Biomarkers for clinical benefit to immune checkpoint blockade treatment in NSCLC Karlijn Hummelink
Biomarkers for clinical benefit to immune checkpoint blockade treatment in NSCLC Karlijn Hummelink
2024, Karlijn Hummelink ISBN: 978-94-6506-465-9 DOI: 10.33540/2395 Cover design and layout: © evelienjagtman.com Printing: ridderprint.nl The printing of this thesis was financially supported by the Netherlands Cancer Institute. This research was financially supported by the Dutch Cancer Society.
Biomarkers for clinical benefit to immune checkpoint blockade treatment in NSCLC Biomarkers voor het voorspellen van therapeutisch voordeel van immuun checkpoint blokkade behandeling in NSCLC (met een samenvatting in het Nederlands) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof. dr. H.R.B.M. Kummeling, ingevolge het besluit van het College voor Promoties in het openbaar te verdedigen op dinsdag 12 november 2024 des middags te 2.15 uur door Karlijn Hummelink geboren op 10 januari 1990 te Lichtenvoorde
Promotoren: Prof. dr. G.A. Meijer Prof. dr. E.F. Smit Copromotoren: Dr. K. Monkhorst Dr. D.S. Thommen Beoordelingscommissie Dr. D. Cohen Prof. dr. P.J. van Diest Prof. dr. M.M. van den Heuvel Prof. dr. W.L. de Laat Prof. dr. E.E. Voest (voorzitter)
“Nae vragt, kom iej neet wies” Voor José, mijn moeder
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Table of Contents Chapter 1 General introduction and thesis outline 9 Outline of this thesis 16 Part 1 PD-1T TILs as precision and clinical applicable biomarker in NSCLC Chapter 2 PD-1T TILs as a predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC 25 Supplemental Material 57 Chapter 3 Head-to-head comparison of composite and individual biomarkers to predict clinical benefit to PD-1 blockade in nonsmall cell lung cancer 81 Supplemental material 107 Chapter 4 A dysfunctional T cell gene signature for predicting nonresponse to PD‑1 blockade in non-small cell lung cancer that is suitable for routine clinical diagnostics 129 Supplemental material 153 Part 2 Serum and pleural effusion as bio-sources for diagnostic biomarker tests Chapter 5 A serum protein classifier identifying patients with advanced non-small cell lung cancer who derive clinical benefit from treatment with immune checkpoint inhibitors 171 Supplemental material 194 Chapter 6 Cell-free DNA in the supernatant of pleural effusion can detect driver and resistance mutations and can guide TKI treatment decisions 211 Supplemental material 222 Part 3 Summary and future perspectives Chapter 7 Summary and future perspectives 233 Chapter 8 Nederlandse samenvatting 251 Appendices Chapter 5 Supplemental material: Methods 263 Chapter 2 Commentary by Anagnostou V and Luke JJ in Clinical Cancer Research 279 List of publications 287 Curriculum Vitae 293 Dankwoord 297
Chapter 1 General introduction and thesis outline
General introduction and thesis outline 11 1 General introduction and thesis outline Epidemiology of lung cancer Lung cancer constitutes a significant global health challenge, with an estimated 2.2 million new cancer cases and 1.8 million deaths reported worldwide in 2020. It stands as the second most frequently diagnosed cancer and is the leading cause of cancer-related mortality1. In the Netherlands, approximately 14.000 new cases of lung cancer were diagnosed in the year 2019, and the mortality rate exceeded 10.000 deaths attributed to lung cancer during the same period2. Tobacco smoking has been associated to the development of lung cancer in 80% of cases3. Over the decades, trends in smoking have influenced the incidence rates on different continents. For example, in the United States, the incidence rate declined from its peak of 67 per 100.000 individuals in 1992 to 43.2 per 100.000 in 20184, primarily due to increased smoking cessation efforts. Approximately 85% of lung cancer patients form a group of histological subtypes collectively known as non-small cell lung cancer (NSCLC)2. The two most common subtypes are adenocarcinoma (AD) and squamous cell carcinoma (SCC)5. In cases where the morphology of the tumor does not show evidence for either AD or SCC, a positive P40 (>50% of tumor cells) and negative TTF1 immunohistochemical (IHC) staining can confirm squamous cell differentiation. Otherwise, the tumor is classified as NSCLC, not otherwise specified (NOS)5. Almost 50% of patients are diagnosed with advanced stage disease2 that is not curable by surgery alone, leaving systemic therapies as treatment of choice. Treatment of advanced NSCLC Historically, the treatment of advanced stage NSCLC has been chemotherapy consisting of a platinum doublet with either carboplatin or cisplatin with gemcitabine, pemetrexed, vinorelbine, or taxanes (paclitaxel or docetaxel). No clinically meaningful differences in outcome have been found among these cytotoxic regimens6, with the exception of the combination pemetrexed-cisplatin which showed shorter overall survival (OS) compared to the gemcitabine-cisplatin combination in SCC7. The treatment landscape of NSCLC dramatically changed after the development of specific targeted therapies for the treatment of e.g., EGFR-mutant, ALK-rearranged, ROS1-rearranged or BRAFV600E‑mutant advanced-stage NSCLC. Several tyrosine kinase inhibitors (TKIs) targeting these molecular alterations have led to remarkable responses in selected patients8. More recently, immunotherapy, particularly immune checkpoint blockade (ICB), has introduced a new era in lung cancer care. In the tumor microenvironment (TME), activated T cells express a protein called programmed cell death 1 (PD-1). When a T
