both arms, indicating that increased levels of PD-L1 are a general predictor of neoadjuvant therapy response. In an exploratory analysis, we identified seven genes that were higher expressed in responders in the durvalumab arm, but not the placebo arm: HLA-A, HLA-B, TAP1, GBP1, CXCL10, STAT1, and CD38. These genes were associated with cellular antigen processing and presentation and IFN signaling.Conclusions:Immune-associated signatures are associated with pCR after chemotherapy, but might be of limited use for the prediction of response to additional immune checkpoint blockade. Gene expressions related to antigen presentation and IFN signaling might be interesting candidates for further evaluation.

PDF file, 66K, Subpopulation Treatment Effect Pattern Plot (STEPP) analysis of the treatment effect of conventional versus response-guided neoadjuvant chemotherapy as measured by mean disease-free survival according to overlapping subpopulations defined by percentages of Ki67 at surgery in patients with hormone-receptor-positive (A) and hormone-receptor-negative (B) disease.

Purpose: The value of Ki67 measured on residual disease after neoadjuvant chemotherapy is not sufficiently described.Experimental Design: Participants of the GeparTrio study with primary breast cancer randomly received neoadjuvant response-guided [8 cycles TAC (docetaxel/doxorubicin/cyclophosphamide) in responding and TAC-NX (vinorelbine/capecitabine) in nonresponding patients] or conventional (6 cycles TAC) chemotherapy according to interim response assessment. Ki-67 levels were centrally measured immunohistochemically after neoadjuvant treatment if tumor tissue was available. Here, we analyze 1,151 patients having a pathologic complete response (pCR; n, 484), or residual disease with low (0–15%), intermediate (15.1–35%), or high (35.1–100%) posttreatment Ki67 levels in 488, 77, and 102 patients, respectively.Results: Patients with high posttreatment Ki67 levels showed higher risk for disease relapse (P < 0.0001) and death (P < 0.0001) compared with patients with low or intermediate Ki67 levels. Patients with low Ki67 levels showed a comparable outcome to patients with a pCR (P = 0.211 for disease-free and P = 0.779 for overall survival). Posttreatment Ki67 levels provided more prognostic information than pretreatment Ki67 levels or changes of Ki67 from pre- to posttreatment. Information on pCR plus posttreatment Ki67 levels surmount the prognostic information of pCR alone in hormone–receptor-positive disease [hazard ratios (HR), 1.82–5.88] but not in hormone–receptor-negative disease (HR: 0.61–1.73). Patients with conventional and response-guided treatment did not show a different distribution of posttreatment Ki67 (P = 0.965).Conclusions: Posttreatment Ki67 levels provide prognostic information for patients with hormone–receptor-positive breast cancer and residual disease after neoadjuvant chemotherapy. Levels were not prognostic for outcome after response-guided chemotherapy. High posttreatment Ki67 indicates the need for innovative postneoadjuvant treatments. Clin Cancer Res; 19(16); 4521–31. ©2013 AACR.

Purpose: Although the central role of the immune system for tumor prognosis is generally accepted, a single robust marker is not yet available.Experimental Design: On the basis of receiver operating characteristic analyses, robust markers were identified from a 60-gene B cell–derived metagene and analyzed in gene expression profiles of 1,810 breast cancer; 1,056 non–small cell lung carcinoma (NSCLC); 513 colorectal; and 426 ovarian cancer patients. Protein and RNA levels were examined in paraffin-embedded tissue of 330 breast cancer patients. The cell types were identified with immunohistochemical costaining and confocal fluorescence microscopy.Results: We identified immunoglobulin κ C (IGKC) which as a single marker is similarly predictive and prognostic as the entire B-cell metagene. IGKC was consistently associated with metastasis-free survival across different molecular subtypes in node-negative breast cancer (n = 965) and predicted response to anthracycline-based neoadjuvant chemotherapy (n = 845; P < 0.001). In addition, IGKC gene expression was prognostic in NSCLC and colorectal cancer. No association was observed in ovarian cancer. IGKC protein expression was significantly associated with survival in paraffin-embedded tissues of 330 breast cancer patients. Tumor-infiltrating plasma cells were identified as the source of IGKC expression.Conclusion: Our findings provide IGKC as a novel diagnostic marker for risk stratification in human cancer and support concepts to exploit the humoral immune response for anticancer therapy. It could be validated in several independent cohorts and carried out similarly well in RNA from fresh frozen as well as from paraffin tissue and on protein level by immunostaining. Clin Cancer Res; 18(9); 2695–703. ©2012 AACR.

