Patent Application
20230374597
The present disclosure generally relates to methods for treating head and neck squamous cell carcinoma patients based on use of blood-based tumor mutation burden, PD-L1 expression, blood based markers, expression levels of immunomodulators, pro-angiogenesis markers and pro-inflammatory markers and/or identification of mutations in circulating tumor DNA.
Recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is a difficult cancer to treat. The standard of care (SoC) in the first-line setting is platinum-based doublet chemotherapy with cetuximab with limited survival benefits in general.
Immune checkpoint inhibitors have demonstrated clinical efficacy in the treatment of R/M HNSCC with anti-PD-1 blockade therapies and approved in first and second line settings. Durvalumab is an immune checkpoint inhibitor that blocks the interaction between programmed cell death ligand 1, or PD-L1, and its receptors. The cytotoxic activity of durvalumab has been found in various solid tumors leading to multiple approvals. Tremelimumab, is a cytotoxic T-lymphocyte—associated antigen 4, or anti—CTLA-4, monoclonal antibody. Since CTLA-4 and PD-L1/PD-1 pathways are largely non-redundant, combining them together could have additive effects and studies are ongoing to assess their clinical activities in different solid tumor types (see Burtness et al., The Lancet, Vol. 394, Issue 10212, P 1915-1928, 2019).
Despite the success of multiple anti-PD-L1 immune checkpoint inhibitors, it is worth noting that clinical response was restricted in a minority of patients with moderate improvement of overall survival, calling for efficient biomarkers to select patients most likely to benefit. Single arm or real-world evidence studies in R/M HNSCC have showed that tumor mutational burden (TMB), as measured in tumor tissue (tTMB), may be associated better with clinical outcomes with immune checkpoint inhibitor treatment. However, these studies failed to determine if TMB is predictive or prognostic and define clear predictivity cut-points for TMB.
The disclosure provides a method of predicting success of head and neck cancer treatment in a patient in need thereof, comprising determining the patient's tumor mutational burden (TMB), wherein a high TMB predicts success of treatment.
The disclosure further provides a method of treating head and neck cancer in a patient in need thereof, comprising: determining the patient's TMB, determining whether the TMB is high or low, and treating or continuing treatment if TMB is high or not treating or discontinuing treatment if TMB is low.
The disclosure further provides a method of treating head and neck cancer in a patient in need thereof, comprising: determining whether the patient has a somatic mutation in at least one of Lysine Methyltransferase 2D (KMT2D) gene or Ataxia-Telangiectasia Mutated (ATM) gene; and treating or continuing treatment if the patient has a somatic mutation in at least one of Lysine Methyltransferase 2D (KMT2D) gene or Ataxia-Telangiectasia Mutated (ATM) gene.
The disclosure further provides a method of predicting success of head and neck cancer treatment in a patient in need thereof, comprising determining PD-L1 expression in the patient's tumor cells and tumor-associated immune cells, wherein ≥50% of tumor cells express PD-L1 and/or ≥25% of tumor-associated immune cells express PD-L1 predicts success of treatment.
The disclosure further provides a method of treating head and neck cancer in a patient in need thereof, comprising: determining PD-L1 expression in the patient's tumor cells and tumor-associated immune cells; and treating or continuing treatment if ≥50% of the tumor cells express PD-L1 and/or ≥25% of the tumor-associated immune cells express PD-L1.
The disclosure further provides a method of predicting success of head and neck cancer treatment in a patient in need thereof, comprising determining levels of one or a plurality of protein biomarkers, wherein the protein biomarker is IL-23, osteocalcin, IL-6, neutrophil-to-lymphocyte ratio (NLR), von Willebrand factor (vWF), or Plasminogen activator inhibitor-1 (PAI-1); wherein an increased level of IL-23 or osteocalcin as compared to a reference level, and/or a decreased level of IL-6, NLR, vWF, or PAI-1 as compared to a reference level, and/or low tumor burden as compared to a reference level predicts success of treatment.
