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

Circulating protein biomarkers of pharmacodynamic activity of sunitinib in patients with metastatic renal cell carcinoma: modulation of VEGF and VEGF-related proteins

<p>Abstract</p> <p>Background</p> <p>Sunitinib malate (SUTENT^®) is an oral, multitargeted tyrosine kinase inhibitor, approved multinationally for the treatment of advanced RCC and of imatinib-resistant or – intolerant GIST. The purpose of this study was to explore potential biomarkers of sunitinib pharmacological activity via serial assessment of plasma levels of four soluble proteins from patients in a phase II study of advanced RCC: VEGF, soluble VEGFR-2 (sVEGFR-2), placenta growth factor (PlGF), and a novel soluble variant of VEGFR-3 (sVEGFR-3).</p> <p>Methods</p> <p>Sunitinib was administered at 50 mg/day on a 4/2 schedule (4 weeks on treatment, 2 weeks off treatment) to 63 patients with metastatic RCC after failure of first-line cytokine therapy. Predose plasma samples were collected on days 1 and 28 of each cycle and analyzed via ELISA.</p> <p>Results</p> <p>At the end of cycle 1, VEGF and PlGF levels increased >3-fold (relative to baseline) in 24/54 (44%) and 22/55 (40%) cases, respectively (P < 0.001). sVEGFR-2 levels decreased ≥ 30% in 50/55 (91%) cases and ≥ 20% in all cases (P < 0.001) during cycle 1, while sVEGFR-3 levels were decreased ≥ 30% in 48 of 55 cases (87%), and ≥ 20% in all but 2 cases. These levels tended to return to near-baseline after 2 weeks off treatment, indicating that these effects were dependent on drug exposure. Overall, significantly larger changes in VEGF, sVEGFR-2, and sVEGFR-3 levels were observed in patients exhibiting objective tumor response compared with those exhibiting stable disease or disease progression (P < 0.05 for each analyte; analysis not done for PlGF).</p> <p>Conclusion</p> <p>Sunitinib treatment in advanced RCC patients leads to modulation of plasma levels of circulating proteins involved in VEGF signaling, including soluble forms of two VEGF receptors. This panel of proteins may be of value as biomarkers of the pharmacological and clinical activity of sunitinib in RCC, and of angiogenic processes in cancer and other diseases.</p

M402, a Novel Heparan Sulfate Mimetic, Targets Multiple Pathways Implicated in Tumor Progression and Metastasis

Heparan sulfate proteoglycans (HSPGs) play a key role in shaping the tumor microenvironment by presenting growth factors, cytokines, and other soluble factors that are critical for host cell recruitment and activation, as well as promoting tumor progression, metastasis, and survival. M402 is a rationally engineered, non-cytotoxic heparan sulfate (HS) mimetic, designed to inhibit multiple factors implicated in tumor-host cell interactions, including VEGF, FGF2, SDF-1α, P-selectin, and heparanase. A single s.c. dose of M402 effectively inhibited seeding of B16F10 murine melanoma cells to the lung in an experimental metastasis model. Fluorescent-labeled M402 demonstrated selective accumulation in the primary tumor. Immunohistological analyses of the primary tumor revealed a decrease in microvessel density in M402 treated animals, suggesting anti-angiogenesis to be one of the mechanisms involved in-vivo. M402 treatment also normalized circulating levels of myeloid derived suppressor cells in tumor bearing mice. Chronic administration of M402, alone or in combination with cisplatin or docetaxel, inhibited spontaneous metastasis and prolonged survival in an orthotopic 4T1 murine mammary carcinoma model. These data demonstrate that modulating HSPG biology represents a novel approach to target multiple factors involved in tumor progression and metastasis

Increased numbers of small circulating endothelial cells in renal cell cancer patients treated with sunitinib

Mature circulating endothelial cell (CEC) as well as endothelial progenitor populations may reflect the activity of anti-angiogenic agents on tumor neovasculature or even constitute a target for anti-angiogenic therapy. We investigated the behavior of CECs in parallel with hematopoietic progenitor cells (HPCs) in the blood of renal cell cancer patients during sunitinib treatment. We analyzed the kinetics of a specific population of small VEGFR2-expressing CECs (CD45(neg)/CD34(bright)), HPCs (CD45(dim)/CD34(bright)), and monocytes in the blood of 24 renal cell cancer (RCC) patients receiving 50 mg/day of the multitargeted VEGF inhibitor sunitinib, on a 4-week-on/2-week-off schedule. Blood was taken before treatment (C1D1), on C1D14, C1D28, and on C2D1 before the start of cycle 2. Also plasma VEGF and erythropoietin (EPO) were determined. Remarkably, while CD34(bright) HPCs and monocytes decreased during treatment, CD34(bright) CECs increased from 69 cells/ml (C1D1) to 180 cells/ml (C1D14; P = 0.001) and remained high on C1D28. All cell populations recovered to near pre-treatment levels on C2D1. Plasma VEGF and EPO levels were increased on C1D14 and partly normalized to pre-treatment levels on C2D1. In conclusion, opposite kinetics of two circulating CD34(bright) cell populations, HPCs and small CECs, were observed in sunitinib-treated RCC patients. The increase in CECs is likely caused by sunitinib targeting of immature tumor vessel

Tumor growth fueled by spurious senescence phenotypes

Cancer treatments can induce a form of senescence that halts cellular division while allowing continued secretion of tumor-promoting proteins. We recently found that antiangiogenic treatment resistance can lead to a transient hijacking of the senescence-controlled secretory machinery that, when therapeutically targeted during treatment cessation, can blunt rebound tumor growth

Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.

Parameter estimates of the metastatic and survival models obtained by likelihood maximization via the SAEM algorithm.</p

Mathematical modeling reveals differential effects of neoadjuvant sunitinib treatment on primary tumor and metastatic growth.

(A) Schematic of the study. Data from an ortho-surgical, human xenograft animal model of neoadjuvant sunitinib breast cancer treatment were fitted using a mixed-effects statistical framework. This provided calibrated parameters for each animal. Machine learning algorithms were used to assess the predictive power of molecular and cellular biomarkers to predict the metastatic dissemination parameter μ and quantify metastatic aggressiveness. (B) Schematic of tested hypotheses of the effect of neoadjuvant sunitinib Tx on primary tumor and metastatic growth and dissemination through mechanistic mathematical modeling. Scenario A = growth arrest on both primary and secondary tumors. Scenario B = growth arrest on primary tumor only. (C) Predicted simulations of Scenarios A and B using parameters calibrated from a previous study involving untreated (vehicle) animals only [4]. Data plotted here (LM2-4LUC+ bioluminescent human breast cancer cells orthotopically injected in mice) was not used to estimate the model parameters. Tx, treatment; PT, primary tumor; MB, metastatic burden. *See methods for additional details on animal experiments, treatment dose and duration, and mechanistic model. The mouse images were drawn using Biorender.</p

Individual parameters vs covariates.

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.</div

Calibration and validation of a kinetics-pharmacodynamics (K-PD) mathematical model for neoadjuvant sunitinib treatment effect on pre- and post-surgical tumor growth.

Pre- and postsurgical growth of LM2-4LUC+ human metastatic breast carcinomas were measured in multiple groups involving different neoadjuvant treatment modalities (doses and durations). The mathematical model was fitted to the experimental data using a mixed-effects population approach (n = 104 animals in total). (A) Comparison of the simulated model population distribution (visual predictive check) for vehicle and neoadjuvant sunitinib treatment (60mg/kg/day) 14 days before surgery. (B) Examples of individual dynamics. Tx, treatment; PT, primary tumor; MB, metastatic burden.</p