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1-10. 16 APPENDICES 16.1 APPENDIX A-P ERFORMANCE STATUS CRITERIA ECOG Pe rformance Status Scale Grade Descriptions 0 Normal activity. Fully active, able to carry on all pre- disease performance without restriction. 1 Symptoms, but ambul atory. Restricted in physically strenuous activity, but ambulatory and able to carry out work of a light or sedentary nature (e.g., light housework, office work). 2 In bed <50% of the time. Ambulatory and capable of all self -care, but unable to carry out any work activities. Up and about more than 50% of waking hours. 3 In bed >50% of the time. Capable of only limited self -care, confined to bed or chair more than 50% of waking hours. 4 100% bedridden. Completely disabled. Cannot carry on any self- care. Totally confined to bed or chair.
5 Dead. Abbreviated T itle: STAT Trial Version Date : 08/17/2022 125 16.2 APPENDIX B: DRUGS KNOWN TO SIGNIFICANTLY PROLONG THE QTC* Generic Name* Brand Name* Aclarubicin Aclacin and others Amiodarone Cordarone and others Anagrelid e Agrylin and others Arsenic trioxide Trisenox Astemizole Hismanal Azithromycin Zithro max and others Bepridil Vascor Chloroquine Aralen Chlorpromazine Thorazine and others Cilostazol Pletal Ciprofloxacin Cipro and others Cisapride Propulsid Citalopram Celexa and others Clarithromycin Biaxin and others Cocaine Cocaine Disopyramide Norpace Dofetilide Tikosyn Domp erido ne Motilium and others Donepezil Aricept Dronedarone Multaq Droperidol Inapsine and others Erythromycin E.E.S. and others Escitalopram Cipralex and othe rs Flecainide Tambocor and others Fluconazole Diflucan and others Gatif loxacin Tequin Grepafloxacin Raxar Halofantrine Halfan Generic Name* Brand Name* Haloperidol Haldol (US & UK) and others Ibogaine None Ibutilide Corvert Levofloxacin Levaquin and others Levomepromazine (methotrimeprazine) Nosinan and others Levomethadyl acetate Orlaam L evosulpiride Lesuride and others Mesoridazine Serentil Methadone Dolophine and others Moxifloxacin Avelox and others Ondansetron Zofran and others Oxaliplatin Eloxatin Papaverine HCl ( Intraco ronary) None Pentamidine (systemic) Pentam Abbreviated T itle: STAT Trial Version Date : 08/17/2022 126 Pimozide Orap Probucol Lorelco Procainamide Pronestyl and others Propofol Diprivan and others Quinidine Quinaglut e and others Roxithromycin Rulide and others Sevoflurane Ultane and others Sotalol Betapace and others Sparfloxacin Zagam Sulpiride Dogmatil and others Sultopride Barnetil and others Terfenadine Seldane Terlipressin Teripre ss and others Terodiline Micturin and others Thioridazine Mellaril and others Vandetanib Caprelsa * CredibleMeds.org Abbreviated T itle: STAT Trial Version Date : 08/17/2022 127 16.3 APPENDIX C PARTICIPANT ’S MEDICATION DIARY ________________________ 16.3.1 Lead In Participant ’s ID _________________________ Treatment LEAD IN:_____________________ Part icipant SX-682 Dose:________ __________ Treatment Dates:______________________ INSTRUCTIONS TO THE PARTICIPANT : 1. Complete this form for two -week per iod on the trial.
2. You will take SX -682 twice a day for every day of two- week period. This drug should be taken in a fasting state, me aning no food 2 hours prior to taking the drug and no food 1 hour after taking the drug. 3. Record the date, the number of tablets that you took, and when you took them. 4. If you have any comments or notice any side effects, ple ase record them in the comment’s column. 5. Please bring this form and your bottles (even it is empty) when you come for your clinic visit. Day Date Oral SX -682 (every 12 hours) Comments (Side effects, reason for missing dose, etc) Morning Dose Evening Do se Time # Tablets Time # Tab lets -14L -13L -12L -11L -10L -9L -8L -7L -6L -5L -4L -3L -2L -1L Participant ’s signature: ___________________________________________________ Abbreviated T itle: STAT Trial Version Date : 08/17/2022 128 16.3.2 Cycles Participant ’s ID _ ________________________ Tr eatment Cycle:_____________________ Participant SX-682 Dose :__________________ Treatment Dates:______________________ INSTRUCTIONS TO THE PARTICIPANT : 1. Complete one form for each cycle on the trial. 2. You will take SX -682 twice a d ay for every day of the cycle. This drug should be taken in a fasting state, meaning no food 2 hours prior to taking the drug and no food 1 hour after taking the drug. 3. Record the date, the number of tablets that you took, and when you took the m. 4. If you ha ve any comments or notice any side effects, please record them in the comment’s column. 5. Please bring this form and your bottles (even it is empty) when you come for your clinic visit. Day Date Oral SX -682 (ev ery 12 hours) Comm ents (Side effects , reason for missin g dose, etc) Morning Dose Evening Dose Time # Tablets Time # Ta blets 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Abbreviated T itle: STAT Trial Version Date : 08/17/2022 129 Day Date Oral SX -682 (ev ery 12 hours) Comm ents (Side effects , reason for missin g dose, etc) Morning Dose Evening Dose Time # Tablets Time # Ta blets 25 26 27 28 Participant ’s signature: ___________________________________________________
REVIEW Preclinical and clinical development of neoantigen vaccines L. Li1,2, S. P. Goedegebuure1,2& W. E. Gillanders1,2* 1Department of Surgery, Washington University School of Medicine, St Louis;2The Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St Louis, USA Correspondence to : Dr William E. Gillanders, Department of Surgery, Washington University School of Medicine, Campus Box 8109, 660 South Euclid Avenue, St Louis, MO 63110, USA. Tel: þ1-314-747-0072; E-mail: [email protected] Cancer neoantigens are antigens that result from somatic mutations present in individual cancers. Neoantigens are considered important targets for cancer immunotherapy because of their immunogenicity and lack of expression in normal tissues. Next-generation sequencing technologies and computational analysis have recently made neoantigen discovery possible. Althoughneoantigens are important targets of checkpoint blockade therapy, neoantigen vaccines are currently being investigated inpreclinical models and early-phase human clinical trials. Preliminary results from these clinical trials demonstrate that dendriticcell, synthetic long peptide, and RNA-based neoantigen vaccines are safe, and capable of inducing both CD8 þand CD4 þ neoantigen-specific T-cell responses. We and others are testing neoantigen vaccines in melanoma, breast cancer, non-small-celllung cancer and other cancer types. Since cancers have evolved mechanisms to escape immune control, it is particularlyimportant to study the efficacy of neoantigen vaccines in combination with other immunotherapies including checkpointblockade therapy, and immune therapies targeting the immunosuppressive tumor microenvironment. Key words :cancer vaccine, neoantigen, immunotherapy, clinical trial Introduction Cancer immunotherapy has evolved into one of the most promis- ing cancer treatment modalities. The goal of cancer immunother- apy is to harness the immune system for the selective destruction of cancers while leaving normal tissues unharmed. Two immunecheckpoints, cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and programmed cell death protein 1 (PD-1), have been the focus of most clinical trials. Antibodies targeting CTLA- 4 and PD-1/PD-L1 are relatively well-tolerated, and can elicit durable antitumor responses in a subset of patients with mela-noma [ 1–3], non-small-cell lung cancer (NSCLC), bladder carcinoma and other cancers [ 4]. Although antibodies targeting CTLA-4 and PD-1/PD-L1 represent the greatest success of cancer immunotherapy, substantial efforts are underway to develop additional immunotherapies that can be used alone or in combi-nation with these checkpoint blockade therapies. A dynamic interplay exists between cancer cells and the immune system, as described in the cancer immunoediting hypothesis [ 5– 7]. It is now understood that the immune system is capable of both suppressing tumor growth, and/or promoting tumor progression.In terms of suppressing tumor growth, the innate and adaptive immune systems can speciﬁcally recognize cancers as nonself and mount antitumor immune responses. Such beneﬁcial immuneresponses can be induced and/or enhanced by checkpoint blockade therapies, adoptive cell therapies or cancer vaccine approaches. In terms of promoting tumor progression, tumors leverage immune regulatory networks to circumvent immune control and promote growth [ 6,8]. To maximize efﬁcacy, it is likely that next- generation immunotherapies will successfully integrate multiple strategies targeting elements of this dynamic interplay. The most promising strategies include, but are not limited to (i) agents thattarget immune checkpoints, (ii) approaches that inhibit immuno- suppression in the tumor microenvironment (e.g. targeting regula- tory T cells, myeloid-derived suppressor cells, tumor-associated macrophages etc.), (iii) adoptive transfer of ex vivo expanded and/ or genetically engineered T-cell receptor (TCR) or chimeric anti-gen receptor T cells, and (iv) cancer vaccines designed to elicit robust antitumor immune responses. In this review we focus on the development of neoantigen vaccines with an emphasis on bothpreclinical development and initial clinical experience. VCThe Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For Permissions, please email: [email protected] of Oncology 28 (Supplement 12): xii11–xii17, 2017 doi:10.1093/annonc/mdx681 Cancer neoantigens Traditionally, cancer vaccines have targeted the so-called tumor- associated antigens (TAA). TAA are typically proteins present in normal tissues but overexpressed in cancers. Examples of TAA include HER2, MART-1, MUC1, tyrosinase, MAGE, mammaglo-bin-A and NY-ESO-1. Cancer vaccines targeting TAA are pre- sumed capable of inducing T-cell responses to these ‘self’ proteins due to one or more of the following reasons (i) incomplete thymic depletion and/or peripheral tolerance of TAA-reactive T cells; (ii) extremely low expression of TAA in the periphery; (iii) low TCR binding afﬁnity of TAA-reactive T cells; or (iv) restricted TAA expression pattern