Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

Habitat escalated adaptive therapy (HEAT): a phase 2 trial utilizing radiomic habitat-directed and genomic-adjusted radiation dose (GARD) optimization for high-grade soft tissue sarcoma.

BACKGROUND: Soft tissue sarcomas (STS), have significant inter- and intra-tumoral heterogeneity, with poor response to standard neoadjuvant radiotherapy (RT). Achieving a favorable pathologic response (FPR ≥ 95%) from RT is associated with improved patient outcome. Genomic adjusted radiation dose (GARD), a radiation-specific metric that quantifies the expected RT treatment effect as a function of tumor dose and genomics, proposed that STS is significantly underdosed. STS have significant radiomic heterogeneity, where radiomic habitats can delineate regions of intra-tumoral hypoxia and radioresistance. We designed a novel clinical trial, Habitat Escalated Adaptive Therapy (HEAT), utilizing radiomic habitats to identify areas of radioresistance within the tumor and targeting them with GARD-optimized doses, to improve FPR in high-grade STS. METHODS: Phase 2 non-randomized single-arm clinical trial includes non-metastatic, resectable high-grade STS patients. Pre-treatment multiparametric MRIs (mpMRI) delineate three distinct intra-tumoral habitats based on apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) sequences. GARD estimates that simultaneous integrated boost (SIB) doses of 70 and 60 Gy in 25 fractions to the highest and intermediate radioresistant habitats, while the remaining volume receives standard 50 Gy, would lead to a > 3 fold FPR increase to 24%. Pre-treatment CT guided biopsies of each habitat along with clip placement will be performed for pathologic evaluation, future genomic studies, and response assessment. An mpMRI taken between weeks two and three of treatment will be used for biological plan adaptation to account for tumor response, in addition to an mpMRI after the completion of radiotherapy in addition to pathologic response, toxicity, radiomic response, disease control, and survival will be evaluated as secondary endpoints. Furthermore, liquid biopsy will be performed with mpMRI for future ancillary studies. DISCUSSION: This is the first clinical trial to test a novel genomic-based RT dose optimization (GARD) and to utilize radiomic habitats to identify and target radioresistance regions, as a strategy to improve the outcome of RT-treated STS patients. Its success could usher in a new phase in radiation oncology, integrating genomic and radiomic insights into clinical practice and trial designs, and may reveal new radiomic and genomic biomarkers, refining personalized treatment strategies for STS. TRIAL REGISTRATION: NCT05301283. TRIAL STATUS: The trial started recruitment on March 17, 2022.

Tumor containment: a more general mathematical analysis.

Clinical and pre-clinical data suggest that treating some tumors at a mild, patient-specific dose might delay resistance to treatment and increase survival time. A recent mathematical model with sensitive and resistant tumor cells identified conditions under which a treatment aiming at tumor containment rather than eradication is indeed optimal. This model however neglected mutations from sensitive to resistant cells, and assumed that the growth-rate of sensitive cells is non-increasing in the size of the resistant population. The latter is not true in standard models of chemotherapy. This article shows how to dispense with this assumption and allow for mutations from sensitive to resistant cells. This is achieved by a novel mathematical analysis comparing tumor sizes across treatments not as a function of time, but as a function of the resistant population size.

Technical feasibility of novel immunostimulatory low-dose radiation for polymetastatic disease with CBCT-based online adaptive and conventional approaches.

