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.

2 Background: Tremendous advances have been made in cancer therapy through the use of immune checkpoint blockade, although responses are not always durable. There is a growing appreciation of the role of the microbiome in cancer-related outcomes and recent evidence in murine models suggests that modulation of the gut microbiome may enhance responses to immune checkpoint blockade in melanoma. However this has not been investigated in patients. Here, we demonstrate that differential bacterial “signatures” exist in the gut microbiome of responders (R) and non-responders (NR) to anti-PD1 therapy at baseline, and that insights gained could be used to derive actionable strategies to enhance responses. Methods: We collected oral (n = 222) and gut microbiome (n = 113) samples on a large cohort of patients with metastatic melanoma (n = 228), with a majority treated with anti-PD1 therapy (n = 110). Patients were classified as either R or NR based on RECIST criteria, and 16S rRNA gene sequencing was performed to characterize the diversity and composition of the microbiomes. Immune profiling (via 7-marker IHC panel of CD3, CD8, PD-1, PD-L1, Granzyme B, RORγT and FoxP3) was performed in available tumors at baseline. Results: In these studies, we observed significant differences in the diversity and composition of the gut microbiome in R versus NR to PD1 blockade at baseline, but no clear differences in oral microbiomes. Specifically, R had a significantly higher alpha diversity compared to NR (p = 0.017). Notable differences were also seen in the composition of the gut microbiome of R versus NR. Immune profiling demonstrated significantly increased immune infiltrates in baseline tumor samples of R, with a positive correlation between CD8, CD3, PD1 and FoxP3 T-cell density and abundance of specific bacteria enriched in R (e.g. Faecalibacterium). Conclusions: Differences exist in the diversity and composition of the gut microbiome in R vs NR to anti-PD1 therapy. These results have far-reaching implications and suggest that modifications to the gut microbiome could potentially enhance therapeutic responses to immune checkpoint blockade.

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.