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BackgroundLiver cancer is the second leading cause of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC) risk factors include chronic hepatitis, cirrhosis, and alcohol abuse, whereby tumorigenesis is induced through inflammation and subsequent fibrotic response. However, a subset of HCC arises in non-cirrhotic livers. We characterized the genomic and transcriptomic landscape of non-cirrhotic HCC to identify features underlying the disease’s development and progression.MethodsWhole genome and transcriptome sequencing was performed on 30 surgically resectable tumors comprised of primarily of non-cirrhotic HCC and adjacent normal tissue. Using somatic variants, capture reagents were created and employed on an additional 87 cases of mixed cirrhotic/non-cirrhotic HCC. Cases were analyzed to identify viral integrations, single nucleotide variants (SNVs), insertions and deletions (INDELS), copy number variants, loss of heterozygosity, gene fusions, structural variants, and differential gene expression.ResultsWe detected 3,750 SNVs/INDELS and extensive CNVs and expression changes. Recurrent TERT promoter mutations occurred in >52% of non-cirrhotic discovery samples. Frequently mutated genes included TP53, CTNNB1, and APOB. Cytochrome P450 mediated metabolism was significantly downregulated. Structural variants were observed at MACROD2, WDPCP and NCKAP5 in >20% of samples. Furthermore, NR1H4 fusions involving gene partners EWSR1, GNPTAB, and FNIP1 were detected and validated in 2 non-cirrhotic samples.ConclusionGenomic analysis can elucidate mechanisms that may contribute to non-cirrhotic HCC tumorigenesis. The comparable mutational landscape between cirrhotic and non-cirrhotic HCC supports previous work suggesting a convergence at the genomic level during disease progression. It is therefore possible genomic-based treatments can be applied to both HCC subtypes with progressed disease.HighlightsNon-cirrhotic HCC genomically resembles cirrhotic HCCComprehensive genome- and transcriptome-wide profiling allows detection of novel structural variants, fusions, and undiagnosed viral infectionsNR1H4 fusions may represent a novel mechanism for tumorigenesis in HCCNon-cirrhotic HCC is characterized by genotoxic mutational signatures and dysregulated liver metabolismClinical history and comprehensive omic profiling incompletely explain underlying etiologies for non-cirrhotic HCC highlighting the need for further researchShort DescriptionThis study characterizes the genomic landscape of hepatocellular carcinomas (HCCs) in non-cirrhotic livers. Using 117 HCCs tumor/normal pairs, we identified 3,750 SNVs/INDELS with high variant frequency in TERT, TP53, CTNNB1, and APOB. CYP450 was significantly downregulated and many structural variants were observed. This characterization could assist in elucidating non-cirrhotic HCC tumorigenesis.

Antibody blockade of programmed death-1 (PD-1) or its ligand, PD-L1, has led to unprecedented therapeutic responses in certain tumor-bearing individuals, but PD-L1 expression's prognostic value in stratifying cancer patients for such treatment remains unclear. Reports conflict on the significance of correlations between PD-L1 on tumor cells and positive clinical outcomes to PD-1/PD-L1 blockade. We investigated this issue using genomically related, clonal subsets from the same methylcholanthrene-induced sarcoma: a highly immunogenic subset that is spontaneously eliminated in vivo by adaptive immunity and a less immunogenic subset that forms tumors in immunocompetent mice, but is sensitive to PD-1/PD-L1 blockade therapy. Using CRISPR/Cas9-induced loss-of-function approaches and overexpression gain-of-function techniques, we confirmed that PD-L1 on tumor cells is key to promoting tumor escape. In addition, the capacity of PD-L1 to suppress antitumor responses was inversely proportional to tumor cell antigenicity. PD-L1 expression on host cells, particularly tumor-associated macrophages (TAM), was also important for tumor immune escape. We demonstrated that induction of PD-L1 on tumor cells was IFNγ-dependent and transient, but PD-L1 induction on TAMs was of greater magnitude, only partially IFNγ dependent, and was stable over time. Thus, PD-L1 expression on either tumor cells or host immune cells could lead to tumor escape from immune control, indicating that total PD-L1 expression in the immediate tumor microenvironment may represent a more accurate biomarker for predicting response to PD-1/PD-L1 blockade therapy, compared with monitoring PD-L1 expression on tumor cells alone. Cancer Immunol Res; 5(2); 106–17. ©2017 AACR.

Characterization of tier 1 mutations identified in WHIM37 through NSG.

Characterization of tier 1 mutations identified in WHIM35 through NSG.

Supplementary Figure S1. Genomic sequence of the PD-L1 locus and expression of immunogenic neoantigens in T3deltaPDL1 lines. Supplementary Figure S2. Expression of PD-L1 and neoantigens in the escape T3deltaPDL1 cells. Supplementary Figure S3. PD-L1 but not PD-L2 is upregulated on MCA sarcomas in vitro and in vivo following exposure to IFNgamma. Supplementary Figure S4. T9-PDL1 cells form progressively growing tumors in WT mice. Supplementary Figure S5. T9-PDL1ovr but not T9-PDL1phy cells inhibit antigen-specific T cell killing in vitro. Supplementary Figure S6. Extrinsic IFNgamma stimulation controls upregulation and maintenance of tumor PD-L1 expression. Supplementary Figure S7. Sixteen immune subsets in MCA sarcomas by flow cytometry. Supplementary Figure S8. Comprehensive analysis of cell types expressing PD-L1 in T3 tumors.

Supplementary Table S1. Staining panels for comprehensive analysis of cell types expressing PD-L1.

Characterization of tier 1 mutations identified in WHIM30 through NSG.

