Steel, C. | bioRxiv Science

bioRxiv2024

FGFR2 promotes resistance to ALK tyrosine kinase inhibitors and its inhibition acts synergistically with lorlatinib in the treatment of ALK-expressing neuroblastoma

Anaplastic Lymphoma Kinase inhibitors (ALK TKIs) are approved for the treatment of ALK-positive non-small cell lung cancer (NSCLC) and are in clinical trial for ALK-aberrant high-risk neuroblastoma (NB) patients, particularly loratinib. However, resistance to ALK inhibitors can occur in patients, via the activation of bypass-signalling pathways, and there is a need to identify these mechanisms as well as drugs that inhibit them to design therapeutic approaches that prevent resistance, and to treat ALK TKI relapsed/refractory disease. Using genome-wide CRISPR-Cas9 overexpression screens, we identified and validated FGFR2 as a desensitizer to lorlatinib in aberrant ALK-expressing high-risk NB. FGFR2 and FGFR2-associated pathways are up-regulated in lorlatinib-resistant NB cells. Moreover, high-throughput screens using a library of 1,430 FDA approved drugs identified kinase inhibitors including those targeting FGFR2 as efficacious in reducing the survival of lorlatinib resistant NB cells. Hence, the FGFR pathway was investigated as a therapeutic target applying the pan-FGFR inhibitor erdafitinib or the multi-kinase inhibitor ponatinib, resulting in reduced survival of lorlatinib-resitant cells in comparison to their lorlatinib-sensitive counterparts. Moreover, both FGFR inhibitors act synergistically with lorlatinib in vitro and in vivo, using patient-derived xenografts (PDXs) and genetically engineered models (GEMM) of ALK-expressing NB. FGFR2 mRNA expression also correlate with a poorer prognosis for NB patients, regardless of sub-type, suggesting that a broader range of patients may benefit from FGFR inhibitors. Overall, our data suggests that FGFR2 potentially plays roles in lorlatinib resistance in NB and that combined pharmacological inhibition of ALK and FGFR constitutes a therapeutic approach to treat high-risk NB.

cancer biology↗

bioRxiv2017

Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels

BackgroundSerologicalantibody levels are a sensitive marker of pathogen exposure, and advances in multiplex assays have created enormous potential for large-scale, integrated infectious disease surveillance. Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups, but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels in low transmission settings. Analysis methods have predominantly maintained a single disease focus, yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays.\n\nMethods/Principal FindingsWe developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance. We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens, including: lymphatic filariasis (Wuchereria bancrofti) measured before and after mass drug administration on Mauke, Cook Islands, malaria (Plasmodium falciparum) before and after a combined insecticide and mass drug administration intervention in the Garki project, Nigeria, and enteric protozoans (Cryptosporidium parvum, Giardia intestinalis, Entamoeba histolytica), bacteria (enterotoxigenic Escherichia coli, Salmonella spp.), and viruses (norovirus groups I and II) in children living in Haiti and the USA. Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity. Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens, assays, and populations. Mean antibody levels correlated strongly with traditional measures of transmission intensity, such as the entomological inoculation rate for P. falciparum (Spearmans rho=0.75). Seroprevalence estimates recapitulated patterns observed in quantitative antibody levels, albeit with lower resolution.\n\nConclusions/SignificanceAge-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission, with greatest sensitivity among young children. The method generalizes to pathogens that can be measured in high-throughput, multiplex serological assays, and scales to surveillance activities that require high spatiotemporal resolution. The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases, malaria, and other infectious diseases with well-defined antigen targets.\n\nAuthor SummaryGlobal elimination strategies for infectious diseases like neglected tropical diseases and malaria rely on accurate estimates of pathogen transmission to target and evaluate control programs. Circulating antibody levels can be a sensitive measure of recent pathogen exposure, but no broadly applicable method exists to measure changes in transmission directly from quantitative antibody levels. We developed a novel method that applies recent advances in machine learning and data science to flexibly fit age-dependent antibody curves. Shifts in age-dependent antibody curves provided remarkably consistent, sensitive measures of transmission changes when evaluated across many globally important pathogens (filarial worms, malaria, enteric infections). The methods generality and performance in diverse applications demonstrate its broad potential for integrated serological surveillance of infectious diseases.

epidemiology↗