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1. BIOLOGIE
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1.4 BIOLOGIE - TECHNOS
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2. ETIOLOGIE
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3.1.1 PRÉVENTION - TABAC - E-CIGS
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4.12 BIOPSIES LIQUIDES
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Liquid biopsy: ‘classifier’ of metastatic breast cancer subgroups [VHIO]
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The authors analyzed a pooled of individual patient data from 18 cohorts, including 2436 patients with metastatic breast cancer. The threshold established for this sub-classification was 5 tumor cells per 7.5ml of blood. Patients with lower values were classified as indolent. Those with higher scorings, as aggressive.
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4.2 DÉP., DIAG. & PRONO. - GÉNOME
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4.7 DÉP., DIAG. & PRONO. - COL DE L'UTÉRUS
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5. TRAITEMENTS
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5.1 TRAITEMENTS - PRÉ-CLINIQUE
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5.12.2 IMMUNOTHÉRAPIES - CAR-T, THÉRAPIES CELLULAIRES
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Off-the-shelf CAR-T and gene-editing player Precision Bio files $100M IPO [Fierce Biotech]
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Precision is built around Arcus, a one-step genome editing platform that uses a synthetic mimic of the homing endonucleases found in nature to make insertions, deletions or other edits to DNA. The biotech thinks its approach is more specific than competing gene-editing technologies like CRISPR, TALEN and zinc finger nucleases with a lower risk of off-target activity and will open up new opportunities in CAR-T as well as gene-modifying therapies.
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5.12.6 IMMUNOTHÉRAPIES - AMM
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5.2 PHARMA
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5.3.4 TRAITEMENTS - AMM (FDA, EMA,...)
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6. LUTTE CONTRE LES CANCERS
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6.3 ASSOCIATIONS/FONDATIONS
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Big Pharma Gave Money To Patient Advocacy Groups Opposing Medicare Changes [KHN]
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Dozens of patient advocacy groups, like the Bonnie J. Addario Lung Cancer Foundation and the National Coalition for Cancer Survivorship, recently appeared in national advertisements objecting to a Trump administration proposal that could limit drugs covered by Medicare providers. But a Kaiser Health News analysis found that about half of the groups representing patients have received funding from the pharmaceutical industry.
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6.7.1 IA/BIOINFORMATIQUE
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Getting smart about artificial intelligence [Wellcome Sanger Institute]
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Though the concept of machine learning has been around since the 1960s, it’s only in the last 10 years that it’s really been applied in genomics. Three factors have converged allowing its potential to be realised – the algorithms are sophisticated enough, the data sets needed to train the algorithms now exist, and the computing power to train those algorithms exists.
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