Next-generation sequencing technologies have provided insights into the biology and mutational landscape of cancer. Here, we evaluate the relevance of cancer neoantigens in human breast cancers. Using patient-derived xenografts from three patients with advanced breast cancer (xenografts were designated as WHIM30, WHIM35, and WHIM37), we sequenced exomes of tumor and patient-matched normal cells. We identified 2,091 (WHIM30), 354 (WHIM35), and 235 (WHIM37) nonsynonymous somatic mutations. A computational analysis identified and prioritized HLA class I–restricted candidate neoantigens expressed in the dominant tumor clone. Each candidate neoantigen was evaluated using peptide-binding assays, T-cell cultures that measure the ability of CD8+ T cells to recognize candidate neoantigens, and preclinical models in which we measured antitumor immunity. Our results demonstrate that breast cancer neoantigens can be recognized by the immune system, and that human CD8+ T cells enriched for prioritized breast cancer neoantigens were able to protect mice from tumor challenge with autologous patient-derived xenografts. We conclude that next-generation sequencing and epitope-prediction strategies can identify and prioritize candidate neoantigens for immune targeting in breast cancer. Cancer Immunol Res; 5(7); 516–23. ©2017 AACR.

Figure Legends for Supplementary Data

Supplementary Figure 1. H&E of WHIM30, WHIM35 and WHIM37 parental tumors. Supplementary Figure 2. Peptides corresponding to prioritized neoantigens can induce strong surface expression of HLA class I molecules. Supplementary Figure 3. Indirect tumor-specific immune response. Supplementary Figure 4. The immune response to neoantigens is HLA-specific. Supplementary Figure 5. Prioritized neoantigens induce immune responses in HLA-A0201 transgenic. Supplementary Table 1. Patient clinical characteristics. Supplementary Table 5. Summary of candidate neoantigens identified by exome sequencing. Supplementary Table 6. HLA typing. Supplementary Table 7. Candidate neoantigens and HLA class I predicted binding affinities for WHIM30. Supplementary Table 8. Candidate neoantigens and HLA class I predicted binding affinities for WHIM35.

Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that ‘polyvalent’ vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole exome sequencing (WES) and RNA sequencing (RNA-Seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-Seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by alignment of B-cell receptor (BCR) CDR3 regions from RNA-Seq data, grouping at the protein level, and comparison to the BCR repertoire of RNA-Seq data from healthy individuals. An average of 52 somatic mutations per patient (range: 2-172) were identified, and two or more (median: 15) high-quality neoantigens were predicted for 56 of 58 samples. The predicted neoantigen peptides were composed of missense mutations (76%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide (SLP) vaccines were successfully synthesized for and administered to all four patients enrolled to date. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field.

Here, we demonstrate a complete clinical response achieved in a patient with HER2+ metastatic pancreatic ductal adenocarcinoma to a coordinated barrage of anti-HER2, personalized vaccine and checkpoint inhibition immunotherapy, radiation, and chemotherapy. Comprehensive organoid profiling with drug sensitivity screening and drug testing suggested a vulnerability to anti-HER2 directed therapy, facilitating personalized treatment selection for our patient, which contributed to her clinical benefit. Immune response monitoring following personalized vaccine, radiation and checkpoint inhibition showed a sustained increase in neoantigen specific T cell response.

PURPOSECancer neoantigens are important targets of cancer immunotherapy. Neoantigen vaccines have the potential to induce or enhance highly specific antitumor immune responses with minimal risk of autoimmunity. We have developed a neoantigen DNA vaccine platform capable of efficiently presenting both HLA class I and II epitopes. To test the safety, feasibility and efficacy of this platform, we performed a phase 1 clinical trial in triple negative breast cancer patients with persistent disease following neoadjuvant chemotherapy, a patient population at high risk of disease recurrence.EXPERIMENTAL DESIGNExpressed somatic mutations were identified by tumor/normal exome sequencing and tumor RNA sequencing. The pVACtools software suite was used to identify and prioritize cancer neoantigens. Neoantigen DNA vaccines were designed and manufactured in an academic GMP facility at Washington University School of Medicine. Neoantigen DNA vaccines were administered via electroporation following completion of standard of care therapy. Safety was measured by clinical and laboratory evaluation. Immune responses were assessed by ELISPOT, flow cytometry and TCR sequencing.RESULTS18 subjects received three doses of a personalized neoantigen DNA vaccine encoding on average 11 neoantigens per patient (range 4-20). The vaccinations were well tolerated with limited adverse events, primarily related to injection site reactions. Neoantigen-specific immune responses were induced in 16/18 patients as measured by ELISPOT and flow cytometry. At a median follow-up of 36 months, progression-free survival was 87.5% (95% CI: 72.7-100%) in the cohort of vaccinated patients compared to 49% (95% CI: 36.4-65.9%) in a cohort of institutional historical control patients (p=0.011).CONCLUSIONSNeoantigen DNA vaccines are safe, feasible, and capable of inducing a neoantigen-specific immune response. There is preliminary evidence of improved disease-free survival compared to historical controls.

