Assessing the Prognostic Significance of Tumor-Infiltrating Lymphocytes in Patients With Melanoma Using Pathologic Features Identified by Natural Language Processing

Immunohistochemistry assessment of tissue neutrophil-to-lymphocyte ratio predicts outcomes in melanoma patients treated with anti-programmed cell death 1 therapy

Elevated neutrophil-to-lymphocyte ratio (NLR) is associated with diminished immunotherapy response in metastatic melanoma. Although NLR assessment in peripheral blood is established, tissue dynamics remain insufficiently explored. This study aimed to evaluate tissue NLR (tNLR)'s predictive potential through immunohistochemistry in immunotherapy-treated melanoma. Fifty melanoma patients who underwent anti-programmed cell death 1 (PD-1) therapy were assessed. Hematological, clinical and tumor features were collected from medical records. Responses were categorized using the Response Evaluation Criteria in Solid Tumors for immunotherapy (iRECIST) guidelines. Immunohistochemistry for tumor-infiltrating T cells (cluster differentiation 3) and neutrophils (myeloperoxidase) was performed on formalin-fixed paraffin-embedded tumor samples. NLR, derived NLR (dNLR) and tNLR were calculated. Overall survival (OS) and survival following immunotherapy (SFI) were calculated from diagnosis or immunotherapy start to loss of follow-up or death. Patients with high tNLR presented improved OS ( P =  0.038) and SFI with anti-PD-1 therapy ( P =  0.006). Both NLR and dNLR were associated with OS ( P =  0.038 and P =  0.046, respectively) and SFI ( P =  0.001 and P =  0.019, respectively). NLR was also associated with immunotherapy response ( P =  0.007). In conclusion, tNLR emerged as a novel potential biomarker of enhanced survival post anti-PD-1 therapy, in contrast to classical NLR and dNLR markers.

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.

Tumor-infiltrating lymphocytes as a prognostic and predictive factor for Melanoma

INTRODUCTION

Tumor-infiltrating lymphocytes (TILs) have been investigated as prognostic factors in melanoma. Recent advancements in assessing the tumor microenvironment in the setting of more widespread use of immune checkpoint blockade have reignited interest in identifying predictive biomarkers. This review examines the function and significance of TILs in cutaneous melanoma, evaluating their potential as prognostic and predictive markers.

AREAS COVERED

A literature search was conducted on papers covering tumor infiltrating lymphocytes in cutaneous melanoma available online in PubMed and Web of Science from inception to 1 December 2023, supplemented by citation searching. This article encompasses the assessment of TILs, the role of TILs in the immune microenvironment, TILs as a prognostic factor, TILs as a predictive factor for immunotherapy response, and clinical applications of TILs in the treatment of cutaneous melanoma.

EXPERT OPINION

Tumor-infiltrating lymphocytes play a heterogeneous role in cutaneous melanoma. While they have historically been associated with improved survival, their status as independent prognostic or predictive factors remains uncertain. Novel methods of TIL assessment, such as determination of TIL subtypes and molecular signaling, demonstrate potential for predicting therapeutic response. Further, while their clinical utility in risk-stratification in melanoma treatment shows promise, a lack of consensus data hinders standardized application.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

Cross‑talk between lymphangiogenesis and malignant melanoma cells: New opinions on tumour drainage and immunization (Review)

