hereditary breast cancer susceptibility: defining the new...

Hereditary Breast Cancer Susceptibility:

Defining the New Frontier

Thomas Slavin, MD, FACMG, DABMD

Assistant Professor,

Department of Medical Oncology,

Division of Clinical Cancer Genetics

Program Member,

Cancer Control and Population Sciences

City of Hope National Medical Center

Disclosures

I do not have anything to disclose.

Outline

• Background

• Results from the SIMPLEXO Consortium

• Conclusions

5

(i.e., HBOC)

(i.e., BCR-ABL)

Why are panels expanding?

NGS, Patents, Pathways, candidate genes and emerging evidence

http://www.rockefeller.edu/fanconi/mutate/

CHEK229%

PALB217%

MUTYH mono14%

ATM14%

NBN10%

BRIP17%

MRE11A3%

RAD502%

RAD51D2%

BARD12%

Results of MGP non-BRCA1/2 testing

Select results from 348 commercial multigene panel tests ordered by Clinical Cancer Genetics Community of Practice

clinicians between January 1, 2014, through October 1, 2014. Adapted from Slavin T.P. et al., Front. Oncol. 2015. 5:208..

Aren’t more genes better?

Genes

Value

In theory

In reality

Slavin TP, Niell-Swiller M, Solomon I, Nehoray B, Rybak C, Blazer KR and Weitzel JN (2015) Clinical application of multigene panels:

challenges of next-generation counseling and cancer risk management. Front. Oncol. 2015. 5:208.

Cause of a given cancer

High Penetrance gene Other factors

Cause of a given cancer

Moderate Penetrance gene Other factors

Guidelines change! (2015)

NCCN Guidelines Version 1 (2015), Genetic/Familial High-Risk Assessment: Breast and Ovarian

Guidelines change! (2016)

NCCN Guidelines Version 1 (2016), Genetic/Familial High-Risk Assessment: Breast and Ovarian

Recommend carriers check back or set-up follow-ups

Even healthy populations have mutations

http://www.mycariboonow.com/wp-content/uploads/2016/02/Population.jpg

PurposeDetail the spectrum of, and refine risk estimates for, known and proposed breast cancer susceptibility genes using BRCA negative FBC cases contrasting against a control population from the Exome Aggregation Consortium (ExAC).

Multi-institutional project

• Centers• Mayo

• Univ of Penn

• British Columbia Cancer Agency

• Memorial Sloan-Kettering Cancer Center

• Stanford

• Dana Farber Cancer Institute

• Fondazione IRCCS IstitutoNazionale dei Tumori Italy,

• COH

• People • Thomas Paul Slavin

• Kara N. Maxwell

• Jenna Lilyquist

• Joseph Vijai

• Susan Neuhausen

• Steve Hart

• Intan Schrader,

• Susan M. Domchek

• Mark E. Robson

• James Ford

• Judy Garber

• Kenneth Offit

• Katherine Nathanson

• Fergus J. Couch

• Jeffrey N. Weitzel

• 2266 BRCA1/2 negative cases with familial breast cancer.

FBC defined as a proband with breast cancer and at least two first to third degree relatives with breast or ovarian cancer under age 70 were selected.

• 200 non-cancer controls for estimating systematic sequencing artifacts of NGS.

Cases

• 26 gene capture: ATM, BARD1, BLM, BRIP1, CDH1, CHEK2, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2. Additionally exon only analysis was available for BAP1, FANCC, FANCM, PPM1D, RINT1

• Illumina HiSeq 2000 to an estimated 100X mean coverage for the target region to yield paired-end reads of 100 bp per sample (24 samples per lane).

DNA Sequencing

• Loss-of-function (nonsense, frameshift, splicing +/-1 or 2, whole gene deletion, truncating CNV) unless after a known truncating polymorphism.

• Missense, splicing +/- 3+ position, intronic or synonymous variants that were classified as pathogenic or likely pathogenic in ClinVar by two or more of the following clinical genetics group

Variant calling

Copy Number Variants

Fromer, M., et al., Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J

Hum Genet, 2012. 91(4): p. 597-607.

