Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of non-identifiability and clinically unrealistic results. Materials and methods:We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients to predict patient-specific responses to subsequent radiation doses. Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R 2 ¼0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index ¼ 0.89).Conclusion: The PSI model may be suited to forecast treatment response for individual patients and offers actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy.

Purpose: Radiotherapy prescription dose and dose fractionation protocols vary little between individual patients having the same tumor grade and stage. To personalize radiotherapy a predictive model is needed to simulate radiation response. Previous modeling attempts with multiple variables and parameters have been shown to yield excellent data fits at the cost of nonidentifiability and clinically unrealistic results. Materials and Methods:We develop a mathematical model based on a proliferation saturation index (PSI) that is a measurement of pre-treatment tumor volume-to-carrying capacity ratio that modulates intrinsic tumor growth and radiation response rates. In an adaptive Bayesian approach, we utilize an increasing number of data points for individual patients for predicting response to subsequent radiation doses.Results: Model analysis shows that using PSI as the only patient-specific parameter, model simulations can fit longitudinal clinical data with high accuracy (R 2 =0.84). By analyzing tumor response to radiation using daily CT scans early in the treatment, response to the remaining treatment fractions can be predicted after two weeks with high accuracy (c-index=0.89). Conclusion:The PSI model may be suited to forecast treatment response for individual patients and offer actionable decision points for mid-treatment protocol adaptation. The presented work provides an actionable image-derived biomarker prior to and during therapy to personalize and adapt radiotherapy. 3

Human papillomavirus (HPV) related oropharyngeal cancer (OPC) is one of the few types of cancers increasing in incidence. HPV+ OPC treatment with radiotherapy (RT) provides 75-95% five-year locoregional control (LRC). Why some but not all patients with similar clinical stage and molecular profile are controlled remains unknown. We propose the proliferation saturation index, PSI, as a mathematical modeling biomarker of tumor growth and RT response. The model predicts that patients with PSI<0.75 are likely to be cured by radiation, and that hyperfractionated radiation could improve response rates for patients with higher PSI that are predicted to fail standard of care RT. Prospective evaluation is currently ongoing.

Prior work by us and others has demonstrated that the extracellular pH (pHe) of solid tumors is acidic, due to a combination of increased fermentative metabolism, resulting in lactic acid production and poor perfusion. This acidity promotes tumor progression and metastasis formation. Recently we have shown in melanoma and pancreatic cancer models that acidity inhibits antitumor immunity by preventing T-cell activation. Reversal of acidity with buffer therapy (200mM NaHCO3) synergized with checkpoint blockade (anti-CTLA4 and anti-PD1) and adoptive T-cell therapy has resulted in cures. While this is promising, concerns are high regarding the ingestion of such large amounts of sodium bicarbonate, which makes clinical translation a challenge. Hence, we hypothesize that alternative pharmacological interventions can neutralize the pHe of tumors and remove this immunosuppressive effect. To study this we first investigated a series of agents for their ability to inhibit metastasis in the PC3 prostate cancer model, which is exquisitely sensitive to inhibition with buffer therapy. In this study, male SCID mice were grouped in to 6 groups (n=5) and treated with tap water, 200 mM bicarbonate ad lib, 30 mg/kg daily (q.d.) intraperitoneal (i.p.) Acetazolamide (CA inhibitor), 1.2 mg/kg q.d.i.p. Furosemide (diuretic), 10 mg/kg q.d.i.p. DH348 (selective CAIX inhibitor) or 2.1 mg/kg q.d.i.p. FX-11 (LDHA inhibitor). Mice were intravenously injected with 5*10^6 PC3M-luc cells and ventral bioluminescence images were acquired at time 0 and weekly thereafter. Our results showed that FX-11, acetazolamide, DH348 and bicarbonate were able to effectively (p&lt;0.004) suppress metastasis formation in this system, whereas furosemide was not. Based on these results, a subsequent study investigated the combination of bicarbonate, FX-11 or DH348 with immune checkpoint blockade in a Panc02 mouse model of pancreatic cancer. Animals were inoculated orthotopically with Panc02 cells and randomized into 8 groups (n=10). Once tumors reached 0.3 cc in size, mice were treated with either 15 mg/kg anti-PD-1 antibody or normal rat IgG controls twice weekly alone or in combination with bicarbonate; FX-11 or DH348 and tumor response was evaluated using US 3D-Mode imaging and volume analysis (FUJIFILM VisualSonics) and histopathology. Anti-PD-1 antibody was ineffective as a monotherapy in this system. Both bicarbonate and DH348 neutralized tumor acidity and reduced tumor growth as monotherapies. Notably, despite the fact that FX-11 was shown to inhibit LDH-A in pancreatic cancer models, it did not affect pH and had no effect on tumor growth as monotherapy. However, FX-11 significantly reduced tumor growth (p &lt;0.01) and metastasis formation in combination with anti-PD-1 antibody, suggesting that inhibition of LDH-A might be effective in combination with checkpoint blockade. Citation Format: Arig A. Ibrahim Hashim, Dominique Abrahams, Liping Xu, Barbra Centeno, Enakshi Sunassee, Rasha Abddelgader, Ludwig Dubois, Philippe Lambin, Robert A. Gatenby, Robert J. Gillies. Targeting tumor acidity with the LDHA inhibitor (FX11) and CAIX inhibitor (DH348) overcomes resistance to PD-1 blockade and inhibits metastasis in a pancreatic cancer model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5932. doi:10.1158/1538-7445.AM2017-5932

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