Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment.

CONFOUNDING FACTORS: In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64). ANALYSIS APPROACH: Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes. ANOVA: ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors. CONCLUSION: Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Predictive performance of a seven-plex antibody array in prenatal screening for Down Syndrome.

We evaluated the use of multiplex antibody array methodology for simultaneous measurement of serum protein markers for first trimester screening of Down Syndrome (DS) and other pregnancy outcomes such as preeclampsia. For this purpose, we constructed an antibody array for indirect ("sandwich") measurement of seven serum proteins: pregnancy-associated plasma protein-A (PAPP-A), free beta subunit of human chorionic gonadotropin (fß-hCG), alpha-fetoprotein (AFP), angiopoietin-like 3 (ANGPTL3), epidermal growth factor (EGF), insulin-like growth factor 2 (IGFII), and superoxide dismutase 1 (SOD1). This array was tested using 170 DS cases and 510 matched controls drawn during the 8th-13th weeks of pregnancy. Data were used for prediction modelling and compared to previously obtained AutoDELFIA immunoassay data for PAPP-A and fß-hCG. PAPP-A and fß-hCG serum concentrations obtained using antibody arrays were highly correlated with AutoDELFIA data. Moreover, DS prediction modeling using (log-MoMmed) antibody array and AutoDELFIA data gave comparable results. Of the other markers, AFP and IGFII showed significant changes in concentration, although adding these markers to a prediction model based on prior risk, PAPP-A and fß-hCG did not improve the predictive performance. We conclude that implementation of antibody arrays in a prenatal screening setting is feasible but will require additional first trimester screening markers.

Comparison of different blood collection, sample matrix, and immunoassay methods in a prenatal screening setting.

We compared how measurements of pregnancy-associated plasma protein A (PAPP-A) and the free beta subunit of human chorionic gonadotropin (fß-hCG) in maternal blood are influenced by different methods for blood collection, sample matrix, and immunoassay platform. Serum and dried blood spots (DBS) were obtained by venipuncture and by finger prick of 19 pregnant women. PAPP-A and fß-hCG from serum and from DBS were measured by conventional indirect immunoassay on an AutoDELFIA platform and by antibody microarray. We compared methods based on the recoveries for both markers as well as marker levels correlations across samples. All method comparisons showed high correlations for both marker concentrations. Recovery levels of PAPP-A from DBS were 30% lower, while those of fß-hCG from DBS were 50% higher compared to conventional venipuncture serum. The recoveries were not affected by blood collection or immunoassay method. The high correlation coefficients for both markers indicate that DBS from finger prick can be used reliably in a prenatal screening setting, as a less costly and minimally invasive alternative for venipuncture serum, with great logistical advantages. Additionally, the use of antibody arrays will allow for extending the number of first trimester screening markers on maternal and fetal health.

A range finding protocol to support design for transcriptomics experimentation: examples of in-vitro and in-vivo murine UV exposure.

In transcriptomics research, design for experimentation by carefully considering biological, technological, practical and statistical aspects is very important, because the experimental design space is essentially limitless. Usually, the ranges of variable biological parameters of the design space are based on common practices and in turn on phenotypic endpoints. However, specific sub-cellular processes might only be partially reflected by phenotypic endpoints or outside the associated parameter range. Here, we provide a generic protocol for range finding in design for transcriptomics experimentation based on small-scale gene-expression experiments to help in the search for the right location in the design space by analyzing the activity of already known genes of relevant molecular mechanisms. Two examples illustrate the applicability: in-vitro UV-C exposure of mouse embryonic fibroblasts and in-vivo UV-B exposure of mouse skin. Our pragmatic approach is based on: framing a specific biological question and associated gene-set, performing a wide-ranged experiment without replication, eliminating potentially non-relevant genes, and determining the experimental 'sweet spot' by gene-set enrichment plus dose-response correlation analysis. Examination of many cellular processes that are related to UV response, such as DNA repair and cell-cycle arrest, revealed that basically each cellular (sub-) process is active at its own specific spot(s) in the experimental design space. Hence, the use of range finding, based on an affordable protocol like this, enables researchers to conveniently identify the 'sweet spot' for their cellular process of interest in an experimental design space and might have far-reaching implications for experimental standardization.

