GATA-3 expression is crucial for T cell development and peaks during commitment to the T cell lineage, midway through the CD4⁻CD8⁻ (double-negative [DN]) stages 1–3. We used RNA interference and conditional deletion to reduce GATA-3 protein acutely at specific points during T cell differentiation in vitro. Even moderate GATA-3 reduction killed DN1 cells, delayed progression to the DN2 stage, skewed DN2 gene regulation, and blocked appearance of the DN3 phenotype. Although a Bcl-2 transgene rescued DN1 survival and improved DN2 cell generation, it did not restore DN3 differentiation. Gene expression analyses (quantitative PCR, RNA sequencing) showed that GATA-3–deficient DN2 cells quickly upregulated genes, including Spi1 (PU.1) and Bcl11a, and downregulated genes, including Cpa3, Ets1, Zfpm1, Bcl11b, Il9r, and Il17rb with gene-specific kinetics and dose dependencies. These targets could mediate two distinct roles played by GATA-3 in lineage commitment, as revealed by removing wild-type or GATA-3–deficient early T lineage cells from environmental Notch signals. GATA-3 worked as a potent repressor of B cell potential even at low expression levels, so that only full deletion of GATA-3 enabled pro–T cells to reveal B cell potential. The ability of GATA-3 to block B cell development did not require T lineage commitment factor Bcl11b. In prethymic multipotent precursors, however, titration of GATA-3 activity using tamoxifen-inducible GATA-3 showed that GATA-3 inhibits B and myeloid developmental alternatives at different threshold doses. Furthermore, differential impacts of a GATA-3 obligate repressor construct imply that B and myeloid development are inhibited through distinct transcriptional mechanisms. Thus, the pattern of GATA-3 expression sequentially produces B lineage exclusion, T lineage progression, and myeloid-lineage exclusion for commitment.

Adoptive cell therapy (ACT) with tumor-infiltrating T cells (TILs) has emerged as a promising therapy for the treatment of unresectable or metastatic solid tumors. One challenge to finding a universal anticancer treatment is the heterogeneity present between different tumors as a result of genetic instability associated with tumorigenesis. As the epitome of personalized medicine, TIL-ACT bypasses the issue of intertumoral heterogeneity by utilizing the patient’s existing antitumor immune response. Despite being one of the few therapies capable of inducing durable, complete tumor regression, many patients fail to respond. Recent research has focused on increasing therapeutic efficacy by refining various aspects of the TIL protocol, which includes the isolation, ex vivo expansion, and subsequent infusion of tumor specific lymphocytes. This review will explore how the therapy has evolved with time by highlighting various resistance mechanisms to TIL therapy and the novel strategies to overcome them.

Metastatic melanoma remains the deadliest form of skin cancer. Immune checkpoint inhibition (ICI) immunotherapy has defined a new age in melanoma treatment, but responses remain inconsistent and some patients develop treatment resistance. The myriad of newly developed small molecular (SM) inhibitors of specific effector targets now affords a plethora of opportunities to increase therapeutic responses, even in resistant melanoma. In this review, we will discuss the multitude of SM classes currently under investigation, current and prospective clinical combinations of ICI and SM therapies, and their potential for synergism in melanoma eradication based on established mechanisms of immunotherapy resistance.

The tumor-draining lymph node (TDLN) plays a role in tumor immunity. Intratumorally administered microspheres (MS) that encapsulate immunomodulatory agents have emerged as a treatment strategy capa...

Metastatic melanoma portends a poor prognosis and patients may present with multiple, simultaneous tumors. Despite recent advances in systemic immunotherapy, a majority of patients fail to respond, or exhibit lesion-specific responses wherein some metastases respond as others progress within the same patient. While intertumoral heterogeneity has been clinically associated with these mixed lesion-specific therapeutic responses, no clear mechanism has been identified, largely due to the scarcity of preclinical models. We developed a novel murine synchronous melanoma model that recapitulates this intertumoral genetic and microenvironmental heterogeneity. We show that genetic differences between tumors are sufficient to generate distinct tumor immune microenvironments (TIME) simultaneously in the same mouse. Furthermore, these TIMEs lead to the independent regulation of PD-1/PD-L1 (programmed cell death protein 1/PD-1 ligand), a popular axis targeted by immune checkpoint therapy, in response to ongoing anti-tumor immunity and the presence of interferon-gamma. Currently, therapeutic selection for metastatic melanoma patients is guided by a single biopsy, which may not represent the immune status of all tumors. As a result, patients can display heterogeneous lesion-specific responses. Further investigations into this synchronous melanoma model will provide mechanistic insight into the effects of intertumoral heterogeneity and guide therapeutic selection in this challenging patient population.

The H-scan analysis has been developed as a sensitive measure of ultrasound scattering types. In this study, we aim to distinguish between two different melanoma tumors: YUMM and YUMMER. To characterize the tumor tissues, H-scan was applied to the melanoma samples, resulting in estimation of several parameters. Multiparametric analysis was performed by measuring the H-scan blue fraction, attenuation coefficient, H-scan signal-to-noise ratio (SNR), and B-scan SNR. Principal component analysis was applied to the parameters, forming clusters in 2D space. These were assigned as the inputs for support vector machine (SVM) classification. H-scan imaging was able to differentiate between YUMM and YUMMER; YUMMER showed as more blue (higher frequency) in color. The H-scan parameters of blue fraction and SNR show significant differences between the two types of melanoma. Combining the four parameters, principal components formed clusters of YUMM and YUMMER. The clusters were separated by the decision planes obtained using SVM analysis with 88.5% classification accuracy. We anticipate clinical use of H-scan parameters for melanoma diagnosis.