PURPOSE: A workflow/planning strategy delivering low-dose radiation therapy (LDRT) (1 Gy) to all polymetastatic diseases using conventional planning/delivery (Raystation/Halcyon = "conventional") and the AI-based Ethos online adaptive RT (oART) platform is developed/evaluated. METHODS: Using retrospective data for ten polymetastatic non-small cell lung cancer patients (5-52 lesions each) with PET/CTs, gross tumor volumes (GTVs) were delineated using PET standardized-uptake-value (SUV) thresholding. A 1 cm uniform expansion of GTVs to account for setup/contour uncertainty and organ motion-generated planning target volumes (PTVs). Dose optimization/calculation used the diagnostic CT from PET/CT. Dosimetric objectives were: Dmin,0.03cc ≥ 95% (acceptable variation (Δ) ≥ 90%), V100% ≥ 95% (Δ ≥ 90%), and D0.03cc ≤ 120% (Δ ≤ 125%). Additionally, online adaptation was simulated. When available, subsequent diagnostic CT was used to represent on-treatment CBCT. Otherwise, the CT from PET/CT used for initial planning was deformed to simulate clinically representative changes. RESULTS: All initial plans generated, both for Raystation and Ethos, achieved clinical goals within acceptable variation. For all patients, Dmin,0.03cc ≥ 95%, V100% ≥ 95%, and D0.03cc ≤ 120% goals were achieved for 84.8%/99.5%, 97.7%/98.7%, 97.4%/92.3%, in conventional/Ethos plans, respectively. The ratio of 50% isodose volume to PTV volume (R50%), maximum dose at 2 cm from PTV (D2cm), and the ratio of the 100% isodose volume to PTV volume (conformity index) in Raystation/Ethos plans were 7.9/5.9; 102.3%/88.44%; and 0.99/1.01, respectively. In Ethos, online adapted plans maintained PTV coverage whereas scheduled plans often resulted in geographic misses due to changes in tumor size, patient position, and body habitus. The average total duration of the oART workflow was 26:15 (min:sec) ranging from 6:43 to 57:30. The duration of each oART workflow step as a function of a number of targets showed a low correlation coefficient for influencer generation and editing (R2 = 0.04 and 0.02, respectively) and high correlation coefficient for target generation, target editing and plan generation (R2 = 0.68, 0.63 and 0.69, respectively). CONCLUSIONS: This study demonstrates feasibility of conventional planning/treatment with Raystation/Halcyon and highlights efficiency gains when utilizing semi-automated planning/online-adaptive treatment with Ethos for immunostimulatory LDRT conformally delivered to all sites of polymetastatic disease.

Targeting Innate Immunity in Glioma Therapy.

Currently, there is a lack of effective therapies for the majority of glioblastomas (GBMs), the most common and malignant primary brain tumor. While immunotherapies have shown promise in treating various types of cancers, they have had limited success in improving the overall survival of GBM patients. Therefore, advancing GBM treatment requires a deeper understanding of the molecular and cellular mechanisms that cause resistance to immunotherapy. Further insights into the innate immune response are crucial for developing more potent treatments for brain tumors. Our review provides a brief overview of innate immunity. In addition, we provide a discussion of current therapies aimed at boosting the innate immunity in gliomas. These approaches encompass strategies to activate Toll-like receptors, induce stress responses, enhance the innate immune response, leverage interferon type-I therapy, therapeutic antibodies, immune checkpoint antibodies, natural killer (NK) cells, and oncolytic virotherapy, and manipulate the microbiome. Both preclinical and clinical studies indicate that a better understanding of the mechanisms governing the innate immune response in GBM could enhance immunotherapy and reinforce the effects of chemotherapy and radiotherapy. Consequently, a more comprehensive understanding of the innate immune response against cancer should lead to better prognoses and increased overall survival for GBM patients.

Response adaptive salvage with KTd and ASCT for functional high-risk multiple myeloma-The Australasian Leukemia and Lymphoma Group (ALLG) MM17 Trial.

We evaluated re-induction incorporating carfilzomib-thalidomide-dexamethasone (KTd) and autologous stem cell transplantation (ASCT) for newly diagnosed multiple myeloma (NDMM) refractory, or demonstrating a suboptimal response, to non-IMID bortezomib-based induction. KTd salvage consisted of thalidomide 100 mg daily and dexamethasone 20 mg orally combined with carfilzomib 56 mg/m2 days 1, 2, 8, 9, 15 and 16, of each 28-day cycle. Following four cycles, patients achieving a stringent complete response proceeded to ASCT whereas those who did not received a further two cycles then ASCT. Consolidation consisted of two cycles of KTd then Td to a total of 12 months post-ASCT therapy. Primary end-point was the overall response rate (ORR) with KTd prior to ASCT. Fifty patients were recruited. The ORR was 78% with EuroFlow MRD negativity of 34% in the intention-to-treat population and 65% in the evaluable population at 12 months post-ASCT. With follow-up >38 months median PFS and OS have not been reached with PFS and OS at 36 months of 64% and 80%, respectively. KTd was well tolerated with grade 3 and grade ≥4 adverse events rates of 32% and 10%, respectively. Response adaptive utilisation of KTd with ASCT is associated with both high-quality responses and durable disease control in functional high-risk NDMM.

Modeling tumour heterogeneity of PD-L1 expression in tumour progression and adaptive therapy.

