Biomarkers of response to neoadjuvant palbociclib plus anastrozole in endocrine-resistant estrogen receptor-positive/HER2-negative breast cancer: a phase 2 trial

Domain: neuroscience
Published: 2026-01-27
Editorial outcome: major_revision

Abstract
CDK4/6 inhibitors (CDK4/6i) improve outcomes for estrogen receptor (ER) positive/HER2-negative breast cancers (BCs), yet intrinsic and acquired resistance exist. Here, we evaluated anastrozole in combination with palbociclib (ANA/PAL) in the NeoPalAna Endocrine-Resistant cohort (NCT01723774). Thirty-four patients with clinical stage II/III ER+/HER2– BCs resistant to standard neoadjuvant endocrine therapy (on-treatment Ki67 > 10%) received neoadjuvant ANA/PAL, with serial biopsies analyzed. The primary endpoint, complete cell cycle arrest (CCCA; Ki67 ≤ 2.7% at cycle 1, day 15), was achieved in 57.6% of patients (95% CI: 39.2–74.5%). Resistance to ANA/PAL (Ki67 > 10%) was associated with higher pre-treatment tumor grade, Ki67, and specific PAM50 subtypes. Resistant tumors demonstrated reduced ER signaling and upregulation of cell cycle, mTOR, interferon, JAK/STAT, and immune checkpoints. Additionally, a 33-gene signature that predicted neoadjuvant Ki67 response to ANA/PAL was prognostic in a metastatic validation cohort. These findings underscore dysregulated oncogenic pathways as potential resistance mechanisms and biomarkers of response to CDK4/6i.

Introduction
A major challenge in treating ER+/HER2− breast cancer (BC) is endocrine resistance1. The discovery of cyclin D/CDK4/6-Rb as the major effector of ER signaling and endocrine resistance mechanisms2–6 led to the development of CDK4/6 inhibitors (CDK4/6is)7. These agents block Rb phosphorylation, which hinders E2F transcription factors from activating the expression of genes required for G1 to S phase transition8. Three CDK4/6is are FDA approved and have revolutionized the treatment for ER+ BC9,10. However, despite their impressive anti-tumor activity, intrinsic and acquired resistance exist. There is a critical unmet need to understand the underlying mechanisms of resistance and developing response biomarkers9,10.

Results
The NeoPalAna trial (NCT01723774) is a multi-cohort single-arm phase 2 neoadjuvant study of the CDK4/6i palbociclib (PAL) plus anastrozole (ANA) in clinical stage II/III ER+/HER2− BC, aimed at assessing the antiproliferative effect of PAL in primary BC and discovering response biomarkers. We have previously reported the results of the initial cohorts (NeoPalAna Initial), including the PIK3CA mutated and the PIK3CA wild-type cohorts11. We observed a potent antiproliferative effect of PAL across a broad range of clinicopathologic and mutation profiles, including BCs resistant to ANA monotherapy. Few patients (6 of 50 patients) did not achieve CCCA (Ki67 ≤ 2.7%). Analysis of these resistant cases suggested an association with high-risk PAM50 types and persistent E2F-target gene expression11.

To further investigate PAL resistance mechanisms in endocrine therapy (ET)-resistant BC, we opened the Endocrine-Resistant (ET-Resistant; ET-R) cohort that enrolled patients with on-treatment Ki67 > 10% after ≥ 4 weeks of standard-of-care neoadjuvant ET (NET). The primary endpoint was CCCA at C1D15. Correlative endpoints included genomic and proteomic analyses of tumor tissue in relation to response categorized by Ki67 ≤ 10% (ANA/PAL-sensitive; Sensitive) or > 10% (ANA/PAL-resistant; Resistant).

Patient characteristics
Between August 2016 and March 2021, 34 patients (17 pre- and 17 post-menopausal), with a median age of 52.8 years (range 30.4–77.6), and clinical stage II-III ER+/HER2− BC were enrolled. One patient lacked Ki67 due to insufficient tumor material, leaving 33 patients evaluable for the primary endpoint (Fig. 1). Patient and tumor characteristics are detailed in Table 1. Notably, all but one patient had either Grade 2 (n = 16) or Grade 3 (n = 16) BC. Median Ki67 levels were 28.8% (range 7.5%–94.3%) at diagnosis (baseline; BL) and 30% (range 12%–96.4%) on NET prior to enrollment (C1D1). The majority had ER-rich BC with Allred scores of 8 (n = 26) and 7 (n = 3). All patients received standard-of-care neoadjuvant ANA (with goserelin if premenopausal) for a median duration of 6 weeks (range 4–12 weeks) prior to enrollment.

Safety and clinical responses
All 34 patients were evaluable for treatment-related adverse events (TRAE) (Supplementary Table 1). The only G3 and above TRAEs were neutropenia (38% G3, 3% G4) and leukopenia (9% G3). Among the 34 patients enrolled, 13 patients went off ANA/PAL early due to Ki67 at C1D15 over 10% per protocol (n = 11) or intolerable AE (n = 2), The clinical response rate was 85.7% (18/21, 95% CI: 63.67–96.95%) in the 21 patients who completed 5 cycles of ANA/PAL (Table 2) and 52.5% (18/34) in the intent-to-treatment population.

Primary endpoint and Ki67 response
Among the 33 evaluable patients, the C1D15 CCCA rate was 57.6% (19/33; 95% CI: 39.2–74.5%), which met the primary endpoint. Three of the 14 patients without CCCA had Ki67 < 10%. Therefore, 22 (67%) were classified as ANA/PAL-sensitive (Sensitive) and 11 (33%) ANA/PAL-resistant (Resistant) using the Ki67 10% cut-point. Resistant tumors were more likely cT3 (p = 0.01), G3 (p = 0.018), and had higher Ki67 (p = 0.015) pre-treatment (Table 1). Race was associated with response (p = 0.036). However, caution should be applied in interpreting this result due to the small sample sizes: American Indian (n = 2), Black (n = 3), and Asian (n = 3), compared to the majority of White (n = 25).

Baseline tumor mutation profile and copy number variations (CNV)
Thirteen patients had sufficient archival diagnostic biopsies for WES. To maximize sample size, we used C1D1 biopsies for 5 patients and C1D15 biopsies for 9 patients. The WES cohort included 8 (30%) Resistant, 18 (67%) Sensitive, and 1 with unknown Ki67, similar to the overall cohort. Given the small sample size, we conservatively focused the analysis on the 83 genes most relevant for BC12. Consistent with the ET-Resistance phenotype, this cohort was enriched with genomic alterations implicated in endocrine resistance (Supplementary Fig. 1a)13–16. The top 4 frequently mutated genes were TP53 (37%), PIK3CA (33%), PTEN (11%), and NF1 (11%; Supplementary Data 1). We then compared the mutation frequencies of TP53 and PIK3CA with the NeoPalAna Initial cohort (19 ET-Sensitive, 10 ET-Resistant)11, Z1031_POL (48 ET-Sensitive, 28 ET-Resistant cases)13, and the TCGA ER+/HER2– BCs (n = 396)17. TP53 mutation frequency was significantly higher in the NeoPalAna ET-R cohort than that of the ET-Sensitive cohort of Z1031_POL (37% vs 8%, Fisher FDR p = 0.00845) (Supplementary Fig. 1b, c), as expected13.

We then examined TP53 and PIK3CA mutation frequencies in Sensitive and Resistant cases within the NeoPalAna ET-R cohort, finding no significant differences. While PIK3CA mutations were common in Sensitive cases, this was not statistically significant (8/18 vs 1/8, P = 0.19). Assessment of CNV identified frequent amplifications in MYC18 and CCNE2, consistent with ET-resistant phenotype (Supplementary Fig. 2a). CNVs of cell cycle mediators and commonly altered genes showed little difference between Sensitive and Resistant baseline tumors. However, significant CNVs in pathways including DNA repair and PI3K/AKT/mTOR signaling were more frequent in Resistant tumors (Supplementary Fig. 2b, c and Supplementary Data 2).

Baseline PAM50 subtype and differentially expressed genes in relation to ANA/PAL response
Baseline (BL) RNA-seq was performed for 20 patients (13 Sensitive and 7 Resistant) with sufficient tumor material (Supplementary Fig. 3a) and subjected to PAM50 subtype determination. Two additional patients had BL tumor RNA-seq-derived PAM50 subtype previously reported19. There were 9 Luminal A (LumA), 8 LumB, and 5 non-luminal BCs (3 Basal-like, 2 HER2E) (Fig. 2A). PAM50 subtype was significantly associated with ANA/PAL response (p < 0.001, Table 1), with resistance observed in all 5 non-luminal, 3 of 8 LumB, and none of the 9 LumA. This is consistent with the higher Ki67 observed in Resistant BCs (Fig. 2B and Table 1).

BL RNA-seq analyses revealed 630 DEGs (FDR adj. p ≤ 0.05) between Sensitive and Resistant cases. Principal component analysis (PCA) of DEGs demonstrated clustering of Sensitive BCs, while Resistant BCs were scattered, with notable tight clustering of the 3 Basal-like BCs (Fig. 2C). Statistically significant, positively enriched DEGs of interest in Resistant BCs included tumor-associated effectors CCNE1, IDO1, IRF4, BRCA2, EZH2, and immune cell markers such as CD38 and CD48 (Fig. 2D), among others (Supplementary Data 3). Top upregulated signaling pathways in Resistant BCs included interferon (IFN) γ signaling, E2F targets, MYC targets, G2M checkpoint, mTORC1, TNF signaling, inflammatory response, IL6-JAK-STAT, and IFNα signaling (Fig. 2E), highlighting oncogenic pathways that govern proliferative advantage and inflammatory response. Estrogen response pathways were significantly downregulated in Resistant BCs (Fig. 2E). To support these findings, we analyzed the NeoPalAna initial cohort (NeoPalAna-Initial)11, which showed consistent shared DEGs and dysregulated oncogenic pathways and estrogen signaling in Resistant tumors (Supplementary Fig. 4a, b). These data reveal intrinsic tumor dependencies impacting response to CDK4/6 inhibitors.

C1D15 on-treatment PAM50 subtype and differentially expressed genes and proteins in relation to ANA/PAL response
Research tumor biopsies collected at C1D15 with adequate tumor content were subjected to RNA-seq (FFPE core, n = 28: 19 Sensitive and 9 Resistant) and mass spectrometry (OCT frozen core, n = 17: 12 Sensitive and 5 Resistant; Supplementary Fig. 3b–d) to assess transcriptomic and proteomic differences between Resistant and Sensitive tumors. PAM50 subtype determination based on C1D15 RNA-seq demonstrated the 19 Sensitive cases being predominantly LumA (n = 18) or normal (n = 1), while the 9 Resistant cases included 3 LumA, 2 LumB, 1 HER2E, and 3 Basal-like BCs (Fig. 3A). Like BL, PAM50 subtype at C1D15 was significantly associated with Resistance (p = 0.0014). Figure 3B shows the Ki67 distribution of Sensitive vs Resistant cases.

