Supplementary Table S3. RAS mutation status in target cell models.

The DestinyBreast (DB)04 and DB06 trials have shown clinical activity of trastuzumab-deruxtecan (T-DXd) in HER2-low and HER2-ultralow metastatic breast cancer. The identification of HER2-low and HER2-ultralow breast cancer is therefore essential for personalized therapy with T-DXd. We evaluated 723 residual tumors from the Penelope-B trial (NCT01864746) and correlated different levels of HER2 protein expression with prognosis and messenger RNA (mRNA) profiles, including HER2 transcripts. In Penelope-B, 57.68% (n = 417) of 723 residual tumors were HER2 low. The HER2-ultralow category was assigned to 109 (15.08%) tumors, and 197 (27.25%) tumors were completely HER2 negative (HER2 0). In Kaplan-Meier analysis, there were no survival differences among these 3 subgroups. There was no significant difference in HER2 mRNA expression between HER2-0 and HER2-ultralow tumors (P = .08). In contrast, there was a highly significant difference in HER2 mRNA expression between HER2-ultralow and HER2-low tumors (P < .0001) and between HER2-low and HER2-positive tumors (P < .0001). The extracellular protease cathepsin L, which has been suggested as a biomarker for extracellular cleavage of T-DXd, was detectable in all HER2-related subgroups and was a negative prognostic factor for invasive disease-free survival and overall survival (P = .0001) in preneoadjuvant core biopsies. In our study, we were able to characterize HER2 low as a clinically relevant and molecular defined tumor group with significantly increased HER2 expression. In contrast, for HER2 ultralow, we did not observe a defined molecular phenotype, despite the clinically relevant regulatory approval of T-DXd also in the ultralow subgroup. Additional investigations are needed to identify biomarkers beyond HER2 for T-DXd response as a basis for refined criteria for treatment eligibility.

PURPOSEPrecision therapies have improved outcomes in pancreatic ductal adenocarcinoma (PDAC) and highlight the utility of germline genetic testing (GGT) in clinical decision making. Here, we investigate the association of GGT of individuals diagnosed with PDAC over time to identify changes in testing patterns and the relationship of testing with clinicopathologic parameters and overall survival (OS).METHODSThe frequency of GGT of 979 patients with PDAC treated at the Roswell Park Comprehensive Cancer Center (RPCCC) between 2017 and 2024 was determined. Factors associated with performing testing and having a germline pathogenic variant (GPV) were assessed. OS and the impact of precision treatment were evaluated.RESULTSPatients with PDAC being treated at RPCCC that had GGT increased from 16.5% in 2017 to 57.6% in 2024. Patients with testing had improved OS compared with patients who did not (HR, 0.46). Individuals of older age, without a personal history of other cancers, without a family history of pancreatic cancer, with advanced disease, and with poorly differentiated tumors were less likely to have testing performed. Individuals with a personal history of other cancers were more likely to have a GPV. Of the 425 patients diagnosed with PDAC that had testing, 58 (13.6%) had a GPV detected. Of patients with testing, 12 had actionable mutations in BRCA1/2, seven of whom were subsequently treated with olaparib. Three patients harbored actionable mutations in MSH6, with one patient subsequently treated with pembrolizumab. Patients who received therapy informed by testing results had improved OS (HR, 0.09).CONCLUSIONGGT prevalence has increased at RPCCC and informed treatment decisions. Universal point-of-care testing is being implemented with the goal of completing testing for all patients with PDAC seen at RPCCC.

Supplementary Figure S5. A-C. Volcano plots displaying differentially expressed transposable elements in CDK4/6i (palbociclib, 0.5 μmol/L) and MEKi (trametinib, 25 nmol/L)-treated PDAC (MIA PaCa-2 (A), PA-TU 8988T(B)), and lung cancer (A549 (C)) cell models. The RNAseq data was downloaded from GEO database (GSE110397).