Identification of interleukin-1 beta, but no other inflammatory proteins, as an early onset pre-eclampsia biomarker in first trimester serum by bead-based multiplexed immunoassays.

OBJECTIVE: This study aimed to determine the predictive value of growth factors, cardiovascular, and immunological markers for first trimester identification of early onset pre-eclampsia (PE). METHODS: In a retrospective case-control study, maternal serum samples of 35 early onset PE cases and 35 controls were analysed by multiplexed immunoassays, to determine serum concentrations of 41 proteins whose functionality can be associated with PE pathogenesis. All levels were converted into multiples of the gestation-specific normal median. For prediction modelling, proteins that were found to be significant were combined with previously obtained values of three established PE markers, that is, placental growth factor, placental protein 13, and pregnancy-associated plasma protein A. Prediction modelling was used to determine predicted detection rates for 5% and 10% false-positive rates. RESULTS: Three of the proteins examined in this study, interleukin-1 beta (IL-1ß), fibrinogen, and carcinoembryonic antigen, showed significantly different serum levels at p < 0.05. In prediction modelling, only IL-1ß added predictive value to the three previously established biomarkers, by increasing detection from 38.2% to 44.1% at a 5% false-positive rate. CONCLUSIONS: This study indicates that IL-1ß has potential to improve first trimester prediction of pre-eclampsia. Studies on larger cohorts will be needed to validate these findings.

Lasting effects on body weight and mammary gland gene expression in female mice upon early life exposure to n-3 but not n-6 high-fat diets.

Exposure to an imbalance of nutrients prior to conception and during critical developmental periods can have lasting consequences on physiological processes resulting in chronic diseases later in life. Developmental programming has been shown to involve structural and functional changes in important tissues. The aim of the present study was to investigate whether early life diet has a programming effect on the mammary gland. Wild-type mice were exposed from 2 weeks prior to conception to 6 weeks of age to a regular low-fat diet, or to high-fat diets based on either corn oil or flaxseed oil. At 6 weeks of age, all mice were shifted to the regular low-fat diet until termination at 10 weeks of age. Early life exposure to a high-fat diet, either high in n-6 (corn oil) or in n-3 (flaxseed oil) polyunsaturated fatty acids, did not affect birth weight, but resulted in an increased body weight at 10 weeks of age. Transcriptome analyses of the fourth abdominal mammary gland revealed differentially expressed genes between the different treatment groups. Exposure to high-fat diet based on flaxseed oil, but not on corn oil, resulted in regulation of pathways involved in energy metabolism, immune response and inflammation. Our findings suggest that diet during early life indeed has a lasting effect on the mammary gland and significantly influences postnatal body weight gain, metabolic status, and signaling networks in the mammary gland of female offspring.

A bead-based multiplexed immunoassay to evaluate breast cancer biomarkers for early detection in pre-diagnostic serum.

This study investigates whether a set of ten potential breast cancer serum biomarkers and cancer antigens (osteopontin (OPN), haptoglobin, cancer antigen 15-3 (CA15-3), carcinoembryonic antigen (CEA), cancer antigen 125 (CA-125), prolactin, cancer antigen 19-9 (CA19-9), α-fetoprotein (AFP), leptin and migration inhibitory factor (MIF)) can predict early stage breast cancer in samples collected before clinical diagnosis (phase III samples). We performed a nested case-control study within the Prospect-EPIC (European Prospective Investigation into Cancer and nutrition) cohort. We examined to what extent the biomarker panel could discriminate between 68 women diagnosed with breast cancer up to three years after enrollment and 68 matched healthy controls (all 56-64 years at baseline). Using a quantitative bead-based multiplexed assay, we determined protein concentrations in serum samples collected at enrollment. Principal Component Analysis (PCA) and Random Forest (RF) analysis revealed that on the basis of all ten proteins, early cases could not be separated from controls. When we combined serum protein concentrations and subject characteristics related to breast cancer risk in the RF analysis, this did not result in classification accuracy scores that could correctly classify the samples (sensitivity: 50%, specificity: 50%). Our findings indicate that this panel of selected tumor markers cannot be used for diagnosis of early breast cancer.

