Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells, bioRxiv, 2019-03-11
SummaryIntermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.
biorxiv cancer-biology 0-100-users 2019Exploiting evolutionary herding to control drug resistance in cancer, bioRxiv, 2019-03-04
AbstractDrug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called ‘evolutionary herding’ that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 108–109 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.
biorxiv cancer-biology 0-100-users 2019Measuring single cell divisions in human cancers from multi-region sequencing data, bioRxiv, 2019-02-26
Cancer is driven by complex evolutionary dynamics involving billions of cells. Increasing effort has been dedicated to sequence single tumour cells, but obtaining robust measurements remains challenging. Here we show that multi-region sequencing of bulk tumour samples contains quantitative information on single-cell divisions that is accessible if combined with evolutionary theory. Using high-throughput data from 16 human cancers, we measured the in vivo per-cell point mutation rate (mean 1.69*10^(-8) bp per cell division) and per-cell survival rate (mean 0.57) in individual patient tumours from colon, lung and renal cancers. Per-cell mutation rates varied 50-fold between individuals, and per-cell survival rates were between nearly-homeostatic and almost perfect cell doublings, equating to tumour ages between 1 and 19 years. Furthermore, reanalysing a recent dataset of 89 whole-genome sequenced healthy haematopoietic stem cells, we find 1.14 mutations per genome per cell division and near perfect cell doublings (per-cell survival rate 0.96) during early haematopoietic development. Our analysis measures in vivo the most fundamental properties of human cancer and healthy somatic evolution at single-cell resolution within single individuals.
biorxiv cancer-biology 100-200-users 2019Tissue structure accelerates evolution premalignant sweeps precede neutral expansion, bioRxiv, 2019-02-11
Cancer has been hypothesized to be a caricature of the renewal process of the tissue of origin arising from (and maintained by) small subpopulations capable of continuous growth1. The strong influence of the tissue structure has been convincingly demonstrated in intestinal cancers where adenomas grow by the fission of stem-cell-maintained glands influenced by early expression of abnormal cell mobility in cancer progenitors2, 3. So-called “born to be bad” tumors arise from progenitors which may already possess the necessary driver mutations for malignancy4, 5 and metastasis6. These tumors subsequently evolve neutrally, thereby maximizing intratumoral heterogeneity and increasing the probability of therapeutic resistance. These findings have been nuanced by the advent of multi-region sequencing, which uses spatial and temporal patterns of genetic variation among competing tumor cell populations to shed light on the mode of tumor evolution (neutral or Darwinian) and also the tempo4, 7–11. Using a classic, well-studied model of tumor evolution (a passenger-driver mutation model12–16) we systematically alter spatial constraints and cell mixing rates to show how tissue structure influences functional (driver) mutations and genetic heterogeneity over time. This model approach explores a key mechanism behind both inter-patient and intratumoral tumor heterogeneity competition for space. Initial spatial constraints determine the emergent mode of evolution (Darwinian to neutral) without a change in cell-specific mutation rate or fitness effects. Driver acquisition during the Darwinian precancerous stage may be accelerated en route to neutral evolution by the combination of two factors spatial constraints and limited cellular mixing.
biorxiv cancer-biology 0-100-users 2019Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body, bioRxiv, 2019-02-06
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer.
biorxiv cancer-biology 200-500-users 2019Endogenous insulin contributes to pancreatic cancer development, bioRxiv, 2019-01-25
Obesity and early-stage type 2 diabetes (T2D) increase the risk for many cancers, including pancreatic ductal adenocarcinoma (PDAC). The mechanisms linking obesity and T2D to cancer have not been established, preventing targeted interventions. Arguments have been made that hyperinsulinemia, hyperglycemia, or inflammation could drive cancer initiation andor progression. Hyperinsulinemia is a cardinal feature of obesity and T2D, and is independently associated with PDAC incidence and mortality, even in non-obese people. Despite ample human epidemiological evidence linking hyperinsulinemia to PDAC, there is no direct in vivo evidence of a causal role for endogenous insulin in cancer in any system. Using mice with reduced insulin gene dosage, we show here that a modest reduction in endogenous insulin production leads to a ~50% reduction in pancreatic intraepithelial neoplasia (PanIN) pre-cancerous lesions in high fat diet-fed mice expressing the KrasG12D oncogene. The significant reduction in PanIN lesions occurred in the absence of changes in fasting glucose. Reduced insulin also led to a ~50% reduction in pancreatic fibrosis, suggesting that endogenous insulin drives PanIN development, in part, via its pro-fibrotic effects on the stroma surrounding acinar cells and PanIN. Collectively, our data indicate that endogenous insulin hypersecretion contributes causally to pancreatic cancer development. This suggests a modest reduction in fasting insulin via lifestyle interventions or therapeutics may be useful in cancer prevention.
biorxiv cancer-biology 200-500-users 2019