Figure S6. P16INK4A is a key determinant of response to BLU-222. A, Heatmap analysis from the cancer dependency map (DepMap) database displaying relative expression of CCNE1 and several endogenous CDK inhibitor genes in a panel of established ovarian and breast cancer cell lines. B, Western blot of RB1-E2F pathway activity in MDA-MB-157 cells following overexpression (OE) of CDK4 with and without BLU-222 treatment. C, Live cell proliferative monitoring of CDK4 WT (left) and overexpressing (OE, right) of MDA-MB-157 cells treated with serial concentrations of BLU-222 [n = 3–4 per group; two-way ANOVA, significance (shown as #) comparing CDK4 OE versus CDK4 WT at 62.5 nM BLU-222 dose; error bars represent SEM]. D, Western blot of RB1-E2F pathway activity in MDA-MB-157 cells following overexpression (OE) of CDK6 with and without BLU-222 treatment. E, Live cell proliferative monitoring of CDK6 WT (left) and overexpressing (OE, right) MDA-MB-157 cells treated with serial concentrations of BLU-222 [n = 3–4 per group; two-way ANOVA, significance (shown as #) comparing CDK6 OE versus CDK6 WT at 250 nM BLU-222 dose; error bars represent SEM]. F, Table summarizing effect of CDK4 or CDK6 overexpression (OE) or CDKN2A knockdown on IC50 to BLU-222 in Kuramochi and MDA-MB-157 cells. G, Hematoxylin and Eosin (H&E) staining and of tumors excised from MDA-MB-157 xenografts in NOD scid gamma (NSG) mice treated with either vehicle or BLU-222; arrows indicate cells with enlarged morphology (scale bar = 100 µm). #, P < 0.05.

PDF file, 193K, Supplemental Figure 1: Demographic data from the datasets that were employed in this study Supplemental Figure 2: Genes comprising the RB- loss signature used in this study Supplemental Figure 3: The sensitivity, specificity, negative and positive predictive value of the RB-signature, p16ink4a, and RB protein is provided for each cohort analyzed Supplemental Figure 4: The association of RB immunohistochemical status with ER, Her2 and Grade is provided, as is the association of RB and ER status with pathological complete response in the TJU cohort. Supplemental Figure 5: Analyses of RB-loss signature in Her2 positive breast cancer cases treated with TFAC.

Differential vulnerabilities to CDK2 versus CDK4/6 inhibition. A, Table of top identified predictive genes in breast and ovarian carcinomas for response to CDK2 inhibition from the publicly available DepMap dataset using CRISPR (top) and RNAi (bottom) screens. B, Cell Titer Glo values normalized to 0 nM treatment of ovarian and breast cancer cells treated with BLU-222 for 5 days. C, Table summarizing breast and ovarian cancer cell line response to BLU-222 treatment. D, COV-504 and PE-O1 ovarian cancer cell lines treated with serial concentrations of BLU-222 or palbociclib for 5 days and monitored with Cellcyte or Incucyte live cell proliferative software (n = 3–4 per group; error bars represent SEM).

Intrinsic and acquired resistance to CDK4/6 inhibitors (CDK4/6i) is a critical challenge in hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC). CDK2 inhibitors (CDK2i) show promise in this context, with several currently under early-phase clinical evaluation. Here, by analyzing tumor and liquid biopsies from patients treated with the selective CDK2i PF-07104091, we observed that PF-07104091 monotherapy achieved disease stabilization in a cohort of CDK4/6i-resistant HR+/HER2- MBC patients. Notably, responses were observed irrespective of RB expression and phosphorylation, whereas non-progressive disease appeared more frequently among patients whose tumors retained wild-type TP53. Studies in established and patient-derived cell lines further substantiated that the growth suppressive effects of CDK2i were independent of RB status in CDK4/6i-resistant models. CDK2i activity reduced DNA replication rate, increased DNA damage, and inhibited mitotic entry, with growth inhibition dependent on p53 expression. These findings indicate that CDK dependency shifts from CDK4/6 toward CDK2 as cells transition from CDK4/6i-sensitive to CDK4/6i-resistant state. The distinct mechanisms of CDK2i and CDK4/6i support enhanced synergistic activity in HR+/HER2- MBC with acquired resistance to CDK4/6i therapy.

Cyclin-dependent kinases (CDKs) are key regulators of cell-cycle progression and have long been recognized as attractive therapeutic targets in oncology. Inhibitors of CDK4 and CDK6 have transformed the treatment of patients with breast cancer, providing proof of principle for the clinical utility of CDK inhibition. However, despite extensive research over the past decade, CDK4/6 inhibitors have so far achieved regulatory approval only in this setting. Nonetheless, recent studies refining the optimal use of these drugs have revealed new opportunities to extend their use in other tumour types. Most notably, new combinatorial approaches, particularly with targeted therapies such as HER2 and PI3K inhibitors, are expanding the therapeutic scope of CDK4/6 inhibitors, and insights into resistance mechanisms have driven the development of highly selective CDK2 inhibitors to treat refractory disease. Additionally, efforts to mitigate haematological toxicity have prompted next-generation CDK4-selective inhibitors. Finally, biomarkers, although still underdeveloped clinically, are beginning to inform patient selection. This Review highlights the changes that have occurred in the clinical use of CDK inhibitors since the first FDA approval 10 years ago and the prospects for realizing their broader potential in cancer therapy.

Supplementary Table S4. RAS mutation status in soft tissue sarcoma cell models.