Malignant melanoma (MM) is a highly aggressive tumour that can easily metastasize through the lymphatic system at the early stages. Lymph node (LN) involvement and lymphatic vessel (LV) density (LVD) represent a harbinger of an adverse prognosis, indicating a strong link between the state of the lymphatic system and the advancement of MM. Permeable capillary lymphatic vessels are the optimal conduits for melanoma cell (MMC) invasion, and lymphatic endothelial cells (LECs) can also release a variety of chemokines that actively attract MMCs expressing chemokine ligands through a gradient orientation. Moreover, due to the lower oxidative stress environment in the lymph compared with the blood circulation, MMCs are more likely to survive and colonize. The number of LVs surrounding MM is associated with tumour-infiltrating lymphocytes (TILs), which is crucial for the effectiveness of immunotherapy. On the other hand, MMCs can release various endothelial growth factors such as VEGF-C/D-VEGFR3 to mediate LN education and promote lymphangiogenesis. Tumour-derived extracellular vesicles are also used to promote lymphangiogenesis and create a microenvironment that is more conducive to tumour progression. MM is surrounded by a large number of lymphocytes. However, both LECs and MMCs are highly plastic, playing multiple roles in evading immune surveillance. They achieve this by expressing inhibitory ligands or reducing antigen recognition. In recent years, tertiary lymphoid structures have been shown to be associated with response to anti-immune checkpoint therapy, which is often a positive prognostic feature in MM. The present review discusses the interaction between lymphangiogenesis and MM metastasis, and it was concluded that the relationship between LVD and TILs and patient prognosis is analogous to a dynamically tilted scale.

pSTAT5 is associated with improved survival in patients with thick or ulcerated primary cutaneous melanoma

Identifying prognostic biomarkers to predict clinical outcomes in stage I and II cutaneous melanomas could guide the clinical application of adjuvant and neoadjuvant therapies. We aimed to investigate the prognostic value of phosphorylated signal transducer and activator of transcription 5 (pSTAT5) as a biomarker in early-stage melanoma. This study evaluated all initially staged Ib and II melanoma patients undergoing sentinel node biopsy at a tertiary centre in Brisbane, Australia between 1994 and 2007, with survival data collected from the Queensland Cancer Registry. Primary melanoma tissue from 189 patients was analysed for pSTAT5 level through immunohistochemistry. Cox regression modelling, with adjustment for sex, age, ulceration, anatomical location, and Breslow depth, was applied to determine the association between pSTAT5 detection and melanoma-specific survival. Median duration of follow-up was 7.4 years. High pSTAT5 detection was associated with ulceration and increased tumour thickness. However, multivariate analysis indicated that high pSTAT5 detection was associated with improved melanoma-specific survival (hazard ratio: 0.15, 95% confidence interval: 0.03-0.67) as compared to low pSTAT5 detection. This association persisted when pSTAT5 detection was limited to immune infiltrate or the vasculature, as well as when sentinel node positivity was accounted for. In this cohort, staining for high-pSTAT5 tumours identified a subset of melanoma patients with increased survival outcomes as compared to low-pSTAT5 tumours, despite the former having higher-risk clinicopathological characteristics at diagnosis. pSTAT5 is likely an indicator of local immune activation, and its detection could represent a useful tool to stratify the risk of melanoma progression.

A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study

OBJECTIVE

To construct a novel tumor-node-morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma.

METHOD

This retrospective study with 1657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, the concordance index (C-index), and the area under the receiver operating curve (AUC).

RESULTS

The precision, recall, and F1 scores of the NLP model were 88.83, 89.89, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (internal validation, 0.58 vs. 0.54, P <0.001; external validation, 0.64 vs. 0.63, P <0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P <0.001; 0.64 vs. 0.60, P= 0.017; 0.69 vs. 0.62, P= 0.001; external validation: 0.69 vs. 0.65, P= 0.098; 0.68 vs. 0.64, P= 0.154; 0.64 vs. 0.55, P= 0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P <0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P <0.001; 0.64 vs. 0.58, P <0.001; 0.67 vs. 0.61, P <0.001; respectively) compared with the TNM system.

CONCLUSION

These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.