during development. Unfortunately, mostclinical trials targeting TAA have failed to demonstrate durable beneﬁcial effects compared with standard of care treatment [ 9]. In contrast, neoantigens are tumor-speciﬁc antigens resulting from somatic DNA alterations [e.g. nonsynonymous point muta-tions, insertion-deletions (so-called indels), gene fusions and/or frameshift mutations]. Neoantigens typically have a high predicted binding afﬁnity to major histocompatibility complex (MHC) molecules. Cancer vaccines targeting neoantigens have generated great enthusiasm given the potential advantages of targeting protein sequences that are not present in normal tissues including decreased central immune tolerance, and improved safety proﬁle.This enthusiasm for targeting neoantigens has been enhanced recently, as a strong correlation between somatic tumor mutation burden and favorable clinical beneﬁt of checkpoint blockade therapy has been established in melanoma [ 10,11], NSCLC [ 12] and colorectal cancer [ 13]. In preclinical models, low mutation burden has been shown to result in a lack of immunoediting in the murine KPC pancreatic cancer model, while introduction of a neoantigen (OVA) results in tumor elimination [ 14]. These observations suggest an important role for neoantigens in the clini- cal response to checkpoint blockade therapy and the potential value of mutation burden as a predictive biomarker [ 15]. A clinical trial is ongoing to study the relationship between tumor mutationburden, predicted neoantigen burden, and clinical response in patients with advanced melanoma or bladder cancer treated with nivolumab ( a-PD-1) or nivolumab plus ipilimumab ( a-CTLA-4) (NCT02553642). Cancer neoantigen identification The mutational landscape of cancer is complex as demonstrated by genomic analyses of breast cancer [ 16], melanoma [ 17], liver cancer [18] and many other major cancer types [ 19,20]. Advances in next- generation sequencing (NGS) technologies have enabled rapid and cost-effective comparisons between tumor and normal sequences, which is the starting point of cancer neoantigen identiﬁcation [ 21] (Figure 1). Many somatic mutations detected by DNA sequencing are not expressed (i.e. noncoding mutations, nonsense mutations or monoallelic expression). For thos e expressed mutations resulting in an altered amino acid sequence, the abnormal amino acid sequences have to be successfully translated and then processed into short peptide fragments and displayed on the cell surface in the context of MHC molecules in order to be recognized by the immune system.Antigen processing and presentation is a complex process involving multiple steps that can impact neoantigen presentation [ 22].Antigen processing is different for MHC class I and class II mole- cules. MHC class I molecules present 8–10 amino acid peptides pro-duced endogenously or acquired by p rofessional antigen presenting cells. MHC class II molecules present longer peptides (11–20 amino acids) derived from exogenous proteins. Therefore, predictingwhether a somatic mutation can create a neoantigen depends onseveral key factors: (i) whether the somatic mutation is expressed atthe protein level, (ii) whether t he mutant protein can be naturally processed into an appropriate peptide for presentation, (iii) thebinding afﬁnity of the mutant peptide to the patient‘s autologousMHC molecules and (iv) the afﬁnity of the mutant peptide/MHCcomplex to the TCR of responding T cells. It is currently believedthat most somatic mutations det ected by sequencing do not result in effective neoantigens [ 23]. With the large number of somatic mutations, and the intrinsic polymorphism of human MHC molecules [i.e. human leukocyteantigen (HLA) molecules], it remains a challenging task to accu-rately predict and prioritize neoantigen candidates. A number ofcomputational tools have been developed, which includesequence-based and structure-based algorithms to predict pep-tide–MHC binding (reviewed in Refs. [ 24–29]). Structure-based approaches require experimentally acquired crystallographicdata for model building as well as signiﬁcant computing powerfor simulations. This limits their current application despite anincreased accuracy in epitope prediction [ 28]. On the other hand, in sequence-based predictions, supervised machine learningmodules are applied with methods ranging from simple position- speciﬁc scoring matrices (PSSM, used by SYFPEITHI [ 30] and BIMAS [ 31]), to more sophisticated artiﬁcial neural networks (ANN, used by NetMHC [ 32]), support vector machines or hid- den Markov models. These algorithms depend heavily on the sizeand quality of the training datasets of known MHC binding pep-tides available, and because of this, the predictive performance ofsome MHC alleles is still in need of further improvement.Nevertheless the computational methods that predict bindingafﬁnity of peptides to MHC class I molecules are the most accu-rate so far and have been used successfully in studies to identifyneoantigens [ 33–36]. The ideal neoantigen prediction approach would integrate ﬁlters based on all of the biologic processes discussed above (i.e. proteasomal cleavage, MHC binding, TCR recognition) in assessing the potential immunogenicity of neoan-tigen candidates. To this end, the Immune Epitope Database andAnalysis Resource (IEDB, www.iedb.org) hosts an array of toolsto facilitate peptide–MHC binding prediction with the options ofintegrating proteasomal processing (NetCHOP and NetCTL)and TAP transport prediction [ 37]. We have found in our pre- clinical studies that median afﬁnity is an effective way to predictneoantigens [ 33] although we are not aware that the median approach and NetMHCpan have been directly compared. Inthese studies, we calculated a median afﬁnity for each neoantigenusing multiple epitope prediction algorithms (NetMHCpan,ANN, SMM and SMMPMBEC). Additional ﬁlters were applied to prioritize neoantigen candidates: (i) elimination of hypotheti- cal (Riken) proteins; (ii) use of NetCHOP, an antigen processingalgorithm to eliminate epitopes that are not likely to be proteo-lytically produced by the constitutive- or immune-proteasomeand (iii) prioritization of neoantigens where the mutant epitopehas a higher predicted binding afﬁnity than the correspondingwildtype sequence.ReviewAnnals of Oncology xii12 \| Li et al. Volume 28 \| Supplement 12 \| December 2017 CD4þT cells, which recognize antigens presented by MHC class II molecules, contribute to antitumor immunity. However,the computational methods available to predict MHC class II epitopes are less informative than the MHC class I algorithms because of the more promiscuous nature of peptide binding toMHC class II molecules [ 38], and lack of robust training datasets. Unlike MHC class I molecules, whose peptide binding groove tends to be closed at both ends, the ends of the MHC class II pep-tide binding grove are open, allowing the accommodation of lon- ger peptides [ 39]. The core binding motif of a set of peptides (with variable lengths) that bind to a particular MHC class IImolecule is more difﬁcult to identify. In addition, the proteolytic degradation process of MHC class II-bound peptides is less well characterized. Despite these difﬁculties, Kreiter et al. [ 40] suc- cessfully prioritized MHC class II neoantigens based solely on expression levels and predicted MHC class II binding afﬁnity using tools available at IEDB. The relevance of these MHC class IIneoantigen predictions was conﬁrmed as vaccination with syn- thetic polyepitope mRNA led to complete rejection of estab- lished, aggressively growing syngeneic tumors (B16F10 andCT26) in mice [ 40]. In addition, recent results from two phase I clinical trials [ 34,35] highlight the importance of neoantigen- speciﬁc CD4 þT-cell responses following neoantigen vaccina- tion. These trials, which will be discussed in more detail below, investigated personalized synthetic long peptide (SLP) [ 34] and polyepitope mRNA neoantigen vaccines [ 35] in patients with advanced melanoma. Both SLP and polyepitope mRNA approaches were able to generate CD8 þand CD4 þneoantigen- speciﬁc T-cell responses. This result is notable because the study by Ott et al. [ 34] did not attempt to identify MHC class II- restricted neoantigens for inclusion in the neoantigen vaccine,and Sahin et al. [ 35] found that /C2420% of the T-cell responses were induced to neoantigens predicted to bind poorly to HLA class I and II. These results underscore the need to furtherimprove the in silico prediction algorithms for both MHC class I and II epitopes. Other methods have been used to identify cancer neoantigens. Mass spectrometry analyses of peptides eluted from peptide–MHC complexes have enabled the characterization of the HLA ligandome or immunopeptidome [ 41,42]. A series of clinical tri- als targeting the HLA ligandome have been completed in HLA-A2 þrenal-cell carcinoma patients [ 43]. Another strategy is based on the functional analysis of an individual patient’s peripheral blood mononuclear cells (PBMC) or tumor-inﬁltratinglymphocytes (TIL). Pre-existing neoantigen-reactive T cells can be stimulated and detected by tetramer/multimer staining (ﬂowcytometry analysis) or cytokine secreting assay (e.g. IFN- c ELISPOT). Recently, novel platforms that screen a patient’sPBMC against a neoantigen library has been proposed. Theseplatforms have been successfully applied to identify CD4 þand CD8þT-cell antigens in infectious diseases and cancer [ 44,45]. However, these types of biologic assays are currently costly, tech-nically challenging, and may also fail to identify subdominant and/or cryptic neoantigens that do not naturally induce immune responses yet can be activated through vaccination [ 46]. Preclinical studies Recent studies have shown that neoantigen vaccine approaches are able to induce robust antitumor responses in mice [ 33,40, 47]. In the B16F10 murine melanoma model, Castle et al. [ 47] vaccinated C57BL/6 mice with SLPs derived from 50 validated mutations. Sixteen of these peptides were immunogenic as deter-mined by IFN- cELISPOT assay. Peptide vaccination against two mutant antigens MUT30 (Kif18b K739N) and MUT44 (Cpsf3lD314N) was able to confer a