A Senescence-Mimicking (Senomimetic) VEGFR TKI Side Effect Primes Tumor Immune Responses via IFN/STING Signaling.

Tyrosine kinase inhibitors (TKIs) that block the vascular endothelial growth factor receptors (VEGFRs) not only disrupt tumor angiogenesis but also have many unexpected side effects that impact tumor cells directly. This includes the induction of molecular markers associated with senescence, a form of cellular aging that typically involves growth arrest. We have shown that VEGFR TKIs can hijack these aging programs by transiently inducting senescence markers (SMs) in tumor cells to activate senescence-associated secretory programs that fuel drug resistance. Here we show that these same senescence-mimicking ("senomimetic") VEGFR TKI effects drive an enhanced immunogenic signaling that, in turn, can alter tumor response to immunotherapy. By using a live cell sorting method to detect ß-galactosidase, a commonly used SM, we found that subpopulations of SM-expressing (SM+) tumor cells have heightened IFN signaling and increased expression of IFN-stimulated genes (ISGs). These ISGs increase under the control of the STimulator of the INterferon Gene (STING) signaling pathway, which we found could be directly activated by several VEGFR TKIs. TKI-induced SM+ cells could stimulate or suppress CD8 T-cell activation depending on host-tumor cell contact while tumors grown from SM+ cells were more sensitive to PDL1 inhibition in vivo, suggesting that offsetting immune-suppressive functions of SM+ cells can improve TKI efficacy overall. Our findings may explain why some (but not all) VEGFR TKIs improve outcomes when combined with immunotherapy and suggest that exploiting senomimetic drug side effects may help identify TKIs that uniquely "prime" tumors for enhanced sensitivity to PDL1-targeted agents.

A senescence-mimicking (senomimetic) VEGFR TKI side-effect primes tumor immune responses via IFN/STING signaling.

Tyrosine kinase inhibitors (TKIs) that block the vascular endothelial growth factor receptors (VEGFRs) disrupt tumor angiogenesis but also have many unexpected side-effects that impact tumor cells directly. This includes the induction of molecular markers associated with senescence, a form of cellular aging that typically involves growth arrest. We have shown that VEGFR TKIs can hijack these aging programs by transiently inducting senescence-markers (SMs) in tumor cells to activate senescence-associated secretory programs that fuel drug resistance. Here we show that these same senescence-mimicking ('senomimetic') VEGFR TKI effects drive an enhanced immunogenic signaling that, in turn, can alter tumor response to immunotherapy. Using a live-cell sorting method to detect beta-galactosidase, a commonly used SM, we found that subpopulations of SM-expressing (SM+) tumor cells have heightened interferon (IFN) signaling and increased expression of IFN-stimulated genes (ISGs). These ISG increases were under the control of the STimulator of INterferon Gene (STING) signaling pathway, which we found could be directly activated by several VEGFR TKIs. TKI-induced SM+ cells could stimulate or suppress CD8 T-cell activation depending on host:tumor cell contact while tumors grown from SM+ cells were more sensitive to PD-L1 inhibition in vivo, suggesting that offsetting immune-suppressive functions of SM+ cells can improve TKI efficacy overall. Our findings may explain why some (but not all) VEGFR TKIs improve outcomes when combined with immunotherapy and suggest that exploiting senomimetic drug side-effects may help identify TKIs that uniquely 'prime' tumors for enhanced sensitivity to PD-L1 targeted agents.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

A phase I/II study of nintedanib and capecitabine for refractory metastatic colorectal cancer.

BACKGROUND: Nintedanib is a tyrosine kinase inhibitor with efficacy in bevacizumab-resistant colorectal cancer models. This phase I/II study evaluated the recommended phase II dose and efficacy of nintedanib and capecitabine in refractory metastatic colorectal cancer. METHODS: Key eligibility criteria included refractory metastatic colorectal cancer and ECOG performance status of 1 or lower. The primary endpoint was 18-week progression-free survival (PFS). A 1-sided binomial test (at α = .1) compared the observed 18-week PFS with a historic control of .25. RESULTS: Forty-two patients were enrolled, including 39 at the recommended phase II dose. The recommended phase II dose was established to be nintedanib 200 mg by mouth twice daily and capecitabine 1000 mg/m2 by mouth twice daily. The protocol was evaluated for efficacy in 36 patients. The 18-week PFS was 42% (15/36 patients; P = .0209). Median PFS was 3.4 mo. Median overall survival was 8.9 mo. Sixteen (44%) patients experienced a grade 3/4 adverse event, most commonly fatigue (8%), palmoplantar erythrodysesthesia (8%), aspartate aminotransferase elevation (6%), asthenia (6%), pulmonary embolus (6%), and dehydration (6%). Osteopontin levels at cycle 1, day 1 and cycle 3, day 1 as well as ΔCCL2 levels correlated to disease control at 18 weeks. CONCLUSIONS: The combination of nintedanib and capecitabine is well tolerated. Clinical efficacy appears to be superior to regorafenib or tipiracil hydrochloride monotherapy. Further investigation of similar combinations is warranted. CLINICALTRIALS.GOV IDENTIFIER: NCT02393755.

Downregulation of IRF8 in alveolar macrophages by G-CSF promotes metastatic tumor progression.

Tissue-resident macrophages (TRMs) are abundant immune cells within pre-metastatic sites, yet their functional contributions to metastasis remain incompletely understood. Here, we show that alveolar macrophages (AMs), the main TRMs of the lung, are susceptible to downregulation of the immune stimulatory transcription factor IRF8, impairing anti-metastatic activity in models of metastatic breast cancer. G-CSF is a key tumor-associated factor (TAF) that acts upon AMs to reduce IRF8 levels and facilitate metastasis. Translational relevance of IRF8 downregulation was observed among macrophage precursors in breast cancer and a CD68hiIRF8loG-CSFhi gene signature suggests poorer prognosis in triple-negative breast cancer (TNBC), a G-CSF-expressing subtype. Our data highlight the underappreciated, pro-metastatic roles of AMs in response to G-CSF and identify the contribution of IRF8-deficient AMs to metastatic burden. AMs are an attractive target of local neoadjuvant G-CSF blockade to recover anti-metastatic activity.