Limitations of the Commercially Available Gene Expression Test in Predicting Cutaneous Squamous Cell Carcinoma Metastasis and Clinical Outcomes.

BACKGROUND: Clinical genetic tests are integral to healthcare decision-making. However, the unclear regulatory framework, especially regarding products that evade stringent FDA oversight, may compromise test validity and transparency. OBJECTIVE: To critically evaluate the DecisionDx® cutaneous squamous cell carcinoma test by Castle Biosciences for its dataset biases, gene panel selection, and reported accuracy metrics, providing insight into broader challenges in the clinical genetic testing landscape. METHODS: Independent analyses of the DecisionDx®-SCC 40-GEP test data from Castle Biosciences were conducted. These included comparisons to clinical genetic testing standards, analysis of prevalence metrics against national cSCC rates, gene ontology of 34 genes for cSCC associations, and evaluation of accuracy metrics. RESULTS: The DecisionDx®-SCC met 11 of 44 CDC's ACCE criteria for clinical genetic testing. Its dataset showed a metastasis prevalence higher than the national average. Out of 34 genes, 15 had known associations with cSCC. Inconsistencies in accuracy metrics presentation were noted, particularly in moderate and high-risk stratifications. CONCLUSION: Analysis of DecisionDx®-SCC indicates potential biases and ambiguities, exacerbated by differences between FDA and CLIA standards. This highlights the need for systematic validation and a unified regulatory approach, stressing the necessity for precise and dependable genetic testing in patient care.

A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo.

Cancer growth is a multistage, stochastic evolutionary process. While cancer genome sequencing has been instrumental in identifying the genomic alterations that occur in human tumors, the consequences of these alterations on tumor growth remain largely unexplored. Conventional genetically engineered mouse models enable the study of tumor growth in vivo, but they are neither readily scalable nor sufficiently quantitative to unravel the magnitude and mode of action of many tumor-suppressor genes. Here, we present a method that integrates tumor barcoding with ultradeep barcode sequencing (Tuba-seq) to interrogate tumor-suppressor function in mouse models of human cancer. Tuba-seq uncovers genotype-dependent distributions of tumor sizes. By combining Tuba-seq with multiplexed CRISPR-Cas9-mediated genome editing, we quantified the effects of 11 tumor-suppressor pathways that are frequently altered in human lung adenocarcinoma. Tuba-seq enables the broad quantification of the function of tumor-suppressor genes with unprecedented resolution, parallelization, and precision.

An in vivo multiplexed small-molecule screening platform.

Phenotype-based small-molecule screening is a powerful method to identify molecules that regulate cellular functions. However, such screens are generally performed in vitro under conditions that do not necessarily model complex physiological conditions or disease states. Here, we use molecular cell barcoding to enable direct in vivo phenotypic screening of small-molecule libraries. The multiplexed nature of this approach allows rapid in vivo analysis of hundreds to thousands of compounds. Using this platform, we screened >700 covalent inhibitors directed toward hydrolases for their effect on pancreatic cancer metastatic seeding. We identified multiple hits and confirmed the relevant target of one compound as the lipase ABHD6. Pharmacological and genetic studies confirmed the role of this enzyme as a regulator of metastatic fitness. Our results highlight the applicability of this multiplexed screening platform for investigating complex processes in vivo.

Tug-of-war between driver and passenger mutations in cancer and other adaptive processes.

Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few driver mutations occur alongside thousands of random passenger mutations--a natural consequence of cancer's elevated mutation rate. Some passengers are deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression describable by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers melt down. We find support for this model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to understand successes and failures of different treatment strategies. A tumor's load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. The collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by current and future therapies.

Impact of deleterious passenger mutations on cancer progression.

Cancer progression is driven by the accumulation of a small number of genetic alterations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional mutations termed passengers. Passengers are widely believed to have no role in cancer, yet many passengers fall within protein-coding genes and other functional elements that can have potentially deleterious effects on cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter the course of neoplastic progression. Our approach combines evolutionary simulations of cancer progression with an analysis of cancer sequencing data. From simulations, we find that passengers accumulate and largely evade natural selection during progression. Although individually weak, the collective burden of passengers alters the course of progression, leading to several oncological phenomena that are hard to explain with a traditional driver-centric view. We then tested the predictions of our model using cancer genomics data and confirmed that many passengers are likely damaging and have largely evaded negative selection. Finally, we use our model to explore cancer treatments that exploit the load of passengers by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Though both approaches lead to cancer regression, the latter is a more effective therapy. Our results suggest a unique framework for understanding cancer progression as a balance of driver and passenger mutations.