marked in vivo antitumor effect in both preventive and therapeutic settings [ 47]. In follow-up stud- ies, the same group found that the majority of the immunogenic‘mutanome’ of B16F10 and CT26 tumors was CD4 þT-cell spe- ciﬁc, and mRNA vaccines encoding CD4 þT-cell neoantigens were able to induce potent antitumor immunity [ 40]. In collabo- ration with Schreiber et al., we identiﬁed two neoantigens respon- sible for tumor rejection following immune checkpoint blockade witha-CTLA-4 or a-PD-1 antibodies [ 33] in the murine sarcoma model T3. Vaccination with SLP incorporating these two mutantepitopes, namely Lama4 G1254V and Alg8 A506T, induced anti-tumor immunity comparable to checkpoint blockade immuno-therapy [ 33]. We have since conﬁrmed and extended these results in the murine 4T1 and E0771 breast tumor models, identifyingneoantigens that can be successfully targeted with both SLP andpolyepitope DNA neoantigen vaccines (unpublished data).Employing similar strategies, Yadav et al. [ 36] successfully identi- ﬁed and validated several neoantigens in the MC-38 and TRAMP-C1 tumors. The immunodeﬁcient NOD.scid.gamma (NSG) [ 48,49] and related mouse models have made it possible to study human can-cer cell lines and patient-derived xenografts (PDX) in vivo .W e have successfully established PDX by injecting human breast Tumor/normal exome sequencing and cDNA- capture sequencing to identify somatic mutationsComputational analysis, variant calling, neoantigen identification and prioritizationNeoantigen vaccine manufacture and product release testsNeoantigen vaccine administration Figure 1. Neoantigen vaccine design and manufacture.Annals of Oncology Review Volume 28 \| Supplement 12 \| December 2017 doi:10.1093/annonc/mdx681 \| xii13 cancer cells into NSG mice [ 50]. Two neoantigens, ROBO3 A1265V and PALB2 H198D were identiﬁed in the WHIM30 PDX and parental tumor by computational analysis and in vitro stud- ies. Adoptive transfer of autologous PBMCs stimulated in vitro with mutant ROBO3 and PALB2 peptides resulted in decreased tumor growth [ 50]. Integrating PDX models into the neoantigen discovery pipeline offers great opportunities. Recently the TRON Cell Line Portal (TCLP) [ 51,52] has been assembled which, among others, catalog the HLA type, expression and neoepitope candidates of 1082 human cancer cell lines. TCLP (available at celllines.tron-mainz.de) is the product of data-mining and re- analyzing the public databases generated by the Catalogue of Somatic Mutations In Cancer (COSMIC) [ 53,54], the Cancer Cell Line Encyclopedia (CCLE) [ 55] and Klijn et al. [ 56]. This val- uable resource will help researchers select cancer cell lines based on the HLA type and expression, as well as provide therapeutic target for the development of cancer immunotherapy. Current clinical trials The presence of neoantigen-speciﬁc CD8 þand CD4 þT cells in TILs from melanoma patients responding to checkpoint block- ade therapy [ 57–60], and promising results from preclinical stud- ies have generated signiﬁcant interest in the clinical development of neoantigen vaccines (Table 1). Results from several phase I clinical trials [ 34,35,46] in patients with advanced melanoma are quite encouraging, even though the number of patients treated in these studies is small. Main characteristics of current neoantigen vaccine platforms are summarized in Table 2. Carreno et al. [ 46] were the ﬁrst to report that neoantigen-pulsed DC can induceneoantigen-speciﬁc T-cell responses in melanoma patients (NCT00683670). The initial report details the results of vaccinat- ing three patients. Seven neoantigens with the highest binding scores to HLA-A02:01 were prioritized in each patient. DC vac- cination augmented pre-existing immunity to neoantigens, and induced neoantigen-speciﬁc T-cell responses. In addition, the frequency of most existing pre-vaccine TCR- bclonotypes was increased and previously undetected clonotypes were revealed, indicating that vaccination promotes a more diverse T-cell reper- toire [ 46]. However, clinical presentation and responses to neoantigen-pulsed DC vaccine were not reported for these mela- noma patients. Two additional papers co-published in Nature by Ott et al. [34] and Sahin et al. [ 35] conﬁrm the potential of neoantigen vac- cines in treating melanoma patients. These two studies employed similar strategies to identify neoantigens based on NGS data from cancers and normal cells. Computational algorithms were used to predict the ability of neoantigens to bind MHC class I molecules and to prioritize candidate neoantigens. Ott et al. vaccinated six patients with SLP (up to 20 total peptides in 4 pools for each patient) after surgical resection of the tumors (NCT01970358). Four of the six patients vaccinated showed no disease recurrence during follow-up of 20–32 months after vaccination. The remain- ing two participants had disease recurrence but both achieved a complete response after treatment with anti-PD-1 antibody. Sahin et al. [ 61] created synthetic RNA vaccines, each encoding ﬁve 27-mer neoantigens (NCT02035956). Such RNA molecules were previously shown to be readily taken up by lymph noderesident DCs. Up to 10 mutations were targeted in each patient’s tumor (two RNA vaccines). Of the 13 patients vaccinated via intranodal injection of the RNA vaccine, 8 remained tumor free throughout the follow-up period. The other ﬁve participants had tumor relapse. However, after PD-1 blockade therapy, tumor regression occurred in one of these patients. Another patient was noted to have outgrowth of b2-microglobulin deﬁcient cancer cells, indicating MHC loss as an acquired immune escape mecha- nism. ‘Off-the-shelf’ RNA vaccines targeting shared TAAs were also administered in patients with NY-ESO-1 þand/or tyrosinaseþmelanoma, but their contribution to the antitumor immunity was not studied. Immune monitoring analyses of patients’ PBMCs (IFN- cELISPOT, intracellular cytokine stain- ing, multimer staining) in both studies revealed that SLP and RNA vaccines can (i) enhance pre-existing but weak neoantigen- speciﬁc T-cell responses and (ii) generate de novo neoantigen- speciﬁc T-cell responses [ 34,35]. As noted above, the majority of the ex vivo IFN-cresponses were generated by CD4 þT cells. Both studies also found that vaccination resulted in an expansion of the neoantigen-speciﬁc T-cell repertoire. Taken together, these studies provide strong rationale for further clinical development and testing of neoantigen vaccines. We and others have initiated clinical trials of neoantigen vac- cines in breast and other cancer types (Table 1). For instance, we have initiated trials and are currently recruiting patients with triple-negative breast cancer (TNBC) who do not have a patho-logic complete response after neoadjuvant chemotherapy. These patients typically have no gross evidence of disease following standard of care therapy (neoadjuvant chemotherapy, surgery and radiation therapy) but are at high-risk for disease recurrence. Targeting this patient population provides a window-of- opportunity to identify neoantigens and manufacture personal- ized cancer vaccines, maximizing the potential beneﬁt from the vaccine as the regulatory networks associated with metastatic dis- ease are not present. One trial (NCT02427581) is designed to vac- cinate TNBC patients with SLP admixed with poly-ICLC as adjuvant. In a companion trial (NCT02348320), we synthesize polyepitope neoantigen DNA vaccines and vaccinate patients intramuscularly using an electroporation device. Exome and RNA sequencing, neoantigen prediction and vaccine production has been completed for 12 patients, and 11 subjects have com- pleted all scheduled vaccinations. The primary objective of these trials is to assess the safety of personalized neoantigen vaccines. Meanwhile, pre- and post-vaccine PBMC have been collected at various time points from vaccinated patients, and cryopreserved. Neoantigen-speciﬁc T-cell responses will be assessed using IFN- c ELISPOT assay, multiparametric ﬂow cytometry analysis, and related techniques. Challenges Despite recent advances, many challenges remain in the develop-ment of neoantigen vaccines. First, both the cost and time to manufacture neoantigen vaccines have to be reduced. Although the cost of DNA/RNA sequencing has decreased signiﬁcantly [21], it remains costly and time-consuming to identify and vali- date candidate neoantigens. Manufacture of neoantigen vaccines under good manufacturing practice conditions is also veryReviewAnnals of Oncology xii14 \| Li et al. Volume 28 \| Supplement 12 \| December 2017 expensive. Currently the time from tissue acquisition to vaccine delivery ranges from 3 to 5 months [ 34,35]. This will need to be improved in order to beneﬁt patients with metastatic disease.Second, neoantigen prediction algorithms require furtheroptimization. These include strategies to better predict MHC class I and II neoantigens, as well as potential neoantigens result-ing from genetical alterations other than missense mutations,such as gene fusions and indels. Given the recent ﬁndings thatTable 1. Selected clinical trials targeting cancer neoantigens ClinicalTrial.gov identifier Phase Enrollment statusaCancer type Formulation Additional intervention Polyepitope plasmid DNA NCT02348320 I Recruiting TNBC Electroporation Neoadjuvant chemotherapyNCT03122106 I Not yet recruiting Pancreatic cancer Electroporation Adjuvant chemotherapy Polyepitope coding RNA NCT02035956 I Not recruiting Melanoma Intranodal injection Pembrolizumab b[33] NCT02316457 I Recruiting TNBC Intranodal injection Synthetic peptide NCT01970358 I Recruiting Melanoma Poly-ICLC (NeoVax) Pembrolizumabb[32] NCT02427581 I Recruiting TNBC Poly-ICLC Neoadjuvant chemotherapyNCT02600949 II By invitation only PDA and CRC IFA, topical imiquimod ChemotherapyNCT02721043 II Recruiting Solid tumors Poly-ICLC LenalidomideNCT02510950 I Recruiting Glioblastoma Poly-ICLC TemozolomideNCT02992977 I Recruiting Advanced cancer AutoSynVax TMc NCT02933073 I Recruiting Ovarian cancer OncoImmunome ChemotherapyNCT02897765 I Recruiting Multiple Poly-ICLC (Neo-PV-01) Nivolumab aAs of 15 August 2017. bOnly to patients with disease recurrence. cHSP70 conjugated