Patent Application
20240417805
The invention relates to the field of cancer. In particular, it relates to the field of immune system directed approaches for tumor treatment, reduction and control. Some aspects of the invention relate to the identification of tumor specific neoantigens, such as those resulting from frameshift mutations, DNA rearrangements, and splicing mutations. Such neoantigens are useful for developing tumor treatments, such as vaccines or cellular immunotherapies and other means of stimulating a neoantigen specific immune response against a tumor in individuals.
There are a number of different existing cancer therapies, including ablation techniques (e.g., surgical procedures and radiation) and chemical techniques (e.g., pharmaceutical agents and antibodies), and various combinations of such techniques. Despite intensive research such therapies are still frequently associated with serious risk, adverse or toxic side effects, as well as varying efficacy.
There is a growing interest in cancer therapies that aim to target cancer cells with a patient's own immune system (such as cancer vaccines or checkpoint inhibitors, or T-cell based immunotherapy). Such therapies may indeed eliminate some of the known disadvantages of existing therapies or be used in addition to the existing therapies for additional therapeutic effect. Cancer vaccines or immunogenic compositions intended to treat an existing cancer by strengthening the body's natural defenses against the cancer and based on tumor-specific neoantigens hold great promise as personalized cancer immunotherapy. Evidence shows that such neoantigen-based vaccination can elicit T-cell responses and can cause tumor regression in patients.
Typically, the immunogenic compositions/vaccines are composed of tumor antigens (antigenic peptides or nucleic acids encoding them) and may include immune stimulatory molecules like cytokines that work together to induce antigen-specific cytotoxic T-cells that target and destroy tumor cells. Many reports describe vaccination based on somatic SNVs (Single Nucleotide Variants) that lead to single amino acid changes in proteins, and hence encode new antigens (neoantigens) that are specific to the tumor. On average 95% of all protein-altering coding somatic mutations in the ORFeome (i.e. the entire collection of all Open Reading Frame sequences in the genome) of tumors (excluding synonymous or truncating SNVs) are missense SNVs (Single Nucleotide Variants), as based on the tumor mutation reports available for the TCGA database. Neoantigens have also been described in e.g., WO2016/191545, US2016/331822 and WO2021172990.
Much of the research in recent years has focused on the prediction (either in silico or by experimental analysis) of which of these many mutations would make for the best neoantigen to use as a vaccine (Schumacher, T. N., Scheper, W. & Kvistborg, P. Cancer Neoantigens. Annu. Rev. Immunol. 37, 173-200 (2019)). Recent experimental estimates suggest that for about 1.6% of gene products encoded by somatic nonsynonymous single nucleotide variations mutation-specific T-cells can be found in cancer patient samples (Parkhurst et al. Cancer Discov Aug. 1, 2019 9(8) 1022-1035). On average (but widely differing per tumor type, see e.g. Priestley, P. et al. Pan-cancer whole genome analyses of metastatic solid tumors. Nature volume 575, 210-216, 2019) a tumor ORFeome contains 200 missense mutations, and the practical limit of the number of peptide vaccines that can be applied to any patient has been set anywhere between 5 and 20, so that at max only a few percent of the neoantigens caused by missense mutations can be used for vaccination (see, e.g., Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234-239 (2019) and Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217-221 (2017). Therefore, the choice of the “best” SNVs is indeed crucial. In this choice it is usually considered that the peptide containing the SNV-neoantigen needs to be presented by the MHC, so that prediction of the presentation by the MHC-type of the patient is essential. For vaccine technologies other than peptides, such as DNA or RNA encoded vaccines, the number of SNVs to be included in a vaccine may be higher than 5-20, but in none of current approaches is the complete set or even the majority of all neoantigenic amino acid sequences included (Hilf, N. et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature 565, 240-245 (2019)).
Accordingly, there is a need for improved methods and compositions for providing subject-specific immunogenic compositions/cancer vaccines. In particular, there is a need for cancer immunogenic compositions that do not rely on predicting which individual neoantigens will be most effective in vivo. One object of the present disclosure is to take the guesswork out of neoantigen selection by identifying a large part of the tumor antigenicity. A further object of the present disclosure is to provide methods for uncovering neoantigens resulting from splicing mutations and/or neoantigens resulting from mutations of stop codons and the use of said neoantigens as immunogenic compositions/cancer vaccines.
The disclosure provides the following preferred embodiments.
In one aspect, the disclosure provides a method for identifying neoantigen sequences, said method comprising:
In some embodiments, step i) comprises performing short-read whole genome sequencing. In some embodiments, step i) comprises performing long-read whole genome sequencing, instead of or in addition to short-read sequencing, of a tumor sample and a healthy sample from the individual. Preferably, the RNA sequencing is performed using long-read direct RNA sequencing, preferably Nanopore sequencing, or long-read cDNA sequencing. Preferably, the method further comprises performing short-read RNA sequencing on RNA or short-read sequencing on the corresponding cDNA from at least one tumor sample. Preferably, the method further comprises performing consensus sequencing on RNA or the corresponding cDNA from at least one tumor sample, preferably wherein the RNA is poly-(A) selected mRNA and/or 5′ cap containing mRNA. Preferably, the method further comprises selecting poly-(A) mRNA from said tumor sample and performing long-read RNA sequencing or long-read cDNA sequencing based on the poly-(A) selected mRNA.
In one aspect, the disclosure provides a method for identifying neoantigen sequences, said method comprising:
In some embodiments, the method further comprises determining the presence of a mutation in a stop codon, wherein the mutation results in a tumor specific open reading frame. In some embodiments, the somatic genomic changes are selected from single nucleotide variants (SNVs), indels, and structural variants.
In one aspect, the disclosure provides a method for identifying neoantigen sequences, said method comprising:
Preferably, said method detects the presence of a) cis-splicing mutations, wherein the mutation results in a tumor specific open reading frame, b) intragenic frameshift mutations in polypeptide encoding sequences, wherein the mutation results in a tumor specific open reading frame, and c) DNA rearrangements resulting in new junctions of DNA sequences, wherein the DNA rearrangement results in a tumor specific open reading frame. Preferably, the method further comprises determining the presence of a mutation in a stop codon, wherein the mutation results in a tumor specific open reading frame.
In some embodiments of the methods disclosed herein, the DNA rearrangements resulting in new junctions of DNA sequences result in
In some embodiments, the RNA sequencing is performed using long-read direct RNA sequencing, preferably Nanopore sequencing, or long-read cDNA sequencing.
In some embodiments, the method further comprises selecting poly-(A) mRNA from said tumor sample and performing long-read RNA sequencing or long-read cDNA sequencing based on the poly-(A) selected mRNA.
In some embodiments, the method comprises mapping the genomic sequences obtained to a human reference sequence to identify somatic genomic changes in the tumor sample, wherein the somatic genomic changes result in new open reading frames.
In some embodiments, the method comprises generating an in silico reconstructed tumor-specific reference genome. In a particular embodiment, the method comprises:
In a particular embodiment, the method comprises:
The disclosure also provides a method for preparing a vaccine or collection of vaccines for the treatment of cancer in an individual, comprising identifying candidate neoantigen peptide sequences according to any of the preceding embodiments and preparing a vaccine or collection of vaccines comprising peptides having said amino acid sequences or comprising nucleic acids encoding said amino acid sequences.
Preferably, the candidate neoantigen peptide sequences comprise amino acid sequences encoded by cis-splicing mutations as defined above.
Preferably, the candidate neoantigen peptide sequences comprise amino acid sequences encoded by nucleic acid sequences comprising a mutation in a stop codon as defined above.
Preferably, the candidate neoantigen peptide sequences comprise amino acid sequences encoded by:
Preferably, said method for preparing a vaccine or collection of vaccines comprises:
Preferably, said vaccine or collection of vaccines comprises essentially all candidate neoantigen peptides identified, or nucleic acids encoding said peptides.
Preferably, the vaccine or collection of vaccines comprises at least 100 amino acids corresponding to the candidate neoantigen peptide sequences encoded by the new open reading frames.
Preferably, the vaccine or collection of vaccines comprises at least 300 or 400, preferably at least 1000, amino acids corresponding to the candidate neoantigen peptide sequences encoded by the new open reading frames.
Preferably, the cancer is not micro-satellite instable (MSI).
In a preferred embodiment, the invention provides a vaccine or collection of vaccines for the treatment of cancer, obtainable by a method as disclosed herein.
In a preferred embodiment, the invention provides a vaccine or collection of vaccines for use in the treatment of cancer in an individual. Methods are also described for treating cancer comprising administering to an individual in need thereof a vaccine or collection of vaccines as disclosed herein and/or as obtainable by a method as disclosed herein.
The invention further provides a vaccine or collection of vaccines for the treatment of cancer wherein the vaccine comprises a neoantigen peptide, or nucleic acid encoding said neoantigen peptide. Preferably, the vaccine or collection of vaccines are obtainable by a method as disclosed herein. In some embodiments, the vaccine comprises at least two different neoantigen peptides. In some embodiments, the at least two different neoantigen peptides are linked, preferably wherein said peptides are comprised within the same polypeptide.
The invention further provides methods of treating an individual in need thereof with said vaccines. In particular, methods for the treatment of cancer are provided comprising administering to an individual in need thereof a vaccine or collection of vaccines as disclosed herein.
In a preferred embodiment the neoantigen peptide or collection of neoantigen peptides can serve as a bait to select or to identify T-cells isolated from a cancer patient, or to stimulate said T-cells. In one aspect the disclosure provides a method for preparing a cellular immunotherapy for the treatment of cancer in an individual, said method comprising contacting T-cells with the candidate neoantigen peptide sequences identified from the individual according to any one of the methods described herein. Preferably, the neoantigen peptide is bound to an MHC-I molecule. In some embodiments, the T-cells are obtained from said individual. In some embodiments, contacting T-cells with the candidate neoantigen peptide sequences results in the stimulation of the T-cells. In some embodiments, the method comprises selecting T-cells having specificity for one or more of said neoantigen peptide sequences. In some embodiments, the method further comprises the in vitro expansion of the stimulated and/or selected T-cells. In some embodiments, the methods may further comprise the isolation of a T-cell receptor or a collection of T-cell receptors with specificity for one or more of said neoantigen peptide sequences.
The disclosure also provides the following preferred embodiments.
