on-117 echo biomarkers 7-13 12p€¦ · philadelphia, pa . program overview. these live teleecho®...

July 13, 2020

Agenda (Track 1): BIOMARKERS I. Overview of Immuno-oncology

a. Immunosurveillance mechanisms by the innate and adaptive immune systems b. Physiologic function of CTLA-4 and PD-1 as immune checkpoints c. MOAs of ICIs alone or in combination d. Brief overview of clinical profiles and indications for FDA-approved ICIs across specific tumor types

II. Immune-related Biomarkers and Testing Methodologies

a. Challenges in immune biomarker discovery b. Prognostic and predictive biomarkers

1. Clinical trial data across tumor types – correlation of markers and clinical outcomes a. Immune markers - tumor lymphocyte infiltrates, PD-L1 expression, MSI status, TMB,

neoantigens c. Identifying new immune-related biomarkers

1. TMB as an evolving immune-related biomarker a. Relationship between TMB and PD-L1

d. Immune-related biomarker testing methodologies e. Use of tumor immune-related biomarkers to select patients that will benefit most from new cancer

immunotherapies administered alone or in combination 1. Pan-tumor clinical trial results can inform personalized treatment-making decisions

a. Definition/Rationale b. Advantages and disadvantages of trial design c. Relevant results d. Ongoing trials

III. Multidisciplinary Oncology Team – Optimizing Patient Care and Survivorship Through Shared Decision Making

a. Educational strategies for the oncology patient 1. Discussing biomarker testing and its relevance to individual treatment plans 2. Shared decision making in the care process – use of decision aids 3. Planning for biomarker testing considering resource availability and healthcare coverage

IV. Interactive Project ECHO Case Studies

a. Role of biomarkers in combining and sequencing immunotherapies b. Can biomarkers be used for monitoring therapeutic responses c. How use of biomarkers affects treatment choice

V. Conclusions VI. Questions & Answers

ECHO Series: Targeting Tumor Immunosuppression with Immune Checkpoint Inhibitors

Biomarkers

Faculty

Shayma Master Kazmi, MD, RPh Medical Director of Thoracic Oncology Cancer Treatment Centers of America

Philadelphia, PA

PROGRAM OVERVIEW These live TeleECHO® sessions will be a faculty-led didactic and case-based lecture focusing on immune-related biomarkers and immune checkpoint inhibition.

TARGET AUDIENCE This initiative is designed to meet the educational needs of medical oncologists, surgical oncologists, oncology pharmacists, oncology nurses and other healthcare professional involved in the management of patients with cancer who are treated or eligible for treatment with immunotherapy.

LEARNING OBJECTIVES

Upon completion of the program, attendees should be able to:

Learning Objectives

• Describe the role of tumor immune evasion in the development of cancer and review the rationale underlying the use of immune checkpoint inhibitors in the management of select solid tumors

• Evaluate data on the use of biomarkers to select therapy options for patients with solid tumors

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Credits: 1.0 ANCC Contact Hour.

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Dr. Kazmi is a consultant and serves on the Speakers Bureau for Eisai, Lilly, Merck,

Immunomedics, and Takeda.

The independent reviewers, staff, planners, and managers reported the following financial

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CME Content Review The content of this activity was independently peer reviewed. The reviewer of this activity has nothing to disclose. CNE Content Review The content of this activity was peer reviewed by a nurse reviewer. The reviewer of this activity has nothing to disclose.

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Posting Questions in Zoom Chat

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Targeting Tumor Immunosuppression with Immune Checkpoint Inhibitors (TeleECHO Session):

Biomarkers

Shayma Kazmi, MD, RPhMedical Director of Thoracic Oncology

Cancer Treatment Centers of America

Philadelphia, PA

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Disclosures

• Dr. Shayma Kazmi discloses that:

–She is a consultant for Eisai, Lilly, Merck, and Takeda

–She is a Speaker Bureau member for Eisai, Immunomedics, Lilly, Merck, and Takeda

• During the course of this lecture, Dr. Kazmi may mention the use of medications for both FDA‐approved and non‐approved indications.

This activity is supported by an educational grant from Merck & Co, Inc.

Learning Objectives

• Describe the role of tumor immune evasion in the development of cancer, and review the rationale underlying the use of immune checkpoint inhibitors in the management of select solid tumors

• Evaluate data on the use of biomarkers to select therapy options for patients with solid tumors

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Overview of Immuno‐oncology

Antitumor Immunity Is a Dynamic Process

Hegde PS, Chen DS. Immunity. 2020;52:17‐35.

APCs = antigen‐presenting cells; CTLs = cytotoxic T lymphocytes.

Lymphnode

Tumor

Bloodvessel

Cancer immunity cycle

Priming and activation

(APCs and T cells)

Cancer antigen presentation

(dendritic cells/APCs)

Release of cancer cell antigens

(cancer cell death)

Synthetic T cellengagement

Trafficking of T cells to tumors (CTLs)

Infiltration of T cells into tumors(CTLs, endothelial cells)

Recognition of cancer cells by T cells(CTLs, cancer cells)

Killing of cancer cells (immune and cancer cells)

1

2

3

4

5

6

7

Ex vivo geneticmodification and

expansion of T cells

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Immune Checkpoints: CTLA‐4, PD‐1, and PD‐L1

Modified from Singh PP, et al. Gastroenterol Rep (Oxf). 2015;3:289‐297. Modified from Chen DS, et al. Clin Cancer Res. 2012;18:6580‐6587.

CTLA = cytotoxic T lymphocyte antigen; PD = programmed (cell) death; PD‐L1 = PD ligand 1; CD = cluster of differentiation; MHC = major histocompatibility complex; TCR = T cell receptor.