RNA-seq at C1D15 revealed 1,148 DEGs (FDR adj. p ≤ 0.05) between Sensitive and Resistant cases. Similar to BL findings, PCA of DEGs at C1D15 showed a distinct distribution of Sensitive vs Resistant cases and clustering of Basal-like BCs (Fig. 3C). Top upregulated genes in Resistant tumors included TOP2A, E2Fs, CCNE1, BUB1, AURKB, and STMN1 (Fig. 3D), reflecting their high proliferative state. Hallmark GSEA demonstrated enrichment of pathways such as E2F targets, G2M checkpoint, MYC targets, mTORC1 signaling, mitotic spindle, DNA repair, and IFN signaling, and lower expression of pathways related to estrogen response, TGFβ signaling, myogenesis, and epithelial-mesenchymal transition in Resistant tumors (Fig. 3E and Supplementary Fig. 5).

To identify genes associated with proliferation despite ANA/PAL treatment in Resistant BCs, we analyzed C1D15 RNA-seq data and compared genes correlating with Ki67 levels between Resistant and Sensitive tumors (Fig. 3F and Supplementary Table 5). As expected, Ki67 levels in Resistant tumors were positively correlated with MYC, cell cycle genes (CCNE1, CCNA1, CCNA2, CDK6, PLK1), and translation factors (EIF4EBP1, EIF4E2, E2F2), consistent with their highly proliferative state, and negatively correlated with ESR1 and AR, suggesting a less dependence on hormone receptor-mediated signaling. Interestingly, Ki67 levels also positively correlated with genes involved in DNA repair (BRCA2), IFN and immune responses (ADAR, IDO1, CD274 [PD-L1]), and inflammation (IRAK1, IL1A) in Resistant tumors (Fig. 3F), suggesting potential relevance of these pathways in ANA/PAL-resistant tumors.

Proteomic analysis confirmed genes and pathways associated with resistance, similar to the transcriptional findings. Figure 3G highlighted significantly upregulated proteins important for cell cycle progression (CDK2, CDK4), mTOR signaling and translation (RPS6, RPS6KB1, EIF4A1), interferon and inflammatory mediators (ADAR, STAT1, STAT3, IRAK1) in Resistant tumors (and Fig. 3H). The dysregulated pathways in Resistant tumors showed high correlation between transcriptomes and proteomes (Spearman correlation r = 0.92) (Fig. 3I). Among the Hallmark pathways of interest, high RNA-protein correlations were observed in interferon and estrogen response pathways, while more discordance was seen in MYC, cell cycle, and DNA repair pathways (Fig. 3J), which may suggest post-transcriptional or post-translational processes in response to ANA/PAL. These integrated analyses highlighted potential oncogenic pathways as therapeutic targets for further investigation (Fig. 3K).

We then assessed treatment-induced changes at C1D15 relative to BL. In cases with paired samples, subtype switching to LumA was uniformly observed in Sensitive cases (BL LumB, n = 5), and in 2 Resistant BCs (1 LumB, and 1 HER2E at BL) (Fig. 4A). One Resistant LumB switched to HER2E. All 3 Basal-like BCs remained Basal-like at C1D15. Among significant DEGs (≥ twofold changes) between BL and C1D15, 3,032 genes were uniquely upregulated in Sensitive cases and 735 in Resistant cases, with 1,208 shared. For downregulated genes, 909 were unique to Sensitive cases and 613 to Resistant cases, with 591 shared (Fig. 4B and Supplementary Data 6). Key cell cycle genes (including CCNB2, AURKB, E2F1 and E2F2) were suppressed at C1D15 in Sensitive, but not Resistant samples (Fig. 4C). While ANA/PAL treatment caused similar overall changes in both Sensitive and Resistant tumors (Fig. 4D), key pathways like cell cycle and interferon signaling, though suppressed at C1D15 compared to BL, remained relatively elevated in Resistant tumors (Fig. 4E). Persistent gene expression and pathway activation despite ANA/PAL establish these modules as prominent resistance mechanisms.

Targeting hyperactive interferon and inflammatory signaling with JAK inhibitors in CDK4/6i-resistant models
To examine the functional relevance of candidate pathways observed in the ANA/PAL-resistant tumors, we generated MCF7 Palbo-R and Abema-R through long-term dose-escalation culture of MCF7 cells in palbociclib and abemaciclib, respectively (Fig. 5A, B). Compared to parental cells, MCF7 Palbo-R and Abema-R showed upregulated cell cycle effectors (i.e., CDKs and cyclins), and enrichment of interferon and TNF signaling by RNA-seq analysis (Fig. 5C–F), consistent with that observed in the ANA/PAL-resistant tumors (Figs. 2E and 3E). Upregulated IFN/inflammatory signaling was confirmed by increased STAT3 phosphorylation and elevated levels of the downstream interferon-stimulated gene 15 on immunoblot (Fig. 5G). Notably, RB expression was retained, as its loss is a known mechanism of CDK4/6i resistance in cell line models20. Elevated AKT phosphorylation was also observed, supporting augmented PI3K/AKT signaling in resistant lines consistent with previous literature21.

We then used these resistant models to assess the effects of compounds targeting the JAK-STAT pathway, the downstream mediators of IFN signaling, and inflammatory pathways. We chose to investigate JAK1/2 inhibitors as they are already clinically approved to treat inflammatory, autoimmune, and neoplastic diseases such as myeloproliferative neoplasms, including ruxolitinib (JAK1/2), fedratinib (JAK2/FLT3), momelotinib (JAK1/2/ACVR1), and pacritinib (JAK2/ACVR1/IRAK1)22,23, and also itacitinib (JAK1), currently under phase 3 investigation for graft-versus-host disease24. Screening with these inhibitors in parental MCF7 and CDK4/6i-resistant cells revealed similar sensitivity across the cell lines, with pacritinib showing the highest anti-cancer potency (Fig. 5H), suggesting that dual inhibition of JAK2 and IRAK1, which targets interferon and TNF signaling via NFKB pathways, could be beneficial. In Palbo-R cells, pacritinib alone effectively suppressed STAT3 phosphorylation and downregulated cyclin D1, with additive anti-cancer effects when combined with palbociclib (Fig. 5I, J). In contrast, combining pacritinib with abemaciclib was necessary to effectively reduce pSTAT3 and Cyclin D1, possibility explaining the synergistic anti-cancer effect in Abema-R cells (Fig. 5I, J). We lastly tested pacritinib and palbociclib in ER+/HER2– patient-derived xenograft (PDX) organoid models. WHIM58 PDX was derived from a metastatic sternal lesion and harbors hotspot mutations in AKT1 and TP53, in addition to having MYC and CCND1 amplifications. WHIM43 was derived from a metastatic rib lesion and was found to have RB1-loss by WES and negative IHC staining of RB25. Both organoid models showed reduced sensitivity to palbociclib (Fig. 5K) but were responsive to pacritinib at low micromolar doses (Fig. 5L), which was comparable to the response observed in MCF7 Palbo-R/Abema-R cells. These data support the potential importance of JAK/STAT signaling in CDK4/6i resistant BCs.

BL and treatment-induced changes in immune cell populations
The upregulation of IFN signaling in both CDK4/6i-resistant cell lines and ANA/PAL-Resistant BCs, along with elevated markers of immune cells and immune checkpoints in Resistant tumors at baseline (Fig. 2) and at C1D15 (Fig. 3), prompted further exploration of the tumor immune microenvironment. Using CIBERSORT analysis, we deconvoluted bulk RNA-seq data to estimate immune cell type fractions at BL and C1D15 (Fig. 6A). Compared to Sensitive tumors, Resistant tumors at BL exhibited a significantly higher abundance of immune cells, including CD8+ T cells and M1 macrophages (Fig. 6B). ANA/PAL treatment increased several immune cell types (CD8 T cells, activated NK cells, plasma cells, macrophages, and monocytes) in Sensitive but not Resistant tumors (Fig. 6B), suggesting a more favorable anti-tumor immune microenvironment in Sensitive tumors.

We further analyzed BL RNA-seq data using an 18-gene T-cell-inflamed TME signature26, previously linked to poor PFS benefit from PAL in metastatic ER+/HER− BC in the PALOMA-2 trial27,28. We found significant upregulation of this signature in resistant tumors in NeoPalAna ET-R (Fig. 6C, D). This, along with a higher abundance of immune cells, including CD8+ T cells and M1 macrophages from CIBERSORT analysis at BL, suggests a pre-existing inflamed but immune suppressive microenvironment in Resistant tumors.

Enhanced IDO1 expression in resistant tumors and association with PAM50 subtype
We further assessed immune checkpoints (ICs) expression in Resistant tumors. There was a significant correlation between ICs, including IDO1 and CD274, and Ki67, suggesting a proliferative role (Fig. 7A). IDO1 canonically suppresses anti-tumor immunity through tryptophan depletion28,29 and was part of the 18-gene immune signature (Fig. 6C). IDO1 was also one of the top upregulated genes in Resistant tumors in BL RNA-seq (Figs. 7B and 2D and Supplementary Fig. 4a). We further performed immunohistochemistry (IHC) for IDO1 on BL biopsies from the NeoPalAna ET-R cohort, which demonstrated elevated expression in Resistant versus Sensitive tumors (Fig. 7C). IDO1 IHC, quantified with Allred score, modestly correlated with mRNA levels (Fig. 7D). Using a 1% cutoff, all 4 Basal-like BCs were IDO1 positive, compared to 5 of 8 LumB and 2 of 8 LumA BCs (Fig. 7E). Basal-like BCs had the highest IDO1 mRNA expression compared to LumB and LumA subtypes (Fig. 7F). The correlation was confirmed in the TCGA BRCA and METABRIC cohorts (Fig. 7G).

NeoPalAna 33-gene resistance signature is prognostic in metastatic ER+ HER− breast cancer receiving ET/CDK4/6i
To validate BL ANA/PAL response biomarkers, we derived a resistance gene signature using BL RNA-seq data from NeoPalAna ET-R and previous microarray data from NeoPalAna Initial to increase sample size (Fig. 8A and Supplementary Fig. 6). Logistic regression models were performed for receiver operating characteristic (ROC) curve analysis for candidate genes across both training cohorts. The 33-gene signature includes key regulators of cell cycle, oncogenic signaling, proliferation, transcription, DNA repair, metabolism, and immune and interferon response mediators (Fig. 8B). PCA of the gene signature effectively separated Resistant and Sensitive tumors (Fig. 8C, D). The gene signature outperformed Ki67, with an ROC curve area under the curve (AUC) of 0.967 (95% CI 0.902−1) in NeoPalAna ET-R and 0.992 (95% CI 0.971−1) in NeoPalAna Initial (Fig. 8E, F and Supplementary Data 7), with minimal improvement when Ki67 was added. We performed bootstrap analysis30 in NeoPalAna ET-R to estimate variability and correct for potential bias in our results, which remained high for Ki67 (corrected AUC = 0.818), the signature alone (corrected AUC = 0.965, individual gene corrections in Supplementary Data 8), or in combination with Ki67 (corrected AUC = 0.963).