Chapter 1 12 cell recognizes a specific tumor antigen presented by the major histocompatibility complex (MHC), it triggers the production of inflammatory cytokines. These cytokines can induce overexpression of programmed cell death 1 ligand 1 (PD-L1) within the tumor. The interaction between PD-L1 and the PD-1 receptor on T cells results in T cell dysfunction and subsequently immunotolerance. Consequently, tumors can protect themselves from cytotoxic (CD8+) T cell-mediated cell killing. Blocking the interaction between PD-1 and PD-L1 can offer an approach to restore T cellmediated antitumor immunity9. The first evidence demonstrating the effectiveness of anti-PD-(L)1 antibodies in treating NSCLC came from studies involving patients with previously treated advanced NSCLC10–12. Since then, these treatments rapidly transitioned to the first-line treatment setting, as multiple clinical trials showed a significant improvement in survival when compared to chemotherapy alone13–16. Unfortunately, approximately 60-70% of patients experience disease progression within six months after initiating treatment14–16, underscoring the need for biomarkers to support shared decision-making for therapeutic strategies. Such biomarkers are essential to improve personalized medicine, to minimize patient exposure to potential adverse effects and to reduce healthcare costs. Biomarkers for predicting response to systemic therapy in advanced NSCLC Although long-lasting clinical responses have been observed for patients treated with TKIs or PD‑(L)1 blockade, this only accounts for a minority of patients. Therefore, biomarkers are urgently needed to estimate the probability of response to specific systemic therapeutic regimens, herein referred to as predictive biomarkers. For TKIs, most predictive biomarkers are characterized by specific genomic alterations associated with the mode of action of the involved TKIs. As a result, comprehensive molecular profiling of tumors is now routinely advised for all newly diagnosed advanced-stage NSCLC patients. This practice allows personalized therapeutic strategies tailored to patients whose tumors harbor targetable oncogenes. However, in the case of immunotherapy, which serves to restore anti-tumor immunity, potential predictive biomarkers differ fundamentally from driver oncogene biomarkers. They exhibit a continuous spectrum rather than a binary categorization, display spatial and temporal variability, and arise from multiple determinants rather than a single dominant determinant. Therefore, biomarker development is more challenging within the context of immunotherapies. The anti-tumor immune response is a complex process, requiring several steps for effective priming, activation, trafficking, and recognition of T cells to eradicate cancer cells, a process known as the cancerimmunity cycle17. Therefore, it is unlikely to find one single biomarker that effectively predicts response18. In addition to predicting which patient will respond to treatment, it is also highly relevant to predict which patients will not respond to
General introduction and thesis outline 13 1 treatment. Biomarkers with a high negative predictive value can reliably predict this lack of therapeutic benefit, thus holding paramount significance for preventing overtreatment. As described earlier, this type of biomarker not only minimizes the risk of unnecessary side effects but also serves to reduce health care costs. Moreover, it offers the possibility of providing patients with alternative treatment options at an early stage. The initial biomarker examined for its predictive potential in advanced NSCLC patients treated with PD-(L)1 blockade was the assessment of PD-L1 expression in tumor tissue. This was done due to its function in the inhibitory PD-L1/PD-1 pathway that is targeted by this treatment. Several studies have shown a positive correlation between high PD-L1 expression and improved response rates and survival13,19,20. For example, the results of the phase III KEYNOTE-024 study demonstrated that pembrolizumab led to significantly prolonged progression-free and overall survival compared to platinum-based chemotherapy in patients with PD-L1 expression levels of ≥50%13,20. Subsequently, PD‑L1 assessment via IHC received clinical approval as a predictive biomarker test. However, different studies have published conflicting results, as some patients with PD‑L1 low or PD-L1 negative tumors have also shown long-term disease control with ICB agents14–16. Furthermore, PD-L1 expression levels can vary due to factors such as interassay