Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes

Histopathological images provide the definitive source of cancer diagnosis, containing information used by pathologists to identify and subclassify malignant disease, and to guide therapeutic choices. These images contain vast amounts of information, much of which is currently unavailable to human interpretation. Supervised deep learning approaches have been powerful for classification tasks, but they are inherently limited by the cost and quality of annotations. Therefore, we developed Histomorphological Phenotype Learning, an unsupervised methodology, which requires no annotations and operates via the self-discovery of discriminatory image features in small image tiles. Tiles are grouped into morphologically similar clusters which appear to represent recurrent modes of tumor growth emerging under natural selection. These clusters have distinct features which can be identified using orthogonal methods. Applied to lung cancer tissues, we show that they align closely with patient outcomes, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype

Adversarial Learning of Cancer Tissue Representations

Deep learning based analysis of histopathology images shows promise in advancing the understanding of tumor progression, tumor micro-environment, and their underpinning biological processes. So far, these approaches have focused on extracting information associated with annotations. In this work, we ask how much information can be learned from the tissue architecture itself. We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations. We show that these representations are able to identify a variety of morphological characteristics across three cancer types: Breast, colon, and lung. This is supported by 1) the separation of morphologic characteristics in the latent space; 2) the ability to classify tissue type with logistic regression using latent representations, with an AUC of 0.97 and 85% accuracy, comparable to supervised deep models; 3) the ability to predict the presence of tumor in Whole Slide Images (WSIs) using multiple instance learning (MIL), achieving an AUC of 0.98 and 94% accuracy. Our results show that our model captures distinct phenotypic characteristics of real tissue samples, paving the way for further understanding of tumor progression and tumor micro-environment, and ultimately refining histopathological classification for diagnosis and treatment (The code and pretrained models are available at: https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations)

Characterization by means of Delaunay triangulation and Voronoi paving of the influence of anti-hormone and/or anti-growth factor antibodies on the in vitro cell growth of human colorectal neoplastic cell lines

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Chemotherapy-induced nuclear alterations of morphologic and genomic characteristics in a human colon cancer grafted onto nude mice.

PURPOSE: A human Dukes B colonic adenocarcinoma was grafted onto 40 nude mice. The mice were divided into four groups, one control and three representing experimental conditions. Animals in the three experimental groups received either adriamycin (ADR), 5-fluorouracil (5-FU), or camptothecin (CPT) over a 25-day period beginning 34 days after grafting. Control animals received saline on an identical schedule. Animals were killed 105 days after grafting. METHODS: The effect of therapy was assessed by three techniques: 1) tumor size was periodically measured during the life of the animals, 2) modifications of APC, Ki-ras, and p53 genes were studied by polymerase chain reaction, dot-blot analysis, restriction analysis, and DNA sequencing, and 3) image cytometry of Feulgen-stained material was used to characterize 15 parameters describing morphometric, densitometric, and textural features of tumor nuclei. RESULTS: When compared with controls, tumor growth (size) was maximally suppressed by treatment with CPT (P < or = 0.001). Growth was inhibited significantly by treatment with 5-FU (P < or = 0.01); no statistical difference in tumor size was observed between controls and animals treated with ADR. Modifications of APC, Ki-ras, and p53 genes were not observed; however, treatment did inhibit amplification of APC and p53 genes. CONCLUSIONS: The 15 morphonuclear parameters were assessed to define populations of cell nuclei altered by chemotherapy. Although CPT maximally suppressed growth, it did not alter nuclear morphology when compared with controls. Treatment with either 5-FU or ADR resulted in nuclear morphologic alterations defined as distinct populations using multivariate analysis. Nonsupervised linear discriminant analysis was used to quantify the relative proportions of these populations. Four morphonuclear parameters were identified, which discriminated nuclei exposed to either ADR or 5-FU from controls.Journal Articleinfo:eu-repo/semantics/publishe

KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance

Summary: Lung cancer treatment has benefited greatly through advancements in immunotherapies. However, immunotherapy often fails in patients with specific mutations like KEAP1, which are frequently found in lung adenocarcinoma. We established an antigenic lung cancer model and used it to explore how Keap1 mutations remodel the tumor immune microenvironment. Using single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. This observation was corroborated in patient samples. CRISPR-Cas9-mediated gene targeting revealed that hyperactivation of the NRF2 antioxidant pathway is responsible for diminished immune responses in Keap1-mutant tumors. Importantly, we demonstrate that combining glutaminase inhibition with immune checkpoint blockade can reverse immunosuppression, making Keap1-mutant tumors susceptible to immunotherapy. Our study provides new insight into the role of KEAP1 mutations in immune evasion, paving the way for novel immune-based therapeutic strategies for KEAP1-mutant cancers