As used herein, the term “open reading frame” or ORF refers to a nucleic acid sequence comprising or encoding a continuous stretch of codons. As used herein the term “neoORF” refers to a tumor-specific open reading frame (i.e., novel open reading frame) arising from a somatic genomic change (i.e., mutation) including point mutations; indels; and DNA rearrangements, in particular structural variants. Such neoORFs are not present in the germline and/or healthy cells of an individual. Peptides arising from such neoORFs are referred to herein as neoantigens or ‘Frames’. The methods described herein have been developed, at least in part, in order to maximize the number of neoantigen amino acids identified from the tumor of an individual. As used herein, the term ‘Framome’ refers to all, or essentially all, of the neoORFs that result from somatic genetic changes as described herein (e.g., frameshift mutations, genomic rearrangements, splicing mutations, mutation of stop codon) that can be identified in a tumor sample using whole genome sequencing.
As used herein the term “sequence” can refer to a peptide sequence, DNA sequence or RNA sequence. The term “sequence” will be understood by the skilled person to mean either or any of these and will be clear in the context provided. For example, when comparing sequences to identify a match, the comparison may be between DNA sequences, RNA sequences or peptide sequences, but also between DNA sequences and peptide sequences. In the latter case the skilled person is capable of first converting such DNA sequence or such peptide sequence into, respectively, a peptide sequence and a DNA sequence in order to make the comparison and to identify the match. As is clear to a skilled person, when sequences are obtained from the genome or exome, the DNA sequences are preferably converted to the predicted peptide sequences. In this way, neo open reading frame peptides are identified. The neoantigens can include a polypeptide sequence or a nucleotide sequence encoding said polypeptide sequence.
As used herein the term “sample” can include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from an individual, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art. The nucleic acid for sequencing is preferably obtained by taking a sample from a tumor of the patient. The skilled person knowns how to obtain samples from a tumor of a patient and depending on the nature, for example location or size, of the tumor. Preferably the sample is obtained from the patient by biopsy or resection. The sample is obtained in such manner that it allows for sequencing of the genetic material obtained therein. The biological material from multiple samples may also be used and/or pooled. As used herein, a sample may also be referred to as a biological sample. The sample may be from a tumor (or comprise tumor cells or tumor DNA).
The sample may also be a healthy sample from healthy tissue, i.e., a non-tumorous sample.
The term ‘individual’ includes mammals, both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines. Preferably, the mammal is a human.
Cancer emerges because of mutational processes that affect the genome of cancer cells (Alexandrov and Stratton, Curr Opin Genet Dev 2014 February; 24(100):52-60). Various types of genetic mutations arise from such mutational processes, such as point mutations, short insertions and deletions and large structural genomic rearrangements. Mutations in cancer genomes may alter the amino acid composition of proteins, thereby leading to formation of neoantigens, which can represent tumor-specific antigens that can be recognized by the immune system and form a target of immunotherapy (Annual Review of Immunology Volume 37, 2019 Schumacher, pp 173-200).
A typical neoantigen is formed by a non-synonymous point mutation in a coding exon, which changes one amino acid of a protein to another amino acid (
The methods described herein identify neoantigen sequences. The use of neoantigen sequences for therapy has been described (e.g., WO2016/191545 and US2016/331822). However, the present methods determine the presence of single nucleotide variants (SNVs), indels, and structural variants that result in tumor specific open reading frames. In particular, the methods comprise determining the presence of cis-splicing mutations, determining the presence of intragenic frameshift mutations, determining the presence of DNA rearrangements and determining the presence of a mutation in a stop codon; wherein the mutations result in a tumor specific open reading frame. The methods combine whole genome sequencing and long-read RNA sequencing
Neoantigens resulting from structural variants, such as frameshifts and “Hidden Frames” are known from WO2021172990 (see, e.g.,
Neoantigens Resulting from Mis-Splicing of mRNA
Recent work has described yet another category of neoantigens, which are a consequence of genetic mutations that alter splice donor and acceptor sites, thereby giving rise to novel (alternatively spliced) transcripts. Mis-splicing mutations have been comprehensively described by Jung et al (Oncogene volume 40:1347-1361 (2021)), who used a combination of whole-genome sequencing (WGS) and short-read transcriptome sequencing (RNA-seq) to classify the effects of genetic mutations on splicing. Related work has characterized splicing-associated genetic variants (SAVs) based on exome sequencing (Shirashi Genome Res. 2018 August; 28(8): 1111-1125). However, both of these studies merely described the splicing effects of mutations, without recognizing the neoantigenic potential of mis-splicing. Possible neoantigenic effects of mis-splicing mutations have been described in related work by Jayasinghe et al (Cell Rep 2018 Apr. 3; 23(1):270-281 reviewed in Smith et al. (Nature Reviews Cancer 2019 19:465-478)) which made use of a set of 8,656 whole-genome (WGS) and transcriptome sequencing data (short-read RNAseq) to characterize splice-creating mutations. Splice-creating mutations were identified using a novel bioinformatic tool (MiSplice, https://github.com/ding-lab/misplice), which integrates mutation data (from whole genome sequencing) and short-read RNA sequencing data and searches for alternative splice-junctions in the vicinity of genetic mutations. In this work, the prediction of neoantigenic peptides derived from novel splice-junctions is based on the transcript structures available in the RefSeq database. However, the actual expressed transcripts are not taken into account, leading to uncertainties and possible errors in the in silico translation process.
A Novel Approach for Detection of Neoantigens from Mis-Splicing Causing Mutations
Short-read RNA sequencing technology only provides a local view on transcript structure, which is mostly restricted to accurate measurement of the connection between consecutive exons. However, from such individual exon-exon connections, the entire structure of a transcript cannot be reliably determined (Hardwick et al, Front. Genet., 16 Aug. 2019). Given the complex and diverse patterns of transcript isoforms that are expressed in human tissues, an end-to-end sequence of the entire structure of a transcript is required to predict the translated sequences that may emerge from aberrant (mis-spliced) transcripts resulting from genetic mutations in cancer cells. Herein, we propose to use long-read cDNA or mRNA sequencing to detect neoantigens derived from genetic mutations that cause mis-splicing of transcripts in tumor cells. Such mutations also include SNVs resulting in the generation of neoantigens.
In one aspect, the disclosure provides a method for identifying candidate neoantigen sequences (“Frames”). The neoantigen sequences are identified from a tumor sample of an individual afflicted with cancer. As described further herein, such neoantigens may be used to prepare a vaccine or other form of immunotherapy for the treatment of cancer.
There are two major advantages of using the Framome, i.e. the entire collection of Frames expressed by a tumor, as target of therapeutic anti-cancer vaccines or other forms of immunotherapy. Firstly, Frames are presumed to be the most antigenic neoantigens encoded by tumor genomes as compared to SNV-antigens. As used herein, the term “SNV-antigen” refers to antigens having a single amino acid change. If the potential antigenicity of a tumor were to be expressed as the number of newly encoded amino acids, the Framome covers much, if not the majority of all antigenicity (see FIG. 2, FIG. 9, and FIG. 10 of WO 2021/172990), and thus largely takes the selection process for the best possible neoantigens out of vaccine or immunotherapy development.
Secondly, Frames have an additional advantage over SNV-antigens in regards to HLA-restriction. Small peptides containing a single amino acid change will be presented within the MHC with only few options for a productive presentation, and thus the precise fit of the chosen peptide within the MHC of the specific HLA type of the patient is a point of serious attention. For long viral antigens it has long been concluded that such concern about HLA-matching is of less importance, since the long and entirely foreign (non-self) sequence will be degraded by the proteasome in so many different ways that along the full length of the neoantigen there will always be stretches that match and are thus productive antigens. This also applies to Frames, which are in this respect no different than e.g. the HPV16 and HPV-17 antigens encoded by the Human Papilloma Virus, and which are used successfully for anti-tumor vaccination (Massarelli et al. JAMA Oncol 2019 5:67-73).
While cancer-specific frameshift mutations and SNV-antigens have previously been described, one object of the disclosure is to identify a larger source of potential neoantigens. This includes, e.g., Frames derived from SNVs. Such mutations may, e.g., cause mis-splicing of transcripts in tumor cells or mutate a stop codon, resulting in a tumor specific open reading frame. The present disclosure is not concerned with neoantigens comprising a single amino acid difference resulting from a SNV (i.e., “SNV-antigens”). Rather, only SNVs that result in the expression of novel Frames are encompassed by the present disclosure.
The methods comprise identifying somatic genomic changes in nucleic acid sequences from at least one tumor sample from the individual, wherein the somatic genomic changes result in new open reading frames. In particular, the methods may comprise determining the presence of single nucleotide variants (SNVs), indels, and structural variants that result in tumor specific open reading frames. It is clear to a skilled person that determining the presence of SNVs also includes determining multiple SNVs, or rather multi-nucleotide variants (MNVs). A number of different mechanisms by which genomic mutations can lead to the encoding of novel neoantigen sequences are discussed below.
Newly synthesized eukaryotic mRNA molecules (i.e., pre-mRNA) are processed by the addition of a 5′ methylated cap and a 3′ poly(A) tail and introns are removed by splicing to form the mature mRNA sequence. Splice junctions are also referred to as splice sites with the 5′ side of the junction often called the “5′ splice site,” or “splice donor site” and the 3′ side the “3′ splice site” or “splice acceptor site.” Donor and acceptor sites are evolutionary conserved and are usually defined by GT and AG nucleotides at the 5′ and 3′ ends of the intron, respectively. After an intron is removed, the exons are contiguous at what is sometimes referred to as the exon/exon junction or boundary in the mature mRNA.
Mutations leading to splice aberrancies of mRNA can be formed by any type of genomic alteration that is found in the genome of cancer cells, e.g., Single nucleotide variants (SNVs), Structural variants (SVs), Short insertions and deletions (indels), and Multi-nucleotide variants (MNVs). Splicing mutations may occur in either introns or exons and may, e.g., disrupt existing splice sites, create new splice sites, or activate cryptic splice sites. In a preferred embodiment, a splice mutation occurs within the coding region (including introns and exons) of a gene. Splice mutations may potentially also occur downstream of the stop codon or at the 3′-end of the gene. Such mutation may induce novel splicing from transcription that continues past the gene 3′ end (read through transcription). Mutations at donor and acceptor sites as well as within 20 nucleotides of said sites are a large source of splicing-mutations. Mutations occurring more than 20 bp away from the nearest intron/exon junction are referred to herein as “deep intronic mutations”. While most deep intronic mutations are silent, some affect canonical and auxiliary splicing cis-elements or generate cryptic GT-AG dinucleotides.
