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Pritchard, J. R.

Publications and source records attributed to Pritchard, J. R..

12 recordsLinked to original sources

Efficient growth factor production stabilizes cooperation in a cellular public goods game

Eukaryotic cells secrete growth factors that facilitate the proliferation and survival of neighboring cells in a coordinated manner essential for tissue development, repair, and homeostasis. However, mutant cells that fail to produce these factors may exploit the benefits provided by neighboring producers, thereby gaining a proliferative advantage that could lead to the extinction of the producer clone. Evolutionary theory predicts that preventing this "tragedy of the commons" requires a fixed spatial structure or other mechanisms that enable cells to interact preferentially with copies of themselves. Contrary to this expectation, we found that, in competition experiments, cells producing the diffusible growth factor interleukin-3 (IL3) resist invasion by non-producing mutant cells even in well-mixed populations, and that non-producers are driven to extinction instead. Using evolutionary game theory, we show that this outcome arises when the cost-to-benefit ratio of growth factor production falls below a critical threshold. We derive this tipping point analytically across the full parameter space of benefit function shape and group size and confirm that IL3 production in our experimental system satisfies this condition with a production cost less than 0.1% of the benefit conferred. These results demonstrate that efficient public good production alone can resolve the tragedy of the commons in populations of cells, and potentially in other biological systems.

evolutionary biology↗

Characterizing Variants of Uncertain Drug Resistance (VUDRs) Using Quantitative Measurements at Clinical Exposures

Somatic missense mutations in oncogenes drive resistance to anticancer drugs, yet many variants remain clinically uncharacterized. Analogous to Variants of Uncertain Significance (VUS) in genetic disorders, these Variants of Uncertain Drug Resistance (VUDRs) in cancer lack the functional annotation needed to guide clinical management. Here, we applied a standards-driven deep mutational scanning platform that connects quantitative concentration-response measurements to human pharmacokinetics across 4922 missense variants (>96% coverage) at approved (400 mg QD) and investigational (400 mg and 500 mg BID) human doses of imatinib. Resistance phenotypes for 18 standards spanning 2 orders of magnitude of drug sensitivity showed strong quantitative performance and clinical concordance. Analyzing 257 clinical VUDRs, >10% conferred modest levels of resistance that might be overcome by dose escalation with generic imatinib instead of a branded alternative, potentially alleviating financial toxicity. Integration with global germline data also revealed ancestry-specific variants with the potential to create private VUDRs. These preclinical data establish the first generalizable framework for high throughput resistance variant classification directly tied to known human doses.

bioengineering↗

Systemic tumor-targeting gene drive vectors proactively eliminate drug resistance in solid tumors

While targeted therapies have revolutionized cancer treatment, drug resistance remains a major barrier to their curative potential. We recently demonstrated biological proof-of-concept for selection gene drive circuits, a technology that overwrites disease evolution to proactively eliminate resistance in vivo, but translation requires a delivery method compatible with disseminated metastatic disease. Now, we demonstrate a clinically feasible delivery solution with novel tumor-targeting lentiviral vectors that selectively install these therapeutic circuits in tumor cells in situ. Systemic administration of these vectors demonstrated durable elimination of visible tumor burden and minimal body weight loss, validating the translational potential of a new class of genetic medicines for long-term control of resistance in cancer.

bioengineering↗

A side-by-side comparison of variant function measurements using deep mutational scanning and base editing

Variant annotation is a crucial objective in mammalian functional genomics. Deep Mutational Scanning (DMS) is a well-established method for annotating human gene variants, but CRISPR base editing (BE) is emerging as an alternative. However, questions remain about how well high-throughput base editing measurements can annotate variant function and the extent of downstream experimental validation required. This study presents the first direct comparison of DMS and BE in the same lab and cell line. Results indicate that focusing on the most likely edits and highest efficiency sgRNAs enhances the agreement between a "gold standard" DMS dataset and a BE screen. A simple filter for sgRNAs making single edits in their window could sufficiently annotate a large proportion of variants directly from sgRNA sequencing of large pools. When multi-edit guides are unavoidable, directly measuring the variants created in the pool, rather than sgRNA abundance, can recover high-quality variant annotation measurements in multiplexed pools. Taken together, our data show a surprising degree of correlation between base editor data and gold standard deep mutational scanning.

