Genomics of breast cancer brain metastases: a meta-analysis and therapeutic implications

Genomics of breast cancer brain metastases: a meta-analysis and therapeutic implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomics of breast cancer brain metastases: a meta-analysis and therapeutic implications Thuy Thi Nguyen, Diaddin Hamdan, Eurydice Angeli, Quang Van Le, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2364912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer brain metastases are challenging daily pratice, and the biological link between gene mutations and metastatic spread to the brain remains to be determined. Here, we performed a meta-analysis on genomic data obtained from primary tumors, extracerebral metastases and brain metastases, to identify gene alterations associated with metastatic processes in the brain. Methods Articles with relevant findings were selected using Medline via PubMed, from January 1999 up to February 2022, and the algorithms were the following: ("Breast Neoplasms"[Mesh] AND "metast*" AND ("Genomics"[Mesh] OR "mutation*")), and "Breast" AND "brain" AND "metast*" AND ("Genom*" OR "mutation*" OR "sequenc*"). A critical review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA). Results Fifty-seven publications were selected for this meta-analysis, including 37,218 patients in all, 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and 1,485 brain metastasis samples. We report overall and sub-group prevalence of gene mutations, including comparisons between primary tumors, extracerebral metastases and brain metastases. In particular, we identified 6 genes with a higher mutation prevalence in brain metastases than in extracerebral metastases, with a potential role in metastatic processes in the brain: ESR1, ERBB2, EGFR, PTEN, BRCA2 and NOTCH1 . We discuss here the therapeutic implications. Conclusion Our results underline the added value of obtaining biopsies from brain metastases to fully explore their biology, to develop personalized treatments. breast cancer brain metastases genomics mutation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Brain metastases are becoming a leading cause of mortality among patients with metastatic cancers, including breast cancer. Despite therapeutic advances, breast cancer brain metastases develop in 15–25% of patients[ 1 ], with a median survival of 16 months[ 2 ]. The treatment of brain metastases remains a major therapeutic challenge, with limited indications for and benefit of curative surgery and radiation therapy[ 3 ]. Systemic drugs have limited effects on brain metastases, because most anti-cancer drugs fail to cross the blood-brain barrier[ 4 ]. Innovative approaches using physical methods or physiological transporters are being explored to facilitate drug penetration across this barrier to deliver them to brain metastases at relevant pharmacological concentrations[ 4 ]. New generation tyrosine kinase inhibitors such as osimertinib and tucatinib have been engineered to cross the blood-brain barrier more efficaciously, with greater, but time-limited, control of brain metastases[ 5 , 6 ]. Following recent improvements in the genomic sequencing of malignant tumors, targetable genetic alterations are being increasingly identified. However, most alterations have been identified in primary tumors and not metastases[ 7 ]. And this is particularly true for brain metastases, because of the difficulty accessing these localizations[ 8 ]. Although primary tumors and metastases can harbor a common genomic signature, significant discrepancies have also been identified between matched samples from a given patient, since metastases can derive from a minority clone in the primary tumor[ 9 ]. In addition, there are few studies comparing genomic data from primary tumors and metastases. Here, we performed the first meta-analysis on genomic data from breast cancer brain metastases. The integrative study we performed enabled us to identify gene alterations associated with brain metastasis, a requisite for the development of new targeted therapies for these localizations. Methods Search strategy and selection criteria We conducted this systematic review following the methods outlined by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA)[ 10 ]. Eligibility criteria Inclusion criteria Our objective was to perform a meta-analysis on genomic data obtained from breast cancer brain metastases. The inclusion criteria were: (1) any study assessing the genomics of breast cancer metastases in any localization, and (2) any article in English from 1999 to the present. Exclusion criteria The following exclusion criteria were applied: (1) studies with unusable or unavailable genomic data on metastases; (2) studies limited to genomic data from primary tumors without available genomic data on metastases; (3) genomic data obtained from samples other than tissue samples ( e.g. circulating DNA); (4) reviews, meta-analyses, letters to the editor; (5) experimental data and non-human studies, (6) articles without full text available. The PRISMA flow diagram template used in this study is detailed in Fig. 1 . Search strategy For a systematic meta-analysis, we searched MEDLINE via PubMed and used the following research algorithm: ("Breast Neoplasms"[Mesh] AND "metast*" AND ("Genomics"[Mesh] OR "mutation*")). A total of 2,776 articles were initially identified. We then tested a second algorithm to focus on brain metastases: "Breast" AND "brain" AND "metast*" AND ("Genom*" OR "mutation*" OR "sequenc*"). We obtained 631 publications. We manually searched the reference lists of all included articles to identify any potentially related articles. Zotero software version 5.0.95.1 was used to manage the references and remove any duplicates. In addition, the references contained in the literature searched and relevant reviews were also considered to avoid eligible articles being missed. Study selection Two authors (TTN and GB) independently screened the papers retrieved, initially by title, then by abstract, and finally by full text. Protocol and registration We registered the review in PROSPERO, an international prospective register of systematic reviews. The protocol can be accessed at: https://www.crd.york.ac.uk/PROSPERO/ Registration number: CRD42021272358 Quality assessment To assess the quality of studies, we used the Q-genie tool. It consists of 11 questions addressing the following aspects of the study methodology: rationale for the study, selection and outcomes, comparability of comparison groups, technical and non-technical exposure, bias, sample size and power, analyses, statistical methods, control for confounders, inferences for genetic analyses and inferences from results. Each question was scored from 1–7 as follows: "1 (poor)", "2", "3 (good)", "4", "5 (very good)", "6" or "7 (excellent)". For studies with a control group, a total score of ≤ 35 indicates poor quality, a score of 36–45 indicates moderate quality and a score of > 45 indicates good quality[ 11 ]. In our meta-analysis on genomic data, as the criterion “non-technical exposure” was not applicable, we considered that a total score of ≤ 28 indicated poor quality, a score between 29 and 38 indicated moderate quality and a score of > 38 indicated good quality. Statistical analysis The data was analyzed using R statistical software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org ). On the basis of the articles selected, we performed a meta-analysis (with the package “meta”) to assess the gene mutation prevalence in breast cancers according to the tumor site: primary tumor, extracerebral metastases and brain metastases. We only considered genes associated with a prevalence ≥ 1% in the primary tumor. For all studies, we assessed the gene mutation prevalence according to: i) the quality of studies: good or moderate/poor; ii) the tumoral site: primary tumor extracerebral metastases and brain metastases for all tumor samples. We also assessed the gene mutation prevalence in matched tumor samples from the same patient between primary tumor, extracerebral and brain metastases; iii) the genomic method (NGS, targeted NGS or other); iv) and the conditions of tissue preservation (fresh frozen or formalin-fixed). For each outcome, to test the sub-group differences, we used the Q-test based on analysis of variance. Here is a typical R script example of a single proportion using logit transformation and taking only random effect into account: metaprop(event = name.database $ variable.number.of.event, n = name.database $ variable.total.number.of.observations, studlab = name.database $ name.of.studies, subgroup = name.database $ variable.subgroup, sm="PLOGIT", common = FALSE, random = TRUE). We performed a pairwise comparison of gene mutation prevalence across tumor sites using z test as follows: primary tumors vs extracerebral metastases, primary tumors vs brain metastases, and extracerebral metastases vs brain metastases. We then selected all genes associated with significant differences in prevalence between subgroups and we retained only those with prevalence in each tumor site (primary, extracerebral and brain metastases) and with greater prevalence in brain metastases than in the primary tumor. On these selected genes, we performed univariate and multivariate meta-regression to take into account the effect size of tumor site, quality of studies (good vs moderate or poor), genomic method (NGS, targeted NGS or other) and tissue preservation condition (frozen or formalin-fixed). Univariate variables yielding P values under 0.20 in the univariate analysis were considered for inclusion in the multivariate analysis. Results of the meta-regression were presented as estimates (ß-coefficient) ± standard error. We assessed the heterogeneity of study results using the I2 indicator and Cochran’s Q-test. I2 values of 0%, 25%, 50% and 75% were considered to indicate absence, low, moderate and marked heterogeneity respectively. A P value ≤ 0.05 of the Q tests indicated significant heterogeneity. Due to a significant heterogeneity of gene mutation prevalence, pooled results were summarized using the random-effect model and ordered in decreasing prevalence values (%) with their 95% Confidence Interval (95%CI), including a comparison of subgroup prevalence. Graphically, the gene mutation prevalence according to the tumor site was presented as a heatmap plot. Gene mutations associated with an increased prevalence in brain metastases were presented as a bar plot of frequencies ordered in decreasing values in primary tumors. Copy-number alterations (LOH, material gain) were also presented as a bar plot of frequencies. All tests were two-tailed and the threshold for statistical significance was set at a P- value of under 0.05. This is a meta-analysis on published data, so the ethical approval was not sought. Results Study selection, characteristics and quality assessment After the literature search and removal of duplicate articles, we identified 3,394 studies. By careful selection on titles and abstracts, 3,337 studies were excluded, mainly because genomic data was not available. A total of 57 studies were finally included in this meta-analysis (Figure 1). The characteristics of the 57 studies selected are summarized in Table S1. Our meta-analysis included a total 37,218 patients with a median age at diagnosis of 53.5 years, and a total of 18,932 samples