The impact of intratumoral heterogeneity (ITH) and the resultant neoantigen landscape on T cell immunity are poorly understood. ITH is a widely recognized feature of solid tumors and poses distinct challenges related to the development of effective therapeutic strategies, including cancer neoantigen vaccines. Here, we performed deep targeted DNA sequencing of multiple metastases from melanoma patients and observed ubiquitous sharing of clonal and subclonal single nucleotide variants (SNVs) encoding putative HLA class I-restricted neoantigen epitopes. However, spontaneous antitumor CD8+ T cell immunity in peripheral blood and tumors was restricted to a few clonal neoantigens featuring an oligo-/monoclonal T cell-receptor (TCR) repertoire. Moreover, in various tumors of the 4 patients examined, no neoantigen-specific TCR clonotypes were identified despite clonal neoantigen expression. Mature dendritic cell (mDC) vaccination with tumor-encoded amino acid-substituted (AAS) peptides revealed diverse neoantigen-specific CD8+ T responses, each composed of multiple TCR clonotypes. Isolation of T cell clones by limiting dilution from tumor-infiltrating lymphocytes (TILs) permitted functional validation regarding neoantigen specificity. Gene transfer of TCRαβ heterodimers specific for clonal neoantigens confirmed correct TCR clonotype assignments based on high-throughput TCRBV CDR3 sequencing. Our findings implicate immunological ignorance of clonal neoantigens as the basis for ineffective T cell immunity to melanoma and support the concept that therapeutic vaccination, as an adjunct to checkpoint inhibitor treatment, is required to increase the breadth and diversity of neoantigen-specific CD8+ T cells.

Neoantigens are novel peptide sequences resulting from somatic mutations in tumors that upon loading onto major histocompatibility complex (MHC) molecules allow recognition by T cells. Accurate neoantigen identification is thus critical for designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly inferring whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are SNVs, changes between wildtype and mutant peptide are subtle and require cautious interpretation. An important, yet underappreciated, variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific HLA alleles. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. However, a systematic method for determining anchor locations for the wide range of HLA alleles present in the population and application of these to evaluate MT/WT peptide pairs arising in tumors has not been reported. As a result, many neoantigen studies have either failed to adequately consider this crucial factor or have used conventional assumptions to guide their neoantigen identification process. Here, we provide a computational workflow for predicting anchor locations for a wide range of HLA alleles, using a reference dataset generated from clinical and The Cancer Genome Atlas (TCGA) patient samples. We calculated high probability anchor positions for different peptide lengths for over 300 common HLA alleles. Analysis of these results showed clusters of different anchor trends among the HLA alleles analyzed. A subset of these HLA anchor results were orthogonally validated using protein crystallography structures. Analysis of 923 tumor samples showed that 7-41% of neoantigen candidates were potentially misclassified in the neoantigen selection process and can be rescued using allele-specific knowledge of anchor positions. These anchor predictions are currently undergoing experimental validation using both peptide-MHC stability assays as well as fluorescence-based competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, such as pVACtools, we hope to formalize and streamline the identification process for relevant clinical studies. Citation Format: Huiming Xia, Joshua McMichael, Michelle Becker-Hapak, Onyinyechi C. Onyeador, Rico Buchli, Ethan McClain, Patrick Pence, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Jasreet Hundal, Susanna Kiwala, S. Peter Goedegebuure, Tanner M. Johanns, Gavin P. Dunn, Antoni Ribas, Christopher A. Miller, William Gillanders, Todd A. Fehniger, Obi L. Griffith, Malachi Griffith. Computational prediction of MHC anchor locations guide neoantigen prediction and prioritization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5639.

Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also affect neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction for major histocompatibility complex class I neoantigen peptides, the overall false discovery rate (incorrect neoantigens predicted) and the false negative rate (strong-binding neoantigens missed) across peptides of lengths 8-11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively.

Neoantigens are novel peptide sequences resulting from sources such as somatic mutations in tumors. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are single nucleotide variants, changes between wildtype and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. We computationally predicted high probability anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6-38% of neoantigen candidates are potentially misclassified and can be rescued using allelespecific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline and improve the identification process for relevant clinical studies.One Sentence SummaryNeoantigen prediction accuracy is significantly influenced by the mutation position within the neoantigen and its relative position to the patient’s allele-specific MHC anchor locations.

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

BACKGROUND The use of next-generation sequencing technologies has enabled the rapid identification of non-synonymous somatic mutations in cancer cells. Neoantigens are mutated peptides derived from somatic mutations not present in normal tissues that may result in the presentation of tumour-specific peptides capable of eliciting antitumour T-cell responses. Personalised neoantigen-based cancer vaccines and adoptive T-cell therapies have been shown to prime host immunity against tumour cells and are under clinical trial development. However, the optimisation and standardisation of neoantigen identification, as well as its delivery as immunotherapy are needed to increase tumour-specific T-cell responses and, thus, the clinical efficacy of current cancer immunotherapies. METHODS In this recommendation article, launched by the European Society for Medical Oncology (ESMO), we outline and discuss the available framework for neoantigen prediction and present a systematic review of the current scientific evidence. RESULTS A number of computational pipelines for neoantigen prediction are available. Most of them provide peptide major histocompatibility complex (MHC) binding affinity predictions, but more recent approaches incorporate additional features like variant allele fraction, gene expression, and clonality of mutations. Neoantigens can be predicted in all cancer types with high and low tumour mutation burden, in part by exploiting tumour-specific aberrations derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other tumour-specific processes that could yield more potently immunogenic tumour neoantigens. Ongoing clinical trials will highlight those cancer types and combinations of immune therapies that would derive the most benefit from neoantigen-based immunotherapies. CONCLUSIONS Improved identification, selection and prioritisation of tumour-specific neoantigens are needed to increase the scope of benefit from cancer vaccines and adoptive T-cell therapies. Novel pipelines are being developed to resolve the challenges posed by high-throughput sequencing and to predict immunogenic neoantigens.

