Medical research for human benefit is greatly impeded by the necessity for human tissues and subjects. However, upon obtaining consent for human specimens, precious samples must be handled with the greatest care in order to ensure integrity of organs, tissues, and cells to the highest degree. Unfortunately, tissue processing by definition requires extraction of tissues from the host, a change which can cause great cellular stress and have major repercussions on subsequent analyses. These stresses could result in the specimen being no longer representative of the site from which it was retrieved. Therefore, a strict protocol must be adhered to while processing these specimens to ensure representativeness. The desired assay(s) must also be taken into consideration in order to ensure that an optimal technique is used for sample processing. Outlined here is a protocol for tissue retrieval, processing and various analyses which may be performed on processed tissue in order to maximize downstream production from limited tissue samples.
Appreciation for genomic and immune heterogeneity in cancer has grown though the relationship of these factors to treatment response has not been thoroughly elucidated. To better understand this, we studied a large cohort of melanoma patients treated with targeted therapy or immune checkpoint blockade (n = 60). Heterogeneity in therapeutic responses via radiologic assessment was observed in the majority of patients. Synchronous melanoma metastases were analyzed via deep genomic and immune profiling, and revealed substantial genomic and immune heterogeneity in all patients studied, with considerable diversity in T cell frequency, and few shared T cell clones (<8% on average) across the cohort. Variables related to treatment response were identified via these approaches and through novel radiomic assessment. These data yield insight into differential therapeutic responses to targeted therapy and immune checkpoint blockade in melanoma, and have key translational implications in the age of precision medicine.
We have made major advances in the treatment of melanoma through the use of targeted therapy and immune checkpoint blockade; however, clinicians are posed with therapeutic dilemmas regarding timing and sequence of therapy. There is a growing appreciation of the impact of antitumor immune responses to these therapies, and we performed studies to test the hypothesis that clinical patterns and immune infiltrates differ at progression on these treatments. We observed rapid clinical progression kinetics in patients on targeted therapy compared to immune checkpoint blockade. To gain insight into possible immune mechanisms behind these differences, we performed deep immune profiling in tumors of patients on therapy. We demonstrated low CD8+ T-cell infiltrate on targeted therapy and high CD8+ T-cell infiltrate on immune checkpoint blockade at clinical progression. These data have important implications, and suggest that antitumor immune responses should be assessed when considering therapeutic options for patients with melanoma.
9575 Background: Although both targeted and immune therapies have significantly improved outcomes for mm pts, only a minority of pts experience durable responses with many pts with multiple SMM demonstrating differential responses to therapy. We performed multidimensional spatial characterization of immune markers in SMM from mm patients treated with targeted and immune therapies to improve our understanding of correlations and determinants of response. Methods: NanoString's Digital Spatial Profiling research platform was used on 6 SMM from 3 pts (treatment-naïve; BRAF + MEK targeted therapy treated; anti-PD-1 immunotherapy treated) for 30 immune and signaling proteins. For analysis, we selected and compared immune-rich (CD45+) and tumor-rich (S100B+) regions across SMM. Results were compared to lesion-specific clinical responses. Results: Striking differences in patterns of expression across SMM from individual pts were detected, including in Ki67, CD68 myeloid cells, and the potent immunosuppressor B7-H3. SMM progressing after targeted therapy demonstrated higher pAKT and PD-L1 expression, consistent with described resistance mechanisms. Large differences in expression of PD-L1 were noted following anti-PD-1 therapy, which could contribute to heterogeneous responses. Differential expression patterns in the TME associated with response were also detected, including in increases in CD4 and CD14 cells in progressing lesions. Conclusions: Striking differences in responding and non-responding SMM were observed, providing potential explanations for the heterogeneous clinical responses frequently observed in mm pts. Studies are ongoing to further characterize interactions and spatial distribution of cell types, as well as integrate these findings with previous molecular and immune profiling data (whole exome sequencing, gene expression profiling, flow cytometry, IHC, TCR sequencing) in these and additional SMM to identify actionable strategies to homogenize responses across metastases in mm pts.