A modified ziggurat algorithm for generating exponentially- and normally-distributed pseudorandom numbers.

The Ziggurat Algorithm is a very fast rejection sampling method for generating PseudoRandom Numbers (PRNs) from statistical distributions. In the algorithm, rectangular sampling domains are layered on top of each other (resembling a ziggurat) to encapsulate the desired probability density function. Random values within these layers are sampled and then returned if they lie beneath the graph of the probability density function. Here, we present an implementation where ziggurat layers reside completely beneath the probability density function, thereby eliminating the need for any rejection test within the ziggurat layers. In the new algorithm, small overhanging segments of probability density remain to the right of each ziggurat layer, which can be efficiently sampled with triangularly-shaped sampling domains. Median runtimes of the new algorithm for exponential and normal variates is reduced to 58% and 53% respectively (collective range: 41-93%). An accessible C library, along with extensions into Python and MATLAB/Octave are provided.

Frequency-dependent ecological interactions increase the prevalence, and shape the distribution, of pre-existing drug resistance.

The evolution of resistance remains one of the primary challenges for modern medicine from infectious diseases to cancers. Many of these resistance-conferring mutations often carry a substantial fitness cost in the absence of treatment. As a result, we would expect these mutants to undergo purifying selection and be rapidly driven to extinction. Nevertheless, pre-existing resistance is frequently observed from drug-resistant malaria to targeted cancer therapies in non-small cell lung cancer (NSCLC) and melanoma. Solutions to this apparent paradox have taken several forms from spatial rescue to simple mutation supply arguments. Recently, in an evolved resistant NSCLC cell line, we found that frequency-dependent ecological interactions between ancestor and resistant mutant ameliorate the cost of resistance in the absence of treatment. Here, we hypothesize that frequency-dependent ecological interactions in general play a major role in the prevalence of pre-existing resistance. We combine numerical simulations with robust analytical approximations to provide a rigorous mathematical framework for studying the effects of frequency-dependent ecological interactions on the evolutionary dynamics of pre-existing resistance. First, we find that ecological interactions significantly expand the parameter regime under which we expect to observe pre-existing resistance. Next, even when positive ecological interactions between mutants and ancestors are rare, these resistant clones provide the primary mode of evolved resistance because even weak positive interaction leads to significantly longer extinction times. We then find that even in the case where mutation supply alone is sufficient to predict pre-existing resistance, frequency-dependent ecological forces still contribute a strong evolutionary pressure that selects for increasingly positive ecological effects (negative frequency-dependent selection). Finally, we genetically engineer several of the most common clinically observed resistance mechanisms to targeted therapies in NSCLC, a treatment notorious for pre-existing resistance. We find that each engineered mutant displays a positive ecological interaction with their ancestor. As a whole, these results suggest that frequency-dependent ecological effects can play a crucial role in shaping the evolutionary dynamics of pre-existing resistance.

Assessing the microbiota of the snail intermediate host of trematodes, Galba truncatula.

BACKGROUND: The microbiome is known to play key roles in health and disease, including host susceptibility to parasite infections. The freshwater snail Galba truncatula is the intermediate host for many trematode species, including the liver and rumen flukes Fasciola hepatica and Calicophoron daubneyi, respectively. The snail-parasite system has previously been investigated. However, the specific interaction between the snail-associated microbiota and intra-snail developmental stages of trematodes has yet to be explored. METHODS: Galba truncatula snails were collected from farms in Northern Ireland and trematode infection was diagnosed using PCR. High-throughput sequencing analysis of the bacterial 16S ribosomal DNA V3-V4 hypervariable regions was subsequently applied to characterise the microbiota of both uninfected and infected snails. RESULTS: We first showed that the snail harboured microbiota that was distinct for its environment. The microbiota of infected snails was found to differ significantly from that of uninfected snails. In particular, the bacterial genera Mycoplasma and Methylotenera were significantly more abundant in infected snails, while genera Sphingomonas and Nocardioides were predominantly associated with uninfected snails. CONCLUSION: These findings pave the way to future studies on the functional roles of bacteria in host-parasite relationships.

Host movement dominates the predicted effects of climate change on parasite transmission between wild and domestic mountain ungulates.

