Functional Analysis of Rare RAS Variants of Unknown Significance

<div>Abstract<p>The <i>RAS</i> gene is frequently mutated in human cancers. Whereas the functional significance of frequent mutations is well established, the significance of rare mutations remains unknown. This study aimed to comprehensively investigate the function of rare <i>RAS</i> variants and provide new insights about their clinical relevance. A total of 298 <i>K/N/HRAS</i> variants (169, 72, and 57 variants, respectively) reported in the COSMIC database v100 were introduced into 3T3 cells. Subsequently, the drug sensitivity of <i>KRAS</i> variants to BI-2865, a noncovalent pan-KRAS inhibitor, was evaluated using the mixed-all-nominated-in-one method. The 3T3 focus formation assay newly identified 35 <i>KRAS</i>, 10 <i>NRAS</i>, and 21 <i>HRAS</i> variants as transforming competent. The oncogenicity assessed in the present study was consistent with that reported in the database. The drug sensitivity assay identified 15 <i>KRAS</i> variants sensitive to BI-2865. BI-2865 treatment inhibited the RAS downstream signaling pathways and induced apoptosis in cells with the sensitive variants. The present study identified 66 new oncogenic <i>RAS</i> variants. The sensitivity of <i>KRAS</i> variants to BI-2865 varies by variant. Functional analysis provides clues for the treatment of patients with rare <i>RAS</i> variants.</p>Significance:<p>This study presents the first comprehensive functional analysis of 298 rare <i>RAS</i> variants, identifying 66 novel oncogenic mutations and 15 <i>KRAS</i> variants sensitive to the noncovalent pan-KRAS inhibitor BI-2865. The heterogeneity in drug responses among <i>KRAS</i> variants underscores the need for variant-specific therapeutic strategies. These findings provide a preclinical framework for guiding personalized treatment in RAS-driven cancers and a valuable resource for understanding the clinical relevance of rare <i>RAS</i> mutations.</p></div>

P-271 Frequency of RAS variants in Bulgarian patients with metastatic colorectal cancer

Extending Rare-Variant Testing Strategies: Analysis of Noncoding Sequence and Imputed Genotypes

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

SummaryPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Genome-wide associations studies (GWAS) have been very successful in identifying common genetic variation associated to numerous complex diseases [1]. However, most of the identified common genetic variants appear to confer modest risk and few causal alleles have been identified [2]. Furthermore, these associations account for a small portion of the total heritability of inherited disease variation [1]. This has led to the reexamination of the contribution of environment, gene-gene and gene-environment interactions, and rare genetic variants in complex diseases [1, 3, 4]. There is strong evidence that rare variants play an important role in complex disease etiology and may have larger genetic effects than common variants [2]. Currently, much of what we know regarding the contribution of rare genetic variants to disease risk is based on a limited number of phenotypes and candidate genes. However, rapid advancement of second generation sequencing technologies will invariably lead to widespread association studies comparing whole exome and eventually whole genome sequencing of cases and controls. A tremendous challenge for enabling these "next generation" medical genomic studies is developing statistical approaches for correlating rare genetic variants with disease outcome. The analysis of rare variants is challenging since methods used for common variants are woefully underpowered. Therefore, methods that can deal with genetic heterogeneity at the trait-associated locus have been developed to analyze rare variants. These methods instead analyzing individual variants analyze variants within a region/gene as a group and usually rely on collapsing. They can be applied to both in cases vs. controls and quantitative trait studies are needed. The paper of Bansal et al. in this volume describes the application of a number of statistical methods for testing associations between rare variants in two genes to obesity. The authors considered the relative merits of the different methods as well as important implementation details, such as the leveraging of genomic annotations and determining p-values. Knowledge of haplotypes can increase the power of GWAS studies and also highlight associations that are impossible to detect without haplotype phase (e.g. loss of heterozygosity). Even more complicated phase-dependent interactions of variants in linkage equilibrium have also been suggested as possible causes of missing heritability. In their work, Hallsorsson et al. formulate algorithmic strategies for haplotype phasing by multi-assembly of shared haplotypes from next-generation sequencing data. These methods would allow testing haplotypes harboring rare variants for association and potentially increase their explanatory power. Since single SNP tests are often underpowered in rare variant association analysis, Zeggini and Asimit propose a locus-based method that has high power in the presence of rare variants and that incorporate base quality scores available for sequencing data. Their results suggest that this multi-marker approach may be best suited for smaller regions, or after some filtering to reduce the number of SNPs that are jointly tested to reduce loss of power due to multiple-testing adjustments. Finally, the paper of Zhou et al., presents a penalized regression framework for association testing on sequence data, in the presence of both common and rare variants. This method also introduces the use of weights to incorporate available biological information on the variants. Although these tactics improve both false positive and false negative rates, they represent an incremental development and there is still significant room for improvement. With the development of sequencing technologies and methods to detect complex trait rare variant associations many new and exciting discovery are imminent. The analysis of rare variants is still in its infancy and the next few years promises to produce many new methods to meet the special demands of analyzing this type of data. Note from Publisher: This article contains the abstract and references.