Next-generation sequencing technologies have provided insights into the biology and mutational landscape of cancer. Here, we evaluate the relevance of cancer neoantigens in human breast cancers. Using patient-derived xenografts from three patients with advanced breast cancer (xenografts were designated as WHIM30, WHIM35, and WHIM37), we sequenced exomes of tumor and patient-matched normal cells. We identified 2,091 (WHIM30), 354 (WHIM35), and 235 (WHIM37) nonsynonymous somatic mutations. A computational analysis identified and prioritized HLA class I–restricted candidate neoantigens expressed in the dominant tumor clone. Each candidate neoantigen was evaluated using peptide-binding assays, T-cell cultures that measure the ability of CD8+ T cells to recognize candidate neoantigens, and preclinical models in which we measured antitumor immunity. Our results demonstrate that breast cancer neoantigens can be recognized by the immune system, and that human CD8+ T cells enriched for prioritized breast cancer neoantigens were able to protect mice from tumor challenge with autologous patient-derived xenografts. We conclude that next-generation sequencing and epitope-prediction strategies can identify and prioritize candidate neoantigens for immune targeting in breast cancer. Cancer Immunol Res; 5(7); 516–23. ©2017 AACR.

Background HER2 (ERBB2) gene amplification and its corresponding overexpression are present in 15–30% of invasive breast cancers. While HER2-targeted agents are effective treatments, resistance remains a major cause of death. The American College of Surgeons Oncology Group Z1041 trial (NCT00513292) was designed to compare the pathologic complete response (pCR) rate of distinct regimens of neoadjuvant chemotherapy and trastuzumab, but ultimately identified no difference. Patients and methods In supplement to tissues from 37 Z1041 cases, 11 similarly treated cases were obtained from a single institution study (NCT00353483). We have extracted genomic DNA from both pre-treatment tumor biopsies and blood of these 48 cases, and performed whole genome (WGS) and exome sequencing. Coincident with these efforts, we have generated RNA-seq profiles from 42 of the tumor biopsies. Among patients in this cohort, 24 (50%) achieved a pCR. Results We have characterized the genomic landscape of HER2-positive breast cancer and investigated associations between genomic features and pCR. Cases assigned to the HER2-enriched subtype by RNA-seq analysis were more likely to achieve a pCR compared to the luminal, basal-like, or normal-like subtypes (19/27 versus 3/15; P=0.0032). Mutational events led to the generation of putatively active neoantigens, but were overall not associated with pCR. ERBB2 and GRB7 were the genes most commonly observed in fusion events, and genomic copy number analysis of the ERBB2 locus indicated that cases with either no observable or low-level ERBB2 amplification were less likely to achieve a pCR (7/8 versus 17/40; P=0.048). Moreover, among cases that achieved a pCR, tumors consistently expressed immune signatures that may contribute to therapeutic response. Conclusion The identification of these features suggests that it may be possible to predict, at the time of diagnosis, those HER2-positive breast cancer patients who will not respond to treatment with chemotherapy and trastuzumab. ClinicalTrials.gov identifiers NCT00513292, NCT00353483

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

e14561 Background: Tumor neoantigens are epitopes derived from tumor-specific mutations that may be incorporated in personalized vaccines to prime T cell responses. DNA vaccines delivered with electroporation have recently shown strong CD8 and CD4 T cell responses in clinical trials. In preclinical studies, DNA-encoded neoantigen vaccines have shown induction of CD8 T cells against 50% of predicted high affinity epitopes with the ability to impact tumor growth. Methods: Two resection samples from a patient with IDH+ MGMT-methylated anaplastic astrocytoma were subject to whole exome and transcriptome sequencing. Epitopes derived from 27 neoantigens and 3 shared tumor-associated antigens were prioritized and included in a personalized vaccine. The patient was treated with surgery, radiotherapy and temozolomide starting June 2018 and received the first dose of the personalized vaccine in June 2019 under a compassionate use single patient IND application with the FDA. Results: As of February 10, 2021, the patient has received 10 doses of the DNA personalized vaccine. No serious adverse events have been reported. Related adverse events are limited to grade 1 injection site reactions. The patient remains progression-free 32 months after surgery and 20 months after starting vaccination. Three weeks following the third dose, a hyperintense image on the tumor bed was identified, which disappeared on the following MRI, 2 weeks following dose 5, being catalogued as pseudo progression. Ex vivo ELISpot have identified T cell responses to 21/30 epitopes (70%), including 18/27 (67%) neoantigens and 3/3 (100%) shared antigens. Flow cytometry analysis has determined that T cell responses are 92.3% CD8 and 69.2% CD4 (30.8% CD8 only; 61.5% both CD8 and CD4; and 7.7% CD4 only). Conclusions: This compassionate use treatment in an adjuvant setting demonstrates manufacturing feasibility, safety, tolerability, immunogenicity, and suggests potential for persistent clinical response of DNA encoded personalized vaccines. The data supports further investigation of DNA-encoded personalized vaccines into newly diagnosed high-grade gliomas.

The genomic events responsible for the pathogenesis of relapsed adult B-lymphoblastic leukemia (B-ALL) are not yet clear. We performed integrative analysis of whole-genome, whole-exome, custom capture, whole-transcriptome (RNA-seq), and locus-specific genomic assays across nine time points from a patient with primary de novo B-ALL. Comprehensive genome and transcriptome characterization revealed a dramatic tumor evolution during progression, yielding a tumor with complex clonal architecture at second relapse. We observed and validated point mutations in EP300 and NF1, a highly expressed EP300-ZNF384 gene fusion, a microdeletion in IKZF1, a focal deletion affecting SETD2, and large deletions affecting RB1, PAX5, NF1, and ETV6. Although the genome analysis revealed events of potential biological relevance, no clinically actionable treatment options were evident at the time of the second relapse. However, transcriptome analysis identified aberrant overexpression of the targetable protein kinase encoded by the FLT3 gene. Although the patient had refractory disease after salvage therapy for the second relapse, treatment with the FLT3 inhibitor sunitinib rapidly induced a near complete molecular response, permitting the patient to proceed to a matched-unrelated donor stem cell transplantation. The patient remains in complete remission more than 4 years later. Analysis of this patient's relapse genome revealed an unexpected, actionable therapeutic target that led to a specific therapy associated with a rapid clinical response. For some patients with relapsed or refractory cancers, this approach may indicate a novel therapeutic intervention that could alter outcome.