including 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and 1,485 brain metastasis samples. For the quality assessment, 29 studies (50.9%) were good quality, 25 studies (43.8%) were moderate quality, and 3 studies (5.3%) were poor quality (Table S2). However, since good quality studies included much larger numbers of tumor samples than other studies, they accounted for 97.3%, 97.5% and 85.3% of the total number of samples for primary tumors, extracerebral and brain metastases respectively. Gene mutation profiles in breast cancer brain metastases Using a threshold of 1% for mutation prevalence in the primary tumor, we identified 53 genes. Considering all pooled tumor samples, we first compared gene mutation prevalence for these 53 genes between studies of good quality and studies of moderate/poor quality. For 16 genes, the mutation prevalence was significantly higher for moderate and poor-quality studies, but with small sample numbers analyzed (Table S3). Then, we compared gene mutation prevalence according to tumor sample site: primary tumor, extracerebral metastases, and brain metastases (Figure 2A). Five of them had a mutation prevalence over 10% in all 3 types of samples: TP53, PIK3CA, MYC, KMT2C, and ATRX . In brain metastases, mutation prevalence was particularly high for TP53 (58%), FOXA1 (43%), FGFR4 (33%), BRCA2 (22%), FGFR2 (20%), BRAF (19%) and PTEN (15%). Among the 53 genes initially retained, 21 were associated with significant differences in prevalence between subgroups (Table S4). When we compared brain metastases with extracerebral metastases, the mutation prevalence was significantly higher for 10 genes. We considered that the mutation prevalence was reliable if a minimum sample number of 100 brain metastases was analyzed. We finally retained 6 genes: TP53, BRCA2, PTEN, NRAS, NOTCH1 and EGFR (Table 1, Figure 2B and Figure 3 for BRCA2, PTEN, NOTCH1 and EGFR ) . We then analyzed matched tumor samples between primary tumor, extracerebral metastases and brain metastases: most differences observed did not reach statistical significance due to small sample numbers (Table S5). When we considered the most frequently mutated genes such as TP53 , PIK3CA or BRCA2 , the gene mutation prevalence was comparable with data obtained from all pooled tumor samples (Table S4 and S5). Heterogeneity of gene mutation prevalence between studies was less marked for brain metastasis samples Among the 53 genes, there was significant heterogeneity across studies except for 26 genes in the brain metastasis samples (Table S4). To address this limitation, we performed several subgroup analyses. We first assessed prevalence of gene mutations in all samples according to the genomic analysis method (NGS, targeted NGS and others) and to tumor preservation condition (frozen vs. formalin-fixed). NGS was the main method used, for 61.4% of the studies and 91% of all tumor samples, with persistent significant heterogeneity across studies. For mutation prevalence, a significant difference across genomic analysis methods was only observed for 9 genes, including 7 genes with a higher mutation prevalence with targeted NGS than with whole exome/genome NGS (Table S6). For the conditions of tumor preservation, freezing is usually considered as the standard condition for whole genome analyses[12]. It accounted for 28% studies and only 7.1% of tumor samples, with less heterogeneity across studies. In addition, mutation prevalence was significantly higher for 16 genes when frozen condition was compared to formalin-fixed condition (Table S7). Since heterogeneity in genomic studies could be linked to tumor heterogeneity itself, we intended to assess gene mutation prevalence comparing multiple sampling with single sampling. However, data was only available for 7% of the studies, accounting for 0.5% of the total number of samples analyzed. Copy-number alterations and loss of heterozygosity in breast cancer brain metastases. For gene amplification, data was obtained for 11,950 patients and 9,286 samples. When we analyzed and compared prevalence between primary tumor, extracerebral metastases and brain metastases, most differences observed did not reach statistical significance due to small sample numbers, especially for extracerebral and brain metastases. Interestingly, for PTEN , the mutation prevalence decreased from 11% to 0% (Table S8). For loss of heterozygosity and other copy-number alterations, only 7 studies with 628 patients and 614 samples provided data. Due to the small number of samples analyzed for each gene and each tumor site, we were not able to perform reliable statistical comparisons (Table S9). Interestingly, in the 3 loci 9p21.3, 10q23.31 and 17q11.2, the prevalence of LOH increased between the extracerebral metastases and brain metastases. For example, for the 10q23.31 locus, comprising PTEN, the prevalence in brain metastases was 75%, but in only 10 brain samples (Figure 2C). ESR1, ERBB2, EGFR, PTEN, BRCA2, and NOTCH1 mutations could be linked to metastatic processes in the brain. For the 6 genes with a minimum sample number of 100 brain metastases analyzed and a mutation prevalence that was higher in brain metastasis samples than in extracerebral metastases, we ran univariate and multivariate meta-regressions to determine whether gene mutation prevalence was associated with sample subgroups. Focusing on brain metastasis localizations, we showed that they significantly influenced mutation prevalence for 4 genes: EGFR, PTEN, BRCA2 and NOTCH1 (Table 2). We also decided to retain ESR1 and ERBB2, since their mutation prevalence increased gradually from primary tumor, to extracerebral metastases and brain metastases (Table S4, Figure 2B and Figure 3), an observation that may have therapeutic implications. For these 6 genes, we produced a cartography of the mutations reported in our meta-analysis (Figure 4). Overall, some gene mutations could be linked to the breast cancer brain metastatic process, with strong therapeutic implications. Discussion Here, we report the first meta-analysis of genomic data concerning 37,218 patients with metastatic breast cancers, including 1,485 brain metastasis samples. We have recently shown that this methodological approach provides more reliable gene mutation prevalence values than data obtained from individual sources[ 7 ]. In addition, the stringent methodology we have used is a strength of our study, with two complementary search algorithms, careful selection of the studies, quality control of the studies, and an approach to explaining heterogeneity across subgroups. In a recent review on genomic data for 164 breast cancer brain metastases, gene mutation prevalence for the two most frequently mutated genes, TP53 and PIK3CA , were similar to those in our meta-analysis[ 13 ]. In contrast, there were many discrepancies for the other genes. In that review, the mutation prevalence was 4% for PTEN and BRCA2 , compared to 15% and 22% respectively in our meta-analysis, in which a much larger number of brain metastasis samples was analyzed. Our meta-analysis highlights the need to sequence brain metastases, and thus to obtain tissue samples, which could be facilitated by the use of imagery-guided biopsies[ 8 ]. Our meta-analysis has also shown the added value of using targeted NGS to provide reliable data on gene mutation prevalence. Compared with whole exome/genome sequencing, targeted NGS is faster and less costly with a greater sensitivity to detect mutations with low allelic frequency[ 14 ]. This type of approach could be proposed to sequence a panel of genes with therapeutic implications on brain metastasis samples. One strong added value of our meta-analysis is that we compared genomic data obtained from brain metastases, extracerebral metastases and primary tumors. Metastatic cells can derive from minority clones within a primary tumor[ 9 ], but also from minority clones within extracerebral metastases[ 15 , 16 ]. Biological factors associated with increased risk of brain metastases are not fully understood. In particular, there is no data demonstrating that some gene mutations could be responsible for the crossing of the blood-brain barrier by cancer cells. In our meta-analysis, we identified 6 genes with high mutation prevalence in brain metastases, of particular interest for their potential role in brain metastatic process and resistance to first-line anti-cancer drugs: ESR1, ERBB2, EGFR, PTEN, BRCA2 and NOTCH1 (Fig. 5 ). ESR1 encodes for the estrogen receptor 1 protein. After estrogen binding, ESR1 translocate to the nucleus, and binds to estrogen receptor elements in enhancer regions of the genome, mediating gene transcription during normal physiological processes, but also in the course of breast cancer tumorigenesis[ 17 ]. Activating mutations in the ligand-binding domain of ESR1 have been observed in 10–40% of metastatic ER + breast cancers, conferring endocrine resistance[ 17 , 18 ]. ERBB2 is a proto-oncogene encoding a member of the epidermal growth factor receptor family. ERBB2 amplification is common in different cancer types[ 19 , 20 ]. ERBB2 mutations are less common, with prevalence ranging from 0.2–12.6%[ 21 ] , [ 22 ]. In breast cancer, ERBB2 mutations have been described in all histological subtypes, usually in the absence of ERBB2 amplification[ 23 , 24 ]. In preclinical studies, ERBB2 mutations located in the extracellular and the C-terminal domains, are usually predictive of sensitivity to trastuzumab, whereas most mutations in the tyrosine kinase domain are resistant mutations. For example, pL755P/S mutations, common in breast cancer, are associated with resistance to lapatinib, an anti-HER2 TKI. In contrast, the A775_776insYVMA mutation, frequently identified in lung cancer brain metastases, is associated with response to afatinib and neratinib[ 21 ] , [ 25 ]. EGFR is a frequently altered oncogene. EGFR activation, through either amplification or mutation, in turn activates numerous downstream signal transduction pathways including the Ras-Raf-MAPK and PI3K/Akt pathways[ 26 ]. EGFR mutations, usually ligand-independent activating mutations, are well-known oncogenic events in non-small-cell lung cancers[ 16 , 27 ]. In addition, in 384 patients with non-small-cell lung cancer, the incidence of brain metastases was 49.5% among patients with L858R EGFR- mutated cancer versus 27.3% among those with wild-type cancer[ 28 ]. PTEN is a tumor suppressor gene. The PTEN protein is mainly involved in the blockade of PI3K/Akt signaling originating from EGFR activation. Inactivating PTEN mutations have been identified in many cancer types, particularly endometrial carcinomas and glioblastomas[ 29 , 30 ]. In 56 brain metastases from different cancer types, the prevalence of PTEN loss was very high in case of lung and breast cancers, sometimes combining LOH and an inactivating mutation, suggesting that PTEN loss of function could contribute to brain metastatic processes[ 31 ]. Loss of PTEN was also found to predict trastuzumab resistance among breast cancer patients[ 32 ]. BRCA2 is also a tumor suppressor gene. The BRCA2 protein plays an important role in DNA repair and transcription regulation. BRCA2 germline mutations are associated with an increased risk of breast, ovarian, and pancreas cancers[ 33 , 34 ]. In breast cancer, BRCA2 germline mutations have been found to be significantly associated with brain metastasis, regardless