An elevated rate of whole-genome duplications in cancers from Black patients.

In the United States, Black individuals have higher rates of cancer mortality than any other racial group. Here, we examine chromosome copy number changes in cancers from more than 1800 self-reported Black patients. We find that tumors from self-reported Black patients are significantly more likely to exhibit whole-genome duplications (WGDs), a genomic event that enhances metastasis and aggressive disease, compared to tumors from self-reported white patients. This increase in WGD frequency is observed across multiple cancer types, including breast, endometrial, and lung cancer, and is associated with shorter patient survival. We further demonstrate that combustion byproducts are capable of inducing WGDs in cell culture, and cancers from self-reported Black patients exhibit mutational signatures consistent with exposure to these carcinogens. In total, these findings identify a type of genomic alteration that is associated with environmental exposures and that may influence racial disparities in cancer outcomes.

The next Nobel Prize in chemistry or in physiology or medicine.

In early October, the Nobel Prizes will honor groundbreaking discoveries. After the anticipated recognition of Katalin Karikó and Drew Weissman in 2023 for the development of RNA modifications that enabled the SARS-CoV-2 mRNA vaccine, we eagerly consider the next topics to be awarded. In the September 30th anniversary special issue of Cell Chemical Biology, we ask researchers from a range of backgrounds, what topic do you think deserves the next Nobel Prize in chemistry or in physiology or medicine, and why?

Evolving copy number gains promote tumor expansion and bolster mutational diversification.

The timing and fitness effect of somatic copy number alterations (SCNA) in cancer evolution remains poorly understood. Here we present a framework to determine the timing of a clonal SCNA that encompasses multiple gains. This involves calculating the proportion of time from its last gain to the onset of population expansion (lead time) as well as the proportion of time prior to its first gain (initiation time). Our method capitalizes on the observation that a genomic segment, while in a specific copy number (CN) state, accumulates point mutations proportionally to its CN. Analyzing 184 whole genome sequenced samples from 75 patients across five tumor types, we commonly observe late gains following early initiating events, occurring just before the clonal expansion relevant to the sampling. These include gains acquired after genome doubling in more than 60% of cases. Notably, mathematical modeling suggests that late clonal gains may contain final-expansion drivers. Lastly, SCNAs bolster mutational diversification between subpopulations, exacerbating the circle of proliferation and increasing heterogeneity.

Inhibition of a lower potency target drives the anticancer activity of a clinical p38 inhibitor.

The small-molecule drug ralimetinib was developed as an inhibitor of the p38α mitogen-activated protein kinase, and it has advanced to phase 2 clinical trials in oncology. Here, we demonstrate that ralimetinib resembles EGFR-targeting drugs in pharmacogenomic profiling experiments and that ralimetinib inhibits EGFR kinase activity in vitro and in cellulo. While ralimetinib sensitivity is unaffected by deletion of the genes encoding p38α and p38ß, its effects are blocked by expression of the EGFR-T790M gatekeeper mutation. Finally, we solved the cocrystal structure of ralimetinib bound to EGFR, providing further evidence that this drug functions as an ATP-competitive EGFR inhibitor. We conclude that, though ralimetinib is >30-fold less potent against EGFR compared to p38α, its ability to inhibit EGFR drives its primary anticancer effects. Our results call into question the value of p38α as an anticancer target, and we describe a multi-modal approach that can be used to uncover a drug's mechanism-of-action.

Aneuploidy in human cancer: new tools and perspectives.

Chromosome copy number imbalances, otherwise known as aneuploidies, are a common but poorly understood feature of cancer. Here, we describe recent advances in both detecting and manipulating aneuploidies that have greatly advanced our ability to study their role in tumorigenesis. In particular, new clustered regularly interspaced short palindromic repeats (CRISPR)-based techniques have been developed that allow the creation of isogenic cell lines with specific chromosomal changes, thereby facilitating experiments in genetically controlled backgrounds to uncover the consequences of aneuploidy. These approaches provide increasing evidence that aneuploidy is a key driver of cancer development and enable the identification of multiple dosage-sensitive genes encoded on aneuploid chromosomes. Consequently, measuring aneuploidy may inform clinical prognosis, while treatment strategies that target aneuploidy could represent a novel method to counter malignant growth.

Oncogene-like addiction to aneuploidy in human cancers.

Most cancers exhibit aneuploidy, but its functional significance in tumor development is controversial. Here, we describe ReDACT (Restoring Disomy in Aneuploid cells using CRISPR Targeting), a set of chromosome engineering tools that allow us to eliminate specific aneuploidies from cancer genomes. Using ReDACT, we created a panel of isogenic cells that have or lack common aneuploidies, and we demonstrate that trisomy of chromosome 1q is required for malignant growth in cancers harboring this alteration. Mechanistically, gaining chromosome 1q increases the expression of MDM4 and suppresses p53 signaling, and we show that TP53 mutations are mutually exclusive with 1q aneuploidy in human cancers. Thus, tumor cells can be dependent on specific aneuploidies, raising the possibility that these "aneuploidy addictions" could be targeted as a therapeutic strategy.

Oncogene-like addiction to aneuploidy in human cancers.