Tumor‐specific T‐cell recognition in the periphery

Tumor‐specific T‐cell recognition in the periphery

Lymphocyte priming to tumor antigens

Lymphocyte priming to tumor antigens

Tumorcell

Antigen‐presenting

cell

T cell

PD‐L1

TCR

MHC

PD‐1PD‐L1 PD‐1

CD80

CD80

CD80

Inhibition

Inhibition

Inhibition

Inhibition

Inhibition

TCRMHC

Tumorantigen

PD‐L1

CD80

CD86CTLA‐4

CD28Activation

Activation

Inhibition

Anti‐CTLA‐4

CTLA‐4 blockade Blocks CTLA‐4 binding to CD80 and CD86

Activation

Activation

–

–

Anti‐PD‐1/PD‐L1

–

–

Anti‐PD‐1/PD‐L1

PD‐1/PD‐L1 blockadeAND also blocks either PD‐1/PD‐L2 or PD‐L1/CD80 interaction

ActivationActivation

The Cancer Immunology Balancing Act

Monjazeb AM, et al. Front Oncol. 2013;3:197. Davies M. Cancer Manag Res. 2014;6:63‐75.

Immune escape• Antigen presentation: loss of

antigen (immune‐editing), HLA• Immune checkpoints:

PD‐1, PD‐L1, CTLA‐4, TIM3• Cytokines: TGF‐β, IL‐4, IL‐6• Immunosuppressive ME: IDO• Cellular immune escape: Tregs,

M2 macrophages, MDSCs• T‐cell anergy

Immune surveillanceImmune system

recognizes malignant cells

HLA = human leukocyte antigen; TIM3 = T‐cell immunoglobulin and mucin‐domain containing‐3; TGF‐β = transforming growth factor beta; ME = microenvironment; IL = interleukin; IDO = indoleamine‐2, 3‐dioxygenase; Treg = regulatory T cell; MDSC = myeloid‐derived suppressor cell.

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Cancer Therapy Through the Ages New Concept—Immunotherapy

1. Scaltriti M, Baselga J. Clin Cancer Res. 2006;12:5268‐5272. 2. Røsland GV, Engelsen AS. Basic Clin Pharmaccol Toxicol. 2015;116:9‐18.

Targets host

ImmunotherapyTargets the tumor

Targeted therapy

1997 targeted therapy

Current FDA‐Approved PD‐1 Inhibitors*

SCT = stem cell transplant; MSI‐H = microsatellite instability‐high; dMMR = mismatch repair deficiency; CRC = colorectal cancer; NSCLC = non‐small cell lung cancer; RCC = renal cell carcinoma; SCLC = small cell lung cancer; HR = high risk; BCG = Bacillus Calmette‐Guerin; CIS = carcinoma in situ.

1.Cemiplimab (Libtayo®) prescribing information (PI), 2019 (www.regeneron.com/sites/default/files/Libtayo_FPI.pdf). 2. Nivolumab (Opdivo®) PI, 2020 (https://packageinserts.bms.com/pi/pi_opdivo.pdf). 3.Pembrolizumab (Keytruda®) PI, 2020 (www.merck.com/product/usa/pi_circulars/k/keytruda/keytruda_pi.pdf).

Agent Target Approved IndicationsCemiplimab1 PD-1 Cutaneous squamous cell carcinoma (2nd line)

Nivolumab2 PD-1 • Bladder cancer (advanced/metastatic, 2nd line)• Head and neck (recurrent/metastatic, 2nd line)• Hepatocellular carcinoma (2nd line)• Hodgkin lymphoma (relapsed/progressed after SCT

or 4th line)

• Melanoma (metastatic and adjuvant)• MSI-H/dMMR CRC (2nd line)• NSCLC (metastatic, 2nd line)• RCC (advanced, 1st and 2nd line)• SCLC (metastatic, 2nd line)

Pembrolizumab3 PD-1 • Bladder cancer (1st and 2nd line metastatic, and HR BCG unresponsive CIS)

• Cervical cancer (2nd line)• Cutaneous squamous cell carcinoma (recurrent or

metastatic, not curable by surgery or radiation)• Endometrial carcinoma (advanced, not MSI-H or

dMMR, 2nd line)• Esophageal cancer (recurrent locally advanced or

metastatic, 2nd line)• Gastric cancer (3rd line)• Head and neck (1st and 2nd line)• Hepatocellular carcinoma (2nd line)

• Hodgkin lymphoma (4th line)• Melanoma (all metastatic and adjuvant)• Merkel cell carcinoma (recurrent locally

advanced or metastatic)• MSI-H or dMMR tumors (1st and 2nd line)• NSCLC (1st and 2nd line)• Primary mediastinal large B-cell

lymphoma (3rd line)• RCC (advanced,1st line)• SCLC (metastatic, 3rd line)• TMB-H tumors (2nd line)

*See prescribing information for complete detailing of approved indications

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Current FDA‐Approved PD‐L1 and CTLA‐4 Inhibitors*

1. Atezolizumab (Tecentriq®) PI, 2019 (www.gene.com/download/pdf/tecentriq_prescribing.pdf). 2. Avelumab (Bevencio®) PI, 2019 (www.emdserono.com/us‐en/pi/bavencio‐pi.pdf). 3. Durvalumab (Imfinzi®) PI, 2020 (www.azpicentral.com/imfinzi/imfinzi.pdf). 4. Ipilimumab (Yervoy®) PI, 2019 (http://packageinserts.bms.com/pi/pi_yervoy.pdf). Accessed 5/22/2020. *See prescribing information for complete detailing of approved indications

ES‐SCLC = extensive‐stage small cell lung cancer; TNBC = triple negative breast cancer; XRT = radiation therapy.

It is very important to become familiar with these agents since the number and breadth of cancer indications are rapidly changing.