variability21, intratumor heterogeneity22–25 and sample characteristics including age, biopsy site, and timing26,27. Another common hurdle is obtaining sufficient tumor tissue samples for PD-L1 testing, as the tumor site is often difficult to reach and invasive procedures are needed. Tumor Mutation Burden (TMB), defined as the total number of nonsynonymous mutations per sequenced coding area of a tumor genome, has subsequently been studied as predictive biomarker for PD-(L)1 blockade monotherapy. It is thought that a higher TMB increases the likelihood of tumor neoantigen production and therefore, potential immunogenicity and the killing of cancer cells28. While TMB has shown predictive potential, no universally applicable TMB threshold has consistently demonstrated the ability to predict overall survival. Also, technical challenges have been reported due to variation across different sequencing platforms29. As PD-(L)1 blockade is thought to reinvigorate dysfunctional T cells30, the presence of tumor-infiltrating lymphocytes (TILs) has been investigated as a predictive biomarker. Although TIL density has shown predictive potential31,32, increasing evidence suggests that not all TILs are in a state to recognize and eliminate tumor cells33,34. Therefore, general TIL density is not an accurate predictive biomarker for ICB response. Previous work showed that CD8+ TILs with high PD-1 expression, referred
Chapter 1 14 to as PD-1T TILs, positively correlated to treatment outcome in a small cohort of NSCLC patients treated with PD-1 blockade monotherapy35. These TILs display a high capacity for tumor recognition, and express and secrete CXCL13, a B cell attractant essential for the formation of tertiary lymphoid structures (TLS). Notably, PD-1T TILs predominantly localize within TLS35. TLS and B cells, a critical TLS component, have been correlated with clinical responses in other tumor types36–39, although comprehensive data from larger NSCLC patient cohorts is currently lacking. The clinical implementation of the aforementioned biomarkers presents challenges due to their continuous nature. Hence, reliable and automated methods are preferred for the assessment and the definition of cut-off values. For example, digital quantification methods have been described for TILs and PD-L140–42. Furthermore, robust platforms such as the Nanostring nCounter platform43 enable the development of predictive mRNA signatures capable of extracting the immune phenotype from the TME. One example of such a signature is the tumor inflammation signature (TIS)44–47. In addition, the combination of biomarkers could be an approach to improve predictive accuracy, as demonstrated in studies combining TMB with PDL148,49 and CD8 with PD-L118,31. Alternative bio-sources for biomarker development One of the predominant challenges in biomarker testing is the availability of sufficient tumor tissue, frequently necessitating invasive procedures. Furthermore, local tissue sampling can introduce biases due to tumor heterogeneity. Notably, more and more tissue material is required to comply to the increasing number of diagnostic biomarker tests. In addition to tumor tissue, blood serves as a viable source for biomarkers. The liquid biopsy method that analyzes cell-free DNA in plasma can detect circulating tumor DNA (ctDNA). This method is minimally invasive and, in contrast to tissue biopsy, can provide a molecular profile of the tumor as well as real-time insights into tumor response dynamics during treatment. Nonetheless, sensitivity challenges persist, primarily because of the often limited fraction of plasma ctDNA available for analysis50. In addition, ctDNA biomarkers are less suitable for predicting responses to immunotherapy, as they do not capture characteristics of the immune infiltrate. A variety of highly sensitive and specific technologies have been rapidly developed, primarily based on multiplex PCR (mPCR) or next-generation sequencing (NGS). These advancements enable the detection of genetic alterations in circulating nucleic acids, encompassing gene mutations, fusions, deletions, amplifications, translocations and epigenetic changes51. Beyond nucleic acid-based approaches,
General introduction and thesis outline 15 1 proteomic-based profiling in liquid biopsies holds significant promise. Given that proteins represent the direct drug targets of many cancer therapies, including ICB, high dimensional proteomic data can be used for biomarker discovery. Furthermore, other body fluids, such as pleural effusion, ascites and cerebrospinal fluid, can also serve as alternative source for biomarker identification52. Among these, pleural effusion stands out as an attractive bio‑source for molecular profiling in NSCLC, particularly considering that approximately 30% of NSCLC patients develop malignant pleural effusion (MPE)53. Unfortunately, the quantity of tumor cells or the tumor cell percentage in MPE often proves insufficient for molecular analysis. Other studies have shown promising results by using cell-free DNA (cfDNA) from the supernatant of MPE54‑59.
