With choice comes decision - Cardiac Oncology

GENOMICS AND PERSONALIZEDMEDICINE TO IDENTIFY THOSE AT RISKFOR CARDIOTOXICITY

Dr Howard L. McLeodMedical Director, DeBartolo FamilyPersonalized Medicine InstituteChair, Department of Individualized Cancer MedicineSenior Member, Division of Population SciencesState of Florida Endowed Chair

The clinical problem•Multiple active regimens for the treatment of most diseases•Variation in response to therapy•Unpredictable toxicity

With choice comes decision

$$$$$$$$$$$$$

‘equal’ options

Probabalistic data is enoughMany clinical interventions are based on increased probability

of a problem occurring

Insulin/oral diabetes drugsStatinsantihypertensives

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1,000

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5-9

10-1

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20-2

425

-29

30-3

435

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465

-69

70-7

475

-79

80-8

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Age at diagnosis

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Increase risk = intervention Colon cancer screening

Pharmacogenomic examples-2018• bcr/abl or 9:22 translocation—imatinib mesylate*• HER2-neu—trastuzumab**• C-kit mutations—imatinib mesylate**• Epidermal growth factor receptor mutations—gefitinib• BRAF-vemurafenib• ALK-Crizotinib• ROS-1_Crizotinib• TPMT-mercaptopurine and azathioprine*• UGT1A1-irinotecan**• CYP2C9/VKORC1-warfarin*• HLA-B*5701-abacavir *• HLA-B*1502-carbamazepine *• IL28B-interferon• CFTR-ivacaftor• CYP2C19-clopidogrel, voriconazole• CYP2D6-5-HT3 receptor antagonists, antidepressants, ADHD drugs, and codeine derivatives*

Pain controlAntiemeticsAntidepressantsADHD drugsAnticoagulantsNot just tumor markers!!

Translation to Cardiooncology?

• Family history/known syndromes

• Clonal hematopoiesis of unknown potential (CHIP)

• Polygenic propensity scores

• A lot more coming, lots to do

• Those with pathogenic mutations in familial cardiomyopathy genes have a predisposition to heart disease

• Risk of anthracycline induced cardiotoxicity caused by asymptomatic familial cardiomyopathy likely similar to (or greater than) other conditions placing patients at risk of heart disease

Pinder et al. JCO 2007, 25(25): 3808-3815

• Age range 66 to 88 years

• 43,338 patients included

• Data-mined the SEER-Medicare-linked database

• HR for familial cardiomyopathy likely similar or higher that HR for pre-existing conditions

Familial Cardiomyopathy and Risk of Anthracycline-Induced Cardiotoxicity

Major Findings References

Autosomal dominant mutations (e.g.,MYH7, TNNT2, LMNA) are associated with familial cardiomyopathy syndromes

***Some phenotypes are mild, but can be exacerbated by environmental factors***

• Morita et al. N Engl J Med. 358, 2008• Hougs et al. Eur J Hum Genet. 13, 2005• Long et al. J Am Hear Assoc. 9, 2015• Pan et al. BMC Med Genet. 16, 2016• Yang et al. Gene. 558, 2015• Coppini et al. J Am Coll Cardiol. 64, 2014• Marsiglia et al. Am Heart J. 166, 2013• Zaragoza et al. PloS One. 16, 2016

Inherited cardiomyopathy syndromes are a risk factor for anthracycline-induced cardiotoxicity

• Van den Berg et al. Eu J Heart Fail. 12, 2010• Shipman et al. JCO. 29, 2011• Wasielewski et al. Open Heart. 18, 2014• Young et al. Ann Oncol. 22, 2011

Approaches for monitoring and preventing anthracycline-induced cardiotoxicity

• Kalam et al. Eur J Cancer. 49, 2013• Conway et al. BMC Cancer. 366, 2015• Vejpongsa et al. J Am Coll Cardiol. 64, 2014






Clonal Expansion and Allelic Fractions.