The disclosure further provides a method of treating head and neck cancer in a patient in need thereof, comprising: determining levels of one or a plurality of protein biomarkers, wherein the protein biomarker is IL-23, osteocalcin, IL-6, neutrophil-to-lymphocyte ratio (NLR), von Willebrand factor (vWF), or Plasminogen activator inhibitor-1 (PAI-1); and treating or continuing treatment if there is an increased level of IL-23 or osteocalcin as compared to a reference level, and/or a decreased level of IL-6, NLR, vWF, or PAI-1 as compared to a reference level, and/or low tumor burden as compared to a reference level.
The present disclosure generally relates to methods for treating head and neck squamous cell carcinoma patients based on use of blood-based tumor mutation burden, PD-L1 expression, expression levels of immunomodulators, pro-angiogenesis markers and pro-inflammatory markers and/or identification of mutations in circulating tumor DNA.
As utilized in accordance with the present disclosure, unless otherwise indicated, all technical and scientific terms shall be understood to have the same meaning as commonly understood by one of ordinary skill in the art. Unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular.
In some embodiments provided herein is method of predicting success of head and neck cancer treatment in a patient in need thereof, comprising determining the patient's tumor mutational burden (TMB), wherein a high TMB predicts success of treatment.
In some embodiments provided herein is a method of treating head and neck cancer in a patient in need thereof, comprising:
“Tumor mutational burden” (TMB) refers to the quantity of mutations found in a tumor. TMB varies among different tumor types. Some tumor types have a higher rate of mutation than others. TMB can be measured by a variety of tools known in the field. In certain embodiments, these tools are the tumor whole exome sequencing. In some embodiments this sequencing can be measured using tools such as the Foundation Medicine and Guardant Health measurement tools. TMB can be determined through both blood and tissue measurements. Determining whether a tumor has high or low levels of tumor mutational burden can be determined by comparison to a reference population having similar tumors and determining median or mean level of expression. In some embodiments, a high TMB is defined as ≥12 to ≥20 mutations/megabase (mut/Mb). In some embodiments, a high TMB is defined as ≥16 mutations/megabase (mut/Mb). In some embodiments, a high TMB is defined as ≥20 mutations/megabase (mut/Mb).
In some embodiments, the patient has a lower neutrophil-to-lymphocyte ratio as compared to a reference level. Determining whether a patient has a lower neutrophil-to-lymphocyte ratio can be determined by comparison to a reference population having a similar cancer or tumor and determining the median or mean of the neutrophil-to-lymphocyte ratio. In some embodiments, a high TMB level and lower neutrophil-to-lymphocyte ratio are used as makers predictive of improved OS in patients receiving durvalumab and/or tremelimumab treatment.
In some embodiments, the patient has low expression of programmed death-ligand 1 (PD-L1) on tumor cells (TCs) and/or immune cells (ICs). In some embodiments, low expression is classified as ≤25% of the patient's tumor-associated immune cells express PD-L1 and ≤50% of the patient's tumor cells express PD-L1. In some embodiments, a high TMB level and low expression of PD-L1 are used as makers predictive of improved OS in patients receiving durvalumab and/or tremelimumab treatment.
In some embodiments, provided herein is a method of predicting success of head and neck cancer treatment in a patient in need thereof, comprising determining PD-L1 expression in the patient's tumor cells and tumor-associated immune cells, wherein ≥50% of tumor cells express PD-L1 and/or ≥25% of tumor-associated immune cells express PD-L1 predicts success of treatment.
In some embodiments, provided herein is a method of treating head and neck cancer in a patient in need thereof, comprising:
In some embodiments, the success of treatment is determined by an increase in OS as compared to standard of care. In some embodiments, the success of treatment is determined by an increase in progression free survival as compared to standard of care. “Standard of care” (SoC) and “platinum-based chemotherapy” refer to chemotherapy treatment comprising at least one of methotrexate, docetaxel, paclitaxel, 5-FU, TS-1 or capecitabine.
As used herein, Overall Survival (OS) relates to the time-period beginning on the date of treatment until death due to any cause. OS may refer to overall survival within a period of time such as, for example, 12 months, 18 months, 24 months, and the like.
As used herein, Progression Free Survival (PFS) relates to the length of time during and after treatment that a patient lives with the head and neck cancer but the cancer does not get worse. PFS may refer to survival within a period of time such as, for example, 12 months, 18 months, 24 months, and the like.
