Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

A new method to accurately identify single nucleotide variants using small FFPE breast samples.

Most tissue collections of neoplasms are composed of formalin-fixed and paraffin-embedded (FFPE) excised tumor samples used for routine diagnostics. DNA sequencing is becoming increasingly important in cancer research and clinical management; however it is difficult to accurately sequence DNA from FFPE samples. We developed and validated a new bioinformatic pipeline to use existing variant-calling strategies to robustly identify somatic single nucleotide variants (SNVs) from whole exome sequencing using small amounts of DNA extracted from archival FFPE samples of breast cancers. We optimized this strategy using 28 pairs of technical replicates. After optimization, the mean similarity between replicates increased 5-fold, reaching 88% (range 0-100%), with a mean of 21.4 SNVs (range 1-68) per sample, representing a markedly superior performance to existing tools. We found that the SNV-identification accuracy declined when there was less than 40 ng of DNA available and that insertion-deletion variant calls are less reliable than single base substitutions. As the first application of the new algorithm, we compared samples of ductal carcinoma in situ of the breast to their adjacent invasive ductal carcinoma samples. We observed an increased number of mutations (paired-samples sign test, P < 0.05), and a higher genetic divergence in the invasive samples (paired-samples sign test, P < 0.01). Our method provides a significant improvement in detecting SNVs in FFPE samples over previous approaches.

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ2 test. Results The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning. © RSNA, 2022.

Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development.

Ductal carcinoma in situ (DCIS) and invasive breast cancer share many morphologic, proteomic, and genomic alterations. Yet in contrast to invasive cancer, many DCIS tumors do not progress and may remain indolent over decades. To better understand the heterogenous nature of this disease, we reconstructed the growth dynamics of 18 DCIS tumors based on the geo-spatial distribution of their somatic mutations. The somatic mutation topographies revealed that DCIS is multiclonal and consists of spatially discontinuous subclonal lesions. Here we show that this pattern of spread is consistent with a new 'Comet' model of DCIS tumorigenesis, whereby multiple subclones arise early and nucleate the buds of the growing tumor. The discontinuous, multiclonal growth of the Comet model is analogous to the branching morphogenesis of normal breast development that governs the rapid expansion of the mammary epithelium during puberty. The branching morphogenesis-like dynamics of the proposed Comet model diverges from the canonical model of clonal evolution, and better explains observed genomic spatial data. Importantly, the Comet model allows for the clinically relevant scenario of extensive DCIS spread, without being subjected to the selective pressures of subclone competition that promote the emergence of increasingly invasive phenotypes. As such, the normal cell movement inferred during DCIS growth provides a new explanation for the limited risk of progression in DCIS and adds biologic rationale for ongoing clinical efforts to reduce DCIS overtreatment.

Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.

In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervised detection models suffer from overfitting and fail to generalize. We present a one-class, semi-supervised framework using a deep convolutional autoencoder trained with over 50,000 images from 11,000 negative-only cases. Since the model learned from only normal breast parenchymal features, calcifications produced large signals when comparing the residuals between input and reconstruction output images. As a key advancement, a structural dissimilarity index was used to suppress non-structural noises. Our selected model achieved pixel-based AUROC of 0.959 and AUPRC of 0.676 during validation, where calcification masks were defined in a semi-automated process. Although not trained directly on any cancers, detection performance of calcification lesions on 1,883 testing images (645 malignant and 1238 negative) achieved 75% sensitivity at 2.5 false positives per image. Performance plateaued early when trained with only a fraction of the cases, and greater model complexity or a larger dataset did not improve performance. This study demonstrates the potential of this anomaly detection approach to detect mammographic calcifications in a semi-supervised manner with efficient use of a small number of labeled images, and may facilitate new clinical applications such as computer-aided triage and quality improvement.

Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ.