Genovese G et al. N Engl J Med 2014;371:2477-2487

CHIP found in ~20% of ‘healthy’ 60 year olds

Presenter

Presentation Notes

Figure 1 Clonal Expansion and Allelic Fractions. Panel A shows a model for the expansion of a single hematopoietic stem cell into a clonal population, under the influence of a somatic mutation (yellow circle), and the potential conversion of the clone into a hematologic cancer through subsequent mutation (black circle with red rim). Mutations present in the founder cell would be present at an appreciable allelic fraction (though <50%) in blood-derived genomic DNA. Panel B shows the distribution of allelic fractions observed in sequencing data for high-confidence, ultra-rare variants ascertained in the 12,380 study participants; the small bump at the left of this distribution represents putative somatic mutations.

Risk of Hematologic Cancer for Participants with Clonal Hematopoiesis.

Genovese G et al. N Engl J Med 2014;371:2477-2487

Clonal hematopoiesis of unknown potential (CHIP) is a risk for primary and therapy-associated leukemia

Presenter

Presentation Notes

Figure 3 Risk of Hematologic Cancer for Participants with Clonal Hematopoiesis. Panels A and D show Kaplan–Meier plots of the proportions of participants who did not receive a diagnosis of hematologic cancer (Panel A) and for surviving participants (Panel D). Panels B and E show hazard ratios for hematologic cancer (Panel B) and death (Panel E) for participants with exactly one putative somatic mutation and no candidate drivers (one mutation), those with exactly two putative somatic mutations and no candidate drivers (two mutations), those with clonal hematopoiesis and unknown drivers (CH-UD), those with clonal hematopoiesis and candidate drivers (CH-CD), and those having clonal hematopoiesis with candidate or unknown drivers (CH), all compared with participants with no candidate drivers and no putative somatic mutations (no mutations). Panel C shows the proportions of participants who had clonal hematopoiesis with candidate or unknown drivers among 31 participants in whom hematologic cancers were diagnosed at least 6 months after DNA sampling, as compared with the proportions in a group of age-matched persons without hematologic cancer over a similar follow-up period.

Association between Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Coronary Heart Disease and Early-Onset Myocardial Infarction.

Jaiswal S et al. N Engl J Med ;377:111-121

Presenter

Presentation Notes

Figure 1 Association between Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Coronary Heart Disease and Early-Onset Myocardial Infarction. Panel A shows a forest plot of hazard ratios for the association between coronary heart disease and CHIP in the BioImage and Malmö Diet and Cancer (MDC) studies. Hazard ratios for coronary heart disease among study participants with CHIP mutations were obtained with the use of a Cox proportional-hazards model after adjustment for age, sex, type 2 diabetes status, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking status, and hypertension. Panel B shows a forest plot of odds ratios for the association between myocardial infarction and CHIP in the Atherosclerosis, Thrombosis, and Vascular Biology Italian Study Group (ATVB) and the Pakistan Risk of Myocardial Infarction Study (PROMIS). The odds ratios were obtained with the use of a logistic-regression model after adjustment for age, sex, type 2 diabetes status, and smoking status.

Association between CHIP and Increased Coronary-Artery Calcification.

Jaiswal S et al. N Engl J Med ;377:111-121

Presenter

Presentation Notes

Figure 3 Association between CHIP and Increased Coronary-Artery Calcification. Panel A shows coronary-artery calcification (CAC) scores in Agatston units among participants with CHIP and those without CHIP in the BioImage study, stratified according to the presence or absence of incident coronary heart disease. The horizontal line in each box indicates the median score, and the top and bottom of the boxes indicate the 75th and 25th percentiles, respectively. Among the participants with no coronary heart disease, the median scores were 92 for no CHIP and 306 for CHIP; among those with coronary heart disease, the median scores were 355 and 650, respectively. P=0.03 for the comparison between CHIP and no CHIP; the P value was obtained with the use of a linear-regression model of logarithm-transformed CAC plus 1, after adjustment for incident coronary heart disease status. Panel B shows a forest plot of odds ratios for the association between a CAC score of 615 or more and mutation status, stratified according to the variant allele fraction (VAF) among participants with incident coronary heart disease in BioImage. The odds ratios were obtained with the use of a logistic-regression model after adjustment for age, sex, type 2 diabetes status, total cholesterol, HDL cholesterol, smoking status, and hypertension. P=0.02 for heterogeneity between a VAF of less than 0.10 and a VAF of 0.10 or more.






