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.

Syngeneic model of carcinogen-induced tumor mimics basal/squamous, stromal-rich, and neuroendocrine molecular and immunological features of muscle-invasive bladder cancer.

Introduction: Bladder cancer is a heterogenous disease and the emerging knowledge on molecular classification of bladder tumors may impact treatment decisions based on molecular subtype. Pre-clinical models representing each subtype are needed to test novel therapies. Carcinogen-induced bladder cancer models represent heterogeneous, immune-competent, pre-clinical testing options with many features found in the human disease. Methods: Invasive bladder tumors were induced in C57BL/6 mice when continuously exposed to N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) in the drinking water. Tumors were excised and serially passed by subcutaneous implantation into sex-matched syngeneic C57BL/6 hosts. Eight lines were named BBN-induced Urothelium Roswell Park (BURP) tumor lines. BURP lines were characterized by applying consensus molecular classification to RNA expression, histopathology, and immune profiles by CIBERSORT. Two lines were further characterized for cisplatin response. Results: Eight BURP tumor lines were established with 3 male and 3 female BURP tumor lines, having the basal/squamous (BaSq) molecular phenotype and morphology. BURP-16SR was established from a male mouse and has a stromal-rich (SR) molecular phenotype and a sarcomatoid carcinoma morphology. BURP-19NE was established from a male mouse and has a neuroendocrine (NE)-like molecular phenotype and poorly differentiated morphology. The established BURP tumor lines have unique immune profiles with fewer immune infiltrates compared to their originating BBN-induced tumors. The immune profiles of the BURP tumor lines capture some of the features observed in the molecular classifications of human bladder cancer. BURP-16SR growth was inhibited by cisplatin treatment, while BURP-24BaSq did not respond to cisplatin. Discussion: The BURP lines represent several molecular classifications, including basal/squamous, stroma-rich, and NE-like. The stroma-rich (BURP-16SR) and NE-like (BURP-19NE) represent unique immunocompetent models that can be used to test novel treatments in these less common bladder cancer subtypes. Six basal/squamous tumor lines were established from both male and female mice. Overall, the BURP tumor lines have less heterogeneity than the carcinogen-induced tumors and can be used to evaluate treatment response without the confounding mixed response often observed in heterogeneous tumors. Additionally, basal/squamous tumor lines were established and maintained in both male and female mice, thereby allowing these tumor lines to be used to compare differential treatment responses between sexes.

Combined BRCA2 and MAGEC3 Expression Predict Outcome in Advanced Ovarian Cancers.

Like BRCA2, MAGEC3 is an ovarian cancer predisposition gene that has been shown to have prognostic significance in ovarian cancer patients. Despite the clinical significance of each gene, no studies have been conducted to assess the clinical significance of their combined expression. We therefore sought to determine the relationship between MAGEC3 and BRCA2 expression in ovarian cancer and their association with patient characteristics and outcomes. Immunohistochemical staining was quantitated on tumor microarrays of human tumor samples obtained from 357 patients with epithelial ovarian cancer to ascertain BRCA2 expression levels. In conjunction with our previously published MAGEC3 expression data, we observed a weak inverse correlation of MAGEC3 with BRCA2 expression (r = −0.15; p < 0.05) in cases with full-length BRCA2. Patients with optimal cytoreduction, loss of MAGEC3, and detectable BRCA2 expression had better overall (median OS: 127.9 vs. 65.3 months, p = 0.035) and progression-free (median PFS: 85.3 vs. 18.8 months, p = 0.002) survival compared to patients that were BRCA2 expressors with MAGEC3 normal levels. Our results suggest that combined expression of MAGEC3 and BRCA2 serves as a better predictor of prognosis than each marker alone.

The Differential Paracrine Role of the Endothelium in Prostate Cancer Cells.

The survival of patients with solid tumors, such as prostate cancer (PCa), has been limited and fleeting with anti-angiogenic therapies. It was previously thought that the mechanism by which the vasculature regulates tumor growth was driven by a passive movement of oxygen and nutrients to the tumor tissue. However, previous evidence suggests that endothelial cells have an alternative role in changing the behavior of tumor cells and contributing to cancer progression. Determining the impact of molecular signals/growth factors released by endothelial cells (ECs) on established PCa cell lines in vitro and in vivo could help to explain the mechanism by which ECs regulate tumor growth. Using cell-conditioned media collected from HUVEC (HUVEC-CM), our data show the stimulated proliferation of all the PCa cell lines tested. However, in more aggressive PCa cell lines, HUVEC-CM selectively promoted migration and invasion in vitro and in vivo. Using a PCa-cell-line-derived xenograft model co-injected with HUVEC or preincubated with HUVEC-CM, our results are consistent with the in vitro data, showing enhanced tumor growth, increased tumor microvasculature and promoted metastasis. Gene set enrichment analyses from RNA-Seq gene expression profiles showed that HUVEC-CM induced a differential effect on gene expression when comparing low versus highly aggressive PCa cell lines, demonstrating epigenetic and migratory pathway enrichments in highly aggressive PCa cells. In summary, paracrine stimulation by HUVEC increased PCa cell proliferation and tumor growth and selectively promoted migration and metastatic potential in more aggressive PCa cell lines.