Whether a mutation is directly causal to a splice aberrancy (i.e. a cis-effect), is primarily determined by the genomic proximity of the mutation to the mis-spliced RNA junction. Hence, a combination of whole genome sequencing and RNA (or cDNA) sequencing is required to effectively identify mutations causing mis-splicing, as well as the exact effects of the mis-splicing on mRNA structure. Herein, we characterize three types of exemplary mutations causing mis-splicing:
Firstly, small mutations (e.g., indels, SNVs, MNVs) that result in gain of a splice site (gain-of-splice [GOS] mutations,
Secondly, small mutations (indels, SNVs, MNVs) that result in loss of a splice site (loss-of-splice [LOS] mutations,
Both GOS and LOS mutations have been described in prior work (Jung et al, Oncogene volume 40, pages 1347-1361 (2021); Jayasinghe et al, Cell Reports VOLUME 23, ISSUE 1, P270-281.E3, Apr. 3, 2018; Shiraishi et al, Genome Research 2018 August, 28(8): 1111-1125).
Thirdly, structural variants (SVs) that result in the rearrangement of splice sites and/or the exon-intron structure of a mRNA (
Exemplary algorithms for identification of mutations causing mis-splicing of mRNA and the neoantigenic sequences caused by such mutations are described below. A skilled person recognizes that other methods or variations of the method may also be used. As will be appreciated by a skilled person, such algorithms involve the use of computers and computer programs.
For gain-of-splice-site (GOS) mutations the preferred steps in the algorithm (herein referred to as A1) can be described as follows.
For loss-of-splice-site (LOS) mutations the preferred steps in the algorithm (herein referred to as A2) can be described as follows.
For structural variants (SVs) in the tumor genome, the effects on mRNA splicing can be diverse and not readily predicted based on the mutation in the DNA. An exemplary algorithm (herein referred to as A3) to detect splicing aberrancies caused by SVs can be defined as follows:
The methods described herein may also be used to identify intragenic frameshift mutations in polypeptide encoding sequences, wherein the mutation results in a change of the reading frame of said polypeptide encoding sequence. Such neoantigens (i.e., Frames) result from insertions and deletions within coding exons of a single gene. As is well-known to a skilled person, a “frame shift mutation” is a mutation causing a change in the frame of the protein, for example as the consequence of an insertion or deletion mutation (other than insertion or deletion of 3 nucleotides, or multitudes thereof). Such frameshift mutations result in new amino acid sequences in the C-terminal part of the protein. These new amino acid sequences (encoded by the new open reading frame) generally do not exist in the absence of the frameshift mutation and thus only exist in cells having the mutation (e.g., in tumor cells and pre-malignant progenitor cells).
Frameshift mutations can be identified based on the exome from the tumor, although whole genome sequencing may be preferred. Expression of relevant Frames resulting from frameshift mutations can be determined by RNA sequencing. Exemplary methods for identifying frameshift mutations and identifying neoantigens resulting from said mutations are also described in WO2021/172990.
Another type of mutation that leads to novel Frames are mutations in stop codons. For example, a SNV can result in the mutation of a stop codon to a codon encoding an amino acid (
Such mutations can be identified based on the exome from the tumor, although whole genome sequencing may be preferred. Expression of relevant Frames resulting from such mutations can be determined by RNA sequencing. Expression analysis involves the identification of the stop codon mutation in individual (long) poly-adenylated and 5′-capped transcript reads. Those transcript reads containing the stop codon mutation are then subjected to in silico translation as outlined herein.
Another type of mutation that leads to novel Frames are DNA rearrangements, in particular structural variations. SVs may result in DNA gain (e.g., copy number variations, such as tandem duplications), DNA loss (e.g., deletions which may disrupt gene function), as well as balanced rearrangements that do not involve loss or gain of chromosomal sequence (e.g. inversions, reciprocal translocations). Each of the possible SV types may possibly lead to new open reading frames.
We have found that in many cases during transcription of a ‘proper’ gene that spans a genomic breakpoint-junction which connects the gene to another piece of the genome, the transcription machinery will seek and find a preferred place for transcription termination and polyadenylation of the RNA and the splicing machinery will seek and find splice sites. The result is a fully processed and translatable mRNA, complete with 5′-CAP and poly-(A)-tail. In our results, we observe that there is often either one or only a few dominant mRNA variants that emerge from the process of transcription across somatic genomic breakpoint-junctions and RNA-processing. These variants result in new open frames and are a large source of tumor antigenicity.
One type of structural variant refers to DNA rearrangements resulting in new junctions of DNA sequences, wherein the rearrangement results in the fusion of at least part of the coding strand of a first gene to at least part of the coding strand of a second gene. Alternatively, the rearrangement results in an intragenic rearrangement, such as an intragenic deletion or (tandem) duplication, thereby creating an intra-genic fusion, between the upstream (5′) part of a gene and the downstream (3′) part (including the poly-(A) signal). In particular, the DNA rearrangement results in a change of the reading frame of a polypeptide encoding sequence, herein referred to as ‘out of frame gene fusions’.
In some embodiments, such mutations result in the fusion of at least part of the coding strand of a first gene to at least part of the coding strand of a second gene (i.e., intergenic genomic rearrangement). The reading frames of the first and second gene are different at the position of the junction in the mRNA, resulting in a novel open reading frame. Such mutations may result from various DNA rearrangements including but not limited to inversions, deletions, or translocations. As is understood by a skilled person, the coding strand (i.e., sense strand) of a gene is the strand comprising the sequence corresponding to the mRNA sequence. Out of frame gene fusions may encode the entire protein corresponding to the first gene or only a part thereof. The out of frame fusion with the coding strand of the second gene results in a Frame (i.e., neoORF). Given that for most genes the introns are much larger than the exons, in some embodiments the mutation results from the fusion of two genes with a genomic junction that maps for each gene within an intron. If splicing were to proceed using the splice sites of the parental genes, the splice product may fuse the downstream partner within the frame of the upstream partner, which can lead to a neoORF. In preferred embodiments, the mutations result in a nucleic acid sequence encoding an mRNA comprising a start codon encoded by the first gene and a poly-(A) signal encoded by the second gene.
In some embodiments, the mutations are intragenic genomic rearrangements which result in a neoORF. For example, such mutations may lead to the fusion of exons of the same gene having different reading frames. Intragenic genomic rearrangements are known to a skilled person and include, but are not limited to, intragenic deletions, intragenic tandem duplications, intragenic dispersed duplications, intragenic inverted duplications, intragenic insertions, and intragenic inversions.
In some embodiments, the said intragenic genomic rearrangements lead to a rearrangement of the natural exon-intron structure of a known gene in the human genome. There are multiple types of rearrangements that can affect the proper splicing of a gene, including, for example, deletions, duplications, inversions, and insertions.
Another type of structural variant refers to DNA rearrangements resulting in new junctions of DNA sequences, wherein the rearrangement results in the fusion at least part of the coding strand (most often an intronic sequence, but exonic or other sequence is also possible) of a first gene to a second sequence selected from intergenic non-coding DNA or to the noncoding strand of a second gene. The fusion results in the coding strand of the first gene being 5′ of the second sequence. Unlike ‘out of frame fusions’ mutations discussed above which fuse two genetic sequences having the same orientation (i.e., the coding strands from two genes are fused), ‘Hidden Frame’ mutations refer to the fusion of a first gene with a second sequence that does not encode for a gene or does not encode for a gene in the same orientation as the first gene. We refer to these neoantigens as “Hidden Frame Neoantigens” since they cannot be accurately predicted based solely on the genomic DNA sequence because the transcription termination and splicing after fusion of two DNA segments is inherently unpredictable.
This second sequence may be (intergenic) non-coding DNA. (Intergenic) non-coding DNA includes DNA which is not predicted to encode a protein. Such non-coding DNA includes repetitive DNA, as well as DNA that regulates expression (e.g., promoters, enhancer elements, etc) and DNA that encodes non-coding RNA (ncRNA). ncRNA refers to RNA that is not translated into protein and includes tRNA, rRNA, microRNAs, etc. See, e.g., FIG. 8 of WO2021/172990 as an exemplary embodiment. The second sequence may be the noncoding strand of a second gene.
See Example 7 of WO2021/172990, which is incorporated by reference herein, for an exemplary embodiment of for carrying out the FramePro method for identifying tumor neoantigens.
In preferred embodiments, the Hidden Frame mutations result in a nucleic acid sequence encoding an mRNA comprising a start codon encoded by the first gene and a poly-(A) signal encoded by the second sequence. The poly-(A) signal encoded by the second sequence may also be referred to as a ‘cryptic’ polyadenylation signal since the poly-(A) signal (without the mutation) is not normally associated with mRNA or a protein encoding sequence.
Another example of a Hidden Frame is the result of a genomic rearrangement outside of a gene resulting in the change of the genomic sequences flanking the 3′ end of a gene. The altered genomic sequences flanking the 3′ end of a gene may contain cryptic splicing signals, which lead to new mRNA structures. In such an embodiment, the SV breakpoint resides downstream of the stop codon, e.g. within 100 kb downstream of the stop codon. Such rearrangement fuses the coding strand of a first gene to a second sequence. The second sequence may be any sequence, e.g., intergenic non-coding DNA or the coding or noncoding strand of a second gene. The mutation results in novel splicing and the expression of a tumor specific open reading frame. An example of such Hidden Frames is depicted in
As is known to a skilled person, messenger RNA is polyadenylated with the addition of a 3′ poly-(A) tail. The poly-(A) tail is involved in a number of processes including nuclear export and protein translation. Polyadenylation signals near the 3′ end of mRNA direct the cell machinery to add a poly-(A) tail. The most common polyadenylation signal on the RNA is AAUAAA. However other variants also exist.
The sequences of such signals and methods for identifying such signals in nucleic acid sequences are well-known in the art and can be predicted by a number of different in silico methods. For example, the genomic sequence of the non-coding second sequence may be analyzed by a sequencing method, such as Illumina sequencing, or the like. In a second step the entire sequence assembled from individual sequencing reads may be screened in silico for the presence of known polyadenylation motifs/signal, e.g. using pattern matching, such as regular expressions, known by persons skilled in the art. Alternatively, one can experimentally test the presence of a poly-(A) tail at the 3′ end of an mRNA, by selecting the mRNAs by binding them to polyT oligonucleotides and removing all non-bound RNA. Using such selected mRNAs for high-throughput sequencing, preferably long-read sequencing, for example Nanopore sequencing, one can determine the sequences of all polyadenylated mRNAs in a tumor specimen or tumor cell. In preferred embodiments, the methods comprise selecting poly(A)-RNA. Such methods do not require a priori any knowledge of whether the corresponding encoding nucleic acid sequence comprises a poly(A) signal.