Follicular lymphoma (FL) is the most common indolent non-Hodgkin's lymphoma; however, it remains incurable with conventional therapies and is poorly responsive to checkpoint blockade. Additionally, because FL develops so slowly (and often asymptomatically), a major research focus has been to avoid chemotherapy treatments to limit the potential development of treatment-related side effects and the risk of therapy-related second cancers. The mutational processes that lead to lymphomagenesis and progression also produce tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system to control malignancies. Personalized cancer vaccines designed for these TSMAs represent a promising new strategy for treatment of FL. However, the feasibility of this approach and precisely how to optimize effective vaccine design, informed by next-generation sequencing data, are not fully understood. We hypothesize that (tumor/normal) whole-exome sequencing (WES) and (tumor) RNA sequencing (RNA-Seq) can be used to predict patient HLA typing and neoepitopes to engineer personalized cancer vaccines for FL. DNA and RNA from 58 patients' FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high-quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high-quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. No fusion genes were identified within the cohort that could have been a source of neoepitope candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These preclinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This preclinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials, and pilot clinical results provide proof of concept for this approach. Citation Format: Cody A. Ramirez, Felix Frenkel, Olga Plotnikova, Vladislav Belousov, Alexander Bagaev, Elena Ocheredko, Susanna Kiwala, Jasreet Hundal, Zachary L. Skidmore, Marcus Watkins, Michelle Becker-Hapak, Thomas B. Mooney, Jason Walker, Catrina Fronick, Robert Fulton, Robert Schreiber, Nancy L. Bartlett, Brad Kahl, Ravshan Ataullakhanov, Malachi Griffith, Obi Griffith, Todd A. Fehniger. Identification of predicted neoantigen vaccine candidates in follicular lymphoma patients [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-56.