of tumor subtype[ 35 ]. NOTCH1 encodes a trans-membrane receptor that belongs to a well-conserved signalling pathway. When NOTCH1 is activated, it splits to form an extracellular domain and an intracellular domain, itself translocated to the nucleus to regulate the transcription of target genes. Activating NOTCH1 mutations have been identified in different cancer types[ 36 ]. In preclinical models of breast cancer, NOTCH1 signalling pathway activation has been associated with an increased risk of brain metastases[ 37 , 38 ]. These genes have also considerable potential therapeutic implications. For ESR1 mutations, estrogen receptor antagonists such as fulvestrant appear broadly effective in vitro, in particular the D538G mutant[ 39 ]. In mice, the combination of fulvestrant and palbociclib or everolimus inhibits tumor growth in breast cancers harboring a D538G or Y537S ESR1 mutations[ 40 ]. In patients with endocrine-resistant breast cancer, the same combinations were efficacious[ 41 ]. Other ESR1 targets such as lasofoxifene and H3B-5942 have proved superior to fulvestrant in inhibiting metastatic processes in breast cancer xenografts harboring Y537S and D538G ESR1 mutants[ 42 , 43 ]. Their benefit for the treatment of women with ER-positive breast cancer with acquired ESR1 mutations are currently being assessed in clinical trials[ 44 , 45 ]. In ERBB2 -mutated cancers, the benefit of anti-HER2 TKIs has been evaluated. In a phase II trial involving 125 patients with ERBB2 mutations across 21 cancer types, treatment with neratinib, a pan HER2-TKI, provided a 24% response rate among breast cancer patients with ERBB2 S310, L755, V777, G778_P780dup and Y772_A775dup mutations[ 46 ]. Among 16 patients with ERBB2 -mutated cervical cancers, response to neratinib was linked to the pS310F mutation[ 47 ]. Typically, EGFR mutations are associated with high response rates to anti-EGFR TKIs in metastatic non-small-cell lung cancer. This is also true for brain localizations, with response rates ranging from 36.5–91%[ 48 , 49 ]. In particular, osimertinib, a third-generation anti-EGFR, has better brain penetration, with response rates of over 70%[ 49 ]. In a preclinical study, osimertinib also showed marked efficacy in EGFR -mutated glioblastoma[ 50 ]. For a metastatic breast cancer patient with EGFR L861Q mutation in a resort situation, treatment with anti-EGFR provided 6 months disease control[ 51 ]. The PI3K/AKT/mTOR pathway is frequently activated in breast cancer brain metastases due to PTEN loss of function and frequent PIK3CA, AKT and mTOR activating mutations, as evidenced in our meta-analysis. In a preclinical model of brain xenografts derived from of HER2-overexpressing breast cancer with PTEN loss, a combination of PI3K and mTOR inhibitors considerably inhibited tumor growth[ 52 ]. Since loss of PTEN decreases homologous recombination and sensitizes tumor cells to polyadenosine diphosphate ribose polymerase (PARP) inhibitors, a combination of PARP-inhibitor with PIK3-inhibitor could be promising for cancers with PTEN loss of function mutations[ 53 ]. PARP inhibitors are currently approved for the treatment of several metastatic cancers with BRCA mutations[ 54 – 56 ]. Since brain metastases occur in approximately half the patients with advanced breast cancer with BRCA mutations[ 35 ], and since BRCA2 mutation prevalence reached 22% in our meta-analysis, PARP inhibitors could be a promising target for the treatment of brain metastases. Indeed, for a woman with brain metastases of endometrial cancer origin and with a circulating BRCA1 mutation, treatment with a PARP inhibitor provided excellent response in brain localizations[ 57 ]. In preclinical models of triple-negative breast cancer brain metastases, carboplatin in combination with veliparib, a PARP inhibitor, decreased tumor volume in the BRCA -mutant[ 58 ]. An ongoing clinical trial is testing the combination of cisplatin and veliparib for breast cancer brain metastases harboring BRCA mutations[ 59 ]. Finally, the NOTCH pathway is frequently activated in metastatic cancers, leading to the development of NOTCH-targeted therapies[ 60 , 61 ]. In preclinical models of breast cancer, γ-secretase inhibitors showed promising activity in brain metastases[ 37 , 38 ]. Our meta-analysis has some limitations. First, it was performed on aggregated data and not individual data. For this reason, some subgroup analyses could not be assessed because of missing data (data on ethnicity, histological subtypes). Second, there was significant heterogeneity across studies for mutation prevalence, usually persisting despite various subgroup analyses to adress this limitation. However, this heterogeneity disappeared for most genes when we considered solely brain metastasis samples. According to the seed and soil hypothesis, brain metastases can derive from a minority clone within a primary tumor or from another metastatic localization[ 9 , 62 ] with possible specific signatures linked to organ-specific metastatic sites[ 63 ]. This highlights the need for biopsy and brain metastasis analyses. This is a first meta-analysis of genomic alterations in breast cancer brain metastases. Our results underline the added value of obtaining biopsies from brain metastases to fully explore their biology, for the development of personalized treatments. Declarations Ethical a pproval Not applicable Availability of data and materials Any additional dataset other than cited published data were available upon request to the corresponding author. Declaration of interests We declare no competing interests Funding This meta-analysis funded by University Sorbonne Paris Nord International Scholarship, Erasmus+ kit mobility. The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Contributors TTN and GB conceptualized and designed the study, and have collected and analyzed the data. GB and QVL have administrated support. DH has collected and analyzed the data. ER has analyzed the data. FP has analyzed statistical. 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Golan T, Hammel P, Reni M, Van Cutsem E, Macarulla T, Hall MJ, et al. Maintenance Olaparib for Germline BRCA-Mutated Metastatic Pancreatic Cancer. N Engl J Med. Massachusetts Medical Society; 2019;381:317–27. de Bono J, Mateo J, Fizazi K, Saad F, Shore N, Sandhu S, et al. Olaparib for Metastatic Castration-Resistant Prostate Cancer. N Engl J Med. Massachusetts Medical Society; 2020;382:2091–102. Wang Q, Zhang F, Gao H, Xu Y. Successful treatment of a patient with brain metastases from endometrial cancer using Niraparib: a case report. Ann Palliat Med. 2021;10:818–27. Karginova O, Siegel MB, Van Swearingen AED, Deal AM, Adamo B, Sambade MJ, et al. Efficacy of carboplatin alone and in combination with ABT888 in intracranial murine models of BRCA-mutated and BRCA-wild-type triple negative breast cancer. Mol Cancer Ther. 2015;14:920–30. National Cancer Institute (NCI). Phase II Randomized Placebo-Controlled Trial of Cisplatin With or Without ABT-888 (Veliparib) in Metastatic Triple-Negative Breast Cancer and/or BRCA Mutation-Associated Breast Cancer, With or Without Brain Metastases [Internet]. clinicaltrials.gov; 2022 Jan. Report No.: NCT02595905. Available from: https://clinicaltrials.gov/ct2/show/NCT02595905 Morgensztern D, Besse B, Greillier L, Santana-Davila R, Ready N, Hann CL, et al. Efficacy and Safety of Rovalpituzumab Tesirine in Third-Line and Beyond Patients with DLL3-Expressing, Relapsed/Refractory Small-Cell Lung Cancer: Results From the Phase II TRINITY Study. Clin Cancer Res Off J Am Assoc Cancer Res. 2019;25:6958–66. Coleman RL, Handley KF, Burger R, Molin GZD, Stagg R, Sood AK, et al. Demcizumab combined with paclitaxel for platinum-resistant ovarian, primary peritoneal, and fallopian tube cancer: The SIERRA open-label phase Ib trial. Gynecol Oncol. 2020;157:386–91. Haffner MC, Mosbruger T, Esopi DM, Fedor H, Heaphy CM, Walker DA, et al. Tracking the clonal origin of lethal prostate cancer. J Clin Invest. 2013;123:4918–22. Brasó-Maristany F, Paré L, Chic N, Martínez-Sáez O, Pascual T, Mallafré-Larrosa M, et al. Gene expression profiles of breast cancer metastasis according to organ site. Mol Oncol. 2022;16:69–87. Tables Table 1. Pairwise comparison of prevalence of gene mutations according to the tumor site P -value for pairwise comparisons of mutation prevalence Gene Primary tumors vs extracerebral metastases Primary tumors vs brain metastases Extracerebral metastases vs brain metastases TP53 <0.0001 <0.0001 <0.0001 PIK3CA 0.06 0.04 0.33 BRCA2 0.002 <0.0001 0.002 PTEN 0.008 <0.0001 <0.0001 CDKN2B NA 0.95 NA BRCA1 <0.0001 0.002 0.72 KDM6A 0.06 0.02 0.34 NRAS <0.0001 0.35 <0.0001 NF1 <0.0001 <0.0001 0.8 ERBB3 0.65 <0.0001 0.8 NOTCH1 <0.0001 <0.0001 <0.0001 MTOR NA 0.001 NA FOXA1 <0.0001 <0.0001 <0.0001 PIK3R1 0.68 <0.0001 0.06 ARID2 0.02 0.004 0.77 ASXL1 <0.0001 <0.0001 0.05 EGFR <0.0001 <0.0001 <0.0001 MLH1 0.001 <0.0001 <0.0001 BRAF 0.0001 <0.0001 <0.0001 FGFR2 0.29 <0.0001 <0.0001 FGFR4 NA <0.0001 NA Bold = significant P -value at the threshold of 0.05, NA: not available Table 2. Univariate and multivariate meta-regression Gene Samples Univariate meta-regression Multivariate meta-regression Estimate Standard error P Estimate Standard error P TP53 Tumor site: Primary tumors 1 (ref) - Extracerebral metastases -0.42 0.27 0.11 Brain metastases 0.44 0.27 0.1 Quality of studies (good) -0.29 0.24 0.22 Method analysis : NGS 1 (ref) - - Targeted NGS 0.24 0.25 0.33 Other -0.5 0.47 0.28 Preservation (Frozen) -0.25 0.29 0.39 BRCA2 Tumor site: Primary tumors 1 (ref) - - 1 (ref) - - Extracerebral metastases -0.24 0.35 0.49 -0.12 0.41 0.77 Brain metastases 0.94 0.35 0.008 0.92 0.37 0.01 Quality of studies (good) -0.79 0.44 0.07 Method analysis: NGS 1 (ref) - - Targeted NGS -0.29 0.4 0.48 Other - - - Preservation (Frozen) -0.87 0.66 0.19 -0.59 0.58 0.31 PTEN Tumor site: Primary tumors 1 (ref) - - 1 (ref) - - Extracerebral metastases 0.46 0.32 0.15 0.47 0.29 0.1 Brain metastases 0.84 0.35 0.01 0.32 0.35 0.36 Quality of studies (good) -1.26 0.32 <0.0001 -1.21 0.37 0.01 Method analysis : NGS 1 (ref) - - Targeted NGS -0.41 0.38 0.27 Other -0.58 0.78 0.45 Preservation (Frozen) 0.88 0.37 0.01 0.23 0.36 0.52 NRAS Tumor site: Primary tumors 1 (ref) - - 1 (ref) - - Extracerebral metastases -3.81 1.12 0.0007 -3.05 0.88 0.0005 Brain metastases 0.88 0.89 0.32 0.65 0.71 0.35 Quality of studies (good) -3.42 1.11 0.002 -1.95 0.64 0.002 Method analysis: NGS 1 (ref) - - Targeted NGS - - - Other -1.62 2.38 0.49 Preservation (Frozen) - - - NOTCH1 Tumor site: Primary tumors 1 (ref) - - 1 (ref) - - Extracerebral metastases -3.55 1.18 0.002 -2.97 1.1 0.006 Brain metastases 1.46 0.87 0.09 1.5 0.76 0.04 Quality of studies (good) -1.52 1.09 0.16 -0.39 0.81 0.62 Method analysis: NGS 1 (ref) - - 1 (ref) - - Targeted NGS 1.8 0.99 0.07 1.04 0.75 0.16 Other - - - - - - Preservation (Frozen) 1.12 1.21 0.35 EGFR Tumor site: Primary tumors 1 (ref) - - 1 (ref) - - Extracerebral metastases -1.89 0.78 0.01 -1.42 0.39 0.0003 Brain metastases 1.38 0.61 0.02 1.46 0.44 0.001 Quality of studies (good) -1.83 0.77 0.01 -1.49 0.46 0.001 Method analysis: NGS 1 (ref) - - Targeted NGS 0.33 0.79 0.67 Other - - - Preservation (Frozen) -0.99 1.67 0.55 Uni- and multivariate meta-regressions were run to assess sample groups significantly associated with prevalence of gene mutations. Sample groups yielding P -values under 0.20 in the univariate analysis were considered for inclusion in the multivariate analysis. Bold = significant P value at the threshold of 0.05. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2364912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":159910350,"identity":"498d1ed2-ccfc-4a38-a262-cb25863c2e5f","order_by":0,"name":"Thuy Thi Nguyen","email":"","orcid":"","institution":"National cancer hospital","correspondingAuthor":false,"prefix":"","firstName":"Thuy","middleName":"Thi","lastName":"Nguyen","suffix":""},{"id":159910351,"identity":"859350d8-13bd-4d9d-9ab6-735d8f47c3d3","order_by":1,"name":"Diaddin Hamdan","email":"","orcid":"","institution":"Hôpital La Porte Verte","correspondingAuthor":false,"prefix":"","firstName":"Diaddin","middleName":"","lastName":"Hamdan","suffix":""},{"id":159910352,"identity":"b6886f4b-7eab-420e-ac01-e2286bc2b92d","order_by":2,"name":"Eurydice Angeli","email":"","orcid":"","institution":"Hôpital Avicenne","correspondingAuthor":false,"prefix":"","firstName":"Eurydice","middleName":"","lastName":"Angeli","suffix":""},{"id":159910353,"identity":"a4276984-c6c5-4829-83ed-145fb751ec28","order_by":3,"name":"Quang Van Le","email":"","orcid":"","institution":"National cancer hospital","correspondingAuthor":false,"prefix":"","firstName":"Quang","middleName":"Van","lastName":"Le","suffix":""},{"id":159910354,"identity":"3478457c-7181-4765-b4ad-425a5539fc59","order_by":4,"name":"Frédéric Pamoukdjian","email":"","orcid":"","institution":"Hôpital Avicenne","correspondingAuthor":false,"prefix":"","firstName":"Frédéric","middleName":"","lastName":"Pamoukdjian","suffix":""},{"id":159910355,"identity":"61e601c1-bf93-471b-8e01-c6e3ad70b3ed","order_by":5,"name":"Guilhem Bousquet","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFACxsbDIMoAxudnYGADUnL4tDSgaJGQbABrMcZrD6oWgwMEtMhHJDccLqjYxmDO3mP84W2bTZ3x8bPHHhcwGOTj0mJ4I7Hh8Iwztxkse86YSc5tS5MwO5OXbjyDwcCyAZeWGUAtvG23GQxu5Jgx87YdljA7kGMmzcPwxwCHDqiWf2Atxp952/5LGPe/AWkxwKlFXgKkpQGsxUCat+2AhIFEDn4tBjwPGw7zHLvNY9lzrExyzrlkyRk33qVJzzDAY0t7+sPHPDW35czZmzd/eFNmx8/fn3tMuqACjy0HIDQPCsnMgFMD0JYGZB5CyygYBaNgFIwCBAAAqFpVAvBwkIcAAAAASUVORK5CYII=","orcid":"","institution":"Institut National de la Santé et de la Recherche Médicale, UMR-S 942","correspondingAuthor":true,"prefix":"","firstName":"Guilhem","middleName":"","lastName":"Bousquet","suffix":""}],"badges":[],"createdAt":"2022-12-10 14:59:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2364912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2364912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30385775,"identity":"3d63256c-80f2-480f-84f6-d37d91f46c8a","added_by":"auto","created_at":"2022-12-15 16:30:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53156,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flowchart for the screening and selection of the studies.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/e000152594279cca77657f56.png"},{"id":30386037,"identity":"1202446a-cfca-476d-a04d-9139cf7c12eb","added_by":"auto","created_at":"2022-12-15 16:38:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85415,"visible":true,"origin":"","legend":"\u003cp\u003eGene mutation prevalence, loss of heterozygosity and copy number alterations in primary tumor, extracerebral metastases and brain metastases\u003c/p\u003e\n\u003cp\u003ePanel A shows a heatmap plot for the prevalence of gene mutations according to the tumor site (primary tumor, extracerebral metastases and brain metastases).\u003c/p\u003e\n\u003cp\u003ePanel B shows the increased prevalence of gene mutations in brain metastases for 8 genes: \u003cem\u003eTP53, BRCA2, PTEN, ESR1, ERBB2, NRAS, NOTCH1, EGFR\u003c/em\u003e. P-values reported here corresponded to those of Table 1 and of pairwise comparison of gene mutation prevalence between brain metastases and extracerebral metastases.\u003c/p\u003e\n\u003cp\u003ePanel C shows the main copy number alterations identified in breast cancer brain metastases.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/d50eb47b46e1c1e6950cfc92.png"},{"id":30384745,"identity":"481ed34c-b0ae-45d2-813f-88ca0feb7041","added_by":"auto","created_at":"2022-12-15 16:22:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":325109,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of mutation prevalence for 6 genes: \u003cem\u003eESR1, ERBB2, EGFR, PTEN, BRCA2\u003c/em\u003e and \u003cem\u003eNOTCH1\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/fe189368382c8f730d7639f0.png"},{"id":30384743,"identity":"83d6b00b-3edf-4b6e-8415-b95d877a81da","added_by":"auto","created_at":"2022-12-15 16:22:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87179,"visible":true,"origin":"","legend":"\u003cp\u003eCartography of the mutations reported in the meta-analysis for the 6 genes with an increased mutation prevalence in brain metastases. Mutations exclusive to brain metastases are identified in green. Those exclusive to extracerebral metastases are in red, and the mutation common to both sites are in black.\u003c/p\u003e\n\u003cp\u003eAF-1: activation function-1, AF-2: activation function-2, TM: transmembrane, JM: juxtamembrane, PBD: PIP2-binding domain, NTD: N-terminal domain, OB folds: oligonucleotide binding folds, T: tower domain, NLS: nuclear localization sequence, EGF-like repeats: epidermal growth factor-like repeats, LNR: Lin12/Notch repeat, HD-N: heterodimerization domain- N terminal, HD-C: \u0026nbsp;heterodimerization domain- C terminal, RAM: Rbp-associated molecule, TAD: transactivation domain, PEST: a region rich in prolone (P), glutamate (E), serine (S) and threonine (T)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/20ee3ed0e172e6a93e0a45d1.png"},{"id":30384748,"identity":"1a5c008b-66ea-4abf-a840-3838acda6a5b","added_by":"auto","created_at":"2022-12-15 16:22:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102554,"visible":true,"origin":"","legend":"\u003cp\u003eGene alterations and signaling pathways involved in breast cancer \u003cbr\u003e\ncarcinogenesis and brain metastatic processes.\u003c/p\u003e\n\u003cp\u003eE: estrogen, CoA: coactivators, EREs: estrogen receptor elements, NEC: Notch1 extracellular cell, NICD: Notch1 intracellular cell domain, CSL (CSL proteins): CBF-1/RBPJ-κ in Homo sapiens/Mus musculus respectively, Suppressor of Hairless in Drosophila melanogaster, Lag-1 in Caenorhabditis elegans, PCNA: proliferating cell nuclear antigen.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/7577b7d0f301ae9788cb9fdc.png"},{"id":30911335,"identity":"647658ce-d145-465f-8ef0-8dd23f571af0","added_by":"auto","created_at":"2022-12-30 02:44:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1702403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/894a96f5-3f6e-4621-8bd3-24d218f39265.pdf"},{"id":30385774,"identity":"a3b4233f-3d35-477f-9b06-f3b3be09404b","added_by":"auto","created_at":"2022-12-15 16:30:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":92726,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2364912/v1/8a4d0675cdad4a88d667692f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomics of breast cancer brain metastases: a meta-analysis and therapeutic implications","fulltext":[{"header":"Background","content":"\u003cp\u003eBrain metastases are becoming a leading cause of mortality among patients with metastatic cancers, including breast cancer. Despite therapeutic advances, breast cancer brain metastases develop in 15–25% of patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with a median survival of 16 months[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe treatment of brain metastases remains a major therapeutic challenge, with limited indications for and benefit of curative surgery and radiation therapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Systemic drugs have limited effects on brain metastases, because most anti-cancer drugs fail to cross the blood-brain barrier[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Innovative approaches using physical methods or physiological transporters are being explored to facilitate drug penetration across this barrier to deliver them to brain metastases at relevant pharmacological concentrations[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. New generation tyrosine kinase inhibitors such as osimertinib and tucatinib have been engineered to cross the blood-brain barrier more efficaciously, with greater, but time-limited, control of brain metastases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing recent improvements in the genomic sequencing of malignant tumors, targetable genetic alterations are being increasingly identified. However, most alterations have been identified in primary tumors and not metastases[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. And this is particularly true for brain metastases, because of the difficulty accessing these localizations[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although primary tumors and metastases can harbor a common genomic signature, significant discrepancies have also been identified between matched samples from a given patient, since metastases can derive from a minority clone in the primary tumor[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, there are few studies comparing genomic data from primary tumors and metastases.\u003c/p\u003e \u003cp\u003eHere, we performed the first meta-analysis on genomic data from breast cancer brain metastases. The integrative study we performed enabled us to identify gene alterations associated with brain metastasis, a requisite for the development of new targeted therapies for these localizations.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003cdiv id=\"Sec5\" class=\"Section4\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section4\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Methods","content":"\u003ch2\u003eSearch strategy and selection criteria\u003c/h2\u003e\u003cp\u003eWe conducted this systematic review following the methods outlined by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eEligibility criteria\u003c/h2\u003e\u003ch2\u003eInclusion criteria\u003c/h2\u003e\u003cp\u003e Our objective was to perform a meta-analysis on genomic data obtained from breast cancer brain metastases. The inclusion criteria were: (1) any study assessing the genomics of breast cancer metastases in any localization, and (2) any article in English from 1999 to the present.\u003c/p\u003e\u003ch2\u003eExclusion criteria\u003c/h2\u003e\u003cp\u003eThe following exclusion criteria were applied: (1) studies with unusable or unavailable genomic data on metastases; (2) studies limited to genomic data from primary tumors without available genomic data on metastases; (3) genomic data obtained from samples other than tissue samples (\u003cem\u003ee.g.\u003c/em\u003e circulating DNA); (4) reviews, meta-analyses, letters to the editor; (5) experimental data and non-human studies, (6) articles without full text available.\u003c/p\u003e\u003cp\u003eThe PRISMA flow diagram template used in this study is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eSearch strategy\u003c/h2\u003e\u003cp\u003eFor a systematic meta-analysis, we searched MEDLINE via PubMed and used the following research algorithm: (\"Breast Neoplasms\"[Mesh] AND \"metast*\" AND (\"Genomics\"[Mesh] OR \"mutation*\")). A total of 2,776 articles were initially identified. We then tested a second algorithm to focus on brain metastases: \"Breast\" AND \"brain\" AND \"metast*\" AND (\"Genom*\" OR \"mutation*\" OR \"sequenc*\"). We obtained 631 publications. We manually searched the reference lists of all included articles to identify any potentially related articles. Zotero software version 5.0.95.1 was used to manage the references and remove any duplicates. In addition, the references contained in the literature searched and relevant reviews were also considered to avoid eligible articles being missed.