Although PD-1/PD-L1 inhibitors show potent and durable anti-tumour effects in some refractory tumours, the response rate in overall patients is unsatisfactory, which in part due to the inherent heterogeneity of PD-L1. In order to establish an approach for predicting and estimating the dynamic alternation of PD-L1 heterogeneity during cancer progression and treatment, this study establishes a comprehensive modelling and computational framework based on a mathematical model of cancer cell evolution in the tumour-immune microenvironment, and in combination with epigenetic data and overall survival data of clinical patients from The Cancer Genome Atlas. Through PD-L1 heterogeneous virtual patients obtained by the computational framework, we explore the adaptive therapy of administering anti-PD-L1 according to the dynamic of PD-L1 state among cancer cells. Our results show that in contrast to the continuous maximum tolerated dose treatment, adaptive therapy is more effective for PD-L1 positive patients, in that it prolongs the survival of patients by administration of drugs at lower dosage.

Exploiting evolutionary trade-offs for posttreatment management of drug-resistant populations.

Antibiotic resistance frequently evolves through fitness trade-offs in which the genetic alterations that confer resistance to a drug can also cause growth defects in resistant cells. Here, through experimental evolution in a microfluidics-based turbidostat, we demonstrate that antibiotic-resistant cells can be efficiently inhibited by amplifying the fitness costs associated with drug-resistance evolution. Using tavaborole-resistant Escherichia coli as a model, we show that genetic mutations in leucyl-tRNA synthetase (that underlie tavaborole resistance) make resistant cells intolerant to norvaline, a chemical analog of leucine that is mistakenly used by tavaborole-resistant cells for protein synthesis. We then show that tavaborole-sensitive cells quickly outcompete tavaborole-resistant cells in the presence of norvaline due to the amplified cost of the molecular defect of tavaborole resistance. This finding illustrates that understanding molecular mechanisms of drug resistance allows us to effectively amplify even small evolutionary vulnerabilities of resistant cells to potentially enhance or enable adaptive therapies by accelerating posttreatment competition between resistant and susceptible cells.

Influence of air mapping errors on the dosimetric accuracy of prostate CBCT-guided online adaptive radiation therapy.

PURPOSE: CBCT-guided online adaptive radiotherapy (oART) plans presently utilize daily synthetic CTs (sCT) that are automatically generated using deformable registration algorithms. These algorithms may have poor performance at reproducing variable volumes of gas present during treatment. Therefore, we have analyzed the air mapping error between the daily CBCTs and the corresponding sCT and explored its dosimetric effect on oART plan calculation. METHODS: Abdominopelvic air volume was contoured on both the daily CBCT images and the corresponding synthetic images for 207 online adaptive pelvic treatments. Air mapping errors were tracked over all fractions. For two case studies representing worst case scenarios, dosimetric effects of air mapping errors were corrected in the sCT images using the daily CBCT air contours, then recalculating dose. Dose volume histogram statistics and 3D gamma passing rates were used to compare the original and air-corrected sCT-based dose calculations. RESULTS: All analyzed patients showed observable air pocket contour differences between the sCT and the CBCT images. The largest air volume difference observed in daily CBCT images for a given patient was 276.3 cc, a difference of more than 386% compared to the sCT. For the two case studies, the largest observed change in DVH metrics was a 2.6% reduction in minimum PTV dose, with all other metrics varying by less than 1.5%. 3D gamma passing rates using 1%/1 mm criteria were above 90% when comparing the uncorrected and corrected dose distributions. CONCLUSION: Current CBCT-based oART workflow can lead to inaccuracies in the mapping of abdominopelvic air pockets from daily CBCT to the sCT images used for the optimization and calculation of the adaptive plan. Despite the large observed mapping errors, the dosimetric effects of such differences on the accuracy of the adapted plan dose calculation are unlikely to cause differences greater than 3% for prostate treatments.

Varian ethos online adaptive radiotherapy for prostate cancer: Early results of contouring accuracy, treatment plan quality, and treatment time.