Biomarker discovery using a comparative omics approach in a mouse model developing heterogeneous mammary cancer subtypes.

Identification of biomarkers for early breast cancer detection in blood is a challenging task, since breast cancer is a heterogeneous disease with a wide range of tumor subtypes. This is envisioned to result in differences in serum protein levels. The p53(R270H/+) WAPCre mouse model is unique in that these mice spontaneously develop both ER- and ER+ tumors, in proportions comparable to humans. Therefore, these mice provide a well-suited model system to identify human relevant biomarkers for early breast cancer detection that are additionally specific for different tumor subtypes. Mammary gland tumors were obtained from p53(R270H/+) WAPCre mice and cellular origin, ER, and HER2 status were characterized. We compared gene expression profiles for tumors with different characteristics versus control tissue, and determined genes differentially expressed across tumor subtypes. By using literature data (Gene Ontology, UniProt, and Human Plasma Proteome), we further identified protein candidate biomarkers for blood-based detection of breast cancer. Functional overrepresentation analysis (using Gene Ontology, MSigDB, BioGPS, Cancer GeneSigDB, and proteomics literature data) showed enrichment for several processes relevant for human breast cancer. Finally, Human Protein Atlas data were used to obtain a prioritized list of 16 potential biomarkers that should facilitate further studies on blood-based breast cancer detection in humans.

Quantitative performance of antibody array technology in a prenatal screening setting.

BACKGROUND: Antibody microarrays (Ab-array) represent a new, innovative proteomics platform for high-throughput protein expression profiling in body fluids. Because they allow for multiplexed measurements in small sample volumes, Ab-arrays are an interesting alternative to conventional indirect sandwich immunoassay (ELISA or DELFIA) tests in clinical or population screening if sets of markers are to be analyzed simultaneously. However, to allow implementation of Ab-arrays in clinical or population screening programs, it is of vital importance to establish that this method is both sensitive and quantitative. METHODS: This study developed and optimized a duplex Ab-array for pregnancy-associated plasma protein A (PAPP-A) and human chorion gonadotropin (fß-hCG), two serum biomarkers currently analyzed by conventional biochemical techniques in prenatal screening. Serum samples from pregnant women, representing the dynamic range of both markers, were analyzed on Ab-arrays, and validated to the, in prenatal screening routinely applied, AutoDelfia system. RESULTS: Two different array hybridization conditions were tested, i.e., direct and indirect labeling, of which the indirect method displayed a sensitive and quantitative performance and a low intra- and inter-assay variation. CONCLUSIONS: Taken together, these findings indicate that Ab-array technology is a promising alternative for ELISA or DELFIA in population screening programs, allowing future quantitative analysis of multiple biomarkers simultaneously in small volumes of serum.

RNA isolation for transcriptomics of human and mouse small skin biopsies.

BACKGROUND: Isolation of RNA from skin biopsies presents a challenge, due to the tough nature of skin tissue and a high presence of RNases. As we lacked the dedicated equipment, i.e. homogenizer or bead-beater, needed for the available RNA from skin isolation methods, we adapted and tested our zebrafish single-embryo RNA-isolation protocol for RNA isolation from skin punch biopsies. FINDINGS: We tested our new RNA-isolation protocol in two experiments: a large-scale study with 97 human skin samples, and a small study with 16 mouse skin samples. Human skin was sampled with 4.0 mm biopsy punches and for the mouse skin different punch diameter sizes were tested; 1.0, 1.5, 2.0, and 2.5 mm. The average RNA yield in human samples was 1.5 µg with an average RNA quality RIN value of 8.1. For the mouse biopsies, the average RNA yield was 2.4 µg with an average RIN value of 7.5. For 96% of the human biopsies and 100% of the mouse biopsies we obtained enough high-quality RNA. The RNA samples were successfully tested in a transcriptomics analysis using the Affymetrix and Roche NimbleGen platforms. CONCLUSIONS: Using our new RNA-isolation protocol, we were able to consistently isolate high-quality RNA, which is apt for further transcriptomics analysis. Furthermore, this method is already useable on biopsy material obtained with a punch diameter as small as 1.5 mm.