For independent validation, we examined a “Real-world” external cohort with RNA-seq analysis using the HTG EdgeSeq Oncology Biomarker Panel (n = 2560 genes) on pre-treatment samples with clinical outcomes for 151 metastatic ER+/HER2− BC patients treated with first-line CDK4/6i (~90% palbociclib) and either an aromatase inhibitor (AI) (n = 115) or fulvestrant (n = 36)31. Blinded to clinical outcomes, we applied the NeoPalAna resistance gene signature to the RNA-seq data, stratifying patients into high, medium, or low score groups by expression score tertiles. Kaplan–Meier (K–M) analyses subsequently performed showed higher gene signature scores significantly associated with shorter progression-free survival (PFS) (Hazard Ratio = 2, p = 0.012, median PFS: Low = 30.18 months, Medium = 22.03 months, High = 18.21 months) in the overall population (Fig. 8G). This association was also significant with AI and a trend with fulvestrant. Higher scores were also associated with a shorter overall survival (OS) across this cohort (HR = 2, p = 0.03) (Fig. 8H). These data provided further validation of biomarkers of resistance from the NeoPalAna cohorts.

Methods
Eligibility
Eligible patients included pre- and post-menopausal women ≥ 18 years, with a clinical stage II-III, ER+ (Allred score ≥ 3 or > 1% ER positivity) and HER2 (IHC 0 or 1+ or FISH negative) BC. Tumor Ki67 > 10% after ≥ 4 weeks of standard-of-care NET by central testing67 was required. Additional key eligibility criteria included: Eastern Cooperative Oncology Group Performance Status 0–2, adequate organ and marrow function, and consenting to research access to archival tumor tissue from biopsies for diagnosis (pre-treatment) and Ki67 on standard-of-care NET prior to enrollment (C1D1). The study was approved by the Institutional Review Board at participating sites and followed the Declaration of Helsinki and Good Clinical Practice guidelines. Written informed consent was obtained.

Study design and treatment
The primary endpoint was CCCA (Ki67 ≤ 2.7%) on ANA/PAL at C1D15. A Simon optimal two stage phase II design was employed to allow a 90% chance of detecting a CCCA rate of ≥ 20% (alpha 0.1). The regimen was considered to have sufficient anti-tumor activity if 4 of 37 patients (stage 1, n = 12; stage 2, n = 25) achieved CCCA. The study was stopped after enrolling 34 patients as the primary endpoint was met, per the data safety and monitoring board.

Eligible patients started ANA (1 mg PO daily) and PAL (125 mg PO daily, days 1–21 of each 28-day cycle) on C1D1. Goserelin (3.6 mg SC every 28 days) was required if premenopausal. Tumor biopsy was performed on C1D15 for central Ki67 analysis. If Ki67 > 10%, protocol therapy was discontinued due to inadequate response. Patients with Ki67 ≤ 10% (or indeterminate) continued ANA/PAL for 5 cycles unless experiencing intolerable side effects, disease progression, or withdrawal from the study. Surgery occurred on Cycle 5 Days 11−13 with the last dose of ANA/PAL on the day prior to surgery. Clinical tape/caliper bi-dimensional tumor measurements and adverse events (AE) assessments by CTCAE 4.0 were performed on day 1 of each cycle and at the completion of 5 cycles of ANA/PAL. Clinical tumor response was assessed according to RECIST 1.1. The response rate was defined as the proportion of patients achieving complete or partial response after completion of 5 cycles of ANA/PAL, evaluated both among patients who completed therapy and, in the intent-to-treatment population.

Ki67 immunohistochemistry (IHC) and Quantification
Ki67 testing on archival tissues from pre-treatment (baseline; BL) and C1D1 biopsies, and research C1D15 biopsies, was performed centrally in the Washington University AMP lab using the CONFIRM anti-Ki67 antibody (clone 30-9) and scored using pathologist-guided imaging analysis or point-counting as previously described67.

Formalin-fixed paraffin-embedded (FFPE) tumor RNA/DNA extraction
FFPE tumor blocks were sectioned and ≥ 50% tumor cellularity was required for RNA/DNA isolation. Microdissection was performed if overall tumor cellularity was < 50%. Deparaffinization was then performed as previously described68. Nucleic acids were isolated using the Allprep DNA/RNA FFPE Kit (Qiagen, 80234). Germline DNA was extracted using the QIAamp DNA kit (Qiagen, 51304).

RNA sequencing (RNA-seq) and analysis
Total RNA was prepared according to manufacturer’s protocol using SeqPlex RNA Amplification Kit (Sigma), indexed, pooled, and sequenced on an Illumina NovaSeq 6000. Sequencing pipeline and data analysis were performed as previously described69. Differential gene expression analysis was conducted using DESeq270. Gene set enrichment analysis (GSEA) was performed using GSEA software71 (version 4.2.2) or pathway enrichment analysis at https://www.gsea-msigdb.org utilizing the Hallmark gene set (Supplementary Data 1). PAM50-based intrinsic subtype assignment was determined as previously described72.

Whole exome sequencing (WES)
Tumor DNA and matched leukocyte germline DNA, when available, were subjected to WES as previously described73. Somatic variants were called from WES tumor and normal paired BAMs using somaticwrapper v2.2 (https://github.com/ding-lab/somaticwrapper), a pipeline designed for detection of somatic variants from tumor and normal exome data73. The pipeline merges and filters variant calls from four callers: Strelka (v2.9.2)74, Mutect (v1.1.7)75, VarScan (v2.3.8)76, and Pindel (v0.2.5)77. Indel calls were obtained from Stralka2, Varscan, and Pindel. The following filters were applied to get variant calls of high confidence: normal VAF ≤ 0.02 and tumor VAF ≥ 0.05; rescue low-VAF variants in SMG gene list by removing tumor VAF ≥ 0.05 requirement; read depth in tumor ≥ 14 and normal ≥ 8; indel length < 100bp; all variants must be called by 2 or more callers; all variants must be exonic; and excluding variants in dbSNP but not in COSMIC. In cases where normal tissue counterparts were unavailable, somatic variants were called using the Mutect2 (v4.1.2.0) best-practice pipeline (https://gatk.broadinstitute.org). Silent mutations were not considered in the analysis. Somatic copy number variations (CNVs) were identified using our in-house GATK4 somatic CNV calling pipeline (https://github.com/gatk-workflows/gatk4-somatic-cnvs).

Mass spectrometry proteomic analysis
Proteomic analysis was performed on 12 Sensitive and 5 Resistant patient samples. Frozen biopsy OCT block sections with tumor cellularity of ≥ 25% were processed and assayed on mass spectrometry as previously described78. Briefly, OCT samples were sectioned with interleaved collection of curls, resulting in equal distribution of material for RNA, Protein and DNA extracts. Curls were then washed with 70% ethanol, followed by washing with water and finally with 100% ethanol. Samples were lysed with 8M Urea and sonicated with a Covaris S220 before being centrifuged. The resulting supernatant was used for downstream proteomics processing. Samples in urea were reduced and alkylated followed by trypsin digestion. The resulting peptides were then loaded onto Evotips and washed with 0.1% formic acid before being loaded onto an Evosep. A 44 min gradient was used with an EV1109 column (8 cm × 150 µm, 1.5 µm). Samples were injected into a Thermo Exploris 480 with FAIMS. A -45 CV was used for all runs with 2200V source voltage. A data independent acquisition method was utilized, where MS1 scans were generated with a 500–740 m/z window followed by 4 m/z DIA windows with optimal window placement. For MS1 a resolution of 30,000, AGC of 300%, and 20 ms max IT were used. For all MS2 scans stepped collision energy of 22, 26, and 30 was used with a resolution of 30,000 and m/z range of 200−1800. An AGC target of 300% and Auto injection times were used. RAW data files were analyzed with DIA-NN 1.8 using a library free deep learning approach. Spectral libraries were generated from an Uniprot human FASTA file with mostly default settings. Precursor charge was set to 2–6 and precursor m/z was 500–740. MBR was used for all runs. Enrichment analysis was performed comparing Resistant and Sensitive tumors using GSEA 4.2.2 following conversion of proteins into gene names.

Drug compounds
Palbociclib, abemaciclib, ruxolitinib, fedratinib, pacritinib, and itacitinib were purchased from Selleck Chemicals (Houston, TX). Momelotinib was purchased from ChemieTek (Indianapolis, IN).

Cell culture
MCF7 (ATCC, HTB-22) cells were cultured in RPMI 1640 (ThermoFisher, MA) supplemented with 10% fetal bovine serum, 1% penicillin/streptomycin, and 10% MEM non-essential amino acids solution. Cell lines were maintained at 37°C and 5% CO2, and regularly tested for mycoplasma. Resistant MCF7 Palbo-R and Abema-R cell lines were generated through prolonged culturing of MCF7 cells in palbociclib or abemaciclib through dose-escalation treatment (10 nM to 1–2 µM) over an 8-month period.

Immunoblotting
Cell pellets were collected and lysed with Laemmli Sample Buffer (BioRad, Hercules, CA) with beta-mercaptoethanol and heated at 95°C. Samples were processed with NuPAGE Novex Gel Electrophoresis Systems (ThermoFisher) followed by standard immunoblotting procedure. Immunoblotting antibodies and dilutions are provided in Supplementary Table 2. Secondary antibodies were utilized at a 1:5000 ratio.

Organoid generation and drug treatment
Organoid models were generated as previously described79. In brief, human tumor samples were implanted into NSG mice and collected, and grown in Matrigel (Corning, NY) with organoid media. For drug treatments, organoids were dissociated and seeded into 384-well plates, incubated for 1 day, and treated with serial dilutions of palbociclib or pacritinib for 5 days. Cell viability was measured with CellTiter Glo 3D (Promega, WI) and luminescence was measured using a Tecan infinite M200 plate reader.

Cell viability assays and synergy calculations
MCF7 cells were treated with the indicated doses and duration of inhibitors. Following, cell viability was measured with CellTiter-Blue (Promega) and fluorescence assessed with a plate reader. Synergy indices between CDK4/6 inhibitors and pacritinib were calculated using SynergyFinder (synergyfinder.org) following website interface80.

IDO1 immunohistochemistry (IHC) and quantification
Five-micron FFPE tissue sections were stained using the Leica BOND automated staining system according to manufacturer’s instructions, as previously described81. Rabbit anti-IDO1 (Cell Signaling, #86630) was used at a 1:300 dilution with epitope retrieval solution 2. IDO1 IHC slides were scored by a pathologist and quantified using the Allred score based on the proportion and intensity of tumor cells stained. IDO1 positivity was defined as at least 1% staining.

IDO1 expression and clinical data across additional cohorts
IDO1 mRNA expression and clinical data from ER+/HER2− BCs were extracted from TCGA17 and METABRIC82 datasets available on cBioPortal.