As is known to a skilled person, messenger RNA normally comprises a five-prime cap (5′ cap). In eukaryotes, mRNA is “capped” at the 5′ end with 7-methylguanylate during transcription. Methods for selecting and enriching for 5′ capped RNA are known in the art. For example, the TeloPrime Full-Length cDNA Amplification Kit V2 from Lexogen uses Cap-Dependent Linker Ligation (CDLL) and long reverse transcription (long RT) technology to select full-length RNA molecules that are both capped and polyadenylated. Other methods include the use of a mRNA 5′ Cap Structure Affinity Column Preparation as described in U.S. Pat. No. 6,187,544B1.
A skilled person will recognize that all classes of mutations discussed above may not be present in a particular tumor or that not all classes of mutations will be represented in the RNA of a tumor sample. However, the methods are suitable for identifying the presence or absence of such mutations.
Neoantigens resulting from many of the classes of mutations described above cannot be predicted based solely on the DNA sequence. This is particularly relevant for neoantigens resulting from structural rearrangement. In a preferred embodiment, the method of the disclosure combines whole genome sequences with whole full-length transcriptome sequencing (in order to obtain the full-length sequence of intact mRNA). Preferably, the method uses three datasets:
In some embodiments, the candidate neoantigen sequences described herein may be identified by a method, comprising
A skilled person can readily identify genomic changes in a sequence. While partial sequencing/targeted/exome sequencing is often used on tumor tissue, such methods primarily identify single nucleotide variants (SNVs), or other small genetic variations present in (protein) coding sequences of the genome. In contrast, the present methods rely on whole genome sequencing.
In order to determine whether such genomic changes are somatic, the sequences obtained from the tumor sample can be compared to sequences from non-tumor tissue (also referred to herein as a “healthy sample”) of the patient, e.g., blood. The comparison of tumor sequences and sequences from non-tumor tissue are often compared via mapping of the sequences to a human reference genome, as is known by a person skilled in the art.
In some embodiments, the method further comprises performing whole genome sequencing of a healthy sample (i.e., a non-tumorous sample) from the individual. Whole genome sequencing is generally performed using a short-read sequencing library (e.g., shotgun sequencing with paired-end sequencing reads of 2×150 bp). In preferred embodiments, the method comprises performing long-read whole genome sequencing on the tumor sample, either alone or preferably in combination with short-read whole genome sequencing. Long-read sequencing is especially useful for tumors having complex genomic rearrangements. Long-read sequencing may also be used to sequence a healthy sample. As described further herein, long-read sequencing methods are often referred to as “third generation sequencing” and include systems from Pacific Biosciences and Oxford Nanopore technologies. As a skilled person will recognize, when using highly accurate long-read sequencing techniques, short-read sequencing is redundant.
The methods identify somatic genomic changes that result in new open reading frames. The new open reading frames are not present in the germline genome of the individual. In some embodiments, the methods comprise comparing the nucleic acid sequences from at least one tumor sample with reference sequences. Sequence comparison can be performed by any suitable means available to the skilled person. Indeed, the skilled person is well equipped with methods to perform such comparison, for example using software tools like BLAST and the like, or specific software to align short or long sequence reads.
In some embodiments, the reference sequences are obtained from sequencing healthy tissue from said individual. A comparison of the sequences between a tumor sample and healthy tissue will identify somatic genomic mutations present in the tumor sample. This comparison often makes use of a comparison of the tumor and the healthy tissue sample to a reference human genome sequence (GRCh37, GRCh38, or the like). The differences with respect to the reference human genome sequence are subsequently compared between tumor and healthy tissue. This provides a list of genetic changes that solely occur in the tumor genome, often referred to as somatic genetic changes. In some embodiments, the reference sequence is a human reference genome such as GRCh37 (the Genome Reference Consortium human genome (build 37) date of release February 2009) or GRCh38 the Genome Reference Consortium human genome (build 38) date of release December 2013.
Analysis of sequence reads and identification of mutations will occur through standard methods in the field. For sequence alignment, aligners specific for short or long reads can be used, e.g. BWA (Li and Durbin, Bioinformatics. 2009 Jul. 15; 25(14):1754-60) or Minimap2 (Li, Bioinformatics. 2018 Sep. 15; 34(18):3094-3100). Subsequently, mutations can be derived from the read alignments and their comparison to a reference sequence using variant calling tools, for example Genome Analysis ToolKit (GATK), MuTect, Varscan, and the like (McKenna et al. Genome Res. 2010 September; 20(9):1297-303), which are often used for identification of short insertions and deletions (indels) or single nucleotide variations. Specific software is available for using read alignments for identification of large structural genomic rearrangements, including but not limited to deletions, duplications, inversions, insertions and translocations. An example of such software is GRIDSS, which uses split-read and read-pair mappings and retrieves the sequences of genomic rearrangement breakpoint-junctions through assembly of discordantly mapping sequence reads (Cameron et al. Genome Res 2017 27:2050-2060). Other existing software tools are Delly (Rausch et al. Bioinformatics 2012 28:i333-i339), or Manta (Chen et al. Bioinformatics 2016 32:1220-2), which are based on similar principles. An overview of the methods to identify genomic rearrangements in cancer genomes can be found in the paper by Kosugi et al (Kosugi et al. Genome Biol 2019 20:117). Following the identification of breakpoint-junctions of genomic rearrangements, one can perform an annotation step to identify Frames, i.e. determining the effects of the genomic rearrangement on the protein sequences, using known information on gene structure, transcript sequences, as available in e.g. the Ensembl database (http://www.ensembl.org/index.html). Methods for annotation of indels and genomic rearrangements resulting in frameshift neoORFs and out of frame fusions are (for example) Annovar (Wang et al. Nucleic Acids Res 2010 38:e164) or Integrate-Neo (Zhang et al. Bioinformatics 2017 33:555-557).
A preferred method for identification of neoantigens, in particular Frames resulting from SVs, comprises the in silico reconstruction of rearranged genomic regions and resulting mRNA sequences by using whole genome sequencing, or more preferably a combination of whole genome sequencing and RNA sequencing. In some embodiments the method uses a combination of whole genome sequencing and ribosome profiling and RNA sequencing, or a combination of whole genome sequencing, long-read whole genome sequencing and ribosome profiling and short-read RNA sequencing and long-read RNA sequencing. An approach for analysis of the neoantigens based on such sequencing data, then may involve the following steps, or variations of these steps:
The identification of neoantigens can be difficult if the identification method only makes used of DNA sequencing, in particular if a new junction is in the mature mRNA is created by a novel splicing event. In many cases it is not possible to predict the neoantigen based solely on the DNA sequence.
For example, Hidden Frames cannot be predicted based solely on DNA sequence using standard methods. The resulting Frame will depend not only on the DNA rearrangement (i.e., structural variation) but also on the splicing machinery. The vast majority of DNA rearrangements occur in non-coding DNA, e.g., in the non-coding region of a gene (e.g., an intron). The sequences immediately surrounding the rearrangement junction will therefore normally not correspond to the splicing junction in the resulting mRNA and will normally not be present in the resulting corresponding mRNA. Similarly, Splicing Frames (resulting from point mutations, indels, or SVs) are also difficult to predict as the resulting Frames also depend on the splicing machinery. In order to address these problems, the methods provided herein comprise both whole genome sequencing as well as long-read RNA sequencing.
General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al. (1997) Current Protocols of Molecular Biology, John Wiley and Sons. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp & Locker (1987) Lab Invest. 56:A67, and De Andres et al., BioTechniques 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions (QIAGEN Inc., Valencia, Calif.). For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Numerous RNA isolation kits are commercially available and can be used in the methods of the invention.
Preferably, the RNA isolated for sequencing is cytosolic RNA that is not tRNA or rRNA. Preferably, the RNA is poly-(A)RNA. Methods for selecting poly-(A) RNA are known to a skilled person and include mixing total RNA with poly-(T) oligomers and retaining only the RNA that is bound to the poly-(T) oligomers. Preferably, the RNA is selected for having a 5′-CAP. More preferably, the RNA is selected for having a 5′-CAP and a 3′-poly-(A) tail (
In some embodiments, the RNA is reversed transcribed to cDNA and the cDNA is sequenced. In some embodiments direct RNA sequencing is performed. “RNA sequencing” and “RNA sequences” as used herein encompass both direct RNA sequencing and cDNA sequences from the corresponding RNA.
While second-generation (or short-read) sequencing provides highly accurate sequence information, in some cases it can be difficult to correctly annotate longer stretches of sequences, in particular when such sequences involve repetitive elements or complex rearrangements. Long-read sequencing has the advantage that longer stretches of nucleic acid can be sequenced. The methods of the disclosure comprise performing long-read RNA sequencing on RNA or long-read sequencing on the corresponding cDNA. Preferably, long-read sequencing methods are also used to determine DNA sequence. Such methods are often referred to as “third generation sequencing” and include systems from Pacific Biosciences and Oxford Nanopore technologies.
Long read sequencing offers the advantage that the structure of the entire mRNA molecule can be determined. Determining the full-length structure of mRNA molecules resulting from the genomic mutations is useful for identifying Frame neopeptide sequences. This is especially useful for complex rearrangements as well as mutations affecting splicing. For example, the splicing pattern of a gene depends on the structure of the primary transcript. Preferably, long read sequencing is used to confirm the splicing events for gene fusions. In regards to Hidden and Splicing Frames, long read sequencing is preferably also used to confirm that a polyadenylated RNA is produced, and to determine possible (cryptic) splicing patterns. Long-read sequencing is also useful to confirm that the mRNA is not subject to extensive non-sense mediated decay. Long read sequencing is preferably also used to confirm the poly-adenylation of RNA products containing stop loss Frames.
Preferably, the long-read molecules that are sequenced are at least 300 nucleotides in length, more preferably at least 500 nucleotides in length, more preferably covering the full-length mRNA molecules for each expressed gene in a tumor sample. To obtain molecules for long read sequencing the RNA is generally not fragmented during isolation and purification. Methods for sequencing long-read RNA molecules are well-known in the art and are disclosed in publications such as Tilgner, H. et al., Proc. Nat'l Acad. Sci., USA, 111(27):9869-9874 (2014), Tseng, E. and Underwood, J., J. Biomol. Techniques., 24 Supplement:545 (2013), Sharon, D., et al., Nature Biotech. 31(10):1009-1014 (2013), Pan. Q., et al., Nature Genetics, 40:1413-1415 (2008), Steijger, T., et al., Nature Methods, 10:1177-1184 (2013) and U.S. Pat. Nos. 8,192,961, 8,501,405 and 8,940,507, all of which are incorporated by reference. Similar methods are useful for long-read whole genome sequencing (see also Logsdon, Nature Reviews Genetics 2020). Preferably, long-read single molecule DNA and/or RNA sequencing technologies are used in the present methods. Such methods can generate reads of at least 1 kb even tens to thousands of kilobases in length. The accuracy of such methods is constantly improving and, as a skilled person will appreciate, if highly accurate long-read sequence data is available, then short-read sequencing is redundant.