short peptides. TNBC, triple-negative breast cancer; PDA, pancreatic ductal adenocarcinoma; CRC, colorectal cancer; IFA, incomplete Freund‘s adjuvant. Table 2. Main characteristics of current neoantigen vaccine platforms Vaccine platform Advantages Disadvantages Clinical trials Synthetic long peptide vaccine•Stable in storage •Low toxicity •Elicit both CD8 and CD4 T-cell responses •Safe •Repeated vaccination possible•Requires co-administration of an appropriateadjuvant •Multiple peptides need to be manufactured •Immune responses may be weak and/or tran-sient [although this appears to be more of aconcern with short peptides (8–10 AA) com-pared with long peptides (25–30 AA)]•NCT01970358 •NCT02427581 •NCT02600949 •NCT02721043 •NCT02510950 RNA vaccine •Activation of TLR3, TLR7, TLR8 •No potential for integration into the genome (cf. DNA vaccine)•Manufacture more complex •Subject to RNase degradation, although modi-ﬁcation could potentially extend the half-life•NCT02035956 •NCT02316457 Dendritic cell vaccine •Dendritic cells represent the most impor- tant cell for CD8 T-cell priming •Dendritic cells can be modiﬁed to expressboth neoantigens and costimulatorymolecules•Labor intensive and high cost •Requires ex vivo expansion, maturation, and activation •Short half-life in vivo•NCT00683670 •NCT01885702 DNA vaccine •Capable of delivery of multiple antigens in a single vaccine •Flexible platform allowing molecularengineering •Relatively straightforward manufacturingprocess that is readily scaled for personal-ized intervention•Limited success in humans with ﬁrst genera-tion delivery platforms, success may bedependent on electroporation •Limited potential for integration into thegenome•NCT02348320 •NCT03122106Annals of Oncology Review Volume 28 \| Supplement 12 \| December 2017 doi:10.1093/annonc/mdx681 \| xii15 CD4þT-cell responses to neoantigen vaccines were more com- mon than CD8 þT-cell responses [ 34,35] even when the neoanti- gens included in the vaccines were prioritized based on predictedMHC class I binding, it is clear that neoantigen prediction algo- rithms can be improved. The clinical response to cancer immunotherapies has distinct kinetics compared with the response to cytotoxic or small mole-cule therapies. Cancer immunotherapies are frequently evaluated using immune-related response criteria (irRC) [ 62,63]. Neoantigen vaccines will also leverage these immune-relatedresponse criteria, but will also rely on effective immune monitor- ing to assess vaccine-induced immune responses before clinical end points are reached. Unfortunately, there is still a lack of reli-able immune response biomarkers that are predictive of antitu- mor immunity and, ultimately a survival beneﬁt. Further investigation is needed to identify relevant immune response bio- markers using a systematic approach. From a technical point-of- view, immunological assays currently available (e.g. ELISPOT,ﬂow cytometry-based multimer staining and intracellular cyto- kine staining) have a reputation of inconsistency among different laboratories. For instance, one study found the inter-laboratoryvariation of ELISPOT can be as high as 50% [ 64]. Clearly, stand- ardized and harmonized procedures from specimen banking, assay validation, to result reporting are warranted for successfulclinical development. Concluding remarks Neoantigen vaccines have shown encouraging results in terms ofinducing neoantigen-speciﬁc T-cell responses [ 34,35]. RNA, SLP, dendritic cell and DNA neoantigen vaccines are being rigor- ously tested in phase I clinical trials. NGS technologies and com- putational algorithms demonstrate great promise but still need tobe optimized to most effectively prioritize candidate neoantigens. Given that cancers can escape immune control through various mechanisms, including some that are not fully understood, it willbe important to explore the efﬁcacy of neoantigen vaccines in combination with other immunotherapies including checkpoint blockade therapy, and emerging therapies targeting the immuno-suppressive tumor microenvironment. Acknowledgement We would like to thank Enid McIntosh for administrativeassistance.
Funding This project was supported by grants from Susan G. Komen forthe Cure (KG111025), the Alvin J. Siteman Cancer Center (Siteman Investment Program grant 4035); the National Cancer Institute at the National Institute of Health (Cancer CenterSupport Grant P30-CA091842, and SPORE in Pancreatic Cancer P50-CA196510); and the Foundation for Barnes-Jewish Hospital (to SPG). This supplement was sponsored by F.
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Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 1Protocol B7791001 A PHASE 1 STUDY TO E VALUATE THE SAFETY, PHARMACOKINETICS AND PHARMACODYNAMICS OF ESCALATING DOSES OF A VACCINE -BASED IMMUNOTHERAPY REGIME N (VBIR) FOR PROSTAT E CANCER (PF -06753512) Statistical Analysis Plan (SAP) Version :Amendment 2 Date: 08-JUN-2020 Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 2TABLE OF CONTENTS LIST OF TABLES................................................................................................................. ....3 LIST OF FIGURES ................................................................................................................
...3 1. AMENDMENTS FROM PREVIOUS VERSION(S) ...........................................................42. INTRODUCTION ................................................................................................................
.5 2.1. Study Design .............................................................................................................5 2.2. Study Objectives .......................................................................................................6 3. INTERIM ANALYSES, FINAL ANALYSES AND UNBLINDING..................................74. HYPOTHESES, SAMPLE SIZE AND DECISION RULES................................................8 4.1. Statistical Hypotheses ...............................................................................................84.2. Statistical Decision Rules..........................................................................................84.3. Sample Size Determination.......................................................................................8 5. ANALYSIS SETS ............................................................................................................... ..9 5.1. Safety Anal ysis Set....................................................................................................9 5.2. Full Analysis Set .......................................................................................................95.3. Per-Protocol Analysis Set..........................................................................................95.4. Modified Intent-to-Treat Analysis Set ......................................................................95.5. Pharmacodynamics Analysis Set ..............................................................................9 5.6. Evaluable Population.................................................................................................95.7. Treatment Misallocations........................................................................................105.8. Protocol Deviations .................................................................................................10 6. ENDPOINTS AND COVARIATES ...................................................................................10 6.1. Part A Primary Endpoint.........................................................................................106.2. Part A Sec ondary E ndpoint.....................................................................................10 6.3. Part A Exploratory Endpoints .................................................................................116.4. Part B Primary Endpoint .........................................................................................126.5. Part B Secondary Endpoint .....................................................................................12 .....................................................................13 6.7. Covariates................................................................................................................
13 7. HANDLING OF MISSING VALUES ................................................................................13 ..................................................................................13 CCI CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 37.2. Immune Responses Lower Limit of Quantitation...................................................13 7.3. Pharmacokinetic Concentrations and Parameters ...................................................137.4. Efficacy Data...........................................................................................................14 8. STATISTICAL METHODOLOGY AND STATISTICAL ANALYSES ..........................14 8.1. Statistical Analyses .................................................................................................15 8.1.1. Baseline Evaluations...................................................................................158.1.2. Safety Analysis ...........................................................................................15 8.1.2.1. Analysis of Pr imary Endpoint ...................................................15 8.1.2.2. Analysis of Secondary Safety Endpoints ..................................168.1.2.3. Analysis of other Secondary Endpoints ....................................16 8.1.3. Exploratory (Part A) / Secondary (Part B) Efficacy Analyses ...................18 ..............................19 8.1.5. Pharmacokinetics........................................................................................21 8.1.5.1. Tremelimumab and PF-06801591 Pharmacokinetics ...............21 8.1.6. Tremelimumab and PF-06801591 Immunogenicity...................................23 ..................................................................................................23 9. REFERENCES ..................................................................................................................
