Identification of Actionable Genomic Alterations Using Circulating Cell-Free DNA

Abstract

PURPOSE

Cell-free DNA (cfDNA) next-generation sequencing is a noninvasive approach for genomic testing. We report the frequency of identifying alterations and their clinical actionability in patients with advanced/metastatic cancer.

PATIENTS AND METHODS

Prospectively consented patients had cfDNA testing performed. Alterations were assessed for therapeutic implications.

RESULTS

We enrolled 575 patients with 37 tumor types. Of these patients, 438 (76.2%) had at least one alteration detected, and 205 (35.7%) had one or more alterations of high potential for clinical action. In diseases with 10 or more patients enrolled, 50% or more had at least one alteration deemed of high potential for clinical action. Trials were identified in 80% of patients (286 of 357) with any alteration and in 92% of patients (188 of 205) with one or more alterations of high potential for clinical action of whom 57.6% (118 of 205) had 6 or more months of follow-up available. Of these patients, 10% (12 of 118) had received genomically matched therapy through enrollment in clinical trials (n = 8), off-label drug use (n = 3), or standard of care (n = 1). Although 88.6% of all patients had a performance status of 0 or 1 upon enrollment, the primary reason for not acting on alterations was poor performance status at next treatment change (28.1%; 27 of 96).

CONCLUSION

cfDNA testing represents a readily accessible method for genomic testing and allows for detection of genomic alterations in most patients with advanced disease. Utility may be higher in patients interested in investigational therapeutics with adequate performance status. Additional study is needed to determine whether utility is enhanced by testing earlier in the treatment course.

INTRODUCTION

Molecular profiling of tumors through circulating cell-free DNA (cfDNA) has gained significant traction in recent years because of its noninvasiveness, sensitivity, and quick turnaround time.^1-4 For patients with advanced cancers, specifically those who have either exhausted their solid tumor block or have quickly progressed on compelling intervening therapy, repeat invasive tissue biopsy is necessary to identify driver mutations and/or resistance mechanisms that arise in response to treatment or natural tumor evolution/progression.^3,5 In addition, a single sample can serve as a global representation of the mutational landscape across multiple metastatic lesions.^6-8 Together, these attributes make cfDNA testing especially attractive for patients with advanced cancer where a quick clinical decision is necessary to identify genomically relevant clinical trials.

However, approaches are needed to discern truncal driver alterations from those that later evolve³ and to prioritize variants for actionability through variant functional annotation, assignment of potential for clinical actionability, and clinical trial matching to guide clinical decision making on the basis of cfDNA testing. In this prospective study of 575 patients with advanced cancers, we provided such postprocessing to evaluate the clinical utility of cfDNA testing in identifying clinically actionable genomic alterations. This entailed the delivery of personalized annotation reports, including functional interpretation of variants and retrieval of genomically relevant clinical trials. However, physicians were allowed to order testing and initiate treatments without waiting for results, as appropriate. We retrospectively reviewed clinical management after cfDNA testing and provide end-to-end evaluation of the clinical potential utility against the true utility of cfDNA testing.

PATIENTS AND METHODS

Patients

From November 2014 through February 2018, 575 patients with advanced cancers were prospectively consented for enrollment (ClinicalTrials.gov identifier: NCT01772771) and underwent cfDNA testing using a Clinical Laboratory Improvement Act–certified, College of American Pathologists–accredited laboratory, New York State Department of Health–approved panel that includes 70 to 73 genes commonly altered in cancer (Guardant360; Guardant Health, Redwood City, CA). Criteria for enrollment were active metastatic/local inoperable advanced cancer, consideration of clinical trial enrollment within the next two lines of therapy, and exhausted tissue block or archival tissue older than 1 year or available tissue block but progression on compelling intervening therapy. Human investigations were performed after approval by the MD Anderson institutional review board and in accordance with an assurance filed with and approved by the US Department of Health and Human Services.

CONTEXT

• Key Objective

• What is the frequency of actionable alterations in patients with advanced/metastatic cancer who undergo cell-free DNA (cfDNA) testing?