Jiang, Y., et al., CODEX: a normalization and copy number variation detection method for whole exome sequencing. Nucleic

Acids Res, 2015. 43(6): p. e39.

• Xhmm

• CODEX

• Overlap

• MLPA

• ExAC minus TCGA, Non-Finnish European (n= 26,375)

• Contributing projects 1000 Genomes

Bulgarian Trios

Finland-United States Investigation of NIDDM Genetics (FUSION)

GoT2D

Inflammatory Bowel Disease

METabolic Syndrome In Men (METSIM)

Jackson Heart Study

Myocardial Infarction Genetics Consortium:

NHLBI-GO Exome Sequencing Project (ESP)

National Institute of Mental Health (NIMH) Controls

SIGMA-T2D

Sequencing in Suomi (SISu)

Swedish Schizophrenia & Bipolar Studies

T2D-GENES

Schizophrenia Trios from Taiwan

Tourette Syndrome Association International Consortium for Genomics (TSAICG)

Case-control analysis

BREAST CANCER CASES (n=2134 cases) n % RACE1

White 1722 80.7% Hispanic 136 6.4%

Asian 69 3.2% Other 69 3.2%

African American 49 2.3% Unknown 89 4.2%

PERSONAL HISTORY OF CANCER2

Second breast cancer 291 13.6% Ovarian cancer 33 1.5%

Other cancer 167 7.8% Avg Age 1st breast cancer 47.9 + 9.4

Avg Age 2nd breast cancer 52.9 + 10.5 FAMILY HISTORY OF CANCER

No FDR/SDR w/breast cancer 166 7.8% 1 FDR/SDR w/breast cancer 419 19.6% 2 FDR/SDR w/breast cancer 729 34.2%

3+ FDR/SDR w/breast cancer 820 38.4% FDR w/breast cancer 1377 64.5%

FDR/SDR w/ovarian cancer 374 17.5% FDR/SDR w/colon cancer 394 18.5%

BREAST CANCER PATHOLOGY (n=2425 cancers) Behavior

Invasive 1727 71.2% In Situ 205 8.5%

Unknown 493 20.3% Histology3

Ductal 1210 49.9% Lobular 164 6.8%

Mixed 132 5.4% Other 183 7.5%

Unknown 868 35.8% Grade

Low 232 10.9% Intermediate 581 27.2%

High 581 27.2% Unknown 1031 42.5%

ER Status Positive 1203 49.6%

Negative 349 14.4% Indeterminate 5 0.2%

Unknown 868 35.8% HER2 Status

Negative 888 36.6% Positive 289 11.9%

Indeterminate 47 1.9% Unknown 1201 49.5%

BREAST CANCER FULL HR STATUS KNOWN (n=1055)

ER+Her2- 651 61.7% ER+Her2+ 163 15.5% ER-Her2+ 93 8.8% ER-Her2- 148 14.0%

RAD51C exon 8 deletion

Summary of results (1)

• Mutations, including large genomic rearrangements, were identified in 8.2% of familial breast cancer cases compared to 6.1% in controls.

• An enrichment of known high and moderate penetrance breast cancer gene mutation carriers (1.4% versus 0.2% and 2.8% versus 1.5%, respectively) was seen in cases compared to controls.

• We confirmed associations (p-value <0.05) and estimated gene specific risk ratios for familial breast cancer associated with the following genes ATM, BARD1, PALB2, TP53, and CHEK2 Truncating variants.

Summary of results (2)

• Significant associations with over 5 cases were not identified for BLM, BRIP1, CDH1, CHEK2 missense, MLH1, MRE11A, MSH2, MSH6, MUTYH missense, NBN, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, XRCC2, BAP1, FANCC, FANCM, PPM1D, or RINT1.

Genotype-Phenotype Correlations (1)

• TP53 mutation carriers had a significantly higher portion of Her2+ breast cancer.

