Artificial intelligence for drug response prediction in disease models

Non peer reviewe

Utility of patient-derived lymphoblastoid cell lines as an ex vivo capecitabine sensitivity prediction model for breast cancer patients.

Capecitabine is commonly used in treating breast cancer; however, therapeutic response varies among patients and there is no clinically validated model to predict individual outcomes. Here, we investigated whether drug sensitivity quantified in ex vivo patients' blood-derived cell lines can predict response to capecitabine in vivo. Lymphoblastoid cell lines (LCLs) were established from a cohort of metastatic breast cancer patients (n = 53) who were prospectively monitored during treatment with single agent capecitabine at 2000 mg/m2/day. LCLs were treated with increasing concentrations of 5'-DFUR, a major capecitabine metabolite, to assess patients' ex vivo sensitivity to this drug. Subsequently, ex vivo phenotype was compared to observed patient disease response and drug induced-toxicities. We acquired an independent cohort of breast cancer cell lines and LCLs derived from the same donors from ATCC, compared their sensitivity to 5'-DFUR. As seen in the patient population, we observed large inter-individual variability in response to 5'-DFUR treatment in patient-derived LCLs. Patients whose LCLs were more sensitive to 5'-DFUR had a significantly longer median progression free survival (9-month vs 6-month, log rank p-value = 0.017). In addition, this significant positive correlation for 5'-DFUR sensitivity was replicated in an independent cohort of 8 breast cancer cell lines and LCLs derived from the same donor. Our data suggests that at least a portion of the individual sensitivity to capecitabine is shared between germline tissue and tumor tissue. It also supports the utility of patient-derived LCLs as a predictive model for capecitabine treatment efficacy in breast cancer patients

Mixed Effects Modeling of Proliferation Rates in Cell-Based Models: Consequence for Pharmacogenomics and Cancer

The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.</p

Spectroscopic confirmation of an ultra-faint galaxy at the epoch of reionization

Within one billion years of the Big Bang, intergalactic hydrogen was ionized by sources emitting ultraviolet and higher energy photons. This was the final phenomenon to globally affect all the baryons (visible matter) in the Universe. It is referred to as cosmic reionization and is an integral component of cosmology. It is broadly expected that intrinsically faint galaxies were the primary ionizing sources due to their abundance in this epoch. However, at the highest redshifts ($z>7.5$; lookback time 13.1 Gyr), all galaxies with spectroscopic confirmations to date are intrinsically bright and, therefore, not necessarily representative of the general population. Here, we report the unequivocal spectroscopic detection of a low luminosity galaxy at $z>7.5$. We detected the Lyman-$\alpha$ emission line at $\sim 10504$ {\AA} in two separate observations with MOSFIRE on the Keck I Telescope and independently with the Hubble Space Telescope's slit-less grism spectrograph, implying a source redshift of $z = 7.640 \pm 0.001$. The galaxy is gravitationally magnified by the massive galaxy cluster MACS J1423.8+2404 ($z = 0.545$), with an estimated intrinsic luminosity of $M_{AB} = -19.6 \pm 0.2$ mag and a stellar mass of $M_{\star} = 3.0^{+1.5}_{-0.8} \times 10^8$ solar masses. Both are an order of magnitude lower than the four other Lyman-$\alpha$ emitters currently known at $z > 7.5$, making it probably the most distant representative source of reionization found to date

COVID-19 mortality may be reduced among fully vaccinated solid organ transplant recipients.

BACKGROUND: Solid organ transplant (SOT) recipients are at increased risk for morbidity and mortality from COVID-19 due to their immunosuppressed state and reduced immunogenicity from COVID-19 mRNA vaccines. This investigation examined the association between COVID-19 mRNA vaccination status and mortality among SOT recipients diagnosed with COVID-19. METHODS & FINDINGS: A retrospective, registry-based chart review was conducted investigating COVID-19 mortality among immunosuppressed solid organ transplant (SOT) recipients in a large metropolitan healthcare system in Houston, Texas, USA. Electronic health record data was collected from consecutive SOT recipients who received a diagnostic SARS-CoV-2 test between March 1, 2020, and October 1, 2021. The primary exposure was COVID-19 vaccination status at time of COVID-19 diagnosis. Patients were considered \u27fully vaccinated\u27 at fourteen days after completing their vaccine course. COVID-19 mortality within 60 days and intensive care unit admission within 30 days were primary and secondary endpoints, respectively. Among 646 SOT recipients who were diagnosed with COVID-19 at Houston Methodist Hospital between March 2020, and October 2021, 70 (10.8%) expired from COVID-19 within 60 days. Transplanted organs included 63 (9.8%) heart, 355 (55.0%) kidney, 108 (16.7%) liver, 70 (10.8%) lung, and 50 (7.7%) multi-organ. Increasing age was a risk factor for COVID-19 mortality, while vaccination within 180 days of COVID-19 diagnosis was protective in Cox proportional hazard models with hazard ratio 1.04 (95% CI: 1.01-1.06) and 0.31 (0.11-0.90), respectively). These findings were confirmed in the propensity score matched cohort between vaccinated and unvaccinated patients. CONCLUSIONS: This investigation found COVID-19 mortality may be significantly reduced among immunosuppressed SOT recipients within 6 months following vaccination. These findings can inform vaccination policies targeting immunosuppressed populations worldwide

Impact of Trucking Network Flow on Preferred Biorefinery Locations in the Southern United States

The impact of the trucking transportation network flow was modeled for the southern United States. The study addresses a gap in existing research by applying a Bayesian logistic regression and Geographic Information System (GIS) geospatial analysis to predict biorefinery site locations. A one-way trucking cost assuming a 128.8 km (80-mile) haul distance was estimated by the Biomass Site Assessment model. The median family income, timberland annual growth-to-removal ratio, and transportation delays were significant in determining mill location. Transportation delays that directly impacted the costs of trucking are presented. A logistic model with Bayesian inference was used to identify preferred site locations, and locations not preferential for a mill location. The model predicted that higher probability locations for smaller biomass mills (feedstock capacity, the size of sawmills) were in southern Alabama, southern Georgia, southeast Mississippi, southern Virginia, western Louisiana, western Arkansas, and eastern Texas. The higher probability locations for large capacity mills (feedstock capacity, the size for pulp and paper mills) were in southeastern Alabama, southern Georgia, central North Carolina, and the Mississippi Delta regions