• Knowledge Generated

• In this study, of 575 patients who had cfDNA testing, 76.2% had at least one alteration detected and 35.7% had one or more alterations of high potential for clinical action. Of patients with alterations of high potential for action, 10% received genomically matched therapy. The primary reason for not acting on alterations was poor performance status at next treatment change.

• Relevance

• There is growing interest in liquid biopsies for treatment selection. Actionable alterations can be detected in cfDNA across a variety of tumor types. Patient selection and optimal timing of testing may enhance clinical utility of testing.

Comprehensive Genomic Testing in Plasma

cfDNA was extracted from whole blood collected in 10-mL Streck tubes. Samples were shipped to a Clinical Laboratory Improvement Act–certified, College of American Pathologists–accredited laboratory (Guardant Health). After double ultracentrifugation, 5 to 30 ng of cfDNA were isolated and analyzed by digital sequencing as previously described.^9-14 Samples were tested on either a 70- or 73-gene panel (DDR2, MAPK1 [ERK2], and MAPK3 [ERK1]) were added) for the detection of point mutations, select insertion-deletion alterations (indels), copy number amplifications, and fusions.³

Actionable Genes Defined

Variants identified by the Guardant360 panel were annotated for their functional significance by the Institute for Personalized Cancer Therapy–Precision Oncology Decision Support team at MD Anderson Cancer Center. Variants were ascribed a clinical potential for actionability using criteria described previously^15,16 and herein. Sixty-seven genes were considered actionable. Only variants that occurred within an actionable gene were annotated.^17,18

A gene is considered actionable if it has an established biologic role in cancer and there is a clinically available drug to which the gene confers sensitivity or resistance, where actionability can be applicable to all or select alteration or tumor types. The 67 panel genes and Precision Oncology Decision Support–designated actionability are listed in the Data Supplement. Of note, other genes are included in the Guardant360 panel, such as tumor suppressor genes, which may not be targetable, but clonal driver mutations in these genes are usually variants at the highest allele fractions, which makes them ideal candidates to index the degree of tumor DNA shedding into the bloodstream and to determine the relative subclonality of other variants in the same sample.

Defining a Variant’s Potential for Clinical Action

Variants reported in an actionable gene were annotated as follows:

• Functional significance (FS): activating, activating inferred, inactivating, inactivating inferred, inactivating and neomorphic, unknown, or likely benign

• Actionable variant call (AVC; from highest level of evidence [LOE] to lowest): yes: literature-based (peer-reviewed scientific literature); yes: functional genomics (Institute for Personalized Cancer Therapy internal functional genomics platform); yes: inferred (intermediate LOE includes inferences dictated by the type of alteration and is mostly applicable to frameshift/truncation alterations where loss of region is inferred to be activating/inactivating); potentially (lower LOE where there are indirect and/or limited data to support the function of a specific alteration); unknown; or no (FS call is likely benign or inactivating alteration in an oncogene)

• Actionable for call: the combination of an FS call and AVC: therapeutic intervention, resistance to drugs, or enrollment in select clinical trials.

Finally, gene actionability can be applicable to all, or specific, tumor types. For example, KRAS is actionable for therapeutic intervention for all tumor types; however, for colorectal tumors, KRAS is also actionable for resistance to cetuximab and panitumumab (Data Supplement).

A fifth parameter to sum these parameters into one final call specifically that describes each variant’s potential for treatment with targeted therapy is the variant’s potential for clinical action: high potential for clinical action (HPCA), low potential for clinical action, or not recommended for clinical action. Although specific Food and Drug Administration–approved indications and/or National Comprehensive Cancer Network guidelines are not included within this parameter, the upstream annotation process depends on gene-level actionability. The workflow for categorizing a variant’s potential for clinical action is illustrated in the Data Supplement.

Clinical Trials Identified on the Basis of Patient Genomic Profile

Trial retrieval was restricted to variants that were deemed actionable and to trials available at the MD Anderson Cancer Center at the time of patient annotation.