Chapter 1 16 Outline of this thesis In spite of the impressive results observed with checkpoint inhibitor-based immunotherapies in a small subset of patients with advanced NSCLC, robust predictive biomarkers are still lacking. Notably, there are no predictive biomarkers capable of accurately identifying patients who do not derive clinical benefit to ICB treatment. In this thesis, we examined direct effectors of the anti-tumor immune response as potential biomarkers to predict response and non-response to PD-1 blockade monotherapy in advanced NSCLC. Additionally, we explored alternative bio-sources for biomarker assessment to avoid the need for invasive and complicated biopsy procedures, particularly in cases where tumor tissue is not available. In Chapter 2 we examine the accuracy of a tumor-reactive TIL population, known as PD‑1T TILs, as predictive biomarker in a cohort of 120 advanced stage NSCLC patients treated with PD-1 blockade. The frequency of PD-1T TILs was quantified using digital image analysis. Additional exploratory analyses addressed the impact of lesionspecific responses, tissue sample properties, and the combination of PD-1T TILs with other biomarkers on their predictive value. Chapter 3 describes a study that investigates whether the predictive performance of biomarkers can be improved by combining them in pairs. The assessed biomarkers included both well-established ones, such as PD-L1, CD8/CD3 TILs and TIS, as well as recently developed biomarkers like PD-1T TILs, CD20+ B cells and TLS. All these biomarkers are known for their pivotal roles in the anti-tumor immune response upon PD‑1 blockade monotherapy. In Chapter 4 we investigate whether a tumor’s PD-1T TIL status can be translated into an mRNA signature using the Nanostring nCounter platform. As digital quantification of PD‑1T TILs requires a substantial user interaction, a PD-1T mRNA signature, developed on a robust clinical grade platform, will facilitates its implementation in a clinical setting. This study develops and validates a PD-1T mRNA signature using gene expression data from 100 advanced NSCLC patients treated with PD-1 blockade from two independent cohorts. In Chapter 5 we highlight the use of liquid biopsies as an alternative bio-source, obviating the need for tissue biopsies. This study develops and validates a serumderived protein signature designed to predict durable clinical benefit in 289 advanced stage NSCLC patients treated with PD-1 blockade.
General introduction and thesis outline 17 1 In Chapter 6 we describe the diagnostic yield of cell free DNA (cfDNA) extracted from pleural effusion as an alternative bio-source for molecular profiling of tumors. Currently, molecular analysis has become the mainstay in diagnostics to guide treatment selection and monitoring in advanced NSCLC. This study highlights the potential of cfDNA analysis in pleural effusion, even when tumor cell purity is low. Last, chapter 7 presents a general discussion of all chapters, providing a holistic view of the research findings and their implications.
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General introduction and thesis outline 21 1 59. Shin, S., Kim, J., Kim, Y., Cho, S. M. & Lee, K. A. Assessment of real-time PCR method for detection of EGFR mutation using both supernatant and cell pellet of malignant pleural effusion samples from non-small-cell lung cancer patients. Clin. Chem. Lab. Med. 55, 1962– 1969 (2017).
PD-1T TILs as precision and clinical applicable biomarker in NSCLC PART 1
Karlijn Hummelink1,2, Vincent van der Noort3, Mirte Muller2, Robert D. Schouten2, Ferry Lalezari4, Dennis Peters5, Willemijn S. M. E. Theelen2, Viktor H. Koelzer6, Kirsten D. Mertz7, Alfred Zippelius8, Michel M. van den Heuvel2,11, Annegien Broeks5, John B. A. G. Haanen9, Ton N. Schumacher10, Gerrit A. Meijer1, Egbert F. Smit2,12, Kim Monkhorst1* and Daniela S. Thommen9* *These authors jointly supervised this work 1Department of Pathology, Division of Diagnostic Oncology, 2Department of Thoracic Oncology, Division of Medical Oncology, 3Department of Biometrics, 4Department of Radiology, Division of Diagnostic Oncology, 5Core Facility Molecular Pathology and Biobanking, Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam, The Netherlands, 6Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland, 7Institute of Pathology, Cantonal Hospital Baselland, Liestal, Switzerland, 8Department of Biomedicine, University Hospital Basel, Basel, Switzerland, 9Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, The Netherlands, 10Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Oncode Institute, Amsterdam, The Netherlands, 11Present address: Department of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, The Netherlands, 12Present address: Department of Pulmonary Diseases, Leiden University Medical Center, Leiden, The Netherlands. Clinical Cancer Research, 2022 Chapter 2 PD-1T TILs as a predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC
Chapter 2 26 Translational relevance Despite the clinical success of anti-PD-1 treatment, robust predictive biomarkers are still lacking. As PD-1 blockade can reinvigorate dysfunctional T cells, we hypothesized that new biomarkers could be developed by assessing such direct effectors of the anti-tumor immune response. We previously identified a tumor-reactive T cell population, termed PD-1T TILs, with predictive potential in a small cohort of nonsmall cell lung cancer (NSCLC) patients. In this study, PD-1T TILs were assessed as a predictive biomarker for durable clinical benefit in two NSCLC cohorts treated with PD-1 blockade, reaching high sensitivity and high negative predictive value. The predictive performance was superior compared to PD-L1 and TLS. Therefore, this biomarker may positively impact treatment decision making in clinical practice, as it improves patient stratification. Importantly, it specifically identifies a patient group that is unlikely to benefit from PD-1 blockade, thereby providing a tool to reduce overtreatment.