Climate change is shifting the transmission of parasites, which is determined by host density, ambient temperature and moisture. These shifts can lead to increased pressure from parasites, in wild and domestic animals, and can impact the effectiveness of parasite control strategies. Understanding the interactive effects of climate on host movement and parasite life histories will enable targeted parasite management, to ensure livestock productivity and avoid additional stress on wildlife populations. To assess complex outcomes under climate change, we applied a gastrointestinal nematode transmission model to a montane wildlife-livestock system, based on host movement and changes in abiotic factors due to elevation, comparing projected climate change scenarios with the historic climate. The wildlife host, Alpine ibex (Capra ibex ibex), undergoes seasonal elevational migration, and livestock are grazed during the summer for eight weeks. Total parasite infection pressure was more sensitive to host movement than to the direct effect of climatic conditions on parasite availability. Extended livestock grazing is predicted to increase parasite exposure for wildlife. These results demonstrate that movement of different host species should be considered when predicting the effects of climate change on parasite transmission, and can inform decisions to support wildlife and livestock health.

An alien parasite in a changing world - Ashworthius sidemi has lost its traditional seasonal dynamics.

A non-native nematode Ashworthius sidemi has emerged in captive fallow deer in Central and Eastern Europe over the last decade. Although this parasite has been spreading in the wild outside it's native distributional range and colonising local European host species since the middle of the last century, limited information has been published on the seasonality of A. sidemi and its susceptibility to anthelmintics. To address this knowledge gap, we conducted a study to investigate seasonal dynamics of the non-native parasite in the current Central European climate conditions. We collected freshly voided faecal pellets at four-week intervals from February 2018 to February 2020 at a fallow deer reserve with a known history of A. sidemi presence. The faecal pellets obtained were pooled after each site visit (n = 25) and coprocultured to obtain the third stage larvae of trichostrongylid nematodes at monthly intervals. Total genomic DNA was extracted from the recovered larvae. Using real-time multiplex PCR, A. sidemi DNA was detected in 17 out of 25 larval samples (68% prevalence). During the monitoring period, the annual administration of ivermectin based premix (Cermix) took place in January 2018, 2019, and 2020, and additionally a mixture of rafoxanide and mebendazole (Rafendazol) was administered once in spring 2019. The probability of parasite presence was significantly influenced by the time since the drug administration (p = 0.048) and the mean temperature at the location (p = 0.013). Larval samples negative for A. sidemi were always identified shortly after the drug administration. However, rapid pasture contamination by the parasite eggs from two to three months after Cermix administration and within one month after Rafendazol administration suggest only a short-lived efficacy of both administered drugs. The abundance of A. sidemi DNA was positively affected by mean temperature (p = 0.044) and remained relatively stable throughout the monitoring period, with the highest peak in August 2018 and 2019. Pasture contamination with A. sidemi eggs occurred almost all year round, with the exception of the beginning of 2018, 2019, and 2020. These findings indicate adaptation of a non-native parasite to the current climatic conditions of the Czech Republic resulted in negligible seasonal patterns of parasite egg shedding.

Unveiling immune checkpoint regulation: exploring the power of in vivo CRISPR screenings in cancer immunotherapy.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer immunotherapy by reinvigorating antitumor immune responses, but their efficacy remains limited in most patients. To address this challenge and optimize Immune check inhibitor treatment, understanding the underlying molecular intricacies involved is crucial. The emergence of CRISPR-Cas9 technology has empowered researchers to precisely investigate gene function and has introduced transformative shifts in identifying key genes for various physiological and pathological processes. CRISPR screenings, particularly in vivo CRISPR screenings, have become invaluable tools in deciphering molecular networks and signaling pathways governing suppressive immune checkpoint molecules. In this review, we provide a comprehensive overview of in vivo CRISPR screenings in cancer immunotherapy, exploring how this cutting-edge technology has unraveled potential novel therapeutic targets and combination strategies. We delve into the latest findings and advancements, shedding light on immune checkpoint regulation and offering exciting prospects for the development of innovative and effective treatments for cancer patients.

Most cancers carry a substantial deleterious load due to Hill-Robertson interference.