• PALB2 carriers were more likely to be ER-Her2-

• ATM and CHEK2 carriers were more likely to be ER+.

• BRIP1, RAD51C, RAD51D, carriers were more likely to have a first or second degree relative with ovarian cancer compared to mutation negative controls (50% vs 17%, p=0.03).

Genotype-Phenotype Correlations (2)

• 5/11 (45%) of TP53 carriers met classic LFS or Chompret criteria, or had breast cancer <31.

• A family history of colon cancer was not higher in MUTYH carriers compared to mutation negative individuals.

Conclusions

• Considering only genes that reached statistical significance with over 5 cases, only 4% of BRCA- FBC cases had mutations in breast cancer susceptibility genes.

• The high rate of mutations in controls (6.1%) challenges the significance of the majority of genes included on many commercial hereditary breast cancer panels.

Future Directions

• Looking at the other 600 genes sequenced in this cohort.

• RNA expression and PRS studies.

Multi-institutional project

• Centers• COH

• Mayo

• Univ of Penn

• British Columbia Cancer Agency

• Memorial Sloan-Kettering Cancer Center

• Stanford

• Fondazione IRCCS IstitutoNazionale dei Tumori Italy,

• Dana Farber Cancer Institute

• People • Kara N.

Maxwell

• Jenna Lilyquist

• Joseph Vijai

• Susan Neuhausen

• Steve Hart

• IntanSchrader,

• Susan M. Domchek

• Mark E. Robson

• James Ford

• Judy Garber

• Kenneth Offit

• Katherine Nathanson

• Fergus J. Couch

• Jeffrey N. Weitzel

• Sharon Sand

• Kai Yang

This work is supported by the Breast Cancer Research Foundation (F.C., K.N., K.O., J.W., K.N.), the NIH (CA176785, CA116167, and NCI SPORE CA116201 to F.C., Abramson Cancer Center Core grant CA016520 to K.N.; 3P30CA008748-4 to K.O.; CA175491, CA165082, and HG007033 to R.K, CA178800 to JV), the U.S. Dept of Defense (W81XWH-13-1-0338 to K.M.; W81XWH-10-1-0341 to F.C.), the American Society of Clinical Oncology (K.M.), the Rooney Family Foundation (K.N.), the Commonwealth of Pennsylvania (K.N.), the Sharon Levine Corzine Cancer Research Fund (K.O.), the Robert and Kate Niehaus Clinical Cancer Initiative (K.O.), the Filomen M. D’Agostino Foundation (K.O.), the Andrew Sabin Family Fund (K.O.), STOP CANCER (T.S.), Oxnard Foundation (T.S., J.W.), ACS (J.W.), Avon Foundation (02-2013-044 to J.W.), and the Morris and Horowitz Families Endowed Professorship (S.N.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Views and opinions of, and endorsements by the authors do not reflect those of the US Army or the Department of Defense. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions.

Acknowledgments

Questions?

RR vs OR

Ways to try to understand absolute risk of disease using non-population data

Relative risks or risk ratio – prospective cohort data– How many more times likely is someone with a positive result going to

develop disease

Odds ratios– from retrospective data– Is an estimate of the RR

Both can vary from zero to infinity– Association

• 0-1 negative • 1+ positive (the bigger the number, the more impressive)• 1= none (i.e, RR of 2 with 95% CI: 0.7-2.6 would be considered a non-

association)

Examples for our purposes OR ≈ RR

– RR of 1.36 = risk of incidence of disease is increased by 36% with a positive test (RR increase = RR-1 x100)

• So for breast cancer, assuming 10% lifetime incidence, lifetime risks would be 13.6% (estimate of ABSOLUTE RISK)

– RR of 2 = risk of incidence of disease is increased by 100%• So for breast cancer, lifetime risk would be 20%

– RR of 0.8 = risk of disease is decreased by 20% with a positive test

• So breast cancer risk would be 8% lifetime

Nice UABSOM resource by Terry Shaneyfelt, MD: https://www.youtube.com/watch?v=FZzm3-RRlI4