..25 10. APPENDICES ................................................................................................................. ..26 10.1. Appendix: Statistical Methods for Immunogenicity (serology)............................2610.2. Appendix: PSA kinetics_AppendiCES .................................................................2810.3. Appendix: Time to Event Data Analysis Censoring Rules ...................................2910.4. Appendix: Immune-related Response Criteria per RECIST 1.1 (irRECIST) .......30 LIST OF TABLES Table 1. Probability of Escalating Dose ...............................................................................8Table 2. Outcome and Event Dates for irPFS and irDR Analyses .....................................32 LIST OF FIGURES Figure 1. Overall Study Design..............................................................................................5CCI CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 41. AMENDMENTS FROM PREVIOUS VERSION(S) Amendment 2 Section 2.1 S tudy design texts are updated to be consistent with the prot ocol amendment .
A new stud y design schema replace sthe previous one. Section 2.2 Objectives are described separately for Part A and Part B. Section 4.3 Sample size languages are updated according to the protocol amendment. Section 6 Endpoints are described separately for Part A and Part B. Section 8.1 .3 Efficacy anal yses languages are updated to provide more clarity . Section 8.1.4 A PSA -50 response rate anal ysis is added. Section 8.1.5.2 Sunitinib PK section is deleted according to the protocol amendment. Section 10.3 A censoring rule is added for COVID -19 caused missing tumor assessments. Amendment 1 Section 2.1 Editorial updates to match the protocol Section 4.3 Added from the protocol Section 5.3 The definition for the per -protocol population is updated to match the protocol. Section 6 Texts about endpoints are updated to match the protocol Section 8.1.2.1 Texts added to define “treatment related AE” Section 8.1.3 Details added about tumor response anal yses and time to event anal yses. Section 8.1.5.3 A section about PF -06801591 PK analy sis is added to match the protocol Section 10.3 Time to event censoring rules added Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 52.INTRODUCTION This document presents the s tatistical anal ysis plan (SAP) for study B77 91001 . The SAP is based on the protocol amendment 7dated November 22, 201 9and protocol amendment 8 dated May 29, 2020.
Note: in this document any text taken directl y from the protocol is italicized . 2.1.Study Design This is a Phase 1, open label, multi- center, multiple dose, safety, PK, PD and immunogenicity study evaluating the components of a vaccine -based immunotherapy regimen for prostate cancer (PrCa VBIR). PrCa VBIR consists of the following components: Adenovirus (AdC68), pDNA andtremelimumab. In addition, cohorts will evaluate PrCa VBIR when given in combination with PF-06801591 . The study will enroll patients with the following stages of prostate cancer: patients with asymptomatic/minimally symptomat ic metastatic castration -resistant prostate cancer (mCPRC) who have not received secondary hormones (M1 pre- secondary hormones), patients with rising PSA at high risk for recurrence (biochemically relapsed), and asymptomatic/minimally symptomatic mCPRC patients for whom secondary hormone treatment failed (M1 post- secondary hormones). The study is divided in to two parts, D ose Escalation (Part A) followed by D ose Expansion (Part B).
The overall study design is presented in Figure 1. Figure 1.Overall Study Design Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 62.2. Study Objectives Part A Primary Objective •To assess safety and tolerability of increasing dose levels of the prostate cancer vaccine-based immunotherapy regimen (PrCa VBIR) components alone and in combination with increasing doses of PF-06801591. •To characterize the dose limiting toxicities (DLTs), if any are observed, and overall safety profile of escalated doses of the PrCa VBIR components alone and in combination with increasing doses of PF-06801591. •To determine the Part B Expansion Dose for the PrCa VBIR components, and in combination with PF-06801591. Part A Secondary Objectives •To evaluate the immune response elicited by the PrCa VBIR to the selected prostate cancer tumor-antigens. •To evaluate the overall safety profile in prostate cancer participants. •To evaluate the PK of tremelimumab after subcutaneous(SC) administration. •To evaluate the PK of PF-06801591 after SC administration •To evaluate the anti-drug antibody (ADA) response of tremelimumab after SC administration with the other PrCa VBIR components. •To evaluate the ADA response of PF-06801591 after SC administration with the other PrCa VBIR components. Part A Exploratory Objectives •To document any preliminary evidence of anti-tumor activity. Part B Primary Objective •To evaluate the overall safety profile of the PrCa VBIR + PF-06801591 in prostate cancer participants. Part B Secondary ObjectivesCCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 7•For Cohort 3B: To evaluate the anti-tumor response induced by treatment in participants with mCRPC utilizing solid tumor response criteria. •For Cohort 3B: To evaluate the anti-tumor response induced by treatment utilizing immune related response criteria. •For Cohort 3B: to evaluate bone metastatic disease outcome in participants with mCRPC. •For Cohorts 3B: To estimate the duration of radiographic Progression-Free Survival (rPFS) in participants with mCRPC. •To evaluate response rate based on 50% reduction of prostate specific antigen (PSA). •To evaluate PSA kinetics. •To evaluate trough concentrations of tremelimumab after SC administration at selected doses. •To evaluate trough concentrations of PF-06801591 after SC administration at selected doses. •To evaluate the anti-drug antibody (ADA) response of tremelimumab after SC administration with the other PrCa VBIR components. •To evaluate the ADA response of PF-06801591 after SC administration with the other PrCa VBIR components. 3.
INTERIM ANALYSES, FINAL ANALYSES AND UNBLINDING There are no formal interim analyses planned in this study. However, this is an open label study, and therefore, the Pfizer team will review safety, immunogenicity, pharmacokinetics, pharmacodynamic, and other data throughout the study.CCI CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 84.HYPOTHESES , SAMPLE SIZE AND DECISION RULES 4.1.Statistical Hypotheses No formal testing of h ypotheses will be conducted in this study . 4.2.Statistical Dec ision Rules The following table shows the probability of escalating to the next dose level for a range of underlying true dose limiting toxicity (DLT)rates.
For example, for a DLT that occurs in 10% of patients, there is a greater than 90% probability of escalating. Conversely, for a DLT that occurs with a rate of 70%, the probability of escalating is 3%. It is assumed that dose escalation occurs wi th either 0/3 or 1/6 patients with DLTs. Table 1. Probability of Escalating Dose True underlying DLT rate 10% 20% 30% 40% 50% 60% 70% 80% 90% Probability of escalating dose 0.91 0.71 0.49 0.31 0.17 0.08 0.03 0.009 0.001 Probabilities of testing sequential doses can be calculated as the 3+3 escalation rules are predefined. For example the probability of escalating to a third dose level is 0.64 under the assumption that DLTs occur in 10% and 20% of patients respectively in the first dose and the second dose. The probability of escalating to a third dose is only 0.35 if DLTs occur in 20% and 30% of patients respectively in the first dose and the second dose tested. 4.3.Sample Size Determination The exact sample size of the 3+3 designs in the Phase 1Part A (Dose Escalation) cannot be pre-specified because of the dynamic feature of the design. The number of patients to be enrolled in the study will depend upon the observed safety profile, which will determine the number of patie nts at each dose level ( eg, 3or 6) and the number of dose levels explored. Typically, at least 3 patients will be treated at each regimen dose level. Dose cohorts with an acceptable safety profile (0/3 or 1/6 patients with DLTs) may be expanded up to N=15 to further assess safety, immune response, pharmacokinetics, and pharmacodynamics. Decisions to enroll additional patients at dose levels already cleared for safety will be based on clinical judgment of the investigators and the sponsor considering all evaluable safety, immunoresponse, and/or pharmacodynamics data. The estimated sample size for Part A would be 24-48 patients and will be dependent on the safety profile observed. The sample size for Cohort 1B (AdC68 + pDNA + treme 80 mg SC for BCR patien ts who had no prior therapy) was determined clinically rather than statistically. Twenty patients were enrolled in that cohort to provide preliminary efficacy, safety, PK and biomarker data for this regimen. For Cohort 3B (AdC + pDNC + treme 80 mg SC + PF- 0689159 300 mg SC. for patients with mCRPC whose disease has progressed despite novel hormonal treatment), 18patients Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 9were enrolled. The main efficacy endpoint for this cohort will be objective response rate (ORR) based on RECIST version 1.1, the key supp ortive efficacy endpoint will be radiographic progression -free survival (rPFS). There are no hypothesis tests for these endpoints.
Bayesian approach may be used to evaluate the efficacy data. For example, Bayesian approach with a non- informative Jeffery’s prior beta (0.5,0.5) will be used to calculate the posterior probability the ORR of the study treatment exceeds various ORR thresholds of interest in the end of the study. For Cohort 5B (AdC68 + pDNA + treme 80 mg SC + PF -0689159 130 mg SC.
for BCR patien ts who had no prior therapy), the sample size was determined clinically rather than statistically. Fifteen patients were enrolled in that cohort to provide preliminary efficacy, safety, PK and biomarker data for the regimen. 5.ANALYSIS SETS 5.1.Safety Analysis Set The safety analysis set includes all enrolled patients who receive at least one dose of one of the components of the regimen. 5.2.Full Analysis Set The full analysis set includes all enrolled patients. 5.3.Per-Protocol Analysis Set The per protocol (PP) analysis set includes all enrolled patients (for each indication) who receive at least one dose of the assigned regimen components administered on Cycle 1 Day 1 of study medication and who do not have major protocol deviations during the 28 days after the first vaccination . Major protocol deviations will be determined before data base release. 5.4.Modified Intent -to-Treat Analysis Set The modified Intention- to-Treat (mITT) is defined as all enrolled patients who received at least one dose of all assigned regimen components administered on Cycle 1 Day 1 of treatment. 5.5. P harmacodynamics A nalysis Set The pharmacod ynamics anal ysis set will be based on the mITT population. Apatient must have at least 1 valid and determinate assay result related to the proposed analy sis.
