The Evolution of Mouse Models of Cancer: Past, Present, and Future.

In the nearly 50 years since the original models of cancer first hit the stage, mouse models have become a major contributor to virtually all aspects of cancer research, and these have evolved well beyond simple transgenic or xenograft models to encompass a wide range of more complex models. As the sophistication of mouse models has increased, an explosion of new technologies has expanded the potential to both further develop and apply these models to address major challenges in cancer research. In the current era, cancer modeling has expanded to include nongermline genetically engineered mouse models (GEMMs), patient-derived models, organoids, and adaptations of the models better suited for cancer immunology research.

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment.

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based).

Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial.

We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11).

Treating Head and Neck Cancer in the Age of Immunotherapy: A 2023 Update.

Most patients diagnosed with head and neck squamous cell carcinoma (HNSCC) will present with locally advanced disease, requiring multimodality therapy. While this approach has a curative intent, a significant subset of these patients will develop locoregional failure and/or distant metastases. The prognosis of these patients is poor, and therapeutic options other than palliative chemotherapy are urgently needed.

Tumor-Specific Major Histocompatibility-II Expression Predicts Benefit to Anti-PD-1/L1 Therapy in Patients With HER2-Negative Primary Breast Cancer.

Purpose: Immunotherapies targeting PD-1/L1 enhance pathologic complete response (pCR) rates when added to standard neoadjuvant chemotherapy (NAC) regimens in early-stage triple-negative, and possibly high-risk estrogen receptor-positive breast cancer. However, immunotherapy has been associated with significant toxicity, and most patients treated with NAC do not require immunotherapy to achieve pCR. Biomarkers discerning patients benefitting from the addition of immunotherapy from those who would achieve pCR to NAC alone are clearly needed.

A dormant TIL phenotype defines non-small cell lung carcinomas sensitive to immune checkpoint blockers.

The biological determinants of sensitivity and resistance to immune checkpoint blockers are not completely understood. To elucidate the role of intratumoral T-cells and their association with the tumor genomic landscape, we perform paired whole exome DNA sequencing and multiplexed quantitative immunofluorescence (QIF) in pre-treatment samples from non-small cell lung carcinoma (NSCLC) patients treated with PD-1 axis blockers. QIF is used to simultaneously measure the level of CD3+ tumor infiltrating lymphocytes (TILs), in situ T-cell proliferation (Ki-67 in CD3) and effector capacity (Granzyme-B in CD3).

Spatially Resolved and Quantitative Analysis of VISTA/PD-1H as a Novel Immunotherapy Target in Human Non-Small Cell Lung Cancer.

Determine the localized expression pattern and clinical significance of VISTA/PD-1H in human non-small cell lung cancer (NSCLC). Using multiplex quantitative immunofluorescence (QIF), we performed localized measurements of VISTA, PD-1, and PD-L1 protein in 758 stage I-IV NSCLCs from 3 independent cohorts represented in tissue microarray format. The targets were selectively measured in cytokeratin tumor epithelial cells, CD3 T cells, CD4 T-helper cells, CD8 cytotoxic T cells, CD20 B lymphocytes and CD68 tumor-associated macrophages.

TGF-beta and cancer.

The relationships between transforming growth factor-beta (TGF-beta) and cancer are varied and complex. The paradigm that is emerging from the experimental evidence accumulated over the past decade or so is that TGF-beta can play two different and opposite roles with respect to the process of malignant progression. During early stages of carcinogenesis, TGF-beta acts predominantly as a potent tumor suppressor and may mediate the actions of chemopreventive agents such as retinoids and nonsteroidal anti-estrogens.

Structural alterations of transforming growth factor-beta receptor genes in human cervical carcinoma.

The development and progression of invasive uterine cervical carcinomas appear to be associated with the progressive loss of sensitivity to transforming growth factor-beta (TGFbeta)-mediated cell cycle arrest. In order to identify possible molecular mechanisms responsible for TGFbeta resistance, we screened the 7 exons of the type II (TbetaR-II) TGFbeta receptor and the 9 exons of the type I (TbetaR-I) TGFbeta receptor genes for mutations in 16 paraffin-embedded primary invasive cervical carcinoma specimens. In one of these carcinomas, we found a novel G-->T transversion in exon 3 of TbetaR-II that introduces a premature stop codon (E142Stop) and presumably results in the synthesis of a truncated soluble exoreceptor.