To improve the quality of the long RNA sequences, multiple approaches known to the skilled person may be used.
In one approach, the RNA sequencing preferably includes short-read cDNA sequencing, in addition to the long-read RNA/cDNA sequencing. The short-read RNA sequences are used in subsequent analytical steps to remove errors inherent to single-molecule long-read sequencing. In some embodiments, short-read sequencing methods such as sequencing-by-ligation (SBL) and sequencing-by-synthesis (SBS) are used. Generally, such short-read sequencing methods provide read lengths of around 100-200 bases. These methods are also referred to as second-generation sequencing or Next-generation sequencing.
In another approach, the long-read RNA sequencing may include consensus sequencing, i.e. repeated sequencing of the same molecule and determining a consensus sequence from the repeatedly sequenced copies. For example, long-read sequencing on a Pacific Biosciences sequencer enables Circular Consensus Sequencing (CCS), which involves repeated sequencing of the same template DNA molecule (or cDNA molecule). The repeated sequences can be collapsed to generate a highly accurate consensus sequence, which reaches a sequence accuracy competitive with short-read (RNA) sequencing methods. Circular consensus sequencing involves the generation of long sequence reads with (inverted) tandemly repeated copies of the original transcript molecule. Such concatemer reads can be used to generate a high-quality consensus sequence. Examples of such approach are described in e.g. Wenger et al, Nature Biotechnology volume 37, pages 1155-1162 (2019). Generation of high-quality mRNA transcript reads with such approach have been described (see review by Byrne et al, Philos Trans R Soc Lond B Biol Sci. 2019 Nov. 25; 374(1786): 20190097). Similar consensus sequencing approaches from long reads with repeated copies have been described in combination with Nanopore sequencing (Gigascience 2016 Aug. 2; 5(1):34 and Nucleic Acids Res 2021 Jul. 9; 49(12):e70).
An alternative approach for consensus sequencing involves the use of Unique Molecular Identifiers (UMIs) coupled to each unique and original DNA (or mRNA or cDNA) molecule. A library of nucleic acid molecules tagged with UMI sequences is subsequently amplified by PCR or the like, thereby producing copies of each unique molecule in the library. Following deep long-read sequencing of the amplified library with nucleic acid sequences each containing a UMI, the resulting sequence reads can be clustered based on the presence of (near) identical UMI sequence. The clusters of sequences are then collapsed into a single consensus sequence with higher accuracy than each of the individual sequence reads within the cluster. An example of UMI-based long-read consensus sequencing has been described by Karst et al, Nature Methods 18, page 165-169 (2021).
Once highly accurate consensus sequences are obtained, each individual consensus read (which corresponds to a single mRNA or cDNA molecule) can be directly translated.
Methods provided herein preferably comprise determining the sequences of full-length RNA transcripts encoded by nucleic acid sequences comprising (or overlapping with) the somatic mutations (e.g., DNA rearrangements or splicing mutations). As is clear to a skilled person, sequences immediately surrounding the DNA rearrangement junction will normally not be represented in the full-length RNA transcripts.
In an exemplary embodiment, a method, referred to herein as ‘FramePro’ or ‘reconstructed tumor genome mapping’, comprises the generation of a tumor-specific human reference genome, based on somatic and germline structural genome variations identified in a tumor sample, followed by mapping of long cDNA/RNA reads to the tumor-specific reference sequences. The method comprises the following steps:
In some embodiments, this step is an iterative process comprising short-read sequencing data and long-read sequencing data to the reconstructed contigs. The short-read data can be used to polish (i.e., correct) the long-read data.
In one embodiment, a method, which we refer to herein as ‘direct-RNA Frame detection’ is provided. Said method comprises the mapping of cDNA/RNA sequencing reads to a normal human reference genome, such as GRCh37, GRCh38 or the like, followed by identification of a possible ‘path’ following genomic rearrangement breakpoint-junctions in the tumor genome that could lead to a contig that places the mapped cDNA/RNA segments together in a small genomic sequence (arbitrarily defined as smaller than e.g. 200 kb). Such method is particularly relevant for identification of Frames emerging from complex genomic rearrangements, such as chromothripsis or the like, which occurs at high-frequency in many human cancers (Cortes-ciriano et al, Nature Genetics volume 52, pages331-341(2020). Complexity of genomic rearrangements may not be fully resolved by short-read WGS or long-read WGS, which makes mapping of long cDNA/RNA reads to the normal human reference a relevant alternative option. The method may involve the following steps or combinations of steps:
In preferred embodiments, the method disclosed herein comprises selecting as candidate neoantigen peptide sequences, peptide sequences whose corresponding RNA, preferably poly-(A) and 5′-capped RNA, sequence is present in the tumor sample.
The methods further comprise determining the (predicted) amino acid sequences encoded by the new open reading frames. As is clear to a skilled person, this step may be performed when identifying somatic genomic changes.
In some embodiments, the method comprises defining tumor specific open reading frames by determining strings of one or more consecutive tumor specific amino acids. One or more of the following criteria may be used to consider an amino acid occurring at the relevant position to be tumor specific.
In some embodiments, criteria A, B, or C may be used to consider an amino acid occurring at the relevant position to be tumor specific. In some embodiments, criteria A and B; B and C; A and C; or A, B, and C may be used to consider an amino acid occurring at the relevant position to be tumor specific.
In order to identify candidate neoantigen peptide sequences with the potential to induce an immune response, neoORFs comprising at least 8, preferably at least 9 contiguous amino acids are selected. A candidate neoantigen peptide sequence preferably comprises at least 8, preferably at least 9 contiguous amino acids encoded by a neoORF. Preferably, the candidate neoantigen peptide sequences comprise at least 15 or at least 20 or at least 25 or more contiguous amino acids encoded by a neoORF. In some embodiments, shorter neoantigen sequences comprising at least 1, 2, 3 or 4 amino acids encoded by a neoORF may also be useful. In those cases, candidate neoantigen peptide sequences comprise additional sequences flanking the neoORF encoded amino acids such that the candidate neoantigen peptide sequences comprise at least 8, preferably at least 9 amino acids (for binding to MHC class I), or up to 25 or more amino acids (for binding to MHC class II). While not wishing to be bound by theory, 8-9 amino acids is considered to be the minimum length of an MHC epitope and peptides having this length are likely to be more amenable to cellular processing and antigen presentation. In some embodiments, candidate neoantigen peptide sequences comprise at least 8 amino acids, wherein at most 7 contiguous amino acids are encoded by the upstream wildtype sequence preceding the tumor-specific neo open reading frame.
In preferred embodiments, the methods further comprise determining whether said neoORFs are expressed in a tumor sample. Expression of neoORFs can be determined by, e.g., determining the presence of the amino acids or peptides encoded by the neoORFs. Methods for determining the sequence of peptides, e.g., using mass spectrometry, are known to a skilled person.
Expression can also be determined by sequencing RNA from at least one tumor sample from the individual. In some embodiments, the sequence of the RNA overlapping the new junctions of DNA sequences resulting from said DNA rearrangements and/or the sequence of the RNA overlapping the mutation is determined. In some embodiments, the entire RNA molecule comprising a neoORF is sequenced.
In some embodiments, neoantigen peptide sequences encoded by RNA sequences that are expressed in the tumor sample at a level of at least 0.1 transcript per million (tpm) are selected. In some embodiments, the transcripts are expressed at a level of at least 1, at least 5, at least 10, or even at a level of at least 100 tpm. TPM represents a relative expression level that is comparable between samples (see, e.g., Zhao et al. 2021 J Translational Medicine 19, 269 (2021). https://doi.org/10.1186/s12967-021-02936-w).
As will be apparent to one of skill in the art, the methods described herein are preferably performed with the aid of a computer. In particular, as is clear to a skilled person, the mapping and/or aligning of such extensive sequencing reads requires the use of computer programs, which are known in the art. In some embodiments, the methods comprise performing whole genome sequencing of a tumor sample to produce at least 100,000, more preferably at least 1,000,000 sequencing reads. In an exemplary embodiment, around 1 billion sequencing reads are produced. In some embodiments, the methods comprise performing long read RNA sequencing to produce at least 10,000, more preferably at least 100,000 sequencing reads. In some embodiments, the methods comprise performing long read RNA sequencing to produce at least 1,000,000, more preferably at least 10,000,000 sequencing reads. In an exemplary embodiment, around 100 million sequencing reads are produced.
As described further herein, the methods described above are particularly useful for identifying the “Framome” of a tumor, which can then be used in the preparation of a vaccine, or other form of immunotherapy, including but not limited to cellular immunotherapy.
The disclosure further provides methods for preparing a vaccine, collection of vaccines, or collection of neoantigens for the immunotherapy-based treatment of cancer in an individual, comprising identifying candidate neoantigen peptide sequences as disclosed herein. Vaccine or collections are prepared comprising peptides having the candidate neoantigen amino acid sequences or comprising nucleic acids encoding said amino acid sequences. Preferably, the vaccine or collection comprises at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20, or at least 50 neoantigens/Frames.
The disclosure further provides methods for preparing antigen or a collection of antigens comprising identifying candidate neoantigen peptide sequences as disclosed herein. The antigens comprise peptides having the candidate neoantigen amino acid sequences or nucleic acids encoding said amino acid sequences. Preferably, the antigen or collection comprises at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20, or at least 50 neoantigens/Frames.
The disclosure provides vaccines, collections of vaccines, and collection of neoantigens for the treatment of cancer obtainable by identifying candidate neoantigens as disclosed herein. The vaccines and collections may comprise peptides having said candidate neoantigen peptide sequences or nucleic acids encoding said peptide sequences. As described herein, said candidate neoantigen peptide sequences may include the entire, or essentially the entire, Framome, or a selection may be made as described herein.
Preferably, vaccines and collections disclosed herein induce an immune response, or rather the neoantigens are immunogenic. Preferably, the neoantigens bind to an antibody or a T-cell receptor. In preferred embodiments, the neoantigens comprise an MHCI or MHCII ligand/epitope.
The major histocompatibility complex (MHC) is a set of cell surface molecules encoded by a large gene family in vertebrates. In humans, MHC is also referred to as human leukocyte antigen (HLA). An MHC molecule displays an antigen and presents it to the immune system of the vertebrate. Antigens (also referred to herein as ‘MHC ligands’) bind MHC molecules via a binding motif specific for the MHC molecule. Such binding motifs have been characterized and can be identified in proteins. See for a review Meydan et al. 2013 BMC Bioinformatics 14:S13.
