Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared with traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols. SIGNIFICANCE: Generation of interpretable and personalized adaptive treatment schedules using a deep reinforcement framework that interacts with a virtual patient model overcomes the limitations of standardized strategies caused by heterogeneous treatment responses.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Searching for Goldilocks: How Evolution and Ecology Can Help Uncover More Effective Patient-Specific Chemotherapies.

Deaths from cancer are mostly due to metastatic disease that becomes resistant to therapy. A mainstay treatment for many cancers is chemotherapy, for which the dosing strategy is primarily limited by patient toxicity. While this MTD approach builds upon the intuitively appealing principle that maximum therapeutic benefit is achieved by killing the largest possible number of cancer cells, there is increasing evidence that moderation might allow host-specific features to contribute to success. We believe that a "Goldilocks Window" of submaximal chemotherapy will yield improved overall outcomes. This window combines the complex interplay of cancer cell death, immune activity, emergence of chemoresistance, and metastatic dissemination. These multiple activities driven by chemotherapy have tradeoffs that depend on the specific agents used as well as their dosing levels and schedule. Here we present evidence supporting the idea that MTD may not always be the best approach and offer suggestions toward a more personalized treatment regime that integrates insights into patient-specific eco-evolutionary dynamics.

The Goldilocks Window of Personalized Chemotherapy: Getting the Immune Response Just Right.

The immune system is a robust and often untapped accomplice of many standard cancer therapies. A majority of tumors exist in a state of immune tolerance where the patient's immune system has become insensitive to the cancer cells. Because of its lymphodepleting effects, chemotherapy has the potential to break this tolerance. To investigate this, we created a mathematical modeling framework of tumor-immune dynamics. Our results suggest that optimal chemotherapy scheduling must balance two opposing objectives: maximizing tumor reduction while preserving patient immune function. Successful treatment requires therapy to operate in a "Goldilocks Window" where patient immune health is not overly compromised. By keeping therapy "just right," we show that the synergistic effects of immune activation and chemotherapy can maximize tumor reduction and control. SIGNIFICANCE: To maximize the synergy between chemotherapy and antitumor immune response, lymphodepleting therapy must be balanced in a "Goldilocks Window" of optimal dosing.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/20/5302/F1.large.jpg.

Clonal hematopoiesis of indeterminate potential and its impact on patient trajectories after stem cell transplantation.

Clonal hematopoiesis of indeterminate potential (CHIP) is a recently identified process where older patients accumulate distinct subclones defined by recurring somatic mutations in hematopoietic stem cells. CHIP's implications for stem cell transplantation have been harder to identify due to the high degree of mutational heterogeneity that is present within the genetically distinct subclones. In order to gain a better understanding of CHIP and the impact of clonal dynamics on transplantation outcomes, we created a mathematical model of clonal competition dynamics. Our analyses highlight the importance of understanding competition intensity between healthy and mutant clones. Importantly, we highlight the risk that CHIP poses in leading to dominance of precancerous mutant clones and the risk of donor derived leukemia. Furthermore, we estimate the degree of competition intensity and bone marrow niche decline in mice during aging by using our modeling framework. Together, our work highlights the importance of better characterizing the ecological and clonal composition in hematopoietic donor populations at the time of stem cell transplantation.

The Immune Checkpoint Kick Start: Optimization of Neoadjuvant Combination Therapy Using Game Theory.

PURPOSE: In an upcoming clinical trial at the Moffitt Cancer Center for women with stage 2/3 estrogen receptor-positive breast cancer, treatment with an aromatase inhibitor and a PD-L1 checkpoint inhibitor combination will be investigated to lower a preoperative endocrine prognostic index (PEPI) that correlates with relapse-free survival. PEPI is fundamentally a static index, measured at the end of neoadjuvant therapy before surgery. We have developed a mathematical model of the essential components of the PEPI score to identify successful combination therapy regimens that minimize tumor burden and metastatic potential, on the basis of time-dependent trade-offs in the system. METHODS: We considered two molecular traits, CCR7 and PD-L1, which correlate with treatment response and increased metastatic risk. We used a matrix game model with the four phenotypic strategies to examine the frequency-dependent interactions of cancer cells. This game was embedded in an ecological model of tumor population-growth dynamics. The resulting model predicts evolutionary and ecological dynamics that track with changes in the PEPI score. RESULTS: We considered various treatment regimens on the basis of combinations of the two therapies with drug holidays. By considering the trade off between tumor burden and metastatic potential, the optimal therapy plan was a 1-month kick start of the immune checkpoint inhibitor followed by 5 months of continuous combination therapy. Relative to a protocol giving both therapeutics together from the start, this delayed regimen resulted in transient suboptimal tumor regression while maintaining a phenotypic constitution that is more amenable to fast tumor regression for the final 5 months of therapy. CONCLUSION: The mathematical model provides a useful abstraction of clinical intuition, enabling hypothesis generation and testing of clinical assumptions.

Yeast response to LA virus indicates coadapted global gene expression during mycoviral infection.

Viruses that infect fungi have a ubiquitous distribution and play an important role in structuring fungal communities. Most of these viruses have an unusual life history in that they are propagated exclusively via asexual reproduction or fission of fungal cells. This asexual mode of transmission intimately ties viral reproductive success to that of its fungal host and should select for viruses that have minimal deleterious impact on the fitness of their hosts. Accordingly, viral infections of fungi frequently do not measurably impact fungal growth, and in some instances, increase the fitness of the fungal host. Here we determine the impact of the loss of coinfection by LA virus and the virus-like particle M1 upon global gene expression of the fungal host Saccharomyces cerevisiae and provide evidence supporting the idea that coevolution has selected for viral infection minimally impacting host gene expression.

Challenges of delivering oral health care to older adults.

With increasing mortality rates and declining fertility, there is expected to be a large increase in the proportion of older adults living in the United States. There are many challenges associated with oral health care delivery to this population, including complex medical conditions, access to care and the availability of adequately trained dentists. The dental community needs to consider the oral health needs of this growing population and address these needs through initiatives in education, research and health-care policy.

Challenges of delivering oral health care to older adults.

With increasing mortality rates and declining fertility, there is expected to be a large increase in the proportion of older adults living in the United States. There are many challenges associated with oral health care delivery to this population, including complex medical conditions, access to care and the availability of adequately trained dentists. The dental community needs to consider the oral health needs of this growing population and address these needs through initiatives in education, research and health-care policy.

Juvenile oral lichen planus: a report of 2 cases.

Lichen planus is a mucocutaneous disease that predominantly affects older patients and occurs much less frequently in the pediatric population. Furthermore, oral lichen planus is extremely rare in childhood with very few cases cited in the literature. The intention of this paper is to contribute two clinically and histologically documented cases of juvenile oral lichen planus cases to the literature. Although a rare occurrence, early recognition and diagnosis of this condition by dental practitioners can have a significant impact on the oral health of affected patients.