Hypoxia promotes aggressive tumor phenotypes and mediates the recruitment of suppressive T cells in invasive breast carcinomas. We investigated the role of hypoxia in relation to T-cell regulation in ductal carcinoma in situ (DCIS). We designed a deep learning system tailored for the tissue architecture complexity of DCIS, and compared pure DCIS cases with the synchronous DCIS and invasive components within invasive ductal carcinoma cases. Single-cell classification was applied in tandem with a new method for DCIS ductal segmentation in dual-stained CA9 and FOXP3, whole-tumor section digital pathology images. Pure DCIS typically has an intermediate level of colocalization of FOXP3+ and CA9+ cells, but in invasive carcinoma cases, the FOXP3+ (T-regulatory) cells may have relocated from the DCIS and into the invasive parts of the tumor, leading to high levels of colocalization in the invasive parts but low levels in the synchronous DCIS component. This may be due to invasive, hypoxic tumors evolving to recruit T-regulatory cells in order to evade immune predation. Our data support the notion that hypoxia promotes immune tolerance through recruitment of T-regulatory cells, and furthermore indicate a spatial pattern of relocalization of T-regulatory cells from DCIS to hypoxic tumor cells. Spatial colocalization of hypoxic and T-regulatory cells may be a key event and useful marker of DCIS progression.

Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer.

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.

Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts.

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

Minimal barriers to invasion during human colorectal tumor growth.

Intra-tumoral heterogeneity (ITH) could represent clonal evolution where subclones with greater fitness confer more malignant phenotypes and invasion constitutes an evolutionary bottleneck. Alternatively, ITH could represent branching evolution with invasion of multiple subclones. The two models respectively predict a hierarchy of subclones arranged by phenotype, or multiple subclones with shared phenotypes. We delineate these modes of invasion by merging ancestral, topographic, and phenotypic information from 12 human colorectal tumors (11 carcinomas, 1 adenoma) obtained through saturation microdissection of 325 small tumor regions. The majority of subclones (29/46, 60%) share superficial and invasive phenotypes. Of 11 carcinomas, 9 show evidence of multiclonal invasion, and invasive and metastatic subclones arise early along the ancestral trees. Early multiclonal invasion in the majority of these tumors indicates the expansion of co-evolving subclones with similar malignant potential in absence of late bottlenecks and suggests that barriers to invasion are minimal during colorectal cancer growth.

Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.

OBJECTIVE: The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our clinical task is to use mammographic features to predict whether ductal carcinoma in situ (DCIS) identified at needle core biopsy will be later upstaged or shown to contain invasive breast cancer. METHODS: To improve the prediction of pure DCIS (negative) versus upstaged DCIS (positive) cases, this study considers the adjunctive roles of two related classes: atypical ductal hyperplasia (ADH), a non-cancer type of breast abnormity, and invasive ductal carcinoma (IDC), with 113 computer vision based mammographic features extracted from each case. To improve the baseline Model A's classification of pure vs. upstaged DCIS, we designed three different strategies (Models B, C, D) with different ways of embedding features or inputs. RESULTS: Based on ROC analysis, the baseline Model A performed with AUC of 0.614 (95% CI, 0.496-0.733). All three new models performed better than the baseline, with domain adaptation (Model D) performing the best with an AUC of 0.697 (95% CI, 0.595-0.797). CONCLUSION: We improved the prediction performance of DCIS upstaging by embedding two related pathology classes in different training phases. SIGNIFICANCE: The three new strategies of embedding related class data all outperformed the baseline model, thus demonstrating not only feature similarities among these different classes, but also the potential for improving classification by using other related classes.

Isolation of RNA from cell lines and cervical cytology specimens stored in BD SurePath preservative fluid and downstream detection of housekeeping gene and HPV E6 expression using real time RT-PCR.

This study was performed to demonstrate that RNA isolated from cell lines and cervical cytology specimens stored in SurePath preservative fluid would be functional in real-time RT-PCR assays. RNA was isolated from cervical cell lines or cytology samples stored in SurePath preservative at room temperature for 2-5 weeks using five commercially available RNA purification kits, three of which contain proteinases. The quality of the RNA was assessed by real time RT-PCR amplification of GAPDH, GUSB, U1A, HPV 16 and 18 E6 mRNAs. RNA was isolated successfully from cells that were stored in SurePath preservative fluid with only the three protocols that contained proteinases. GAPDH was amplified in 98-100% of the samples, GUSB in 90-98%, and the least abundant transcript, U1A, was amplified in 81-96% of the samples. HPV 16 and 18 E6 transcripts were detected in 56% of high grade, 39% of low grade and 2% of normal samples, with a concordance between DNA genotype and E6 mRNA expression of 97%. We demonstrated that RNA can be extracted from cervical cell lines and cytology specimens stored in BD SurePath preservative fluid with three different procedures that all contain proteinases. This RNA is suitable for real-time RT-PCR applications.

