Tumor-specific T helper (Th) cells have a central role in the immune response against cancer. However, there exist distinct Th cell subsets with very different and antagonizing properties. Some Th subsets such as Th1 protect against cancer, while others (Th2, T regulatory/Treg) are considered detrimental or of unknown significance (T follicular helper/Tfh, Th17). The Th composition of human solid tumors remains poorly characterized. Therefore, we established a four-color multiplex chromogenic immunohistochemical assay for detection of Th1, Th2, Th17, Tfh and Treg cells in human tumor sections. The method was used to analyze resected primary lung tumors from 11 patients with non-small cell lung cancer (NSCLC). Four microanatomical regions were investigated: tumor epithelium, tumor stroma, peritumoral tertiary lymphoid structures (TLS) and non-cancerous distal lung tissue. In tumor epithelium and stroma, most CD4⁺T cells identified had either a Th2 (GATA-3⁺CD3⁺CD8^-) or Treg (FOXP3⁺CD3⁺CD8^-) phenotype, whereas only low numbers of Th1, Th17, and Tfh cells were observed. Similarly, Th2 was the most abundant Th subset in TLS, followed by Treg cells. In sharp contrast, Th1 was the most frequently detected Th subset in non-cancerous lung tissue from the same patients. A higher Th1:Th2 ratio in tumor stroma was found to be associated with increased numbers of intratumoral CD8⁺T cells. The predominance of Th2 and Treg cells in both tumor stroma and tumor epithelium was consistent for all the 11 patients investigated. We conclude that human primary NSCLC tumors are Th2-skewed and contain numerous Treg cells. If human tumors are Th2-skewed, as our data in NSCLC suggest, reprogramming the type of immune response from a detrimental Th2 to a beneficial Th1 may be critical to increase the response rate of immunotherapy.

The analysis of tumour‐associated macrophages (TAMs) has a high potential to predict cancer recurrence and response to immunotherapy. However, the heterogeneity of TAMs poses a challenge for quantitative and qualitative measurements. Here, we critically evaluated by immunohistochemistry and flow cytometry two commonly used pan‐macrophage markers (CD14 and CD68) as well as some suggested markers for tumour‐promoting M2 macrophages (CD163, CD204, CD206 and CD209) in human non–small cell lung cancer (NSCLC). Tumour, non‐cancerous lung tissue and blood were investigated. For immunohistochemistry, CD68 was confirmed to be a useful pan‐macrophage marker although careful selection of antibody was found to be critical. The widely used anti‐CD68 antibody clone KP‐1 stains both macrophages and neutrophils, which is problematic for TAM quantification because lung tumours contain many neutrophils. For TAM counting in tumour sections, we recommend combined labelling of CD68 with a cell membrane marker such as CD14, CD163 or CD206. In flow cytometry, the commonly used combination of CD14 and HLA‐DR was found to not be optimal because some TAMs do not express CD14. Instead, combined staining of CD68 and HLA‐DR is preferable to gate all TAMs. Concerning macrophage phenotypic markers, the scavenger receptor CD163 was found to be expressed by a substantial fraction (50%‐86%) of TAMs with a large patient‐to‐patient variation. Approximately 50% of TAMs were positive for CD206. Surprisingly, there was no clear overlap between CD163 and CD206 positivity, and three distinct TAM sub‐populations were identified in NSCLC tumours: CD163⁺CD206⁺, CD163⁺CD206⁻ and CD163⁻CD206⁻. This work should help develop macrophage‐based prognostic tools for cancer.

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p < 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p < 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

We propose a unified multitasking deep learning method to identify and segment structural regions and detect immune cells of non-small cell lung cancer tissue. This approach combines multitask learning and deep supervision to significantly reduce training time compared with single-task models, while achieving competitive performance with 50% fewer parameters. We balance tasks using homoscedastic uncertainty and address class imbalance through a combination of focal loss and class weight adjustment. Tested on multiplexed WSI, our method overcomes challenges related to variability and limited annotations, while extracting robust spatial information from the tumor microenvironment (TME). This data can be leveraged to quantify tumor-immune dynamics and intercellular communication, thereby providing objective insights into the TME's structural and functional complexity.