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Age is among the main risk factors for cancer and any cancer study in adults is faced with an aging tissue and organism. Yet, pre-clinical studies are carried out using young mice and are not able address the impact of aging and associated comorbidities on disease biology and treatment outcomes. Here, we discuss the limitations of current mouse cancer models and suggest strategies for developing novel models to address these major gaps in knowledge and experimental approaches.

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.

Introduction Presently, programmed death ligand 1 is the most commonly used biomarker to predict response to immune checkpoint inhibitors (ICIs) in NSCLC. Owing to its several limitations, there is continuous search for more precise and reliable markers. Frameshift mutations by insertion or deletion (fsindels) are suggested to induce more immunogenic tumor-specific neoantigens, conferring better response to ICIs. Positive correlation of fsindels with ICI response has been studied in melanoma and renal cell carcinoma. We investigated the implication of fsindels in the clinical outcomes and immune landscape of patients with NSCLC treated with ICIs. Methods We utilized The Cancer Genome Atlas data set to analyze tumor mutational burden, neoantigen burden, and immune landscape in relation to fsindel status. In addition, utilizing the clinical data from 122 patients treated with ICIs, we evaluated the influence of fsindels on disease response rates and survival outcomes. Results A positive correlation between fsindel burden and tumor mutational burden and activated CD4/CD8 T-cell infiltration was shown. Presence of fsindels was also associated with significant prolongation of progression-free survival in patients treated with ICIs (median 6.2 versus 2.7 months [p = 0.01]). In addition, significant differences in the overall response rates (26% versus 12% [p = 0.04]) and disease control rates (68% versus 48% [p = 0.02]) were observed in patients with fsindels. Conclusion Our findings suggest that fsindels may have a predictive role for ICI response in NSCLC.

Here we analyzed patient samples of metastatic melanoma, and found that tumor-infiltrating T cells are primarily juxtaposed to CD14+ monocytes/macrophages rather than melanoma cells. Using novel immunofluorescence-guided laser capture microdissection, we analyzed transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages and melanoma cells in non-dissociated tissue. CD14+ cells localized within tumor nests displayed a specific transcriptional signature distinct from CD14+ cells localized in tumor stroma. When applied to TCGA cohorts, this stromal macrophage gene set distinguished patients with significantly prolonged survival in cutaneous melanoma and as well as other cancers. This signature contains CD2 and Ly75, a gene linked with antigen capture and regulation of tolerance and immunity, and comprises markers of monocyte-derived dendritic cells (DCs). Thus, the stromal CD14+ cell signature represents a novel candidate biomarker and suggests that reprogramming of stromal macrophages to acquire DC function may offer a therapeutic opportunity for numerous metastatic cancers.

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