In some embodiments, provided herein are methods of treating head and neck cancer in a patient in need thereof, comprising determining whether the patient has a somatic mutation in at least one of Lysine Methyltransferase 2D (KMT2D) gene or Ataxia-Telangiectasia Mutated (ATM) gene; and treating or continuing treatment if the patient has a somatic mutation in at least one of Lysine Methyltransferase 2D (KMT2D) gene or Ataxia-Telangiectasia Mutated (ATM) gene. In some embodiments, mutations in KMT2D and ATM are used as a biomarker predictive of improved OS in patients receiving durvalumab and/or tremelimumab treatment.
The term “KMT2D” encompasses “full-length” unprocessed KMT2D as well as any form of KMT2D that results from processing in the cell. The term also encompasses naturally occurring variants of KMT2D, e.g., splice variants or allelic variants.
The term “ATM” encompasses “full-length” unprocessed ATM as well as any form of ATM that results from processing in the cell. The term also encompasses naturally occurring variants of ATM, e.g., splice variants or allelic variants.
In some embodiments, provided herein is a method of predicting success of cancer treatment in a patient in need thereof, comprising determining levels of one or a plurality of protein biomarkers, wherein the protein biomarker is IL-23, osteocalcin, IL-6, neutrophil-to-lymphocyte ratio (NLR), von Willebrand factor (vWF), or plasminogen activator inhibitor-1 (PAI-1), wherein an increased level of IL-23 or osteocalcin as compared to a reference level, and/or a decreased level of IL-6, NLR, vWF, or PAI-1 as compared to a reference level, and/or low tumor burden as compared to a reference level predicts success of treatment. In some embodiments, IL-23, osteocalcin, IL-6, NLR, vWF, and PAI-1 are used as biomarkers predictive of improved OS in patients receiving durvalumab treatment.
In some embodiments provided herein is a method of treating head and neck cancer in a patient in need thereof, comprising:
In some embodiments, the method comprises treatment with durvalumab. The term “durvalumab” as used herein refers to an antibody that selectively binds PD-L1 and blocks the binding of PD-L1 to the PD-1 and CD80 receptors, as disclosed in U.S. Pat. No. 9,493,565 (wherein durvalumab is referred to as “2.14H9OPT”), which is incorporated by reference herein in its entirety. The fragment crystallizable (Fc) domain of durvalumab contains a triple mutation in the constant domain of the IgG1 heavy chain that reduces binding to the complement component C1q and the Fcγ receptors responsible for mediating antibody-dependent cell-mediated cytotoxicity (ADCC). Durvalumab can relieve PD-L1-mediated suppression of human T-cell activation in vitro and inhibits tumor growth in a xenograft model via a T-cell dependent mechanism.
In some embodiments, the methods disclosed herein comprise treatment with tremelimumab. The term “tremelimumab” as used herein refers to an antibody that selectively binds a CTLA-4 polypeptide, as disclosed in U.S. Pat. No. 8,491,895 (wherein tremelimumab is referred to as “clone 11.2.1”), which is incorporated by reference herein in its entirety. Tremelimumab is specific for human CTLA-4, with no cross-reactivity to related human proteins. Tremelimumab blocks the inhibitory effect of CTLA-4, and therefore enhances T-cell activation. Tremelimumab shows minimal specific binding to Fc receptors, does not induce natural killer (NK) ADCC activity, and does not deliver inhibitory signals following plate-bound aggregation.
In some embodiments, the methods disclosed herein comprise treatment with durvalumab and tremelimumab. In some embodiments, the methods disclosed herein comprise treatment with durvalumab. In some embodiments, the methods disclosed herein comprise treatment with tremelimumab
The term “patient” is intended to include human and non-human animals, particularly mammals.
In some embodiments, the methods disclosed herein relate to treating a subject for a tumor disorder and/or a cancer disorder. In some embodiments, the cancer is head and neck cancer. In some embodiments, the head and neck cancer is a squamous cell carcinoma. In some embodiments, the cancer is recurrent and/or metastatic.
