Identifying modules of cooperating cancer drivers

Prevalence and Associations of Co-occurrence of NFE2L2 Mutations and Chromosome 3q26 Amplification in Lung Cancer

Background   NFE2L2 (nuclear factor erythroid-2-related factor-2) encodes a basic leucine zipper (bZIP) transcription factor and exhibits variations in various tumor types, including lung cancer. In this study, we comprehensively investigated the impact of simultaneous mutations on the survival of NFE2L2 -mutant lung cancer patients within specific subgroups. Methods  A cohort of 1,103 lung cancer patients was analyzed using hybridization capture-based next-generation sequencing. Results  The NFE2L2 gene had alterations in 3.0% (33/1,103) of lung cancer samples, including 1.5% (15/992) in adenocarcinoma and 16.2% (18/111) in squamous cell carcinoma. Thirty-four variations were found, mainly in exons 2 (27/34). New variations in exon 2 (p.D21H, p.V36_E45del, p.F37_E45del, p.R42P, p.E67Q, and p.L76_E78delinsQ) were identified. Some patients had copy number amplifications. Co-occurrence with TP53 (84.8%), CDKN2A (33.3%), KMT2B (33.3%), LRP1B (33.3%), and PIK3CA (27.3%) mutations was common. Variations of NFE2L2 displayed the tightest co-occurrence with IRF2 , TERC , ATR , ZMAT3 , and SOX2 ( p  < 0.001). In The Cancer Genome Atlas Pulmonary Squamous Carcinoma project, patients with NFE2L2 variations and 3q26 amplification had longer median survival (63.59 vs. 32.04 months, p  = 0.0459) and better overall survival. Conclusions   NFE2L2 mutations display notable heterogeneity in lung cancer. The coexistence of NFE2L2 mutations and 3q26 amplification warrants in-depth exploration of their potential clinical implications and treatment approaches for affected patients.

Mutations, substitutions, and selection: Linking mutagenic processes to cancer using evolutionary theory

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Negative prognostic impact of Co-mutations in TGFβ and TP53 pathways in surgically resected rectal tumors following neoadjuvant chemoradiotherapy

BACKGROUND

Preoperative neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME) is a common approach for treating patients with locally advanced rectal cancer. Nevertheless, the mutational profile and its prognostic impact in surgically resected tumor specimens after nCRT remains to be clarified.

METHODS

The comprehensive analysis of mutational landscape was retrospectively conducted by target regions sequencing approach that covered 150 tumor-related genes. Univariate and multivariate logistic regression and Cox regression was used to examine the association of mutation status in genes and pathways with pathological response and prognosis. Data from Memorial Sloan Kettering Cancer Center (MSK) cohort was used for comparison with our results.

RESULTS

The top five commonly mutated genes in resected rectal tumor tissue samples following nCRT were TP53 (42%), APC (31%), KRAS (27%), PIK3CA (14%) and FBXW7 (11%). Mutations in the WNT pathway, which was mainly represented by APC mutation, were found to be significantly associated with tumor regression grade (TRG) 3. In our cohort, co-mutations in the receptor tyrosine kinase (RTK)/RAS and WNT pathways were found to be independently associated with reduced risk of recurrent and significantly associated with longer disease-free survival (DFS). In both our cohort and the MSK cohort, co-mutations in the TGF-β and TP53 pathways were significantly associated with worse DFS.

CONCLUSIONS

Resected rectal tumor samples from patients without complete pathological response can be appropriately used to detect mutations. Co-mutations in the TGF-β and TP53 pathways may provide more prognostic information beyond commonly used clinical factors.

Pairwise and higher-order epistatic effects among somatic cancer mutations across oncogenesis

Cancer occurs as a consequence of multiple somatic mutations that lead to uncontrolled cell growth. Mutual exclusivity and co-occurrence of mutations imply-but do not prove-that mutations exert synergistic or antagonistic epistatic effects on oncogenesis. Knowledge of these interactions, and the consequent trajectories of mutation and selection that lead to cancer has been a longstanding goal within the cancer research community. Recent research has revealed mutation rates and scaled selection coefficients for specific recurrent variants across many cancer types. However, there are no current methods to quantify the strength of selection incorporating pairwise and higher-order epistatic effects on selection within the trajectory of likely cancer genotoypes. Therefore, we have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation (flux), dependent on somatic cancer genotype. Coupling this continuous-time Markov chain model with a deconvolution approach provides estimates of underlying mutation rates and selection across the trajectory of oncogenesis. We demonstrate computation of fluxes and selection coefficients in a somatic evolutionary model for the four most frequently variant driver genes (TP53, LRP1B, KRAS and STK11) from 565 cases of lung adenocarcinoma. Our analysis reveals multiple antagonistic epistatic effects that reduce the possible routes of oncogenesis, and inform cancer research regarding viable trajectories of somatic evolution whose progression could be forestalled by precision medicine. Synergistic epistatic effects are also identified, most notably in the somatic genotype TP53 LRP1B for mutations in the KRAS gene, and in somatic genotypes containing KRAS or TP53 mutations for mutations in the STK11 gene. Large positive fluxes of KRAS variants were driven by large selection coefficients, whereas the flux toward LRP1B mutations was substantially aided by a large mutation rate for this gene. The approach enables inference of the most likely routes of site-specific variant evolution and estimation of the strength of selection operating on each step along the route, a key component of what we need to know to develop and implement personalized cancer therapies.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Estimation of Neutral Mutation Rates and Quantification of Somatic Variant Selection Using cancereffectsizeR

