Supplementary Fig. S3. Summary of protein expression changes after 24 weeks of endocrine treatment (ET) and/or Palbociclib (palbo) treatment.

Supplementary Fig. S2. Comprehensive analysis of protein expression changes before and after endocrine treatment

Supplementary Fig. S6. Effect of hormone deprivation and NFKB pathway activation HR+ breast cancer cells.

Supplementary Fig. S4. Schematic of flow cytometry experiments and differential impact of IFNg stimulation on HR+ breast cancer cells.

Supplementary Fig. S8. Comprehensive analysis of fulvestrant and birinapant treatment in HR+ breast cancer cell.

Supplementary Fig. S1. Comprehensive analysis of protein expression data in immune and invasive cancer epithelial regions from in various patient cohorts.

BACKGROUND:Adenoid cystic carcinoma (ACC) is a malignancy for which currently no therapies are approved by the FDA. Thus, there is an urgent need to identify novel treatments. Although imaging is the standard to evaluate responses to experimental drugs, accurate radiologic assessments can be challenging. CT and MRI may lead to an underestimation of the tumor burden, whereas PET is limited by the slow growth rate of ACC. Here, we demonstrate that liquid biopsy to detect ACC-derived cell-free DNA can serve as a complementary marker for assessing tumor burden and therapeutic futility. PATIENTS AND METHODS:We investigated 47 blood samples from 15 patients with advanced ACC who received all-trans retinoic acid in a phase II clinical trial and determined the kinetics of circulating tumor DNA (ctDNA) over time. We established two independent methods for ctDNA detection: an unbiased approach based on low-pass whole genome sequencing (lpWGS) (<1x) and a targeted sequencing approach focused on known somatic mutations. RESULTS:lpWGS performed better in detecting the ctDNA than targeted sequencing (in 47 vs. 20 % of patients who provided plasma samples, respectively), and has the advantage of not requiring a prior knowledge of the mutational landscape of the tumor. Overall, the presence of ctDNA and higher tumor fraction pointed toward a correlation with shorter progression-free survival and time spent on therapy as well as higher probability of progressive versus stable disease, although statistical significance was not achieved, possibly due to the limited size of the patient cohort. CONCLUSION:Our findings suggest that ctDNA may serve as an additional tool for therapeutic guidance in ACC patients.

Supplementary Fig. S7. Impact of treatment with fulvestrant, birinapant, and their combination on a PDX model of HR+ breast cancer.

Supplementary Fig. S5. Analysis of the impact of the ER axis on the response to IFNg stimulation in HR+ breast cancer cells.

While most patients initially respond to CAR-T cell treatment, responses often are not durable and subsequent lines of immunotherapy show diminishing success. In this study, we investigated the co-evolutionary dynamics between CAR-T cells and the immune microenvironment in myeloma patients undergoing anti-BCMA CAR-T cell therapy at single-cell resolution. Our findings highlight the transformative impact of CAR-T cell treatment on the endogenous T cell landscape. We identify a novel transitional CD8 + T cell population that is predictive of poor treatment outcomes. The emergence of this population coincides with the depletion of the endogenous T cell repertoire and compositional evolution of functional T cell subsets. These changes in the endogenous T cell compartment induced by CAR-T cell therapy may contribute to inadequate immune capacity and tumor control. Our findings highlight the potential of targeting TIM3/GAL9 interactions to mitigate T cell exhaustion, apoptosis and lack of persistence, offering promising avenues for optimizing T cell-based cancer immunotherapies. We provide a framework for assessing and manipulating the ‘mileage’ of the immune system as predictive marker and therapeutic opportunity to prevent repeated immunotherapies from becoming increasingly less successful, even when targeting distinct antigens.

Brain metastatic breast cancer is particularly lethal largely due to therapeutic resistance. Almost half of the patients with metastatic HER2-positive breast cancer develop brain metastases, representing a major clinical challenge. We previously described that cancer-associated fibroblasts are an important source of resistance in primary tumors. Here, we report that breast cancer brain metastasis stromal cell interactions in 3D cocultures induce therapeutic resistance to HER2-targeting agents, particularly to the small molecule inhibitor of HER2/EGFR neratinib. We investigated the underlying mechanisms using a synthetic Notch reporter system enabling the sorting of cancer cells that directly interact with stromal cells. We identified mucins and bulky glycoprotein synthesis as top-up-regulated genes and pathways by comparing the gene expression and chromatin profiles of stroma-contact and no-contact cancer cells before and after neratinib treatment. Glycoprotein gene signatures were also enriched in human brain metastases compared to primary tumors. We confirmed increased glycocalyx surrounding cocultures by immunofluorescence and showed that mucinase treatment increased sensitivity to neratinib by enabling a more efficient inhibition of EGFR/HER2 signaling in cancer cells. Overexpression of truncated MUC1 lacking the intracellular domain as a model of increased glycocalyx-induced resistance to neratinib both in cell culture and in experimental brain metastases in immunodeficient mice. Our results highlight the importance of glycoproteins as a resistance mechanism to HER2-targeting therapies in breast cancer brain metastases.