Integrative data mining to identify novel candidate serum biomarkers for pre-eclampsia screening.

OBJECTIVE: Pre-eclampsia (PE) is a serious complication that affects approximately 2% of pregnant women worldwide. At present, there is no sufficiently reliable test for early detection of PE in a screening setting that would allow timely intervention. To help future experimental identification of serum biomarkers for early onset PE, we applied a data mining approach to create a set of candidate biomarkers. METHODS: We started from the disease etiology, which involves impaired trophoblast invasion into the spiral arteries. On the basis of this, we used a three-stage filtering strategy consisting of selection of tissue-specific genes, textmining for further gene prioritization, and identifying blood-detectable markers. RESULTS: This approach resulted in 38 candidate biomarkers. These include the best three first-trimester serum biomarkers for PE found to date LGALS13 (placental protein 13, PP13), PAPPA (pregnancy-associated plasma protein-A, PAPP-A), and PGF (placental growth factor, PlGF), as well as five proteins previously identified as biomarker after the first-trimester or disease onset. This substantiates the effectiveness of our approach and provides an important indication that the list will contain several new biomarkers for PE. CONCLUSIONS: We anticipate this list can serve in prioritization of future experimental studies on serum biomarkers for early onset PE.

Gene expression profiling in a mouse model identifies fetal liver- and placenta-derived potential biomarkers for Down Syndrome screening.

BACKGROUND: As a first step to identify novel potential biomarkers for prenatal Down Syndrome screening, we analyzed gene expression in embryos of wild type mice and the Down Syndrome model Ts1Cje. Since current Down Syndrome screening markers are derived from placenta and fetal liver, these tissues were chosen as target. METHODOLOGY/PRINCIPAL FINDINGS: Placenta and fetal liver at 15.5 days gestation were analyzed by microarray profiling. We confirmed increased expression of genes located at the trisomic chromosomal region. Overall, between the two genotypes more differentially expressed genes were found in fetal liver than in placenta. Furthermore, the fetal liver data are in line with the hematological aberrations found in humans with Down Syndrome as well as Ts1Cje mice. Together, we found 25 targets that are predicted (by Gene Ontology, UniProt, or the Human Plasma Proteome project) to be detectable in human serum. CONCLUSIONS/SIGNIFICANCE: Fetal liver might harbor more promising targets for Down Syndrome screening studies. We expect these new targets will help focus further experimental studies on identifying and validating human maternal serum biomarkers for Down Syndrome screening.

Identification of breast cancer biomarkers in transgenic mouse models: A proteomics approach.

PURPOSE: Transgenic mouse models for cancer circumvent many challenges that hamper human studies aimed at biomarker discovery. Lower biological variances among mice combined with controllable factors such as food uptake and health status may enable the detection of more subtle protein expression differences. This is envisioned to result in the identification of biomarkers better discriminating cancer cases from controls. EXPERIMENTAL DESIGN: The current study used two innovative mouse models for breast-cancer to identify new serum biomarkers. Multi-analyte profiling technique was used to analyze 70 proteins in individual serum samples of non-tumor and mammary tumor-bearing Tg.NK (MMTV/c-neu) mice. RESULTS: A small set of proteins fully differentiated tumor samples from controls. These comprised osteopontin, interleukin-18, cystatin C and CD40 antigen. Comparison of protein expression in another breast-cancer mouse model, the humanized p53.R270H mice, showed common discriminatory expression of osteopontin. However, other biomarkers showed distinct expression in the two different breast-cancer models, indicating that different mammary tumor sub-types with respect to molecular and estrogen receptor status reveal divergent serum biomarker sets. CONCLUSIONS AND CLINICAL RELEVANCE: The current study supports the concept that serum proteins can discriminate mammary tumor cases from controls, and yielded interesting biomarkers that need further testing and validation in human studies.