MHC-class I molecules typically present the antigen to CD8 positive T-cells whereas MHC-class II molecules present the antigen to CD4 positive T-cells. The terms “cellular immune response” and “cellular response” or similar terms refer to an immune response directed to cells characterized by presentation of an antigen with class I or class II MHC involving T cells or T-lymphocytes which act as either “helpers” or “killers”. The helper T cells (also termed CD4+ T cells) play a central role by regulating the immune response and the killer cells (also termed cytotoxic T cells, cytolytic T cells, CD8+ T cells or CTLs) kill diseased cells such as cancer cells, preventing the production of more diseased cells.
In preferred embodiments, the present disclosure involves the stimulation of an anti-tumor CTL response against tumor cells expressing one or more tumor-expressed antigens (i.e., Frames) and preferably presenting such tumor-expressed antigens with class I MHC.
Frames may be analysed by known means in the art in order to identify potential MHC binding peptides (i.e., MHC ligands). Suitable methods are described herein in the examples and include in silico prediction methods (e.g., ANNPRED, BIMAS, EPIMHC, HLABIND, IEDB, KISS, MULTIPRED, NetMHC, PEPVAC, POPI, PREDEP, RANKPEP, SVMHC, SVRMHC, and SYFFPEITHI, see Lundegaard 2010 130:309-318 for a review). MHC binding predictions depend on HLA genotypes, furthermore it is well known in the art that different MHC binding prediction programs predict different MHC affinities for a given epitope. See also Schmidt et al, Cell Reports Medicine, February 2021.
As will be clear to a skilled person, the neoantigen sequences may also be provided as a collection of tiled sequences, wherein such a collection comprises two or more peptides that have an overlapping sequence. Such ‘tiled’ peptides have the advantage that several peptides can be easily synthetically produced, while still covering a large portion of the Frame. In an exemplary embodiment, a collection comprising at least 3, 4, 5, 6, 10, or more tiled peptides each having between 10-50, preferably 12-45, more preferably 15-35 amino acids, is provided. As will be clear to a skilled person, a collection of tiled peptides comprising a candidate neoantigen peptide sequence indicates that when aligning the tiled peptides and removing the overlapping sequences, the resulting tiled peptides provide the amino acid sequence of the candidate sequence, albeit present on separate peptides.
In some embodiments, the entire candidate neoantigen peptide sequence (i.e., Frame) may be provided as the vaccine (e.g., peptide or nucleic acid). Preferred Frames are at least 8, preferably at least 9 amino acids in length, more preferably at least 20 amino acids in length, more preferably at least 30 amino acids, and most preferably at least 50 amino acids in length. While not wishing to be bound by theory, it is believed that neoantigens longer than 10 amino acids can be processed into shorter peptides, e.g., by antigen presenting cells, which then bind to MHC molecules.
In some embodiments, fragments of a Frame can also be presented as the neoantigen. The fragments comprise at least 8 consecutive amino acids of the Frame, preferably at least 10 consecutive amino acids, and more preferably at least 20 consecutive amino acids, and most preferably at least 30 amino acids. In some embodiments, the fragments can be about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 21, about 22, about 23, about 24, about 25, about 26, about 27, about 28, about 29, about 30, about 31, about 32, about 33, about 34, about 35, about 36, about 37, about 38, about 39, about 40, about 41, about 42, about 43, about 44, about 45, about 46, about 47, about 48, about 49, about 50, about 60, about 70, about 80, about 90, about 100, about 110, or about 120 amino acids or greater. Preferably, the fragment is between 8-50, between 8-30, or between 10-20 amino acids. As will be understood by the skilled person, fragments greater than about 10 amino acids can be processed to shorter peptides, e.g., by antigen presenting cells. In an exemplary embodiment, a fragment of a neoantigen peptide sequence as identified herein may be selected based on MHC binding prediction.
In some embodiments, the neoantigens (i.e., peptides) are directly linked. Preferably, the neoantigens are linked by peptide bonds, or rather, the neoantigens are present in a single polypeptide. Accordingly, the disclosure provides polypeptides comprising at least two peptides (i.e., neoantigens). In some embodiments, the polypeptide comprises 3, 4, 5, 6, 7, 8, 9, 10 or more peptides (i.e., neoantigens). In an exemplary embodiment, a polypeptide may comprise 10 different neoantigens, each neoantigen having between 10-400 amino acids. Thus, the polypeptide may comprise between 100-4000 amino acids, or more. As is clear to a skilled person, the final length of the polypeptide is determined by the number of neoantigens selected and their respective lengths. A collection may comprise two or more polypeptides comprising the neoantigens which can be used to reduce the size of each of the polypeptides.
In some embodiments, the amino acid sequences of the neoantigens are located directly adjacent to each other in the polypeptide. For example, a nucleic acid molecule may be provided that encodes multiple neoantigens in the same reading frame. In some embodiments, a linker amino acid sequence may be present. Preferably a linker has a length of 1, 2, 3, 4 or 5, or more amino acids. The use of linker may be beneficial, for example for introducing, among others, signal peptides or cleavage sites. In some embodiments at least one, preferably all of the linker amino acid sequences have the amino acid sequence VDD.
As will be appreciated by the skilled person, the peptides and polypeptides disclosed herein may contain additional amino acids, for example at the N- or C-terminus. Such additional amino acids include, e.g., purification or affinity tags or hydrophilic amino acids in order to decrease the hydrophobicity of the peptide. In some embodiments, the neoantigens may comprise amino acids corresponding to the adjacent, wild-type amino acid sequences of the relevant gene, e.g., amino acid sequences located 5′ to the frame shift mutation that results in the neo open reading frame. Preferably, each neoantigen comprises no more than 20, more preferably no more than 10, and most preferably no more than 5 of such wild-type amino acid sequences.
The peptides and polypeptides can be produced by any method known to a skilled person. In some embodiments, the peptides and polypeptide are chemically synthesized. The peptides and polypeptide can also be produced using molecular genetic techniques, such as by inserting a nucleic acid into an expression vector, introducing the expression vector into a host cell, and expressing the peptide. Preferably, such peptides and polypeptide are isolated, or rather, substantially isolated from other polypeptides, cellular components, or impurities. The peptide and polypeptide can be isolated from other (poly)peptides as a result of solid phase protein synthesis, for example. Alternatively, the peptides and polypeptide can be substantially isolated from other proteins after cell lysis from recombinant production (e.g., using HPLC).
The disclosure further provides nucleic acid molecules encoding the peptides and polypeptide disclosed herein. Based on the genetic code, a skilled person can determine the nucleic acid sequences which encode the (poly)peptides disclosed herein. Based on the degeneracy of the genetic code, sixty-four codons may be used to encode twenty amino acids and translation termination signal.
The nucleic acid molecule may comprise deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or the combination thereof. Nucleic acid molecules include genomic DNA, cDNA, mRNA, recombinantly produced and chemically synthesized molecules. Preferably, the nucleic acid molecule is mRNA. The nucleic acid molecule may be single-stranded or double-stranded and linear or covalently circularly closed molecule. Preferably the nucleic acid molecule is isolated. The nucleic acid molecule may be recombinantly produced or chemically synthesized. RNA, e.g., can be prepared by in vitro transcription from a DNA template.
In some embodiments, the nucleic acid molecule is modified. The chemical modification may comprise replacing or substituting an atom of a pyrimidine base with an amine, SH, an alkyl (e.g., methyl, or ethyl), or a halo (e.g., chloro or fluoro). The chemical modification may also comprise modifications of the sugar moiety and/or phosphate backbone. Chemical modification of the phosphate backbone comprising phosphorothioate linkages can increase nuclease resistance and ensure a longer half-life in the cellular environment. Preferably, the nucleic acid molecule is RNA (preferably mRNA) having one or more modifications. In some embodiments, the nucleic acid is RNA and comprises pseudouridine or another modified nucleoside. Preferably, the nucleic acid molecule is not modified or comprises one or more modified nucleosides selected from 1-methylpseudouridine. Preferably, the nucleosides of the nucleic acid molecule are not modified, except for the optional 5′ cap structure.
Modified nucleosides optionally comprise 1-methyl-3-(3-amino-3-carboxypropyl) pseudouridine, 2′-O-methylpseudouridine, 5-methyldihydrouridine, 5-methoxyuridine, 5-methylcytidine, 2′-O-methyuridine, 1-methylpseudouridine, pyridin-4-one ribonucleoside, 5-aza-uridine, 2-thio-5-aza-uridine, 2-thiouridine, 4-thio-pseudouridine, 2-thio-pseudouri-dine, 5-hydroxyuridine, 3-methyluridine, 5-carboxymethyluridine, 1-carboxymethyl-pseudouridine, 5-propynyluridine, 1-propynyl-pseudouridine, 5-taurinomethyluridine, 1-taurinomethylpseudouridine, 5-taurinomethyl-2-thio-uridine, 1-taurinomethyl-4-thio-uridine, 5-methyl-uridine, 1-methyl-pseudouridine, 4-thio-1-methyl-pseudouridine, 2-thio-1-methyl-pseudouridine, 1-methyl-1-deaza-pseudouridine, 2-thio-1-methyl-1-deaza-pseudouridine, dihydrouridine, dihydropseudouridine, 2-thio-dihydrouridine, 2-thio-dihydropseudouridine, 2-methoxyuridine, 2-methoxy-4-thio-uridine, 4-methoxy-pseudouridine, and 4-methoxy-2-thio-pseudouridine, 5-aza-cytidine, pseudoisocytidine, 3-methyl-cytidine, N4-acetylcytidine, 5-formylcytidine, N4-methylcytidine, 5-hydroxymethylcytidine, 1-methyl-pseudoisocytidine, pyrrolo-cytidine, pyrrolo-pseudoisocytidine, 2-thio-cytidine, 2-thio-5-methyl-cytidine, 4-thio-pseudoisocytidine, 4-thio-1-methyl-pseudoisocytidine, 4-thio-1-methyl-1-deaza-pseudoisocytidine, 1-methyl-1-deaza-pseudoisocytidine, Zebularine, 5-aza-Zebularine, 5-methyl-Zebularine, 5-aza-2-thio-Zebularine, 2-thio-Zebularine, 2-methoxy-cytidine, 2-methoxy-5-methyl-cytidine, 4-methoxy-pseudoisocytidine, 4-methoxy-1-methyl-pseudoisocytidine, 2-aminopurine, 2,6-diaminopurine, 7-deaza-adenine, 7-deaza-8-aza-adenine, 7-deaza-2-aminopurine, 7-deaza-8-aza-2-aminopurine, 7-deaza-2,6-diaminopurine, 7-deaza-8-aza-2,6-diaminopurine, 1-methyladenosine, 2-methyladenosine, N6-methyladenosine, N6-isopentenyladenosine, N6-(cis-hydroxyisopentenyl)adenosine, 2-methylthio-N6-(cis-hydroxyisopentenyl)adenosine, N6-glycinylcarbamoyladenosine, N6-threonylcarbamoyladenosine, 2-methylthio-N6-threonyl carbamoyladenosine, N6.N6-dimethyladenosine, 7-methyladenine, 2-methylthio-adenine, 2-methoxy-adenine, inosine, 1-methyl-inosine, Wyosine, Wybutosine, 7-deaza guanosine, 7-deaza-8-aza-guanosine, 6-thio-guanosine, 6-thio-7-deaza-guanosine, 6-thio-7-deaza-8-aza-guanosine, 7-methyl-guanosine, 6-thio-7-methyl-guanosine, 7-methylinosine, 6-methoxy-guanosine, 1-methylguanosine, N2-methylguanosine, N2.N2-dimethylguanosine, 8-oxo-guanosine, 7-methyl-8-oxo-guanosine, 1-methyl-6-thioguanosine, N2-methyl-6-thio-guanosine, and N2.N2-dimethyl-6-thio-guanosine.