Isolation of RNA from residual BD SurePath liquid-based cytology specimens and detection of HPV E6/E7 mRNA using the PreTectt HPV-Proofer assay.

Infection with high-risk human papillomavirus (HPV) is known to be associated directly with the development of cervical cancer. Recent data suggests that the detection of E6/E7 mRNA from high-risk HPV types may serve as a better diagnostic method for detecting the presence of cervical pre-cancer than HPV DNA testing. This report details a commercially available nucleic acid isolation protocol which can be used to isolate reproducibly RNA from residual BD SurePath liquid-based cytology specimens stored for up to 28 days, and have demonstrated the quality and quantity of mRNA is sufficient for detection with the NorChip PreTect HPV-Proofer assay. Of the 242 specimens tested in this study, 236 (97.5%) tested positive for U1A internal control gene expression. HPV type 16, 18, 31, 33 or 45 mRNA was detected in 16/20 (80%) of the analyzed high-grade squamous intraepithelial lesion (HSIL) specimens, with a low frequency of HPV mRNA detected in the normal lesions (3%). The presence of HPV E6 expression in a subset of HPV positive specimens was also detected by real-time RT-PCR. These findings confirm that RNA of sufficient quality can be isolated from residual BD SurePath cervical cytology specimens for use in downstream NASBA and RT-PCR-based assays.

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. METHODS: In this retrospective study, digital mammographic magnification views were collected for 99 subjects with DCIS at biopsy, 25 of which were subsequently upstaged to invasive cancer. A deep convolutional neural network model that was pretrained on nonmedical images (eg, animals, plants, instruments) was used as the feature extractor. Through a statistical pooling strategy, deep features were extracted at different levels of convolutional layers from the lesion areas, without sacrificing the original resolution or distorting the underlying topology. A multivariate classifier was then trained to predict which tumors contain occult invasive disease. This was compared with the performance of traditional "handcrafted" computer vision (CV) features previously developed specifically to assess mammographic calcifications. The generalization performance was assessed using Monte Carlo cross-validation and receiver operating characteristic curve analysis. RESULTS: Deep features were able to distinguish DCIS with occult invasion from pure DCIS, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval, 0.68-0.73). This performance was comparable with the handcrafted CV features (area under the curve = 0.68; 95% confidence interval, 0.66-0.71) that were designed with prior domain knowledge. CONCLUSIONS: Despite being pretrained on only nonmedical images, the deep features extracted from digital mammograms demonstrated comparable performance with handcrafted CV features for the challenging task of predicting DCIS upstaging.

Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

RATIONALE AND OBJECTIVES: This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy. MATERIALS AND METHODS: In this retrospective study, we analyzed data from 99 subjects with DCIS (74 pure DCIS, 25 DCIS with occult invasion). We developed a computer-vision algorithm capable of extracting 113 features from magnification views in mammograms and combining these features to predict whether a DCIS case will be upstaged to invasive cancer at the time of definitive surgery. In comparison, we also built predictive models based on physician-interpreted features, which included histologic features extracted from biopsy reports and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists. The generalization performance was assessed using leave-one-out cross validation with the receiver operating characteristic curve analysis. RESULTS: Using the computer-extracted mammographic features, the multivariate classifier was able to distinguish DCIS with occult invasion from pure DCIS, with an area under the curve for receiver operating characteristic equal to 0.70 (95% confidence interval: 0.59-0.81). The physician-interpreted features including histologic features and Breast Imaging Reporting and Data System-related mammographic features assessed by two radiologists showed mixed results, and only one radiologist's subjective assessment was predictive, with an area under the curve for receiver operating characteristic equal to 0.68 (95% confidence interval: 0.57-0.81). CONCLUSIONS: Predicting upstaging for DCIS based upon mammograms is challenging, and there exists significant interobserver variability among radiologists. However, the proposed computer-extracted mammographic features are promising for the prediction of occult invasion in DCIS.