Nature Genetics 2018 September






• Quality Improvement Pilot

• The Primary goals are to:

‒ Identify those genetically predisposed to adverse drug affects

‒ Guide drug selection and dosing

‒ Reduce untoward drug effects

‒ Improve the quality of patient care

Therapeutic Risk mItigation plUs optiMizedPharmacotHerapy (TRIUMPH)

• Preemptive, initiated at first contact/first return visit

A Broader Strategy

Neuropathy riskCardiotoxicity riskBone marrow ‘opathy’ riskGastropathy risk

Hereditary cancer riskEligibility for PARP inhibitorsCriteria for immunotherapy

Drug selection and dosing• Pain control• Antiemetics• Antifungals• Anesthesia risks• Coagulation risks

Practical choicesSelection treatment from amongst ‘equals’We have large cancer cohorts, so let’s look!Quality improvement is needed to find the right fit for most health systems – don’t just copy the eggheads

‘acceptable’* levels of toxicity We have to ask!*to the patient, not prescriber

Preemptive assessment of benefit:risk, to AVOID risk and ASSURE the best change of benefit

AcknowledgementsPersonalized Medicine Clinical ServiceHoward McLeod, PharmD (Medical Director, Chair)Heather Blanford (PMCS Admin Assistant)Tim Block, MPA, HSA (Administrator)J. Kevin Hicks, PharmD, PhD (Attending, Clinical Service)Sapna Joshi (Executive Assistant)Todd Knepper, PharmD (Attending, Clinical Service) Neil Mason, MA, MBA, PSM (Strategist) Daryoush Saeed-Vafa , MD (Fellow)Christine Walko, PharmD, BCOP (Attending, CGAC Chair) Pam Wilson, RN, MBA, MSN, CPHRM (Program Director)

Clinical Service ConsultantsTerry Boyle, MD, PhDAndy Brohl, MDMohammad Hussaini, MDEric Padron, MDTeresa Vo, PharmD

GeneticsXia Wang, MD, PhD, FACMGLaura Barton, MA, MS, CGCChristine Bruha, MS, CGCJennifer Brzosowicz, MS, CGCCarolyn Haskin, MS, GCGKathleen Ray, MGC, CGCBioinformaticsRichard Lu, PhDJamie Teer, PhDGuillermo Gonzalez-Calderon, PhDRodrigo Carvajal, PhD

Outcomes ResearchMargaret Byrne, PhDDeborah Cragun, MS, CGC, PhDKristine Donovan, PhDHeather Jim, PhDSusan Vadaparampil, PhD

AcknowledgementsEHR ITRanda Perkins, MD (CMIO)Alastair MacGregor, MD (Associate CMIO)Jennifer Greenman (CIO)Kerry Kelly, MT-ASCP Joseph Markowitz, MD

PathologyAnthony Magliocco, MD (Chair)Lynn Moscinski, MD (Laboratory Medicine Chair)Thomas WatsonClinical Action Genomics Committee Therapeutic Risk Mitigation Panel Michael Fradley, MD (Physician Champion, Cardiooncology)Sepideh Mokhtari, MD (Physician Champion, Neurology)Sephalie Patel, MD (Physician Champion, Anesthesia)Bijal Shah MD (Physician Champion, Lymphoma)Mian Shahzad, MD, PhD (Physician Champion, Ovarian)Hatem Soliman, MD (Physician Champion, Breast)

CYP2C19-Voriconazole ProjectJohn Greene, MDRebecca Nelson, PharmDYanina Pasikhova, PharmDRod Quilitz, PharmDWonhee So, PharmDPGx – Antidepressant ProjectMargarita Bobonis, MDBarbara Lubrano Di Ciccone, MDSteven Sutton, PhD

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With choice comes decision - Cardiac Oncology