The efficacy of the highly selective RET inhibitor selpercatinib is now established in RET-driven cancers, and we sought to characterize the molecular determinants of response and resistance. We find ...that the pre-treatment genomic landscape does not shape the variability of treatment response except for rare instances of RAS-mediated primary resistance. By contrast, acquired selpercatinib resistance is driven by MAPK pathway reactivation by one of two distinct routes. In some patients, on- and off-target pathway reactivation via secondary RET solvent front mutations or MET amplifications are evident. In other patients, rare RET-wildtype tumor cell populations driven by an alternative mitogenic driver are selected for by treatment. Multiple distinct mechanisms are often observed in the same patient, suggesting polyclonal resistance may be common. Consequently, sequential RET-directed therapy may require combination treatment with inhibitors targeting alternative MAPK effectors, emphasizing the need for prospective characterization of selpercatinib-treated tumors at the time of monotherapy progression.
Abstract
Circulating cell-free DNA from blood plasma of cancer patients can be used to non-invasively interrogate somatic tumor alterations. Here we develop MSK-ACCESS (Memorial Sloan Kettering - ...Analysis of Circulating cfDNA to Examine Somatic Status), an NGS assay for detection of very low frequency somatic alterations in 129 genes. Analytical validation demonstrated 92% sensitivity in de-novo mutation calling down to 0.5% allele frequency and 99% for a priori mutation profiling. To evaluate the performance of MSK-ACCESS, we report results from 681 prospective blood samples that underwent clinical analysis to guide patient management. Somatic alterations are detected in 73% of the samples, 56% of which have clinically actionable alterations. The utilization of matched normal sequencing allows retention of somatic alterations while removing over 10,000 germline and clonal hematopoiesis variants. Our experience illustrates the importance of analyzing matched normal samples when interpreting cfDNA results and highlights the importance of cfDNA as a genomic profiling source for cancer patients.
The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and ...its role in response to therapy is lacking. Here, we perform single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we find a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibit similar transcriptional responses to chemotherapy and do not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observe decreased ligand-receptor interactions in treated samples, particularly between TIGIT on CD8 + T cells and its receptor on cancer cells, and identify TIGIT as the major inhibitory checkpoint molecule of CD8 + T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.
The isoquinoline plant alkaloid berberine has anti‐tumor effects on a variety of carcinoma cells, mainly through inhibition of cell proliferation, apoptosis induction and cell cycle arrest. However, ...the mechanisms underlying its role in tumor progression are unknown. In the present study, we investigated the molecular mechanisms involved in berberine‐induced cell death in human hepatoma carcinoma cell (HCC) lines HepG2 and SMMC7721. Our results showed that berberine inhibited tumor cell viability in a dose‐ and time‐dependent manner, and induced cell death via apoptosis and autophagy. Moreover, berberine treatment significantly inhibited CD147 expression by HCC cells in a dose‐dependent manner. Overexpression of CD147 protein markedly reduced berberine‐induced cell death. Our data provide the first experimental evidence that berberine induces cell death in HCC cells via downregulation of CD147 and suggest a new mechanism to explain its anti‐tumor effects. (Cancer Sci 2011; 102: 1287–1292)
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial ...therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.
Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs ...targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.
Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To ...address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.
Chronic overnutrition, for instance, high-fat diet (HFD) feeding, is a major cause of rapidly growing incidence of metabolic syndromes. However, the mechanisms underlying HFD-induced adverse effects ...on human health are not clearly understood. HFD-fed C57BL6/J mouse has been a popular model employed to investigate the mechanisms. Yet, there is no systematic and comprehensive study of the impact of HFD on the protein profiles of the animal. Here, we present a proteome-wide study of the consequences of long-term HFD feeding. Utilizing a powerful technology, stable isotope labeling of mammals, we detected and quantitatively compared 965 proteins extracted from livers of chow-diet-fed and HFD-fed mice. Among which, 122 proteins were significantly modulated by HFD. Fifty-four percent of those 122 proteins are involved in metabolic processes and the majority participate in lipid metabolism. HFD up-regulates proteins that play important roles in fatty acid uptake and subsequent oxidation and are linked to the transcription factors PPARα and PGC-1α. HFD suppresses lipid biosynthesis-related proteins that play major roles in de novo lipogenesis and are linked to SREBP-1 and PPARγ. These data suggest that HFD-fed mice tend to develop enhanced fat utilization and suppressed lipid biosynthesis, understandably a self-protective mechanism to counteract to excessive fat loading, which causes liver steatosis. Enhanced fatty acid oxidation increases reactive oxygen species and inhibits glucose oxidation, which are associated with hyperglycemia and insulin resistance. This proteomics study provides molecular understanding of HFD-induced pathology and identifies potential targets for development of therapeutics for metabolic syndromes.
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