NeoPalAna resistance gene signature construction and validation
Gene expression data from the NeoPalAna ET-R and NeoPalAna Initial cohorts were analyzed to identify 33 genes combined to distinguishing between Sensitive and Resistant patient groups. Gene expression data from all patients available from NeoPalAna ET-R (n = 13 Sensitive, 7 Resistant) and NeoPalAna Initial (n = 26 Sensitive, 5 Resistant) cohorts were included. From these two training cohorts, candidate genes were first selected based on fulfilment at least one of the following criteria: (1) Differentially expressed genes (DEGs) between Resistant and Sensitive groups from the NeoPalAna ET-R cohort (915 DEGs from NeoPalAna ET-R identified using DEseq2 with adjusted p-value < 0.05, of which 332 were shared with NeoPalAna Initial cohort identified using Limma with a p-value < 0.05 given the small sample size of Resistant samples); (2) annotation in key enriched molecular pathways identified through GSEA with FDR p-value < 0.05 including E2F targets, G2M checkpoint, Interferon alpha response, Interferon gamma response, MTORC1 signaling, MYC targets V1, and MYC targets V2, in addition to Hallmark Estrogen response early, Hallmark Estrogen response late, and KEGG cell cycle; (3) genes of interest including MTOR (not annotated in the Hallmark MTORC1 signaling gene set), key breast cancer markers (ERBB2 and ESR1), and those mediating immunological pathways (Tumor inflammation signature and HLA genes). Logistic regression models and ROC curve analysis and AUC calculations were performed to assess the predictive value of individual genes, followed by the construction of a multi-gene signature which encompassed representative candidates (Supplementary Table 8). Bootstrapping30 analysis was performed to identify potential bias and to evaluate optimism correction for AUC (Additional description in Supplementary Information).

In the “Real world” metastatic cohort31, patients were stratified into “low”, “medium”, or “high” score groups based on the gene expression score derived from the HTG EdgeSeq Oncology Biomarker Panel (n = 2560 genes), of which 27 of the 33 predefined genes from our panel were available for analysis. The low scoring group was utilized as reference group for survival outcome comparisons using log rank tests. Figures were plotted using the survival (v3.6-4), survminer (v0.4.9) packages. Signature construction and reporting were performed considering TRIPOD-AI recommendation83 (Supplementary Data 2).