Curious cases of GATA2 deficiency: clonal evolution or dual diagnoses?

e15632 Background: While commercially available hotspot testing detects over 99% of the activating mutations in KRAS located at codons 12, 13, 61, and 146, rare oncogenic variants beyond these regions can significantly impact treatment decisions in colorectal cancer (CRC). Herein we showcase the utility of comprehensive gene panel testing in identifying low frequency yet clinically relevant KRAS alterations to enable informed clinical treatment decisions. Methods: 575 CRC specimens were analyzed using CANCERPLEX over a three-year period (KEW, Inc; Cambridge, MA). CANCERPLEX is a comprehensive large gene panel comprising 435 cancer associated genes. A rare KRAS alteration was defined as a variant with a frequency > 0.02% in COSMIC. Variants with clinical actionability in CRC were defined as activating mutations with inferred therapeutic resistance to anti-EGFR antibody therapy. The analytical sensitivity and specificity for single nucleotide polymorphisms (SNPs) in CANCERPLEXwas 99.2% and > 99.9%, respectively. Results: KRAS Q22K, L19F, and G60D were identified as rare oncogenic variants in our analysis of 575 CRC cases. Q22K, L19F, and G60D has been previously reported in 0.009% (7/73000), 0.017% (13/73000), 0.003% (2/73000), respectively, in CRC cases (COSMIC). All three variants have been reported to have transforming potential in vitro and in vivo and trigger activation of downstream oncogenic pathways [PMID:11095964;26284123; 17150185; 20949621]. As activating mutations in KRAS predict lack of response to EGFR antibody therapies, cetuximab and panitumumab were reported as contraindications in all three cases. While not a rare variant, we detected activating mutations in codon 146 in 3% (17/575) of our cohort; strikingly, 5 of the KRAS A146 variants were not identified by prior less comprehensive modes of RAS testing. Conclusions: Low frequency, clinically relevant KRAS variants in CRC may be overlooked using commercially available RAS hotspot assays. In this study we demonstrate that a comprehensive gene panel may detect clinically consequential rare KRAS mutations that may define patients’ treatment and clinical course.