Loss of MAGEC3 Expression Is Associated with Prognosis in Advanced Ovarian Cancers.

Rare variants in MAGEC3 are associated with BRCA negative, early-onset ovarian cancers. Given this association, we evaluated the impact of MAGEC3 protein expression on prognosis and transcription. We quantified normal and tumor protein expression of MAGEC3 via immunohistochemistry in n = 394 advanced ovarian cancers, assessed the correlation of these values with clinicopathologic and immunological features and modeled survival using univariate and multivariate models. To extend these results, we quantified MAGEC3 protein expression in n = 180 cancers and used matching RNA sequencing data to determine MAGEC3-associated differentially expressed genes and to build an RNA-based model of MAGEC3 protein levels. This model was tested in a third independent cohort of patients from TCGA's OV dataset (n = 282). MAGEC3 protein was sporadically lost in ovarian cancers, with half of the cases falling below the 9.5th percentile of normal tissue expression. Cases with MAGEC3 loss demonstrated better progression-free survival [HR = 0.71, p = 0.004], and analyses performed on predicted protein scores were consistent [HR = 0.57 p = 0.002]. MAGEC3 protein was correlated with CD8 protein expression [Pearson's r = 0.176, p = 0.011], NY-ESO-1 seropositivity, and mRNA expression of tumor antigens at Xq28. Results of gene set enrichment analysis showed that genes associated with MAGEC3 protein expression cluster around G2/M checkpoint (NES = 3.20, FDR < 0.001) and DNA repair (NES = 2.28, FDR < 0.001) hallmark pathways. These results show that MAGEC3 is a prognostic biomarker in ovarian cancer.

Patient derived models of bladder cancer enrich the signal of the tumor cell transcriptome facilitating the analysis of the tumor cell compartment.

The evolving paradigm of the molecular classification of bladder cancer requires models that represent the classifications with less heterogeneity. Robust transcriptome based molecular classifications are essential to address tumor heterogeneity. Patient derived models (PDMs) are a powerful preclinical tool to study specific tumor compartments. We tested if the consensus molecular subtype analysis was applicable to PDMs and evaluated the tumor compartment each model represents. PDMs derived from surgical specimens were established as xenografts (PDX), organoids (PDO), and spheroids (PDS). The surgical specimens and PDMs were molecularly characterized by RNA sequencing. PDMs that were established in immune deficient mice or in vitro significantly downregulated transcripts related to the immune and stromal compartments compared to the surgical specimens. However, PDMs upregulate a patient-specific bladder cancer cell signal which allowed for analysis of cancer cell pathways independent of the tumor microenvironment. Based on transcriptomic signatures, PDMs are more similar to their surgical specimen than the model type; indicating that the PDMs retained unique features of the tumor from which the PDM was derived. When comparing models, PDX models were the most similar to the surgical specimen, while PDO and PDS models were most similar to each other. When the consensus molecular subtype classification system was applied to both the surgical samples and the three PDMs, good concordance was found between all samples indicating that this system of classification can be applied to PDO and PDS models. PDMs reduce tumor heterogeneity and allow analysis of tumor cells while maintaining the gene expression profile representative of the original tumor.

USP1 Regulates TAZ Protein Stability Through Ubiquitin Modifications in Breast Cancer.

The Hippo signaling pathway is an evolutionarily conserved pathway that was initially discovered in Drosophila melanogaster and was later found to have mammalian orthologues. The key effector proteins in this pathway, YAP/TAZ, are often dysregulated in cancer, leading to a high degree of cell proliferation, migration, metastasis and cancer stem cell populations. Due to these malignant phenotypes it is important to understand the regulation of YAP/TAZ at the protein level. Using an siRNA library screen of deubiquitinating enzymes (DUBs), we identified ubiquitin specific peptidase 1 (USP1) as a novel TAZ (WWTR1) regulator. We demonstrated that USP1 interacts with TAZ and increases TAZ protein stability. Conversely, loss of function of USP1 reduces TAZ protein levels through increased poly-ubiquitination, causing a decrease in cell proliferation and migration of breast cancer cells. Moreover, we showed a strong positive correlation between USP1 and TAZ in breast cancer patients. Our findings facilitate the attainment of better understanding of the crosstalk between these pathways and may lead to potential therapeutic interventions for breast cancer patients.

Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors.

Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and-more notably-logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of the age of a tumor from limited data at diagnosis. The code and data used in our analysis are publicly available at https://github.com/cristinavaghi/plumky.

Enhanced efficacy of sitravatinib in metastatic models of antiangiogenic therapy resistance.

Tyrosine kinase inhibitors (TKIs) that primarily target angiogenesis are approved to treat several cancers in the metastatic setting; however, resistance is common. Sequential treatment or 'switching' from one TKI to another following failure can be effective, but predicting which drugs will have cross-over sensitivity remains a challenge. Here we examined sitravatinib (MGCD516), a spectrum-selective TKI able to block MET, TAM (TYRO3, AXL, MerTK) and multiple receptor families (including PDGFRs, VEGFRs, and Ephs). Transcriptomic analysis of several mouse and human cell lines revealed diverse molecular changes after resistance to two TKIs (sunitinib and axitinib) with multiple sitravatinib targets found to be upregulated. Sitravatinib treatment in vitro resulted in enhanced anti-proliferative effects in resistant cells and was improved compared to TKIs with similar target profiles. In vivo, primary tumor growth inhibition after sitravatinib treatment in mice was enhanced in resistant tumors and metastasis suppression improved when tumors were surgically removed. Together, these results suggest that the diverse and often inconsistent compensatory signaling mechanisms found to contribute to TKI resistance may paradoxically improve the tumor-inhibiting effects of broad-spectrum TKIs such as sitravatinib that are able to block multiple signaling pathways. Sitravatinib in the second-line setting following antiangiogenic TKI treatment may have enhanced inhibitory effects in local and disseminated disease, and improve outcomes in patients with refractory disease.

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.

A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal.

VEGF receptor tyrosine kinase inhibitors (VEGFR TKIs) approved to treat multiple cancer types can promote metastatic disease in certain limited preclinical settings. Here, we show that stopping VEGFR TKI treatment after resistance can lead to rebound tumor growth that is driven by cellular changes resembling senescence-associated secretory phenotypes (SASPs) known to promote cancer progression. A SASP-mimicking antiangiogenic therapy-induced secretome (ATIS) was found to persist during short withdrawal periods, and blockade of known SASP regulators, including mTOR and IL-6, could blunt rebound effects. Critically, senescence hallmarks ultimately reversed after long drug withdrawal periods, suggesting that the transition to a permanent growth-arrested senescent state was incomplete and the hijacking of SASP machinery ultimately transient. These findings may account for the highly diverse and reversible cytokine changes observed in VEGF inhibitor-treated patients, and suggest senescence-targeted therapies ("senotherapeutics")-particularly those that block SASP regulation-may improve outcomes in patients after VEGFR TKI failure.

Tumor-Independent Host Secretomes Induced By Angiogenesis and Immune-Checkpoint Inhibitors.

The levels of various circulating blood proteins can change in response to cancer therapy. Monitoring therapy-induced secretomes (TIS) may have use as biomarkers for establishing optimal biological effect (such as dosing) or identifying sources of toxicity and drug resistance. Although TIS can derive from tumor cells directly, nontumor "host" treatment responses can also impact systemic secretory programs. For targeted inhibitors of the tumor microenvironment, including antiangiogenic and immune-checkpoint therapies, host TIS could explain unexpected collateral "side effects" of treatment. Here, we describe a comparative transcriptomic and proteomic analysis of host TIS in tissues and plasma from cancer-free mice treated with antibody and receptor tyrosine kinase inhibitors (RTKI) of the VEGF, cMet/ALK, and PD-1 pathways. We found that all cancer therapies elicit TIS independent of tumor growth, with systemic secretory gene change intensity higher in RTKIs compared with antibodies. Our results show that host TIS signatures differ between drug target, drug class, and dose. Notably, protein and gene host TIS signatures were not always predictive for each other, suggesting limitations to transcriptomic-only approaches to clinical biomarker development for circulating proteins. Together, these are the first studies to assess and compare "off-target" host secretory effects of VEGF and PD-1 pathway inhibition that occur independent of tumor stage or tumor response to therapy. Testing treatment impact on normal tissues to establish host-mediated TIS signatures (or "therasomes") may be important for identifying disease agnostic biomarkers to predict benefits (or limitations) of drug combinatory approaches. Mol Cancer Ther; 17(7); 1602-12. ©2018 AACR.