\u003c/p\u003e\u003ch2\u003eStudy selection\u003c/h2\u003e\u003cp\u003eTwo authors (TTN and GB) independently screened the papers retrieved, initially by title, then by abstract, and finally by full text.\u003c/p\u003e\u003ch2\u003eProtocol and registration\u003c/h2\u003e\u003cp\u003e We registered the review in PROSPERO, an international prospective register of systematic reviews. The protocol can be accessed at:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://www.crd.york.ac.uk/PROSPERO/\u003c/span\u003e \u003cspan address=\"https://www.crd.york.ac.uk/PROSPERO/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e\u003cp\u003eRegistration number: CRD42021272358\u003c/p\u003e\u003ch2\u003eQuality assessment\u003c/h2\u003e\u003cp\u003eTo assess the quality of studies, we used the Q-genie tool. It consists of 11 questions addressing the following aspects of the study methodology: rationale for the study, selection and outcomes, comparability of comparison groups, technical and non-technical exposure, bias, sample size and power, analyses, statistical methods, control for confounders, inferences for genetic analyses and inferences from results. Each question was scored from 1–7 as follows: \"1 (poor)\", \"2\", \"3 (good)\", \"4\", \"5 (very good)\", \"6\" or \"7 (excellent)\". For studies with a control group, a total score of ≤ 35 indicates poor quality, a score of 36–45 indicates moderate quality and a score of \u003cem\u003e\u0026gt;\u003c/em\u003e 45 indicates good quality[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In our meta-analysis on genomic data, as the criterion “non-technical exposure” was not applicable, we considered that a total score of ≤ 28 indicated poor quality, a score between 29 and 38 indicated moderate quality and a score of \u003cem\u003e\u0026gt;\u003c/em\u003e 38 indicated good quality.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe data was analyzed using R statistical software (version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). On the basis of the articles selected, we performed a meta-analysis (with the package “meta”) to assess the gene mutation prevalence in breast cancers according to the tumor site: primary tumor, extracerebral metastases and brain metastases. We only considered genes associated with a prevalence ≥ 1% in the primary tumor.\u003c/p\u003e\u003cp\u003e For all studies, we assessed the gene mutation prevalence according to: i) the quality of studies: good or moderate/poor; ii) the tumoral site: primary tumor extracerebral metastases and brain metastases for all tumor samples. We also assessed the gene mutation prevalence in matched tumor samples from the same patient between primary tumor, extracerebral and brain metastases; iii) the genomic method (NGS, targeted NGS or other); iv) and the conditions of tissue preservation (fresh frozen or formalin-fixed). For each outcome, to test the sub-group differences, we used the Q-test based on analysis of variance.\u003c/p\u003e\u003cp\u003eHere is a typical R script example of a single proportion using logit transformation and taking only random effect into account:\u003c/p\u003e\u003cp\u003emetaprop(event = name.database\u003cspan\u003e$\u003c/span\u003evariable.number.of.event, n = name.database\u003cspan\u003e$\u003c/span\u003evariable.total.number.of.observations, studlab = name.database\u003cspan\u003e$\u003c/span\u003ename.of.studies, subgroup = name.database\u003cspan\u003e$\u003c/span\u003evariable.subgroup, sm=\"PLOGIT\", common = FALSE, random = TRUE).\u003c/p\u003e\u003cp\u003eWe performed a pairwise comparison of gene mutation prevalence across tumor sites using z test as follows: primary tumors vs extracerebral metastases, primary tumors vs brain metastases, and extracerebral metastases vs brain metastases. We then selected all genes associated with significant differences in prevalence between subgroups and we retained only those with prevalence in each tumor site (primary, extracerebral and brain metastases) and with greater prevalence in brain metastases than in the primary tumor. On these selected genes, we performed univariate and multivariate meta-regression to take into account the effect size of tumor site, quality of studies (good \u003cem\u003evs\u003c/em\u003e moderate or poor), genomic method (NGS, targeted NGS or other) and tissue preservation condition (frozen or formalin-fixed). Univariate variables yielding \u003cem\u003eP\u003c/em\u003e values under 0.20 in the univariate analysis were considered for inclusion in the multivariate analysis. Results of the meta-regression were presented as estimates (ß-coefficient) ± standard error.\u003c/p\u003e\u003cp\u003eWe assessed the heterogeneity of study results using the I2 indicator and Cochran’s Q-test. I2 values of 0%, 25%, 50% and 75% were considered to indicate absence, low, moderate and marked heterogeneity respectively. A \u003cem\u003eP\u003c/em\u003e value ≤ 0.05 of the Q tests indicated significant heterogeneity. Due to a significant heterogeneity of gene mutation prevalence, pooled results were summarized using the random-effect model and ordered in decreasing prevalence values (%) with their 95% Confidence Interval (95%CI), including a comparison of subgroup prevalence.\u003c/p\u003e\u003cp\u003e Graphically, the gene mutation prevalence according to the tumor site was presented as a heatmap plot. Gene mutations associated with an increased prevalence in brain metastases were presented as a bar plot of frequencies ordered in decreasing values in primary tumors. Copy-number alterations (LOH, material gain) were also presented as a bar plot of frequencies.\u003c/p\u003e\u003cp\u003eAll tests were two-tailed and the threshold for statistical significance was set at a \u003cem\u003eP-\u003c/em\u003evalue of under 0.05.\u003c/p\u003e\u003cp\u003eThis is a meta-analysis on published data, so the ethical approval was not sought.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStudy selection, characteristics and quality assessment\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter the literature search and removal of duplicate articles, we identified 3,394 studies. By careful selection on titles and abstracts, 3,337\u0026nbsp;studies were excluded, mainly because genomic data was not available. A total of 57 studies were finally included in this meta-analysis (Figure\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eThe characteristics of the 57 studies selected are summarized in Table S1. \u0026nbsp;Our meta-analysis included a total 37,218 patients with a median age at diagnosis of 53.5 years, and a total of 18,932 samples including 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and 1,485 brain metastasis samples.\u003c/p\u003e\n\u003cp\u003eFor the quality assessment, 29 studies (50.9%) were good quality, 25 studies (43.8%) were moderate quality, and 3 studies (5.3%) were poor quality (Table S2). However, since good quality studies included much larger numbers of tumor samples than other studies, they accounted for 97.3%, 97.5% and 85.3% of the total number of samples for primary tumors, extracerebral and brain metastases respectively.\u003c/p\u003e\n\u003ch2\u003eGene mutation\u0026nbsp;profiles in breast cancer brain metastases\u003c/h2\u003e\n\u003cp\u003eUsing a threshold of 1% for mutation prevalence in the primary tumor, we identified 53 genes. Considering all pooled tumor samples, we first compared gene mutation prevalence for these 53 genes between studies of good quality and studies of moderate/poor quality. For 16 genes, the mutation prevalence was significantly higher for moderate and poor-quality studies, but with small sample numbers analyzed (Table S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen, we compared gene mutation prevalence according to tumor sample site: primary tumor, extracerebral metastases, and brain metastases (Figure\u0026nbsp;2A). Five of them had a mutation prevalence over 10% in all 3 types of samples: \u003cem\u003eTP53, PIK3CA, MYC, KMT2C,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;ATRX\u003c/em\u003e. In brain metastases, mutation prevalence was particularly high for \u003cem\u003eTP53\u003c/em\u003e (58%),\u0026nbsp;\u003cem\u003eFOXA1\u003c/em\u003e (43%),\u0026nbsp;\u003cem\u003eFGFR4\u003c/em\u003e (33%),\u0026nbsp;\u003cem\u003eBRCA2\u003c/em\u003e (22%), \u003cem\u003eFGFR2\u003c/em\u003e (20%), \u003cem\u003eBRAF\u003c/em\u003e (19%)\u0026nbsp;and \u003cem\u003ePTEN\u003c/em\u003e (15%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 53 genes initially retained, 21 were associated with significant differences in prevalence between subgroups\u0026nbsp;(Table S4). When\u0026nbsp;we compared brain metastases with extracerebral metastases, the mutation prevalence was significantly higher for 10 genes. We considered that the mutation prevalence was reliable if a minimum sample number of 100 brain metastases was analyzed. We finally retained 6 genes: \u003cem\u003eTP53, BRCA2, PTEN, NRAS, NOTCH1\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;EGFR\u0026nbsp;\u003c/em\u003e(Table 1, Figure\u0026nbsp;2B and Figure 3 for \u003cem\u003eBRCA2, PTEN, NOTCH1\u003c/em\u003e and \u003cem\u003eEGFR\u003c/em\u003e)\u003cem\u003e.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;then\u0026nbsp;analyzed matched tumor samples between primary tumor, extracerebral metastases and\u0026nbsp;brain metastases: most differences observed\u0026nbsp;did not reach statistical significance due\u0026nbsp;to\u0026nbsp;small sample numbers\u0026nbsp;(Table S5). When we considered the most frequently mutated genes such as \u003cem\u003eTP53\u003c/em\u003e\u003cem\u003e, PIK3CA or\u003c/em\u003e\u003cem\u003e\u0026nbsp;BRCA2\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003ethe gene mutation prevalence was comparable with data obtained from all pooled tumor samples (Table S4 and S5).\u003c/p\u003e\n\u003ch2\u003eHeterogeneity of gene mutation prevalence between studies was less marked for brain metastasis samples\u003c/h2\u003e\n\u003cp\u003eAmong the 53 genes, there was significant heterogeneity across studies except for 26 genes in the brain metastasis samples (Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this limitation, we performed several subgroup analyses. We first\u0026nbsp;assessed prevalence of gene mutations in all samples according to the genomic analysis method (NGS, targeted NGS and others)\u0026nbsp;and\u0026nbsp;to tumor preservation condition (frozen vs. formalin-fixed). NGS was the main method used, for 61.4% of the studies and 91% of all\u0026nbsp;tumor samples, with persistent significant heterogeneity across studies. For mutation prevalence, a significant difference across genomic analysis methods was only observed for\u0026nbsp;9 genes, including 7 genes with a higher\u0026nbsp;mutation prevalence\u0026nbsp;with targeted NGS\u0026nbsp;than with whole exome/genome NGS (Table S6).