Characterization and clinical validation of MCM2 and TOP2A monoclonal antibodies in the BD ProEx™ C assay: An immunoassay which detects aberrant S-phase induction in cervical tissue.

BACKGROUND: The Papanicolaou (Pap) screen has been successful in reducing cervical cancer; but exhibits low sensitivity when detecting cervical dysplasia. Use of molecular biomarkers in Pap tests may improve diagnostic accuracy. DESIGN: Monoclonal antibodies to Minichromosome Maintenance Protein 2 (MCM2) and DNA Topoisomerase II α (TOP2A) were selected for use in IHC based on their ability to differentiate normal from diseased cervical tissues in tissue microarrays. Enhanced Green Fluorescent Protein Western blot analysis was used to help identify binding epitopes specific to MCM2 and TOP2A antibody clones. Antibody affinity was determined by solution phase affinity measurement and immunohistochemistry was performed using high affinity MCM2 or TOP2A antibodies on serial histological sections. RESULTS: Antibody clones to MCM2 and TOP2A clones were selected based on their ability to detect over expression in abnormal cervical epithelia. In IHC, MCM2-27C5.6 and MCM2-26H6.19 demonstrated superior staining in abnormal cervical tissue over the MCM2-CRCT2.1 antibody. A combination of MCM2 and TOP2A antibodies showed greater staining when compared to staining with any of the antibodies alone on serial histological sections. Distinct linear epitopes were elucidated for each of the MCM2 and TOP2A clones. Affinity values (Kd) for MCM2 or TOP2A antibodies had a similar range. In a research study, the MCM2 and TOP2A (BD ProEx™ C) antibody cocktail showed increased epithelia staining with increasing dysplasia. The use of BD ProEx™ C in combination with H&E staining enhanced immunohistochemical discrimination of dysplastic and non-dysplastic FFPE cervical tissue specimens. CONCLUSIONS: BD ProEx™ C containing MCM2 and TOP2A antibodies showed strong specific nuclear staining that correlated with increased dysplasia and lesion severity. Enhanced performance of the antibodies was linked to their unique topography recognition. BD ProEx™ C incorporates antibodies that enhance detection of CIN2+ cervical disease.

Analytical performance of RNA isolated from BD SurePath™ cervical cytology specimens by the PreTect™ HPV-Proofer assay.

Several commercial HPV ancillary tests are available for detection of E6/E7 RNA. It is not clear how storage of a cervical Pap affects the analytical and clinical performance of the PreTect™ HPV-Proofer assay. To investigate the qualitative performance of RNA extracted from BD SurePath™ liquid-based cytology (LBC) specimens for the detection of human papillomavirus (HPV) E6/E7 mRNA using the PreTect™ HPV-Proofer assay, studies including stability, reproducibility, residual specimen analysis, and storage medium comparison assays were performed. Cervical cytology specimens were collected and stored in BD SurePath™ LBC preservative fluid and/or PreTect™ Transport Media. RNA was isolated using the RecoverAll™ Total Nucleic Acid Isolation kit and RNA integrity was evaluated in the PreTect™ HPV-Proofer assay. The performance of RNA isolated from cervical cells collected and stored in BD SurePath™ LBC preservative fluid or PreTect™ Transport Media was also evaluated through a storage medium comparison study. The RNA was found to be stable for a minimum of 21 days when stored at ambient temperature and displayed high reproducibility with the mean percentage reproducibility ranging from 90.5% to 100% for the HPV types detected by the PreTect™ HPV-Proofer assay. The prevalence rate of HPV types in this study cohort was consistent with published reports. A 93.7% first pass acceptance rate was demonstrated across all cytology grades. The positive human U1 snRNP specific A protein (U1A) and HPV rate for BD SurePath™ LBC and PreTect™ Transport Media specimens was statistically equivalent for both normal and abnormal specimens. This data support the use of RNA isolated from BD SurePath™ LBC for ancillary HPV testing and demonstrates the feasibility of using BD SurePath™ preservative fluid as a specimen type with the PreTect™ HPV-Proofer assay.