Clock-like Mutation Signature May Be Prognostic for Worse Survival Than Signatures of UV Damage in Cutaneous Melanoma

Novel treatment modalities comprising immune checkpoint inhibitors and targeted therapies have revolutionized treatment of metastatic melanoma. Still, some patients suffer from rapid progression and decease within months after a diagnosis of stage IV melanoma. We aimed to assess whether genomic alterations may predict survival after the development of stage IV disease, irrespective of received therapy. We analyzed tumor samples of 79 patients with stage IV melanoma using a custom next-generation gene-sequencing panel, MelArray, designed to detect alterations in 190 melanoma-relevant genes. We classified the patients: first, as short survivors (survival ≤6 months after stage IV disease, n = 22) and long survivors (survival >6 months, n = 57); second, by using a cut-off of one year; and third, by comparing the longest surviving 20 patients to the shortest surviving 20. Among analyzed genes, no individual gene alterations, or combinations of alterations, could be dichotomously associated with survival. However, the cohort's mutational profiles closely matched three known mutational signatures curated by the Catalog of Somatic Mutations in Cancer (COSMIC): UV signature COSMIC_7 (cosine-similarity 0.932), clock-like signature COSMIC_5 (cosine-similarity 0.829), and COSMIC_30 (cosine-similarity 0.726). Patients with UV signature had longer survival compared to patients with clock-like and COSMIC 30 (p < 0.0001). Subgroup dichotomization at 6 months showed that 75% of patients with UV signature survived longer than 6 months, and about 75% of patients with clock-like signature survived less than 6 months after development of stage IV disease. In our cohort, clock-like COSMIC_5 mutational signature predicted poor survival while a UV signature COSMIC_7 predicted longer survival. The prognostic value of mutational signatures should be evaluated in prospective studies.

Combining the guidelines and multiphoton imaging methods to improve the prognostic value of tumor-infiltrating lymphocytes in breast cancer

Multiphoton microscopy (MPM) was introduced to label-freely obtain tumor-infiltrating lymphocytes (TILs) images from a total of 611 patients, and the prognostic value of TILs in breast cancer was assessed by the MPM method (TILs-MPM) and guidelines method proposed by the International Immuno-Oncology Biomarker Working Group (TILs-WG), respectively. Moreover, the clinical (CLI) model, TILs-WG + TILs-MPM model, and full model (CLI + TILs-WG + TILs-MPM) were developed to investigate the prognostic value of TILs. The results show that TILs-WG performs better in estrogen receptor (ER)-negative subgroup, and TILs-MPM is comparable with TILs-WG in the ER-negative subgroup, but has the best performance in the ER-positive subgroup. Furthermore, the TILs-WG + TILs-MPM model can significantly improve the prognostic power compared with the TILs-WG model, and the full model has excellent performance, with high area under the curve (AUC) and hazard ratio (HR) in both ER-positive, ER-negative subgroups, and the complete cohort. Our results suggest that the combination of TILs-WG with TILs-MPM model can greatly improve the prognostic value of TILs.

The Genetic and Immunologic Landscape Underlying the Risk of Malignant Progression in Laryngeal Dysplasia

(1) Background: The development of laryngeal cancer is a multistep process involving structural alterations of the epithelial mucosa, from dysplasia (LDy) to invasive carcinoma. In this study, we define new biomarkers, prognostic for malignant transformation, in patients affected by LDy. (2) Methods: We used targeted next-generation sequencing and immunohistochemical analysis to define the mutational and immunological landscape of 15 laryngeal dysplasia progressing to invasive cancer (progressing dysplasia), as well as 31 cases of laryngeal dysplasia that did not progress to carcinoma (non-progressing dysplasia). Two pathologists independently analyzed the presence of tumor-infiltrating lymphocytes in LDy pre-embedded paraffin-fixed specimens. The RNA-based next-generation sequencing panel OIRRA was used to evaluate the expression of 395 genes related to immune system activation. (3) Results: High TILs are significantly correlated with a higher risk of malignant transformation. The non-brisk pattern was significantly associated with an 86% reduced risk of malignant progression (OR = 0.16, 95% CI: 0.03-0.5, p = 0.008). TILs showed a highly positive correlation with CCR6, CD83, HLA-DPB1, MX1 and SNAI1, and they were inversely correlated with CD48, CIITA, CXCR4, FCER1G, IL1B, LST1 and TLR8. (4) Conclusions: TILs have a great potential to identify high-risk progression dysplasia and thus to define surveillance protocols and prevention programs.