Purpose: A common characteristic of mammary carcinomas is an inverse relationship between the estrogen receptor (ER) status and the proliferative activity of the tumor. Yet, a subset of ER-positive breast cancers is characterized by a high proliferation, suggesting malfunctions in ER responsiveness that influence the biological and therapeutic behavior of tumor cells. The expression of several ER-dependent genes seems to be dysregulated among those “uncoupled” tumors. One of those genes is plexin B1, a cell-surface receptor for the semaphorin Sema4D (CD 100). However, the biological role of plexin B1 in breast cancer is largely unknown.Experimental Design: Expression data of plexin B1 were obtained from Affymetrix microarray analysis of n = 119 breast cancer specimens. Validation was done by quantitative real-time PCR and protein expression was evaluated by immunohistochemistry. Expression data were compared with clinical characteristics as well as follow-up data of the disease.Results: Low plexin B1 expression levels characterize a more aggressive tumor phenotype. The expression of plexin B1 is strongly correlated with the ER status. However, even among ER-positive tumors, loss of plexin B1 is associated with an impaired prognosis of breast cancer patients in both univariate (all patients, P = 0.0062; ER positive, P = 0.0107) and multivariate analyses (all patients, P = 0.032; ER positive, P = 0.022). Immunohistochemistry reveals that the tumor cells themselves and not the endothelial cells are the major source of plexin B1 expression in the tumor.Conclusion: Plexin B1 acts not only as a new important prognostic but should also represent a predictive marker indicating an endocrine resistance. These data give a new insight in markers that could be involved in endocrine dysregulation of breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive, heterogeneous disease with poor prognosis and no effective targeted therapies. EGFR is highly expressed in basal-like TNBC and is considered as a potential therapeutic target. However, EGFR targeting exerts only marginal clinical benefits, possibly due to activation of compensatory signaling pathways, which are frequently associated with HER3 upregulation. Here we show that concomitant targeting of EGFR and the nonreceptor tyrosine kinases PYK2/FAK synergistically inhibits the proliferation of basal-like TNBC cells in vitro and attenuates tumor growth in a mouse xenograft model. Dual targeting of EGFR and PYK2/FAK inhibited complementary key growth and survival pathways mediated by AKT, S6K, STAT3, and ERK1/2 activation. PYK2 inhibition also abrogated HER3 upregulation in response to EGFR antagonists, thereby circumventing HER3-associated drug resistance. Mechanistically, PYK2 inhibition facilitated the proteasomal degradation of HER3 while inducing upregulation of NDRG1 (N-myc downstream regulated 1 gene). NDRG1 enhanced the interaction of HER3 with the ubiquitin ligase NEDD4, while PYK2, which interacts with NEDD4 and HER3, interfered with NEDD4–HER3 binding, suggesting that the PYK2–NDRG1–NEDD4 circuit has a critical role in receptor degradation, drug response, and resistance mechanism. Our studies offer a preclinical proof of concept for a strategy of cotargeting the EGFR and PYK2/FAK kinases to improve TNBC therapy. Cancer Res; 77(1); 86–99. ©2016 AACR.

Supplementary Data from Predictive Markers of Response to Neoadjuvant Durvalumab with Nab-Paclitaxel and Dose-Dense Doxorubicin/Cyclophosphamide in Basal-Like Triple-Negative Breast Cancer

Purpose:We examined gene expression, germline variant, and somatic mutation features associated with pathologic response to neoadjuvant durvalumab plus chemotherapy in basal-like triple-negative breast cancer (bTNBC).Experimental Design:Germline and somatic whole-exome DNA and RNA sequencing, programmed death ligand 1 (PD-L1) IHC, and stromal tumor-infiltrating lymphocyte scoring were performed on 57 patients. We validated our results using 162 patients from the GeparNuevo randomized trial.Results:Gene set enrichment analysis showed that pathways involved in immunity (adaptive, humoral, innate), JAK–STAT signaling, cancer drivers, cell cycle, apoptosis, and DNA repair were enriched in cases with pathologic complete response (pCR), whereas epithelial–mesenchymal transition, extracellular matrix, and TGFβ pathways were enriched in cases with residual disease (RD). Immune-rich bTNBC with RD was enriched in CCL-3, -4, -5, -8, -23, CXCL-1, -3, -6, -10, and IL1, -23, -27, -34, and had higher expression of macrophage markers compared with immune-rich cancers with pCR that were enriched in IFNγ, IL2, -12, -21, chemokines CXCL-9, -13, CXCR5, and activated T- and B-cell markers (GZMB, CD79A). In the validation cohort, an immune-rich five-gene signature showed higher expression in pCR cases in the durvalumab arm (P = 0.040) but not in the placebo arm (P = 0.923) or in immune-poor cancers. Independent of immune markers, tumor mutation burden was higher, and PI3K, DNA damage repair, MAPK, and WNT/β-catenin signaling pathways were enriched in germline and somatic mutations in cases with pCR.Conclusions:The TGFβ pathway is associated with immune-poor phenotype and RD in bTNBC. Among immune-rich bTNBC RD, macrophage/neutrophil chemoattractants dominate the cytokine milieu, and IFNγ and activated B cells and T cells dominate immune-rich cancers with pCR.