genomics↗

Mutant scaling laws reveal that accelerated mutant evolution via gene amplifications requires spatially structured population growth

Principles of evolution in spatially structured expanding populations have recently received much attention, but more work remains to be performed, especially for complex, multi-step evolutionary processes, where mutations are accumulated in an expanding population. A key limitation is that the simulation of spatially explicit stochastic computational models is essential, but not feasible for larger population sizes characteristic of prokaryotic and eukaryotic cell populations. We describe a methodological advance by deriving scaling laws that allow the straightforward prediction of the number of single-hit, double-hit and multi-hit mutants as a function of wild-type population size in spatially expanding populations. While this is a versatile tool to address a range of cutting-edge evolutionary questions, here we apply this methodology to reconcile apparently contradicting data from experimental evolution studies regarding the role of gene amplifications for the emergence of point mutations in bacteria. Applying the scaling laws, we demonstrate that in populations that expand in a 2D or a 3D spatial setting, gene amplifications can significantly promote mutant emergence, and that this is not possible in well-mixed populations. In support of the predictions, experiments that do show accelerated mutant evolution through gene amplifications grew bacteria in spatially restricted lawns, while those that failed to show an effect grew bacteria in non-spatial liquid media.

evolutionary biology↗

Agent-based models help interpret patterns of clinical drug resistance by contextualizing competition between distinct drug failure modes.

Structured AbstractO_ST_ABSIntroductionC_ST_ABSModern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that known microenvironmental resistance mechanisms should not be able to compete for outgrowth with genetic resistance within a tumor, and yet evidence of microenvironmental resistance is often observed in the clinic. Quantitatively understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse. Materials and MethodsWe employed spatial agent-based models to explore regimes where spatial constraints enable microenvironmental resistance to significantly compete with genetically resistant subclones. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts (CAFs) in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates. Results and DiscussionOur simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic. ConclusionOur model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.

bioengineering↗

Refined quantification of infection bottlenecks and pathogen dissemination with STAMPR

Pathogen population dynamics during infection are critical determinants of infection susceptibility and define patterns of dissemination. However, deciphering these dynamics, particularly founding population sizes in host organs and patterns of dissemination between organs, is difficult because measuring bacterial burden alone is insufficient to observe these patterns. Introduction of allelic diversity into otherwise identical bacteria using DNA barcodes enables sequencing-based measurements of these parameters, in a method known as STAMP (Sequence Tag-Based Analysis of Microbial Population dynamics). However, bacteria often undergo unequal expansion within host organs, resulting in marked differences in the frequencies of barcodes in input and output libraries. Here, we show that these differences confound STAMP-based analyses of founding population sizes and dissemination patterns. We present STAMPR, a successor to STAMP that accounts for such population expansions. Using data from systemic infection of barcoded Extraintestinal Pathogenic E. coli we show that this new framework along with the metrics it yields enhances the fidelity of measurements of bottlenecks and dissemination patterns. STAMPR was also validated on an independent, barcoded Pseudomonas aeruginosa dataset, uncovering new patterns of dissemination within the data. This framework (available at https://github.com/hullahalli/stampr_rtisan), when coupled with barcoded datasets, enables a more complete assessment of within-host bacterial population dynamics. ImportanceBarcoded bacteria are often employed to monitor pathogen population dynamics during infection. The accuracy of these measurements is diminished by unequal bacterial expansion rates. Here, we develop computational tools to circumvent this limitation and establish additional metrics that collectively enhance the fidelity of measuring within-host pathogen founding population sizes and dissemination patterns. These new tools will benefit future studies of the dynamics of pathogens and symbionts within their respective hosts and may have additional barcode-based applications beyond host-microbe interactions.

microbiology↗

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Beyond a database search, our approach empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Our approach is straightforward to implement, well documented and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology.

bioinformatics↗

A common genetic architecture enables the lossy compression of large CRISPR libraries