The terms “treatment” or “treat” as used herein refer to both therapeutic treatment and prophylactic or preventative measures. Those in need of treatment include subjects having cancer as well as those prone to having cancer or those in cancer is to be prevented. In some embodiments, the methods disclosed herein can be used to treat cancer. In other embodiments, those in need of treatment include subjects having a tumor as well as those prone to have a tumor or those in which a tumor is to be prevented. In certain embodiments, the methods disclosed herein can be used to treat tumors. In other embodiments, treatment of a tumor includes inhibiting tumor growth, promoting tumor reduction, or both inhibiting tumor growth and promoting tumor reduction.
The terms “administration” or “administering” as used herein refer to providing, contacting, and/or delivering a compound or compounds by any appropriate route to achieve the desired effect. Administration may include, but is not limited to, oral, sublingual, parenteral (e.g., intravenous, subcutaneous, intracutaneous, intramuscular, intraarticular, intraarterial, intrasynovial, intrasternal, intrathecal, intralesional, or intracranial injection), transdermal, topical, buccal, rectal, vaginal, nasal, ophthalmic, via inhalation, or using implants.
The terms “pharmaceutical composition” or “therapeutic composition” as used herein refer to a compound or composition capable of inducing a desired therapeutic effect when properly administered to a subject. In some embodiments, the disclosure provides a pharmaceutical composition comprising a pharmaceutically acceptable carrier and a therapeutically effective amount of at least one antibody of the disclosure.
The terms “pharmaceutically acceptable carrier” or “physiologically acceptable carrier” as used herein refer to one or more formulation materials suitable for accomplishing or enhancing the delivery of one or more antibodies of the disclosure.
Without limiting the disclosure, a number of embodiments of the disclosure are described below for purpose of illustration.
The Examples that follow are illustrative of specific embodiments of the disclosure, and various uses thereof. They are set forth for explanatory purposes only, and should not be construed as limiting the scope of the invention in any way.
EAGLE (NCT02369874) was a randomized, open-label, phase 3 trial study that evaluated the efficacy of durvalumab (D) or durvalumab plus tremelimumab (D+T) versus chemotherapy in patients with recurrent/metastatic head and neck squamous cell carcinoma. Patients with disease progression after platinum-based CT were randomized 1:1:1 to durvalumab (10 mg/kg every 2 weeks), durvalumab plus tremelimumab (durvalumab 20 mg/kg every 4 weeks plus tremelimumab 1 mg/kg every 4 weeks for 4 doses, then durvalumab 10 mg/kg every 2 weeks), or chemotherapy (cetuximab, a taxane, methotrexate, or a fluoropyrimidine). The primary endpoint of overall survival with durvalumab versus chemotherapy, and overall survival with durvalumab plus tremelimumab versus chemotherapy was not met in the EAGLE trial; there were no statistically significant differences in overall survival with durvalumab or durvalumab plus tremelimumab versus chemotherapy. However, overall survival at landmark timepoints (12, 18, and 24 months) was higher with durvalumab than with chemotherapy, demonstrating clinical activity for durvalumab.
Plasma samples were profiled to identify somatic alterations including single-nucleotide variants, small indels and copy number amplifications using GuardantOMNI next-generation sequencing platform (Guardant Health, Redwood City, CA) comprising 500 genes (2.145 Mb). The OMNI TMB algorithm incorporates somatic synonymous and non-synonymous single nucleotide variants (SNVs) and short insertions/deletions (indels) at all variant allele fractions across 1.0 Mb of genomic coding sequence and is optimized to calculate TMB on plasma samples with low cell-free circulating tumor DNA content. Alterations associated with clonal hematopoiesis, germline and oncogenic driver or drug resistance mechanisms were excluded from the TMB calculation. Samples with low tumor shedding (e.g., maximum somatic allele fraction <0.3%) or low unique molecule coverage were considered bTMB-unevaluable.
A series of bTMB cutoff values from 5 to 20 mut/Mb were examined to determine the optimal hazard ratio of OS for durvalumab as compared with SoC in the bTMB high cohort. Two-fold cross validation analyses were performed and a minimum p value approach based on Cox proportional hazard (PH) model was used to select the optimal cut-point from the above values. The most frequently selected cut-points in the Cox PH models in training sets were considered as potential optimal cutoffs. These potential optimal cutoffs in the training set were then validated based on HR distribution in the validation set.