The Varian Ethos system allows for online adaptive treatments through the utilization of artificial intelligence (AI) and deformable image registration which automates large parts of the anatomical contouring and plan optimization process. In this study, treatments of intact prostate and prostate bed, with and without nodes, were simulated for 182 online adaptive fractions, and then a further 184 clinical fractions were delivered on the Ethos system. Frequency and magnitude of contour edits were recorded, as well as a range of plan quality metrics. From the fractions analyzed, 11% of AI generated contours, known as influencer contours, required no change, and 81% required minor edits in any given fraction. The frequency of target and noninfluencer organs at risk (OAR) contour editing varied substantially between different targets and noninfluencer OARs, although across all targets 72% of cases required no edits. The adaptive plan was the preference in 95% of fractions. The adaptive plan met more goals than the scheduled plan in 78% of fractions, while in 15% of fractions the number of goals met was the same. The online adaptive recontouring and replanning process was carried out in 19 min on average. Significant improvements in dosimetry are possible with the Ethos online adaptive system in prostate radiotherapy.

Dosimetric advantages of daily adaptive strategy in IMPT for high-risk prostate cancer.

PURPOSE: To evaluate the dosimetric advantages of daily adaptive radiotherapy (DART) in intensity-modulated proton therapy (IMPT) for high-risk prostate cancer by comparing estimated doses of the conventional non-adaptive radiotherapy (NART) that irradiates according to an original treatment plan through the entire treatment and the DART that uses an adaptive treatment plan generated by using daily CT images acquired before each treatment. METHODS: Twenty-three patients with prostate cancer were included. A treatment plan with 63 Gy (relative biological effectiveness (RBE)) in 21 fractions was generated using treatment planning computed tomography (CT) images assuming that all patients had high-risk prostate cancer for which the clinical target volume (CTV) needs to include prostate and the seminal vesicle (SV) in our treatment protocol. Twenty-one adaptive treatment plans for each patient (total 483 data sets) were generated using daily CT images, and dose distributions were calculated. Using a 3 mm set-up uncertainty in the robust optimization, the doses to the CTV, prostate, SV, rectum, and bladder were compared. RESULTS: Estimated accumulated doses of NART and DART in the 23 patients were 60.81 ± 3.47 Gy (RBE) and 63.24 ± 1.04 Gy (RBE) for CTV D99 (p < 0.01), 62.99 ± 1.28 Gy (RBE) and 63.43 ± 1.33 Gy (RBE) for the prostate D99 (p = 0.2529), and 59.07 ± 5.19 Gy (RBE) and 63.17 ± 1.04 Gy (RBE) for SV D99 (p < 0.001). No significant differences were observed between NART and DART in the estimated accumulated dose for the rectum and bladder. CONCLUSION: Compared with the NART, DART was shown to be a useful approach that can maintain the dose coverage to the target without increasing the dose to the organs at risk (OAR) using the 3 mm set-up uncertainty in the robust optimization in patients with high-risk prostate cancer.

Modeling the synergistic properties of drugs in hormonal treatment for prostate cancer.

Prostate cancer is one of the most prevalent cancers in men, with increasing incidence worldwide. This public health concern has inspired considerable effort to study various aspects of prostate cancer treatment using dynamical models, especially in clinical settings. The standard of care for metastatic prostate cancer is hormonal therapy, which reduces the production of androgen that fuels the growth of prostate tumor cells prior to treatment resistance. Existing population models often use patients' prostate-specific antigen levels as a biomarker for model validation and for finding optimal treatment schedules; however, the synergistic effects of drugs used in hormonal therapy have not been well-examined. This paper describes the first mathematical model that explicitly incorporates the synergistic effects of two drugs used to inhibit androgen production in hormonal therapy. The drugs are cyproterone acetate, representing the drug family of anti-androgens that affect luteinizing hormones, and leuprolide acetate, representing the drug family of gonadotropin-releasing hormone analogs. By fitting the model to clinical data, we show that the proposed model can capture the dynamics of serum androgen levels during intermittent hormonal therapy better than previously published models. Our results highlight the importance of considering the synergistic effects of drugs in cancer treatment, thus suggesting that the dynamics of the drugs should be taken into account in optimal treatment studies, particularly for adaptive therapy. Otherwise, an unrealistic treatment schedule may be prescribed and render the treatment less effective. Furthermore, the drug dynamics allow our model to explain the delay in the relapse of androgen the moment a patient is taken off treatment, which supports that this delay is due to the residual effects of the drugs.

Initial analysis of the dosimetric benefit and clinical resource cost of CBCT-based online adaptive radiotherapy for patients with cancers of the cervix or rectum.