Cancer genomes exhibit surprisingly weak signatures of negative selection (Martincorena et al., 2017; Weghorn, 2017). This may be because selective pressures are relaxed or because genome-wide linkage prevents deleterious mutations from being removed (Hill-Robertson interference; Hill and Robertson, 1966). By stratifying tumors by their genome-wide mutational burden, we observe negative selection (dN/dS ~ 0.56) in low mutational burden tumors, while remaining cancers exhibit dN/dS ratios ~1. This suggests that most tumors do not remove deleterious passengers. To buffer against deleterious passengers, tumors upregulate heat shock pathways as their mutational burden increases. Finally, evolutionary modeling finds that Hill-Robertson interference alone can reproduce patterns of attenuated selection and estimates the total fitness cost of passengers to be 46% per cell on average. Collectively, our findings suggest that the lack of observed negative selection in most tumors is not due to relaxed selective pressures, but rather the inability of selection to remove deleterious mutations in the presence of genome-wide linkage.

An improved model for the population dynamics of cattle gastrointestinal nematodes on pasture: parameterisation and field validation for Ostertagia ostertagi and Cooperia oncophora in northern temperate zones.

Gastrointestinal nematodes (GIN) are amongst the most important pathogens of grazing ruminants worldwide, resulting in negative impacts on cattle health and production. The dynamics of infection are driven in large part by the influence of climate and weather on free-living stages on pasture, and computer models have been developed to predict infective larval abundance and guide management strategies. Significant uncertainties around key model parameters limits effective application of these models to GIN in cattle, however, and these parameters are difficult to estimate in natural populations of mixed GIN species. In this paper, recent advances in molecular biology, specifically ITS-2 rDNA 'nemabiome' metabarcoding, are synthesised with a modern population dynamic model, GLOWORM-FL, to overcome this limitation. Experiments under controlled conditions were used to estimate rainfall constraints on migration of infective L3 larvae out of faeces, and their survival in faeces and soil across a temperature gradient, with nemabiome metabarcoding data permitting species-specific estimates for Ostertagia ostertagi and Cooperia oncophora in mixed natural populations. Results showed that L3 of both species survived well in faeces and soil between 0 and 30 °C, and required at least 5 mm of rainfall daily to migrate out of faeces, with the proportion migrating increasing with the amount of rainfall. These estimates were applied within the model using weather and grazing data and use to predict patterns of larval availability on pasture on three commercial beef farms in western Canada. The model performed well overall in predicting the observed seasonal patterns but some discrepancies were evident which should guide further iterative improvements in model development and field methods. The model was also applied to illustrate its use in exploring differences in predicted seasonal transmission patterns in different regions. Such predictive modelling can help inform evidence-based parasite control strategies which are increasingly needed due climate change and drug resistance. The work presented here also illustrates the added value of combining molecular biology and population dynamics to advance predictive understanding of parasite infections.

Tracking gastrointestinal nematode risk on cattle farms through pasture contamination mapping.

Gastrointestinal nematode (GIN) parasites in grazing cattle are a major cause of production loss and their control is increasingly difficult due to anthelmintic resistance and climate change. Rotational grazing can support control and decrease reliance on chemical intervention, but is often complex due to the need to track grazing periods and infection levels, and the effect of weather on larval availability. In this paper, a simulation model was developed to predict the availability of infective larvae of the bovine GIN, Ostertagia ostertagi, at the level of individual pastures. The model was applied within a complex rotational grazing system and successfully reproduced observed variation in larval density between fields and over time. Four groups of cattle in their second grazing season (n = 44) were followed throughout the temperate grazing season with regular assessment of GIN faecal egg counts, which were dominated by O. ostertagi, animal weight and recording of field rotations. Each group of cattle was rotationally grazed on six group-specific fields throughout the 2019 grazing season. Maps and calendars were produced to illustrate the change in pasture infectivity (density of L3 on herbage) across the 24 separate grazing fields. Simulations predicted differences in pasture contamination levels in relation to the timing of grazing and the return period. A proportion of L3 was predicted to persist on herbage over winter, declining to similar intensities across fields before the start of the following grazing season, irrespective of contamination levels in the previous year. Model predictions showed good agreement with pasture larval counts. The model also simulated differences in seasonal pasture infectivity under rotational grazing in systems that differed in temperature and rainfall profiles. Further application could support individual farm decisions on evasive grazing and refugia management, and improved regional evaluation of optimal grazing strategies for parasite control. The integration of weather and livestock movement is inherent to the model, and facilitates consideration of climate change adaptation through improved disease control.

Quantitative In Vivo Analyses Reveal a Complex Pharmacogenomic Landscape in Lung Adenocarcinoma.