PD-1T TILs as precision biomarker in NSCLC 27 2 Abstract Purpose Durable clinical benefit to PD-1 blockade in NSCLC is currently limited to a small fraction of patients, underlining the need for predictive biomarkers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC. Methods PD-1T TILs were digitally quantified in 120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was Disease Control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties and combination with other biomarkers on the predictive value of PD-1T TILs. Results PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months, respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI: 0.24-0.63, P<0.0001) and overall survival (HR 0.46, 95% CI: 0.28-0.76, P<0.01). Predictive performance was increased when lesionspecific responses and samples obtained immediately before treatment were assessed. Notably, the predictive performance of PD-1T TILs was superior to PD-L1 and TLS in the same cohort. Conclusion This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in advanced NSCLC patients. Most importantly, the high NPV demonstrates an accurate identification of a patient group without benefit.
Chapter 2 28 Introduction Immune checkpoint blockade (ICB) targeting the programmed cell death-1 (PD-1)/PD-ligand 1 (PD-L1) pathway has dramatically changed the treatment of advanced stage non-small cell lung cancer (NSCLC) patients. Significant improvement in survival, quality of life and a favorable safety profile compared to chemotherapy has led to the rapid and broad clinical implementation of this treatment modality1–6. However, approximately 60 to 70% of patients progress within 6 months after treatment initiation3,5,6. Hence, predictive biomarkers are needed, in particular to identify patients that are less likely to benefit to reduce overtreatment. In analogy to molecular biomarkers that have been used for identification of patients with targetable oncogenes7, it has been assumed that PD-L1 expression in tumors could predict benefit of anti-PD-1/PD-L1 therapy. Previous studies indeed have shown that pretreatment stratification based on high expression of PD-L1 can identify patient subgroups with improved response rates and survival1,2,8, leading to the approval of PD-L1 testing for newly diagnosed advanced NSCLC. However, PD-L1 is not a perfect biomarker since multiple studies have shown conflicting results with regard to its predictive potential3,5,6. As PD-1/PD-L1 blockade is thought to reactivate dysfunctional T cells9, an alternative strategy may be to develop biomarkers that reflect the capacity of a tumor to mount an anti-tumor immune response. We previously showed that the presence of a specific CD8+ tumor-infiltrating lymphocyte (TIL) subpopulation, termed PD-1T TILs, correlated with response and survival in a small cohort of NSCLC patients treated with PD-1 blockade10. PD-1T TILs are a subset of PD-1+ T cells characterized by high, tumor-associated expression levels of PD-1, and are transcriptionally and functionally distinct from other TIL populations with lower or no PD-1 expression. Importantly, PD-1T TILs show high tumor reactivity10 consistent with subsequent work in other tumor types demonstrating that the capacity for tumor recognition is strongly enriched in the dysfunctional T cell population that expresses high levels of PD-111,12. Moreover, tumor infiltration by PD-1T lymphocytes was recently associated with immunological response to PD-1 blockade in a number of other tumor types13. Finally, PD-1T TILs predominantly localize in tertiary lymphoid structures (TLS)10, which have been correlated with clinical and immunological response to ICB in other cancer types13–16. Collectively, these observations suggest that the presence of PD-1T TILs in a tumor may indicate that a tumor-specific T cell response has been mounted, and thereby represent
PD-1T TILs as precision biomarker in NSCLC 29 2 a potential biomarker to preselect patients for treatment with PD-1 blockade. Particularly, the absence of PD-1T TILs in a tumor may signify the lack of a tumorreactive T cell population and hence identify patients that are unlikely to benefit. In this retrospective observational study we analyzed pretreatment samples from two independent cohorts of NSCLC patients treated with PD-1 blockade to (1) train and validate PD-1T TILs as a predictive biomarker, (2) explore whether certain sample characteristics such as sample type, sample location or time of sampling influence the predictive value of this biomarker, and (3) evaluate the potential for clinical implementation, by comparing and combining PD-1T TILs with other predictive markers such as PD-L1 and TLS.