Patient Follow-Up

Patients were followed for at least 6 months after the initial annotation to collect treatment decisions, performance status (PS), and trial screening/enrollment (and reasons for eligibility/ineligibility).

RESULTS

Patient Characteristics

We enrolled 575 patients into the study. Patient characteristics and disease types are listed in Table 1. The major diseases represented are those with 10 or more patients enrolled.

TABLE 1.

Patient Characteristics and Disease Types

Overall Utility of cfDNA in Identifying Actionable Alterations in Patients With Advanced Cancers

Of the 575 patients enrolled, 76.2% (n = 438) had at least one alteration reported in any gene; of these 438 patients, 81.5% (n = 357) had at least one alteration reported within an actionable gene, and of these 357, 54.7% (n = 205) had at least one HPCA alteration (see Patients and Methods). Thus, 35.6% of the sample (205 of 575 patients) had at least one HPCA alteration (Fig 1).

FIG 1.

Flow diagram that depicts the overall utility of cell-free DNA (cfDNA) in identifying actionable alterations in patients with advanced cancers. A total of 575 patients was enrolled, of whom 76.2% (n = 438) had at least one alteration reported. Of these 438, 81.5% (n = 357) had at least one alteration reported within an actionable gene, of whom in turn, 54.7% (n = 205) had at least one high potential for clinical action alteration. Hence, of the original 575 patients tested, 35.6% (n = 205) had at least one alteration classified as having a high potential for clinical action.

Overall Landscape of Molecular Alterations

The panel used offered extensive point mutation coverage for 70 to 73 genes (amplifications, 18 genes; fusions, six genes; indels, 23 genes). Consistent with this, single nucleotide variants were the most commonly reported alteration type, followed by amplifications, indels, and fusions. The results reflected the genomic landscape reported across tumor types through solid tumor testing^19,20 and is shown in Figure 2A. Amplification levels detected ranged from positive, strongly positive, to very strongly positive, as previously defined.^9-14 Genes most frequently reported with very strong positive amplification levels were CCNE1, FGFR1, MET, ERBB2, and AR. The remaining genes usually reported positive or strongly positive amplifications levels (Fig 2B). Aneuploidy/polysomy versus focal amplification can be ascertained when multiple genes reside on the same chromosome. For example, BRAF, CDK6, EGFR, and MET all reside on chromosome 7, so when all are amplified, aneuploidy is assumed. Similarly, more than four genes are included on chromosome 17, so focal amplification of ERBB2 (HER2) can be determined.

FIG 2.

Landscape of molecular alterations identified. (A) Commonly reported alterations by gene and alteration type. (B) Amplification levels of genes with copy number alterations. (C) Most frequently altered genes with consideration of each variant’s potential for clinical action. Shown are the most frequently altered genes in 205 patients with at least one alteration classified as high potential for clinical action. Indel, insertion/deletion alteration; SNV, single nucleotide variant.

Molecular Landscape in the Context of Potential for Clinical Action

Among the overall molecular landscape for the 357 patients with at least one alteration in an actionable gene, the most frequently altered genes (> 40 patients) were KRAS (22.7%; n = 81), PIK3CA (21%; n = 75), NF1 (16.5%; n = 59), EGFR (15.1%; n = 56), MET (15.1%; n = 54), BRAF (14.6%; n = 52), and ERBB2 (12.9%; n = 46). However, if the specific alteration’s potential for clinical action is factored in, the number of patients with HPCA alterations decreased from 357 to 205. Likewise, the order of most actionable genes within the context of these 205 patients shifted, with PIK3CA (18%; n = 37) being the most represented followed by BRAF (13.7%; n = 28), KRAS (10.2%; n = 21), NF1 (9.8%; n = 20), CDKN2A (7.8%; n = 16), ERBB2 (7.3%; n = 15), CCNE1 (7.3%; n = 15), and FGFR1 (7.3%; n = 15). Again, these results highlight the importance of assessing each alteration’s biologic FS individually and in the disease context (Fig 2C).