Supplemental File 1. BLU-222 combinatorial drug screen data from MDA-MB-231 (tab 1) and COV-504 cells (tab 2) reported in Figure 5. Data reported as percent growth compared to sole treatment with 0.1% DMSO.

Supplementary Figure S3. Volcano plots displaying differentially expressed genes in CDK4/6i (palbociclib, 2 μmol/L) (A) and MEKi (pimasertib, 0.5 μmol/L) (B)-treated HT1080 cells. The RNAseq data was downloaded from the GEO database (GSE180265). C. Heatmap displaying log2 fold change of select cell cycle regulatory genes from HT1080 cells treated with CDK4/6i (palbociclib, 2 μmol/L) or MEKi (pimasertib, 0.5 μmol/L). D. Heatmap displaying log2 fold change of select immune-related genes from HT1080 cells treated with CDK4/6i (palbociclib, 2 μmol/L) or MEKi (pimasertib, 0.5 μmol/L).

Figure S5. Cooperative and antagonistic combination strategies with BLU-222. A, BLISS synergistic analysis for antagonism between BLU-222 and CHK1 checkpoint kinase inhibitors in MDA-MB-231 cells (n = 3 per group; BLISS scores calculated with SynergyFinder v3). B, Live cell proliferative monitoring of the indicated cell lines treated with BLU-222, palbociclib, or in combination (n = 3–4 per group; one-way ANOVA; error bars represent SEM). C, Quantification of mVenus-DHB and mCherry-RB CDK activity sensor localization to the cytosol or nucleus in MDA-MB-231 and HCC-1806 cells following treatment with either 500 nM BLU-222, palbociclib, or combination. D, Quantification of mVenus-DHB and mCherry-RB CDK activity sensor localization to the cytosol or nucleus in MDA-MB-231 and HCC-1806 cells following treatment with siCCNE1, siCCND1 or combination. E, Live-cell imaging endpoint stills for HCC-1806 cells treated for 5 days with 500 nM BLU-222, palbociclib, or combination (scale bar = 100 µm). ns, not significant; *, P < 0.05; **, P < 0.01; ****, P < 0.0001.

Figure S4. Loss of RB1 shifts phase of arrest and sensitivity to BLU-222. A, Heatmap of RNA sequencing data comparing differential gene expression between BLU-222-treated Kuramochi (125 nM BLU-222) and OVCAR-8 (250 nM BLU-222) cells normalized to respective DMSO-treated controls. B, Cell cycle profiling from univariate flow cytometric analysis from OVCAR-8 cells at baseline, after 1 to 5 days of treatment with 500 nM BLU-222, and after 1 to 5 days of drug washout. C, Western blot for abrogation of G2/M checkpoint signaling in OVCAR-8 cells following doxycycline (Dox) induction of the RB1 expression vector and treatment with BLU-222 compared with RB1 deficient controls. D, Live cell proliferative monitoring of OVCAR-8 cells following Dox induction of the RB1 expression vector (RB1 Dox +) with non-induced controls (RB1 Dox -) and treatment with serial concentrations of palbociclib (n = 3–4 per group; error bars represent SEM). E, Table summarizing effect of RB1 induction on IC50 to BLU-222 in OVCAR-8 cells. F, Live cell proliferative monitoring of OVCAR-8 cells following doxycycline induction of the RB1 expression vector (RB1 Dox +) with non-induced controls (RB1 Dox -) and transfection with siNT, siCCNE1, or siCCNA2 [n = 3–4 per group; two-way ANOVA, significance (shown as #) for combined RB1 and siRNA effect; error bars represent SEM]. G, Live-cell imaging endpoint stills for the OVCAR-8 cells in D (scale bar = 100 µm). H, Western blot of RB1-E2F pathway activity in Kuramochi cells with RB1 knockdown or siNT control and treatment with BLU-222. I, Cell cycle profiling from univariate flow cytometric analysis from Kuramochi cells with RB1 knockdown or siNT control and treatment with BLU-222. J, Western blot of RB1-E2F pathway activity in MDA-MB-157 cells with and without deletion (DEL) of RB1 and treatment with BLU-222. K, Cell cycle profiling from univariate flow cytometric analysis from MDA-MB-157 cells with and without deletion (DEL) of RB1 and treatment with BLU-222. L, Table summarizing effect of RB1 knockdown/deletion on IC50 to BLU-222 in Kuramochi and MDA-MB-157 cells, respectively. ####, P < 0.0001. PI, propidium iodide.