Agent Target Approved IndicationsAtezolizumab1 PD-L1 • Bladder cancer (1st and 2nd line)

• ES-SCLC (1st line)

• NSCLC (1st and 2nd line)

• TNBC (PD-L1+ unresectable, LA or metastatic)

Avelumab2 PD-L1 • Bladder cancer (LA/metastatic, 2nd line)

• Merkel cell carcinoma (metastatic)

• RCC (advanced,1st line)

Durvalumab3 PD-L1 • Bladder cancer (LA/metastatic, 2nd line)

• ES-SCLC (1st line)

• NSCLC (unresectable, stage III, without disease progression following platinum-based chemo-XRT)

Agent Target Approved IndicationsIpilimumab4 CTLA-4 • Melanoma (unresectable or metastatic,

adjuvant resected)• HCC (2nd line)

• RCC (untreated advanced, 1st line)• MSI-H or dMMR CRC (2nd line)• NSCLC (metastatic, 1st line)

Immune Biomarkers Across Multiple Tumor Types

Challenges in Immune Biomarker Development

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Unmet Need for Immunotherapy Biomarkers: Background

• Clinical successes in cancer immunotherapy and across multiple tumor types highlight critical need for biomarkers1

• Predictive—who is most likely to benefit from the therapies?

• Prognostic—factors that predict outcomes irrespective of treatment

• Mechanism of action of biomarkers—how therapy functions in order to inform decision making

1. Butterfield LH. Semin Cancer Biol. 2018;52:12‐15. 2. Dobbin KK, et al. J Immunother Cancer. 2016;4:77.

Evaluating the performance of a predictive biomarker1,2

A trial designed to assess the clinical validity of a predictive biomarker must predefine its clinically meaningful performance metrics.

Guidelines for informative reporting of studies on prognostic as well as diagnostic markers exist; apply them to cancer immunotherapy.

Choice of specific performance metric and benchmark performance level that must be attained is dependent on intended clinical use (ie, determine predictive vs prognostic value of a biomarker).

Clinical utility vs clinical validity: there must be evidence suggesting that the use of the test is likely to lead to clinically meaningful benefit to the patient beyond current standards of care.

Current Immune Biomarker Status

1. Hirsch FR, et al. J Thorac Oncol. 2017;12:208‐222. 2. Vilar E, Gruber SB. Nat Rev Clin Oncol. 2010;7:153‐162. 3. Hause RJ, et al. Nat Med. 2016;22:1342‐1350. 4. Astor L. Targeted Oncol. 2020. (www.targetedonc.com/view/fda‐approves‐pembrolizumab‐for‐tmb‐high‐solid‐tumors). Accessed 6/17/2020. 5. EfremovaM, et al. Front Immunol. 2017;8:1679. 6. Rooney MS, et al. Cell. 2015;160:48‐61. 7. Yuan J, et al. J Immunother Cancer. 2016;4:3. 8. Zhao SG, et al. J Natl Cancer Inst. 2019;111:301‐310. 9. Ma W, et al. J Hematol Oncol. 2016;9:47. 10. Galon J, et al. Science. 2006;313:1960‐1964. 11. Okla K, et al. Crit Rev Clin Lab Sci. 2018;55:376‐407. 12. Santegoets SJ, et al. Cancer Immunol Immunother. 2015;64:1271‐1286. 13. Du W, et al. DiscovMed. 2018;25:277‐290. 14. Chen DS, Mellman I. Nature. 2017;541:321‐330. 15. Gibney GT, et al. Lancet Oncol. 2016;17:e542‐e551.

TMB = tumor mutational burden; TILs = tumor‐infiltrating lymphocytes; LAG‐3 = lymphocyte activation gene‐3.

Under investigation

Neoantigens5,6

PD‐L28 TILs9,10

Inflammatory gene signatures7

LAG‐313

Tregs12

MDSCs11

IDO115

Host factors14

MSI‐H/dMMR2,3

PD‐L11

Current

TMB4

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Challenges in Biomarker Development Tumor Heterogeneity

• Tumor heterogeneity is a well‐known trait of most cancers, originating from genomic instability diverse populations of cells1

• Requires analysis of multiple tumor samples for discovery and validation1

– Invasive, non‐repeatable over time, risky, inaccessible, and not fully reflective of tumor heterogeneity2

1. Gerlinger M, et al. Eur Urol. 2015;67:729‐737. 2. Appierto V, et al. Semin Cancer Biol. 2017;44:106‐116. 3. Burrell RA, et al. Nature. 2013;501:338‐345.

Intertumor and intratumor heterogeneity3

Intratumor heterogeneityIntertumor heterogeneitySubclone 1

Subclone 2 Subclone 3

Clonal heterogeneity

Intercellular geneticand non‐geneticheterogeneity

Challenges in Biomarker DevelopmentExpression

Brooks JD. Genome Res. 2012;22:183‐187.

Gene expression profiling has identified numerous genes expressed at higher levels in cancer, but many are not uniquely elevated in cancer

Lack of consistent correlation between transcript levels and

cognate protein levels

Candidate transcripts or proteins show only a relative increase

in expression vs normal tissue

Accessibility to assays

(ie, biomarkers expressed in nucleus or cytoplasm)

Expression can change over the course of disease

Assay limitations

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Challenges in Biomarker DevelopmentCutoffs

Masucci GV, et al. J Immunother Cancer. 2016;4:76. National Institutes of Health (NIH) Genetics home reference. 2019 (https://ghr.nlm.nih.gov/primer/testing/validtest). Accessed 5/22/2020.

IHC = immunohistochemistry; TPS = tumor proportion score; CPS = combined proportion score.