PURPOSE: This provides a benchmark of dosimetric benefit and clinical cost of cone-beam CT-based online adaptive radiotherapy (ART) technology for cervical and rectal cancer patients. METHODS: An emulator of a CBCT-based online ART system was used to simulate more than 300 treatments for 13 cervical and 15 rectal cancer patients. CBCT images were used to generate adaptive replans. To measure clinical resource cost, the six phases of the workflow were timed. To evaluate the dosimetric benefit, changes in dosimetric values were assessed. These included minimum dose (Dmin) and volume receiving 95% of prescription (V95%) for the planning target volume (PTV) and the clinical target volume (CTV), and maximum 2 cc's (D2cc) of the bladder, bowel, rectum, and sigmoid colon. RESULTS: The average duration of the workflow was 24.4 and 9.2 min for cervical and rectal cancer patients, respectively. A large proportion of time was dedicated to editing target contours (13.1 and 2.7 min, respectively). For cervical cancer patients, the replan changed the Dmin to the PTVs and CTVs for each fraction 0.25 and 0.25 Gy, respectively. The replan changed the V95% by 9.2 and 7.9%. The D2cc to the bladder, bowel, rectum, and sigmoid colon for each fraction changed -0.02, -0.08, -0.07, and -0.04 Gy, respectively. For rectal cancer patients, the replan changed the Dmin to the PTVs and CTVs for each fraction of 0.20 and 0.24 Gy, respectively. The replan changed the V95% by 4.1 and 1.5%. The D2cc to the bladder and bowel for each fraction changed 0.02 and -0.02 Gy, respectively. CONCLUSIONS: Dosimetric benefits can be achieved with CBCT-based online ART that is amenable to conventional appointment slots. The clinical significance of these benefits remains to be determined. Managing contours was the primary factor affecting the total duration and is imperative for safe and effective adaptive radiotherapy.

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory.

Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.

An effective method to reduce the interplay effects between respiratory motion and a uniform scanning proton beam irradiation for liver tumors: A case study.

PURPOSE: For scanning particle beam therapy, interference between scanning patterns and interfield organ motion may result in suboptimal dose within target volume. In this study, we developed a simple offline correction technique for uniform scanning proton beam (USPB) delivery to compensate for the interplay between scanning patterns and respiratory motion and demonstrate the effectiveness of our technique in treating liver cancer. METHODS: The computed tomography (CT) and respiration data of two patients who had received stereotactic body radiotherapy for hepatocellular carcinoma were used. In the simulation, the relative beam weight delivered to each respiratory phase is calculated for each beam layer after treatment of each fraction. Respiratory phases with beam weights higher than 50% of the largest weight are considered "skipped phases" for the next fraction. For the following fraction, the beam trigger is regulated to prevent beam layers from starting irradiation in skipped phases by extending the interval between each layer. To calculate dose-volume histogram (DVH), the dose of the target volume at end-exhale (50% phase) was calculated as the sum of each energy layer, with consideration of displacement due to respiratory motion and relative beam weight delivered per respiratory phase. RESULTS: For a single fraction, D1% , D99% , and V100% were 114%, 88%, and 32%, respectively, when 8 Gy/min of dose rate was simulated. Although these parameters were improved with multiple fractions, dosimetric inhomogeneity without motion management remained even at 30 fractions, with V100% 86.9% at 30 fractions. In contrast, the V100% values with adaptation were 96% and 98% at 20 and 30 fractions, respectively. We developed an offline correction technique for USPB therapy to compensate for the interplay effects between respiratory organ motion and USPB beam delivery. CONCLUSIONS: For liver tumor, this adaptive therapy technique showed significant improvement in dose uniformity even with fewer treatment fractions than normal USPB therapy.

[Long-term effect of risk-adaptive therapy for early stage extranodal NK/T cell lymphoma].

