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The accumulation of somatic mutations in healthy human tissues has been extensively characterized, but the mutational landscape of the healthy breast is still poorly understood. Our analysis of whole-genome sequencing shows that in line with other healthy organs, the healthy breast during the reproduction years accumulates mutations with age, with the rate of accumulation in the epithelium of 15.24 ± 5 mutations/year. Both epithelial and stromal compartments contain mutations in breast-specific driver genes, indicative of subsequent positive selection. Parity- and age-associated differences are evident in the mammary epithelium, partly explaining the observed difference in breast cancer risk amongst women of different childbearing age. Parity is associated with an age-dependent increase in the clone size of mutated epithelial cells, suggesting that older first-time mothers have a higher probability of accumulating oncogenic events in the epithelium compared to younger mothers or nulliparous women. In conclusion, we describe the reference genome of the healthy female human breast during reproductive years and provide evidence of how parity affects the genomic landscape of the mammary gland.
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Neoplasias da Mama , Mama , Gravidez , Humanos , Feminino , Adulto , Paridade , Neoplasias da Mama/genética , Mutação , Células EpiteliaisRESUMO
The investigation of differentially expressed genes (DEGs) and their interactome could provide valuable insights for the development of markers to optimize cervical intraepithelial neoplasia (CIN) screening and treatment. This study investigated patients with cervical disease to identify gene markers whose dysregulated expression and protein interaction interface were linked with CIN and cervical cancer (CC). Literature search of microarray datasets containing cervical epithelial samples was conducted in Gene Expression Omnibus and Pubmed/Medline from inception until March 2021. Retrieved DEGs were used to construct two protein-protein interaction (PPI) networks. Module DEGs that overlapped between CIN and CC samples, were ranked based on 11 topological algorithms. The highest-ranked hub gene was retrieved and its correlation with prognosis, tissue expression and tumor purity in patients with CC, was evaluated. Screening of the literature yielded 9 microarray datasets (GSE7803, GSE27678, GSE63514, GSE6791, GSE9750, GSE29570, GSE39001, GSE63678, GSE67522). Two PPI networks from CIN and CC samples were constructed and consisted of 1704 and 3748 DEGs along 21393 and 79828 interactions, respectively. Two gene clusters were retrieved in the CIN network and three in the CC network. Multi-algorithmic topological analysis revealed PCNA as the highest ranked hub gene between the two networks, both in terms of expression and interactions. Further analysis revealed that while PCNA was overexpressed in CC tissues, it was correlated with favorable prognosis (log-rank P=0.022, HR=0.58) and tumor purity (P=9.86 × 10-4, partial rho=0.197) in CC patients. This study identified that cervical PCNA exhibited multi-algorithmic topological significance among DEGs from CIN and CC samples. Overall, PCNA may serve as a potential gene marker of CIN progression. Experimental validation is necessary to examine its value in patients with cervical disease.
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A screen by Kost-Alimova et al.1 suggests that the FDA-approved SYK inhibitor fostamatinib inhibits MUC1 in the respiratory tract and has the potential to treat serious outcomes of coronavirus COVID-19, including acute respiratory distress syndrome (ARDS) and acute lung injury (ALI).
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Lesão Pulmonar Aguda/tratamento farmacológico , Aminopiridinas/uso terapêutico , Tratamento Farmacológico da COVID-19 , Reposicionamento de Medicamentos , Morfolinas/uso terapêutico , Pirimidinas/uso terapêutico , SARS-CoV-2/patogenicidade , Lesão Pulmonar Aguda/etiologia , Lesão Pulmonar Aguda/metabolismo , COVID-19/complicações , COVID-19/metabolismo , Humanos , Mucina-1/metabolismo , Síndrome do Desconforto Respiratório/tratamento farmacológico , Síndrome do Desconforto Respiratório/etiologia , Síndrome do Desconforto Respiratório/metabolismo , Quinase Syk/antagonistas & inibidoresRESUMO
We used data obtained by Caris Life Sciences, to evaluate the benefits of tailoring treatments for a breast carcinoma cohort by using tumor molecular profiles to inform decisions. Data for 92 breast cancer patients from the commercial Caris Molecular Intelligence database was retrospectively divided into two groups, so that the first always followed treatment recommendations, whereas in the second group all patients received at least one drug after profiling that was predicted to lack benefit. The biomarker and drug associations were based on tests including fluorescent in situ hybridization and DNA sequencing, although immunohistochemistry was the main test used. Patients whose drugs matched those recommended according to their tumor profile had an average overall survival of 667 days, compared to 510 days for patients that did not (P=0.0316). In the matched treatment group, 26% of patients were deceased by the last time of monitoring, whereas this was 41% in the unmatched group (P=0.1257). We therefore confirm the ability of tumor molecular profiling to improve survival of breast cancer patients. Immunohistochemistry biomarkers for the androgen, estrogen and progesterone receptors were found to be prognostic for survival.