Measurement of immune response variables often continuous

• Resultant variability regarding analytical performance, criterial and clinical relevance, particularly cutoff points for clinical decision-making

• Cutoff needs to be determined empirically through correlation with clinical outcomes in a clinical trial

Example of PD-1/PD-L1: IHC diagnostic kits and assays vary in:

• Different percentages of positive cells

• Scoring systems/testing platforms

• Cutoff values (from 1–50%)

• Cells scores (tumor cells and/or infiltrating immune cells)

• Subcellular localization of staining (membrane vs cytoplasmic)

Analytical validity refers to how well the test predicts the presence or absence of a particular gene or genetic change/variant

Challenges in Biomarker DevelopmentSpecimen Source

Tissue1,2

Invasive, fresh vs frozen, fixation (time, period), spatial and temporal inter‐ and intratumor heterogeneity

Biopsy core 1

Biopsy core 2

Liquid biopsy2–4

Less invasive, repeat sampling, fresh material; clinical utility has not yet been comprehensively demonstrated, and risk for false negatives

(low circulating ctDNA)

1. Duffy MJ, et al. Clin Chem. 2015;61:809‐820. 2. Ilié M, Hofman P. Transl Lung Cancer Res. 2016;5:420‐423. 3. Cabel L, et al. Nat Rev Ciln Oncol. 2018;15:639‐650. 4. College of American Pathologists (CAP). The “liquid” biopsy. 2020 (www.cap.org/member‐resources/articles/the‐liquid‐biopsy).

Image courtesy of Fred Hirsh.

DNA = deoxyribonucleic acid; cfcDNA = cell free circulating DNA; ctDNA= circulating tumor DNA; NGS = next generation sequencing; PCR = polymerase chain reaction.

ctDNA fraction andtechnical limits of detection

cfcDNA

ctDNA

cfcDNA from normal cells

cfcDNA from clonal hematopoietic cells

ctDNA fraction andin cfcDNA

Technical limitations of detection

Background mutationalnoise from clonalhematopoiesis

Sanger

Digital PCR

Real‐time PCRand standard

NGS

OptimizedPCR

Bloodsample

DNAextraction ctDNA

quantification

100%

10%

1%

0.1%

0.01%

SinglectDNA

molecule

NGS‐based ctDNA detection

•Standard NGS

•Optimized NGS– Reduced base‐position error rate– Unique molecular identifiers

ABEFE

Advantages: enables analysis of several genes, TMB, neoepitope discovery, and dMMR status assessment

Limitations: high cost, limited sensitivity (for standard NGS), bioinformatic turnaround time

Advantages: sensitive, low cost and quick; allows real‐time serial monitoring in large cohorts

Limitations: only one or a few mutations detected, and no TMB or neoepitope prediction

Digital PCR‐based ctDNA detection

•Droplet‐digital PCR

•BEAMing

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Challenges in Biomarker Development The Example of PD‐L1

1. Kluger HM, et al. Clin Cancer Res. 2017;23:4270‐4279. 2. McLaughlin J, et al. JAMA Oncol. 2016;2:46‐54. 3. Patel SP, Kurzrock R. Mol Cancer Ther. 2015;14:847‐856. 4. Gradecki SE, et al. Am J Surg Pathol. 2018;42:1090‐1094. 5. Bigras G, et al. Appl Immunohistochem Mol Morphol. 2018;26;701‐708. 6. Rojkó L, et al. J Cancer Res Clin Oncol. 2018;144:1219‐1226. 7. Vilain RE, et al. Clin Cancer Res. 2017;23:5024‐5033. 8. Hirsch FR, et al. J Thorac Oncol. 2017;12:208‐222.

PD‐L1 expression

Defining a positive result

Biopsy type

Other considerations

Interval between biopsy and treatment and effect of other therapies6,7

• Antibody and staining conditions8

• Correlation between assays—ie, Blueprint PD‐L1 IHC Assay Comparison Project8

Biopsy type (ie, core biopsy vs resection specimens)4

Biopsy size (ie, misclassification of small biopsy samples)4,5

Differential expression across tumor types1

Expression of PD‐L1 is heterogeneous within tumors2

Cell type expressing PD‐L1, distribution, tumor/immune interface, % “positive” cells (cutoffs, assays)1–3

Immune Biomarkers Across Multiple Tumor Types

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PD‐1/PD‐L1 Expression as Biomarker for Immunotherapy

• Predictive significance: expression of PD‐L1 on the cell surface of tumor or immune cells generally higher likelihood of response to treatment with anti‐PD‐1/PD‐L1 agents

• Conflicting observations of PD‐1/PD‐L1 expression as a predictive or prognostic biomarker

– Could be assay‐ and/or setting‐dependent

Arkenau HT. ESMO biomarker factsheet (https://oncologypro.esmo.org/Education‐Library/Factsheets‐on‐Biomarkers/PD‐L1‐in‐Cancer#eztoc1489281_0_0_4). Accessed 5/22/2020. Passiglia F, et al. Oncotarget.2016;7:19738‐19747.

Study and Cancer Type

PD-L1 Positive PD-L1 Negative Weight(%)

OR IV, Random(95% CI)

OR, Random(95% CI)Events Patients Events Patients

NSCLCAntonia et al 2016 14 49 1 9 0.7 3.20 (0.37–28.01)Borghaei et al 2015 34 95 14 136 4.0 4.86 (2.43–9.72)Brahmer et al 2015 9 42 11 75 2.7 1.59 (0.60–4.21)Fehrenbacher et al 2016 11 50 10 94 2.8 2.37 (0.93– 6.05)Garon et al 2015 50 176 3 28 1.9 3.31 (0.96–11.45)Gettinger et al 2015 5 33 3 35 1.4 1.90 (0.42–8.70)Gettinger et al 2016 8 26 3 20 1.4 2.52 (0.57–11.10)Herbst et al 2014 1 7 6 33 0.7 0.75 (0.08–7.44)Herbst et al 2016 86 290 40 400 5.9 3.79 (2.51–5.73)Rittmeyer et al 2017 29 129 28 292 4.8 2.73 (1.55–4.83)Rizvi et al 2015 6 25 7 51 2.0 1.98 (0.59–6.70)Spiegel et al 2015 14 43 28 136 3.6 1.86 (0.87–3.99)Verschraegen et al 2016 6 28 1 16 0.7 4.09 (0.45–37.53)Wakelee et al 2016 86 302 122 659 6.7 1.75 (1.28–2.41)