Proportion of Patients With Alterations Identified, by Disease Type

Given the diverse set of advanced cancers (37 types) of patients enrolled in this study, we investigated whether differences existed in the suitability of cfDNA testing. Among diseases with 10 or more patients enrolled, most tumor types had at least one alteration reported within an actionable gene, as follows: colorectal, 95%; breast, 91%; gall bladder, 81%; prostate, 80%; lung, 74%; cholangiocarcinoma, 68%; hepatocellular, 63%; pancreatic, 61%; head and neck, 52%; ovarian, 50%; neuroendocrine, 46%; sarcoma, 40%; and appendiceal, 27.6%. For all other disease types combined, 46.3% of patients had at least one alteration that occurred within an actionable gene (Fig 3A).

FIG 3.

Identification of clinically actionable alterations and clinical trials by disease type. (A) Distribution of clinically actionable alterations by disease type. (B) Identification of clinical trials across all disease types in the context of variant’s potential for clinical action.

The actual variant actionability, or potential for clinical action, was used to stratify patients by disease type into three categories: HPCA, low potential for clinical action, and not recommended for clinical action. Among diseases with 10 or more patients enrolled, the proportion of patients who had at least one HPCA alteration was as follows: breast, 67.6%; colorectal, 60%; lung, 56.5%; gall bladder, 56.3%; ovarian, 50%; head and neck, 43.5%; cholangiocarcinoma, 43.3%; prostate, 40%; hepatocellular, 25.4%; pancreatic, 19.5%; neuroendocrine, 15.4%; sarcoma, 14.9%; and appendiceal, 10.6% (Fig 4A).

FIG 4.

(A) Clinical management outcomes of patients with at least one variant of high potential for clinical action identified. (B) Reasons why genomic alterations were not acted upon in patients with at least one variant of high clinical potential identified. ^a2. ^b3. ^c5. ^d6. ^e7. ^f11. ^g26. ^h27. Mets, metastases; PS, performance status.

Overall, these findings demonstrate that cfDNA testing is informative across all tumor types included in this study. Specifically, among tumor types with greater representation (≥ 10 patients enrolled), 50% or more patients had at least one HPCA alteration. Tumors with slightly lower proportions of patients having at least one HPCA alteration were neuroendocrine tumors (15.4%), sarcoma (14.9%), and appendiceal tumors (10.6%).

Comparison With Previous Solid Tumor Testing

This study was not designed to compare cfDNA testing with tissue testing, and thus, patients did not have acquisition and testing of tumor and plasma samples simultaneously. The median time between the dates plasma and the archival tumor tissue that was tested were obtained was 20 months (range, 19 days to 13.6 years). When we compared cfDNA testing and previous tumor testing in 184 patients with previous solid tumor testing, regardless of the time interval but accounting for panel coverage among alterations in actionable genes detected in previous testing, there was a 36% concordance rate (56 of 157 alterations). If only including cfDNA panels that detected one or more alterations (143 patients), the concordance rate was 44% (56 of 127 alterations). The concordance rate for mutations was 42% (41 of 98 mutations). There were 135 alterations in actionable genes reported on cfDNA testing that were not covered by the previous, usually more limited, tumor testing. In addition, there were 182 alterations detected in actionable genes on cfDNA testing that were not detected in previous panels, although they were covered by the assay. These alterations potentially represent genomic evolution and mechanisms of acquired resistance and included detection of a PIK3CA mutation after treatment with a BRAF inhibitor and BRAF and MET mutations after treatment with AKT/mTOR inhibitors.

On the other hand, among 391 patients with no previous solid tumor testing attempted, 529 alterations in actionable genes were reported on cfDNA testing. In addition, of 14 patients who had previous solid tumor testing attempted but the tumor genomic assays failed (eg, because of an insufficient amount of DNA obtained from the sample), cfDNA testing reported 17 alterations in actionable genes, with 10 patients having any alteration detected, seven having at least one alteration in an actionable gene, and two having at least one specific alteration that was actionable at the time of annotation.