Chapter 2 30 Methods Patient enrollment and study endpoints In this study, 164 stage IV NSCLC patients were identified from two independent cohorts who started second or later line monotherapy with nivolumab (n=128) or pembrolizumab (n=36) between March 2015 and April 2018 at the Netherlands Cancer Institute/Antoni van Leeuwenhoek hospital (NKI-AVL), The Netherlands. All patients had pathologically confirmed stage IV NSCLC. Absence of sensitizing EGFR mutations or ALK translocations was confirmed in 145 patients, while in 19 patients the mutation status was unknown. Patients received single agent nivolumab 3 mg/ kg, administered as an IV infusion, every two weeks for at least one dose or single agent pembrolizumab 200 mg as an IV infusion every 3 weeks. Nivolumab was provided within the Expanded Access Programme (EAP) from Bristol Myers Squibb or in regular care after the drug was registered. Pembrolizumab treated patients were part of the control arm in the PEMBRO-RT study (NCT02492568)17. Patients were randomized into a training and validation set. Randomization was stratified by type of treatment (nivolumab vs pembrolizumab) and treatment outcome at 6 months. Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 was used to assess efficacy. Patients with progressive disease (PD) who were not evaluable for response by RECIST were determined by the treating physician as PD. Disease Control (DC) (complete response (CR)/partial response (PR) or stable disease (SD)) at 6 months following initiation of treatment was used as the primary clinical outcome measure. We assessed DC at 12 months (CR/PR/SD that lasted ≥12 months), progression-free survival (PFS) and overall survival (OS) as secondary outcome measures to predict long-term efficacy to PD-1 blockade. PFS and OS were defined as the time from the date of initiation of treatment with PD-1 blockade to the date of progression or death (for PFS) or death (for OS). Patients who had not progressed or died were censored at the date of their last follow-up. Pretreatment formalin-fixed paraffin embedded (FFPE) tumor tissue samples were collected from all patients. Written informed consent was obtained from all patients for research usage of material not required for diagnostic use by institutionally implemented opt-out procedure. The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Research Board of NKI (CFMPB586). 44 patients (27%) were excluded based on the following criteria: samples contained less than 10,000 cells in the tumor area on a single cross-sectional slide (n=15), were obtained more than 2 years before start of PD-1 blockade (n=14), were obtained from endobronchial lesions (n=8), contained normal lymphoid
PD-1T TILs as precision biomarker in NSCLC 31 2 tissue (n=3), showed fixation and/or staining artefacts (n=3), or were non-NSCLC histology (n=1) (Fig. 1A, Table S1). We excluded bronchial biopsies as they frequently showed unspecific antibody staining due to mechanical damage, and lymph node resections due to the presence of PD-1 bright T cells in normal lymphoid tissue, which could potentially lead to false positive results. Prespecified subgroup analyses were performed to compare (i) samples derived from tumor resections and biopsies, (ii) samples from primary and metastatic sites, and (iii) samples that were obtained either directly before the start of nivolumab or pembrolizumab or before any prior line of systemic treatment. Fresh tumor samples were collected from 16 patients with NSCLC undergoing primary surgical treatment between July 2017 and February 2019 at NKI-AVL. The study was approved by the Institutional Research Board of NKI-AVL (CFMPB484). All patients consented to research usage of material not required for diagnostic use either by opt-out procedure or via prior informed consent (after May 23, 2018). Representative tumor tissue samples were procured from surgical resection specimens by a pathologist. Half of each sample was formalin fixed and embedded in paraffin for further histological analysis, the other half was immediately processed into tumor fragments that were cryopreserved until further usage (see sample processing and flow cytometry analysis). Sample processing and flow cytometry analysis For flow cytometry analysis, cryopreserved tissue fragments were thawed and processed into single-cell suspensions by enzymatic digestion using RPMI1640 medium (Thermo Fisher) supplemented with 1% Penicillin-Streptomycin (Roche), 12.6µg/ml Pulmozyme (Roche) and 1mg/ml Collagenase type IV (Sigma), as described previously14. Samples were then washed in PBS (Sigma), filtered over a 150µM filter mesh, resuspended in 50µL PBS, and incubated with Fc receptor blocking agent (eBioscience) and with live/dead Zombie UV (Biolegend) for 20 min at 4°C. Cells were washed, resuspended in 50µl of staining buffer (PBS (Sigma), 0.5% bovine serum albumin (Sigma), 0.1% NaN3 (Invitrogen)) containing the below-described antibodies, and incubated for 20 min at 4°C. After washing twice, cells were taken up in 200µl IC Fixation Buffer (eBioscience) and incubated for 20 min. Subsequently, samples were washed twice before data acquisition. For staining the following antibodies were used: anti-CD45 PerCP Cy5.5 (2D1, RRID:AB_1548697) from Invitrogen; anti-CD8 BUV563 (RPA-T8, RRID:AB_2870199), -PD-1 PE-Cy7 (EH12.1, RRID:AB_10611585), all from BD Biosciences; anti-CD3 FITC (SK7, RRID_ AB2043993), -CD4 BV421 (SK3 RRID:AB_2566015), all from Biolegend. PD-1T lymphocytes
