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 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.

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.

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.

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.

Immunization with short peptide particles reveals a functional CD8+ T-cell neoepitope in a murine renal carcinoma model.

BACKGROUND: Induction of CD8+ T cells that recognize immunogenic, mutated protein fragments in the context of major histocompatibility class I (MHC-I) is a pressing challenge for cancer vaccine development. METHODS: Using the commonly used murine renal adenocarcinoma RENCA cancer model, MHC-I restricted neoepitopes are predicted following next-generation sequencing. Candidate neoepitopes are screened in mice using a potent cancer vaccine adjuvant system that converts short peptides into immunogenic nanoparticles. An identified functional neoepitope vaccine is then tested in various therapeutic experimental tumor settings. RESULTS: Conversion of 20 short MHC-I restricted neoepitope candidates into immunogenic nanoparticles results in antitumor responses with multivalent vaccination. Only a single neoepitope candidate, Nesprin-2 L4492R (Nes2LR), induced functional responses but still did so when included within 20-plex or 60-plex particles. Immunization with the short Nes2LR neoepitope with the immunogenic particle-inducing vaccine adjuvant prevented tumor growth at doses multiple orders of magnitude less than with other vaccine adjuvants, which were ineffective. Nes2LR vaccination inhibited or eradicated disease in subcutaneous, experimental lung metastasis and orthotopic tumor models, synergizing with immune checkpoint blockade. CONCLUSION: These findings establish the feasibility of using short, MHC-I-restricted neoepitopes for straightforward immunization with multivalent or validated neoepitopes to induce cytotoxic CD8+ T cells. Furthermore, the Nes2LR neoepitope could be useful for preclinical studies involving renal cell carcinoma immunotherapy.

A Novel 4-Gene Score to Predict Survival, Distant Metastasis and Response to Neoadjuvant Therapy in Breast Cancer.

We generated a 4-gene score with genes upregulated in LM2-4, a metastatic variant of MDA-MB-231 (DOK 4, HCCS, PGF, and SHCBP1) that was strongly associated with disease-free survival (DFS) in TCGA cohort (hazard ratio [HR]>1.2, p < 0.02). The 4-gene score correlated with overall survival of TCGA (HR = 1 .44, p < 0 .001), which was validated with DFS and disease-specific survival of METABRIC cohort. The 4-gene score was able to predict worse survival or clinically aggressive tumors, such as high Nottingham pathological grade and advanced cancer staging. High score was associated with worse survival in the hormonal receptor (HR)-positive/Her2-negative subtype. High score enriched cell proliferation-related gene sets in GSEA. The score was high in primary tumors that originated, in and metastasized to, brain and lung, and it predicted worse progression-free survival for metastatic tumors. Good tumor response to neoadjuvant chemotherapy or hormonal therapy was accompanied by score reduction. High scores were also predictive of response to neoadjuvant chemotherapy for HR-positive/Her2-negative subtype. High score tumors had increased expression of T cell exhaustion marker genes, suggesting that the score may also be a biomarker for immunotherapy response. Our novel 4-gene score with both prognostic and predictive values may, therefore, be clinically useful particularly in HR-positive breast cancer.

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.

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.

Prodding the Beast: Assessing the Impact of Treatment-Induced Metastasis.

The arsenal of treatments for most cancers fit broadly into the categories of surgery, chemotherapy, radiation, and targeted therapy. All represent proven and successful strategies, yet each can trigger local (tumor) and systemic (host) processes that elicit unwanted, often opposing, influences on cancer growth. Under certain conditions, nearly all cancer treatments can facilitate metastatic spread, often in parallel (and sometimes in clear contrast) with tumor reducing benefits. The paradox of treatment-induced metastasis (TIM) is not new. Supporting preclinical studies span decades, but are often overlooked. With recent evidence of prometastatic effects following treatment with targeted agents blocking the tumor microenvironment, a closer inspection of this literature is warranted. The TIM phenomena may diminish the impact of effective therapies and play a critical role in eventual resistance. Alternatively, it may simply exemplify the gap between animal and human studies, and therefore have little impact for patient disease and treatment. This review will focus on the preclinical model systems used to evaluate TIM and explore the mechanisms that influence overall treatment efficacy. Understanding the role of TIM in established and emerging drug treatment strategies may help provide rationales for future drug combination approaches with antimetastatic agents to improve outcomes and reduce resistance.

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.

Classical mathematical models for description and prediction of experimental tumor growth.

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.