In infectious disease, polyclonal T cell responses against immunodominant epitopes drive successful immune responses. In cancer, neoepitopes (neoE) derived from non-synonymous mutations, similarly to the immunodominant epitopes in viral infections, are potentially highly immunogenic because the T cells recognizing these antigens are not subjected to the mechanisms of tolerance. Indeed, early studies support that neoE derived from non-synonymous mutations are the primary target of T cell responses induced by immune checkpoint blockade therapy and have been successfully targeted by adoptively transferred T cell therapies (ACT) in multiple cancer histologies. However, there is limited knowledge on the immunodominance and evolution of neoE's, or the clonality of the T cell responses against these neoE. Furthermore, little is known regarding the correlation between the presence and expansion of neoE-specific T cells and the clinical response to immunotherapy in patients. To characterize the neoE-specific T cell responses induced after immunotherapy, we collected peripheral blood mononuclear cells (PBMCs) over time (longitudinally) and established expanded tumor infiltrating lymphocyte cultures (TILs) and autologous tumor cell lines from the patient's tumor biopsies. We performed whole exome and RNA sequencing of the tumor and normal tissue controls for the computational prediction and ranking of patient-specific neoEs. We then generated a library of capture reagents consisting of the patient HLA class I molecules loaded with predicted neoE (Peng et al. AACR 2019) and isolated neoE-specific T cells from the patients' PBMC or TIL samples. Once isolated, the paired neoE-specific TCR alpha and beta chains (neoTCR) were obtained by single cell sequencing. For functional characterization of the neoTCRs, healthy donor primary human T cells were modified to express the neoTCR using CRISPR-based, non-viral precision genome engineering by replacing the endogenous TCR with the respective neoTCR (Jacoby et al., AACR 2019, Sennino et al., AACR 2019). These gene-edited T cells were then used in co-culture experiments with the patient autologous cell lines. We analyzed T cell responses in three patients (PT1, PT2, and PT3) with metastatic melanoma receiving immunotherapy. PT1 had a fast and durable anti-tumor response to anti-PD-1 therapy. Sequencing identified 2556 somatic coding mutations. A library of 243 neoE-specific pMHC capture reagents across 3 HLA types, HLA-A*03:01, A*24:01, and C*12:03 was generated and used for screening of PBMCs or TILs derived from multiple longitudinal time points. Several hundred neoE-specific T cells were isolated. Importantly, this neoE-specific T cell response was comprised of 17 different neoE-specific T cells clones targeting only 5 different HLA-neoE complexes supporting the immunodominance hypothesis. On the other hand, PT2 and PT3 showed marginal responses to immunotherapy. Patient two progressed after being treated with anti-PD1. This patient had 24 somatic coding mutations. Seventeen neoE-HLA reagents across 3 HLAs, B*35:03, C*12:03, and C*08:01 were generated and used to capture neoE-specific T cells from TILs and PBMCs. While 14 different TCRs targeting 7 HLA-neoE complexes were identified from expanded TILs, no neoE-reactive T cells were captured from the peripheral blood. PT3 presented with progressive disease after being treated with local TVEC. This patient had 61 somatic coding mutations; 78 neoE-specific pHLA capture reagents covering HLA-A*02:01, A*03:01, B*07:02, C*05:01, and C*07:02 were generated and used to screen for neoE-specific T cells in the patient's TIL and PBMCs. In contrast to PT2, 2 different neoTCRs targeting the same HLA-neoE complexes were isolated from PBMCs, but none from TILs. To further characterize the T cell responses from patients that responded or did not respond to immunotherapy, we generated 18 separate T cell products, each expressing a different neoTCR isolated from PT1, PT2 and PT3. For PT1, we characterized 14 different neoTCRs specific for neoE's in the mutated IL8, PUM1 and TPP2 genes. All 14 T cell products displayed specific cytotoxicity against the matched autologous melanoma cell line established from a biopsy of patient one (50-75% tumor growth inhibition compared to melanoma cell line growth in co-culture with a mismatched control TCR, 96 hour assay using a product to target ratio (P:T) of 1:1, p < 0.000001 for each comparison). No cytotoxic effect against an unmatched human melanoma cell line was observed. Furthermore, neoE TCR T cells upregulated 4-1BB and OX-40, secreted IFNγ, IL-2, TNFα, and IL6, and induced T cell proliferation and degranulation. Again, no unspecific T cell activation was observed when T cells were co-cultured with unmatched targets. Interestingly, precision genome engineered T cell products expressing neoTCRs identified from patients that did not respond to therapy (PT2 and PT3), also potently killed autologous tumor cells. Four neoTCRs were studied (2 TCR for PT2 and 2 TCRs for PT3), and three of them showed specific cytotoxicity against the matched autologous melanoma cell line (50-100% tumor growth inhibition compared to melanoma cell line growth in co-culture with a mismatched control TCR, 96 hour assay using P:T 5:1, p < 0.05 for each comparison). Additionally, upon co-culture with the matched melanoma cell line, but not against an unmatched melanoma cell line control, neoE TCR T cells upregulated 4-1BB and OX-40, secreted IFNγ, IL-2, TNFα, and IL6, and induced T cell proliferation and degranulation. These data demonstrate that even patients that did not respond to immunotherapy harbor neoTCRs that, when expressed in ‘fresh' T cells, are able to kill the autologous tumor cell lines. Using newly developed techniques to isolate and capture neoE-specific single T cells, as well as non-viral gene editing, we isolated and characterized neoE-specific T cells that can recognize the cancer cells and induce an anti-tumor response. We also studied the neoE immunodominance and TCR clonality over time of the natural T cell repertoire that induce anti-tumor responses to ICB therapy. Our results show that in a patient with a good response to anti-PD-1, there is a polyclonal response that targets a limited number of neoE-HLA complexes (2% of the neoE tested in the case of patient one) highlighting the immunodominance of these epitopes. Interestingly, different T cell clonotypes targeting the same mutations evolve over time, suggesting functional differences amongst the TCRs. In addition, our results demonstrate that even patients that did not respond to these therapies harbor neoE-specific T cells, as we were able to isolate neoE-specific T cells that recognized and killed patient-derived cancer cells. This suggests that even in patients that do not respond to immunotherapy, neoE-specific TCRs can be isolated and could be potentially used for personalized ACT. Finally, our results also show how non-viral precision genome engineering can successfully redirect T cells to neoE-expressing tumors, enabling the personalized ACT. Citation Format: Cristina Puig-Saus, Barbara Sennino, Bhamini Purandare, Duo An, Boi Quach, Songming Peng, Huiming Xia, Sidi Zhao, Zheng Pan, Yan Ma, Justin Saco, Sameeha Jilani, Christine Shieh, Katharine Heeringa, Olivier Dalmas, Robert Moot, Diana Nguyen, William Lu, Kyle Jacoby, Andrew Conroy, Jasreet Hundal, Malachi Griffith, Stefanie Mandl, Alex Franzusoff, Antoni Ribas. Landscape analysis of neoepitope-specific T-cell responses to immunotherapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr NG11.

8054 Background: Follicular lymphoma (FL) is incurable with conventional therapies and poorly responsive to immune checkpoint blockade. There is a need for new therapies without long-term complications of chemotherapy and with curative potential. We hypothesize that FL contains tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system by vaccination. Recent reports have highlighted the potential for unique immunoglobulin peptides to elicit immune response in lymphomas. We utilized whole exome sequencing (WES) and RNA sequencing (RNA-Seq) of FL patient samples to infer HLA genotype, and predict TSMAs with the goal of designing a personalized cancer vaccine, supported by recent reports of this approach in solid cancers. Methods: DNA and RNA from 58 patients’ FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. Results: An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These pre-clinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. Conclusions: TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This pre-clinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials and pilot clinical results provide proof of concept for this approach.

The original version of this Article contained errors in the depiction of confidence intervals in the NF1 BCSS data illustrated in Figure 3b. These have now been corrected in both the PDF and HTML versions of the Article. The incorrect version of Figure 3b is presented in the associated Author Correction.

Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction (q = 0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer., Unravelling the link between somatic mutation and prognosis in estrogen positive (ER+) breast cancer requires the use of long-term follow-up data. Here, combining archival formalin-fixed paraffin embedded tissue and targeted sequencing in three cohorts of ER+ breast cancer, the authors find associations with clinical outcome for NF1 frame-shift nonsense mutations, PIK3R1 mutation, and DDR1 mutations.

