Epithelial cell size dysregulation in human lung adenocarcinoma

U-Shape Pillar Strip for 3D Cell-Lumped Organoid Model (3D-COM) Mimicking Lung Cancer Hypoxia Conditions in High-Throughput Screening (HTS)

Hypoxia is a representative tumor characteristic associated with malignant progression in clinical patients. Engineered in vitro models have led to significant advances in cancer research, allowing for the investigation of cells in physiological environments and the study of disease mechanisms and processes with enhanced relevance. In this study, we propose a U-shape pillar strip for a 3D cell-lumped organoid model (3D-COM) to study the effects of hypoxia on lung cancer in a high-throughput manner. We developed a U-pillar strip that facilitates the aggregation of PDCs mixed with an extracellular matrix to make the 3D-COM in 384-plate array form. The response to three hypoxia-activated prodrugs was higher in the 3D-COM than in the 2D culture model. The protein expression of hypoxia-inducible factor 1 alpha (HIF-1α) and HIF-2α, which are markers of hypoxia, was also higher in the 3D-COM than in the 2D culture. The results show that 3D-COM better recapitulated the hypoxic conditions of lung cancer tumors than the 2D culture. Therefore, the U-shape pillar strip for 3D-COM is a good tool to study the effects of hypoxia on lung cancer in a high-throughput manner, which can efficiently develop new drugs targeting hypoxic tumors.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Alveolar Organoids in Lung Disease Modeling

Lung organoids display a tissue-specific functional phenomenon and mimic the features of the original organ. They can reflect the properties of the cells, such as morphology, polarity, proliferation rate, gene expression, and genomic profile. Alveolar type 2 (AT2) cells have a stem cell potential in the adult lung. They produce and secrete pulmonary surfactant and proliferate to restore the epithelium after damage. Therefore, AT2 cells are used to generate alveolar organoids and can recapitulate distal lung structures. Also, AT2 cells in human-induced pluripotent stem cell (iPSC)-derived alveolospheres express surfactant proteins and other factors, indicating their application as suitable models for studying cell-cell interactions. Recently, they have been utilized to define mechanisms of disease development, such as COVID-19, lung cancer, idiopathic pulmonary fibrosis, and chronic obstructive pulmonary disease. In this review, we show lung organoid applications in various pulmonary diseases, drug screening, and personalized medicine. In addition, stem cell-based therapeutics and approaches relevant to lung repair were highlighted. We also described the signaling pathways and epigenetic regulation of lung regeneration. It is critical to identify novel regulators of alveolar organoid generations to promote lung repair in pulmonary diseases.

Cancer organoid-based diagnosis reactivity prediction (CODRP) index-based anticancer drug sensitivity test in ALK-rearrangement positive non-small cell lung cancer (NSCLC)

BACKGROUND

Recently, cancer organoid-based drug sensitivity tests have been studied to predict patient responses to anticancer drugs. The area under curve (AUC) or IC50 value of the dose-response curve (DRC) is used to differentiate between sensitive and resistant patient's groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers the cancer stage and cancer cell growth rate, which represent the severity of cancer patients, in the sensitivity test.

METHODS

On the CODRP platform, patient-derived organoids (PDOs) that recapitulate patients with lung cancer were implemented by applying a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a very small number of cells due to limited patient cells. A drug sensitivity test was performed using PDO from the patient tissue and the primary cancer characteristics of PDOs were confirmed by pathological comparision with tissue slides.

RESULTS

The conventional index of drug sensitivity is the AUC of the DRC. In this study, the CODRP index for drug sensitivity test was proposed through multi-parameter analyses considering cancer cell proliferation rate, the cancer diagnosis stage, and AUC values. We tested PDOs from eight patients with lung cancer to verify the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for the three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not classify into sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and was verified to be consistent with the clinical drug treatment response.

CONCLUSIONS

Therefore, the PDO-based HTS and CODRP index drug sensitivity tests described in this paper may be useful for predicting and analyzing promising anticancer drug efficacy for patients with lung cancer and can be applied to a precision medicine platform.

Expression of oncogenic HRAS G12V causes defects in control of cell size in NIH 3T3 cells

Severe defects in control of cell size are closely associated with cancer. However, the mechanisms that drive cell size defects in cancer remain unknown and it is unclear whether they are a direct consequence of signals from primary oncogenic drivers or a secondary consequence of mutations that accumulate during evolution of cancer cells. Here, we report that expression of oncogenic HRAS G12V is sufficient to cause cell size defects in NIH 3T3 cells, which suggests that the cell size defects of cancer cells are a direct consequence of primary oncogenic drivers.

Hyperactive Ras disrupts cell size control and a key step in cell cycle entry in budding yeast

Severe defects in cell size are a nearly universal feature of cancer cells. However, the underlying causes are unknown. A previous study suggested that a hyperactive mutant of yeast Ras (ras2G19V) that is analogous to the human Ras oncogene causes cell size defects, which could provide clues to how oncogenes influence cell size. However, the mechanisms by which ras2G19V influences cell size are unknown. Here, we found that ras2G19V inhibits a critical step in cell cycle entry, in which an early G1 phase cyclin induces transcription of late G1 phase cyclins. Thus, ras2G19V drives overexpression of the early G1 phase cyclin Cln3, yet Cln3 fails to induce normal transcription of late G1 phase cyclins, leading to delayed cell cycle entry and increased cell size. ras2G19V influences transcription of late G1 phase cyclins via a poorly understood step in which Cln3 inactivates the Whi5 transcriptional repressor. Previous studies found that yeast Ras relays signals via protein kinase A (PKA); however, ras2G19V appears to influence late G1 phase cyclin expression via novel PKA-independent signaling mechanisms. Together, the data define new mechanisms by which hyperactive Ras influences cell cycle entry and cell size in yeast. Hyperactive Ras also influences expression of G1 phase cyclins in mammalian cells, but the mechanisms remain unclear. Further analysis of Ras signaling in yeast could lead to discovery of new mechanisms by which Ras family members control expression of G1 phase cyclins.

The human cell count and size distribution

Cell size and cell count are adaptively regulated and intimately linked to growth and function. Yet, despite their widespread relevance, the relation between cell size and count has never been formally examined over the whole human body. Here, we compile a comprehensive dataset of cell size and count over all major cell types, with data drawn from >1,500 published sources. We consider the body of a representative male (70 kg), which allows further estimates of a female (60 kg) and 10-y-old child (32 kg). We build a hierarchical interface for the cellular organization of the body, giving easy access to data, methods, and sources (https://humancelltreemap.mis.mpg.de/). In total, we estimate total body counts of ≈36 trillion cells in the male, ≈28 trillion in the female, and ≈17 trillion in the child. These data reveal a surprising inverse relation between cell size and count, implying a trade-off between these variables, such that all cells within a given logarithmic size class contribute an equal fraction to the body's total cellular biomass. We also find that the coefficient of variation is approximately independent of mean cell size, implying the existence of cell-size regulation across cell types. Our data serve to establish a holistic quantitative framework for the cells of the human body, and highlight large-scale patterns in cell biology.