\u003c/p\u003e\n\u003cp\u003eFor the conditions of tumor preservation, freezing is usually considered as the standard condition for whole genome analyses[12]. It accounted for 28% studies and only 7.1% of tumor samples, with less heterogeneity across studies. In addition, mutation prevalence was significantly higher for 16 genes when frozen condition was compared to formalin-fixed condition (Table S7).\u003c/p\u003e\n\u003cp\u003eSince heterogeneity\u0026nbsp;in genomic studies could be linked to tumor heterogeneity itself, we intended to assess gene mutation prevalence comparing multiple sampling with single sampling. However, data was only available for 7%\u0026nbsp;of the studies, accounting for 0.5% of the total number of samples analyzed.\u003c/p\u003e\n\u003ch2\u003eCopy-number alterations and loss of heterozygosity in breast cancer brain metastases.\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFor gene amplification, data was obtained for 11,950 patients and 9,286 samples. When we analyzed and compared prevalence between primary tumor, extracerebral metastases and\u0026nbsp;brain metastases, most differences observed\u0026nbsp;did not reach statistical significance due\u0026nbsp;to\u0026nbsp;small sample numbers, especially for extracerebral and brain metastases. Interestingly, for \u003cem\u003ePTEN\u003c/em\u003e, the mutation prevalence decreased from 11% to 0% (Table S8).\u003c/p\u003e\n\u003cp\u003eFor loss of heterozygosity and other copy-number alterations, only 7 studies with 628 patients and 614 samples provided data. Due to the small number of samples analyzed for each gene and each tumor site, we were not able to perform reliable statistical comparisons (Table S9).\u0026nbsp;Interestingly, in the 3 loci 9p21.3, 10q23.31 and 17q11.2, the prevalence of LOH increased between the extracerebral metastases and brain metastases. For example, for the 10q23.31 locus, comprising \u003cem\u003ePTEN,\u003c/em\u003e the prevalence in brain metastases was 75%, but in only 10 brain samples (Figure\u0026nbsp;2C).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eESR1, ERBB2, EGFR, PTEN,\u0026nbsp;\u003c/em\u003e\u003cem\u003eBRCA2,\u003c/em\u003e and \u003cem\u003eNOTCH1\u003c/em\u003e mutations could be linked to metastatic processes in the brain.\u003c/h2\u003e\n\u003cp\u003eFor\u0026nbsp;the 6\u0026nbsp;genes with a minimum sample number of 100 brain metastases analyzed and a mutation prevalence that was higher in brain metastasis samples than in extracerebral metastases, we ran\u0026nbsp;univariate and multivariate meta-regressions to determine whether gene mutation prevalence was associated with sample subgroups. Focusing on brain metastasis localizations, we showed that they significantly influenced mutation prevalence for 4 genes: \u003cem\u003eEGFR, PTEN, BRCA2\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;NOTCH1\u003c/em\u003e (Table 2). We\u0026nbsp;also\u0026nbsp;decided to retain \u003cem\u003eESR1\u0026nbsp;\u003c/em\u003eand \u003cem\u003eERBB2,\u003c/em\u003e since their mutation prevalence\u0026nbsp;increased gradually\u0026nbsp;from\u0026nbsp;primary tumor, to extracerebral metastases and brain metastases (Table S4, Figure 2B and Figure 3),\u0026nbsp;an observation that may have therapeutic implications. For these 6 genes, we produced a cartography of the mutations reported in our meta-analysis (Figure\u0026nbsp;4).\u003c/p\u003e\n\u003cp\u003eOverall, some gene mutations could be linked to the breast cancer brain metastatic process, with strong therapeutic implications.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere, we report the first meta-analysis of genomic data concerning 37,218 patients with metastatic breast cancers, including 1,485 brain metastasis samples. We have recently shown that this methodological approach provides more reliable gene mutation prevalence values than data obtained from individual sources[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, the stringent methodology we have used is a strength of our study, with two complementary search algorithms, careful selection of the studies, quality control of the studies, and an approach to explaining heterogeneity across subgroups. In a recent review on genomic data for 164 breast cancer brain metastases, gene mutation prevalence for the two most frequently mutated genes, \u003cem\u003eTP53\u003c/em\u003e and \u003cem\u003ePIK3CA\u003c/em\u003e, were similar to those in our meta-analysis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, there were many discrepancies for the other genes. In that review, the mutation prevalence was 4% for \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e, compared to 15% and 22% respectively in our meta-analysis, in which a much larger number of brain metastasis samples was analyzed.\u003c/p\u003e \u003cp\u003eOur meta-analysis highlights the need to sequence brain metastases, and thus to obtain tissue samples, which could be facilitated by the use of imagery-guided biopsies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our meta-analysis has also shown the added value of using targeted NGS to provide reliable data on gene mutation prevalence. Compared with whole exome/genome sequencing, targeted NGS is faster and less costly with a greater sensitivity to detect mutations with low allelic frequency[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This type of approach could be proposed to sequence a panel of genes with therapeutic implications on brain metastasis samples.\u003c/p\u003e \u003cp\u003eOne strong added value of our meta-analysis is that we compared genomic data obtained from brain metastases, extracerebral metastases and primary tumors. Metastatic cells can derive from minority clones within a primary tumor[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but also from minority clones within extracerebral metastases[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Biological factors associated with increased risk of brain metastases are not fully understood. In particular, there is no data demonstrating that some gene mutations could be responsible for the crossing of the blood-brain barrier by cancer cells.\u003c/p\u003e \u003cp\u003eIn our meta-analysis, we identified 6 genes with high mutation prevalence in brain metastases, of particular interest for their potential role in brain metastatic process and resistance to first-line anti-cancer drugs: \u003cem\u003eESR1, ERBB2, EGFR, PTEN, BRCA2\u003c/em\u003e and \u003cem\u003eNOTCH1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eESR1\u003c/em\u003e encodes for the estrogen receptor 1 protein. After estrogen binding, ESR1 translocate to the nucleus, and binds to estrogen receptor elements in enhancer regions of the genome, mediating gene transcription during normal physiological processes, but also in the course of breast cancer tumorigenesis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Activating mutations in the ligand-binding domain of \u003cem\u003eESR1\u003c/em\u003e have been observed in 10\u0026ndash;40% of metastatic ER\u0026thinsp;+\u0026thinsp;breast cancers, conferring endocrine resistance[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eERBB2\u003c/em\u003e is a proto-oncogene encoding a member of the epidermal growth factor receptor family. \u003cem\u003eERBB2\u003c/em\u003e amplification is common in different cancer types[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. \u003cem\u003eERBB2\u003c/em\u003e mutations are less common, with prevalence ranging from 0.2\u0026ndash;12.6%[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In breast cancer, \u003cem\u003eERBB2\u003c/em\u003e mutations have been described in all histological subtypes, usually in the absence of \u003cem\u003eERBB2\u003c/em\u003e amplification[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In preclinical studies, \u003cem\u003eERBB2\u003c/em\u003e mutations located in the extracellular and the C-terminal domains, are usually predictive of sensitivity to trastuzumab, whereas most mutations in the tyrosine kinase domain are resistant mutations. For example, pL755P/S mutations, common in breast cancer, are associated with resistance to lapatinib, an anti-HER2 TKI. In contrast, the A775_776insYVMA mutation, frequently identified in lung cancer brain metastases, is associated with response to afatinib and neratinib[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eEGFR\u003c/em\u003e is a frequently altered oncogene. \u003cem\u003eEGFR\u003c/em\u003e activation, through either amplification or mutation, in turn activates numerous downstream signal transduction pathways including the Ras-Raf-MAPK and PI3K/Akt pathways[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eEGFR\u003c/em\u003e mutations, usually ligand-independent activating mutations, are well-known oncogenic events in non-small-cell lung cancers[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, in 384 patients with non-small-cell lung cancer, the incidence of brain metastases was 49.5% among patients with L858R \u003cem\u003eEGFR-\u003c/em\u003emutated cancer versus 27.3% among those with wild-type cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003ePTEN\u003c/em\u003e is a tumor suppressor gene. The PTEN protein is mainly involved in the blockade of PI3K/Akt signaling originating from EGFR activation. Inactivating \u003cem\u003ePTEN\u003c/em\u003e mutations have been identified in many cancer types, particularly endometrial carcinomas and glioblastomas[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In 56 brain metastases from different cancer types, the prevalence of \u003cem\u003ePTEN\u003c/em\u003e loss was very high in case of lung and breast cancers, sometimes combining LOH and an inactivating mutation, suggesting that PTEN loss of function could contribute to brain metastatic processes[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Loss of PTEN was also found to predict trastuzumab resistance among breast cancer patients[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eBRCA2\u003c/em\u003e is also a tumor suppressor gene. The BRCA2 protein plays an important role in DNA repair and transcription regulation. \u003cem\u003eBRCA2\u003c/em\u003e germline mutations are associated with an increased risk of breast, ovarian, and pancreas cancers[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In breast cancer, \u003cem\u003eBRCA2\u003c/em\u003e germline mutations have been found to be significantly associated with brain metastasis, regardless of tumor subtype[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eNOTCH1\u003c/em\u003e encodes a trans-membrane receptor that belongs to a well-conserved signalling pathway. When NOTCH1 is activated, it splits to form an extracellular domain and an intracellular domain, itself translocated to the nucleus to regulate the transcription of target genes. Activating \u003cem\u003eNOTCH1\u003c/em\u003e mutations have been identified in different cancer types[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In preclinical models of breast cancer, NOTCH1 signalling pathway activation has been associated with an increased risk of brain metastases[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese genes have also considerable potential therapeutic implications.