Purpose: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. Methods: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer’s confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. Results: After reading the SOP, average accuracy in somatic variant identification increased by 16.7% (p value = 0.0298) and average interreviewer agreement increased by 12.7% (p value

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.MethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.ResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewer’s confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.ConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

Background: Relationships between recurrent somatic mutations and outcome in estrogen receptor positive (ER+) breast cancer have not been extensively studied as the original discovery efforts were from either heterogeneously treated patients or follow up was too brief. Targeted massively parallel sequencing (MPS) analysis was therefore conducted on DNA extracted from archived formalin-fixed breast primaries from a cohort of over 600 patents from British Columbia treated with five years of adjuvant tamoxifen monotherapy and followed for over 10 years (Nielsen et al CCR 16:5222, 2010). Methods: Genes were selected for targeted sequencing by meta-analysis of five large-scale breast cancer sequencing studies and manual review of breast cancer literature. In total, 83 genes were identified and 3286 probes were designed to tile across all known exons. Minimum starting input DNA was 50ng (mean=189.1ng). Illumina sequencing libraries were constructed, indexed, pooled, and enriched for target sequences by hybrid-capture followed by paired-end 100bp reads. The Genome Modeling System was used to perform single-tumor somatic variant prediction. Variant calls were filtered to include only targeted regions and exclude variants with global minor allele frequencies greater than 0.1% in unmatched non-tumor samples from 1000 genomes, NHLBI exome, and TCGA datasets. Kaplan-Meier univariate and multivariate survival analyses (including mutation status, clinical features and intrinsic subtype by qPCR) were performed for breast-cancer-specific and relapse free survival. Results: A total of 625 samples met minimum quality controls of 80% targeted space covered at 20X or greater. On average, each sample had 332M aligned bases and a mean coverage of 134.3X. In total, 3,628 variants were identified including 2,066 missense, 188 nonsense, and 298 frame shift insertions or deletions. Novel hot spots for recurrent mutation were identified in several genes including a splice site mutation in CBFB. Results indicate significant associations between mutation status and improved survival for MAP3K1, ERBB3, ARID1B, PIK3CA and SMG1 or worse survival for DDR1, NF1, FOXC1 and TP53. Six Y537S/C, two E380Q and 5 potentially novel ligand-binding-domain mutations were identified in ESR1. Such mutations were recently reported to be associated with resistance to hormone therapy but were discovered here in as many as 2.1% of pre-treatment samples. Analysis will be presented regarding the use of relapse events to differentiate passenger from driver events. Conclusion: Multiple recurrently mutated genes have both positive and negative associations with prognosis in tamoxifen monotherapy treated breast cancer populations. Associations with poor outcome suggest that DDR1, NF1 and FOXC1 are high priorities for pharmacological interventions. Citation Format: Obi L. Griffith, Malachi Griffith, Nicholas C. Spies, Jingqin Luo, Jasreet Hundal, Christopher A. Miller, David E. Larson, Robert Fulton, Shuzhen Liu, Samuel Leung, Richard K. Wilson, Torsten O. Nielsen, Elaine R. Mardis, Matthew J. Ellis. Recurrent mutations of hormone-positive breast cancer and association with outcome. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-06.

T cell immunity directed against tumor-encoded amino acid substitutions occurs in some melanoma patients. This implicates missense mutations as a source of patient-specific neoantigens. However, a systematic evaluation of these putative neoantigens as targets of antitumor immunity is lacking. Moreover, it remains unknown whether vaccination can augment such responses. We found that a dendritic cell vaccine led to an increase in naturally occurring neoantigen-specific immunity and revealed previously undetected human leukocyte antigen (HLA) class I–restricted neoantigens in patients with advanced melanoma. The presentation of neoantigens by HLA-A*02:01 in human melanoma was confirmed by mass spectrometry. Vaccination promoted a diverse neoantigen-specific T cell receptor (TCR) repertoire in terms of both TCR-β usage and clonal composition. Our results demonstrate that vaccination directed at tumor-encoded amino acid substitutions broadens the antigenic breadth and clonal diversity of antitumor immunity.

Therapy-related acute myeloid leukaemia (t-AML) and therapy-related myelodysplastic syndrome (t-MDS) are well-recognized complications of cytotoxic chemotherapy and/or radiotherapy. There are several features that distinguish t-AML from de novo AML, including a higher incidence of TP53 mutations, abnormalities of chromosomes 5 or 7, complex cytogenetics and a reduced response to chemotherapy. However, it is not clear how prior exposure to cytotoxic therapy influences leukaemogenesis. In particular, the mechanism by which TP53 mutations are selectively enriched in t-AML/t-MDS is unknown. Here, by sequencing the genomes of 22 patients with t-AML, we show that the total number of somatic single-nucleotide variants and the percentage of chemotherapy-related transversions are similar in t-AML and de novo AML, indicating that previous chemotherapy does not induce genome-wide DNA damage. We identified four cases of t-AML/t-MDS in which the exact TP53 mutation found at diagnosis was also present at low frequencies (0.003-0.7%) in mobilized blood leukocytes or bone marrow 3-6 years before the development of t-AML/t-MDS, including two cases in which the relevant TP53 mutation was detected before any chemotherapy. Moreover, functional TP53 mutations were identified in small populations of peripheral blood cells of healthy chemotherapy-naive elderly individuals. Finally, in mouse bone marrow chimaeras containing both wild-type and Tp53(+/-) haematopoietic stem/progenitor cells (HSPCs), the Tp53(+/-) HSPCs preferentially expanded after exposure to chemotherapy. These data suggest that cytotoxic therapy does not directly induce TP53 mutations. Rather, they support a model in which rare HSPCs carrying age-related TP53 mutations are resistant to chemotherapy and expand preferentially after treatment. The early acquisition of TP53 mutations in the founding HSPC clone probably contributes to the frequent cytogenetic abnormalities and poor responses to chemotherapy that are typical of patients with t-AML/t-MDS.

