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TRANSCRIPT
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
ACCREDITATION AND DESIGNATION STATEMENTS
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Credit Designation Statement Med Learning Group designates this live activity for a maximum of 1.0 AMA Category 1 CreditTM. Physicians should claim only the credit commensurate with the extent of their participation in the live activity. Nursing Credit Information Purpose: This program would be beneficial for nurses involved in the care of patients with immunotherapy.
Credits: 1.0 ANCC Contact Hour.
Accreditation Statement Ultimate Medical Academy/CCM is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation. Awarded 1.0 contact hour of continuing nursing education of RNs and APNs.
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DISCLOSURE OF FINANCIAL RELATIONSHIPS
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
relationships or relationships to products or devices they or their spouse/life partner have
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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.
Staff, Planners and Managers
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During this lecture, the faculty may mention the use of medications for both FDA-approved
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Posting Questions in Zoom Chat
• If you would like to post a question during the presentation, please submit your inquiry in the chat feature.
• Remember to direct all questions to the “co‐host.” There is a toggle button above the typing space that allows you to specify the location of your message delivery.
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)
Subtotal 1267 720 29.6 2.04 (1.19–3.49)Total events 454 222
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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Study and Cancer Type
PD-L1 Positive PD-L1 Negative Weight(%)
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)
Subtotal 221 436 14.6 2.20 (1.33–3.64)Total events 64 66
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)
Subtotal 84 139 5.4 2.34 (1.12–4.88)Total events 21 19
Heterogeneity: τ2 = 0.00; χ2 = 0.27, df = 3 (P= .97); I2 = 0%. Test for overall effect: Z = 2.27 (P= .02)
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)
Subtotal 37 95 2.5 3.85 (1.00–14.75)Total events 8 6
Heterogeneity: τ2 = 0.28; χ2 = 2.44, df = 2 (P= .29); I2 = 18%. Test for overall effect: Z = 1.96 (P= .05)
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
Favors PD‐L1 negative Favors PD‐L1 positive
Study and Cancer Type
PD-L1 Positive PD-L1 Negative Weight(%)
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)
Subtotal 10 59 1.4 2.38 (0.52–10.97)Total events 3 9
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
Favors PD‐L1 negative Favors PD‐L1 positive
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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
Chan TA, et al. Ann Oncol. 2019;30:44‐56.
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
Chan TA, et al. Ann Oncol. 2019;30:44‐56.
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.
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Questions and Answers
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Targeting Tumor Immunosuppression with ICIs