Most cancers exhibit aneuploidy, but its functional significance in tumor development is controversial. Here, we describe ReDACT (Restoring Disomy in Aneuploid cells using CRISPR Targeting), a set of chromosome engineering tools that allow us to eliminate specific aneuploidies from cancer genomes. Using ReDACT, we created a panel of isogenic cells that have or lack common aneuploidies, and we demonstrate that trisomy of chromosome 1q is required for malignant growth in cancers harboring this alteration. Mechanistically, gaining chromosome 1q increases the expression of MDM4 and suppresses TP53 signaling, and we show that TP53 mutations are mutually-exclusive with 1q aneuploidy in human cancers. Thus, specific aneuploidies play essential roles in tumorigenesis, raising the possibility that targeting these "aneuploidy addictions" could represent a novel approach for cancer treatment.

Extensive protein dosage compensation in aneuploid human cancers.

Aneuploidy is a hallmark of human cancers, but the effects of aneuploidy on protein expression remain poorly understood. To uncover how chromosome copy number changes influence the cancer proteome, we conducted an analysis of hundreds of human cancer cell lines and tumors with matched copy number, RNA expression, and protein expression data. We found that a majority of proteins show dosage compensation and fail to change by the degree expected based on chromosome copy number alone. We uncovered a variety of gene groups that were recurrently buffered upon both chromosome gain and loss, including protein complex subunits and cell cycle genes. Several genetic and biophysical factors were predictive of protein buffering, highlighting complex post-translational regulatory mechanisms that maintain appropriate gene product dosage. Finally, we established that chromosomal aneuploidy has a moderate effect on the expression of oncogenes and tumor suppressors, showing that these key cancer drivers can be subject to dosage compensation as well. In total, our comprehensive analysis of aneuploidy and dosage compensation across cancers will help identify the key driver genes encoded on altered chromosomes and will shed light on the overall consequences of aneuploidy during tumor development.

Synthesis and Structure-Activity relationships of cyclin-dependent kinase 11 inhibitors based on a diaminothiazole scaffold.

Cyclin-dependent kinases (CDK) are attractive targets for drug discovery due to their wide range of cellular functions. CDK11 is an understudied CDK with roles in transcription and splicing, cell cycle regulation, neuronal function, and apoptosis. In this study, we describe a medicinal chemistry campaign to identify a CDK11 inhibitor. Employing a promising but nonselective CDK11-targeting scaffold (JWD-047), extensive structure-guided medicinal chemistry modifications led to the identification of ZNL-05-044. A combination of biochemical evaluations and NanoBRET cellular assays for target engagement guided the SAR towards a 2,4-diaminothiazoles CDK11 probe with significantly improved kinome-wide selectivity over JWD-047. CDK11 inhibition with ZNL-05-044 leads to G2/M cell cycle arrest, consistent with prior work evaluating OTS964, and impacts CDK11-dependent mRNA splicing in cells. Together, ZNL-05-044 serves as a tool compound for further optimization and interrogation of the consequences of CDK11 inhibition.

Genome-wide identification and analysis of prognostic features in human cancers.

Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development.

Chromosomal instability and aneuploidy as causes of cancer drug resistance.

High levels of aneuploidy and chromosomal instability (CIN) are correlated with poor patient outcomes, though the mechanism(s) underlying this relationship have not been established. Recent evidence has demonstrated that chromosome copy number changes can function as point mutation-independent sources of drug resistance in cancer, which may partially explain this clinical association. CIN generates intratumoral heterogeneity in the form of gene dosage alterations, upon which the selective pressures induced by drug treatments can act. Thus, although CIN and aneuploidy impair cell fitness under most conditions, CIN can augment cellular adaptability, establishing CIN as a bet-hedging mechanism in tumor evolution. CIN may also endow cancers with unique vulnerabilities, which could be exploited therapeutically to achieve better patient outcomes.

Field-theoretic density estimation for biological sequence space with applications to 5' splice site diversity and aneuploidy in cancer.

Density estimation in sequence space is a fundamental problem in machine learning that is also of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy (i.e., calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates). Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data are plentiful while still maintaining a conservative maximum entropy character in regions of sequence space where data are sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyperparameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5' splice sites found in the human genome and to understand patterns of chromosomal abnormalities across human cancers.

Chromosomal instability accelerates the evolution of resistance to anti-cancer therapies.

Aneuploidy is a ubiquitous feature of human tumors, but the acquisition of aneuploidy typically antagonizes cellular fitness. To investigate how aneuploidy could contribute to tumor growth, we triggered periods of chromosomal instability (CIN) in human cells and then exposed them to different culture environments. We discovered that transient CIN reproducibly accelerates the acquisition of resistance to anti-cancer therapies. Single-cell sequencing revealed that these resistant populations develop recurrent aneuploidies, and independently deriving one chromosome-loss event that was frequently observed in paclitaxel-resistant cells was sufficient to decrease paclitaxel sensitivity. Finally, we demonstrated that intrinsic levels of CIN correlate with poor responses to numerous therapies in human tumors. Our results show that, although CIN generally decreases cancer cell fitness, it also provides phenotypic plasticity to cancer cells that can allow them to adapt to diverse stressful environments. Moreover, our findings suggest that aneuploidy may function as an under-explored cause of therapy failure.