Patients who have no valid and determinate assay result related to any proposed analysis will be excluded from the pharmacodynamics analysis set. 5.6.Evaluable Population As a consequence of its mechanisms of action, PrCa VBIR may require time after administration to induce a prostate cancer -specific immune response and subsequent tumor response. Therefore, tumors in patients treated with cancer vaccines may show early progression followed by subsequent response, therefore, supportive analysis populations may be based on the evaluable population (EP) which willconsist of all patients in the mITT Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 10population who have been dosed through Cycle 1 Day 57, Day 85 or Day 113(Day 113 is Cycle 2Day 1). To implement this, 3 sub-populations will be created : EP C1D57, EP C1D85, and EP C2D1. EP C1D57 will include all patients who have received all assigned doses at least until C1D57 . EP C1D85 and EP C2D1 are defined in the same manner. Additionally, tumor response and PD analyses may be repeated in the mITT population in patients who have no major protocol deviations during the first and second treatment cycle . 5.7.Treatment Misallocations For patients with errors in treatment allocation the following ap proach will be followed: If a patient was: Enrolled but not treated, then they will be reported under their enrolled treatment group for demographic analy ses onl y. These patients will be excluded from the immunogenicit y, efficacy and safet y analyses as the actual treatment is missing. Enrolled but took incorrect treatment, then they will be reported under their enrolled treatment group for efficacy analy sesbased on mITT ,excluded from anal yses based on PP, but will be reported under the treatment they a ctuall y received for all safet y, PK, immune responses, and anti- tremelimumab or anti -PF-0680159 immunogenicity analyses. 5.8. Protocol Deviations Major protocol deviation swillbe determined on an ongoing basis per medical data review. Any major protocol deviation will prevent the patient from being included in the per -protocol population. A full list of major protocol deviation s will be compiled prior to database closure. Once the final list of major protocol deviations is determined the per -protocol population flag will be updated. 6.ENDPOINTS AND COVARI ATES 6.1.Part A Primary Endpoint Incidence and grade of treatment- emergent adverse events including DLTs as graded by National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE version 4.03). 6.2.Part A Secondary Endpoint Immune response including T cells specific to the three selected prostate cancer tumor- antigens. T cell immune response will bedetermined as the frequency of IFN-spot forming cells (SFC) /million peripheral blood mononuclear cells (PBMC) in response to Prostate -Specific Membrane Antigen ( PSMA ), Prostate Stem Cell Antigen (PSCA )and Prostate -Specific Antigen (PSA)as measured intheIFN- ELISPOT assay . For analy sis in the IFN -ELISPOT assay , the PSMA antigen will Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 11be divided into three sub regions (peptide pools) for which the IFN- γspot forming cells/million PBMCs will be reported separately (ie, 5 reported values) or by antigen (3 PSMA summed) or all pooled. The PSCA and PSA assays will each be analyzed as a single antigen (ie, 1 reported value for each antigen). ELISPOT assay reported variables, by the Lab, are specified and listed in the respective CSAP document. •Antibody response specific to the PSMA antigen (Geometric Means and Seroconversion): will be determined as the titer (U/mL) of serum IgG antibodies elicited against the PSMA antigen as measured in the Luminex-based assay. •Laboratory abnormalities as characterized by type, frequency, severity (as graded by NCI CTCAE v 4.03) and timing. •Tremelimumab single-dose PK parameters , including the maximum concentration (C max), time to maximum concentration (T max), and area under the concentration versus time curve (AUC) from time zero to the last quantifiable time point prior to the second tremelimumab dose (AUC last) and if data permit, AUC from time zero extrapolated to infinity (AUC 0-inf); and trough concentrations after multiple dosing (Ctrough). •PF-06801591 single-dose PK parameters , including C max, Tmax, AUC last, and if data permit, AUC inf; and C trough after multiple dosing. •Incidence and titers of ADA and neutralizing antibodies against tremelimumab. •Incidence and titers of ADA and neutralizing antibodies against PF-06801591. 6.3. Part A Exploratory Endpoints •Objective tumor response, as assessed using the Response Evaluation Criteria in Solid Tumor (RECIST) version 1.1 by calculating the Objective Response Rate (ORR) and radiographic Progression-Free Survival (PFS). •Antitumor response based on total measurable tumor burden as assessed by the Immune-Related Response Criteria (irRECIST) and irPFS. •Bone outcome according to Prostate Cancer Working Group 3 (PCWG3) criteria. CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 12 6.4. Part B Primary Endpoint •Incidence and grade of treatment-emergent adverse events including DLTs as graded by National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE version 4.03). 6.5. Part B Secondary Endpoint •For Cohort 3B: Objective response rate (ORR) and duration of response, as assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. •For Cohort 3B: Antitumor response and tumor control duration based on total measurable tumor burden as assessed by the Immune-Related Response Criteria Derived from RECIST 1.1 (irRECIST). •For Cohort 3B: Bone outcome according to Prostate Cancer Working Group 3 (PCWG3) criteria. •For Cohort 3B: Radiographic Progression-Free Survival (rPFS) by RECIST 1.1, irRECIST and PCWG3 Criteria in participants with mCRPC. •PSA-50 response rate and duration of response.
•Baseline and changes from baseline for PSA, PSA velocity, PSA slope and PSA doubling time (PSADT). •Trough concentrations after multiple dosing (Ctrough).
•Ctrough after multiple dosing. •Incidence and titers of ADA and neutralizing antibodies against tremelimumab. •Incidence and titers of ADA and neutralizing antibodies against PF-06801591.CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 13 6.7. Covariates Analyses of immune response and analyses of pharmacodynamic parameters may be adjusted (or stratified) by demographic and prognostics variables (eg, age, gleason score, baseline PSA, baseline PSADT etc).
7. HANDLING OF MISSING VALUES All the safety, immune responses, pharmacokinetic, pharmacodynamics, and immunogenicity analyses and summaries will be based on data as observed and no explicit imputation will be applied. 7.2. Immune Responses Lower Limit of Quantitation Titers below the assay detection limit will be assigned a value of one-half that limit. 7.3. Pharmacokinetic Concentrations and Parameters Drug concentrations below the limit of quantification In all data presentations (except listings), drug concentrations below the limit of quantification (BLQ) will be set to zero. In the listings BLQ values will be reported as “<LLQ”, where LLQ will be replaced with the value for the lower limit of quantification.CCI CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 14Deviations, missing concentrations and anomalous values Patients who experience eve nts that may affect their PK (eg, incomplete dosing) may be excluded from the PK analy sis. In summary tables and plots of mean profiles of PK, statistics will be calculated having set concentrations to missing if 1 of the following cases is true: 1.A concent ration has been collected as ND (ie, not done) or NS (ie, no sample); 2. A deviation in sampling time is of sufficient concern or a concentration has been flagged anomalous b y the PK anal yst. Note that summary statistics will not be presented at a particular time point if more than 50% of the data are missing. An anomalous concentration value is one that, after verification of bioanal ytical validity , is grossl y inconsistent with other concentration data from the same individual or from other patients. For exa mple, a BLQ concentration that is between quantifiable values from the same dose is considered as anomalous. Anomalous concentration values may be excluded from PK anal ysis at the discretion of the PK anal yst. Pharmacokinetic parameters Actual PK sampling times will be used in the derivation of PK parameters. If a PK parameter cannot be derived from a patient’s concentration data, the parameter will be coded as NC (ie not calculated). In summary tables, statistics will be calculated b y setting NC values t o missing; and statistics will not be presented for a particular treatment if more than 50% of the data are NC. For statistical analy ses (ie analy sis of variance), PK parameters coded as NC will also be set to missing . 7.4.Efficacy Data For the time-to-event endpoints, the missing data handling method will be censoring. Censoring rules for time -to-event endpoints are detailed in Appendix 10.3. 8. STATISTICAL METHODOL OGY AND STATISTICAL ANALYSES In general, all continuous endp oints will be summarized descriptive lyby cohort. If data are categorical then the standard contingency tables with counts and percent by group will be display ed. Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 158.1.
Statistical Analyses 8.1.1. Baseline Evaluations The baseline evaluation summaries and lis tings will display patient demographics, including age and performance status, PSA and Gleason score at the time of diagnosis. Details and dates of prior hormonal and nonhormonal therapies should be display ed, along with additional PSA measurements that can be used to estimate PSA doubling times (PSADT). Evaluable data on screened, but not enrolled patients, will be presented as listing s.
