Explore the vast range of titles available.

Metastatic melanoma is the most aggressive and obstinate skin cancer with poor prognosis. Variant novel applicable regimens have emerged during the past decades intensively, while the most profound approaches are oncogene-targeted therapy and T-lymphocyte mediated immunotherapy. Although targeted therapies generated remarkable and rapid clinical responses in the majority of patients, acquired resistance was developed promptly within months leading to tumor relapse. By contrast, immunotherapies elicited long-term tumor regression. However, the overall response rate was limited. In view of the above, either targeted therapy or immunotherapy cannot elicit durable clinical responses in large range of patients. Interestingly, the advantages and limitations of these regimens happened to be complementary. An increasing number of preclinical studies and clinical trials proved a synergistic antitumor effect with the combination of targeted therapy and immunotherapy, implying a promising prospect for the treatment of metastatic melanoma. In order to achieve a better therapeutic effectiveness and reduce toxicity in patients, great efforts need to be made to illuminate multifaceted interplay between targeted therapy and immunotherapy.

Venetoclax in combination with hypomethylating agents (HMAs) or low-dose cytarabine (LDAC) has demonstrated exceptional activity in elderly and unfit patients with newly diagnosed acute myeloid leukemia (AML). Notably, the safety profile of venetoclax-based induction regimens was favorable, with a low rate of early treatment-related mortality, even in frail study participants. Thus, the introduction of venetoclax has transformed the landscape of AML therapy in elderly patients. Given these promising results, venetoclax in combination with other agents is now being studied as a frontline therapy in younger patients with AML, as well as in relapsed/refractory AML patients. Here, we review clinical data for venetoclax-based therapy in AML, both from prospective as well as retrospective studies, and highlight ongoing novel studies of venetoclax-containing regimens and discuss future research directions.

Harnessing the broad immunostimulatory capabilities of chemotherapy in combination with immune checkpoint inhibitors has improved immunotherapy outcomes in patients with cancer. Certain chemotherapeutic agents can extensively modify the tumor microenvironment (TME), resulting in the reprogramming of local immune responses. Although chemotherapeutic agents with an enhanced generation of potent anti-tumor immune responses have been tested in preclinical animal models and clinical trials, this strategy has not yet shown substantial therapeutic efficacy in selected difficult-to-treat cancer types. In addition, the efficacy of chemotherapeutic agent-based monotherapy in eliciting a long-term anti-tumor immune response is restricted by the immunosuppressive TME. To enhance the immunomodulatory effect of chemotherapy, researchers have made many attempts, mainly focusing on improving the targeted distribution of chemotherapeutic agents and designing combination therapies. Here, we focused on the mechanisms of the anti-tumor immune response to chemotherapeutic agents and enumerated the attempts to advance the use of chemo-immunotherapy. Furthermore, we have listed the important considerations in designing combinations of these drugs to maximize efficacy and improve treatment response rates in patients with cancer.

Opinion statement Remaining the most common cancer in women through the 21 st century, breast cancer and the development of treatment strategies continue to highlight advances made in our understanding of the pathogenesis of cancer development and resistance to therapies. Despite significant progress in the treatment of breast cancer, resistance to chemotherapeutic agents remains a consistent obstacle in terms of treatment success. Anthracyclines, first used over 30 years ago, and the more recent addition of taxanes to the treatment armamentarium are integral components for both newly diagnosed and recurrent breast cancer. Unfortunately, along with other constituents of combination chemotherapy for metastatic breast cancer, these agents ultimately become ineffective in controlling disease. With the emergence of a resistant phenotype, tumors are deemed to be drug resistant - frequently multidrug resistant (MDR). A number of processes have been identified that can underlie clinical drug resistance; observations stemming largely from in vitro laboratory-based studies in human cancer cell lines. Recognized mechanisms of resistance include altered expression of the adenosine triphosphate-binding cassette (ABC) superfamily of transporters, alteration in DNA repair pathways, mutations in cellular targets, resistance to initiation of the apoptotic pathway and the development of constitutively activated signaling pathways. As our understanding of mechanisms of resistance expands, the ability to select specific drugs or drug combinations specific to the phenotype of the cancer will become more specific. Illustrative of these advancements are the reported benefits from the use of newer microtubule-targeting agents in triple negative breast cancer, such as eribulin and ixabepilone; drugs which may be less susceptible to common pathways of drug resistance. Likewise, the combination usage of agents which intersect in receptor crosstalk, such as between the estrogen receptor and the mammalian target of rapamycin (mTOR), have demonstrated synergy in antitumor effects. The recent report of exemestane used in combination with everolimus, have shown great promise in this regard. For patients with HER2 positive disease, a combination approach with trastuzumab and investigational agents such as pertuzumab appear to result in a more complete blockage of HER2 signaling, and improved progression free survival. Thus, as our understanding of the interconnectedness of signaling pathways in breast cancer improves, the ability to rationally design appropriate chemotherapy regimens and delay emerging resistance will improve.