Identification of Clinical Trials for Variants Identified Through cfDNA

The utility of cfDNA testing depends on the ability to match patients to relevant targeted therapy delivered through clinical trials, standard of care, or off-label use. In addition to variant annotation, for patients with HPCA variants and/or select low potential for clinical action variants (ie, FS, unknown; AVC, potentially; actionable for call, therapeutic intervention), the decision support team identified genotype-matched trials for the patient’s tumor type, which accounted for additional biomarker inclusion/exclusion criteria, where applicable. The portfolio of clinical trials available for matching consisted of those available across MD Anderson at the time of a patient’s annotation. On the basis of these criteria, trials were identified in 286 (80%) of 357 patients (Fig 3B). Among patients who had at least one HPCA alteration, identification of trials was even higher, with potential trials identified for 188 (92%) of 205 patients (Fig 3B).

Clinical Course of Action After cfDNA Testing

Of the 205 patients with at least one HPCA variant, 118 had reached the 6-month follow-up period at the time of data freeze (March 6, 2018). At this time, 11% of patients (13 of 118) had been treated with genotype-matched targeted therapy on the basis of cfDNA testing, where a match included enrollment into a clinical trial (n = 9), off-label drug use (n = 3), or standard of care (n = 1). Of note, 7.6% of patients (nine of 118) who were not matched had already acted on this alteration on the basis of prior molecular testing (Fig 4A). Thus, we sought to understand why the genomic alterations in the rest of the patients (n = 96) were not acted upon. We found that most unmatched patients received standard of care (38.5%) or did not begin a new treatment regimen (36.5%), whereas a smaller subset of patients continued on a treatment regimen established either on or before the ordering of cfDNA testing (9.4%), enrolled in another clinical trial (genomic and nongenomically driven; 12.5%), had surgery (2.1%), or had additional tumor testing ordered (1%; one of 96; Fig 4A). Moreover, a major contributor to not acting on genomic alterations was the overall PS, with 28% of patients (27 of 96) reporting brain metastasis and/or other comorbidities at the time of trial consideration (Fig 4B).

DISCUSSION

Among the 118 patients with 6 months or longer follow-up in our study, only 13 (11%) with HPCA alterations underwent genotype-matched targeted therapy on the basis of cfDNA testing. Although we predicted higher enrollment because of cfDNA turnaround time, this is in line with our previous report from genomic testing with a targeted tissue panel, which was also 11%.²¹ However, we have previously found that with routine implementation of decision support with variant annotation and trial matching, trial enrollment is much higher for patients with actionable alterations (20.6%)¹⁸.

We retrospectively reviewed clinical management after cfDNA testing to understand the causes of low trial enrollment rates. On the basis of at least 6 months of follow-up, a major contributor to not acting on a genomic alteration was the PS of patients at the time of trial consideration. Reports from the University of California, Los Angeles, also found that biopsy samples required for trial eligibility led to delays and decline in PS, which resulted in reduced enrollment by approximately one half.²² Similarly, a review of 55 non–small-cell lung cancer trials at Princess Margaret Hospital reported a significant reduction in enrollment because of declining PS or insufficient tissue biopsy sample material when required.²³ In addition, we evaluated a novel and important factor—initiation of alternative therapy when ordering cfDNA testing—that may have reduced the number of trial-eligible patients because of a decline of PS during next-line therapy. Of note, cfDNA testing has a short enough turnaround time to facilitate selection of next-line therapy (a median of 7 calendar days now is achieved at the high-volume laboratory used herein).^3,24 Thus, its use at the point of care may increase its clinical utility before a decline in PS. Furthermore, outside select diseases with drug approvals linked to a genomic marker, often genomic testing is initiated late in the treatment course. Testing earlier, when PS is better, also is likely to optimize patient outcomes from investigational therapies.

There have been several reports on cfDNA testing utility.^3,25-27 Laufer-Geva et al²⁸ reported that treatment decisions were changed in 23% to 32% of patients with non–small-cell lung cancer dependent on the clinical scenario. Of note, we accrued very few patients with lung cancer and accrued predominantly tumor types without standard-of-care, genomically informed treatment options. This may have affected overall actionability rates as well as the number of patients who received genomically matched therapy.