Particularly preferred modifications and methods for generating nucleic acid molecules are described in US2020030460, which is hereby incorporated by reference, including the modifications described at paragraphs 0072 and 0292 of US2020030460. Such modifications reduce the immunogenicity of RNA. In preferred embodiments, the modified nucleotide is 2′-O-methylpseudouridine, 2′-O-methyluridine, 5-methoxyuridine, 1-methylpseudouridine, N6-methyladenosine, 2-thiouridine, 5-methylcytidine, 5-methyluridine, pseudouridine, or a combination thereof. In a preferred embodiment, mRNA is provided wherein at least a portion of uridine nucleotides are replaced by 1-methylpseudouridine, 2′-O-methyluridine, 2-thiouridine, 5-methyluridine, 5-methoxyuridine, pseudouridine, or a combination thereof. In some embodiments, mRNA is provided wherein at least a portion of cytidine nucleotides are replaced by 5-methylcytidine.
In a preferred embodiment, the nucleic acid molecules are codon optimized. As is known to a skilled person, codon usage bias in different organisms can affect gene expression level. Various computational tools are available to the skilled person in order to optimize codon usage depending on which organism the desired nucleic acid will be expressed. Preferably, the nucleic acid molecules are optimized for expression in mammalian cells, preferably in human cells. Table 2 lists for each acid amino acid (and the stop codon) the most frequently used codon as encountered in the human exome.
In preferred embodiments, at least 50%, 60%, 70%, 80%, 90%, or 100% of the amino acids are encoded by a codon corresponding to a codon presented in Table 2.
In some embodiments, the nucleic acid molecule is mRNA, self-amplifying replicon RNA, circular RNA, or viral RNA. Preferably, the nucleic acid molecule is mRNA. The disclosure further provides vectors comprising the nucleic acids molecules disclosed herein. A “vector” is a recombinant nucleic acid construct, such as plasmid, phase genome, virus genome, cosmid, or artificial chromosome, to which another nucleic acid segment may be attached. The term “vector” includes both viral and non-viral means for introducing the nucleic acid into a cell in vitro, ex vivo or in vivo. The disclosure contemplates both DNA and RNA vectors. The disclosure further includes self-replicating RNA with (virus-derived) replicons, including but not limited to mRNA molecules derived from mRNA molecules from alphavirus genomes, such as the Sindbis, Semliki Forest and Venezuelan equine encephalitis viruses.
Vectors, including plasmid vectors, eukaryotic viral vectors and expression vectors are known to the skilled person. Vectors may be used to express a recombinant gene construct in eukaryotic cells depending on the preference and judgment of the skilled practitioner (see, for example, Sambrook et al., Chapter 16). For example, many viral vectors are known in the art including, for example, retroviruses, adeno-associated viruses, and adenoviruses. Other viruses useful for introduction of a gene into a cell include, but are not limited to, adenovirus, arenavirus, herpes virus, mumps virus, poliovirus, Sindbis virus, and vaccinia virus, such as, canary pox virus. The methods for producing replication-deficient viral particles and for manipulating the viral genomes are well known. In preferred embodiments, the vaccine comprises an attenuated or inactivated viral vector comprising a nucleic acid disclosed herein.
Preferred vectors are expression vectors. It is within the purview of a skilled person to prepare suitable expression vectors for expressing the antigens disclosed herein. An “expression vector” is generally a DNA element, often of circular structure, having the ability to replicate autonomously in a desired host cell, or to integrate into a host cell genome and also possessing certain well-known features which, for example, permit expression of a coding DNA inserted into the vector sequence at the proper site and in proper orientation. Such features can include, but are not limited to, one or more promoter sequences to direct transcription initiation of the coding DNA and other DNA elements such as enhancers, polyadenylation sites and the like, all as well known in the art. Suitable regulatory sequences including enhancers, promoters, translation initiation signals, and polyadenylation signals may be included. Additionally, depending on the host cell chosen and the vector employed, other sequences, such as an origin of replication, additional DNA restriction sites, enhancers, and sequences conferring inducibility of transcription may be incorporated into the expression vector. The expression vectors may also contain a selectable marker gene which facilitates the selection of host cells transformed or transfected. Examples of selectable marker genes are genes encoding a protein such as G418 and hygromycin which confer resistance to certain drugs, β-galactosidase, chloramphenicol acetyltransferase, and firefly luciferase.
The expression vector can also be an RNA element that contains the sequences required to initiate translation in the desired reading frame, and possibly additional elements that are known to stabilize or contribute to replicate the RNA molecules after administration. Therefore, when used herein, the terms DNA and RNA when referring to an isolated nucleic acid encoding a neoantigen peptide should be interpreted as referring to DNA from which the peptide can be transcribed or RNA molecules from which the peptide can be translated.
The nucleic acid molecule according to the present disclosure optionally comprises a 5′ untranslated region (UTR) and/or a 3′ UTR. The nucleic acid molecule may comprise a poly-A tail. A poly-A tail sequence may mostly or entirely be of adenine nucleotides, analogs or derivates thereof. A poly-A tail may be located adjacent to a 3′ UTR. The nucleic acid molecule may comprise a 5′ cap structure. For example, a natural mRNA cap may include a guanine nucleotide and a guanine (G) nucleotide methylated at the 7 position joined by a triphosphate linkage at their 5′ positions, e.g., m7G(5′)ppp(5′)G, commonly written as m7GpppG. A 5′ cap may also be an anti-reverse cap analog. Cap species include m7GpppG, m7Gpppm7G, m73′ dGpppG, m27,O3,GpppG, m27,O3,GppppG, m27,O2,GppppG, m7Gpppm7G, etc. Preferably, the cap structure is a Cap-1, e.g., a m7G(5′)ppp(5′)(2′OMeA)pG cap. Such a cap can be produced using the CleanCap technology from TriLink Biotechnologies. A cap structure may be located adjacent to a 5′ UTR. Preferably, the nucleic acid molecule according to the present disclosure is mRNA comprising a poly-A tail or a 5′ cap structure. Preferably, the nucleic acid molecule according to the present disclosure is mRNA comprising a poly-A tail and a 5′ cap structure.
Also provided for is a host cell comprising a nucleic acid molecule or a vector as disclosed herein. The nucleic acid molecule may be introduced into a cell (prokaryotic or eukaryotic) by standard methods. As used herein, the terms “transformation” and “transfection” are intended to refer to a variety of art recognized techniques to introduce a DNA into a host cell. Such methods include, for example, transfection, including, but not limited to, liposome-polybrene, DEAE dextran-mediated transfection, electroporation, calcium phosphate precipitation, microinjection, or velocity driven microprojectiles (“biolistics”). Such techniques are well known by one skilled in the art. See, Sambrook et al. (1989) Molecular Cloning: A Laboratory Manual (2 ed. Cold Spring Harbor Lab Press, Plainview, N.Y.). Alternatively, one could use a system that delivers the DNA construct in a gene delivery vehicle. The gene delivery vehicle may be viral or chemical. Various viral gene delivery vehicles can be used with the present invention. In general, viral vectors are composed of viral particles derived from naturally occurring viruses. The naturally occurring virus has been genetically modified to be replication defective and does not generate additional infectious viruses, or it may be a virus that is known to be attenuated and does not have unacceptable side effects.
Preferably, the host cell is a mammalian cell, such as MRC5 cells (human cell line derived from lung tissue), HuH7 cells (human liver cell line), CHO-cells (Chinese Hamster Ovary), COS-cells (derived from monkey kidney (African green monkey), Vero-cells (kidney epithelial cells extracted from African green monkey), Hela-cells (human cell line), BHK-cells (baby hamster kidney cells, HEK-cells (Human Embryonic Kidney), NSO-cells (Murine myeloma cell line), C127-cells (nontumorigenic mouse cell line), PerC6®-cells (human cell line, Crucell), and Madin-Darby Canine Kidney (MDCK) cells. In some embodiments, the disclosure comprises an in vitro cell culture of mammalian cells expressing the neoantigens obtained as disclosed herein. Such cultures are useful, for example, in the production of cell-based vaccines, such as viral vectors expressing the neoantigens disclosed herein.
As is clear to a skilled person, if multiple neoantigens are used, they may be provided in a single composition (e.g., a single vaccine composition) or in several different compositions to make up a collection (such as a vaccine collection). The disclosure thus provides collections (such as a vaccine collection) comprising a collection of tiled peptides, collection of peptides, as well as nucleic acid molecules, vectors, or host cells. As is clear to a skilled person, such collections may be administered to an individual simultaneously or consecutively (e.g., on the same day) or they may be administered several days or weeks apart.
Various known methods may be used to administer the vaccines and other therapeutic compounds disclosed herein to an individual in need thereof. For instance, one or more neoantigens can be provided as a nucleic acid molecule directly, as “naked DNA”. Neoantigens can also be expressed by attenuated viral hosts, such as vaccinia or fowlpox. This approach involves the use of a virus as a vector to express nucleotide sequences that encode the neoantigen. Upon introduction into the individual, the recombinant virus expresses the neoantigen peptide, and thereby elicits a host CTL response. Vaccination using viral vectors is well-known to a skilled person and vaccinia vectors and methods useful in immunization protocols are described in, e.g., U.S. Pat. No. 4,722,848. Another vector is BCG (Bacille Calmette Guerin) as described in Stover et al. (Nature 351:456-460 (1991)).