Objective: This study aimed to evaluate the role of extended involved-field intensity modulated radiation therapy (IMRT) for patients with early stage extranodal nasal-type NK/T-cell lymphoma (NKTCL) in a single center, and to explore the long-term effect of risk-adaptive therapy. Methods: Among 238 patients with early stage NKTCL, there were 191 in high-risk group [any risk factor of age > 60, elevated serum lactate dehydrogenase (LDH), Eastern Cooperative Oncology Group (ECOG) score≥2, primary tumor invasion, or Ann Arbor stage Ⅱ] and 47 in low-risk group (no risk factor). A total of 204 patients received radiotherapy combined with chemotherapy, 15 received radiotherapy alone and 19 received chemotherapy alone. One-hundred and eighty-six patients had radiotherapy at a dose ≥50 Gy and 159 patients received chemotherapy with asparaginase-based regimen. Results: The 5-year overall survival (OS) rate and progression- free survival (PFS)rate of all patients were 66.2% and 57.5%. Five-year OS and PFS rates in low-risk group were 91.8% and 88.0%, while 59.3% and 49.3% in high-risk group. The survival of patients in low-risk group were better than those in high-risk group (both P<0.001). The five-year OS rate in combined therapy group, radiotherapy group and chemotherapy group were 71.7%, 52.3% and 20.7%, respectively (P<0.001). The five-year PFS rate were 63.5%, 23.3% and 24.1%, respectively (P<0.001). Among 219 patients receiving radiotherapy, the 5-year OS and PFS rate of patients with primary site radiotherapy dose ≥ 50 Gy were 72.6% and 66.1%, higher than those of patients with a dose <50 Gy (57.3% and 30.9%, respectively; P=0.031, P<0.001). In the high-risk group, the 5-year OS and PFS rate of the patients who received radiotherapy combined with more than 4 cycles of chemotherapy were 66.5% and 62.6%, higher than those of patients received less than 4 cycles of chemotherapy (56.7% and 46.4%, P=0.045 and 0.020, respectively). Cox multivariate analysis showed that ECOG score (HR=2.208, P=0.001), primary site (HR=4.887, P<0.001), primary tumor invasion (HR=3.265, P=0.001) and radiation dose (HR=1.895, P=0.011) were independent factors of OS in early NKTCL patients. Conclusion: Extended-involved field IMRT with radiation dose more than 50 Gy was the main treatment for patients with early stage NKTCL. Radiotherapy combined with adequate cycle chemotherapy significantly improved prognosis of high-risk patients.

Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer.

In metastatic castrate resistant prostate cancer (mCRPC), abiraterone is conventionally administered continuously at maximal tolerated dose until treatment failure. The majority of patients initially respond well to abiraterone but the cancer cells evolve resistance and the cancer progresses within a median time of 16 months. Incorporating techniques that attempt to delay or prevent the growth of the resistant cancer cell phenotype responsible for disease progression have only recently entered the clinical setting. Here we use evolutionary game theory to model the evolutionary dynamics of patients with mCRPC subject to abiraterone therapy. In evaluating therapy options, we adopt an optimal control theory approach and seek the best treatment schedule using nonlinear constrained optimization. We compare patient outcomes from standard clinical treatments to those with other treatment objectives, such as maintaining a constant total tumor volume or minimizing the fraction of resistant cancer cells within the tumor. Our model predicts that continuous high doses of abiraterone as well as other therapies aimed at curing the patient result in accelerated competitive release of the resistant phenotype and rapid subsequent tumor progression. We find that long term control is achievable using optimized therapy through the restrained and judicious application of abiraterone, maintaining its effectiveness while providing acceptable patient quality of life. Implementing this strategy will require overcoming psychological and emotional barriers in patients and physicians as well as acquisition of a new class of clinical data designed to accurately estimate intratumoral eco-evolutionary dynamics during therapy.

Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment.

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoner's dilemma game theoretic mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor burden, but subsequently "release" the resistant cells from competition to re-populate and re-grow the tumor in a resistant form. The evolutionary model allows us to quantify responses to conventional (continuous) therapeutic strategies as well as to design adaptive strategies.These novel adaptive strategies are robust to small perturbations in timing and extend simulated time to relapse from continuous therapy administration.

Novel Approaches to Apoptosis-Inducing Therapies.

Induction of apoptotic programmed cell death is one of the underlying principles of most current cancer therapies. In this review, we discuss the limitations and drawbacks of this approach and identify three distinct, but overlapping strategies to avoid these difficulties and further enhance the efficacy of apoptosis-inducing therapies. We postulate that the application of multi-targeted small molecule inhibitor cocktails will reduce the risk of the cancer cell populations developing resistance towards therapy. Following from these considerations regarding population genetics and ecology, we advocate the reconsideration of therapeutic end points to maximise the benefits, in terms of quantity and quality of life, for the patients. Finally, combining both previous points, we also suggest an altered focus on the cellular and molecular targets of therapy, i.e. targeting the (cancer cells') interaction with the tumour microenvironment.