Activating mutations in RAS genes ( KRAS , HRAS , and NRAS ) are oncogenic drivers arising in about one-third of human malignancies (1, 2). Cancers with oncogenic RAS mutations are among those with the poorest prognosis, the most notorious example being pancreatic cancer with a 95% mutation frequency in KRAS and a 7% survival rate beyond 5 y of diagnosis. As such, targeting oncogenic RAS proteins or their functional output has been a longstanding priority for development of effective cancer therapies. Unfortunately, therapeutic targeting of oncogenic RAS proteins directly or of their individual downstream effector pathways has not been successful for the treatment of the vast majority of human cancers, suggesting that functional redundancies provide workarounds that sustain oncogenic activity. In PNAS, Lee et al. (3) use a novel combinatorial knockdown screening approach to identify essential RAS signaling and stress adaptation programs that, when cotargeted, compromise RAS-mediated cancer cell survival. RAS proteins are small GTPases that transduce signals from upstream growth factor receptors to downstream signaling pathways to stimulate growth, proliferation, and survival. In cancers, oncogenic mutations in RAS proteins such as KRAS G12V render them in the constitutively “on” position, decoupling regulatory growth signals from effector mechanisms. These unregulated growth signals drive the cancer phenotype through constitutive activation of the downstream RAF, RalGDS, and PI3K pathways (Fig. 1) (1, 2). Fig. 1. Essential codependency of RAS-driven cancers on BRAF, CRAF, and autophagy. BRAF and CRAF provide key functional oncogenic signaling downstream of RAS that requires autophagy mediated by ATG7 to sustain survival. Coordinate blockade of BRAF, CRAF, and ATG7 provides the one-two punch and lethal blow to Ras-driven cancer cells. Targeting oncogenic RAS proteins directly has proved difficult, with the possible exception of the KRAS V12C mutation in a small subset of human cancers in which the cysteine residue … [↵][1]1Email: epwhite{at}cinj.rutgers.edu. [1]: #xref-corresp-1-1

Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth. However, image analysis software is often not adapted to the three-dimensionality of PDTOs, requires fluorescent viability dyes, or is not compatible with a 384-well microplate format. This paper describes a semi-automated methodology to seed, treat, and image PDTOs in a high-throughput, 384-well format using conventional, widefield, live-cell imaging systems. In addition, we developed viability marker-free image analysis software to quantify growth rate-based drug response metrics that improve reproducibility and correct growth rate variations between different PDTO lines. Using the normalized drug response metric, which scores drug response based on the growth rate normalized to a positive and negative control condition, and a fluorescent cell death dye, cytotoxic and cytostatic drug responses can be easily distinguished, profoundly improving the classification of responders and non-responders. In addition, drug-response heterogeneity can by quantified from single-organoid drug response analysis to identify potential, resistant clones. Ultimately, this method aims to improve the prediction of clinical therapy response by capturing a multiparametric drug response signature, which includes kinetic growth arrest and cell death quantification.

BackgroundVariations in pharmacogenes that regulate drug absorption, distribution, metabolism, and excretion (ADME) contribute to approximately 20–30% of interindividual differences in drug response. While many common variants are successfully utilized in clinical settings to predict individual drug responses, a significant portion of the genetic basis underlying this variability remains unidentified. This includes rare variants, which are estimated to account for 4–6% of drug response variability.ResultsTo comprehensively elucidate the functional consequences and molecular mechanisms of rare variants, we conducted in vitro enzyme expression studies combined with in silico structure–function analyses. We selected 11 rare variants in the CYP2C19 and CYP2D6 genes identified among participants within the Estonian Biobank. Variant cDNAs were heterologously expressed in HEK-293 cells, and detailed enzyme activity analyses were performed. The experimental results were further validated against average scores from five optimized in silico prediction models: LRT, Mutation Assessor, PROVEAN, VEST3, and CADD. To explore structure–activity relationships, we performed in silico docking of substrates into available 3D enzyme structures. Our findings reveal that most of the rare genetic variants caused significant functional alterations, including: (i) Likely impairments in substrate transport to the active site due to narrowing of access channels; (ii) Changes in catalytic rates; and (iii) Potential effects on substrate extrusion rates from the active site. The in silico prediction tools accurately anticipated the functional impact of 6 out of the 11 variants (54%). ConclusionsEvaluating the functionality of rare variants will become increasingly essential as rapid and cost-effective whole-genome sequencing technologies continue to advance. Our results highlight the need for further refinement of in silico prediction models, particularly those leveraging 3D crystal enzyme structures, to enhance the accuracy of functional predictions for rare genetic variants.

Distinguishing neutral from deleterious mutations in growing populations

Quantitative Imaging of Morphometric and Metabolic Signatures Reveals Heterogeneity in Drug Response of Three-Dimensional Mammary Tumor Spheroids.

Exome Sequencing Implicates an Increased Burden of Rare Potassium Channel Variants in the Risk of Drug-Induced Long QT Interval Syndrome

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans