DO NOT REMOVE FROM
`
`i CURRENT PERIODICALS
`ROOM
`
`Cancer Immunology and
`Immunotherapy
`
`UNIV. CHICAGO EX. 2030
`
`SPECIAL ISSUE
`
`Genome & Co. v. Univ. of Chicago
`PG R201 9-00002
`
`

`

`
`\\\'\\\\\\\
`
`
`
`40 & 102
`A look at what keeps engines
`running smoothly
`
`
`
`INTRODUCTION
`
`SPECIAL SECTION
`
`_ Cancer Immunology
`' and Immunotherapy
`ON THE COVER
`
`54 Realizing the promise
`REVI we
`
`58 The future of immune checkpoint
`therapy R Shea-ma and .I. P. Allison
`
`62 Adoptive cell transfer as personal-
`ized immunotherapy for human
`cancer 8. A. Rosenberg and N. P. Reson
`
`69 Neoantigens in cancer
`immunotherapy 12 NE Schanwcher
`and R. D. Schreiber
`
`14 T cell exclusion, immune
`privilege, and the tumor microenvi-
`ronment J. A. Joyce and D. T. Fear-on
`
`30 Cancer and the microbiota
`W. S. Garrett
`
`
`
`Cancer immunotherapy
`harnesses the power
`of the immune system
`to kill tumors. These
`therapies aim to activate
`and expand T cells, such
`as those shown in blue,
`to specifically kill tumors
`(black). Current approaches
`include antibodies targeting inhibitory
`proteins on T cells, adoptive T cell therapy, and
`tumor vaccines, among others. See page 54‘.
`Itinetmtt‘oru ValerikAltounian/Science
`
`SEE ALSO b PERSPECTIVE P. 45 r BOOKS ET
`AL I-‘. 49 b REPORTS PP. 124 r3136 > REPORT BY
`B.h-1.CARRENO ETAL10.1126.‘scnence.aa33828
`r SCIENCE CAREERS STORY El‘r’ R. BERNSTEIN
`
`HESEIIIIIH
`
`IN BRIEF
`
`95 IIIIIOSOME
`The structure of the human
`mitochondrial ribosome A. Ammo; et a1.
`b RESEARCH ARTICLE BY B. J. GREBER ET .41..
`lO.1126/science.aaa38?2
`
`8? From Science and other journals
`
`REPORTS
`
`109 TIIEIIMIIELECTltlcs
`Dense dislocation arrays embedded
`in grain boundaries for high-
`performance bulk thermoelectrics
`S. I. Kim et a].
`
`114 STELLAR PHYSICS
`Observing the onset of outflow
`collimation in a massive protostar
`C. Corrosive-Gonzalez et al.
`r PERSPECTIVE P. 44
`
`11? IIIROLOGT
`Mutation rate and genotype
`variation of Ebola virus from Mali
`
`case sequences 1’: Hoenen et al.
`
`120 PLANT BIOLOGY
`Suppression of endogenous
`gene silencing by bidirectional
`cytoplasmic RNA decay in
`Arabidopsts X. Zhang et al.
`
`124 CANCER IMMUNOLOGY
`Mutational landscape
`determines sensitivity to PD-l
`blockade in non—small cell lung
`cancer N. A. Rizui et al.
`P CANCER IMMUNOLOGY AND
`IMMUNOTHERAPY SECTION P. 54
`
`128 GENE EXPRESSION
`MicroRNA control of protein
`expression noise J. M. Schmiedel ct al.
`) PERSPECTIVE P. 41
`
`132 EPIGENE‘I’IGS
`Restricted epigenetic
`inheritance of H3K9 methylation
`P! N C. B. Audergon et a1.
`, RESEARCH ARTICLE P. so
`
`RESEARCH ARTICLES
`
`90 EPIGENE‘IIOS
`Epigenetic inheritance uncoupled
`from sequence-specific recruitment
`K. Ragunathan et a1.
`RESEARCH ARTICLE SUMMARY: FoR FULL TEXT:
`dauloi.orgx‘10.1126/science.1258699
`P REPORT 9.132
`
`99 MOLECULAR PHYSICS
`Production of trilobite Rydberg molecule
`dimers with kilo-Debye permanent
`electric dipole moments D. Booth et al.
`
`102 TIIIIIIJLOGI'
`Mechanisms of antiwear tribofilm growth
`revealed in situ by single-asperity sliding
`contacts N. N. Gommt‘ et a].
`, PERSPECTIVE P. 40
`
`136 ANTITIIMOR IMMUNITY
`A shed NKG2D ligand that
`promotes natural killer cell
`91 COGNITIVE DEVELOPMENT
`activation and tumor rejection
`106 FIIIJSTRATEIJ MAGNETISM
`Observing the unexpected enhances
`W Deng et al.
`infants’ learning and exploration
`Large thermal Hall conductivity of
`h PERSPECTIVE P. 45: CANCER EMMUNOLOG‘I’
`neutral spin excitations in a frustrated
`A. E. Srahl and L. Fez'genson
`AND IMMUNOTHERAPY SECTION P. 54
`, PERSPECTWE P. 42
`quantum magnet M. Hirschberger et a1.
`
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`8
`
`3 APRIL 2015 0 VOL 2348 ISSUE 6230
`
`aciencemagorg SCIENCE
`
`

`

`
`This material may be protected by Copyright law (Title 17 U.S. Code)
`
`
`
`REVIEWS
`
`Neoantigens in
`cancer immunotherapy
`
`Ton N. Schumacher‘* and Robert D. Scltreiberh
`
`The clinical relevance of Tcells in the control of a diverse set of human cancers is now beyond
`doubt. However. the nature of the antigens that allow the immune system to distinguish cancer
`cells from noncancer cells has long remained obscure. Recent technological innovations have
`made it possible to dissect the immune response to patient-specific neoantigens that arise as a
`consequence of tumor-specific mutations. and emerging data suggest that recognition of such
`neoantigens is a major factor in the activity of clinical immunotherapies. These observations
`indicate that neoantigen load may form a biomarker in cancer immunotherapy and provide an
`Incentive for the development of novel therapeutic approaches that selectively enhance Tcell
`reactivity against this class of antigens.
`
`mmunotherapies that boost the ability of en-
`dogenous T cells to destroy cancer cells have
`demonstrated therapeutic efficacy in a vari-
`ety of human malignancies. Until recently,
`evidence that the endogenous T cell com-
`partment could help control tumor growth was
`in large part restricted to preclinical mouse tu-
`mor models and to human melanoma. Specif-
`ically, mice lacking an intact immune system
`were shown to be more susceptible to carcinogen-
`induced and spontaneous cancers compared with
`their immunocompetent counterparts (1). With
`respect to human studies, the effects of the T cell
`cytokine interleukin—2 in a small subset of mel~
`anoma patients provided early clinical evidence
`of the potential of immunotherapy in this dis-
`ease. In 2010, the field was revitalized by a
`landmark randomized clinical trial that dem-
`onstrated that treatment with ipilimumab, an
`antibody that targets the T cell checkpoint pro-
`tein CTLAA‘, improved overall survival of pa—
`tients with metastatic melanoma (2). As a direct
`test of the tumoricidal potential of the endoge-
`nous T cell compartment, work by Rosenberg
`and colleagues demonstrated that infusion of
`autologous ex vivo expanded tumor-infiltrating
`lymphocytes can induce objective clinical re—
`sponses in metastatic melanoma (3), and at least
`part of this clinical activity is due to cytotoxic
`T cells (4). Importantly, recent studies demon-
`strate that T cell-based immunotherapies are
`also effective in a range of other human malig-
`nancies. In particular, early-phase trials of anti-
`bodies that interfere with the T cell checkpoint
`molecule PD—l have shown clinical activity in
`tumor types as diverse as melanoma1 lung can»
`cer, bladder cancer, stomach cancer, renal cell
`cancer, head and neck cancer, and Hodgkin's
`l.‘i’lnphoma (5). Based on the relationship between
`
`
`
`1Division of immunology, Netherlands Cancer Institute.
`Plesrnanlaan 121, 1066 CK. Amsterdam. Netherlands.
`apartment of Pathology and Immunology. Washington
`University School of Medicine. 560 South Euclid Avenue.
`St. Louis. no 53110. USA.
`'Correspcnding author. Email: tschumachar®nltinl (T.N.5.):
`a:hreibertoimmunclogymustledu 01.0.5.)
`
`pretherapy CDS+ T cell infiltrates and response
`to 1’le blockade in melanoma, cytotoxic T cell
`activity also appears to play a central role in this
`form of cancer immunotherapy (6).
`An implicit conclusion from these clinical data
`is that in a substantial fraction of patients, the
`endogenous T cell compartment is able to rec-
`ognize peptide epitopes that are displayed on
`major histocompatibility complexes (MHCs) on
`the surface of the malignant cells. On theoretical
`grounds, such cancer rejection epitopes may be
`derived from two classes of antigens. A first class
`of potential cancer rejection antigens is formed
`by nonmutated proteins to which T cell tolerance
`is incomplete—for instance, bemuse of their re-
`stricted tissue expression pattern. A second class
`of potential cancer rejection antigens is formed
`by peptides that are entirely absent from the
`normal human genome, so-called neoantigens.
`For the large group of human tumors without a
`viral etiology, such neo-epitopes are solely created
`by manor-specific DNA alterations that result in
`the formation of novel protein sequences. For
`virus-associated tumors, such as cervical cancer
`and a subset of head and neck cancers, epitopes
`derived from viral open reading frames also con-
`tribute to the pool of neoantigens.
`As compared with nonmutated self-antigens,
`neoantigens have been postulated to be of par-
`ticular relevance to tumor control, as the quality
`of the T cell pool that is available for these an—
`tigens is not affected by central T cell tolerance
`(7). Although a number of heroic studies pro-
`vided early evidence for the immunogenicity of
`mutation-derived neoantigens [reviewed in (8)],
`technology to systemically analyze T cell reactiwty
`against these antigens only became available
`recently. Here, we review our emerging underv
`standing of the role of patient-specific neo—
`antigens in current cancer immunotherapies
`and the implications of these data for the de-
`velopment of next-generation immunotherapies
`
`Exome-guided neoantlgen
`identification: Process considerations
`
`A large fraction of the mutations in human
`tumors is not shared between patients at
`
`
`
`
`sICIENIIHE. sciencemagorg
`
`Obtain tumor material
`
`
`
`Identify tumor-specific
`mutations within
`expressed genes
`
`Filter in silico
`
`Filter by
`MS analysis
`
`Assess T cell recognition
`
`neoantigen
`
`
`
`_.___._._-- __ x).
`
`Fig. 1. Cancer ammo-based identification of
`neoantlgens. Tumor material is analyzed for now
`synonymous somatic mutations. When available.
`RNA sequencing data are used to focus on mu—
`tations in expressed genes. Peptide stretches
`containing any of the identified nonsynonymous
`mutations are generated in silico and are eithe‘r
`left unfiltered (16.17). filtered through the use of
`prediction algorithms [e.g.. (10—13)], or used to
`identify MHC-associated neoantigens in mass
`spectrometry data (15. 20). Modeling of the ef-
`fect of mutations on the resulting peptide—MHC
`complex may be used as an additional filter (20),
`Resulting epitope sets are used to identify phys—
`iologically occurring neoantigen-specific Tcell re-
`sponses by MHC multimer-based screens (13. 22)
`or functional assays [e.g.. (ll. 12)]. within both
`008+ [e.g.. (11-13. 19, 39)] and CD4+ (16, 18) T
`cell populations. Alternatively. T cell induction strat~
`egies are used to validate predicted neoantigens
`[e.g.. (10.20)].
`
`3 APRIL 1015 I VOL 34!!! ISSUE 6230 69
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`

`

`KPH-Chi L SECTION
`
`CANCER IMMUNOLOGYAND IMMUNOTHERAPY
`
`meaningful frequencies and may therefore be
`considered patient—specific. Because of this, tech—
`nologies to interrogate T cell reactivity against
`putative mutation-derived neoantigens need to
`be based on the genome of an individual tumor.
`With the development of deep-sequencing tech-
`nologies, it has become feasible to identify the
`mutations present within the proteinrencoding
`part of the genome (the exome) of an individual
`tumor with relative ease and thereby predict
`potential neoantigens (9). Two studies in mouse
`models provided the first direct evidence that
`such a cancer exome-based approach can be used
`to identify neoantigens that can be recognized
`by T cells (10, II). in brief, for all mutations that
`resulted in the formation of novel protein se—
`quence, potential MHC binding peptides were
`predicted, and the resulting set of potential neo-
`antigens was used to query T cell reactivity. Sub-
`sequent studies have demonstrated that cancer
`exome—based analyses can also be exploited in a
`clinical setting, to dissect T cell reactivity in pa-
`tients who are treated by either honor—infiltrating
`lymphocyte (TELJ cell therapy or checkpoint block-
`ade (12, I3). Furthermore, following this early
`work, the identification of neoantigens on the
`basis of cancer exome data has been docamented
`in a variety of experimental model systems and
`human malignancies (10—22).
`The technological pipeline used to identify
`neoantigens in these different studies has varied
`substanlially, and further optimization is likely pos-
`sible (Fig. 1). Accepting the limitations of probing
`the mutational profile of a tumor in a single biopsy
`(23), the genetic analysis of the tumor itself can be
`considered a robust process. Specifically, based on
`the analysis of neoantigens previously identified
`by other means, the false—negative rate of cancer
`
`
`
`exome sequencing is low—i.e., the vast majority of
`neoantigens occur within exonic sequence for
`which coverage is sufficient (2%). At the same time,
`it is apparent from unbiased screening efforts—in
`which the entire collection of identified muta-
`tions was used to query T cell reactivity—that the
`vast majority of mutations within expressed genes
`do not lead to the formation of neoantigens that
`are recognized by autologous T cells {16, i7). Bernese
`of this, a robust pipeline that can be used for the
`filtering of cancer exome data is essential, in par—
`ticular for tumors with high mutational loads
`How can such filtering be performed? “fith
`the set of mutations within expressed genes as a
`starting point, two additional requirements can
`be formulated. First, a mutated protein needs to
`be processed and then presented as a mutant
`peptide by MHC molecules. Second, T cells need
`to be present that can recognize this peptide—
`MHC complex. In two recent preclinical studies,
`presentation of a handful of predicted neoanti-
`gens by MHC molecules was experimentally dem-
`onstrated by mass spectrometry (15, 20), and this
`approach may form a valuable strategy to further
`optimize MHC presentation algorithms. At the
`same time, the sensitivity of mass spectrometry
`is presently still limited, thereby likely resulting
`in a substantial fraction of false negatives. For this
`reason, but also because of logistical issues, imple-
`mentation of this approach in a clinical setting is
`unlikehr to happen soon. Iacltingdirect evidence for
`MHC presentation, as can be provided by mass
`spectrometry, presentation of neoantigens by MHC
`class I molecules may be predicted using previously
`established algorithms that analyse aspects such as
`the likelihood of proteasomal processing, transport
`into the endoplasmic reticulum, and affinity for
`the relevant MHC class I alleles. In addition,
`
`
`
`gene expression levels [or perhaps preferably
`protein translation levels) may potentially also
`be used to help predict epitope abundance (25).
`Although most neoantigen identification studies
`have successfully used criteria for epitope predic-
`tion that are similar to those previously estab-
`lished for the identification of pathogen-derived
`epitopes [e.g., (12, 13)], Sn'vastava and colleagues
`have argued that neoantigens in a transplantable
`mouse tumor model display very different prop-
`erties from viral antigens and generally have a
`very low affinity for MHC class I (14). Although
`lacking a. satisfactory explanation to remncile
`these findings, we do note that the vast majority
`of human neoantigens that have been identified
`in unbiased screens do display a high predicted
`MHC binding affinity (24-, 26). likewise, minor
`histocompatibility antigens, an antigen class that
`is conceptually similar to neoantigens, are cor-
`rectly identified by classical MHC binding algo—
`rithms (2?). Moreover, the mutations that were
`identified in a recent preclinical study as forming
`timer-specific mutant antigens that could in-
`duce therapeutic tumor rejection when used in
`tumor vaccines (15) were not predicted to be sig-
`nificant using the Srivastava approach. Another
`potential filter step that has been suggested
`examines whether the mutation is expected to
`improve MHC binding. rather than solely alter the
`'1‘ cell receptor (TCR)—exposed surface of the mu-
`tant peptide. However, with examples of both
`categories in both mouse models and human
`data, the added value of such a filter may be
`relatively modest (11, I5, 20, 26). For MHC class
`I restricted neoaniigens, conceivably the biggest
`gain in prediction algorithms can be made with
`respect to identification of the subset of MHC
`binding peptides thatcan succwfullybe recognized
`
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