Mechanisms of resistance to immune checkpoint inhibitors in melanoma: What we have to overcome?

Marching into the second decade after the approval of ipilimumab, it is clear that immune checkpoint inhibitors (ICIs) have dramatically improved the prognosis of melanoma. Although the current edge is already high, with a 4-year OS% of 77.9% for adjuvant nivolumab and a 6.5-year OS% of 49% for nivolumab/ipilimumab combination in the metastatic setting, a high proportion of patients with advanced melanoma have no benefit from immunotherapy, or experience an early disease relapse/progression in the first few months of treatment, surviving much less. Reasonably, the primary and acquired resistance to ICIs has entered into the focus of clinical research with positive (e.g., nivolumab and relatlimab combination) and negative feedbacks (e.g., nivolumab with pegylated-IL2, pembrolizumab with T-VEC, nivolumab with epacadostat, and combinatorial triplets of BRAF/MEK inhibitors with immunotherapy). Many intrinsic (intracellular or intra-tumoral) but also extrinsic (systematic) events are considered to be involved in the development of this resistance to ICIs: i) melanoma cell immunogenicity (e.g., tumor mutational burden, antigen-processing machinery and immunogenic cell death, neoantigen affinity and heterogeneity, genomic instability, melanoma dedifferentiation and phenotypic plasticity), ii) immune cell trafficking, T-cell priming, and cell death evasion, iii) melanoma neovascularization, cellular TME components(e.g., T_regs, CAFs) and extracellular matrix modulation, iv) metabolic antagonism in the TME(highly glycolytic status, upregulated CD39/CD73/adenosine pathway, iDO-dependent tryptophan catabolism), v) T-cell exhaustion and negative immune checkpoints, and vi) gut microbiota. In the present overview, we discuss how these parameters compromise the efficacy of ICIs, with an emphasis on the lessons learned by the latest melanoma studies; and in parallel, we describe the main ongoing approaches to overcome the resistance to immunotherapy. Summarizing this information will improve the understanding of how these complicated dynamics contribute to immune escape and will help to develop more effective strategies on how anti-tumor immunity can surpass existing barriers of ICI-refractory melanoma.

A large language model for electronic health records

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .

Quantitative spatial evaluation of tumor-immune interactions in the immunotherapy setting of metastatic melanoma lymph nodes

Introduction

Immune cell infiltration into the tumor microenvironment is generally associated with favorable clinical outcomes in solid tumors. However, the dynamic interplay among distinct immune cell subsets within the tumor-immune microenvironment as it relates to clinical responses to immunotherapy remains unresolved. In this study, we applied multiplex immunofluorescence (MxIF) to spatially characterize tumor-immune interactions within the metastatic melanoma lymph node.

Methods

Pretreatment, whole lymph node biopsies were evaluated from 25 patients with regionally metastatic melanoma who underwent subsequent anti-PD1 therapy. Cyclic MxIF was applied to quantitatively and spatially assess expression of 45 pathologist-validated antibodies on a single tissue section. Pixel-based single cell segmentation and a supervised classifier approach resolved 10 distinct tumor, stromal and immune cell phenotypes and functional expression of PD1.

