Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis.

Standard-of-care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5-year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor-infiltrating lymphocytes (TILs), which are the main effectors of antitumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune-desert (48%), immune-excluded (34%), and inflamed (18%). Immune-desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low antitumor immune response. Immune-excluded IP predominantly consisted of BRAF V600E-mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high antitumor immune response, as demonstrated by a high cytolytic score, immune-related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune-related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue-based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.

Transcriptional profiling of macrophages in situ in metastatic melanoma reveals localization-dependent phenotypes and function.

Modulation of immune function at the tumor site could improve patient outcomes. Here, we analyze patient samples of metastatic melanoma, a tumor responsive to T cell-based therapies, and find that tumor-infiltrating T cells are primarily juxtaposed to CD14+ monocytes/macrophages rather than melanoma cells. Using immunofluorescence-guided laser capture microdissection, we analyze transcriptomes of CD3+ T cells, CD14 + monocytes/macrophages, and melanoma cells in non-dissociated tissue. Stromal CD14+ cells display a specific transcriptional signature distinct from CD14+ cells within tumor nests. This signature contains LY75, a gene linked with antigen capture and regulation of tolerance and immunity in dendritic cells (DCs). When applied to TCGA cohorts, this gene set can distinguish patients with significantly prolonged survival in metastatic cutaneous melanoma and other cancers. Thus, the stromal CD14+ cell signature represents a candidate biomarker and suggests that reprogramming of stromal macrophages to acquire DC function may offer a therapeutic opportunity for metastatic cancers.

Human KIT+ myeloid cells facilitate visceral metastasis by melanoma.

Metastasis of melanoma significantly worsens prognosis; thus, therapeutic interventions that prevent metastasis could improve patient outcomes. Here, we show using humanized mice that colonization of distant visceral organs with melanoma is dependent upon a human CD33+CD11b+CD117+ progenitor cell subset comprising <4% of the human CD45+ leukocytes. Metastatic tumor-infiltrating CD33+ cells from patients and humanized (h)NSG-SGM3 mice showed converging transcriptional profiles. Single-cell RNA-seq analysis identified a gene signature of a KIT/CD117-expressing CD33+ subset that correlated with decreased overall survival in a TCGA melanoma cohort. Thus, human CD33+CD11b+CD117+ myeloid cells represent a novel candidate biomarker as well as a therapeutic target for metastatic melanoma.

Whole-exome sequencing capture kit biases yield false negative mutation calls in TCGA cohorts.

The Cancer Genome Atlas (TCGA) provides a genetic characterization of more than ten thousand tumors, enabling the discovery of novel driver mutations, molecular subtypes, and enticing drug targets across many histologies. Here we investigated why some mutations are common in particular cancer types but absent in others. As an example, we observed that the gene CCDC168 has no mutations in the stomach adenocarcinoma (STAD) cohort despite its common presence in other tumor types. Surprisingly, we found that the lack of called mutations was due to a systematic insufficiency in the number of sequencing reads in the STAD and other cohorts, as opposed to differential driver biology. Using strict filtering criteria, we found similar behavior in four other genes across TCGA cohorts, with each gene exhibiting systematic sequencing depth issues affecting the ability to call mutations. We identified the culprit as the choice of exome capture kit, as kit choice was highly associated with the set of genes that have insufficient reads to call a mutation. Overall, we found that thousands of samples across all cohorts are subject to some capture kit problems. For example, for the 6353 samples using the Broad Institute's Custom capture kit there are undercalling biases for at least 4833 genes. False negative mutation calls at these genes may obscure biological similarities between tumor types and other important cancer driver effects in TCGA datasets.