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eESR1\u003c/em\u003e mutations, estrogen receptor antagonists such as fulvestrant appear broadly effective in vitro, in particular the D538G mutant[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In mice, the combination of fulvestrant and palbociclib or everolimus inhibits tumor growth in breast cancers harboring a D538G or Y537S \u003cem\u003eESR1\u003c/em\u003e mutations[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In patients with endocrine-resistant breast cancer, the same combinations were efficacious[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Other ESR1 targets such as lasofoxifene and H3B-5942 have proved superior to fulvestrant in inhibiting metastatic processes in breast cancer xenografts harboring Y537S and D538G \u003cem\u003eESR1\u003c/em\u003e mutants[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Their benefit for the treatment of women with ER-positive breast cancer with acquired \u003cem\u003eESR1\u003c/em\u003e mutations are currently being assessed in clinical trials[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eERBB2\u003c/em\u003e-mutated cancers, the benefit of anti-HER2 TKIs has been evaluated. In a phase II trial involving 125 patients with \u003cem\u003eERBB2\u003c/em\u003e mutations across 21 cancer types, treatment with neratinib, a pan HER2-TKI, provided a 24% response rate among breast cancer patients with \u003cem\u003eERBB2\u003c/em\u003e S310, L755, V777, G778_P780dup and Y772_A775dup mutations[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Among 16 patients with \u003cem\u003eERBB2\u003c/em\u003e-mutated cervical cancers, response to neratinib was linked to the pS310F mutation[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTypically, \u003cem\u003eEGFR\u003c/em\u003e mutations are associated with high response rates to anti-EGFR TKIs in metastatic non-small-cell lung cancer. This is also true for brain localizations, with response rates ranging from 36.5\u0026ndash;91%[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In particular, osimertinib, a third-generation anti-EGFR, has better brain penetration, with response rates of over 70%[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In a preclinical study, osimertinib also showed marked efficacy in \u003cem\u003eEGFR\u003c/em\u003e-mutated glioblastoma[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For a metastatic breast cancer patient with \u003cem\u003eEGFR\u003c/em\u003e L861Q mutation in a resort situation, treatment with anti-EGFR provided 6 months disease control[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PI3K/AKT/mTOR pathway is frequently activated in breast cancer brain metastases due to PTEN loss of function and frequent \u003cem\u003ePIK3CA, AKT\u003c/em\u003e and \u003cem\u003emTOR\u003c/em\u003e activating mutations, as evidenced in our meta-analysis. In a preclinical model of brain xenografts derived from of HER2-overexpressing breast cancer with PTEN loss, a combination of PI3K and mTOR inhibitors considerably inhibited tumor growth[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Since loss of PTEN decreases homologous recombination and sensitizes tumor cells to polyadenosine diphosphate ribose polymerase (PARP) inhibitors, a combination of PARP-inhibitor with PIK3-inhibitor could be promising for cancers with \u003cem\u003ePTEN\u003c/em\u003e loss of function mutations[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePARP inhibitors are currently approved for the treatment of several metastatic cancers with \u003cem\u003eBRCA\u003c/em\u003e mutations[\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Since brain metastases occur in approximately half the patients with advanced breast cancer with \u003cem\u003eBRCA\u003c/em\u003e mutations[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and since \u003cem\u003eBRCA2\u003c/em\u003e mutation prevalence reached 22% in our meta-analysis, PARP inhibitors could be a promising target for the treatment of brain metastases. Indeed, for a woman with brain metastases of endometrial cancer origin and with a circulating \u003cem\u003eBRCA1\u003c/em\u003e mutation, treatment with a PARP inhibitor provided excellent response in brain localizations[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In preclinical models of triple-negative breast cancer brain metastases, carboplatin in combination with veliparib, a PARP inhibitor, decreased tumor volume in the \u003cem\u003eBRCA\u003c/em\u003e-mutant[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. An ongoing clinical trial is testing the combination of cisplatin and veliparib for breast cancer brain metastases harboring \u003cem\u003eBRCA\u003c/em\u003e mutations[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, the NOTCH pathway is frequently activated in metastatic cancers, leading to the development of NOTCH-targeted therapies[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In preclinical models of breast cancer, γ-secretase inhibitors showed promising activity in brain metastases[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur meta-analysis has some limitations. First, it was performed on aggregated data and not individual data. For this reason, some subgroup analyses could not be assessed because of missing data (data on ethnicity, histological subtypes). Second, there was significant heterogeneity across studies for mutation prevalence, usually persisting despite various subgroup analyses to adress this limitation. However, this heterogeneity disappeared for most genes when we considered solely brain metastasis samples. According to the seed and soil hypothesis, brain metastases can derive from a minority clone within a primary tumor or from another metastatic localization[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] with possible specific signatures linked to organ-specific metastatic sites[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This highlights the need for biopsy and brain metastasis analyses.\u003c/p\u003e \u003cp\u003eThis is a first meta-analysis of genomic alterations in breast cancer brain metastases. Our results underline the added value of obtaining biopsies from brain metastases to fully explore their biology, for the development of personalized treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical\u0026nbsp;a\u003c/strong\u003e\u003cstrong\u003epproval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny additional dataset other than cited published data were available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis meta-analysis funded by University Sorbonne Paris Nord International Scholarship, Erasmus+ kit mobility.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTTN and GB conceptualized and designed the study, and have collected and analyzed the data. GB and QVL have administrated support. DH has collected and analyzed the data. ER has analyzed the data. FP has analyzed statistical. TTN, FP and GB drafted the manuscript. Both authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Ms. Angela Swaine for the revision of the English language\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCagney DN, Martin AM, Catalano PJ, Redig AJ, Lin NU, Lee EQ, et al. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro-Oncol. 2017;19:1511\u0026ndash;21.\u003c/li\u003e\n\u003cli\u003eSperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, et al. 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Lasofoxifene as a potential treatment for therapy-resistant ER-positive metastatic breast cancer. Breast Cancer Res BCR. 2021;23:54.\u003c/li\u003e\n\u003cli\u003ePuyang X, Furman C, Zheng GZ, Wu ZJ, Banka D, Aithal K, et al. Discovery of Selective Estrogen Receptor Covalent Antagonists for the Treatment of ER\u0026alpha;WT and ER\u0026alpha;MUT Breast Cancer. Cancer Discov. 2018;8:1176\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eTeam E. Lasofoxifene Alone or Combined with a CDK Inhibitor may Limits Progression in AI Resistant Tumors - Onco\u0026rsquo;Zine [Internet]. 2022 [cited 2022 May 1]. Available from: https://www.oncozine.com/lasofoxifene-alone-or-combined-with-a-cdk-inhibitor-may-limits-progression-in-ai-resistant-tumors/, https://www.oncozine.com/lasofoxifene-alone-or-combined-with-a-cdk-inhibitor-may-limits-progression-in-ai-resistant-tumors/\u003c/li\u003e\n\u003cli\u003eH3 Biomedicine Inc. A Phase 1-2 Multicenter, Open Label Trial of H3B-6545, a Covalent Antagonist of Estrogen Receptor Alpha, in Women With Locally Advanced or Metastatic Estrogen Receptor-positive, HER2 Negative Breast Cancer [Internet]. clinicaltrials.gov; 2022 Apr. Report No.: NCT03250676. Available from: https://clinicaltrials.gov/ct2/show/NCT03250676\u003c/li\u003e\n\u003cli\u003eHyman DM, Piha-Paul SA, Won H, Rodon J, Saura C, Shapiro GI, et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature. Nature Publishing Group; 2018;554:189\u0026ndash;94.\u003c/li\u003e\n\u003cli\u003eOaknin A, Friedman CF, Roman LD, D\u0026rsquo;Souza A, Brana I, Bidard F-C, et al. Neratinib in patients with HER2-mutant, metastatic cervical cancer: Findings from the phase 2 SUMMIT basket trial. Gynecol Oncol. 2020;159:150\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eBai H, Xiong L, Han B. The effectiveness of EGFR-TKIs against brain metastases in EGFR mutation-positive non-small-cell lung cancer. OncoTargets Ther. 2017;10:2335\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eReungwetwattana T, Nakagawa K, Cho BC, Cobo M, Cho EK, Bertolini A, et al. CNS Response to Osimertinib Versus Standard Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Patients With Untreated EGFR-Mutated Advanced Non\u0026ndash;Small-Cell Lung Cancer. J Clin Oncol. Wolters Kluwer; 2018;36:3290\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eChagoya G, Kwatra SG, Nanni CW, Roberts CM, Phillips SM, Nullmeyergh S, et al. Efficacy of osimertinib against EGFRvIII+ glioblastoma. Oncotarget. 2020;11:2074\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eSingh GK, Bajpai J, Joshi S, Prabhash K, Choughule A, Patil A, et al. Excellent response to erlotinib in breast carcinoma with rare EGFR mutation\u0026mdash;a case report. ecancermedicalscience. 2020;14:1092.\u003c/li\u003e\n\u003cli\u003eNi J, Ramkissoon SH, Xie S, Goel S, Stover DG, Guo H, et al. Combination inhibition of PI3K and mTORC1 yields durable remissions in orthotopic patient-derived xenografts of HER2-positive breast cancer brain metastases. Nat Med. 2016;22:723\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003ePhilip C-A, Laskov I, Beauchamp M-C, Marques M, Amin O, Bitharas J, et al. Inhibition of PI3K-AKT-mTOR pathway sensitizes endometrial cancer cell lines to PARP inhibitors. BMC Cancer. 2017;17:638.\u003c/li\u003e\n\u003cli\u003eGelmon KA, Tischkowitz M, Mackay H, Swenerton K, Robidoux A, Tonkin K, et al. Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: a phase 2, multicentre, open-label, non-randomised study. Lancet Oncol. Elsevier; 2011;12:852\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eGolan T, Hammel P, Reni M, Van Cutsem E, Macarulla T, Hall MJ, et al. Maintenance Olaparib for Germline BRCA-Mutated Metastatic Pancreatic Cancer. N Engl J Med. Massachusetts Medical Society; 2019;381:317\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003ede Bono J, Mateo J, Fizazi K, Saad F, Shore N, Sandhu S, et al. Olaparib for Metastatic Castration-Resistant Prostate Cancer. N Engl J Med. Massachusetts Medical Society; 2020;382:2091\u0026ndash;102.