Previous studies have reported higher concordance rates for cfDNA results and tumor testing.^24,29 Leighl et al³⁰ reported that in untreated nonsquamous cell carcinoma, Food and Drug Administration–approved target (EGFR, ALK, ROS1, BRAF) concordance was greater than 98.2%, with a 100% positive predictive value for cfDNA versus tissue (34 of 34 patients with EGFR-, ALK-, or BRAF-positive mutations). Our study was not designed to compare cfDNA and tissue testing; thus, it had inherent limitations to investigate this issue, including intervening time between sample collections, intervening treatments and resulting tumor evolution, tumor heterogeneity of solid tumor testing versus global representation of primary and metastatic sites from cfDNA, and tumor type dependence on the extent of cfDNA shedding. However, cfDNA testing reported potentially clinically relevant alterations that otherwise may have been missed because solid tumor testing was not attempted or not technically feasible because of lack of tissue or did not have sufficient coverage.

Our study has other limitations. First, as previously discussed, we prospectively enrolled patients into a study that offered cfDNA testing and retrospectively assessed clinical utility. Although there may have been greater use of cfDNA results for treatment selection if patients were prospectively tested and treatment initiated after results, we believe that our study design gives insight into clinical practice patterns. Second, we enrolled patients with a variety of tumor types. This design allowed us to determine that the detection of cfDNA and the detection of actionable alterations vary by tumor type. However, the variable histologies made some clinical outcome assessments challenging. Third, actionability was assessed at the time of testing by a designated decision support team, but trial matching was performed automatically on the basis of gene-drug associations. Where there were trial matches, additional clinical exclusion criteria possibly made patients ineligible for selected trials. Finally, for actionability, we focused on positive predictive biomarkers only. cfDNA also can identify actionable resistance mutations, and including those would have potentially enhanced clinical utility.³

In summary, we demonstrated that cfDNA testing detects actionable alterations in patients with advanced cancers across a variety of tumor types. Additional study is needed to determine how to enhance clinical utility and determine optimal testing timing, especially in patients interested in investigational therapeutics.

AUTHOR CONTRIBUTIONS

Conception and design: Nora S. Sánchez, Chetna Wathoo, Milind Javle, Funda Meric-Bernstam

Financial support: Kenna R. Mills Shaw

Administrative support: Kenna R. Mills Shaw

Provision of study material or patients: Milind Javle, Ahmed Kaseb, Vivek Subbiah, Victoria M. Raymond, Kenna R. Mills Shaw

Collection and assembly of data: Nora S. Sánchez, Michael P. Kahle, Ann Marie Bailey, Chetna Wathoo, Kavitha Balaji, Mehmet Esat Demirhan, Dong Yang, Vivek Subbiah, Filip Janku, Victoria M. Raymond, Richard B. Lanman, Kenna R. Mills Shaw, Funda Meric-Bernstam

Data analysis and interpretation: Nora S. Sánchez, Michael P. Kahle, Ann Marie Bailey, Chetna Wathoo, Milind Javle, Ahmed Kaseb, Cathy Eng, Vivek Subbiah, Victoria M. Raymond, Richard B. Lanman, Funda Meric-Bernstam

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.

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Nora S. Sánchez

Employment: Foundation Medicine

Stock and Other Ownership Interests: Roche

Ann Marie Bailey

Employment: QIAGEN

Travel, Accommodations, Expenses: QIAGEN

Kavitha Balaji

Employment: Lexicon

Stock and Other Ownership Interests: Lexicon

Travel, Accommodations, Expenses: Lexicon

Dong Yang

Employment: Molecular Health

Milind Javle

Consulting or Advisory Role: QED Therapeutics, Oncosil Medical, Incyte, Mundipharma

Other Relationship: Rafael Pharmaceuticals, Incyte, Pieris Pharmaceuticals, Merck, Merck Serono, Novartis, Seattle Genetics, BeiGene, QED Therapeutics, Bayer AG