Sunitinib Stimulates Expression of VEGFC by Tumor Cells and Promotes Lymphangiogenesis in Clear Cell Renal Cell Carcinomas.

Sunitinib is an antiangiogenic therapy given as a first-line treatment for renal cell carcinoma (RCC). While treatment improves progression-free survival, most patients relapse. We hypothesized that patient relapse can stem from the development of a lymphatic network driven by the production of the main growth factor for lymphatic endothelial cells, VEGFC. In this study, we found that sunitinib can stimulate vegfc gene transcription and increase VEGFC mRNA half-life. In addition, sunitinib activated p38 MAPK, which resulted in the upregulation/activity of HuR and inactivation of tristetraprolin, two AU-rich element-binding proteins. Sunitinib stimulated a VEGFC-dependent development of lymphatic vessels in experimental tumors. This may explain our findings of increased lymph node invasion and new metastatic sites in 30% of sunitinib-treated patients and increased lymphatic vessels found in 70% of neoadjuvant treated patients. In summary, a therapy dedicated to destroying tumor blood vessels induced the development of lymphatic vessels, which may have contributed to the treatment failure. Cancer Res; 77(5); 1212-26. ©2017 AACR.

Nucleoporin 62 and Ca(2+)/calmodulin dependent kinase kinase 2 regulate androgen receptor activity in castrate resistant prostate cancer cells.

BACKGROUND: Re-activation of the transcriptional activity of the androgen receptor (AR) is an important factor mediating progression from androgen-responsive to castrate-resistant prostate cancer (CRPC). However, the mechanisms regulating AR activity in CRPC remain incompletely understood. Ca(2+) /calmodulin-dependent kinase kinase (CaMKK) 2 was previously shown to regulate AR activity in androgen-responsive prostate cancer cells. Our objective was to further explore the basis of this regulation in CRPC cells. METHODS: The abundance of CaMKK2 in nuclear fractions of androgen-responsive prostate cancer and CRPC, cells were determined by subcellular fractionation and Western blotting. CaMKK2 association with nuclear pore complexes (NPCs) and nucleoporins (Nups) including Nup62, were imaged by structured illumination and super-resolution fluorescence microscopy and co-immunoprecipitation, respectively. The abundance and subcellular localization of CaMKK2 and Nup62 in human clinical specimens of prostate cancer was visualized by immunohistochemistry. The role of Nups in the growth and viability of CRPC cells was assessed by RNA interference and cell counting. The involvement of CaMKK2 and Nup62 in regulating AR transcriptional activity was addressed by RNA interference, chromatin immunoprecipitation, androgen response element reporter assay, and Western blotting. RESULTS: CaMKK2 was expressed at higher levels in the nuclear fraction of CPRC C4-2 cells, than in that of androgen-responsive LNCaP cells. In C4-2 cells, CaMKK2 associated with NPCs of the nuclear envelope and physically interacted with Nup62. CaMKK2 and Nup62 demonstrated pronounced, and similar increases in both expression and perinuclear/nuclear localization in human clinical specimens of advanced prostate cancer relative to normal prostate. Knockdown of Nup62, but not of Nups, 98 or 88, reduced growth and viability of C4-2 cells. Knockdown of Nup62 produced a greater reduction of the growth and viability of C4-2 cells than of non-neoplastic RWPE-1 prostatic cells. Nup62, CaMKK2, and the AR were recruited to androgen response elements of the AR target genes, prostate specific antigen, and transmembrane protease, serine 2. Knockdown of CaMKK2 and Nup62 reduced prostate specific antigen expression and AR transcriptional activity driven by androgen response elements from the prostate-specific probasin gene promoter. CONCLUSION: Nup62 and CaMKK2 are required for optimal AR transcriptional activity and a potential mechanism for AR re-activation in CRPC.

Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach.