Background NR2F6 has been proposed as an alternative cancer immune checkpoint in the effector T cell compartment. However, a realistic assessment of the in vivo therapeutic potential of NR2F6 requires acute depletion. Methods Employing primary T cells isolated from Cas9-transgenic mice for electroporation of chemically synthesized sgRNA, we established a CRISPR/Cas9-mediated acute knockout protocol of Nr2f6 in primary mouse T cells. Results Analyzing these Nr2f6 CRISPR/Cas9 knockout T cells, we reproducibly observed a hyper-reactive effector phenotype upon CD3/CD28 stimulation in vitro, highly reminiscent to Nr2f6 −/− T cells. Importantly, CRISPR/Cas9-mediated Nr2f6 ablation prior to adoptive cell therapy (ACT) of autologous polyclonal T cells into wild-type tumor-bearing recipient mice in combination with PD-L1 or CTLA-4 tumor immune checkpoint blockade significantly delayed MC38 tumor progression and induced superior survival, thus further validating a T cell-inhibitory function of NR2F6 during tumor progression. Conclusions These findings indicate that Nr2f6 CRISPR/Cas9 knockout T cells are comparable to germline Nr2f6 −/− T cells, a result providing an independent confirmation of the immune checkpoint function of lymphatic NR2F6. Taken together, CRISPR/Cas9-mediated acute Nr2f6 gene ablation in primary mouse T cells prior to ACT appeared feasible for potentiating established PD-L1 and CTLA-4 blockade therapies, thereby pioneering NR2F6 inhibition as a sensitizing target for augmented tumor regression. B_1fr6kneZ4txc6uQGfNdf Video abstract. Graphical abstract

Metastatic renal cell carcinoma (MRCC) exhibits primary resistance to both chemotherapy and radiotherapy. As an immunogenic cancer, MRCC is relatively sensitive to immunotherapy such as that with cytokines, immune checkpoint inhibitors and adoptive T-cell therapy. In addition, many targeted agents developed over the past decade exhibit greater efficacy than cytokines and have become the standard first-line therapy for MRCC. Several preclinical studies have shown that the targeted agent sorafenib possesses an immunomodulation function and may be suitable for combination with immunotherapy. Here, combinatorial therapy consisting of sorafenib and cytokine-induced killer cell immunotherapy was administered to an MRCC patient resulting in a synergistic effect without serious adverse effects. These results suggest a potential combinatorial regimen for MRCC patients.

Bayesian inference is predominantly formulated in a continuous framework, in which posterior beliefs are represented by smooth probability densities. However, an alternative discrete representation—already implicit in Bayes’s original construction—remains conceptually distinct and structurally informative. This paper develops a representation-level analysis of Bayesian updating in the binomial setting and shows that discrete and continuous posteriors may exhibit qualitatively distinct behavior under finite parameter resolution. In particular, coarse discretization can induce regime-dependent divergence from the continuous posterior, even when the algebraic form of the likelihood is identical. The analysis further demonstrates that divergence is not determined solely by grid resolution but also by the balance between prior strength and sample size. By introducing a scale-dependent perspective in which representational resolution and prior magnitude jointly define distinct regimes of inference, the paper clarifies how structural and analytic descriptions interact under finite conditions.

We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution. We formulate this as a covariance-regularized maximum likelihood problem, which can be reduced to a combinatorial optimization problem of searching over the possible breakpoints, or segment boundaries. This problem can be solved using dynamic programming, with complexity that grows with the square of the time series length. We propose a heuristic method that approximately solves the problem in linear time with respect to this length, and always yields a locally optimal choice, in the sense that no change of any one breakpoint improves the objective. Our method, which we call greedy Gaussian segmentation (GGS), easily scales to problems with vectors of dimension over 1000 and time series of arbitrary length. We discuss methods that can be used to validate such a model using data, and also to automatically choose appropriate values of the two hyperparameters in the method. Finally, we illustrate our GGS approach on financial time series and Wikipedia text data.

This paper deals with the ruin problem of an insurance company investing its capital reserve in a risky asset with the price dynamics given by a conditional geometric Brownian motion whose parameters depend on a Markov process describing random variations in the economic and financial environments. We prove a sufficient condition on the distribution of jumps of the business process ensuring the smoothness of the ruin probability as a function of the initial capital and obtain for this function an integro-differential equation.

Large deviations for random graph models has been a topic of significant recent research activity. Much work in this area is focused on the class of dense random graph models (number of edges in the graph scale as n², where n is the number of vertices) where the theory of graphons has emerged as a principal tool in the study of large deviation properties. These tools do not give a good approach to large deviation problems for random graph models in the sparse regime. The aim of this paper is to study an approach for large deviation problems in this regime by establishing large deviation principles (LDP) on suitable path spaces for certain exploration processes of the associated random graph sequence. Exploration processes are an important tool in the study of sparse random graph models and have been used to understand detailed asymptotics of many functionals of sparse random graphs, such as component sizes, surplus, deviations from trees, etc. In the context of rare event asymptotics of interest here, the point of view of exploration process transforms a large deviation analysis of a static random combinatorial structure to the study of a small noise LDP for certain stochastic dynamical systems with jumps. Our work focuses on one particular class of random graph models, namely the configuration model; however, the general approach of using exploration processes for studying large deviation properties of sparse random graph models has broader applicability. The goal is to study asymptotics of probabilities of nontypical behavior in the large network limit. The first key step for this is to establish a LDP for an exploration process associated with the configuration model. A suitable exploration process here turns out to be an infinite-dimensional Markov process with transition probability rates that diminish to zero in certain parts of the state space. Large deviation properties of such Markovian models is challenging due to poor regularity behavior of the associated local rate functions. Our proof of the LDP relies on a representation of the exploration process in terms of a system of stochastic differential equations driven by Poisson random measures and variational formulas for moments of nonnegative functionals of Poisson random measures. Uniqueness results for certain controlled systems of deterministic equations play a key role in the analysis. Next, using the rate function in the LDP for the exploration process we formulate a calculus of variations problem associated with the asymptotics of component degree distributions. The second key ingredient in our study is a careful analysis of the infinite-dimensional Euler–Lagrange equations associated with this calculus of variations problem. Exact solutions of these systems of nonlinear differential equations are identified which then provide explicit formulas for decay rates of probabilities of nontypical component degree distributions and related quantities.