Ahmed Kaseb

Stock and Other Ownership Interests: Gilead Sciences

Honoraria: Merck, Exelixis, Bayer AG, Bristol-Myers Squibb

Consulting or Advisory Role: Bayer AG, Bristol-Myers Squibb, Merck, Exelixis

Research Funding: Bristol-Myers Squibb, Merck, Bayer AG, Onyx Pharmaceuticals, Genentech

Travel, Accommodations, Expenses: Exelixis, Merck, Bayer AG, Onyx Pharmaceuticals, Bristol-Myers Squibb

Cathy Eng

Honoraria: Roche, Bayer AG

Consulting or Advisory Role: Roche, Genentech, Bayer Schering Pharma, Taiho, Terumo Clinical Supply

Travel, Accommodations, Expenses: Genentech, Roche, Bayer AG, Sirtex Medical

Vivek Subbiah

Consulting or Advisory Role: MedImmune

Research Funding: Novartis (Inst), GlaxoSmithKline (Inst), NanoCarrier (Inst), Northwest Biotherapeutics (Inst), Genentech (Inst), Roche (Inst), Berg Pharma (Inst), Bayer AG (Inst), Incyte (Inst), Fujifilm (Inst), PharmaMar (Inst), D3 Oncology Solutions (Inst), Pfizer (Inst), Amgen (Inst), AbbVie (Inst), Multivir (Inst), Blueprint Medicines (Inst), Loxo Oncology (Inst), Vegenics (Inst), Takeda Pharmaceuticals (Inst), Alfasigma (Inst), Agensys (Inst), Idera (Inst), Boston Medical (Inst), Inhibrx (Inst), Exelixis (Inst)

Travel, Accommodations, Expenses: PharmaMar, Bayer AG

Filip Janku

Stock and Other Ownership Interests: Trovagene

Consulting or Advisory Role: Deciphera, Trovagene, Novartis, Sequenom, Foundation Medicine, Guardant Health, Immunome, Synlogic, Valeant, Dendreon, IFM Therapeutics, Sotio, PureTech

Research Funding: Novartis (Inst), BioMed Valley Discoveries (Inst), Roche (Inst), Agios (Inst), Astellas Pharma (Inst), Deciphera (Inst), Plexxikon (Inst), Piqur (Inst), Fujifilm (Inst), Symphogen (Inst), Bristol-Myers Squibb (Inst), Asana Biosciences (Inst), Astex Pharmaceuticals (Inst)

Other Relationship: Bio-Rad

Victoria M. Raymond

Employment: Trovagene, Guardant Health

Stock and Other Ownership Interests: Trovagene, Guardant Health

Richard B. Lanman

Employment: Guardant Health, Veracyte

Leadership: Guardant Health, Biolase

Stock and Other Ownership Interests: Guardant Health, Biolase, Forward Medical

Consulting or Advisory Role: Forward Medical

Research Funding: Guardant Health

Kenna R. Mills Shaw

Research Funding: Guardant Health (Inst), Tempus (Inst)

Funda Meric-Bernstam

Honoraria: Sumitomo Group, Dialectica

Consulting or Advisory Role: Genentech, Inflection Biosciences, Pieris Pharmaceuticals, Clearlight Diagnostics, Darwin Health, Samsung Bioepis, Spectrum Pharmaceuticals, Aduro Biotech, Origimed, Xencor, Debiopharm Group, Mersana, Seattle Genetics

Research Funding: Novartis, AstraZeneca, Taiho Pharmaceutical, Genentech, Calithera Biosciences, Debiopharm Group, Bayer AG, Aileron Therapeutics, Puma Biotechnology, CytomX Therapeutics, Jounce Therapeutics, Zymeworks, Curis, Pfizer, eFFECTOR Therapeutics, AbbVie, Boehringer Ingelheim (I), Guardant Health (Inst), Daiichi Sankyo, GlaxoSmithKline

Speakers’ Bureau: Chugai Biopharmaceuticals

No other potential conflicts of interest were reported.