The application of modern high-throughput genomics to the study of cancer genomes has exploded in the past few years, yielding unanticipated insights into the myriad and complex combinations of genomic alterations that lead to the development of cancers. Coincident with these genomic approaches have been computational analyses that are capable of multiplex evaluations of genomic data toward specific therapeutic end points. One such approach is called "immunogenomics" and is now being developed to interpret protein-altering changes in cancer cells in the context of predicted preferential binding of these altered peptides by the patient's immune molecules, specifically human leukocyte antigen (HLA) class I and II proteins. One goal of immunogenomics is to identify those cancer-specific alterations that are likely to elicit an immune response that is highly specific to the patient's cancer cells following stimulation by a personalized vaccine. The elements of such an approach are outlined herein and constitute an emerging therapeutic option for cancer patients.

We previously identified missense mutations in the U2AF1 splicing factor affecting codons S34 (S34F and S34Y) or Q157 (Q157R and Q157P) in 11% of the patients with de novo myelodysplastic syndrome (MDS). Although the role of U2AF1 as an accessory factor in the U2 snRNP is well established, it is not yet clear how these mutations affect splicing or contribute to MDS pathophysiology. We analyzed splice junctions in RNA-seq data generated from transfected CD34+ hematopoietic cells and found significant differences in the abundance of known and novel junctions in samples expressing mutant U2AF1 (S34F). For selected transcripts, splicing alterations detected by RNA-seq were confirmed by analysis of primary de novo MDS patient samples. These effects were not due to impaired U2AF1 (S34F) localization as it co-localized normally with U2AF2 within nuclear speckles. We further found evidence in the RNA-seq data for decreased affinity of U2AF1 (S34F) for uridine (relative to cytidine) at the e-3 position immediately upstream of the splice acceptor site and corroborated this finding using affinity-binding assays. These data suggest that the S34F mutation alters U2AF1 function in the context of specific RNA sequences, leading to aberrant alternative splicing of target genes, some of which may be relevant for MDS pathogenesis.

Antibody blockade of programmed death-1 (PD-1) or its ligand, PD-L1, has led to unprecedented therapeutic responses in certain tumor-bearing individuals, but PD-L1 expression's prognostic value in stratifying cancer patients for such treatment remains unclear. Reports conflict on the significance of correlations between PD-L1 on tumor cells and positive clinical outcomes to PD-1/PD-L1 blockade. We investigated this issue using genomically related, clonal subsets from the same methylcholanthrene-induced sarcoma: a highly immunogenic subset that is spontaneously eliminated in vivo by adaptive immunity and a less immunogenic subset that forms tumors in immunocompetent mice, but is sensitive to PD-1/PD-L1 blockade therapy. Using CRISPR/Cas9-induced loss-of-function approaches and overexpression gain-of-function techniques, we confirmed that PD-L1 on tumor cells is key to promoting tumor escape. In addition, the capacity of PD-L1 to suppress antitumor responses was inversely proportional to tumor cell antigenicity. PD-L1 expression on host cells, particularly tumor-associated macrophages (TAM), was also important for tumor immune escape. We demonstrated that induction of PD-L1 on tumor cells was IFNγ-dependent and transient, but PD-L1 induction on TAMs was of greater magnitude, only partially IFNγ dependent, and was stable over time. Thus, PD-L1 expression on either tumor cells or host immune cells could lead to tumor escape from immune control, indicating that total PD-L1 expression in the immediate tumor microenvironment may represent a more accurate biomarker for predicting response to PD-1/PD-L1 blockade therapy, compared with monitoring PD-L1 expression on tumor cells alone. Cancer Immunol Res; 5(2); 106–17. ©2017 AACR.

e21064 Background: Merkel cell carcinoma (MCC) is an immunogenic neuroendocrine malignancy with promising responses to immune checkpoint blockade (ICB). Although ICB is standard of care in advanced MCC patients, approximately 50% of patients are resistant to ICB. Thus, it is crucial to identify biomarkers predictive of response. Methods: To understand the genomic landscape of MCC, we performed exome capture sequencing on 27 tumor and matched normal samples from 25 patients with MCC treated at Memorial Sloan Kettering. Herein, we report the analysis of 16 paired samples from 14 patients. Nonsynonymous, high-confidence somatic mutations were identified and tumor reads aligned to the Merkel cell polyoma virus (MCPyV) were quantified. Results: Tumor and matched normal samples were sequenced to a median target coverage depth of 53x and 79x reads, respectively. One sample was not analyzed due to inadequate coverage. The MCPyV genome was detected in 12 of 13 patients (92%). The median somatic mutation burden among analyzed samples was 19 nonsynonymous variants per exome (range: 8 - 120). No recurrent driver mutations were identified in any sample. Four samples lacked potential driver mutations and, among the remaining 11 samples, 36 putatively oncogenic mutations were detected in 33 genes (variant allele frequency: 0.08 – 1), including genes involved in the cell cycle checkpoint ( TP53, RB1), DNA damage repair ( ERCC4, FANCA, FANCD2), PI3K-AKT-mTOR ( PIK3CA, PIK3CG), and Notch ( NOTCH1, NOTCH2) pathways. One sample with undetected MCPyV DNA demonstrated loss of heterozygosity of both TP53 and RB1. Four samples contained strand coordinated clusters of mutations in more than 20 distinct gene regions, suggesting an APOBEC-high mutagenesis signature. Conclusions: The MCPyV genome was detected in most tumors analyzed and tumor mutation burden was low in such tumors, consistent with published literature. Analysis of an additional 11 sample pairs is ongoing, along with personalized neoantigen binding predictions on all samples. Immunohistochemistry (IHC) for PDL1 and CD8 expression is in progress. Associations between the aforementioned and response to ICB will be reported.