8.1.2. Safety Analysis The main analyses of DLTs will be based on the Per Protocol analysis set. Patients not meeting the cr iteria for inclusion in the Per Protocol Analysis set (i e,not evaluable for assessment of DLTs) will be replaced. Summaries and analyses of other safety parameters will include all patients in the Safety Analysis Set. Note only the safet y data before the start of any new anti -cancer sy stemic therapy will be included in safet y analysis. 8.1.2.1. Analysis of Primary Endpoint DLT is a primary endpoint of the study. The properties of the statistical methods for the analyses of DLTs are described in Section 4. Adverse events constituting DLTs are detailed in Section 3.2 of the study protocol. Adverse events and adverse events constituting DLTs will be listed per dose level (ie,by cohort) . A binary variable will be created at thepatient level to indicate whether or not a patient has experienced an y of the adverse events that are considered DLT. If required, a summary table will be created by cohort to present number and percentage of patients experiencing DLT and each specific adverse event constituting DLT. Adverse Events (AEs) will be graded by the investigator according to the CTCAE version 4.03 and code d using the Medical Dictionary for Regulatory Activities (MedDRA). The focus of AE summaries will be on Treatment Emergent Adverse Events, those with initial onset or increasing in severity after the first dose of study treatment and before the start of any new anti -cancer sy stemic therap y. The number and percentage of patients who experienced any AE, SAE, treatment related AE, and treatment related SAE will be summarized. The summaries will present AEs both on the entire study period by dose (i.e., by cohort) and by cycle . The number and percentage of patients who discontinued from study medications due to AE or discontinued from the study due to AE s will also be presented. AE, SAE, treatment related AE, treatment related SAE will be presented b y system organ class and preferred term for each cohort. Severity summary tables will also be presented where AE, SAE, treatment related AE, and treatment related SAE will be presented for each cohort by system organ class and preferred term according to the wor st toxicity grade. Treatment related AE in this study refers to those adverse events that are determined by investigator as related to either adenovirus, or plasmid DNA, or tremelimumab, or PF - 06801591. If deemed necessary , further summary of component -related AE may be presented.
Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 168.1.2.2. Analysis of Secondary Safety Endpoints These safet y endpoints will be analyzed according to the Pfizer Data Standard. To better characterize the safet y profile of the different regimen components anal yses of safet y will be cond ucted b y cohort, by cycle ( i.e., C ycle 1,2, maintenance cy cle) and by period (Day 1 to Day 28, Day 29 to Day 85). Laboratory Tests Abnormalities The number and percentage of patients who experienced laboratory test abnormalities will be summarized according to worst toxicity grade observed for each lab assay. The analyses will summarize laboratory tests by cohort and visit. The most recent measure ment prior to dosing is considered as baseline. For laboratory tests without CTCAE grade definitions, results will be categorized as normal, abnormal or not done . ECG The analysis of ECG results will be based on patients in the safety analysis set with baseline and on -treatment ECG data. Baseline is defined as the pre -dose ECG collected before the first dose of any component of the study treatment. ECG measurements (an average of the triplicate measurements) will be used for the statistical analysis and all data presentations. Any data obtained from ECGs repeated for safety reasons after the nominal time- points will not be averaged along with the preceding triplicates. Interval measurements from repeated ECGs will be included in the outlier analysis (categorical analysis) as individual values obtained at unscheduled time points. QT intervals will be corrected for heart rate (QTc) using standard correction factors (ie,Frederica’s). Data will be summarized and listed for QT, HR,PR, QRS, and QTcF by cohort. Individual QT intervals will be listed by time and cohort. Descriptive statistics (n,mean, median, standard deviation, minimum, and maximum) will be used to summarize the absolute corrected QT interval and changes from baseline in corrected QT a fter treatment by cohort and time point. For each patient and by treatment, the maximum change from baseline will be calculated as well as the maximum post- baseline interval across time-points. Categorical analysis will be conducted for the maximum chang e from baseline in corrected QT and the maximum post -baseline QT interval. Shift tables will be provided for baseline vs worst on treatment corrected QT. If applicable, the effect of relevant drug concentrations on corrected QT change from baseline will be explored graphically. Additional concentration -corrected QT analyses may be performed. Data may be pooled with other study results and/or explored further with PK/PD models.
8.1.2.3. Analysis of other Secondary Endpoints 1.T cell immune response Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 17The number of antigen -specific T cell s(expressed as SFC/million PBMC) for each prostate cancer antigen (or antigenic sub region) will be compared between pre-treatment and subsequent post treatment time points in two rounds of analy sis. The first round (Cycle 1 anal ysis)will compare the pretreatment response (Cy cle1 Day 1) to Cy cle 1 Day 15, Cy cle 1 Day 29, C ycle 1 Day 43 and C ycle 1 Day 71; the second round (C ycle 2 analy ses) will compare Cy cle 2 Day 1with remaining timepoints as Cycle 2 Day 29 and C ycle2 Day 99,or End of Treatment , and Cy cle 1 Day 1). Data from the maintenance period may be anal yzed in the same manner if deemed necessary . As appropriate, anal ysis of the immune response endpoints will include fold -change from baseline b y patient a nd fold -change in geometric mean (GM FR) by cohort (by Cycle). The immune response against each peptide pool (5 values: 1 for PSA, 1 for PSCA and 3 for PSMA ) may be anal yzed separately or per antigen (eg 3 PSMA summed) or all pooled to provide the overall T cell response to the selected prostate cancer antigens. In case the re is failure for Cycle 1 Day 1,the S creening sample would be used as the pretreatment sample. Number of t reatment- induced immune responses against the prostate cancer antigens will be defined as the numberof patients with an X -fold (e g,where Xcould defined as 2-fold, 4 -fold) increase in at least Y sampling time points (eg,Y=one time, Y=two timepoints) after treatment as compared to the Cycle1Day 1 (or Screening/Baseline sample if failure for C ycle 1 Day 1) for the same patient . Negative (media only )and positive (anti-CD3) control responses, as we ll as specific T cell responses observed to CMV, EBV, Flu and Tetanus Toxoid (CEFT) will be reported in a similar manner to the prostate cancer antigen data (ie,in two rounds of analy sis). 2.Antibody response specific to the PSMA antigen (Geometric Mean and Seroconversion) The LLOQ for the assay will be provided by the testing labs. Titers below the assay detection limit will be assigned a value of one -half that limit. Geometric Mean Titer :For the anal yses on antibody titer, least squares (ie,estimated) Geometric mean titer (GMT) will be calculated at baseline and post baseline . All statistical analy seswill be performed on the logarithmically (natural base) transformed titer values. See 10.1. Geometric Mean Fold Rise : The least squares ( ie,estimated) GMFR of th e post-vaccination titer value to theprevious titer level (other than at baseline) will be calculated, as well as the associated confidence interval and the median, minimal, and maximal n -fold increase. See 10.1. Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 18Seroconversion : Number of patients from negative to positive (eg, k fold above the assay detection ) or with at least an X- foldincrease from baseline (eg ,k=2,3; X=2,4). 8.1.3. Exploratory (Part A) / Secondary (Part B) Efficacy Analyses In this First -In-Patient study anti -tumor activity is an exploratory objective. The main analysis populations will be based on mITT and EP. Tumor response, using RECIST , irRECIST , will be presented in the form of p atient data listings which include, but are not limited to tumor response assessed in comparison to baseline. Traditional response measures in solid tumors, such as ORR and PFS were developed to evaluate chemotherapies and can be unreliable for tracking re sponse to immunotherapies such as cancer vaccines . For example, apparent tumor burden may increase during the first few months of cancer vaccine treatment because of inflammation/T cell infiltration into tumors even in patients who go on to have durable responses. T ime to progression and number of patients with progression could be calculated from first vaccination ( Cycle 1 Day 1), but may also be calculated excluding those progress ions that happened before some specific timepoints ( eg,Day 57, Day 113).Time to response will be calculated from first vaccination ( Cycle 1 Day 1) to time of first response (ie, CR or PR). Additionally, total measurable tumor burden will be evaluated by the irR ECIST criteria that include responses after disease progression that are not captured by RECIST evaluation criteria in solid tumors. For tumor response data, the following anal yses may be performed: 1.Tumor response (CR, PR, SD, PD, etc.) b y cohort a nd visit, separatel y for RECI ST 1.1 and irRECI ST. Investigator provided tumor response will be presented by descriptive statistics (frequency and percentage). 2.Best overall response, b y cohort, across all available tumor assessments from both treatment per iod and maintenance period , separatel y for RECIST 1.1 and irRECI ST. Best overall response will be derived programmaticall y. In the tabular data presentation, a row of CR+PR will be added to show the results of ORR. With RECI ST 1.1, tumor response (CR or PR )confirmation is optional as response rate is not the primary endpoint of the study , hence unconfirmed best overall response will be derived and presented. With irRECI ST, tumor response (irCR or irPR) and disease progression (irPD) are required to be confirmed, however in this study tumor response confirmation is not required, therefore unconfirmed best overall response will be derived and presented. Descriptive statistics (frequency and percentage) will be provided. Selected a d-hoc efficacy anal yses(eg, ORR and radiographic PFS) combining p articipants from Cohorts 7A and 3B may be conducted, as participants in those two cohorts have identical or similar eligibility criteria for study entry and are scheduled to receive the same regimen. Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 19Swimmer plots may be used to display duration of treatment and tumor response at each applicable time point. Waterfall plot for individual tumor size percent change from baseline,and spider plot for individual tumor size percent change from baseline over time may bepresented. These plots, if generated, will be presented for RECIST 1.1 and irRECIST separately. For the rPFS analysis in Part B, the Kaplan-Meier analysis may be performed if deemed necessary. If the Kaplan-Meier analysis is performed for radiographic progression-free survival, an event is defined as the first occurrence of “PD” status by investigator, or death, or a PCWG3 defined bone disease progression, whichever comes first, under RECIST. Time zero for all time-to-event analyses will be defined as the first vaccination on Cycle 1 Day 1. This analysis may be performed for the combined cohorts 7A and 3B. For duration of response per RECIST and time to response (from the first vaccination date to the time of the first response, CR or PR) in Part B, descriptive statistics will be provided.Kaplan-Meier analysis may be performed if deemed necessary. These analyses may be performed for the combined cohorts 7A and 3B.