Statistical analysis
The rates of CCCA (overall and by subgroups) were defined as the percentage of patients with tumor Ki67 ≤ 2.7% at C1D15. The corresponding 90% confidence intervals (CI) were calculated using normal approximation or binomial exact CI as appropriate. A two-sample t-test was used for age, Wilcoxon rank sum test for Ki67, and Fisher’s exact test for categorical characteristics, with all tests being two-tailed.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Biomarkers of Response to Neoadjuvant Palbociclib Plus Anastrozole in Endocrine-Resistant Breast Cancer\nThe manuscript investigates potential biomarkers of response to a combination treatment of palbociclib and anastrozole\n(ANA/PAL) in estrogen receptor-positive (ER+)/HER2-negative breast cancer that is resistant to standard endocrine therapy.\nIn this study, the authors analyze tumor biopsies from 34 patients to identify molecular features associated with treatment\nresistance or sensitivity. The primary endpoint was the rate of complete cell cycle arrest (CCCA), defined by a significant\ndecrease in the proliferation marker Ki67.\nKey findings include:\n-A 57.6% CCCA rate was observed among patients receiving ANA/PAL, indicating that this treatment combination may\nsuppress proliferation in a subset of endocrine-resistant tumors.\n-Resistance to ANA/PAL was associated with certain intrinsic subtypes (e.g., LumB and HER2-E) and the activation of\nsignaling pathways related to cell cycle progression (e.g., MYC and mTOR), immune response (e.g., interferon), and\nimmune tolerance markers such as IDO1.\n-The authors propose that certain molecular markers, particularly gene signatures related to cell cycle control\nand immune modulation, could serve as predictive biomarkers of response to ANA/PAL.\nMajor Issues\nWhile this study provides a detailed descriptive analysis of biomarkers in a small cohort of endocrine-resistant breast cancer\npatients, the following key issues limit its suitability for publication in a high-impact journal like Nature Communications:\n1.Small Sample Size (34 Patients) The small sample size significantly limits the generalizability and statistical power of the\nstudy’s findings. With only 34 patients, there is a higher risk of variability and limited ability to draw reliable conclusions\nabout potential biomarkers of resistance or sensitivity to ANA/PAL. For studies of predictive biomarkers in cancer, larger\ncohorts are typically needed to achieve statistical robustness and validate associations between molecular characteristics\nand treatment outcomes.\n2.Descriptive Nature Without Experimental Validation The study is largely descriptive, identifying potential biomarkers\nwithout functional experiments to validate their roles in modulating response to ANA/PAL. It is just a number of -omic studies\nconducted in a sampleset with a clinical outcome, but not real hypothesis-driven scientific work. For a biomarker study\nintended for a journal like Nature Comms experimental validation is essential. This could involve in vitro or in vivo assays to\nconfirm whether the identified markers directly influence treatment efficacy. Without such experiments, the study does not\nestablish causative relationships, making it less impactful for the field.\n3.Lack of an Independent Validation Cohort An independent validation set is absent in this study, which limits the\nreproducibility and reliability of the findings (kind of in line with point 1 – but equally essential). High-impact studies in\nbiomarker research typically require validation in a separate cohort to confirm that observed associations are not specific to\nthe initial sample. The absence of a validation cohort reduces confidence that these biomarkers will be effective in broader\npatient populations.\n4.Redundancy of Certain Findings Some findings, such as the association of intrinsic subtype with response to CDK4/6\ninhibitors, are already well-documented in the literature. The association between high-risk subtypes like LumB and reduced\nresponsiveness to CDK4/6 inhibitors has been previously established (e.g., Aleix Prat et al, Journal of Clinical Oncology,\n2021; 39(13): 1458-67), which diminishes the novelty of this manuscript. While the study provides additional detail on\nmolecular signatures, the insights presented do not substantially advance current knowledge.\nConclusion\nIn summary, while the manuscript offers an informative descriptive analysis of potential biomarkers in endocrine-resistant\nbreast cancer, these limitations in sample size, experimental work, lack of independent validation, and the redundancy of\nsome findings preclude its publication in a high-impact journal like Nature Communications. Strengthening the study with a\nlarger cohort, experimental validation of biomarkers, and a validation set would significantly enhance its impact and\nsuitability for such a journal.", "reviewer_2": "Biomarkers of response to neoadjuvant palbociclib plus anastrozole in endocrine-resistant estrogen 1 receptor-\npositive/HER2-negative breast cancer\nKong et, al described a translational study based on the data collected from the NeoPalAna trial in which stage II/III\nER+/HER2- BC patients were treated with ANA/PAL. Responding patients were compared with the resistant ones, on\nmultiple -omics levels, with the purpose to understand the mechanism of resistance and to seek for response biomarkers.\nThe study highlighted several dysregulated oncological pathways, different immune microenvironment, and protein IDO1\nwhich is associated with PAM50 subtype, in patients who did not respond to ANA/PAL.\nThe paper is well presented, and the data is valuable for audience in oncology research community. The conclusion made\nso far has been properly underpinned by the analysis results. However, the authors have primarily focused on innate\nresistance mechanism while barely touched response biomarkers, as opposed to what its title suggested. The novelty of the\nconclusions is limited. The data could be further analysed to deliver its full potential.\nMain points:\n1. The study has a classical design: patients were categorised into responding and resistant cohorts, while samples were\ntaken at pre-treatment and post-treatment, collectively forming a two-by-two data structure. Throughout comparisons have\nbeen made between the responding and resistant cohorts, at each individual time point, while little was compared between\nthe pre- and post- treatment, within each patient cohort. The authors should characterise the changes at transcriptomics and\nproteomics level following ANA/PAL, and define the CHANGEs that differentiate the responding vs. resistant patients.\n2. The authors have rightly pointed out that using ki-67 as a surrogate for treatment response has its limitation. While\nacceptable in clinical trial design, this have a more prominent impact in biomarker discovery. In this study ki-67 acted as a\nperfect biomarker for response, leaving no chance a novel treatment-specific biomarker to be discovered. The authors\nshould introduce objective response and other clinical endpoints for response biomarker discovery, followed with\nexploration on the relationship between the identified biomarkers and ki-67.\n3. Genomic, transcriptomic, proteomic, and cellular data were present separately without any attempt to provide an\nintegrated view.\nMinor points:\n1. The authors have associated -omics changes with ki-67 (267-290), but only provide description on a handful of selected\ngene/proteins. A better overview at -omics level, maybe in the format of correlation network topology and its changes, will\nhelp the audience understand the systematic omics-landscape shifting related to cell-proliferation as a result of ANA/PAL\ntreatment.\n2. The number of DEGs are large. Maybe the limited sample size on the homogenous responding patients played a role?\nPCA showed heterogeneous omics profile in different BC subtypes. Is there a subtype-specific pattern in the DEG?\n3. ANA/PAL-resistant and ANA/PAL-R have been used in a mixed way (234-235, and more).", "reviewer_3": "In the manuscript reviewed, Kong, et al. utilizes the NeoPalAna endocrine-resistant cohort to explore the potential benefits of\nadding a CDK4/6 inhibitor to endocrine therapy (ET). Their findings demonstrate that combining palbociclib (PAL) with\nanastrozole (ANA) effectively suppresses cell proliferation resistant to ANA monotherapy, achieving complete cell cycle\narrest (CCCA) in 57.6% of ET-resistant ER+/HER2- breast cancer (BC) cases by cycle 1, day 15 of treatment (C1D15). They\nthen investigated potential mechanisms of resistance to the ANA/PAL combination, through a detailed genomics analysis of\nsensitive and resistant tumors. While no major genetic differences were identified, ANA/PAL-resistant tumors exhibited\nupregulation of cell cycle genes, suppression of G2M checkpoint genes, and activation of MYC and mTOR signaling\npathways—consistent with continued proliferation (Ki-67 positivity). Most interesting is that the PAM50 classification was\nable to separate responders and non-responders. Additionally, some differences were observed in immune cell fraction\ninduction (via CIBERSORT) such as presence of higher T-cell–inflamed TME and IDO1 levels in resistant tumors which\ncould warrant the use of immune checkpoint inhibitors. Overall, despite the limited cohort size, this dataset represents a\nvaluable, well-characterized resource for advancing research in ET resistance and potential therapeutic strategies in\nER+/HER2- BC. Moreover, the results provide significant starting point for a biomarker panel that can identify responders to\nANA/PAL and those who will need other treatments. Importantly, the genomic data suggests alternative paths for these\nresistant individuals.\nQuestions for authors:\n1. The primary findings of significant clinical interest are: that 1) at baseline (BL), luminal A tumors are uniformly sensitive\nwhereas HER2+/Basal were insensitive with Luminal B tumors being split in sensitivity and resistance; 2) higher BL Ki67 is\nclearly associated with resistance, and 3) evidence for immune activation in resistant tumors at baseline. However, simple\nclinical characteristics can also discern responders and non-responders. It would be helpful to address whether the\nmolecular characteristics (e.g. taking the best biomarkers for response, like Ki67) would improve the prediction of response.\n2. There were issues that the authors do not comment on such as the relevance of T stage and more interestingly, race\nbeing significantly associated with response (Table 1). The observations are limited by the cohort size but how does this\ncompare with other datasets? For example, do white patients respond better and Asian patients do not? Importantly, all\npatients underwent resection after the cycles of ANA/PAL and I assume that Table 2 represents these data, but surprisingly,\nthere was no attempt to address whether any of the biomarkers were correlated with this important endpoint. This seems like\na major flaw that is easily amenable in a revision. For example, there were PAM50 shifts at C1D15. Did any of those with\nshifts correlate with CR or PR?\n3. While these BL observations are noteworthy and potentially clinical important, the impact of the C1D15 seems less clear\nbut has significant biological interest. That there were such significant expression changes only 15 days after initiation of\nANA/PAL treatment is intriguing and is not likely to be related to clone fluxes rather to direct transcriptional effects of the\ntherapy. Yet, other than a descriptive narrative, the authors seemed to have avoided much conjecture as to the potential\nbiology, nor did they integrate these data into the final narrative. Moreover, Figure 4, which depicts the C1D15 data is poorly\nannotated so it is not clear to this reviewer how to interpret some of the data. For example, the labels for the X and Y axis in\npanel G does not explain what sensitive r means and what is the interpretation of why CCND1 and AKT1 is below the dotted\nline, This all seems like a wasted opportunity. The shift in PAM. That the immune markers for IFN signalling (Figure 4E)\nactually increased in C1D15 is of interest, perhaps the authors could, in the discussion, explore this a bit further. This seems\nto be contrary to the cibersort results in Figure 6 where the C1D15 levels of the different immune subtypes do not appear to\nbe going up. Again, this may be the confusion of what each of the figures are referring to – BL, or C1D15, For the patient\ntumors that were classified as ANA/PAL sensitive at C1D15, it appears that most of them maintained their relative sensitivity\nbased on clinical response reported in Table 2. What were the three tumors that had stable disease? Did they have any\nspecific characteristics (PAM50, grade, genetic or transcriptional signals etc) compared to the tumors that had partial or\ncomplete response?\n4. Similarly, since PAM50 subtype switching is observed in the tumors before and after treatment, it would be simpler to\nvisualize the data in a manner (plot or table) that shows the subtypes before and after treatment for each tumor instead of just\na Venn diagram showing the subtypes pre and post treatment in Fig. 3A and 4A. Is there any association with PAM50\nsubtyping and response at C1D15? Also Table 1 has “PAM50” notes as “PAM5”.\n5. Since WES data was generated, have the authors looked for mutation calls across of genes vs just the 83 genes reported\nhere (Fig.2)? Were any other highly occurring mutations observed in the cohort? Were any of them associated with\nresistance or sensitivity? What are the criteria for selection of genes shown in copy number heatmap in Fig.2B? All the\ngenomic analysis does not appear to be comprehensive enough to conclude that there is no mutation of copy number\nchanges associated with response.\n6. The limited size of the dataset was probably why no significance could be established on the relevance of the\nPIK3CA mutation incidence in ANA/PAL-resistant vs sensitive cases. Are there any other publicly available datasets that could be\nanalyzed for this? The authors referred to other similar studies with biomarkers but there did not seem to be a systematic\ncomparison of the biomarkers across these studies.\n7. In Fig.2A, the authors should describe what the S and R barplots on the right side are. Is it the percentage of all samples\nwith the mutation in each category? The barplot in the top panel of the figure also has no y axis label. In Fig.2B please add\nin legend for sensitive/resistance sample annotation at the side of the heatmap.\n8. “Top positively enriched DEGs..” described in line 235 don’t really all appear to be “top” DEGs in the volcano plot in\nFig.3D by fold change or fdr. Please describe the “top” DEGs and rephrase the rationale for highlighting the genes listed.\n9. In Figure 3E and 4E, please include fdr or p value for the pathway enrichment analysis. Only NES is shown. The text\n(lines 236-240) reports the pathway enrichment analysis but don’t really highlight the relevance of this analysis. Please\nrephrase text accordingly.\n10. In PCA plots in Fig.3C and 4C, the basal subtype tumors always separate out from all other tumors (irrespective of being\nsensitive or resistant) on PC1. Would you see a separation of the resistant and sensitive tumors if you remove the three\nbasal tumors from the analysis?\n11. In Fig.5D and 7A please mention what the change in the circle size represent.