Somatic nucleotide mutations can contribute to cancer cell survival, proliferation, and pathogenesis. Although research has focused on identifying which mutations are "drivers" versus "passengers," quantifying the proliferative effects of specific variants within clinically relevant contexts could reveal novel aspects of cancer biology. To enable researchers to estimate these cancer effects, we developed cancereffectsizeR, an R package that organizes somatic variant data, facilitates mutational signature analysis, calculates site-specific mutation rates, and tests models of selection. Built-in models support effect estimation from single nucleotides to genes. Users can also estimate epistatic effects between paired sets of variants, or design and test custom models. The utility of cancer effect was validated by showing in a pan-cancer dataset that somatic variants classified as likely pathogenic or pathogenic in ClinVar exhibit substantially higher effects than most other variants. Indeed, cancer effect was a better predictor of pathogenic status than variant prevalence or functional impact scores. In addition, the application of this approach toward pairwise epistasis in lung adenocarcinoma showed that driver mutations in BRAF, EGFR, or KRAS typically reduce selection for alterations in the other two genes. Companion reference data packages support analyses using the hg19 or hg38 human genome builds, and a reference data builder enables use with any species or custom genome build with available genomic and transcriptomic data. A reference manual, tutorial, and public source code repository are available at https://townsend-lab-yale.github.io/cancereffectsizeR. Comprehensive estimation of cancer effects of somatic mutations can provide insights into oncogenic trajectories, with implications for cancer prognosis and treatment.

SIGNIFICANCE

An R package provides streamlined, customizable estimation of underlying nucleotide mutation rates and of the oncogenic and epistatic effects of mutations in cancer cohorts.

Epistasis and evolutionary dependencies in human cancers

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

Big data in basic and translational cancer research

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

APOBEC mutagenesis and selection for NFE2L2 contribute to the origin of lung squamous-cell carcinoma

Lung squamous-cell carcinoma originates as a consequence of oncogenic molecular variants arising from diverse mutagenic processes such as tobacco, defective homologous recombination, aging, and cytidine deamination by APOBEC proteins. Only some of the many variants generated by these processes actually contribute to tumorigenesis. Therefore, molecular investigation of mutagenic processes such as cytidine deamination by APOBEC should also determine whether the mutations produced by these processes contribute substantially to the growth and survival of cancer. Here, we determine the processes that gave rise to mutations of 681 lung squamous-cell carcinomas, and quantify the probability that each mutation was the product of each process. We then calculate the contribution of each mutation to increases in cellular proliferation and survival. We performed in vitro experiments to determine cytidine deamination activity of APOBEC3B against oligonucleotides corresponding with genomic sequences that give rise to variants of high cancer effect size. The largest APOBEC-related cancer effects are attributable to mutations in PIK3CA and NFE2L2. We demonstrate that APOBEC effectively deaminates NFE2L2 at the locations that confer high cancer effect. Overall, we demonstrate that APOBEC activity can lead to mutations in NFE2L2 that have large contributions to cancer cell growth and survival, and that NFE2L2 is an attractive potential target for therapeutic intervention.

The Good and Bad of Nrf2: An Update in Cancer and New Perspectives in COVID-19

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a well-known transcription factor best recognised as one of the main regulators of the oxidative stress response. Beyond playing a crucial role in cell defence by transactivating cytoprotective genes encoding antioxidant and detoxifying enzymes, Nrf2 is also implicated in a wide network regulating anti-inflammatory response and metabolic reprogramming. Such a broad spectrum of actions renders the factor a key regulator of cell fate and a strategic player in the control of cell transformation and response to viral infections. The Nrf2 protective roles in normal cells account for its anti-tumour and anti-viral functions. However, Nrf2 overstimulation often occurs in tumour cells and a complex correlation of Nrf2 with cancer initiation and progression has been widely described. Therefore, if on one hand, Nrf2 has a dual role in cancer, on the other hand, the factor seems to display a univocal function in preventing inflammation and cytokine storm that occur under viral infections, specifically in coronavirus disease 19 (COVID-19). In such a variegate context, the present review aims to dissect the roles of Nrf2 in both cancer and COVID-19, two widespread diseases that represent a cause of major concern today. In particular, the review describes the molecular aspects of Nrf2 signalling in both pathological situations and the most recent findings about the advantages of Nrf2 inhibition or activation as possible strategies for cancer and COVID-19 treatment respectively.