Chapter 2 32 were identified by using peripheral blood T cells from healthy donors as external reference to establish the cut-off as described previously10. Data acquisition was carried out on a BD LSR II SORP cell analyzer (BD Biosciences). Data was collected using the BD FACS Diva Software version 8.0.1, and further analyzed with FlowJo v10.6.1 (Tree Star Inc.) and GraphPad Prism v8.0e (GraphPad Software Inc.). Immunohistochemistry Separate immunohistochemistry (IHC) stainings of consecutive FFPE tumor tissue sections were performed on a BenchMark Ultra autostainer Instrument (Ventana Medical Systems). Paraffin sections were cut at 3 µm. Sections for PD-1 staining were dried overnight at room temperature and stained within 48 hours to reduce background staining. Prior to staining, sections were initially baked at 75°C for 28 minutes and deparaffinised in the instrument with EZ prep solution (Ventana Medical Systems). Heat-induced antigen retrieval was carried out using Cell Conditioning 1 (CC1, Ventana Medical Systems) for 32 minutes (CD68 and CD20CD3 double staining) or 48 minutes (PD-1 and PD-L1) at 95°C. PD-1 was detected using clone NAT105 (Lot number V0002089, Ready-to-Use, 16 minutes at RT, Roche Diagnostics (Cat. # 7099029001). PD-L1 was detected using clone 22C3 (1/40 dilution, 1 hour at RT, Agilent/DAKO) and CD68 was detected using clone KP1 (1/10000 dilution, 32 minutes at 37°C, Agilent/DAKO). Bound antibody was detected using the OptiView DAB Detection Kit (Ventana Medical Systems). Slides were counterstained with Hematoxylin and Bluing Reagent (Ventana Medical Systems). For double staining of CD20 (Yellow) and CD3 (Purple), CD20 was detected in the first sequence using clone L26 (1/800 dilution, 32 minutes at 37°C, Agilent/DAKO). CD20 bound antibody was visualized using anti-Mouse NP (Ventana Medical systems) for 12 minutes at 37°C followed by anti-NP AP (Ventana Medical systems) for 12 minutes at 37°C, followed by the Discovery Yellow detection kit (Ventana Medical Systems). In the second sequence of the double staining procedure CD3 was detected using clone SP7 (1:100 dilution, 32 minutes at 37°C, Thermo Scientific). CD3 was visualized using anti-Rabbit HQ (Ventana Medical systems) for 12 minutes at 37°C followed by antiHQ HRP (Ventana Medical systems) for 12 minutes at 37°C, followed by the Discovery Purple Detection Kit (Ventana Medical Systems). Slides were counterstained with Hematoxylin and Bluing Reagent (Ventana Medical Systems). PD-1T, PD-L1 and CD68 immunostainings were scanned at x20 magnification with a resolution of 0.50 per µm2 using an Aperio slide AT2 scanner (Leica Biosystems). CD20-CD3 immunostaining was scanned at x20 magnification with a resolution of
PD-1T TILs as precision biomarker in NSCLC 33 2 0.24 per µm2 using a 3DHistech P1000 scanner. For manual scoring, PD-L1 and CD68 IHC images were uploaded on Slidescore, a digital pathology slide web platform that integrates a slide viewer with a scoring sheet (https://www.slidescore.com). PD-1T TILs, CD20 and TLS were digitally scored as described below. Digital quantification of PD-1T TILs PD-1T TILs are a subset of PD-1+ T cells in the tumor tissue that can be identified both by flow cytometry and by immunohistochemistry (IHC). To quantify PD-1T TILs in FFPE tissue, a digital workflow using a PD-1T IHC scoring algorithm was previously established10. For the current study, the automated detection of PD-1T TILs was recalibrated using the Multiplex IHC v1.2 module of the HALO™ software, v2.3.2089.69 (Indica Labs). To this end, an independent set of 16 NSCLC tumor samples was used to perform flow cytometry and IHC analysis in parallel. PD-1T TILs are defined by bright, tumor-associated PD-1 expression at levels that exceed those observed on peripheral blood T cells10. Hence, to determine the frequency of PD-1T TILs in the NSCLC samples, PD-1 expression on intratumoral lymphocytes was assessed by flow cytometry and compared to peripheral blood T cells as external reference to establish the threshold for tumor-associated PD-1 expression (Fig. S1A). Next, a digital IHC algorithm to quantify PD-1+ lymphocytes in matched FFPE samples was generated (Fig. S1B). The optical density (OD) measured by this approach is reflective of staining intensity and thereby PD-1 levels. To identify the optimal OD cut-off resulting in similar frequencies of PD-1T TILs by IHC as by flow cytometry, Pearson correlation coefficients were determined using thresholds varying from 0.2 to 0.5 OD. The percentage PD-1 bright lymphocytes obtained for each OD threshold in FFPE samples were normalized to total lymphocyte counts and compared to the flow cytometry-guided annotation of PD-1T lymphocytes. An OD of 0.25 showed the highest Pearson correlation coefficient (R2=0.615, P<0.001) (Fig. S1C,D) and was selected as the threshold for further automated PD-1T quantification in FFPE tumor tissue. For prediction of clinical benefit to PD-1 blockade, the tumor areas were measured and the number of PD-1T TILs per mm2 tumor area was determined (Table S2). To this end, tumor areas were annotated with a 0.5 mm margin from the tumor border and necrotic areas were excluded with a 0.5 mm margin. Digital image analysis was carried out by a trained MD (K.H.) and supervised by an experienced pathologist (K.M.), blinded for clinical outcome. Receiver operator characteristic (ROC) curves were used in the training set to establish an optimal cut-off of 90 PD-1T TILs per mm2 for discriminating patients with and without clinical benefit (see Results).