The lack of knowledge about the relationship between tumor genotypes and therapeutic responses remains one of the most critical gaps in enabling the effective use of cancer therapies. Here, we couple a multiplexed and quantitative experimental platform with robust statistical methods to enable pharmacogenomic mapping of lung cancer treatment responses in vivo. The complex map of genotype-specific treatment responses uncovered that over 20% of possible interactions show significant resistance or sensitivity. Known and novel interactions were identified, and one of these interactions, the resistance of KEAP1-mutant lung tumors to platinum therapy, was validated using a large patient response data set. These results highlight the broad impact of tumor suppressor genotype on treatment responses and define a strategy to identify the determinants of precision therapies. SIGNIFICANCE: An experimental and analytical framework to generate in vivo pharmacogenomic maps that relate tumor genotypes to therapeutic responses reveals a surprisingly complex map of genotype-specific resistance and sensitivity.

Seasonal epidemiology of gastrointestinal nematodes of cattle in the northern continental climate zone of western Canada as revealed by internal transcribed spacer-2 ribosomal DNA nemabiome barcoding.

BACKGROUND: Gastrointestinal nematode (GIN) epidemiology is changing in many regions of the world due to factors such as global warming and emerging anthelmintic resistance. However, the dynamics of these changes in northern continental climate zones are poorly understood due to a lack of empirical data. METHODS: We studied the accumulation on pasture of free-living infective third-stage larvae (L3) of different GIN species from fecal pats deposited by naturally infected grazing cattle. The field study was conducted on three organic farms in Alberta, western Canada. Grass samples adjacent to 24 fecal pats were collected from each of three different pastures on each farm. Internal transcribed spacer-2 nemabiome metabarcoding was used to determine the GIN species composition of the harvested larvae. The rotational grazing patterns of the cattle ensured that each pasture was contaminated only once by fecal pat deposition. This design allowed us to monitor the accumulation of L3 of specific GIN species on pastures under natural climatic conditions without the confounding effects of pasture recontamination or anthelmintic treatments. RESULTS: In seven out of the nine pastures, grass L3 counts peaked approximately 9 weeks after fecal deposition and then gradually declined. However, a relatively large number of L3 remained in the fecal pats at the end of the grazing season. Nemabiome metabarcoding revealed that Cooperia oncophora and Ostertagia ostertagi were the two most abundant species on all of the pastures and that the dynamics of larval accumulation on grass were similar for both species. Daily precipitation and temperature across the whole sampling period were similar for most of the pastures, and multiple linear regression showed that accumulated rainfall 1 week prior to sample collection had a significant impact on the pasture L3 population, but accumulated rainfall 3 weeks prior to sample collection did not. CONCLUSIONS: The results suggest that the pasture L3 population was altered by short-term microclimatic conditions conducive for horizontal migration onto grass. Overall, the results show the importance of the fecal pat as a refuge and reservoir for L3 of cattle GIN on western Canadian pastures, and provide an evidence base for the risk assessment of rotational grazing management in the region.

A Functional Taxonomy of Tumor Suppression in Oncogenic KRAS-Driven Lung Cancer.

Cancer genotyping has identified a large number of putative tumor suppressor genes. Carcinogenesis is a multistep process, but the importance and specific roles of many of these genes during tumor initiation, growth, and progression remain unknown. Here we use a multiplexed mouse model of oncogenic KRAS-driven lung cancer to quantify the impact of 48 known and putative tumor suppressor genes on diverse aspects of carcinogenesis at an unprecedented scale and resolution. We uncover many previously understudied functional tumor suppressors that constrain cancer in vivo. Inactivation of some genes substantially increased growth, whereas the inactivation of others increases tumor initiation and/or the emergence of exceptionally large tumors. These functional in vivo analyses revealed an unexpectedly complex landscape of tumor suppression that has implications for understanding cancer evolution, interpreting clinical cancer genome sequencing data, and directing approaches to limit tumor initiation and progression. SIGNIFICANCE: Our high-throughput and high-resolution analysis of tumor suppression uncovered novel genetic determinants of oncogenic KRAS-driven lung cancer initiation, overall growth, and exceptional growth. This taxonomy is consistent with changing constraints during the life history of cancer and highlights the value of quantitative in vivo genetic analyses in autochthonous cancer models.This article is highlighted in the In This Issue feature, p. 1601.