Results

Single cell analysis across 416 pathologist-annotated tumor core regions of interest yielded 5.5 million cells for spatial evaluation. Cellular composition of tumor and immune cell subsets did not differ in the tumor core with regards to recurrence outcomes (p>0.05) however spatial patterns significantly differed in regional and paracrine neighborhood evaluations. Specifically, a regional community cluster comprised of primarily tumor and dendritic cells was enriched in patients that did not experience recurrence (p=0.009). By an independent spatial approach, cell-centric neighborhood analyses identified an enrichment for dendritic cells in cytotoxic T cell (CTL) and tumor cell-centric neighborhoods in the no recurrence patient response group (p<0.0001). Further evaluation of these neighborhoods identified an enrichment for CTL-dendritic cell interactions in patients that did not experience recurrence (p<0.0001) whereas CTL-macrophage interactions were more prevalent in CTL-centric neighborhoods of patients who experienced recurrence (p<0.0001).

Discussion

Overall, this study offers a more comprehensive evaluation of immune infiltrates and spatial-immune signatures in the metastatic tumor-immune microenvironment as it informs recurrence risk following immunotherapy.

The role of peritumoral CD8 + /TIA1 + lymphocytes in hepatocellular carcinoma aggressiveness and recurrence after surgical resection

Hepatocellular carcinoma (HCC) is characterized by a low mutation burden and a relatively low number of tumor-infiltrating lymphocytes (TILs), making still difficult to identify targets for specific therapies. The aim of this study was the identification of the prognostic role of TILs in HCC, focusing on their distribution and status of activation. We retrospectively enrolled 41 patients, undergone to liver resection for HCC. A significant increase of CD8 + intratumoral lymphocytes was observed in HCCs with prevalent solid architecture, but with a higher PD-1/TIA1 ratio, suggesting that HCCs with solid architecture have more peri-tumoral lymphocytes, but with minor functionality. At multivariate and univariate analyses, TIA1/CD8 ratio correlated with tumor recurrence, meaning that HCC with more activated TILs are characterized by a higher tumor aggressiveness. The use of a feasible and cheap immunohistochemical panel can help in post-surgical prognostic stratification, focusing not only in the raw number and density of TILs, but more on their state of activation and morphology.

Establishing a Prognostic Model Based on Ulceration and Immune Related Genes in Melanoma Patients and Identification of EIF3B as a Therapeutic Target

Ulceration and immune status are independent prognostic factors for survival in melanoma patients. Herein univariate Cox regression analysis revealed 53 ulcer-immunity-related DEGs. We performed consensus clustering to divide The Cancer Genome Atlas (TCGA) cohort (n = 467) into three subtypes with different prognosis and biological functions, followed by validation in three merged Gene Expression Omnibus (GEO) cohorts (n = 399). Multiomics approach was used to assess differences among the subtypes. Cluster 3 showed relatively lesser amplification and expression of immune checkpoint genes. Moreover, Cluster 3 lacked immune-related pathways and immune cell infiltration, and had higher proportion of non-responders to immunotherapy. We also constructed a prognostic model based on ulceration and immune related genes in melanoma. EIF3B was a hub gene in the intersection between genes specific to Cluster 3 and those pivotal for melanoma growth (DepMap, https://depmap.org/portal/download/). High EIF3B expression in TCGA and GEO datasets was related to worst prognosis. In vitro models revealed that EIF3B knockdown inhibited melanoma cell migration and invasion, and decreased TGF-β1 level in supernatant compared with si-NC cells. EIF3B expression was negatively correlated with immune-related signaling pathways, immune cell gene signatures, and immune checkpoint gene expression. Moreover, its low expression could predict partial response to anti-PD-1 immunotherapy. To summarize, we established a prognostic model for melanoma and identified the role of EIF3B in melanoma progression and immunotherapy resistance development.

Development of an Image Analysis-Based Prognosis Score Using Google's Teachable Machine in Melanoma

BACKGROUND

The increasing number of melanoma patients makes it necessary to establish new strategies for prognosis assessment to ensure follow-up care. Deep-learning-based image analysis of primary melanoma could be a future component of risk stratification.

OBJECTIVES

To develop a risk score for overall survival based on image analysis through artificial intelligence (AI) and validate it in a test cohort.