Chapter 2 34 PD-L1 scoring Tumor PD-L1 expression was assessed according to the instruction manual of the qualitative, clinical grade LDT IHC assay (22C3 pharmDx, Dako) as used in routine clinical practice at NKI-AVL. As high concordance between the 22C3 and 22-8 PD-L1 antibodies has been reported18,19, the 22C3 clone was also used to assess the predictive value of PD-L1 for nivolumab. PD-L1 expression levels were manually scored by a trained MD (K.H.) under the supervision of an experienced pathologist (K.M.) blinded for clinical outcome. The PD-L1 Tumor Proportion Score (TPS) was determined by calculating the percentage of PD-L1+ tumor cells of total viable tumor cells (Table S2). PD-L1 positivity was defined as tumor cells showing circumferential and/or partial linear expression (at any intensity) of PD-L1 on the plasma cell membrane. A CD68 staining was manually evaluated and compared with PD-L1 stained slides to avoid false positive results due to PD-L1 expressing macrophages in between tumor cells. PD-L1 IC was manually scored as the proportion of tumor area that is occupied by PD-L1+ immune cells (ICs) of any intensity (IC0: <1%, IC1: ≥1% and <5%, IC2: ≥5% and <10% and IC3: ≥10%) as described20,21. Scoring of tertiary lymphoid structures A CD20 (yellow)/CD3 (purple) double staining was used to identify tertiary lymphoid structures (TLS). CD20-CD3 IHC images were scanned and analyzed using HALO™. Lymphoid niches were manually identified based on the presence of B cell (CD20+) clusters and T cell (CD3+) zones as described22,23. Next, areas were measured in HALOTM and assigned as TLS (>60,000 µm2) or lymphoid aggregate (LA) (10,000-60,000 µm2)16. Finally, tumor areas were digitally annotated as described above and the number of TLS per mm2 and the combined number of TLS and LA (TLS+LA) per mm2 tumor area were determined (Table S2). CD20 quantification by digital image analysis The Area Quantification v1.0 module of the HALOTM software was used to generate an analysis algorithm to measure the total area with CD20 expression on the CD20/ CD3 images. The total CD20 positive area was selected because the dense clustering of CD20+ cells in TLS precluded the setup of a reliable algorithm to quantify cell numbers. Tumor areas were digitally annotated as described above and the CD20positive area was normalized per mm2 tumor area (Table S2). Statistical analysis Patient characteristics were descriptively reported using mean ±s.d., interquartile range (IQR) or frequencies (percentages). Differences in patient and sample characteristics between cohorts (training and validation), between outcome groups (disease control vs PD) and between groups created by the biomarker were assessed
PD-1T TILs as precision biomarker in NSCLC 35 2 using the Mann-Whitney test for continuous data, Fisher's exact test for categorical data, the linear-by-linear association test for ordinal variables, the unpaired t-test for variables with two levels and the Kruskal-Wallis test for variables with more than two levels. Differences were considered statistically significant if *P<0.05, **P<0.01, ***P<0.001 or ****P<0.0001. Calculation of the area under the ROC curve (AUC) was used as a measure of discriminatory ability for the biomarkers considered. The predictive performance of different biomarkers or biomarker combinations on the same patient population was described in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and compared using the McNemar test. The predictive accuracy of the same biomarker on different samples (e.g. resections vs. biopsies) was assessed using AUCs and compared in a one-sided permutation test. Survival curves were plotted using the Kaplan-Meier method and compared between groups identified by the various biomarkers using the log-rank test. To assess the predictive performance of PD-1T TILs (discretized at 90 per mm2) and PD-L1 (discretized at either 1% or 50%) in combination, bivariate models were constructed using the validation cohort. We considered two types of models: in one case, patients were considered to have clinical benefit if both (PD-L1 and PD1T TILs), or one of the two markers were above their respective threshold. Patients were considered to experience disease progression if both markers were below their respective threshold. In the other case, patients were considered to have clinical benefit only if both markers (PD-L1 and PD-1T TILs) were above their respective threshold. Patients were considered to experience disease progression if both, or one of the two markers were below their respective threshold. As the first model yielded the better predictive performance, we used this model to test the two choices for the PD-L1 threshold. Bivariate models of PD-L1 TPS (discretized at either 1% and 50%) and PD-L1 IC (discretized at a score of 2) were constructed using all nivolumab treated patients (n=94). The same type of model was used as described for PD-1T and PD-L1 TPS above. Correlations between PD-L1 TPS and PD-L1 IC or PD-1T TILs and PDL1 TPS, respectively, were evaluated using linear regression analysis.
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