TAM family receptors in conjunction with MAPK signalling are involved in acquired resistance to PI3Kα inhibition in head and neck squamous cell carcinoma.

BACKGROUND: Aberrant activation of the phosphatidylinositol 3-kinase (PI3K) pathway is common in many malignancies, including head and neck squamous cell carcinoma (HNSCC). Despite pre-clinical and clinical studies, outcomes from targeting the PI3K pathway have been underwhelming and the development of drug resistance poses a significant barrier to patient treatment. In the present study, we examined mechanisms of acquired resistance to the PI3Kα inhibitor alpelisib (formerly BYL719) in HNSCC cell lines and patient-derived xenografts (PDXs). METHODS: Five unique PDX mouse models and three HNSCC cell lines were used. All cell lines and xenografts underwent genomic characterization prior to study. Serial drug treatment was conducted in vitro and in vivo to develop multiple, clinically-significant models of resistance to alpelisib. We then used reverse phase protein arrays (RPPAs) to profile the expression of proteins in parental and drug-resistant models. Top hits were validated by immunoblotting and immunohistochemistry. Flow cytometric analysis and RNA interference studies were then used to interrogate the molecular mechanisms underlying acquired drug resistance. RESULTS: Prolonged treatment with alpelisib led to upregulation of TAM family receptor tyrosine kinases TYRO3 and AXL. Importantly, a significant shift in expression of both TYRO3 and AXL to the cell surface was detected in drug-resistant cells. Targeted knockdown of TYRO3 and AXL effectively re-sensitized resistant cells to PI3Kα inhibition. In vivo, resistance to alpelisib emerged following 20-35 days of treatment in all five PDX models. Elevated TYRO3 expression was detected in drug-resistant PDX tissues. Downstream of TYRO3 and AXL, we identified activation of intracellular MAPK signalling. Inhibition of MAPK signalling also re-sensitized drug-resistant cells to alpelisib. CONCLUSIONS: We have identified TYRO3 and AXL receptors to be key mediators of resistance to alpelisib, both in vitro and in vivo. Our findings suggest that pan-TAM inhibition is a promising avenue for combinatorial or second-line therapy alongside PI3Kα inhibition. These findings advance our understanding of the role TAM receptors play in modulating the response of HNSCC to PI3Kα inhibition and suggest a means to prevent, or at least delay, resistance to PI3Kα inhibition in order to improve outcomes for HNSCC patients

Mutational analysis of head and neck squamous cell carcinoma stratified by smoking status.

Smoking has historically been recognized as a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). This study aimed to assess the mutational differences between heavy smokers (\u3e20 pack years) and never smokers among the HNSCC patients within The Cancer Genome Atlas (TCGA). Single nucleotide variation and copy number aberration differences between heavy smokers and never smokers were compared within human papillomavirus-positive (HPV-positive) (n = 67) and HPV-negative (n = 431) TCGA cohorts with HNSCC, and the impact of these mutations on survival were assessed. No genes were differentially mutated between smoking and never-smoking patients with HPV-positive tumors. By contrast, in HPV-negative tumors, NSD1 and COL1A11 were found to be more frequently mutated in heavy smokers, while CASP8 was more frequently altered in never smokers. HPV-negative patients with NSD1 mutations experienced significantly improved overall survival compared with NSD1 WT patients. This improved prognosis was validated in an independent cohort of 77 oral cavity cancer patients and a meta-analysis that included 2 additional data sets (688 total patients, hazard ratio for death 0.44, 95% CI, 0.30-0.65). NSD1 mutations are more common in HPV-negative heavy smokers, define a cohort with favorable prognosis, and may represent a clinically useful biomarker to guide treatment deintensification for HPV-negative patients

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Asylum Seekers, Refugees, Community Services, Occupation, Meaning, Cultural Relevance

The measurement of multiply isotopically substituted (‘clumped isotope’) carbonate groups provides a way to reconstruct past mineral formation temperatures. However, dissolution-reprecipitation (i.e., recrystallization) reactions, which commonly occur during sedimentary burial, can alter a sample’s clumped-isotope composition such that it partially or wholly reflects deeper burial temperatures. Here we derive a quantitative model of diagenesis to explore how diagenesis alters carbonate clumped-isotope values. We apply the model to a new dataset from deep-sea sediments taken from Ocean Drilling Project site 807 in the equatorial Pacific. This dataset is used to ground truth the model. We demonstrate that the use of the model with accompanying carbonate clumped-isotope and carbonate δ^(18)O values provides new constraints on both the diagenetic history of deep-sea settings as well as past equatorial sea-surface temperatures. Specifically, the combination of the diagenetic model and data support previous work that indicates equatorial sea-surface temperatures were warmer in the Paleogene as compared to today. We then explore whether the model is applicable to shallow-water settings commonly preserved in the rock record. Using a previously published dataset from the Bahamas, we demonstrate that the model captures the main trends of the data as a function of burial depth and thus appears applicable to a range of depositional settings

TAM family receptors in conjunction with MAPK signalling are involved in acquired resistance to PI3Kα inhibition in head and neck squamous cell carcinoma.