PSA kineticsPSA, PSA velocity, PSA slope and PSA doubling time (PSADT); PSA will be provided from laboratory data. PSA velocity is defined as the slope of the linear regression line of PSA against time in month. PSA slope is the slope of the linear regression line of natural log of PSA against time in month. PSA doubling time is defined as the natural log of 2 divided by the slope of the linear regression line of the natural log of PSA against time in month (see 10.2). These analyses will be conducted both for Central PSA and Local PSA in separate tables. PSA kinetics will be presented at individual level by timepoint (listings and plots). Mean % and absolute change from baseline in PSA kinetics may be presented by cohort. To report PSA-based outcomes, PCWG3 recommends that the percentage of change in PSA from baseline, as well as the maximum decline in PSA, PSA velocity, PSADT and PSA slope that occurs at any point after treatment be reported for each patient using a waterfall plot. Waterfall plots provide a broader and more sensitive display of data, and are more informative until a validated surrogate of clinical benefit is available. A PSA-50 response rate may be calculated by cohort . PSA-50 response rate is defined as the proportion of patients whose on-study PSA declined from baseline by at least 50% at two consecutive measurements at least 3 weeks apart, prior to other systematic anti-cancer therapy. For the subset of PSA-50 responders, a duration of PSA-50 response will be summarized. Duration of PSA-50 response is defined as the period between the first CCI CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 20measurement when PSA -50 response was achieved to the measurement when PSA -50 response no longer holds. Swimmer plots may be used to display duration of treatment and PSA values at each applicable time point. CTC The frequency of “traditional” and “all candidate” CTCs in whole blood each expressed as CTC/mL will be assessed in pre treatment (Screening, C ycle 1 Day 1) and post treatment ( eg. Cycle 1 Day 71, C ycle 2 Day 1, Cycle 2 Day 29, Cy cle 2 Day 99,and End of Treatment) samples. Anal ysis of the CTC endpoints may include absolute CTC and fold- change from baseline (pretreatment) by patient a nd % change and mean fold -change b y cohort. MDSC The percentage of MDSCs will be assessed in pretreatment (Screening, Cy cle 1 Day 1) and post treatment samples. MDSC levels will be presented per patient and per cohort b y GMTs and GM FRs. Anal yses of MD SC endpoints may include absolute and fold -change from baseline (pretreatme nt) by patient and % change and mean fold -change b y cohort. Bone scan assessment Given the frequency of bone involvement in patients with progressive, castration -resistant disease, the decreased emphasis of earl y changes in PSA, and the increased availabi lity of cytostatic agents, reliable methods to assess changes in bone are of increasing importance. PCWG 3recognizes that standards for using MRI and PET to assess bone metastases are under active investigation, so only radionuclide bone scans are conside red here. PCWG 3 also recognizes that there are no validated criteria for response on radionuclide bone scan. For control/relieve/eliminate end points, the PCWG 3recommends that post -treatment changes be recorded simply as either “no new lesions” or “new lesions.” However, progression at the first scheduled assessment should be confirmed on a second scan performed 6 or more weeks later, in the absence of clearl y worsening disease or disease -related s ymptoms. In the event of visible lesions disappear ing, this should also be confirmed at the next scheduled assessment. For prevent/delay end points, progressing disease on bone scan is considered when a minimum of two new lesions is observed. PCWG 3does not recommend performing a follow -up bone scan before 12 weeks of treatment unless clinically indicated. At the first 12-week reassessment, defining disease progression requires a confirmatory scan (which shows additional new lesions compared with the first follow- up scan) performed 6 or more weeks later, because lesions visible at the first 12-week assessment may represent disease that was not detected on the pretreatment scan. When further progression is documented on the confirmatory scan, the date of progression recorded for the trial, is the date of th e first scan that shows the change. Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 21Correlative analyses Plots may be generated to display PSA kinetics vs. PSA antibodies (serology: Luminex assay). Similar plots may be generated to evaluate individual patient trends on MDSC, CTC and T-cell response.
8.1.5. Pharmacokinetics Patients who receive the designated investigational product of interest and have at least one post-dose drug concentration measurement will be included in the PK data analysis. The actual time of sample collection will be used in PK parameter calculation. In the event that the actual sampling time is not available, the nominal time may be used if there is no evidence that the actual sampling time deviates substantially from the nominal time.
8.1.5.1. Tremelimumab and PF-06801591 Pharmacokinetics Presentation of Tremelimumab and PF-06801591 concentration-time data The concentration-time data of tremelimumab and PF-06801591 will be presented as below: •a listing of all concentrations by cohort, subject ID and nominal time for each compound. The concentration listings will also include the actual times.
Deviations from the nominal time will be given in a separate listing for each compound. •a summary of concentrations for each compound by cohort and nominal time, where the set of statistics will include n, mean, standard deviation, median, coefficient of CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 22variation (cv), minimum, maximum and the number of concentrations above the lower limit of quantification. for the concentration -time data after the first dose median concentrations time plots (on both linear and semi -log scales) against nominal time post dose by cohort (all cohorts on the same plot per scale, based on the summary of concentrations by cohort and time postdose) for tremelimumab (for Cohorts 3A and 4A onl y) and PF - 06801591 (for Cohorts 6A through 9A). for the concentration -time data after the first dose mean concentrations time plots (on both linear and semi- log scales) against nominal time postdose by cohort (all cohorts on the same plot per scale, based on the summary of concentrations by cohort and time postdose) for tremelimumab (for Cohor ts 3A and 4A only ) and PF -06801591 (for Cohorts 6A through 9A). For drug concentration summary statistics, median and mean plots by sampling time, the nominal PK sampling time will be used. Calculation of tremelimumab PK parameters For patients of Cohorts 3A and 4 A, the concentration- time data of tremelimumab after the first dose will be analyzed individually by non -compartmental methods to determine the PK parameters. For patients of Cohorts 6A through 9A, the concentration time data of PF-06801591 after the first dose will be anal yzed individually by non compartmental methods to determine the PK parameters. For each compound, the PK parameters to be estimated will include the maximum drug concentration (C max), time to maximum drug concentration (T max), and area under the concentration versus time curve (AUC) from time zero to the last quantifiable time point prior to the second tremelimumab or PF -06801591 dose (AUC last), and if data permit, AUC from time zero extrapolated to infinity (AUC inf), terminal elimination half -life (t 1/2), and apparent clearance (CL/F). In addition, the accumulation ratio (Rac) as calculated by the ratio of the trough concentration prior to the fifth tremelimumab or PF -06801591 dose (on Cycle 2 Day 1) to the concent ration prior to the second tremelimumab or PF -06801591 dose (on Cycle 1, Day 29) will be determined individually if data permit. PK parameters will be calculated using standard non- compartmental methods: Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 23Parameter Method of Determination AUC last Linear/log trapezoidal method AUC infaAUC last+ (C last/k el), where C last is the predicted concentration at the last quantifiable time point (T) estimated from the log-linear regression analysis, and k el is the terminal phase rate constant calculated by a linear regression of the log-linear concentration-time curve. The terminal log-linear phase will be determined from a minimum of 3 concentration-time data points, and will be verified with the r2 value. Cmax Observed directly from data CL/FaDose/AUC inf t½aln2/k el Tmax Observed directly from data aif data permit. The PK parameters of each compound will be summarized as below: Parameter Summary statistics AUClast, AUC inf, Cmax, and CL/FN, arithmetic mean, median, cv%, standard deviation, minimum, maximum, geometric mean. t1/2 N, arithmetic mean, median, cv%, standard deviation, minimum, maximum.
Tmax N, median, minimum, maximum.
8.1.6. Tremelimumab and PF-06801591 Immunogenicity For patients receiving tremelimumab, the percentage of subjects with positive ADA and neutralizing antibodies will be summarized by dosing cohort. For patients with positive ADA, the magnitude (titer), time of onset, and duration of ADA response will also be described, if data permit. For patients receiving PF-06801591, the percentage of subjects with positive ADA and neutralizing antibodies will be summarized by dosing cohort. For patients with positive ADA, the magnitude (titer), time of onset, and duration of ADA response will also be described, if data permit. CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 24 CCI Protocol B7791001 Statistical Analysis Plan PFIZER CONFIDENTIAL Page 259.REFERENCES 1. Nishino M, Jagannathan JP, Krajewski KM, O'Regan K, Hatabu H, Shapiro G, Ramaiy a NH. Personalized tumor response assessment in the era of molecular medicine: cancer - specific and therap y-specific response criteria to complement pitfalls of RECI ST. AJR Am J Roentgenol 2012;198(4):737 –745.
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