\n12. In fig.6B, despite seeing a higher level of CD8+ T cells and M1 macrophages at baseline, the tumors still acquire\nresistance to ANA-PAL treatment. Moreover, at C1D15 there is no significant difference in the levels of T cells, NK cells, M1\nand M2 macrophages between sensitive and resistant tumors. The interpretation of these results can be commented on\nmore in the discussion. For example, looking at absolute levels of immune cells post treatment may not be indicative of\nresponse in these tumors?\n13. The first p-value line in Suppl. Fig S2A, appear to be marked wrong. Are the authors comparing NeoPalAna ET-resistant\ncohort (pink) compared to that of the Z1031_POL ET-sensitive cohort (yellow)?", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The new data and experiments improve the overall quality of the manuscript, addressing the majority of the concerns rised\nby this reviewer. Although the number of patients cant be changed, the validations and the experimetns make the whole\nwork more convincing and suitable for this journal.", "reviewer_2": "Substantial changes have been made to the manuscript following the reviewers' comments. With additional datasets and\nnew analysis, the authors\nwere able to characterise -omics landscape related to cell proliferation and changes following CDK4/6i. The comparison\nbetween resistant and response samples, at molecular levels and functional levels, provide insight to the mechanism\nunderpinning treatment response. The new 33 gene baseline model is a good add-on.\nThe sample size unfortunately limited the depth of this research, nonetheless I am satisfied with the level of evidence\nunderpinning the main conclusion of the paper, and found that the major concerns from the reviewers have been addressed\nto a satisfactory level.\nThe only addition I would like to suggest is to include in this manuscript is a summary table of RNA/protein data related to\nthe 33 gene (BL predictors) in the supplementary material.", "reviewer_3": "On the whole, this revision of the work is improved. Integration of RNA-seq and mass spectrometry analysis and\nTranscriptomic changes between C1D15 and baseline in Resistant versus Sensitive tumors improve the paper significantly.\nMoreover, the addition of the validation cohort was very helpful.\nHowever, there are several issues that need some attention:\nThe additional analysis with the 33 genes signature on the three datasets indeed looks promising and makes good\nutilization of all the data generated. The authors however, should clearly elaborate on how the 33 genes were\nderived/nominated based on the NeoPalAna ET-R and NeoPalAna Initial data. The number 33 seems arbritrary and without\nany rationale. Were they picked because they were overlapping genes in both the dataset sensitive vs resistant\ncomparisons or had molecular pathway relevance etc? In Fig.9B could you highlight the genes based on the different\ncategories described (cell cycle, oncogenic signaling, proliferation, and immune and interferon response mediators etc).\nThis is a major oversight.\nThe discussion around figure 6 is rather confusing. While the expression analysis and western blot data on the MCF7 in vitro\nanalysis looks promising the in vitro drug response data is not convincing. For example - Authors justify the use of\nJAK/STAT inhibitors based on the expression analysis on Palbo-R vs parental and Abema-R MCF7 vs parental cells.\nHowever there is no real difference (increased sensitivity) observed between parental and Palbo-R or Abema-R cell lines for\nany drug or dose (Fig.6H). In fact, it seems that the resistant cell lines are perhaps less sensitive than the sensitive line. The\ndata just shows that all the 3 cell lines have increased cell death for increasing concentrations of most drugs - which would\nbe expected? If the data does not show that Palbo-R or Abema-R MCF7 cells have increased sensitivity to JAK inhibitors\ncompared to parental cells what are the authors trying to state with the data?\n\"Screening with these inhibitors and the JAK1 selective inhibitor itacitinib in parental MCF7 and CDK4/6i-resistant cells\nsuggested a higher dependency on JAK2 than JAK1, with similar sensitivity across the cell lines (Fig. 6H).\"\nThat Pacritinib is more effective on higher doses tested (in all three cell lines) compared to other drugs could just be a\nfunction of concentrations tested in the experiment. That no cell death is observed in response to Itacitinib could just be\ninadequate concentrations tested in this experiment. In order to draw this conclusion of increased dependency on JAK2 vs\nJAK1 authors will have to normalize to the JAK molecule inhibitory effect of the drugs for a given concentration or perform\ngene knock out experiments to confirm the JAK1 vs JAk2 (and IRAK1) sensitivity.\nFor the in vitro drug data on organoids derived from PDX tumors (Fig.6K and 6L), the authors state that \"Both organoid\nmodels showed reduced sensitivity to palbociclib (Fig. 9K) but were responsive to pacritinib at low micromolar doses (Fig.\n9L), which was comparable to the response observed in MCF7 Palbo-R/Abema-R cells. These data support the potential\nimportance of JAK/STAT signaling in CDK4/6i resistant BCs.\" Not sure if the data shown is enough to compare sensitivities\nbetween drugs and make this statement - these are two different drugs with two completely different mechanisms of action.\nSo if WHIM43 for example has an IC50 of 1.9uM to Pacritinib (JAK2 inhibitor) and 3uM to Palbociclib (CDK4/6 infinite) does\nit have any significant biological implication? Also figures 6K and 6L have been referred to wrongly in the text.\n\"Zero Interaction Potency (ZIP) drug synergy calculations of CDK4/6i and pacritinib at indicated doses.\" What are the\ndoses? There is no description of how this synergistic experiment was performed and how should one interpret the colors\netc. The legend was not helpful.\nAuthors should describe in results or legend data in figured better - for example in fig 6G -western blot - There is no mention\nof AKT and RB results.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_5": "As a statistical reviewer, I will focus this review on the methodology of the clinical trial and the development of the molecular signature.\nTo summarize this work, the authors studied the mechanisms of resistance in ER+ breast cancer to the combination of\nanastrozole + palbociclib. Translational experiments are performed on samples of patients included in a single-arm phase II\nSimon’s two-stage study. In this study, the combination is used in a neoadjuvant setting, for patients with large tumors (T2 or\nmore) whatever the lymph node status and without metastatic disease.\nThe clinical study was stopped before the end of recruitment after the decision of the data and safety monitoring board for\nefficacy, which is an unusual situation, as Simon two-stage design allow to stop the study for futility, not for efficacy.\nThe primary endpoint is the rate of complete cell cycle arrest (CCCA) after 1 month of combination of anastrozole +\npalbociclib, assessed by Ki67% immunohistochemistry and centrally reviewed. The sample size is reproducible, though the\nhypotheses are very pessimistic (unacceptable rate of 5%, acceptable rate of 20% of CCCA).\nIn the analysis of the primary endpoint, there were 22/33 (66%) of patients that exhibited CCCA at C1D15.\nMajor comments:\n- My main concern on that work is about the 33 genes signature that would be predictive of resistance to the combination of\nanastrozole + palbociclib (ANA+PAL). There is first a strong issue of reporting. The authors can look at the TRIPOD\nrecommendations, that have been recently updated for AI-based prediction models (PMID: 38626948). Even though these\nrecommendations are for dedicated work on prediction models (not integrated to a translational work), it could serve as a\nbasis for the reporting of this part of the work. There is no info on the selection process of the variables (and their possible\ntransformation) into the model, nor the nature of the model used to estimate the probability of being resistant (logistic\nregression model? Others models?).\n- Then, probably the biggest issue, there is only 33 patients to develop this model (with 11 patients that presented the\nendpoint “resistance to ANA+PAL”) which is very problematic if genes are introduced as unique variables in the analysis (33\npotential variables). For example, a very loosie rule of thumb is 10 events for 1 variable in a logistic regression model\n(therefore in the case of 11 events, only 1 variable can explain the outcome). And finally, there is no correction of optimism\nwhen estimating the performances of the gene signature (e.g. AUC) by any technique (for example, bootstrap, see PMID:\n38191193). The optimism is linked to the fact that the same data are used to choose the model and evaluate its\nperformances.\n- Regarding the external validation of the score, there is several issues. First, the score has been developed to predict the\nANA+PAL resistance (binary, yes or no) in the neoadjuvant setting, and is validated on another outcome (progression-free\nsurvival). This is therefore not a good external validation (for more information, see PMID: 38224968). Second, there is no\ninformation on how are chosen the thresholds “low”, “medium” or “high” for expression of the signature (quartile? distributions?). On the Kaplan-Meier curves, the 95% confidence intervals are not displayed, but when looking at the number of patients at risk, we can see that the subgroups are quite small.\n- Regarding clinical response, only 21/34 patients that completed the 5 cycles had a reported clinical response. This means\nthat this response rate is evaluated only in sensitive patients and not in resistant patients that stopped the treatment. This\nshould be more transparent in the narrative. In addition, in an intention-to-treat analysis, which is recommended to avoid to\ninflate the effect of the treatment, the denominator of the response rate should be 34 and not only 21 as current.\n- It is written that it was a RECIST evaluation (radiological evaluation based on target lesions), but the rhythm of assessment\nis not defined. It is a response at a given time point (e.g. at surgery) ? Is it the best overall response on treatment?\nThis point deserves some clarification.\nMinor comments:\n- The authors stated in the responses to the rebuttal letter that “As discussed earlier in response to previous reviewers’\ncomments, clinical characteristics were insufficient to discern responders vs non-responders (Table 1)”, which is not\ncompletely true, as tumor size (T3 versus T2) and grade (III versus I-II) were associated with resistance, and may have been\nincluded in the model that predicts resistance to ANA+PAL\n- The CCCA seems to be used more and more in the neoadjuvant setting for breast cancer as an endpoint, but its relevance\nfor patients could be debated. Notably, the rate of patients that managed to undergo surgery is probably a more meaningful\nclinical endpoint, even though it is understandable in the setting of this translational study that this endpoint has been\nchosen\n- The one-sided error rate is 10% for the computation of the sample size, therefore if the authors want to compute confidence\nintervals that mirror the sample size, it is not a 90% coverage but an 80% coverage that should be chosen. Anyways, as this\nis not a sample size based on confidence interval, it is not mandatory to reduce the coverage of confidence intervals\nTo conclude, from the statistical perspective, the paper does not meet the standards of a well-conducted clinical research\nstudy. I would expect that Nature Communications would require a better methodology for clinical research, as they do for\nbasic science."}, "author_response": "We thank the reviewers for the thoughtful comments for our manuscript titled: “Biomarkers of Response to\nNeoadjuvant Palbociclib Plus Anastrozole in Endocrine-Resistant Breast Cancer”. We have read all the\ncomments in detail and have addressed all of the concerns. Below, we provide a summary of the major additions\nto the manuscript as well as a point-by-point response to each of the reviewers' comments. We hope that our\nrevisions and responses meet with your approval.\nOur major additions to the manuscript are summarized below:\n1. We performed additional analyses of the proteogenomic data, including:\na. Transcriptomic changes between C1D15 and baseline in Resistant versus Sensitive tumors\n(New Fig. 5).\nb. Integration of RNA-seq and mass spectrometry analysis for C1D15 samples (New Fig. 4).\nc. Validation of differentially expressed genes and pathways in Resistant versus Sensitive tumors\nat baseline using gene expression data from the NeoPalAna Initial cohort (New Suppl. Fig. 4).\n2. We performed in vitro validation of JAK/STAT signaling, which is downstream of IFN and inflammatory\npathways, in resistant ER+ breast cancer cells:\na. We analyzed differentially expressed genes and pathways in MCF7 cells with acquired\nresistance to CDK4/6 inhibitors through chronic treatment with palbociclib or abemaciclib (Fig.\n6).\nb. We examined the anti-tumor activity of a series of clinical-grade JAK1/2 inhibitors in CDK4/6\ninhibitor resistant MCF7 cell lines and ER positive patient-derived xenograft (PDX) organoid\nmodels (New Fig. 6).\n3. We derived a CDK4/6i-resistance 33-gene signature using data from the NeoPalAna ET-R cohort and\nthe data from the NeoPalAna initial cohort, and validated it in an independent external cohort below:\na. Metastatic HR+/HER2- cohort (NCT04526587), treated with CDK4/6i in combination an\naromatase inhibitor (AI) or fulvestrant (FUL): 151 patients (115 AI + CDK4/6i; 36 fulvestrant +\nCDK4/6i) (New Fig 9).\nWe believe that these findings have strengthened the robustness of our findings.\nPlease find our point-by-point responses to each of the reviewers’ comments below:"}, {"round": 3, "reviewer_comments": {"reviewer_3": "I have reviewed this revision of the manuscript and believe it is significantly improved. Though the numbers are small, the\nuse of a validation cohort, and the underlying biology uncovered is sufficiently interesting and should direct the\ninvestigations in larger cohorts.\n(Remarks on code availability)", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\n(Remarks on code availability)", "reviewer_5": "Statistical review.\nRegarding my main concern—the development of the signature—the authors have significantly improved the transparency\nof the reporting of their results. However, several issues remain:\n• Could you please annotate the code regarding correction for optimism of AUC ? I am not sure to understand what have\nbeen done.\nSee 10.1136/ bmj-2023-074819 “Bootstrapping is a resampling technique, where a bootstrap sample is created by randomly\nsampling (with replacement) from the original data. In the enhanced bootstrap, a model is developed (repeating the model\nbuilding steps used to develop the model on all the data) in each bootstrap sample and its performance evaluated in this\nsample as well as the original dataset to get an estimate of optimism of model performance. This process is repeated many\ntimes and the average optimism calculated, which is then subtracted from the apparent performance.”\n• Why does the methods section not mention that the signature is defined as the averaged Z-score of the genes, as stated by\nthe authors in their rebuttal letter?\n• Why was bootstrap validation performed on each gene independently, rather than on the composite model using the\nsignature and/or Ki67 as the dependent covariate?