The Kaplan-Meier method was used to calculate univariate survival estimates for progression-free survival and overall survival. Minimum p value approach based on Cox PH model, 2 folds cross validation analyses were performed. The most frequently selected minimum p value cutoffs in the Cox PH models for training sets will be consider as potential optimal cutoffs. These potential optimal cutoffs will be validated based on HR distribution from validation set. The optimal cutoff will be determined based on further exploration of the efficacy differentiation by the cutoffs using full dataset. A Cox proportional hazard model was used to define the association of mutational status of genes with PFS and OS. P-values were assessed using the log-rank test. Wilcoxon rank-sum test and Kruskal-Wallis test were used when comparing continuous variables. All p-values are two-sided. 10,000-fold cross-validation was performed to evaluate PFS and OS performance at all cutoffs evaluated. Analyses were performed using SAS and R (version 3.4.3, R Foundation, Vienna, Austria).
The retrospective analysis of the EAGLE trial included 736 intent-to-treat patients and 247 were evaluable for bTMB (BEP). Baseline characteristics were generally well balanced among the intention to treat population, patients enrolled in the plasma collection period, and the blood TMB evaluable populations, and were representative of a patient population with platinum-refractory recurrent/metastatic head and neck squamous cell carcinoma. When comparing all patients enrolled in the biomarker evaluable population sample collection period with the biomarker evaluable population, overall survival with durvalumab remained unchanged; however, overall survival in the chemotherapy group was higher in all samples than in the biomarker evaluable population (
Guardant OMNI panel was applied to 300 plasma samples from baseline, and data were successfully generated for 286 (95%) patients. Somatic SNVs or indels were detected in 279 (98%) patients and the median variant count per sample is 12 (
Somatic mutations were identified in 387 genes and were found in more than 20% of samples in 7 genes, including TP53 (79%), KMT2D (33%), FAT1 (26%), LRP1B (23%), TERT (23%), PIK3CA (22%) and NOTCHI (21%). The prevalence of TP53 mutations is comparable with 72% reported by TCGA, and a higher prevalence (86%) was found in HPV-ve patients, consistent with previous observation (Leemans et al., Nat. Rev. Cancer 18(5): 269-82 (2018)). The prevalence of FAT1, LRP1B, PIK3CA, and NOTCH1 mutations is also similar to that in TCGA cohort (23%, 20%, 21% and 19%, respectively), suggesting the somatic mutational landscape in R/M HNSCC is generally consistent with treatment naïve HNSCC. Notably in the cohort, KMT2D gene showed increased mutation frequency as compared with TCGA cohort (33% versus 18%), which may imply more prevalent epigenetic rewiring in R/M HNSCC. 59 TERT promoter mutations in 57 patients were also reported, including two recurrent mutations (−124 G>A, N=34 and −146 G>A, N=11). Expression of TERT tend to be enhanced by bearing those promoter mutations and could promote unlimited cell growth (Shay et al., Semin. Cancer Biol. 21(6): 349-53 (2011)), highlighting its critical role in HNSCC carcinogenesis. High mutation frequencies of several homologous recombination DNA damage repair genes (Heeke et al., JCO Precis. Oncol. (2018), doi: 10.1200/P0.17.00286) were observed in the R/M HNSCC cohort, including ATM (15%), CHEK2 (12%), and ARID1A (11%), which are significantly elevated in contrast to treatment naïve cohort (3%, 2%, and 4%, respectively).