Subtotal 1295 1984 39.5 2.51 (1.99–3.17)Total events 359 277

Heterogeneity: τ2 = 0.03; χ2 = 15.46, df = 13 (P= .28); I2 = 16%. Test for overall effect: Z = 7.72 (P <.001)

MelanomaDuad et al 2016 118 364 6 57 3.1 4.08 (1.70–9.77)Hamid et al 2913 4 15 3 15 1.1 1.45 (0.26–8.01)Hodi et al 2014 8 18 3 23 1.4 5.33 (1.16–24.60)Larkin et al 2015 46 80 86 208 5.1 1.92 (1.14–3.24)Ribas et al 2015 51 193 14 93 4.3 2.03 (1.06–3.89)Robert et al 2015 39 74 45 136 4.7 2.25 (1.26–4.02)Robert et al 2015 154 446 47 101 5.8 0.61 (0.39–0.94)Weber et al 2015 34 77 18 87 4.1 3.03 (1.53–6.02)


Heterogeneity: τ2 = 0.42; χ2 = 31.39, df = 7 (P <.001); I2 = 78%. Test for overall effect: Z = 2.59 (P= .010)

0.01 0.1 1 10 100

Favors PD‐L1 negative Favors PD‐L1 positive

Predictive Value of PD‐L1 Expression in Patients Treated With ICIs

Khunger M, et al. JCO Precision Oncol. 2017;1:May 18 (http://ascopubs.org/doi/pdf/10.1200/PO.16.00030) (see Khunger et al for full references of studies cited). Accessed 5/22/2020.

ICI = immune checkpoint inhibitor; OR = odds ratio; CI = confidence interval.

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OR, Random(95% CI)

OR, Random(95% CI)Events Patients Events Patients

Bladder cancerApolo et al 2016 4 10 2 22 0.9 6.67 (0.97–45.79)Balar et al 2017 9 32 18 87 2.9 1.50 (0.59–3.80)Massard et al 2016 7 15 6 27 1.7 3.06 (0.79–11.94)Plimack et al 2015 6 18 1 11 0.7 5.00 (0.51–48.75)Powles et al 2014 6 21 9 37 2.0 1.24 (0.37–4.17)Rosenberg et al 2016 26 100 19 210 4.3 3.53 (1.84–6.76)Sharma et al 2016 6 25 11 42 2.2 0.89 (0.28–2.80)


Heterogeneity: τ2 = 0.11; χ2 = 7.90, df = 6 (P= .25); I2 = 24%. Test for overall effect: Z = 3.07 (P= .002)

RCCChouieri et al 2014 4 18 3 38 1.2 3.33 (0.66–16.85)McDermott et al 2016 6 33 2 22 1.1 2.22 (0.41–12.18)Motzer et al 2015 9 29 14 78 2.7 2.06 (0.77–5.46)Topalian et al 2012 2 4 0 1 0.3 3.00 (0.08–115.34)



Gastroesophageal cancerChung et al 2016 2 9 1 46 0.6 12.86 (1.03–161.26)Le et al 2016 4 23 4 35 1.4 1.63 (0.36–7.30)Nishina et al 2016 2 5 1 14 0.5 8.67 (0.58–130.11)



Predictive Value of PD‐L1 Expression in Patients Treated With ICIs (continued 1)

Khunger M, et al. JCO Precision Oncol. 2017;1:May 18 (http://ascopubs.org/doi/pdf/10.1200/PO.16.00030) (see Khunger et al for full references of studies cited).

0.01 0.1 1 10 100




OR IV, Random(95% CI)

OR IV, Random(95% CI)Events Patients Events Patients

Head-and-neck cancerFerris et al 2016 12 54 12 107 3.1 2.26 (0.94–5.45)Segal et al 2015 4 22 3 37 1.3 2.52 (0.51–12.50)

Subtotal 76 144 4.4 2.32 (1.07–5.01)Total events 16 15Heterogeneity: τ2 = 0.00; χ2 = 0.01, df = 1 (P= .91); I2 = 0%. Test for overall effect: Z = 2.14 (P= .03)

Merkel cell carcinomaKaufman et al 2016 20 58 3 16 1.6 2.28 (0.58–8.95)Nghiem et al 2016 8 12 6 11 1.2 1.67 (0.31–9.01)

Subtotal 70 27 2.8 2.01 (0.70–5.83)Total events 28 9Heterogeneity: τ2 = 0.00; χ2 = 0.08, df = 1 (P= .78); I2 = 0%. Test for overall effect: Z = 1.29 (P= .20)

Small-cell lung cancerAntonia et al 2016 3 10 9 59 1.4 2.38 (0.52–10.97)


Heterogeneity: Not applicable. Test for overall effect: Z = 1.11 (P= .27)

Total 3060 3604 100 2.26 (1.85–2.75)Total events 953 623

Heterogeneity: τ2 = 0.13; χ2 = 65.80, df = 40 (P= .006); I2 = 39%. Test for overall effect: Z = 8.12 (P <.001)

Test for subgroup differences: χ2 = 1.20, df = 7 (P= .99); I2 = 0%

Predictive Value of PD‐L1 Expression in Patients Treated With ICIs (continued 2)

Khunger M, et al. JCO Precision Oncol. 2017;1:May 18 (http://ascopubs.org/doi/pdf/10.1200/PO.16.00030) (see Khunger et al for full references of studies cited).

