Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic. Tumor growth curves display relatively simple time curves that can be quantified using mathematical models. Herein we exploited two experimental animal systems to assess the descriptive and predictive power of nine classical tumor growth models. Several goodness-of-fit metrics and a dedicated error model were employed to rank the models for their relative descriptive power. We found that the model with the highest descriptive power was not necessarily the most predictive one. The breast growth curves had a linear profile that allowed good predictability. Conversely, not one of the models was able to accurately predict the lung growth curves when using only a few data points. To overcome this issue, we considered a method that uses the parameter population distribution, informed from a priori knowledge, to estimate the individual parameter vector of an independent growth curve. This method was found to considerably improve the prediction success rates. These findings may benefit preclinical cancer research by identifying models most descriptive of fundamental growth characteristics. Clinical perspective is also offered on what can be expected from mathematical modeling in terms of future growth prediction.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteins bind to host mitochondrial proteins, likely inhibiting oxidative phosphorylation (OXPHOS) and stimulating glycolysis. We analyzed mitochondrial gene expression in nasopharyngeal and autopsy tissues from patients with coronavirus disease 2019 (COVID-19). In nasopharyngeal samples with declining viral titers, the virus blocked the transcription of a subset of nuclear DNA (nDNA)–encoded mitochondrial OXPHOS genes, induced the expression of microRNA 2392, activated HIF-1α to induce glycolysis, and activated host immune defenses including the integrated stress response. In autopsy tissues from patients with COVID-19, SARS-CoV-2 was no longer present, and mitochondrial gene transcription had recovered in the lungs. However, nDNA mitochondrial gene expression remained suppressed in autopsy tissue from the heart and, to a lesser extent, kidney, and liver, whereas mitochondrial DNA transcription was induced and host-immune defense pathways were activated. During early SARS-CoV-2 infection of hamsters with peak lung viral load, mitochondrial gene expression in the lung was minimally perturbed but was down-regulated in the cerebellum and up-regulated in the striatum even though no SARS-CoV-2 was detected in the brain. During the mid-phase SARS-CoV-2 infection of mice, mitochondrial gene expression was starting to recover in mouse lungs. These data suggest that when the viral titer first peaks, there is a systemic host response followed by viral suppression of mitochondrial gene transcription and induction of glycolysis leading to the deployment of antiviral immune defenses. Even when the virus was cleared and lung mitochondrial function had recovered, mitochondrial function in the heart, kidney, liver, and lymph nodes remained impaired, potentially leading to severe COVID-19 pathology.

Ligand-induced activation of peroxisome proliferator-activated receptor γ (PPARγ) inhibits proliferation in cancer cells in vitro and in vivo; however, the downstream targets remain undefined. We report the identification of a peroxisome proliferator response element in the promoter region of the Na⁺/H⁺ transporter gene NHE1, the overexpression of which has been associated with carcinogenesis. Exposure of breast cancer cells expressing high levels of PPARγ to its natural and synthetic agonists resulted in downregulation of NHE1 transcription as well as protein expression. Furthermore, the inhibitory effect of activated PPARγ on tumor colony-forming ability was abrogated on overexpression of NHE1, whereas small interfering RNA–mediated gene silencing of NHE1 significantly increased the sensitivity of cancer cells to growth-inhibitory stimuli. Finally, histopathologic analysis of breast cancer biopsies obtained from patients with type II diabetes treated with the synthetic agonist rosiglitazone showed significant repression of NHE1 in the tumor tissue. These data provide evidence for tumor-selective downregulation of NHE1 by activated PPARγ in vitro and in pathologic specimens from breast cancer patients and could have potential implications for the judicious use of low doses of PPARγ ligands in combination chemotherapy regimens for an effective therapeutic response. [Cancer Res 2009;69(22):8636–44]

Spaceflight induces molecular, cellular and physiological shifts in astronauts and poses myriad biomedical challenges to the human body, which are becoming increasingly relevant as more humans venture into space^1–6. Yet current frameworks for aerospace medicine are nascent and lag far behind advancements in precision medicine on Earth, underscoring the need for rapid development of space medicine databases, tools and protocols. Here we present the Space Omics and Medical Atlas (SOMA), an integrated data and sample repository for clinical, cellular and multi-omic research profiles from a diverse range of missions, including the NASA Twins Study⁷, JAXA CFE study^8,9, SpaceX Inspiration4 crew^10–12, Axiom and Polaris. The SOMA resource represents a more than tenfold increase in publicly available human space omics data, with matched samples available from the Cornell Aerospace Medicine Biobank. The Atlas includes extensive molecular and physiological profiles encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome datasets, which reveal some consistent features across missions, including cytokine shifts, telomere elongation and gene expression changes, as well as mission-specific molecular responses and links to orthologous, tissue-specific mouse datasets. Leveraging the datasets, tools and resources in SOMA can help to accelerate precision aerospace medicine, bringing needed health monitoring, risk mitigation and countermeasure data for upcoming lunar, Mars and exploration-class missions.