BackgroundAberrant activation of the phosphatidylinositol 3-kinase (PI3K) pathway is common in many malignancies, including head and neck squamous cell carcinoma (HNSCC). Despite pre-clinical and clinical studies, outcomes from targeting the PI3K pathway have been underwhelming and the development of drug resistance poses a significant barrier to patient treatment. In the present study, we examined mechanisms of acquired resistance to the PI3Kα inhibitor alpelisib (formerly BYL719) in HNSCC cell lines and patient-derived xenografts (PDXs).MethodsFive unique PDX mouse models and three HNSCC cell lines were used. All cell lines and xenografts underwent genomic characterization prior to study. Serial drug treatment was conducted in vitro and in vivo to develop multiple, clinically-significant models of resistance to alpelisib. We then used reverse phase protein arrays (RPPAs) to profile the expression of proteins in parental and drug-resistant models. Top hits were validated by immunoblotting and immunohistochemistry. Flow cytometric analysis and RNA interference studies were then used to interrogate the molecular mechanisms underlying acquired drug resistance.ResultsProlonged treatment with alpelisib led to upregulation of TAM family receptor tyrosine kinases TYRO3 and AXL. Importantly, a significant shift in expression of both TYRO3 and AXL to the cell surface was detected in drug-resistant cells. Targeted knockdown of TYRO3 and AXL effectively re-sensitized resistant cells to PI3Kα inhibition. In vivo, resistance to alpelisib emerged following 20-35 days of treatment in all five PDX models. Elevated TYRO3 expression was detected in drug-resistant PDX tissues. Downstream of TYRO3 and AXL, we identified activation of intracellular MAPK signalling. Inhibition of MAPK signalling also re-sensitized drug-resistant cells to alpelisib.ConclusionsWe have identified TYRO3 and AXL receptors to be key mediators of resistance to alpelisib, both in vitro and in vivo. Our findings suggest that pan-TAM inhibition is a promising avenue for combinatorial or second-line therapy alongside PI3Kα inhibition. These findings advance our understanding of the role TAM receptors play in modulating the response of HNSCC to PI3Kα inhibition and suggest a means to prevent, or at least delay, resistance to PI3Kα inhibition in order to improve outcomes for HNSCC patients

Does HPV Subtype Predict Outcomes in Head and Neck Cancers?

Background. Recently, reanalysis of The Cancer Genome Atlas study demonstrated that human papillomavirus (HPV) genotypes in head and neck cancers other than HPV-16 have inferior survival to HPV-16-positive tumors. We aimed to examine the association of HPV subtypes and survival in a large cohort of patient samples from our institution. Methods. Fresh frozen primary site biopsy samples were collected either in clinic or at the time of surgery. Patient demographic, staging, and survival data were also collected. Tumors were tested for HPV subtypes by quantitative polymerase chain reaction (qPCR). Univariable and multivariable analyses were performed using Cox proportional hazards regression. Results. 280 patient biopsy samples were collected between 2011 and 2017. Mean ± standard deviation (SD) age was 61.9 ± 11.1 years and most patients (78%) were male. The majority of cancers were of the oral cavity (60%) or oropharynx (25%) and 30% had HPV-positive disease. Median follow-up was 3.76 years and 96/280 patients (34%) developed recurrences. Patients with p16-positive versus negative disease had significantly improved 5-year overall survival (OS, 77.6% vs. 53.3%; p=0.009) and progression-free survival (PFS, 67.3% vs. 41.0%, p=0.006). Similarly improved 5-year OS and PFS were observed for patients with HPV-positive versus negative disease (65.0% vs. 55.0%, p=0.084; 53.3% vs. 43.2%, p=0.072, resp.). Patients with HPV-16 compared to other HPV diseases had worse 5-year OS and PFS (62.1% vs. 88.9%, p=0.273; 49.0% vs. 88.9%, p=0.081, resp.). Conclusions. In contrast to the data derived from The Cancer Genome Atlas, patients with HPV-16 tumors trended towards decreased PFS and OS compared with tumors driven by other HPV genotypes. Further larger multi-institutional studies are necessary to understand the relationship between other HPV genotypes and survival in head and neck squamous cell carcinomas

AxFoundation/strax: v1.5.4

What's Changed Split compare_metadata into utils.compare_meta by @dachengx in https://github.com/AxFoundation/strax/pull/754 Change endtime - time >= 0 to endtime >= time by @JYangQi00 in https://github.com/AxFoundation/strax/pull/756 Mandatorily wrap _read_chunk in a check_chunk_n decorator by @dachengx in https://github.com/AxFoundation/strax/pull/758 New Contributors @JYangQi00 made their first contribution in https://github.com/AxFoundation/strax/pull/756 Full Changelog: https://github.com/AxFoundation/strax/compare/v1.5.3...v1.5.