\u003c/li\u003e\n\u003cli\u003eWang Q, Zhang F, Gao H, Xu Y. Successful treatment of a patient with brain metastases from endometrial cancer using Niraparib: a case report. Ann Palliat Med. 2021;10:818\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eKarginova O, Siegel MB, Van Swearingen AED, Deal AM, Adamo B, Sambade MJ, et al. Efficacy of carboplatin alone and in combination with ABT888 in intracranial murine models of BRCA-mutated and BRCA-wild-type triple negative breast cancer. Mol Cancer Ther. 2015;14:920\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eNational Cancer Institute (NCI). Phase II Randomized Placebo-Controlled Trial of Cisplatin With or Without ABT-888 (Veliparib) in Metastatic Triple-Negative Breast Cancer and/or BRCA Mutation-Associated Breast Cancer, With or Without Brain Metastases [Internet]. clinicaltrials.gov; 2022 Jan. Report No.: NCT02595905. Available from: https://clinicaltrials.gov/ct2/show/NCT02595905\u003c/li\u003e\n\u003cli\u003eMorgensztern D, Besse B, Greillier L, Santana-Davila R, Ready N, Hann CL, et al. Efficacy and Safety of Rovalpituzumab Tesirine in Third-Line and Beyond Patients with DLL3-Expressing, Relapsed/Refractory Small-Cell Lung Cancer: Results From the Phase II TRINITY Study. Clin Cancer Res Off J Am Assoc Cancer Res. 2019;25:6958\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003eColeman RL, Handley KF, Burger R, Molin GZD, Stagg R, Sood AK, et al. Demcizumab combined with paclitaxel for platinum-resistant ovarian, primary peritoneal, and fallopian tube cancer: The SIERRA open-label phase Ib trial. Gynecol Oncol. 2020;157:386\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eHaffner MC, Mosbruger T, Esopi DM, Fedor H, Heaphy CM, Walker DA, et al. Tracking the clonal origin of lethal prostate cancer. J Clin Invest. 2013;123:4918\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eBras\u0026oacute;-Maristany F, Par\u0026eacute; L, Chic N, Mart\u0026iacute;nez-S\u0026aacute;ez O, Pascual T, Mallafr\u0026eacute;-Larrosa M, et al. Gene expression profiles of breast cancer metastasis according to organ site. Mol Oncol. 2022;16:69\u0026ndash;87.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Pairwise comparison of prevalence of gene mutations according to the tumor site\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"677\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"83.16100443131462%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value for pairwise comparisons of mutation prevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.838995568685377%\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.13293943870015%\"\u003e\n \u003cp\u003ePrimary tumors \u003cem\u003evs\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eextracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.156573116691284%\"\u003e\n \u003cp\u003ePrimary tumors \u003cem\u003evs\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ebrain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtracerebral metastases \u003cem\u003evs\u003c/em\u003e brain metastases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003ePIK3CA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eCDKN2B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eBRCA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eKDM6A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNRAS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eNF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eERBB3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNOTCH1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eMTOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFOXA1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003ePIK3R1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eARID2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBRAF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFGFR2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.838995568685377%\"\u003e\n \u003cp\u003e\u003cem\u003eFGFR4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.13293943870015%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.156573116691284%\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.87149187592319%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBold = significant \u003cem\u003eP\u003c/em\u003e-value at the threshold of 0.05, NA: not available\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Univariate and multivariate meta-regression \u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"1008\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5952380952380952%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"9.523809523809524%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSamples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"29.761904761904763%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate meta-regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate meta-regression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"1.0101010101010102%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.151515151515152%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.131313131313131%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.141414141414142%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"13.095238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u003c/em\u003e\u003c/strong\u003e (good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis\u003c/em\u003e\u003c/strong\u003e: NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBRCA2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u003c/em\u003e\u003c/strong\u003e (good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis:\u003c/em\u003e\u003c/strong\u003e NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u003c/em\u003e\u003c/strong\u003e (Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePTEN\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u003c/em\u003e\u003c/strong\u003e (good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis\u003c/em\u003e\u003c/strong\u003e: NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNRAS\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.0005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u003c/em\u003e\u003c/strong\u003e (Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNOTCH1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u003c/em\u003e\u003c/strong\u003e (good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"8\" width=\"10.119047619047619%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTumor site:\u003c/em\u003e\u003c/strong\u003e Primary tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Extracerebral metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0003\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Brain metastases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality of studies\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e-1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMethod analysis:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"9.271523178807946%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"14.56953642384106%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"7.947019867549669%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Targeted NGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.45631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.563106796116505%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.359223300970875%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.09933774834437%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreservation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e(Frozen)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.933774834437086%\"\u003e\n \u003cp\u003e-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.609271523178808%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.271523178807946%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.56953642384106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.947019867549669%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"0.5952380952380952%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"31.547619047619047%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.928571428571429%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.738095238095238%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.095238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUni- and multivariate meta-regressions were run to assess sample groups significantly associated with prevalence of gene mutations.\u0026nbsp;Sample groups yielding \u003cem\u003eP\u003c/em\u003e-values under 0.20 in the univariate analysis were considered for inclusion in the multivariate analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBold = significant \u003cem\u003eP\u003c/em\u003e value at the threshold of 0.05.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, brain metastases, genomics, mutation","lastPublishedDoi":"10.21203/rs.3.rs-2364912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2364912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer brain metastases are challenging daily pratice, and the biological link between gene mutations and metastatic spread to the brain remains to be determined. Here, we performed a meta-analysis on genomic data obtained from primary tumors, extracerebral metastases and brain metastases, to identify gene alterations associated with metastatic processes in the brain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArticles with relevant findings were selected using Medline via PubMed, from January 1999 up to February 2022, and the algorithms were the following: (\"Breast Neoplasms\"[Mesh] AND \"metast*\" AND (\"Genomics\"[Mesh] OR \"mutation*\")), and \"Breast\" AND \"brain\" AND \"metast*\" AND (\"Genom*\" OR \"mutation*\" OR \"sequenc*\"). A critical review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-seven publications were selected for this meta-analysis, including 37,218 patients in all, 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and 1,485 brain metastasis samples. We report overall and sub-group prevalence of gene mutations, including comparisons between primary tumors, extracerebral metastases and brain metastases. In particular, we identified 6 genes with a higher mutation prevalence in brain metastases than in extracerebral metastases, with a potential role in metastatic processes in the brain: \u003cem\u003eESR1, ERBB2, EGFR, PTEN, BRCA2 \u003c/em\u003eand \u003cem\u003eNOTCH1\u003c/em\u003e. We discuss here the therapeutic implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur results underline the added value of obtaining biopsies from brain metastases to fully explore their biology, to develop personalized treatments.\u003c/p\u003e","manuscriptTitle":"Genomics of breast cancer brain metastases: a meta-analysis and therapeutic implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-15 16:21:58","doi":"10.21203/rs.3.rs-2364912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b0f48d0c-fdf2-4415-9479-6cc4549e41fd","owner":[],"postedDate":"December 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-30T02:44:10+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-15 16:21:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2364912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2364912","identity":"rs-2364912","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}