Supplementary Figure S1. A. Synergistic inhibition of various MEK inhibitors with CDK4/6 inhibitor palbociclib on HT1080 cell growth rate. B. BrdU incorporation in HT1080 cells treated with DMSO, CDK4/6i (palbociclib, 100 nmol/L), MEKi (pimasertib, 250 nmol/L), or in combination for 72 h. C. Heatmap illustrating relative BrdU incorporation in HT1080 cells after 72 h of CDK4/6i/MEKi (palbociclib/pimasertib) treatment (left) and synergy plot of CDK4/6i and MEKi treatment (right), determined using the BLISS method. D. BrdU incorporation in MIA PaCa-2 cells treated with DMSO, CDK4/6i (palbociclib, 100 nmol/L), MEKi (pimasertib, 250 nmol/L), or in combination, for 72 h. E. Heatmap illustrating relative BrdU incorporation in MIA PaCa-2 cells after 72 h of CDK4/6i/MEKi (palbociclib/pimasertib) treatment (left) and synergy plot of CDK4/6i and MEKi treatment (right), determined using the BLISS method. Data displayed as mean ± SD in triplicate. **p < 0.01, ****p < 0.0001 as determined by one-way ANOVA.

Figure S2. Contextual biochemical response to CDK inhibition. A, Western blot of RB1-E2F pathway activity in OVSAHO and MDA-MB-157 cells following BLU-222 or palbociclib treatment. B, Schematic depicting mVenus-DHB and mCherry-RB CDK activity reporter constructs. C, Representative images of mCherry-RB sensor activity in OVCAR-3 and T-47D cells treated with 500 nM BLU-222 or palbociclib (scale bar = 100 µm). D, Quantification of mCherry-RB CDK activity sensor localization to the cytosol or nucleus in the indicated ovarian and breast cancer cell lines following treatment with either 500 nM BLU-222 or palbociclib. E, Representative images of mCherry-RB sensor activity in OVCAR-3 cells transfected with siNT, siCCNE1 or siCCND1 (scale bar = 100 µm). F, Quantification of mCherry-RB CDK activity sensor localization to the cytosol or nucleus in OVCAR-3 and MDA-MB-157 cells following knockdown of CCNE1 or CCND1 with siRNA constructs.

Immunotherapy with checkpoint inhibitors targeting the PD1/PD-L1 and CTLA4 pathways has limited activity in patients with microsatellite stable (MSS) colorectal adenocarcinoma (CRC). In a prior study, the combination of cetuximab and pembrolizumab failed to improve outcomes for patients with advanced RAS wild-type (RAS(wt)) CRC. In this post hoc secondary analysis, we show that the cetuximab and pembrolizumab-treated patients with TP53 mutant (p53(mt)) tumors had significantly higher progression-free survival (PFS) and a decrease in tumor burden compared to patients with TP53 wild-type (p53(wt)) tumors but no difference in overall survival compared to patients with p53(wt) tumors. The gene set enrichment analysis showed a uniform upregulation of multiple metabolic and immune gene sets, including NK-mediated immunity and IL-12 pathway, while the IL6 pathway was downregulated. There were no overlapping transcriptional alterations between the p53(mt) and p53(wt) groups with treatment that remain constant despite the therapeutic intervention. Functional overlap with treatment in both groups in the proliferative, immune, and metabolic pathways were identified. In the baseline tumor samples, the number of PD-L1(+) tumor cells was significantly higher in p53(mt) tumors while the number of OX40(-)/AE1_AE3(-)/PD-L1(-) non-tumor cells, positive for either LAG3, CTLA4 or TIM3, was significantly higher in p53(wt) tumors. In conclusion, TP53 status was prognostic of improved PFS with cetuximab plus pembrolizumab in RAS(wt) CRC. Future studies evaluating immune-oncology agents in patients with MSS, RAS(wt) CRC should include TP53 as an integrated biomarker and evaluate its performance as a positive predictive biomarker (ClinicalTrials.gov NCT02713373).

Supplementary Table S1. Oligonucleotide real-time qPCR primers used.

Figure S7. Prevalence of Cyclin E1-P16INK4Ahigh tumors that express RB. A, Kaplan Meier curves based on CCNE1 status generated from The Cancer Genome Atlas (TCGA) serous ovarian carcinoma database. HR, hazard ratio; DFS, disease-free survival; OS, overall survival. Amp, amplification. B, Oncoprint of the serous ovarian carcinoma TCGA database for the indicated cell cycle genes. C, Normalized intensity for Cyclin D1, Cyclin E1, or P16INK4A expression within clusters 1, 3, and 4 (n = 82002 for cluster 1, n = 150037 for cluster 3, and n = 34088 for cluster 4; unpaired t-test between clusters 1 and 4). D, SCIMAP analysis of unique spatial single cell protein expression signatures of all 159 cores from the patient-derived gynecological tumor microarray (TMA). E, Computational algorithm-based identification of Cyclin E1high/P16high/RB+ cores from the gynecological TMA expected to be sensitive to CDK2 inhibition.