0.01 0.1 1 10 100


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Limitations of PD‐L1 as a Biomarker

• A low or absent expression does not mean absence of clinical benefit

• Different tumors have different associations of OS with PD‐L1 levels

• Some assays measure expression on tumor cells, immune cells, or both, depending on tumor type

• There is heterogeneity among tumors within a patient and within a tumor type

• Generally, there is an association between improved outcomes and level of PD‐L1 expression

• Outcomes with chemotherapy combinations and immunotherapy may be less dependent on PD‐L1 expression

• At the end of the day, PD‐L1 is not a robust, reproducible, and reliable predictive marker

Patel SP, Kurzrock R. Mol Cancer Ther. 2015;14:847‐856. Forde PM, et al. N Engl J Med. 2018;378:1976‐1986. Dagogo‐Jack I, Shaw AT. Nat Rev Clin Oncol. 2018;15:81‐84. Watanable T, et al. Oncotarget. 2018;9:20869‐20780. Wang X, et al. Onco Targets Ther. 2016;9:5023‐5039.

OS = overall survival.

Tumor‐Infiltrating Lymphocytes (TILs) as Predictive Biomarker for Immunotherapy

Predictive significance: accumulation of TILs generally better response to immunotherapy in melanoma and TNBC, but may vary based on type of tumor, such as RCC

Badalamenti G, et al. Cell Immunol. 2019;343:103753.

RCC: presence of specific intratumoral CD8+ T‐cell subsets, co‐expressing PD‐1 and Tim‐3, confers a higher risk of relapse and a poorer overall survival

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Limitations of TILs as a Biomarker

• The immunohistochemical assay for CD8 T cell density is quite operator‐dependent and time‐consuming

• T cells may be intratumoral or in the stroma at the tumor margin; pattern of staining may have predictive implications, and differentiating staining is complex and labor intensive

• Tumoral T cell infiltrates, like tumor PDL‐1 staining, may vary intratumorally or between tumors from the same patient

Teng F, et al. Cancer Lett. 2018;414:166‐173. Yusko E, et al. Cancer Immunol Res. 2019;7:458‐465. Pinato DJ, et al. Oncoimmunology. 2016;5:e1213934. Reuben A, et al. J Immunotherapy Cancer. 2014; (suppl 3): abstract P200. Li J, et al. Immunity. 2018;49:178‐193.e7. Saltz J, et al. Cell Rep. 2018;23:181‐193.e7. Hofman P, et al. Cancers (Basel). 2019;11:283.

SITC = Society for Immunotherapy of Cancer.

DNA Repair Mechanisms and Genomic Stability

Lord CJ, Ashworth A. Nature. 2012;481:287‐294.

A

G

CH3

Proteins

Tumor types

Drugs

Breast, ovarian, pancreatic

PARP inhibitors, platinum salts

Xerodermapigmentosa

Platinum salts

Glioma

Temozolomide

Colorectal

Methotrexate

BRCA2 ERCC1 MLH1BRCA1 ERCC4 MGMTMSH2

DNA‐PKKU70/80PARP1

XRCC1LIGASE 3

RAD51

PALB2ATMCHEK1CHEK2

NER Directreversal

Mismatchrepair

Bulkyadducts

Base alkylation

Single‐strand break

Double‐strand break

Base mismatches, insertions, and

deletions

BER Homologousrecombination

NHEJ

double‐strand break repair

BER = base excision repair; NER = nucleotide excision repair; NHEJ = non‐homologous end‐joining; PARP = polymeric (ADP[adenosine diphosphate]‐ribose) polymerase.

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Microsatellite Instability and/or Mismatch Repair DeficiencyPredictive Biomarker for Immunotherapy

• High levels of microsatellite instability (MSI) due to mismatch repair (MMR) deficiency are found in many tumor types, including colorectal, endometrial, cervical, esophageal, gastric, thyroid, and hepatocellular cancers

• Predictive significance: tumors with deficiencies in DNA MMR (dMMR) are more sensitive to immunotherapy due to increased mutational load, resulting in neoepitope formation

Dudley JC, et al. Clin Cancer Res. 2016;22:813‐820. Le DT, et al. Science. 2017;357:409‐413.

Proportion of dMMR across tumor type

18%

16%

14%

12%

10%

8%

6%

4%

2%

0%

Late stageEarly stage

Limitations of MSI and dMMR as Biomarkers

• Few limitations, although assays to define MSI may vary from institution to institution

• The genetic profile of MSI‐high tumors may be broader than previously suspected, suggesting that prior assessments of proportions of MSI‐high tumors were an underestimate

• Response rates are not 100%

Shirazi M, Sepulveda AR. Adv Mol Pathol. 2018;1:193‐208. Abida W, et al. JAMA Oncol. 2019;5:471‐478. Duffy MJ, Crown J. Clin Chem. 2019;65:1228‐1238.

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Evolution of TMB as an IO Biomarker

Chan TA, et al. Ann Oncol. 2019;30:44‐56.

2014 2015 2016 2017 2018

CheckMate 032:High TMB associated

with survival in 1L+ SCLC

CheckMate 038:TMB associated with survival in IPI‐naive patients with 2L+ melanoma

CheckMate 275:High TMB associated with survival in 2L bladder

CheckMate 026:High TMB associated with response in

1L NSCLC

First report of TMB effect on response to ICB in

melanoma

KEYTrial/clinical dataOther key data

KEYNOTE‐001:TMB associated with

durable clinical benefit in 2L+ NSCLC

IMvigor210:Response to

atezolizumab is related to TMB in

2L+ bladder

FIR/BIRCH/POPLAR:TMB associated with

efficacy in 1L and 2L+ NSCLC

FIR/BIRCH/POPLAR:TMB assessment by FoundationOne* in

2L+ NSCLC

IMvigor210:TMB

associated with response in 1L bladder

KEYNOTE‐012/KEYNOTE‐028:TMB associatedwith best overall

response in 1L+ solid tumors

High TMBassociated withresponse in

resectable NSCLCtreated with

neoadjuvant NIVO

TMB and GEPpredict forresponse in

Keynote trials,multiple cancers

treated with ICB

OAK/POPLAR:TMB analysis in

2L+ NSCLCBFAST and B‐F1RST:

TMB assay validation in 1L NSCLC

Zehir:Prospective sequencing of over 10,000 tumors using

MSK‐IMPACT assay

Chalmers:Association of TMB with patient characteristics and tumor types using 100,000 human cancer

genomes

FDA approval/authorization of

FoundationOne® CDxand MSK‐IMPACT

Rooney:Genetic properties of tumor associated with cytolytic

activity

CheckMate 227:High TMB associated with

survival in NIVO+IPI patients 1L NSCLC

IO = immuno‐oncology; 1L = first line; 2L = second line; NIVO = nivolumab; IPI = ipilimumab; ICB = immune checkpoint blockade.