\nThe manuscript would also benefit from clarifying some points:\n• Line 213, page 9: The phrase “in an unbiased manner” should be removed. The authors incorporated expert knowledge in\nthe selection of covariates, which, while acceptable in model development, cannot be considered unbiased.\n• Line 226, page 9: The term “iterative selection” is unclear. The authors should clarify what this process entailed\n• Line 459, page 19: The authors should specify the exact sample size of the cohort used to develop the score (i.e., patients\nwith RNA-seq data). Based on the current study, this appears to be n = 20, but it is unclear whether additional samples were\nincluded.\n• Line 568, page 24: The statement “This provided validation…” should be tempered. The results supporting the gene\nsignature are fragile (very small sample sizes for the development and validation cohorts, different outcomes for the\ndevelopment and the validation of the score) and should be presented with appropriate caution. As mentioned by the\nauthors in the rebuttal letter “future large studies are needed to confirm and generalize these findings.”\n(Remarks on code availability)\nThe code is provided, but cannot be run without the source file, which is understandable given that it contains patient data.\nTo enhance clarity, additional annotations would be helpful, in paritcular for the procedure of correction of AUC for optimism\n(see previous comment)."}, "author_response": "Response to Reviewers’ Comments\nWe thank the reviewers for the insightful comments. Please see our point-by-point response below.\nReviewer #1 (Remarks to the Author)\nThe new data and experiments improve the overall quality of the manuscript, addressing the majority of the\nconcerns rised by this reviewer. Although the number of patients cant be changed, the validations and the\nexperimetns make the whole work more convincing and suitable for this journal.\nResponse: We thank the reviewer for the positive assessment of our revised manuscript.\nReviewer #2 (Remarks to the Author)\nSubstantial changes have been made to the manuscript following the reviewers' comments. With additional\ndatasets and new analysis, the authors were able to characterise -omics landscape related to cell proliferation\nand changes following CDK4/6i. The comparison between resistant and response samples, at molecular levels\nand functional levels, provide insight to the mechanism underpinning treatment response. The new 33 gene\nbaseline model is a good add-on.\nThe sample size unfortunately limited the depth of this research, nonetheless I am satisfied with the level of\nevidence underpinning the main conclusion of the paper, and found that the major concerns from the reviewers\nhave been addressed to a satisfactory level.\nThe only addition I would like to suggest is to include in this manuscript is a summary table of RNA/protein data\nrelated to the 33 gene (BL predictors) in the supplementary material.\nResponse: We thank the reviewer for the positive feedback and guidance to further strengthen our\nmanuscript. As requested, we have added a summary table of the 33 genes, including the area under curve\n(AUC) for each candidate in predicting resistance to anastrozole in combination with palbociclib in the\nNeoPalAna ET-R and NeoPalAna Initial cohorts, along with their annotated biological functions (new\nSupplementary Table 8). In addition, Fig. 9B has also been updated to highlight the canonical, biological\nfunction of each candidate.\nReviewer #3 (Remarks to the Author)\nOn the whole, this revision of the work is improved. Integration of RNA-seq and mass spectrometry analysis\nand Transcriptomic changes between C1D15 and baseline in Resistant versus Sensitive tumors improve the\npaper significantly. Moreover, the addition of the validation cohort was very helpful.\nResponse: We thank the reviewer for the positive assessment of our revised manuscript.\nHowever, there are several issues that need some attention:\nThe additional analysis with the 33 genes signature on the three datasets indeed looks promising and makes\ngood utilization of all the data generated. The authors however, should clearly elaborate on how the 33 genes\nwere derived/nominated based on the NeoPalAna ET-R and NeoPalAna Initial data. The number 33 seems\narbritrary and without any rationale. Were they picked because they were overlapping genes in both the\ndataset sensitive vs resistant comparisons or had molecular pathway relevance etc? In Fig.9B could you\nhighlight the genes based on the different categories described (cell cycle, oncogenic signaling, proliferation,\nand immune and interferon response mediators etc). This is a major oversight.\nResponse: We thank the reviewer for the opportunity to clarify our methods for deriving the gene signature.\nWe identified the signature based on both statistical evidence and a priori knowledge following a workflow described\nbelow. From the two training cohorts (the NeoPalAna ET-R and NeoPalAna Initial cohorts), candidate genes\nwere first unbiasedly selected based on fulfilment at least one of the following criteria: 1) Differentially expressed\ngenes (DEGs) between Resistant and Sensitive groups from the NeoPalAna ET-R cohort, of which included\noverlapping DEGs from the NeoPalAna Initial cohort (915 DEGs from NeoPalAna ET-R identified using DEseq2\nwith adjusted p-value < 0.05, of which 332 were shared with NeoPalAna Initial cohort identified using Limma with\na p-value < 0.05 given the small sample size of Resistant samples); 2) annotation in key enriched molecular\npathways identified through GSEA with FDR p-value < 0.05 including E2F targets, G2M checkpoint, Interferon\nalpha response, Interferon gamma response, MTORC1 signaling, MYC targets V1, and MYC targets V2, in\naddition to Hallmark Estrogen response early, Hallmark Estrogen response late, and KEGG cell cycle; 3) genes\nof interest including MTOR (not annotated in the Hallmark MTORC1 signaling gene set), key breast cancer\nmarkers (ERBB2 and ESR1), and those mediating immunological pathways (Tumor inflammation signature and\nHLA genes). Logistic regression models were performed for ROC curve analysis for each candidate gene. The\nwell-known biological and clinically validated markers, such as ESR1 (estrogen receptor) and ERBB2 (HER2),\nwere included in the final signature despite having a weak AUC in the NeoPalAna Initial cohort (AUCs 0.55 and\n0.59, respectively). These criteria resulted in generation of the 33-gene signature. 23 of 33 genes were significant\nin the differential expression analysis from the NeoPalAna ET-R cohort, highlighting that this dataset unbiasedly\ncontributed substantially to the selection of genes.\nOf the 33 genes in the signature, 27 were available in the metastatic validation cohort, which was profiled using\nthe HTG EdgeSeq Oncology Biomarker Panel (covering 2,560 genes). Although six candidates (ARPC5L,\nBCL11A, CDCA8, SKA1, TCEAL4, and TES) were missing in this panel, the remaining genes in the gene\nsignature retained sufficient discriminatory power to stratify patients and demonstrated meaningful associations\nwith survival outcomes. We have updated the Methods section accordingly. A schematic of the workflow is\nprovided below and in new Supplementary Fig. 6.\nAs requested, we have now provided a summary table of the 33 genes, including the area under curve (AUC)\nfor each candidate in the NeoPalAna ET-R and NeoPalAna Initial cohorts, along with their annotated biological\nfunctions and workflow schematic (New Supplementary Table 8, New Supplementary Fig. 6, and below).\nThe discussion around figure 6 is rather confusing. While the expression analysis and western blot data on the\nMCF7 in vitro analysis looks promising the in vitro drug response data is not convincing. For example - Authors\njustify the use of JAK/STAT inhibitors based on the expression analysis on Palbo-R vs parental and Abema-R\nMCF7 vs parental cells. However there is no real difference (increased sensitivity) observed between parental\nand Palbo-R or Abema-R cell lines for any drug or dose (Fig.6H). In fact, it seems that the resistant cell lines\nare perhaps less sensitive than the sensitive line. The data just shows that all the 3 cell lines have increased\ncell death for increasing concentrations of most drugs - which would be expected? If the data does not show\nthat Palbo-R or Abema-R MCF7 cells have increased sensitivity to JAK inhibitors compared to parental cells\nwhat are the authors trying to state with the data?\n\"Screening with these inhibitors and the JAK1 selective inhibitor itacitinib in parental MCF7 and CDK4/6i-\nresistant cells suggested a higher dependency on JAK2 than JAK1, with similar sensitivity across the cell lines\n(Fig. 6H).\"That Pacritinib is more effective on higher doses tested (in all three cell lines) compared to other\ndrugs could just be a function of concentrations tested in the experiment. That no cell death is observed in\nresponse to Itacitinib could just be inadequate concentrations tested in this experiment. In order to draw this\nconclusion of increased dependency on JAK2 vs JAK1 authors will have to normalize to the JAK molecule\ninhibitory effect of the drugs for a given concentration or perform\ngene knock out experiments to confirm the JAK1 vs JAk2 (and IRAK1) sensitivity.\nFor the in vitro drug data on organoids derived from PDX tumors (Fig.6K and 6L), the authors state that \"Both\norganoid models showed reduced sensitivity to palbociclib (Fig. 9K) but were responsive to pacritinib at low\nmicromolar doses (Fig. 9L), which was comparable to the response observed in MCF7 Palbo-R/Abema-R\ncells. These data support the potential importance of JAK/STAT signaling in CDK4/6i resistant BCs.\" Not sure if\nthe data shown is enough to compare sensitivities between drugs and make this statement - these are two\ndifferent drugs with two completely different mechanisms of action. So if WHIM43 for example has an IC50 of\n1.9uM to Pacritinib (JAK2 inhibitor) and 3uM to Palbociclib (CDK4/6 infinite) does it have any significant\nbiological implication? Also figures 6K and 6L have been referred to wrongly in the text.\nResponse: Thank you for these insightful comments. As noted, there were no substantial differences in the\nsensitivity of the panel to JAK2 inhibitors between the parental and Palbo-R/Abema-R MCF7 cell lines. These\ndata suggest that JAK-STAT pathway may not be the sole driver of drug resistance, but rather may also represent\na downstream consequence of other signaling pathways. particularly in the in vivo breast cancer context where\ninterplay with the tumor microenvironment exists. Further studies are warranted to clarify the role of JAK–STAT\npathway upregulation in CDK4/6i-resistant breast cancer, particularly in the in vivo breast cancer setting where\ninteractions with the tumor microenvironment can profoundly influence signaling dynamics and therapeutic\nresponse. For instance, suppression of the JAK-STAT pathway could attenuate both oncogenic signaling and\nimmune modulators such as PD-L1 based on previous studies (PMID: 26155422). We have updated our\ndiscussion to reflect these points.\nThank you for the comments regarding JAK2/JAK1. We have revised our interpretation of the JAK2/JAK1\ncomparison.\nWith respect to defining sensitivity vs resistance to palbociclib or pacritinib in the PDX-derived organoid studies\nshown in Fig. 6K and Fig. 6L, it should be noted that in vitro IC values cannot be directly compared across\nthese two agents. Instead, they should be interpreted in the context of steady-state plasma concentrations\nachieved in patients receiving approved doses (~0.13-0.28 µM₅ f₀or palbociclib [PMID: 35890213]; ~12–13 µM\nfor pacritinib [PMID: 33232476]). On this basis, the observed IC of 1-2 µM for pacritinib falls well within the\nclinically relevant exposure range, whereas the ~3 µM IC for palbociclib is far above concentrations achieved\nin patients and thus not therapeutically attainable. These data su₅₀pport the conclusion of sensitivity of the WHIM\nPDX organoid models being resistant to palbociclib but se₅n₀sitive to pacritinib. Also as shown in Fig. 6I that\npacritinib at 1 µM suppressed pSTAT3 signaling in MCF7 Palbo-R, consistent with our prior work in\nmyeloproliferative neoplasm models where 1 µM pacritinib effectively inhibited STAT1, STAT3, and STAT5\nphosphorylation in CD34 progenitor cells (PMID: 37203407), demonstrating on-target JAK-STAT pathway\nsuppression. Nonetheless, we agree that further mechanistic studies are warranted to delineate the precise\npathways through which ⁺pacritinib exerts its anti-tumor effects in ER /HER2 CDK4/6i-resistant breast cancer.\n⁺ ⁻\nWe have updated the references to Figs. 6K and 6L in the text.\n\"Zero Interaction Potency (ZIP) drug synergy calculations of CDK4/6i and pacritinib at indicated doses.\" What\nare the doses? There is no description of how this synergistic experiment was performed and how should one\ninterpret the colors etc. The legend was not helpful.\nResponse: We apologize for the lack of clarity in this figure. These synergy experiments were performed\nutilizing multiple drug doses by 2-fold serial dilution (palbociclib/abemaciclib doses: 0, 0.5, 1, 2, 4, and 8 μM;\npacritinib doses: 0, 0.16, 0.31, 0.63, 1.25 μM). We have now included the doses in the updated figures,\nupdated the scale bar, and updated the figure legend to include that green coloring represents a negative ZIP\nscore (antagonism when ZIP < -10) and red coloring represents a positive ZIP score (synergism when ZIP >\n10).\nAuthors should describe in results or legend data in figured better - for example in fig 6G -western blot - There\nis no mention of AKT and RB results.\nResponse: We have now expanded on these findings in the results section to highlight that 1) RB expression\nwas retained, which is important since its loss is a well-established mechanism of CDK4/6i resistance in\npreclinical models and 2) elevated AKT phosphorylation to support augmented oncogenic signaling in resistant\nlines.\nReviewer #4 (Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the\nNature Communications initiative to facilitate training in peer review and to provide appropriate recognition for\nEarly Career Researchers who co-review manuscripts.\nResponse: We thank this reviewer for their time in reviewing our manuscript.\nReviewer #5 (Remarks to the Author)\nAs a statistical reviewer, I will focus this review on the methodology of the clinical trial and the development of\nthe molecular signature.\nTo summarize this work, the authors studied the mechanisms of resistance in ER+ breast cancer to the\ncombination of anastrozole + palbociclib. Translational experiments are performed on samples of patients\nincluded in a single-arm phase II Simon’s two-stage study. In this study, the combination is used in a\nneoadjuvant setting, for patients with large tumors (T2 or more) whatever the lymph node status and without\nmetastatic disease.\nThe clinical study was stopped before the end of recruitment after the decision of the data and safety\nmonitoring board for efficacy, which is an unusual situation, as Simon two-stage design allow to stop the study\nfor futility, not for efficacy.\nThe primary endpoint is the rate of complete cell cycle arrest (CCCA) after 1 month of combination of\nanastrozole + palbociclib, assessed by Ki67% immunohistochemistry and centrally reviewed. The sample size\nis reproducible, though the hypotheses are very pessimistic (unacceptable rate of 5%, acceptable rate of 20%\nof CCCA).\nIn the analysis of the primary endpoint, there were 22/33 (66%) of patients that exhibited CCCA at C1D15.