Single nucleotide polymorphisms in the CYP2J2 and CYP2C8 genes and the risk of hypertension.

CYP2J2 and CYP2C8 metabolize arachidonic acid (AA) to cis-epoxyeicosatrienoic acids (EETs), which play a central role in regulating renal tubular fluid-electrolyte transport and vascular tone. We hypothesized that functionally relevant polymorphisms in the CYP2J2 or CYP2C8 genes influence hypertension risk. We examined associations between CYP2J2*7 (G-50 T promoter) and CYP2C8*3 (Arg139Lys and Lys399Arg, which are in 100% linkage disequilibrium) polymorphisms and hypertension in a biethnic population from Tennessee. CYP2J2*7 variant allele frequency was significantly higher in African-Americans versus Caucasians (14.1% versus 7.7%, P=0.01), irrespective of hypertension status. When analysed separately by race, the genotype distribution of the CYP2J2*7 variant allele was not significantly different among African-Americans with/without hypertension, but was significantly different among Caucasians with/without hypertension (P=0.03). Indeed, the odds ratio of having hypertension attributable to carrying the CYP2J2*7 variant allele adjusted for age, gender, body mass index and family history was 0.39 (95% confidence interval 0.17-0.89) among Caucasians, suggesting a protective effect. Additional subgroup analyses revealed a significantly lower CYP2J2*7 variant allele frequency in hypertensive versus normotensive Caucasian males (5.6% versus 12.5%, P=0.02) and in hypertensive versus normotensive Caucasians without a family history of hypertension (1.5% versus 11.0%, P=0.03). With respect to the CYP2C8*3 variant, genotype distribution and allele frequencies were similar between normotensive and hypertensive subjects. This study provides evidence for an association between CYP2J2*7 genotype and hypertension in Caucasian males and Caucasians without a family history of hypertension, but suggests no association between CYP2C8*3 genotype and hypertension. Confirmation of these findings in additional populations is warranted.

Cloning of CYP2J2 gene and identification of functional polymorphisms.

CYP2J2 is abundant in cardiovascular tissue and active in the metabolism of arachidonic acid to eicosanoids that possess potent anti-inflammatory, vasodilatory, and fibrinolytic properties. We cloned and sequenced the entire CYP2J2 gene (approximately 40.3 kb), which contains nine exons and eight introns. We then sequenced the CYP2J2 exons and intron-exon boundaries in 72 healthy persons representing African, Asian, and European/white populations as part of the National Institutes of Health/National Institute of Environmental Health Sciences Environmental Genome Single Nucleotide Polymorphism Program. A variety of polymorphisms were found, four of which resulted in coding changes (Arg158Cys, Ile192Asn, Asp342Asn, and Asn404Tyr). A fifth variant (Thr143Ala) was identified by screening a human heart cDNA library. All five variant cDNAs of CYP2J2 were generated by site-directed mutagenesis and expressed in Sf9 insect cells by using a baculovirus system. The recombinant wild-type and variant CYP2J2 proteins immunoreacted with peptide-based antibodies to CYP2J2 and displayed typical cytochrome P450 (P450) CO-difference spectra; however, the Asn404Tyr and Ile192Asn variants also had prominent spectral peaks at 420 nm. The ability of these variants to metabolize arachidonic acid and linoleic acid was compared with that of wild-type CYP2J2. Three variants (Asn404Tyr, Arg158Cys, and Thr143Ala) showed significantly reduced metabolism of both arachidonic acid and linoleic acid. The Ile192Asn variant showed significantly reduced activity toward arachidonic acid only. The Asp342Asn variant showed similar metabolism to wild-type CYP2J2 for both endogenous substrates. Based on these data, we conclude that allelic variants of the human CYP2J2 gene exist and that some of these variants result in a P450 protein that has reduced catalytic function. Insofar as CYP2J2 products have effects in the cardiovascular system, we speculate that these variants may be functionally relevant.