<p>Supplementary Figure 1. Immune profiling of pre-treatment, on-treatment and progression CTLA-4 blockade samples by immunohistochemistry. Supplementary Figure 2. Myeloid cell profiling of pre-treatment, on-treatment and progression CTLA-4 blockade samples by immunohistochemistry. Supplementary Figure 3. Increased contact between CD8 T cells and CD68 myeloid cells in non-responding patients to anti-CTLA-4 and anti-PD-1 therapy at pre-treatment CTLA-4 blockade, pre-treatment PD-1 blockade, and on-treatment PD-1 blockade time points. Supplementary Figure 4. Immune profiling of pre anti-PD-1, on-treatment anti-PD-1 and progression anti-PD-1 samples by immunohistochemistry. Supplementary Figure 5. Longitudinal increase in CD8, PD-1, and PD-L1 expression in responders to anti-PD-1 therapy. Supplementary Figure 6. Relative increase in CD8 T cell infiltrate at tumor center in responders to anti-PD-1 on treatment. Supplementary Figure 7. Significant increase in immune infiltrate between responders and non-responders to PD-1 blockade in absence of prior anti-CTLA-4 therapy. Supplementary Figure 8. Immune profiling of myeloid cells atpre-treatment and on-treatment PD-1 blockade time pointsby immunohistochemistry. Supplementary Figure 9. Heatmap of 54 NanoString samples. Supplementary Figure 10. Gene-specific NanoString concordance with immune profiling by IHC in pre-treatment, on-treatment and progression CTLA-4 blockade samples. Supplementary Figure 11. Gene-specific NanoString concordance with immune profiling by IHC in pre-treatment, on-treatment and progression PD-1 blockade samples. Supplementary Figure 12. Prior CTLA-4 blockade is not required for PD-1 early on-treatment profile. Supplementary Figure 13. Hierarchical clustering of gene expression across 54 samples confirms lack of batch effect.</p>
The low response rates to immunotherapy in uveal melanoma (UM) sharply contrast with reputable response rates in cutaneous melanoma (CM) patients. To characterize the mechanisms responsible for resistance to immunotherapy in UM, we performed immune profiling in tumors from 10 metastatic UM patients and 10 metastatic CM patients by immunohistochemistry (IHC). Although there is no difference in infiltrating CD8+ T cells between UM and CM, a significant decrease in programmed death-1 (PD-1)-positive lymphocytes was observed and lower levels of programmed death ligand-1 (PD-L1) in UM metastases compared with CM metastases. Tumors from metastatic UM patients showed a lower success rate of tumor-infiltrating lymphocyte (TIL) growth compared with metastatic CM (45% vs. 64% success), with a significantly lower quantity of UM TIL expanded overall. These studies suggest that UM and CM are immunologically distinct, and provide potential explanation for the impaired success of immunotherapy in UM.
Immune checkpoint inhibitors (ICIs), including CTLA-4- and PD-1-blocking antibodies, can have profound effects on tumor immune cell infiltration that have not been consistent in biopsy series reported to date. Here, we analyze seven molecular datasets of samples from patients with advanced melanoma (N = 514) treated with ICI agents to investigate clinical, genomic, and transcriptomic features of anti-PD-1 response in cutaneous melanoma. We find that prior anti-CTLA-4 therapy is associated with differences in genomic, individual gene, and gene signatures in anti-PD-1 responders. Anti-CTLA-4-experienced melanoma tumors that respond to PD-1 blockade exhibit increased tumor mutational burden, inflammatory signatures, and altered cell cycle processes compared with anti-CTLA-4-naive tumors or anti-CTLA-4-experienced, anti-PD-1-nonresponsive melanoma tumors. We report a harmonized, aggregate resource and suggest that prior CTLA-4 blockade therapy is associated with marked differences in the tumor microenvironment that impact the predictive features of PD-1 blockade therapy response.
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