Legends for supplementary figures

Supplementary Figures S1-S8. Suppl. Fig. S1. Meta-analysis of CD73 and CD39 in ovarian cancer gene expression datasets. Suppl. Fig. S2. CD73 and CD8 staining in high grade serous ovarian cancer. Suppl. Fig. S3. Flow cytometry analysis of CD73 expression on 12 human ovarian cancer cell lines. Suppl. Fig. S4. Prognostic value of CD73 protein expression and intraepithelial CD8+ cell density. Suppl. Fig. S5. CD73 gene silencing in OV4485 and SKOV3 human ovarian cancer cells. Suppl. Fig. S6. CD39 and adenosine receptor expression in human ovarian cancer cells. Suppl. Fig. S7. CD73-expressing cancer-associated fibroblasts promote tumor growth in mice. Suppl. Fig. S8. Flow cytometry gating strategy and representative staining of ID8-OVA tumor cell suspension.

The cell surface nucleotidase CD73 is an immunosuppressive enzyme involved in tumor progression and metastasis. Although preclinical studies suggest that CD73 can be targeted for cancer treatment, the clinical impact of CD73 in ovarian cancer remains unclear. In this study, we investigated the prognostic value of CD73 in high-grade serous (HGS) ovarian cancer using gene and protein expression analyses. Our results demonstrate that high levels of CD73 are significantly associated with shorter disease-free survival and overall survival in patients with HGS ovarian cancer. Furthermore, high levels of CD73 expression in ovarian tumor cells abolished the good prognosis associated with intraepithelial CD8+ cells. Notably, CD73 gene expression was highest in the C1/stromal molecular subtype of HGS ovarian cancer and positively correlated with an epithelial-to-mesenchymal transition gene signature. Moreover, in vitro studies revealed that CD73 and extracellular adenosine enhance ovarian tumor cell growth as well as expression of antiapoptotic BCL-2 family members. Finally, in vivo coinjection of ID8 mouse ovarian tumor cells with mouse embryonic fibroblasts showed that CD73 expression in fibroblasts promotes tumor immune escape and thereby tumor growth. In conclusion, our study highlights a role for CD73 as a prognostic marker of patient survival and also as a candidate therapeutic target in HGS ovarian cancers. Cancer Res; 75(21); 4494–503. ©2015 AACR.

Purpose: We examined gene expression, germline variant, and somatic mutation features associated with pathologic response to neoadjuvant durvalumab plus chemotherapy in basal-like triple-negative breast cancer (bTNBC). Experimental Design: Germline and somatic whole-exome DNA and RNA sequencing, programmed death ligand 1 (PD-L1) IHC, and stromal tumor-infiltrating lymphocyte scoring were performed on 57 patients. We validated our results using 162 patients from the GeparNuevo randomized trial. Results: Gene set enrichment analysis showed that pathways involved in immunity (adaptive, humoral, innate), JAK–STAT signaling, cancer drivers, cell cycle, apoptosis, and DNA repair were enriched in cases with pathologic complete response (pCR), whereas epithelial–mesenchymal transition, extracellular matrix, and TGFβ pathways were enriched in cases with residual disease (RD). Immune-rich bTNBC with RD was enriched in CCL-3, -4, -5, -8, -23, CXCL-1, -3, -6, -10, and IL1, -23, -27, -34, and had higher expression of macrophage markers compared with immune-rich cancers with pCR that were enriched in IFNγ, IL2, -12, -21, chemokines CXCL-9, -13, CXCR5, and activated T- and B-cell markers (GZMB, CD79A). In the validation cohort, an immune-rich five-gene signature showed higher expression in pCR cases in the durvalumab arm (P = 0.040) but not in the placebo arm (P = 0.923) or in immune-poor cancers. Independent of immune markers, tumor mutation burden was higher, and PI3K, DNA damage repair, MAPK, and WNT/β-catenin signaling pathways were enriched in germline and somatic mutations in cases with pCR. Conclusions: The TGFβ pathway is associated with immune-poor phenotype and RD in bTNBC. Among immune-rich bTNBC RD, macrophage/neutrophil chemoattractants dominate the cytokine milieu, and IFNγ and activated B cells and T cells dominate immune-rich cancers with pCR.