Mouse models for the p53 R72P polymorphism mimic human phenotypes.

The p53 tumor suppressor gene contains a common single nucleotide polymorphism (SNP) that results in either an arginine or proline at position 72 of the p53 protein. This polymorphism affects the apoptotic activity of p53 but the mechanistic basis and physiologic relevance of this phenotypic difference remain unclear. Here, we describe the development of mouse models for the p53 R72P SNP using two different approaches. In both sets of models, the human or humanized p53 proteins are functional as evidenced by the transcriptional induction of p53 target genes in response to DNA damage and the suppression of early lymphomagenesis. Consistent with in vitro studies, mice expressing the 72R variant protein (p53R) have a greater apoptotic response to several stimuli compared with mice expressing the p53P variant. Molecular studies suggest that both transcriptional and nontranscriptional mechanisms may contribute to the differential abilities of the p53 variants to induce apoptosis. Despite a difference in the acute response to UV radiation, no difference in the tumorigenic response to chronic UV exposure was observed between the polymorphic mouse models. These findings suggest that under at least some conditions, the modulation of apoptosis by the R72P polymorphism does not affect the process of carcinogenesis.

Down syndrome screening: imagining the screening test of the future.

Prenatal screening for Down syndrome (DS) is performed by risk calculation based on biochemical and biometric parameters. This way, approximately 75-85% of all DS cases can be detected. A way to improve detection rates is to search for new screening markers. Since the majority of biomarkers used in current DS screening are predominantly produced by the placenta, and the presence of an extra chromosome (as in DS) complicates placental development and function, it is plausible to assume that new potential screening markers may also originate from the placenta. Any alterations in these markers can be attributed to abnormal placental development and function. This article focuses on normal early placental development and function compared with that in DS pregnancies. Using this knowledge, we reason towards candidate biomarkers that may be useful in screening for DS.

Serious complications in gene-expression studies with stress perturbation: An example of UV-exposed p53-mutant mouse embryonic fibroblasts.

Reanalysis of our UV study of p53-mutant mouse embryonic fibroblasts revealed an intriguing orchestration of massive transcriptome responses. However, close scrutiny of the data uncovered an affected mRNA/rRNA ratio, effectively inhibiting valid data analysis. UV-dose range-finding showed low-dose UV specific- and high-dose stress-related responses, which represent a plea for UV dose range-finding in experimental design.

Discovery of novel serum biomarkers for prenatal Down syndrome screening by integrative data mining.

BACKGROUND: To facilitate the experimental search for novel maternal serum biomarkers in prenatal Down Syndrome screening, we aimed to create a set of candidate biomarkers using a data mining approach. METHODOLOGY/PRINCIPAL FINDINGS: Because current screening markers are derived from either fetal liver or placental trophoblasts, we reasoned that new biomarkers can primarily be found to be derived from these two tissues. By applying a three-stage filtering strategy on publicly available data from different sources, we identified 49 potential blood-detectable protein biomarkers. Our set contains three biomarkers that are currently widely used in either first- or second-trimester screening (AFP, PAPP-A and fbeta-hCG), as well as ten other proteins that are or have been examined as prenatal serum markers. This supports the effectiveness of our strategy and indicates the set contains other markers potentially applicable for screening. CONCLUSIONS/SIGNIFICANCE: We anticipate the set will help support further experimental studies for the identification of new Down Syndrome screening markers in maternal blood.

Finding transcriptomics biomarkers for in vivo identification of (non-)genotoxic carcinogens using wild-type and Xpa/p53 mutant mouse models.