Percent of Solid Tumors with TMB ≥10 mut/Mb


Tumors with TMB >10 m

ut/Mb

by F1CDx NGS assay (%

)

100%

80%

60%

40%

20%

0%

mut/Mb = mutations/megabase; SCC = squamous cell carcinoma; NOS = not otherwise specified.

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TMB Predicts Survival After Immunotherapy Combination or Monotherapy)

Samstein RM, et al. Nat Genet. 2019;51:202‐206 (plus supplement).

HR comparing OS after ICI in patients with ≥10% highest TMB

Cohort of 1662 patients who had received atezolizumab, avelumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, or tremelimumab as monotherapy (n = 1402) or in combination (n = 260)

HR comparing OS after ICI in patients with ≥30% highest TMB

ER = estrogen receptor

Limitations of TMB as a Biomarker

• Requires next‐generation sequencing (NGS), which is expensive

• 10 mutations per MB may be an arbitrary cutoff

• Utility varies from tumor to tumor

– Relationship of TMB in NSCLC with no noted difference in survival outcomes (CheckMate‐227) or any associated greater likelihood of response (Keynote‐021, Keynote‐089)

• Unclear whether non‐clonal or non‐truncal mutations are important

• No clear‐cut cutoff below which no benefit would be seen in most tumors

Berland L, et al. J Thorac Dis. 2019;11(suppl 1):S71‐S80. Buder‐Bakhaya K, Hassel JC. Front Immunol. 2018;9:1474. Columbus G. Targeted Oncol. 2019 (www.targetedonc.com/news/bms‐withdraws‐nivolumabipilimumab‐application‐in‐tmbhigh‐nsclc). Highleyman L. Cancer Health. 2019 (www.cancerhealth.com/article/tumor‐mutation‐burden‐immunotherapy). Burns E. Targeted Oncol. 2019 (www.targetedonc.com/publications/targeted‐therapy‐news/2019/October2‐2019/identification‐of‐certain‐tumor‐characteristics‐enhances‐immunotherapy‐response‐in‐nsclc). Accessed 5/22/2020. Samstein RM, et al. Nat Genet. 2019;51:202‐206. Büttner R, et al. ESMO Open. 2019;4:e000442.

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Emerging Therapies in using TMB as a Biomarker

• June 2020—pembrolizumab granted FDA approval for treatment of adult and pediatric patients with:

– Unresectable or metastatic solid tumors with high tumor tissue TMB (TMB‐H) who have progressed following prior treatment and have no satisfactory treatment options

– TMB ≥10 mut/Mb

– KEYNOTE‐158 trial (phase 2), with pembrolizumab 200 mg IV administered every 3 weeks

Melillo G. Am J Manag Care. 2/17/2020 (www.ajmc.com/newsroom/fda‐approves‐second‐biomarkerbased‐indication‐for‐pembrolizumab). Astor L. Targeted Oncol. 2020. (www.targetedonc.com/view/fda‐approves‐pembrolizumab‐for‐tmb‐high‐solid‐tumors). Accessed 6/17/2020. Marabelle A, et al. Ann Oncol. 2019;30(suppl 5):v478 (abstract 11920).

IV = intravenous; NR = not reached; ORR – overall response rate; PFS = progression free survival; DoR = duration of response.

TMB-H non-TMB-H

ORR (excluding MSI-H tumors) 27.1% 6.7%

Median PFS (at 2 years) 18.9% 6.5%

OS (at 2 years) 34.5% 31.1%

Median OS 11.7 mo 13.0 mo

Median DoR NR NR

Mutations, Neoantigens, and ICB


High TMB

Tumor Tumor

Somatic mutations• Mutations (ultraviolet radiation, smoking, other carcinogens)

• Hereditary or acquired dMMR• Age‐related DNA replications errors

Non‐inflamed:Decreased neoantigenpresentation, poor chemokine expression, dense stroma, MDSCs, Tregs

Abnormal proteins derived from somatic mutations

Inflamed:neoantigens presented on MHC and recognized by CD8 T cells

Combination immune checkpoint blockade

PD‐L1 immune checkpoint blockade

CD8 T cells

PD‐L1(+) PD‐L1(–)

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Biomarkers in Relation to Immune Classification and Immunoscore

• Predictive accuracy of traditional staging fails to address host immune response

• Immunoscore—approach to classification with predictive and prognostic value

Galon J, et al. J Pathol. 2014;232;199‐209.

Tumor‐cell extension/ invasion

Ways to classify

Tumor‐cell characteristics

Host immune response

Immunoscore CD3+ T cells CD8+ T cells Density Location (CT, IM)

Morphology Cell of origin Molecular pathway Mutation status Gene expression

Cribriform comedo

Mucinous Enterocyte CIN BRAF CCS 1

Medullary Goblet‐like MSI APC CCS 2

Adeno, NOS Transit‐amplifying‐R CIMP KRAS CCS 3

Serrated Transit‐amplifying‐S TP53

Signet‐ring cellInflammatory CTNNB 1

MicropapillaryStem‐like

T‐STAGE N‐STAGE M‐STAGE

CIN = chromosomal unstable; CIMP = CpG island methylator phenotype; CT = core of the tumor; IM = invasive margin.