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity.

BACKGROUND: Antivirals are needed to combat the COVID-19 pandemic, which is caused by SARS-CoV-2. The clinically-proven protease inhibitor Camostat mesylate inhibits SARS-CoV-2 infection by blocking the virus-activating host cell protease TMPRSS2. However, antiviral activity of Camostat mesylate metabolites and potential viral resistance have not been analyzed. Moreover, antiviral activity of Camostat mesylate in human lung tissue remains to be demonstrated. METHODS: We used recombinant TMPRSS2, reporter particles bearing the spike protein of SARS-CoV-2 or authentic SARS-CoV-2 to assess inhibition of TMPRSS2 and viral entry, respectively, by Camostat mesylate and its metabolite GBPA. FINDINGS: We show that several TMPRSS2-related proteases activate SARS-CoV-2 and that two, TMPRSS11D and TMPRSS13, are robustly expressed in the upper respiratory tract. However, entry mediated by these proteases was blocked by Camostat mesylate. The Camostat metabolite GBPA inhibited recombinant TMPRSS2 with reduced efficiency as compared to Camostat mesylate. In contrast, both inhibitors exhibited similar antiviral activity and this correlated with the rapid conversion of Camostat mesylate into GBPA in the presence of serum. Finally, Camostat mesylate and GBPA blocked SARS-CoV-2 spread in human lung tissue ex vivo and the related protease inhibitor Nafamostat mesylate exerted augmented antiviral activity. INTERPRETATION: Our results suggest that SARS-CoV-2 can use TMPRSS2 and closely related proteases for spread in the upper respiratory tract and that spread in the human lung can be blocked by Camostat mesylate and its metabolite GBPA. FUNDING: NIH, Damon Runyon Foundation, ACS, NYCT, DFG, EU, Berlin Mathematics center MATH+, BMBF, Lower Saxony, Lundbeck Foundation, Novo Nordisk Foundation.

Aneuploidy as a promoter and suppressor of malignant growth.

Aneuploidy has been recognized as a hallmark of tumorigenesis for more than 100 years, but the connection between chromosomal errors and malignant growth has remained obscure. New evidence emerging from both basic and clinical research has illuminated a complicated relationship: despite its frequency in human tumours, aneuploidy is not a universal driver of cancer development and instead can exert substantial tumour-suppressive effects. The specific consequences of aneuploidy are highly context dependent and are influenced by a cell's genetic and environmental milieu. In this Review, we discuss the diverse facets of cancer biology that are shaped by aneuploidy, including metastasis, drug resistance and immune recognition, and we highlight aneuploidy's distinct roles as both a tumour promoter and an anticancer vulnerability.

Camostat mesylate inhibits SARS-CoV-2 activation by TMPRSS2-related proteases and its metabolite GBPA exerts antiviral activity.

Antiviral therapy is urgently needed to combat the coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The protease inhibitor camostat mesylate inhibits SARS-CoV-2 infection of lung cells by blocking the virus-activating host cell protease TMPRSS2. Camostat mesylate has been approved for treatment of pancreatitis in Japan and is currently being repurposed for COVID-19 treatment. However, potential mechanisms of viral resistance as well as camostat mesylate metabolization and antiviral activity of metabolites are unclear. Here, we show that SARS-CoV-2 can employ TMPRSS2-related host cell proteases for activation and that several of them are expressed in viral target cells. However, entry mediated by these proteases was blocked by camostat mesylate. The camostat metabolite GBPA inhibited the activity of recombinant TMPRSS2 with reduced efficiency as compared to camostat mesylate and was rapidly generated in the presence of serum. Importantly, the infection experiments in which camostat mesylate was identified as a SARS-CoV-2 inhibitor involved preincubation of target cells with camostat mesylate in the presence of serum for 2 h and thus allowed conversion of camostat mesylate into GBPA. Indeed, when the antiviral activities of GBPA and camostat mesylate were compared in this setting, no major differences were identified. Our results indicate that use of TMPRSS2-related proteases for entry into target cells will not render SARS-CoV-2 camostat mesylate resistant. Moreover, the present and previous findings suggest that the peak concentrations of GBPA established after the clinically approved camostat mesylate dose (600 mg/day) will result in antiviral activity.

Discovering and validating cancer genetic dependencies: approaches and pitfalls.

Cancer 'genetic dependencies' - genes whose products are essential for cancer cell fitness - are promising targets for therapeutic development. However, recent evidence has cast doubt on the validity of several putative dependencies that are currently being targeted in cancer clinical trials, underscoring the challenges inherent in correctly identifying cancer-essential genes. Here we review several common techniques and platforms for discovering and characterizing cancer dependencies. We discuss the strengths and drawbacks of different gene-perturbation approaches, and we highlight the use of poorly validated genetic and pharmacological agents as a common cause of target misidentification. A careful consideration of the limitations of current technologies and cancer models will improve our ability to correctly uncover cancer genetic dependencies and will facilitate the development of improved therapeutic agents.