Since it is challenging to identify copy number loss in plasma circulating free DNA (cfDNA), only amplifications were reported in this study. In total, 878 amplifications were identified in 98 genes and 145 patients. For patients with amplifications, a median of three were found. Consistent with TCGA cohort, cyclin D1 (CCND1) on 11q13 was the most frequently observed amplification, presented in 25% of patients. HPV−ve tumors were more prone to CCND1 amplification as compared with HPV+ve tumors (29% versus 10%, P=0.0034, Fisher's test), indicating potential different mechanisms in tumor development. The other genes with recurrent amplifications in more than 10% of the cohort include FGF3 (25%), FGF19 (19%), PIK3CA (18%), and PIK3CB (17%), in general concordance with previous reports. Notably, CCND1, FGF3, and FGF19 were all on 11q13 and they were co-amplified in most of patients.
bTMB data from 247 patients enrolled in EAGLE was generated. The median bTMB of EAGLE cohort was 12.6 (mut/Mb). 74 (30%) or 50 (20%) patients showed bTMB ≥16 or ≥20, respectively. The bTMB distribution across all three arms was similar (
Patients were stratified into bTMB high and bTMB low subgroups using different cutoffs. In the bTMB high cohort, a clear signal of significantly improved overall survival was found in both durvalumab and durvalumab plus tremelimumab treatment arms as compared with SoC arm, when using bTMB cutpoints greater than or equal to 16 mutations per megabase (
Cross-validation also supported 16 mutations per megabase was the optimal bTMB cut-point in the EAGLE study. When incorporating this cut-point to stratify patients, no link was found between bTMB level and human papillomavirus status, PD-L1 status, age, or gender. Smoking and progression within 6 months on multi-modality chemotherapy in localized disease trended with higher bTMB. Other parameters with a greater than 5% difference between bTMB high and low subgroups include primary tumor location or Eastern Cooperative Oncology Group (ECOG) performance status and complete response rate.
Overall survival and progression free survival in EAGLE was improved in bTMB high subgroup for durvalumab and durvalumab plus tremelimumab (
Patients with pathogenic or likely pathogenic mutations in KMT2D, a head and neck squamous cell carcinoma tumor suppressor gene, showed improved overall survival for durvalumab plus tremelimumab versus chemotherapy, with a hazard ratio of 0.39 and a 95% confidence interval of 0.17 to 0.85. A trend of improved overall survival for durvalumab plus tremelimumab versus chemotherapy was also seen in patients with ATM mutations.
With the availability of efficacy data from HAWK and CONDOR studies (Zandberg et al., Eur. J Cancer. 107: 142-52 (2019); Siu et al., JAMA Oncol. 5(2): 195-203 (2019)), it has been possible to analyze larger data sets, and use overall survival data to derive a PD-L1 diagnostic algorithm which is more predictive of OS. This Example illustrates the analysis used to determine an optimal algorithm for HNSCC, and the methodology used to score PD-L1 in tumors of patients. The optimal algorithm was determined as ≥50% of tumor cell or ≥25% of tumor-associated immune cells (TC≥50 or IC≥25) membrane staining for PD-L1 at any intensity, as assessed by the VENTANA PD-L1 (SP263) Assay.
Data was used from D4193C00001 (HAWK) and D4193C00003 (CONDOR) Phase II studies, in 2nd line R/M HNSCC patients. Both studies required PD-L1 status as enrollment criteria, and at screening, patient's tumor specimens were stained and scored with the VENTANA PD-L1 (SP263) Assay. Tumor cell PD-L1 expression data was available in the following bins: <1, 1-4, 5-9, 10-19, 20-24 (CONDOR), 25, 30 (26-34), 40 (35-44), 50 (45-54), 60 (55-64), 70 (65-74), 75, 80 (76-84), 90 (85-94), and 100 (95-100) (HAWK). Exploratory data was collected for immune cells, using a raw score for immune cell positivity.
Overall survival data for the patients treated with monotherapy durvalumab in these two studies was pooled. Data from the durvalumab +tremelilumab combination was not used, because there was no data from patients with PD-L1 TC≥25%. The pooled monotherapy data was from a total of 179 subjects (112 subjects from the HAWK study and 67 subjects from the CONDOR study). The PD-L1 prevalence in the pooled monotherapy group was 62%, whereas the prevalence in a natural population is 25-30%.
There was a general trend of increasing survival (median and 6 month) with increasing Tumor Cell PD-L1 expression (
The algorithm TC>=50% or IC>=25% was considered most technically feasible. Based on experiences with the Urothelial Cancer SP263 (Zajac et al., 2016, European Society Medical Oncology (ESMO) Poster 26P), IC≥25% was considered likely to be more reproducible (higher intra-reader precision) than IC10 or IC1, and thus would make a more robust diagnostic assay in the clinic.