\nMajor comments:\n- My main concern on that work is about the 33 genes signature that would be predictive of resistance to the\ncombination of anastrozole + palbociclib (ANA+PAL). There is first a strong issue of reporting. The authors can\nlook at the TRIPOD recommendations, that have been recently updated for AI-based prediction models (PMID:\n38626948). Even though these recommendations are for dedicated work on prediction models (not integrated\nto a translational work), it could serve as a basis for the reporting of this part of the work. There is no info on\nthe selection process of the variables (and their possible transformation) into the model, nor the nature of the\nmodel used to estimate the probability of being resistant (logistic regression model? Others models?).\n- Then, probably the biggest issue, there is only 33 patients to develop this model (with 11 patients that\npresented the endpoint “resistance to ANA+PAL”) which is very problematic if genes are introduced as unique\nvariables in the analysis (33 potential variables). For example, a very loosie rule of thumb is 10 events for 1\nvariable in a logistic regression model (therefore in the case of 11 events, only 1 variable can explain the\noutcome). And finally, there is no correction of optimism when estimating the performances of the gene\nsignature (e.g. AUC) by any technique (for example, bootstrap, see PMID: 38191193). The optimism is linked\nto the fact that the same data are used to choose the model and evaluate its performances.\nResponse: We thank the reviewer for highlighting the TRIPOD recommendations. We agree that the added\ndetail and greater transparency of our study protocol and analysis would be beneficial for the community. As\nsuch, we have amended our study reporting of relevant topics according to the TRIPOD+AI checklist (New\nSupplementary Data 2).\nWe are aware of the rule of thumb that 10 observations are roughly needed to support one variable in traditional\nstatistical modeling, and that high dimensional omics data suffers from the curse of dimensionality of “small N,\nbig P” and calls for caution and handling of the high dimensionality. We selected each biomarker first through\nunivariate analyses based on differential gene expression (DE) analysis (where we corrected for multiplicity in\npicking differential genes) and ROC analysis using the metric of AUC to assess the discriminative ability of each\nindividual gene. We also complemented the selection process with a priori knowledge in the field. See details of\ncandidate gene selection in our reply to Reviewer 3 and in the updated methods section. We then exploratively\nassessed the overall discriminative ability of the selected genes collectively in an unsupervised manner using\nspare principal component analysis (Fig. 3C). To the end, we constructed the averaged Z-score of the selected\ngenes (where all the genes were oriented the same direction to have higher expression) as the composite\nsignature score to circumvent the issue of “curse of dimensionality”. In this revision, as suggested by the\nreviewer, we conducted additional bootstrapping analyses, as part of the internal validation, to report the\noptimism-corrected AUC as a robust evaluation of the performance of individual biomarkers we selected and the\nsignature. We found the bootstrapping based results agreed greatly with the results we originally reported (New\nSupplementary Table 9, new Supplementary Data 3, and below).\nWe agree with the reviewer that the small sample size of the study may limit our ability to develop a robust and\ngeneralizable gene signature and have acknowledged this in our discussion. While we are fully aware of this\nlimitation, unfortunately, small sample size is the reality for many studies, as it is for our study. We thus adopted\nthe standard strategy universally adopted by most biomarker signature studies: we derived the signature using\na training set, followed by a completely independent external validation. In the external validation experiment,\nwe were fully blinded to the patient phenotypes and clinical efficacy data. Using the RNA sequencing data, we\nderived individual patient signatures and provided them to the validation team, who independently conducted\nthe association analyses to evaluate the predictive ability of the signature in their validation cohort. The rigorous\nexternal validation (Fig. 9G-H) supports the predictive value of the signature, though future large studies are\nneeded to confirm and generalize these findings. Importantly, while the signature was derived using an\nintermediate endpoint (C1D15 Ki67, 10% cutoff), the external validation employed long-term outcomes (PFS and\nOS), making it a more rigorous test.\n- Regarding the external validation of the score, there is several issues. First, the score has been developed to\npredict the ANA+PAL resistance (binary, yes or no) in the neoadjuvant setting, and is validated on another\noutcome (progression-free survival). This is therefore not a good external validation (for more information, see\nPMID: 38224968). Second, there is no information on how are chosen the thresholds “low”, “medium” or “high”\nfor expression of the signature (quartile? distributions?). On the Kaplan-Meier curves, the 95% confidence\nintervals are not displayed, but when looking at the number of patients at risk, we can see that the subgroups\nare quite small.\nResponse: We appreciate the reviewer’s perspective. However, we believe that validating the signature against\nsurvival outcomes strengthens the analysis, as survival endpoints are universally regarded as the gold standard.\nWhile Ki67 provides an early readout correlated with long-term outcomes, the ultimate goal of our signature is to\npredict survival, making this validation both appropriate and clinically meaningful. Obtaining an external\nvalidation cohort in CDK4/6i-resistant populations is challenging, making the metastatic breast cancer validation\ndataset particularly valuable. The consistent identification of candidate mediators and predictors across cohorts\nreinforces the robustness and clinical relevance of our findings.\nFor the validation cohort, patients were stratified into tertiles according to their gene expression score (also\nupdated in our methods section). We have also regenerated the Kaplan-Meier curves with the 95% confidence\nintervals for this reviewer (see below). Although the subgroups are relatively small as indicated by this reviewer,\nthe survival data remain both statistically significant and clinically meaningful, particularly for this metastatic\ndataset.\n- Regarding clinical response, only 21/34 patients that completed the 5 cycles had a reported clinical response. This means\nthat this response rate is evaluated only in sensitive patients and not in resistant patients that stopped the treatment. This\nshould be more transparent in the narrative. In addition, in an intention-to-treat analysis, which is recommended to avoid to\ninflate the effect of the treatment, the denominator of the response rate should be 34 and not only 21 as current.\nResponse: The Result section describing clinical response has been updated to clarify the response rate both\nin patients who completed all cycles of therapy and the ITT population.\n- It is written that it was a RECIST evaluation (radiological evaluation based on target lesions), but the rhythm\nof assessment is not defined. It is a response at a given time point (e.g. at surgery) ? Is it the best overall\nresponse on treatment? This point deserves some clarification.\nResponse: The definition of clinical response and the corresponding assessment timepoint have been\nupdated in the Methods section.\nMinor comments:\n- The authors stated in the responses to the rebuttal letter that “As discussed earlier in response to previous\nreviewers’ comments, clinical characteristics were insufficient to discern responders vs non-responders (Table\n1)”, which is not completely true, as tumor size (T3 versus T2) and grade (III versus I-II) were associated with\nresistance, and may have been included in the model that predicts resistance to ANA+PAL\nResponse: We apologize for this erroneous inclusion in the initial rebuttal letter. We would like to clarify that\nclinical characteristics including tumor size and stage were NOT included in the resistance gene signature.\n- The CCCA seems to be used more and more in the neoadjuvant setting for breast cancer as an endpoint, but\nits relevance for patients could be debated. Notably, the rate of patients that managed to undergo surgery is\nprobably a more meaningful clinical endpoint, even though it is understandable in the setting of this\ntranslational study that this endpoint has been chosen\nResponse: We agree with the reviewer that endpoints should reflect outcomes meaningful to patients. We\nselected complete cell-cycle arrest (CCCA; Ki67 ≤2.7%) at Cycle 1 Day 15 (C1D15) as the primary endpoint\nbecause it is an on-target, early pharmacodynamic marker for CDK4/6 inhibition.\n- The one-sided error rate is 10% for the computation of the sample size, therefore if the authors want to\ncompute confidence intervals that mirror the sample size, it is not a 90% coverage but an 80% coverage that\nshould be chosen. Anyways, as this is not a sample size based on confidence interval, it is not mandatory to\nreduce the coverage of confidence intervals\nResponse: While the confidence coverage level is not directly related to the signature itself, the reviewer is\ncorrect that the confidence interval accompanies the 1-sided error rate of 10% is usually100*(1-2*alpha) % and\nthus 80% coverage. We chose to report the traditional two-sided 90% confidence interval following the\nrecommendation by Steiger (Psychological Methods 2004, Vol. 9, No. 2, 164–182,\nhttps://doi.org/10.1037/1082-989X.9.2.164, see the top texts on the Page 174 under Section “The Relationship\nBetween Confidence Intervals and Hypothesis Tests—Choosing the Appropriate Interval”).\nTo conclude, from the statistical perspective, the paper does not meet the standards of a well-conducted\nclinical research study. I would expect that Nature Communications would require a better methodology for\nclinical research, as they do for basic science.\nResponse: We appreciate the reviewer’s thoughtful comments and have revised the manuscript to strengthen\nthe scientific rigor. While certain limitations (e.g., sample size) are inherent, we have a transparent signature\ndevelopment process where candidate genes were selected based on statistical evidence and a priori field\nknowledge and most importantly, the findings were validated in an independent cohort in a blinded fashion. We\nbelieve these revisions enhance the transparency and methodological robustness of the manuscript and align\nit with the standards expected by Nature Communications for clinical and translational research contributions.\nReviewer #5 (Remarks on code availability):\nThe code is provided, but cannot be run without the source file, which is understandable\ngiven that it contains patient data. To enhance clarity, additional annotations would be\nhelpful, in paritcular for the procedure of correction of AUC for optimism (see previous\ncomment).\nResponse: We have now annotated the R function for AUC correction and also other\nfunctions in the codes.\nWe performed the overoptimism correction via bootstrapping, with the following\ndetailed steps:\n(1) Calculate the AUC of an individual gene (or a composite) using the original data,\ndenoted by AUC\noriginal\n(2) Generate bootstrap data sets (k=1, 2, …, K and K set at1000) by random\nsampling with replacement.\n(3) Within each bootstrap dataset k, we recalculate the AUC of an individual gene\n(or composite) as AUC\nk\n(4) Calculate the difference between a bootstrap AUC and the original AUC as\nΔAUC = AUC - AUC\nk k original\n(5) Calculate the averaged AUC difference across all the K bootstrapping datasets\nas ΔAUC. ΔAUC should presumably be negative, as we expect lower AUC from\nbootstrapped data as compared to using the original data.\n(6) Calculate the corrected AUC by subtracting the optimism part from the original\nAUC: AUC = AUC +ΔAUC\noptimism-corrected original\nThe steps of optimism correction are now incorporated into the Supplemental\nInformation.\nWhy does the methods section not mention that the signature is defined as the averaged Z-score of the genes,\nas stated by the authors in their rebuttal letter?\nThe averaged Z-score is used as the composite signature. However, we are in the\nprocess of a patent application and thus prefer not to disclose this detail of\nconstructing the signature.\nWhy was bootstrap validation performed on each gene independently, rather than on the\ncomposite model using the signature and/or Ki67 as the dependent covariate?\nThe performance of the composite relies on the performance of the individual genes.\nThus, we started by evaluating the individual genes. We now performed a similar\nbootstrapping validation on the composite signature without and with ki67 and\nreported the optimism-corrected AUCs for ki67 alone, for composite signature alone,\nand for ki67+composite signature io the Results section.\nThe manuscript would also benefit from clarifying some points:\nLine 213, page 9: The phrase “in an unbiased manner” should be removed. The authors\nincorporated expert knowledge in the selection of covariates, which, while acceptable in\nmodel development, cannot be considered unbiased.\nWe have removed this phrase following the reviewer’s suggestion.\nLine 226, page 9: The term “iterative selection” is unclear. The authors should clarify what\nthis process entailed.\nTo avoid confusion, we now removed the phrase “iterative selection” as the sentences\npreceding this phrase in that paragraph already reflected the selection procedure that\nwe unbiasedly identified differentially expressed genes between resistant and\nsensitive tumors and calculated their AUC, and we then added back the genes from\nthe pathway analyses and the a priori breast cancer genes.\nLine 459, page 19: The authors should specify the exact sample size of the cohort used to\ndevelop the score (i.e., patients with RNA-seq data). Based on the current study, this\nappears to be n = 20, but it is unclear whether additional samples were included.\nThe reviewer is correct that 20 patients have RNA-seq data. The sample size of 20 is\nclearly indicated in this sentence and in the flow diagram in Supplemental Figure 1:\n“Baseline (BL) RNA-seq was performed for 20 patients (13 Sensitive and 7 Resistant)\nwith sufficient tumor material (Supplemental Fig. 1) ”.\nLine 568, page 24: The statement “This provided validation…” should be tempered. The\nresults supporting the gene signature are fragile (very small sample sizes for the\ndevelopment and validation cohorts, different outcomes for the\ndevelopment and the validation of the score) and should be presented with appropriate caution. As mentioned by\nthe authors in the rebuttal letter “future large studies are needed to confirm and generalize\nthese findings.”\nWe agree that future larger studies are needed and now add a sentence after the\nstatement to acknowledge the limited sample size and call for future validation\nstudies.\nReviewer #5 (Remarks on code availability):\nThe code is provided, but cannot be run without the source file, which is understandable\ngiven that it contains patient data. To enhance clarity, additional annotations would be\nhelpful, in paritcular for the procedure of correction of AUC for optimism\n(see previous comment).\nResponse: We have now annotated the R function for AUC correction and also other\nfunctions in the codes."}]

Decision letter