Endocrine treatment shapes tumor immune microenvironment in primary hormone receptor–positive breast cancer. A–F, Digital spatial profiling proteomic levels (log2 expression levels) of immune cell surface markers ranking from highest to lowest expression. A, Baseline levels within the immune regions in tumors from patients who received endocrine therapy [F(11, 312) = 57.1, P < 2e−16, one-way ANOVA]. B, Protein expression levels within the immune regions after 2 weeks of endocrine treatment [F(11, 276) = 41.7, P < 2e−16, one-way ANOVA]. C, Protein expression levels within the immune regions after 24 weeks of endocrine treatment at the time of surgery [F(11, 1212) = 305.2, P < 2e−16, one-way ANOVA]. D, Baseline levels within the invasive epithelial cell regions in tumors from patients who received endocrine therapy [F (11, 732) = 126.3, P < 2e−16, one-way ANOVA]. E, Protein expression levels within the invasive epithelial regions after 2 weeks of endocrine treatment (P < 2e−16, one-way ANOVA). F, Protein expression levels within the invasive epithelial cell regions after 24 weeks of endocrine treatment at the time of surgery (P < 2e−16, one-way ANOVA). Boxplots show median, 25th, and 75th percentiles as boxes, the minimum of the 75th percentile + 1.5 × IQR, and the maximum observation as the upper whisker and the maximum of the 25th percentile −1.5 × IQR and the minimum observation as the lower whisker. G, Trajectory plot of TIL fractions between baseline and 2 weeks. P-val, P value (paired Wilcoxon signed-rank test with continuity correction). H, Trajectory plot of TIL fractions between 2 weeks and surgery for all patients given endocrine treatment that have TIL observations at all three time points. Each trajectory corresponds to a single patient. P-val, P value (paired Wilcoxon signed-rank test with continuity correction). I, GSVA of the T-cell accumulation gene set in primary ER+ breast cancer biopsies from pre- and post-neoadjuvant AI treatment. J, Enrichment plot of the top-ranked gene set (estrogen response) enriched in the ESR1 highest (fourth quartile) versus ESR1 lowest (first quartile) ER–positive breast cancer samples from the TCGA cohort. K–M, GSVA of RNA-seq from the ER–positive breast cancer samples from TCGA divided into quartiles based on ESR1 mRNA levels testing the enrichment score (y-axis) for signatures of immune-checkpoint blockade (ICB) resistance (K), T-regulatory (Treg) accumulation (L), and cytotoxic T-cell accumulation (M). Comparison between the quartiles was done with a t test. N, number of patients included in the corresponding analysis.

Supplementary Fig. S5 shows additional ATAC-seq data analyses.

NF-κB pathway activation via RelA phosphorylation and binding is enhanced in hormone-deprived conditions. A, Immunoblot of whole-cell lysates for the NF-κB subunits, RelA and RelB, and ER in MCF7 cells grown with E2 10 nmol/L or in HD conditions in response to IFNγ 10 ng/mL stimulation. B, Whole-cell lysate immunoblots of ER, RelA, and phospho-RelA (Ser536) in MCF7 cells. Hormone-deprived cells were stimulated with E2 (10 nmol/L) for 4 days. Protein was extracted every 24 hours. GAPDH was used as a loading control. C, Tornado plots of RelA binding sites in HD cells or treated with 10 nmol/L E2 with or without IFNγ stimulation (10 ng/mL for 1 hour). D, Volcano plot showing differential expression from RNA-seq of MCF7 cells in HD conditions versus E2-treated conditions. Blue dots (True), genes that are differentially expressed based on RNA-seq and predicted to be regulated by RelA based on RelA ChIP-seq and BETA minus analysis. Orange dots (false), genes that are differentially expressed between HD and E2 conditions but not predicted to be regulated by RelA based on the RelA ChIP-seq data. The P value represents the significance of the association between RelA ChIP-seq and RNA-seq up HD (P = 1.9 E−11) or down in HD (P = 0.156) compared with E2-stimulated cells without IFNγ based on BETA basic. E, GSVA of the HD_RelA gene set (541 genes) in primary ER+ breast cancers pre- and post-neoadjuvant treatment with an AI. F and G, RNA-seq differential expression after RelA KO compared with control. Volcano plot highlighting genes differentially expressed between RelA KOs and RelA WT cells grown in HD conditions (F), treated with fulvestrant (10 nmol/L; G), or grown in E2 conditions (H) for 72 hours and stimulated with IFNγ (10 ng/mL) for the last 6 hours (h). n, number of genes differentially expressed. I, RelA ChIP-seq tracks showing examples of RelA peaks at the promoter region of IFNγ-associated genes in MCF7 cells grown in the presence of E2 or in HD conditions and stimulated ± IFNγ (10 ng/mL) for one hour. J, mRNA expression levels of ICAM1, HLA-A, TAP1, B2M, and CXCL10 in E2 and HD conditions without and with RELA silencing KO after 6 hours of IFNγ stimulation. *, P < 0.05; **, P < 0.01; ***, P < 0.001. Error bars, mean ± SD of at least two replicates per each KO. Two-way ANOVA.

RNA-seq of bulk B16F10 tumors that underwent different treatments

Table S5. List of Genes Used to Generate NK Cell Activation Scores.

Effect of different drug treatment on the expression of genes encoding MHC-I, TAP1, PD-L1, and PD-L2

RNA-seq and H3K27ac ChIP-seq of MHC-I-high and MHC-I-low primary melanoma samples

Supplementary Fig. S4 shows additional RNA-seq data analyses.