A later analysis of more mature data was used to confirm the cut-off. Data maturity was 68% for OS and 85% for PFS. The HR was 0.758 (adjusted). PFS was 3.4 months v 1.9 months in PD-L1 high v PD-L1 low (
Pooled data from the HAWK/CONDOR studies did not represent a natural prevalence. In order to model the all-comers population a boot-strapping OS hazard ratio (HR) analysis was performed across the various TC/IC subgroups. Data showed the cut-point of TC≥50 or IC≥25% was optimal with the lowest HR (
In order to classify HNSCC patients based on the PD-L1 TC/IC scoring algorithm, PD-L1 expression in tumor cell (TC) and tumor-associated immune cells (IC) was detected by VENTANA PD-L1 (SP263) Assay in formalin-fixed, paraffin-embedded (FFPE) head and neck squamous cell carcinoma (HNSCC). An isotype matched negative control antibody was used to evaluate the presence of background in test samples and establish a baseline staining intensity.
PD-L1 status and expression was assigned by a trained pathologist based on their evaluation of the percentage of specific staining for both tumor and tumor-associated immune cells (macrophages, dendritic cells, and lymphocytes). PD-L1 status was determined by the percentage of tumor cells with any membrane PD-L1 staining above background or by the percentage of tumor-associated immune cells with PD-L1 staining at any intensity above background.
Immune cell scoring was performed by first calculating the percentage of immune cells present as a proportion of the tumor environment (ICP-value) on the H&E section. The ICP value was expressed in individual percentages. The IC-score was generated by expressing the percentage of positive PD-L1 immune cells as a proportion of the ICP-value. PD-L1 high expression level was greater than or equal to 50% tumor cells with PD-L1 membrane staining or greater than or equal to 25% immune cell PD-L1 staining. PD-L1 low was defined as both <50% TC and <25% IC with membrane staining for PD-L1 at any intensity (Table 4).
In cases where the ICP was equal to 1%, IC positivity (IC+) was scored as either 0%, <100%, or 100% due to the difficulties in estimating the percent staining in small volumes of immune cells in low measures. The small amount of PD-L1 staining observed in cases with <100% IC positivity, should be considered as <25% PD-L1 expression.
A retrospective analysis was performed to evaluate TMB and other biomarkers for their predictive potential in patients benefiting from durvalumab (D) or durvalumab +tremelimumab (D+T) in 2 trials of R/M HNSCC. In the single-arm, Phase II HAWK study (Zandberg et al., Eur. J. Cancer. 107: 142-52 (2019)), 112 patients (PD-L1 tumor cell [TC] staining ≥25%) received D (10 mg/kg every 2 weeks [Q2W] for ≤12 months [mo]). In the randomized, open-label, Phase II CONDOR trial (Siu et al., JAMA Oncol. 5(2): 195-203 (2019)), 67 patients (PD-L1TC<25%) received D (10 mg/kg Q2W for ≤12 mo), 133 received D+T (D 20 mg/kg every 4 weeks [Q4W], T 1 mg/kg Q4W for ≤12 mo), and 67 received T (10 mg/kg Q4W for 7 doses then Q12W for 2 additional doses for ≤12 mo). Interactions of PD-L1 and TMB as predictive biomarkers were also evaluated.
Paired formalin-fixed, paraffin-embedded (FFPE) archival tumor and peripheral blood mononuclear cell (PBMC) samples (as germline controls) in the HAWK and CONDOR trials were evaluated by whole exome sequencing (WES). HLA class 1 types were obtained using WES of PBMC. Human papillomavirus (HPV) was assessed locally using any WES method or centrally using p16 immunohistochemistry. Neutrophil-to-lymphocyte ratio (NLR) was assessed locally. Statistical analyses included the Wilcoxon test, log-rank test, and Cox proportional hazards model. PD-L1 expression status was determined using the VENTANA PD-L1 (SP263) Assay and a cutoff of TC≥25%.