The carcinogenic potential of chemicals and pharmaceuticals is traditionally tested in the chronic, 2 year rodent bioassay. This assay is not only time consuming, expensive and often with a limited sensitivity and specificity but it also causes major distress to the experimental animals. A major improvement in carcinogenicity testing, especially regarding reduction and refinement of animal experimentation, could be the application of toxicogenomics. The ultimate aim of this study is to demonstrate a proof-of-principle for transcriptomics biomarkers in various tissues for identification of (subclasses of) carcinogenic compounds after short-term in vivo exposure studies. Both wild-type and DNA repair-deficient Xpa(-/-)/p53(+/-) (Xpa/p53) mice were exposed up to 14 days to compounds of three distinct classes: genotoxic carcinogens (GTXC), non-genotoxic carcinogens (NGTXC) and non-carcinogens. Subsequently, extensive transcriptomics analyses were performed on several tissues, and transcriptomics data were screened for potential biomarkers using advanced statistical learning techniques. For all tissues analyzed, we identified multigene gene-expression signatures that are, with a high confidence, predictive for GTXC and NGTXC exposures in both mouse genotypes. Xpa/p53 mice did not perform better in the short-term bioassay. We were able to achieve a proof-of-principle for the identification and use of transcriptomics biomarkers for GTXC or NGTXC. This supports the view that toxicogenomics with short-term in vivo exposure provides a viable tool for classifying (geno)toxic compounds.

lacZ mouse embryonic fibroblasts detect both clastogens and mutagens.

The clastogenic effects of MMC and BLM and the mutagenic effects of B[a]P, N-ac-AAF and ENU were studied in mouse embryonic fibroblasts derived from wild-type (WT) and Rad54/Rad54B-deficient mice. Clastogens as well as mutagens showed a statistically significant induction of mutations in the lacZ reporter gene both in a WT and Rad54/Rad54B-deficient genetic background. Rad54/Rad54B MEFs appeared equally sensitive to the clastogens compared to WT MEFs, except for MMC. The type of mutations induced by the different compounds was investigated further by hybridizing the mutant colonies with total mouse DNA. An obvious increased number of mouse DNA positive clones was observed after BLM and MMC exposure, indicating that after these treatments genome rearrangements/translocations had occurred. In this hybridization assay, Rad54/Rad54B MEFs did not show more rearrangements/translocations than WT MEFs. As expected, the mutagens used showed no increase in chromosomal rearrangements or transloctions in MEFs derived from both genotypes. These results show that WT MEFs carrying the lacZ reporter gene on a plasmid are capable to detect both clastogenic as well as mutagenic effects of compounds in vitro. Deletion of the Rad54 and Rad54B genes did not further enhance the sensitivity of MEFs towards clastogens.

High incidence of protein-truncating TP53 mutations in BRCA1-related breast cancer.

Approximately half of all hereditary breast cancers are compromised in their DNA repair mechanisms due to loss of BRCA1 or BRCA2 function. Previous research has found a strong correlation between BRCA mutation and TP53 mutation. However, TP53 mutation status is often indirectly assessed by immunohistochemical staining of accumulated p53 protein. We sequenced TP53 exons 2 to 9 in 21 BRCA1-related breast cancers and 37 sporadic breast tumors. Strikingly, all BRCA1-related breast tumors contained TP53 mutations, whereas only half of these tumors stained positive for p53 accumulation. Positive p53 staining correlates with the presence of TP53 hotspot mutations in both BRCA1-related and sporadic breast tumors. However, whereas the majority of sporadic breast tumors that stained negative for p53 accumulation had wild-type TP53, the majority of BRCA1-associated breast tumors that stained negative for p53 accumulation had protein-truncating TP53 mutations (nonsense, frameshift, and splice mutations). Therefore, the strong selection for p53 loss in BRCA1-related tumors is achieved by an increase of protein-truncating TP53 mutations rather than hotspot mutations. Hence, immunohistochemical detection of TP53 mutation could lead to misdiagnosis in approximately half of all BRCA1-related tumors. The presence of deleterious TP53 mutations in most, if not all, BRCA1-related breast cancers suggests that p53 loss of function is essential for BRCA1-associated tumorigenesis. BRCA1-related tumors may therefore be treated not only with drugs that target BRCA1 deficiency [e.g., poly(ADP-ribose) polymerase inhibitors] but also with drugs that selectively target p53-deficient cells. This raises interesting possibilities for combination therapies against BRCA1-deficient breast cancers and BRCA1-like tumors with homologous recombination deficiency.