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We evaluated the benefit of tailoring treatments for a colorectal adenocarcinoma cancer cohort according to tumor molecular profiles, by analyzing data collected on patient responses to treatments that were guided by a tumor profiling technology from Caris Life Sciences. DNA sequencing and immunohistochemistry were the main tests that predictions were based upon, but also fragment analysis, and in situ hybridization. The status of the IHC biomarker for the thymidylate synthase receptor was a good indicator for future survival. Data collected for the clinical treatments of 95 colorectal adenocarcinoma patients was retrospectively divided into two groups: the first group was given drugs that always matched recommended treatments as suggested by the tumor molecular profiling service; the second group received at least one drug after profiling that was predicted to lack benefit. In the matched treatment group, 19% of patients were deceased at the end of monitoring compared to 49% in the unmatched group, indicating a benefit in mortality by tumor molecular profiling colorectal adenocarcinoma patients.
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We evaluated the effect of tailoring treatments based on predictions informed by tumor molecular profiles across a range of cancers, using data from Caris Life Sciences. These included breast carcinoma, colorectal adenocarcinoma, female genital tract malignancy, lung non-small cell lung cancer, neuroendocrine tumors, ovarian surface epithelial carcinomas, and urinary tract cancers. Molecular profiles using mostly immunohistochemistry (IHC) and DNA sequencing for tumors from 841 patients had been previously used to recommend treatments; some physicians followed the suggestions completely while some did not. This information was assessed to find out if the outcome was better for the patients where their received drugs matched recommendations. The IHC biomarker for the progesterone receptor and for the androgen receptor were found to be most prognostic for survival overall. The IHC biomarkers for P-glycoprotein (PGP), tyrosine-protein kinase Met (cMET) and the DNA excision repair protein ERCC1 were also shown to be significant predictors of outcome. Patients whose treatments matched those predicted to be of benefit survived for an average of 512 days, compared to 468 days for those that did not (P = 0.0684). In the matched treatment group, 34% of patients were deceased at the completion of monitoring, whereas this was 47% in the unmatched group (P = 0.0001).
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In this study we utilized data on patient responses to guided treatments, and we evaluated their benefit for a non-small cell lung cancer cohort. The recommended therapies used were predicted using tumor molecular profiles that involved a range of biomarkers but primarily used immunohistochemistry markers. A dataset describing 91 lung non-small cell lung cancer patients was retrospectively split into two. The first group's drugs were consistent with a treatment plan whereby all drugs received agreed with their tumor's molecular profile. The second group each received one or more drug that was expected to lack benefit. We found that there was no significant difference in overall survival or mortality between the two groups. Patients whose treatments were predicted to be of benefit survived for an average of 402 days, compared to 382 days for those that did not (P = 0.7934). In the matched treatment group, 48% of patients were deceased by the time monitoring had finished compared to 53% in the unmatched group (P = 0.6094). The immunohistochemistry biomarker for the ERCC1 receptor was found to be a marker that could be used to predict future survival; ERCC1 loss was found to be predictive of poor survival.
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[This corrects the article DOI: 10.18632/oncotarget.23675.].
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[This corrects the article DOI: 10.18632/oncotarget.24257.].
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[This corrects the article DOI: 10.18632/oncotarget.24258.].
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Tumor molecular profiling has enabled selection of targeted therapies in a host of solid tumors. Here we used a retrospective clinical cohort, to evaluate the benefit of tailoring treatments for female genital tract malignancy, using tumor molecular profiles. Clinical outcome data for 112 patients was retrospectively separated into two groups. These either followed a matched treatment plan that incorporated at least one drug recommended according to their tumor profile and none that were expected to have no benefit (64 patients), or was unmatched with suggested treatments and received at least one drug that was anticipated to lack benefit for that tumor (48 patients). In the group of patients whose drugs matched those recommended by molecular profiling of their tumor, their overall survival was 593 days on average, compared to 449 days for patients that did not; removing drugs predicted to have no benefit from treatment regimens received after profiling increased survival by 144 days on average (P = 0.0265). In the matched treatment group, 30% of patients had died by the last time of monitoring, whereas this was 40% in the unmatched group (P = 0.2778). The IHC biomarker for the progesterone receptor was demonstrated to be prognostic for survival.