In the HAWK and CONDOR trials, 153 patients had evaluable FFPE samples (
Pooled longitudinal tumor size, survival, and dropout data from 4 trials involving 467 patients with HNSCC were used to develop tumor size-driven hazard models (1108: NCT01693562, CONDOR: NCT02319044, HAWK: NCT02207530, and EAGLE: NCT02369874). A Tumor Growth Inhibition (TGI) Model was developed using non-linear mixed effects methods to characterize the longitudinal tumor size data. The model primarily assumed that the total tumor volume (Ttotal) included sensitive (Tsens) and insensitive (Tinsens) tumor compartments to anti-programmed death ligand-1 treatment. A capacity-limited logistic growth function was used to model growth in the insensitive compartment whereas the sensitive compartment was modeled with first order growth (kg) and second order shrinkage rate (kkkill).
T′
sens=[(kg×Tsens)]−kkill×T2sens×Rim(t)
T
insens=[(kg×Tinsens)]×[(1−Tinsens/Tmax)])
where Rim(t) denotes a delay function constrained between 0 and 1 via transduction through transit compartments where maximum tumor shrinkage effect occurs at Rim(t)=1. The fraction of sensitive tumor cells at baseline is estimated as Fsens (=Tsens(0)/Ttotal(0)). The mean transit time for the delay function (DTIM) was characterized using a mixture model, with 2 distinct populations:
h_OS (t)=HZos×exp(0TS)×TS (t)
h_DO (t)=HZdo×α×tα×exp(0PCT_TS)×PCT_TS (t)×exp(0TBSL)×TBSL,
where h_OS(t) and h_DO(t) are hazard of death and dropout at time t, respectively; HZos and HZdo respectively denote the baseline hazard for death and dropout; α is the shape parameter of the Weibull function; TS (t), and PCT_TS (t) are model-predicted tumor size and change from baseline tumor size, at time t for each individual, respectively. TBSL is the tumor size at baseline.
Covariate analyses were performed on the TGI, OS, and dropout models. The full model approach covariate modeling followed by univariate backward elimination (based on a type-I error of 5%) was used to identify significant biomarkers. A panel of 66 serum protein biomarkers at baseline and 4 relevant clinical markers from 346 out of 413 patients treated with durvalumab (all studies except 1108) were initially screened to select a pool of 21 candidate covariates. The criteria for dimensionality reduction comprised correlation strength between biomarkers and pharmacological hypotheses pertaining to a prior analysis (inflammation, immunomodulation, tumor burden, and angiogenesis).
Cut-point and regression analysis using the final baseline predictors of survival to identify subsets of patients with substantial survival benefits were used. Similar baseline tumor burden and most inflammatory markers were observed across the legacy studies (Table 5). Of note, cross study effects were observed for some of the measured serum cytokines (data not shown), which were assessed and accounted for during the multivariate analysis.
The final tumor size model highlighted that high tumor burden was associated with faster tumor growth while patients with lower baseline tumor burden had an increase in net tumor shrinkage (
Patients with a favorable biomarker profile had high baseline levels of immunomodulators (IL-23, osteocalcin), low systemic inflammation (IL-6, NLR), low tumor burden, and low angiogenesis factors (vWF, plasminogen activator inhibitor-1 (PAI-1)) were associated with survival benefit for patients with HNSCC treated with durvalumab. Specifically, patients with a favorable biomarker profile had a combination of baseline levels of low serum PAI-1<229 pg/mL, low serum IL-6<5.4 pg/mL, high serum IL-23>2.1 pg/mL and/or high osteocalcin>32 pg/MI (
The tumor size model covariate analysis results revealed that as compared to the median, patients with elevated (90th percentile) serum LDH and NLR had on average 40% faster tumor growth.
All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference. Citation or identification of any reference in any section of this application shall not be construed as an admission that such reference is available as prior art to the present invention.
This application is a U.S. national phase application under 35 U.S.C. 371 of International Patent Application No.: PCT/EP2021/062707, filed May 12, 2021, which claims the benefit of both U.S. Provisional Application No. 63/031,238, filed on May 28, 2020, and U.S. Provisional Application No. 63/023,582, filed on May 12, 2020, the disclosures of each of which are incorporated by reference herein in their entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2021/062707 | 5/12/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63023582 | May 2020 | US | |
| 63031238 | May 2020 | US |