There are thousands of ubiquitously expressed mammalian genes, yet a genetic knockout can be lethal to one cell, and harmless to another. This context specificity confounds our understanding of genetics and cell biology. 2 large collections of pooled CRISPR screens offer an exciting opportunity to explore cell specificity. One explanation, synthetic lethality, occurs when a single "private" mutation creates a unique genetic dependency. However, by fitting thousands of machine learning models across millions of omic and CRISPR features, we discovered a "public" genetic architecture that is common across cell lines and explains more context specificity than synthetic lethality. This common architecture is built on CRISPR loss-of-function phenotypes that are surprisingly predictive of other loss-of-function phenotypes. Using these insights and inspired by the in silico lossy compression of images, we use machine learning to identify small "lossy compression" sets of in vitro CRISPR constructs where reduced measurements produce genome-scale loss-of-function predictions.

systems biology↗

Pairwise tests for conditional selection use evolutionary logic to predict intrinsic drug resistance in ALK alterations

Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Conditional selection, which includes mutual exclusivity, is a signal that has been empirically useful for identifying mutations that may be sensitive to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants and prevent robust conclusions from genomic data. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. This effectively creates positive control guideposts of mutual exclusivity in known driver genes that normalizes differences in mutation abundance. We applied this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI, which has been the subject of a recent controversy in the literature. We reproduced some of the original cell transformation experiments, performed rescue experiments, and analyzed drug response data to revisit the original ALKATI findings. We found that ALKATI is not as mutually exclusive with BRAF or NRAS as BRAF and NRAS genes are with each other. We performed in vitro transformation assays and rescue assays that suggested that alternative transcript initiation in ALK is not likely to be sufficient for cellular transformation or growth and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment. The progress of other experimental agents in late-stage melanoma and our experimental and computational re-analysis led us to conclude that further single agent testing of ALK inhibitors in patients with ALKATI should be limited to cases where no other treatment hypotheses can be identified.

bioengineering↗

Exploiting the 'survival of the likeliest' to enable evolution-guided drug design

Theoretical treatments of evolutionary dynamics tend to model the probability that a single "resistant" species will arise in a population. However, experimental studies have identified a diversity of mutations that can lead to genetic resistance. By quantitatively predicting mutations that occur across an entire drug target during treatment, we identify and bridge a fundamental gap in drug resistance theory: that nucleotide/codon substitution biases can dictate which resistant variants arise in the clinic. We find that the likeliest mutation can beat the most resistant mutation. This creates a new paradigm in drug resistance that we term "survival of the likeliest". We use epidemiological evidence in leukemia, isogenic experiments, stochastic dynamics, and large-scale simulations to support this theory. In addition, this work has strong implications for drug design because not all resistance liabilities are created equal. In pathogenic populations that exhibit survival of the likeliest, exploiting the least likely evolutionary path can minimize resistance across a population during widespread drug use, even when a vulnerability-free molecule or combination cannot be made. Data and Code Availabilityhttps://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/

evolutionary biology↗

Evolution of the nonsense-mediated decay (NMD) pathway is associated with decreased cytolytic immune infiltration

BackgroundThe somatic co-evolution of tumors and the cellular immune responses that combat them drives the diversity of immune-tumor interactions. This includes tumor mutations that generate neo-antigenic epitopes that elicit cytotoxic T-cell activity and subsequent pressure to select for genetic loss of antigen presentation. Most studies have focused on how tumor missense mutations can drive tumor immunity, but frameshift mutations have the potential to create far greater antigenic diversity. However, expression of this antigenic diversity is potentially regulated by Nonsense Mediated Decay (NMD) and NMD has been shown to be of variable efficiency in cancers. MethodsUsing TCGA datasets, we derived novel patient-level metrics of NMD burden and interrogated how different mutation and most importantly NMD burdens influence cytolytic activity using machine learning models and survival outcomes. ResultsWe find that NMD is a significant and independent predictor of immune cytolytic activity. Different indications exhibited varying dependence on NMD and mutation burden features. We also observed significant co-alteration of genes in the NMD pathway, with a global increase in NMD efficiency in patients with NMD co-alterations. Finally, NMD burden also stratified patient survival in multivariate regression models. ConclusionsOur work suggests that beyond selecting for mutations that elicit NMD in tumor suppressors, tumor evolution may react to the selective pressure generated by inflammation to globally enhance NMD through coordinated amplification and/or mutation.

cancer biology↗