METHODS

Hematoxylin and eosin (H&E) stained sections of 831 melanomas, diagnosed from 2012-2015 were photographed and used to perform deep-learning-based group classification. For this purpose, the freely available software of Google's teachable machine was used. Five hundred patient sections were used as the training cohort, and 331 sections served as the test cohort.

RESULTS

Using Google's Teachable Machine, a prognosis score for overall survival could be developed that achieved a statistically significant prognosis estimate with an AUC of 0.694 in a ROC analysis based solely on image sections of approximately 250 × 250 µm. The prognosis group "low-risk" (n = 230) showed an overall survival rate of 93%, whereas the prognosis group "high-risk" (n = 101) showed an overall survival rate of 77.2%.

CONCLUSIONS

The study supports the possibility of using deep learning-based classification systems for risk stratification in melanoma. The AI assessment used in this study provides a significant risk estimate in melanoma, but it does not considerably improve the existing risk classification based on the TNM classification.

Prognostic Significance of Primary Tumor-Infiltrating Lymphocytes in a Contemporary Melanoma Cohort

BACKGROUND

The prognostic impact of tumor-infiltrating lymphocytes (TILs) on outcomes and treatment efficacy for patients with melanoma in the contemporary era remains poorly characterized.

METHODS

Consecutive patients who underwent wide excision and sentinel lymph node biopsy for cutaneous melanoma 1 mm thick or thicker at a single institution were identified (2006-2019). The patients were stratified based on primary tumor TIL status as brisk (bTILs), non-brisk (nbTILs), or absent (aTILs). Associations between patient factors and outcomes were analyzed using multivariable analysis.

RESULTS

Of the 1017 patients evaluated, 846 (83.2 %) had primary TILs [nbTILs (n = 759, 89.7 %) and bTILs (n = 87, 10.3 %)]. In the multivariable analysis, the patients with any type of TILs had higher rates of regression [odds ratio (OR), 1.86; p = 0.016], lower rates of acral lentiginous histology (OR, 0.22; p < 0.001), and lower rates of SLN positivity (OR, 0.64; p = 0.042) than those without TILs. The multivariable analysis found no association between disease-specific survival and bTILs [hazard ratio (HR), 1.04; p = 0.927] or nbTILs (HR, 0.89; p = 0.683). An association was found between bTILs and recurrence-free survival (RFS) advantage [bTILs (HR 0.46; p = 0.047), nbTILs (HR 0.71; p = 0.088)], with 5-year RFS rates of 84 % for bTILs, 71.8 % for nbTILs, and 68.4 % for aTILs (p = 0.044). For the 114 immune checkpoint blockade (ICB)-naïve patients who experienced a recurrence treated with ICB therapy, no association was observed between progression-free survival and bTILs (HR, 0.64; p = 0.482) or nbTILs (HR, 0.58; p = 0.176).

CONCLUSIONS

The prognostic significance of primary TILs in the contemporary melanoma era appears complex. Further studies characterizing the phenotype of TILs and their association with regional metastasis and responsiveness to ICB therapy are warranted.

Let's talk about sex: a biological variable in immune response against melanoma

As science culture gravitates toward a more holistic inclusion of both males and females in research design, the outlining of sex differences and their respective intersections with disease physiology and pathophysiology should see reciprocal expansion. Melanoma skin cancer, for example, has observed a female advantage in incidence, mortality, and overall survival since the early 1970s. The exact biological mechanism of this trend, however, is unclear and further complicated by a layering of clinical variables such as skin phototype, age, and body mass index. In this perspective, we highlight epidemiological evidence of sex differences in melanoma and summarize the landscape of their potential origin. Among several biological hallmarks, we make a note of sex‐specific immune profiles—along with divergent hormonal regulation, social practices, DNA damage and oxidative stress responses, body composition, genetic variants, and X‐chromosome expression—as probable drivers of disparity in melanoma initiation and progression. This review further focuses the conversation of sex as an influencing factor in melanoma development and its potential implication for disease management and treatment strategies.