Rapid improvements in the detection and tracking of early-stage tumor progression aim to guide decisions regarding cancer treatments as well as predict metastatic recurrence in patients following surgery. Mathematical models may have the potential to further assist in estimating metastatic risk, particularly when paired with in vivo tumor data that faithfully represent all stages of disease progression. Herein, we describe mathematical analysis that uses data from mouse models of spontaneous metastasis developing after surgical removal of orthotopically implanted primary tumors. Both presurgical (primary tumor) growth and postsurgical (metastatic) growth were quantified using bioluminescence and were then used to generate a mathematical formalism based on general laws of the disease (i.e., dissemination and growth). The model was able to fit and predict pre/postsurgical data at the level of the individual as well as the population. Our approach also enabled retrospective analysis of clinical data describing the probability of metastatic relapse as a function of primary tumor size. In these data-based models, interindividual variability was quantified by a key parameter of intrinsic metastatic potential. Critically, our analysis identified a highly nonlinear relationship between primary tumor size and postsurgical survival, suggesting possible threshold limits for the utility of tumor size as a predictor of metastatic recurrence. These findings represent a novel use of clinically relevant models to assess the impact of surgery on metastatic potential and may guide optimal timing of treatments in neoadjuvant (presurgical) and adjuvant (postsurgical) settings to maximize patient benefit.

The Challenges of Modeling Drug Resistance to Antiangiogenic Therapy.

Drug resistance remains an ongoing challenge for the majority of patients treated with inhibitors of the vascular endothelial growth factor (VEGF) pathway, a key regulator of tumor angiogenesis. Preclinical models have played a significant role in identifying multiple complex mechanisms of antiangiogenic treatment failure. Yet questions remain about the optimal methodology to study resistance that may assist in making clinically relevant choices about alternative or combination treatment strategies. The origins of antiangiogenic treatment failure may stem from the tumor vasculature, the tumor itself, or both together, and preclinical methods that define resistance are diverse and rarely compared. We performed a literature search of the preclinical methodologies used to examine resistance to VEGF pathway inhibitors and identified 109 papers from more than 400 that use treatment failure as the starting point for mechanistic study. We found that definitions of resistance are broad and inconsistent, involve only a small number of reagents, and derive mostly from in vitro and in vivo methodologies that often do not represent clinically relevant disease stages or progression. Together, this literature analysis highlights the challenges of studying inhibitors of the tumor microenvironment in the preclinical setting and the need for improved methodology to assist in qualifying (and quantifying) treatment failure to identify mechanisms that will help predict alternative strategies in patients.

Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy.

Thousands of cancer patients are currently in clinical trials evaluating antiangiogenic therapy in the neoadjuvant setting, which is the treatment of localized primary tumors prior to surgical intervention. The rationale is that shrinking a tumor will improve surgical outcomes and minimize growth of occult micrometastatic disease-thus delaying post-surgical recurrence and improving survival. But approved VEGF pathway inhibitors have not been tested in clinically relevant neoadjuvant models that compare pre- and post-surgical treatment effects. Using mouse models of breast, kidney, and melanoma metastasis, we demonstrate that primary tumor responses to neoadjuvant VEGFR TKI treatment do not consistently correlate with improved post-surgical survival, with survival worsened in certain settings. Similar negative effects did not extend to protein-based VEGF pathway inhibitors and could be reversed with altered dose, surgical timing, and treatment duration, or when VEGFR TKIs are combined with metronomic 'anti-metastatic' chemotherapy regimens. These studies represent the first attempt to recapitulate the complex clinical parameters of neoadjuvant therapy in mice and identify a novel tool to compare systemic antiangiogenic treatment effects on localized and disseminated disease.

Modeling spontaneous metastatic renal cell carcinoma (mRCC) in mice following nephrectomy.

One of the key challenges to improved testing of new experimental therapeutics in renal cell carcinoma (RCC) is the development of models that faithfully recapitulate early- and late-stage metastatic disease progression. Typical tumor implantation models utilize ectopic or orthotopic primary tumor implantation, but few include systemic spontaneous metastatic disease that mimics the clinical setting. This protocol describes the key steps to develop RCC disease progression stages similar to patients. First, it uses a highly metastatic mouse tumor cell line in a syngeneic model to show orthotopic tumor cell implantation. Methods include superficial and internal implantation into the sub-capsular space with cells combined with matrigel to prevent leakage and early spread. Next it describes the procedures for excision of tumor-bearing kidney (nephrectomy), with critical pre- and post- surgical mouse care. Finally, it outlines the steps necessary to monitor and assess micro-and macro-metastatic disease progression, including bioluminescent imaging as well provides a detailed visual necropsy guide to score systemic disease distribution. The goal of this protocol description is to facilitate the widespread use of clinically relevant metastatic RCC models to improve the predictive value of future therapeutic testing.