Society for Immunotherapy of Cancer (SITC) Biomarkers Task Force Recommendations

Butterfield LH. Semin Cancer Biol. 2018;52:12‐15.

High‐priority candidate biomarkers and technology approaches for immunotherapy to consider in clinical trial designs and immune monitoring assessments

WES = whole exome sequencing; RNAseq = ribonucleic acid sequencing; CyTOF = cytometry by time of flight; ELISPOT = enzyme‐linked immunospot.

Key questions:

Tumor infiltrated with lymphocytes?

Tumor expression of PD‐L1?

Tumor mutation load

Circulating MDSC/Tregs

Circulating anti‐tumor T cells

Serum proteins and antibodies

Key technologies:

IHC, multispectral image

IHC, multispectral image

WES, RNAseq

Flow cytometry, CyTOF

Flow cytometry, CyTOF, ELISPOT

Luminex®/MesoScale, protein array

Standardized specimen‐processing, banking, and assessment with validated assay techniques; appropriate biostatistical data analysis approaches to combine with clinical data

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Future of Biomarker Discovery for Immunotherapy

1. Masucci GV, et al. J Immunother Cancer. 2016;4:76. 2. Melero I, et al. Nat Rev Cancer. 2015;15:457‐472.

• Complexity of immune response and tumor biology unlikely that a single biomarker is sufficient1

• Integration of multiple tumor and immune response parameters (eg, protein expression, genomics, and transcriptomics) may be necessary1

Biomarker discovery for combination

immunotherapy and new

management concepts based on biomarkers 2

PBMC = peripheral blood mononuclear cell; FFPE = formalin‐fixed and paraffin‐embedded.

Biomarkers and Multidisciplinary Care

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Personalized Treatment Decisions Using Clinical Trials

• No biomarkers are currently being used for selection in melanoma trials, but PD‐L1 and TMB are often stratification factors

• Pembrolizumab has been tested successfully in multiple trials in which PD‐L1 has been used for selection

• TMB has been used as a biomarker for lung cancer trials with pembrolizumab and nivolumab as well as ipilimumab + nivolumab trials

• No trials are currently being carried out using a multi‐biomarker algorithm

Hellmann MD, et al. N Engl J Med. 2018;378:2093‐2104.

Conclusions

• Immune characteristics of tumor infiltrates involve both good and bad cells, the balance of which can change over time, with an impact on the prognosis of patients with cancer

• Clinical trial data across tumor types have yielded information on prognostic and predictive tumor immune‐related biomarkers

• Best practices for using tumor immune‐related biomarkers to select patients that will benefit most from new cancer immunotherapies continue to evolve

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Case Studies

Biomarker Challenges—CASE STUDY

Epidermal Growth Factor Receptor (EGFR)/Anaplastic Lymphoma Kinase (ALK)

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EGFR/ALK: Biomarker Challenges

A 57‐year‐old woman initially presenting with cough and back pain is diagnosed with stage IV lung adenocarcinoma with bone metastases.

Past Medical History: Hypertension

Past Surgical History: Cholecystectomy

Social History: Non‐smoker

Family History: Noncontributory

Molecular Testing: NGS consistent with EML4‐ALK translocation, PD‐L1 50%

NGS = next generation sequencing; EML4 = echinoderm microtubule‐associated protein‐like 4.

Do biomarkers influence treatment choice?

NL10NL11NL12NL13

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Molecular profile and treatment strategy

NL2NL3NL4NL5NL6NL7NL8

Considerations

• There are first‐line agents for NSCLC (pembrolizumab and ipilimumab/nivolumab), however, without EGFR or ALK mutations

• Potential for increased risk of irAEs in patients with ALK/EGFR mutations treated with ICIs

Treatment modalities

• Chemotherapy

• Palliative radiation to bone metastasis

• Erlotinib

EGFR/ALK: Biomarker Challenges

Schoenfeld AJ, et al. Ann Oncol. 2019;30:839‐844.

NSCLC = non–small‐cell lung cancer; ICI = immune checkpoint inhibitor; irAE = immune‐related adverse event.

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Biomarker Challenges—CASE STUDY

Tumor Mutational Burden (TMB)

TMB: Biomarker Challenges

A 68‐year‐old man initially presenting with SOB and abdominal pain is diagnosed with stage IV squamous cell carcinoma of the lung with liver metastases and malignant pleural effusions.

Past Medical History: Type 2 diabetes mellitus

Past Surgical History: Cholecystectomy

Social History: 1 ppd smoker x 40 years

Family History: Depression

Molecular Testing: NGS consistent with no actionable mutations, PD‐L1 0%, TMB 24 mut/Mb

ppd = pack per day; SOB = shortness of breath.

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Do biomarkers influence treatment choice?

NL10NL11NL12NL13

Molecular profile and treatment strategy

NL2NL3NL4NL5NL6NL7NL8

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TMB: Biomarker Challenges

Considerations

• Data show promise of ICIs in solid tumors with TMB‐high ≥ 10 mut/Mb

• Response with combination chemotherapy/ICIs in PD‐L1 negative and PD‐L1 high settings can be robust

Treatment modalities

• Combination chemotherapy/ICI

• Chemotherapy

• Anti‐VEGF agents

OncLive. FDA grants pembrolizumab priority review for TMB‐high tumors. 2020 (https://www.onclive.com/web‐exclusives/fda‐grants‐pembrolizumab‐priority‐review‐for‐tmbhigh‐tumors). Accessed June 8, 2020. Chumsri S, et al. JNCCN. 2020;18:517‐521.

VEGF = vascular endothelial growth factor.

Thank you!

Questions and Answers

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