Uninformative BRCA Tests. Rebecca Sutphen, M.D. Professor, USF College of Medicine Chief Medical Officer, InformedDNA
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1 Uninformative RCA Tests Rebecca Sutphen, M.D. Professor, USF College of Medicine Chief Medical Officer, InformedDNA
2 Uninformative RCA tests 1. R/OV cancer patient with normal results 2. Cancer-free person with normal results 3. Genetic variants
3 R/OV cancer patient with normal results 1. Test missed mutation - standard RCA testing ~ 95% (but > 95% in Jewish individuals) - ART testing identifies ~ 2% (available if risk > 30%)
4 R/OV cancer patient with normal results 2. Another gene is involved: - HNPCC/PTEN - unknown genes
5 R/OV cancer patient with normal results 3. Could be not genetic - combination of genes/environment
6 Uninformative RCA tests 1. R/OV cancer patient with normal results 2. Cancer-free person with normal results 3. Genetic variants
7 Cancer-free patient with normal results 1. Test missed mutation - standard RCA testing ~ 95% (but > 95% in Jewish individuals) - ART testing identifies ~ 2% (available if risk > 30%)
8 Cancer-free patient with normal results 2. Another gene is involved: - HNPCC/PTEN - unknown genes
9 Cancer-free patient with normal results 3. Could be not genetic - combination of genes/environment
10 Cancer-free patient with normal results 4. Could still be RCA in the family - test other family members
11 Uninformative RCA tests 1. R/OV cancer patient with normal results 2. Cancer-free person with normal results 3. Genetic variants
12 Genetic Variant 1. Suspected deleterious 2. Suspected polymorphism 3. Variant of uncertain significance
13 d. 45 d. 86 d. 67 d. 74 COD: accident Goiter COD: diabetes COD: heart disease d. 53 reast 49 Colon 62 COD: suicide Uterine 56 reast 38
14
15 Cowden syndrome PTEN reast cancer 25 to 50% risk May occur at younger age Some male breast cancer Thyroid cancer 10% Endometrial (uterine) cancer 5 10% Kidney cancer unknown risk level Colon cancer unknown risk level Skin cancer unknown risk level
16 Other features of PTEN Fibrocystic breast disease Uterine fibroids Thyroid goiter, thyroiditis Lipomas Hemangiomas GI polyps 50% chance to pass down the mutation to each child
17 Management of PTEN Yearly thyroid ultrasound age 18 Yearly mammo and MRI 30 or 10 yrs younger than youngest age at diagnosis Yearly endometrial biopsy 35 or 10 yrs younger than youngest age at diagnosis Yearly urine cytology Yearly transvaginal ultrasound postmenopause Yearly colonoscopy age 50
18 d. 45 d. 86 d. 67 d. 74 COD: accident reast 64 COD: diabetes COD: heart disease d. 53 Ovarian 59 Colon 54 COD: suicide Uterine 50 Ovarian 62
19
20 Mismatch Repair Genes - HNPCC Colon cancer 80% risk Uterine cancer 70% risk Ovarian cancer 12% risk Stomach, gall bladder, pancreas, other GI cancers, brain, urinary unknown risks
21 r sequencing r 32 * r 42 DNA extraction no changes deleterious mutation found? variant
22 RCA1 RCA1
23 ? good or bad but.? good? bad
24 Mutation versus variant Figuring out the impact of the change agt gta gca ctc tac cag tgc cag ctg SER VAL ALA LEU TYR GLN CYS GLN GLU agt gta gca ctc tac cag tga cag ctg SER VAL ALA LEU TYR GLN STP nonsense good bad agt gta ca g ct tac cag tgc cag ctg SER VAL HIS LEU PRO VAL PRO ALA frameshift Almost always ut sometimes.. agt gta gca ctc tcc cag tgc cag ctg SER VAL ALA LEU SER GLN CYS GLN GLU missense?
25 How to Classify Variants Guilty-by-association studies cases controls cases controls cases controls 4% 1% 1% 5% 1% 1% bad good good ut sometimes.. cases controls 0% 0%
26 How to Classify Variants Family studies Pr Pr r 70 o Pr Pr r 70 o r 31 Pr r 45 r 31 Pr r 45 r 35 r 35 r 47 r 43 r 35 r 35 r 47 r 43 bad good ut sometimes.. Pr Pr r 70? r 31? Pr r 45 o r 35 r 35 r 47? r 43
27 How to Classify Variant Co-occurrence analysis (RCA1 only) mice fine humans Normal risk Mammary tumors High risk Non viable never found Therefore ut sometimes good? good
28
29 How to Classify Variants Alternative methods: Evolutionary conservation Computational Prediction Functional assays
30 Evolutionary conservation Homo sapiens Pan troglodytes Gorilla gorilla os taurus Rattus norvegicus Mus musculus Monodelphis domestica Gallus gallus Xenopus laevis Tetraodon nigroviridis Canis familiaris Macaca mulatta Pongo pygmaeus (1732) Homo sapiens(1660) Pan troglodytes(1660) os taurus(1653) Canis familiaris(1663) Rattus norvegicus(1606) Mus musculus(1603) Gallus gallus(1551) Xenopus laevis(1384) Consensus(1732) EEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARES-- EEFMLVYKFARKHHITLTNLITEETTHVVMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKMLNEHDFEVRGDVVNGRNHQGPKRARES-- KELMLVQKFARKHHVTLTNLITEETTHVIMKTDPEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKEGKMLDEHDFEVRGDVVNGRNHQGPKRARES-- KEFMLVHKFARKHHISLTNLISEETTHVIMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKILDEHDFEVRGDVVNGRNHQGPKRARESQD KEVMIVQKFAEKYRLALTDVITEETTHVIIKTDAEFVCERTLKYFLGIAGGKWIVSYSWVIKSIQERKLLSVHEFEVKGDVVTGSNHQGPRRSRES-- KEVMTVQKFAEKYRLTLTDAITEETTHVIIKTDAEFVCERTLKYFLGIAGGKWIVSYSWVVRSIQERRLLNVHEFEVTGDVVTGRNHQGPRRSRES-- SEHLMVQKFARKTQSTFSNHITDGTTHVIMKTDEELVCERTLKYFLGIAGRKWVVSYQWIIQSFKEGRILDEEHFEVKGDVINGRNHQGPKRARQS-- CEMALVQRFSKTTQSILSSRITDSTTHVIMKTDAELVCERTLKYFQGIASRKWVVSYEWVVQSFREGQILDEYDFEVKGDVINGRNHRGPRRSRLG-- KEVMLVQKFARKHHITLTNLITEETTHVIMKTDAEFVCERTLKYFLGIAGGKWVVSYFWVTQSIKERKILDEHDFEVRGDVVNGRNHQGPKRARES L A good R W bad N D good
31 Computational Prediction Derive general rules 1753 R T bad
32 Functional assays bad good RCA1 RCA2
33 SUMMARY Figuring out the impact of the change Guilty-by-association studies Family studies Co-occurrence analysis (RCA1 only) Evolutionary conservation Computational Prediction Functional assays Improved risk assessment
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