In preferred embodiments, the neoantigens are provided as one or more RNA or DNA vaccines. Such RNA and DNA based vaccines, as well as their preparation, formulation, and therapeutic administration are well-known to a skilled person. See, e.g., U.S. Pat. No. 9,334,328, which is hereby incorporated by reference, which describes pharmaceutical compositions comprising modified nucleosides, nucleotides, and nucleic acids for treating disorders and diseases. The vaccines may also include one or more so-called IRES (“internal ribosomal entry site) An IRES can be used to allow the translation of several peptides or polypeptides independently of one another (“multicistronic” or “polycistronic” mRNA).
Preferably, the vaccine and other therapeutic compositions disclosed herein comprise a pharmaceutically acceptable excipient and/or an adjuvant. The compositions may contain pharmaceutically acceptable auxiliary substances as required to approximate physiological conditions, such as pH adjusting and buffering agents, tonicity adjusting agents, wetting agents and the like. Suitable adjuvants are well-known in the art and include, aluminum (or a salt thereof, e.g., aluminium phosphate and aluminium hydroxide), monophosphoryl lipid A, squalene (e.g., MF59), and cytosine phosphoguanine (CpG). A skilled person is able to determine the appropriate adjuvant, if necessary, and an immune-effective amount thereof. As used herein, an immune-effective amount of adjuvant refers to the amount needed to increase the vaccine's immunogenicity in order to achieve the desired effect.
The disclosure further provides a pharmaceutical composition comprising the nucleic acid molecule as disclosed herein and a lipid-based carrier. Natural lipid-based carriers include cells and cellular membranes. Artificial lipid-based carriers include liposomes, nanoliposomes, micelles, nanoparticles, and lipoplexes. Preferably the lipid-based carrier is selected from lipid nanoparticles, liposomes, lipoplexes, and nanoliposomes. Preferably, the lipid based carrier is a lipid nanoparticle.
In some embodiments, the lipid-based carriers comprise at least one lipid selected from a cationic lipid or ionizable lipid, a neutral lipid or phospholipid, a steroid or steroid analog, an aggregation-reducing lipid, or any combinations thereof. In a preferred embodiment the lipid based carriers comprise i) at least one cationic or cationizable lipid, ii) at least one neutral lipid or phospholipid, iii) at least one steroid or steroid analogue, and iv) at least one aggregation-reducing lipid.
Preferably, the vaccine, peptide antigen, nucleic acid molecule encoding said peptide antigen or collection of vaccines, antigens, and nucleic acid molecules; respectively comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual. Preferably, the vaccine, peptide antigen, nucleic acid molecule encoding said peptide antigen or collection of vaccines, antigens, and nucleic acid molecules; respectively comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual which are also expressed in the tumor (e.g., RNA encoding said neoantigens is present in the tumor). While not wishing to be bound by theory, the use of the full Framome as a vaccine is believed to increase the success rate of the vaccine.
The therapeutic compounds and compositions disclosed herein (e.g., vaccine, peptide antigen, and nucleic acid molecule encoding said peptide antigen) are preferably designed to maximize the number of neoantigen amino acids provided (either as peptides or nucleic acids encoding said peptides) to an individual afflicted with cancer. In some embodiments, the vaccine is an F50 or F100 product, i.e, the vaccine comprises at least 50 or at least 100 neoantigen amino acids encoded in the tumor genome and resulting from neoORFs (Framome), preferably, detected in the RNA of the tumor. In some embodiments, the vaccine is an F200, F500, or F1000 product, i.e, the vaccine comprises at least 200, 500, or 1000, respectively, neoantigen amino acids encoded in the tumor genome and, preferably, detected in the RNA of the tumor. Similarly, in some embodiments, a peptide antigen or a collection of peptide antigens comprises at least 50, at least 100, at least 200, at least 500, or at least 1000 amino acids encoded by the tumor specific open reading frames. The disclosure further provides nucleic acid molecules encoding said antigens.
In some embodiments, there may be reasons to select a subset of the Framome for preparation of a vaccine. For example, if the vaccine is produced as a peptide, or collection of peptides, then a set of between 5-20 peptides preferably having between 20-30 amino acids per peptide may be used. In which case, such an exemplary vaccine would cover a Framome of between 100-500 amino acids.
In some embodiments, the neoantigens are selected based on cysteine content. As known to a skilled person, when the vaccine is a synthetic peptide, or collection of synthetic peptides, the amino acid content may be evaluated to determine whether peptide synthesis and mixing of peptides is possible. Peptide cysteine content is an important factor since cysteines can form disulfide bridges, which may lower solubility and trigger clutting. Frames with the lowest cysteine content are therefore preferred. The simplest method for determining cysteine content is defined as Qcys=N/L, where N is defined as the number of Cysteines in a Frame and L the total length in amino acids of the Frame. However, other methods are considered as well, for example the number of subsequences of a Frame of defined length L, which have a cysteine content (Q) larger than a predefined value, where L∈{5, 6, 7, 8, 9, 10, 11, . . . , n} with n being the entire length of the Frame sequence in amino acids, and Q being the cysteine content of a Frame subsequence defined as above (N/L). In preferred embodiments, the cysteine content for each peptide is 30% or less, more preferably, 5% or less. In preferred embodiments, methods are provided for identifying neoantigen sequences wherein the cysteine content for each peptide is 30% or less, where cysteine content (Qcys) is defined as the number of cysteines in said sequence divided by the total number of amino acids in said sequence.
In some embodiments, “self-peptides” are not included in the neoantigen vaccine or collection. In preferred embodiments, methods are provided for identifying neoantigen sequences wherein the tumor specific open reading frames do not share a contiguous stretch of at least 4 amino acids with human protein reference sequences. Preferably, the candidate neoantigen peptide sequences, or rather the sequence encoded by the tumor specific open reading frames, do not share a contiguous stretch of at least 4, preferably at least 6, amino acids with human protein reference sequences. Such human reference sequences are available at the NCBI RefSeq database. Other protein databases for identifying a matching pattern include, for example uniprot (https://www.uniprot.org/) or proteomics databases (https://www.proteomicsdb.org/).
In some embodiments, candidate neoantigen sequences are selected on the basis of genomic variant allele frequency (VAF), to select clonal (or truncal) neoantigen sequences, i.e. neoantigens present in all tumor cells of a tumor and not in only a subset of the tumor cells. As used herein, VAF is defined as: VAF=Rmut/Rtot where Rmut is the number of sequencing reads in the genome sequencing data containing the frameshift mutation or genomic rearrangement breakpoint junctions, and Rtot is the total number of sequencing reads covering the frameshift mutation locus. A corrected VAF (VAFcor) can be subsequently calculated based on the estimated tumor purity. Preferably, candidate sequences have a VAF or VAFcor of at least 0.1, more preferably >0.1, more preferably >0.2. In preferred embodiments, methods are provided for identifying neoantigen sequences wherein the genomic variant allele frequency of the respective somatic mutation in the tumor cells of a tumor sample is at least 0.1.
In some embodiments, candidate neoantigen sequences are selected which are predicted to comprise an MHC I or MHC II binding epitope, as disclosed further herein. In preferred embodiments, methods are provided for identifying neoantigen sequences wherein the peptides are predicted to comprise one or more MHC I and/or MHC II binding epitopes.
In some embodiments, candidate neoantigen sequences are selected to optimize the physical spread of Frames across the chromosomes. In particular, candidate neoantigen sequences are selected for which the underlying somatic mutations have a maximum distance with regard to chromosomal location. While not wishing to be bound by theory, a single neoORF may be lost, for example via chromosome loss or deletion. However, the chance that two neoORFs located on different chromosomal arms are both lost is highly unlikely. The use of neoORFs distally located from each other is therefore a useful strategy to reduce the risk of antigen loss. The selection of such neoORFs may be useful if the use of the full Framome as a vaccine or other therapeutic composition has practical limitations. In some embodiments, methods are provided for identifying and selecting neoantigen sequences for which the underlying somatic mutations have a maximum distance with regard to chromosomal location, preferably wherein each mutation is separated by at least 20 Mb, at least 50 Mb, or at least 100 Mb In preferred embodiments, methods are provided for identifying and selecting neoantigen sequences for which the underlying somatic mutations have a maximum distance with regard to chromosomal location, preferably wherein each mutation is located on a different chromosomal arm.
There are multiple ways to choose a set of Frames based on their chromosomal locations. One possible approach is as follows. Let d be the number of Frames to be selected. Let F={f1, f2, . . . , fn} be the set of all Frames within a patient. Let cf
be the set of unique subsets of d Frames taken from F. The preferred combination of Frames is
In some embodiments, neoantigen peptide sequences are selected wherein each somatic mutation corresponding to the neoantigen is located on a different chromosomal arm.
In preferred embodiments, the vaccine, peptide antigen, nucleic acid encoding said peptide antigen or collection of same, respectively; comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual which are also expressed in the tumor (e.g., RNA encoding said neoantigens is present in the tumor) and which are not “self-peptides” as disclosed herein.
In preferred embodiments, the vaccine, peptide antigen, nucleic acid encoding said peptide antigen or collection of same, respectively; comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual which are also expressed in the tumor (e.g., RNA encoding said neoantigens is present in the tumor), which are not “self-peptides” as disclosed herein, and have a VAF or VAFcor of at least 0.1.
In preferred embodiments, the vaccine, peptide antigen, nucleic acid encoding said peptide antigen or collection of same, respectively; comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual which are also expressed in the tumor (e.g., RNA encoding said neoantigens is present in the tumor) and have a VAF or VAFcor of at least 0.1.
In preferred embodiments, the vaccine, peptide antigen, nucleic acid encoding said peptide antigen or collection of same, respectively; comprises all of the candidate neoantigen peptide sequences identified in the tumor sample of an individual which are also expressed in the tumor (e.g., RNA encoding said neoantigens is present in the tumor), which are not “self-peptides” as disclosed herein, have a VAF or VAFcor of at least 0.1, and comprise a predicted MHC I or MHC II binding epitope.
The methods describe determining the presence of cis-splicing mutations that result in tumor specific open reading frames. In some embodiments, the methods further comprise comparing the splice junction resulting from the cis-splicing mutation with a database of mRNA wild-type splice junctions and selecting as candidate neoantigen peptide sequences those sequences where said splice junction is not present in the database of mRNA wild-type splice junctions. Databases comprising human mRNA wild-type splice junctions are known to the skilled person and include the GTex database (see the world wide web at gtexportal.org/home), the RJunBase database (see the world wide web at rjunbase.org), H-DBAS—Human-transcriptome DataBase for Alternative Splicing (see the world wide web at h-invitational.jp/h-dbas/), and the Alternative Splicing Database (ASD) (see Stefan Stamm, et al. ASD: a bioinformatics resource on alternative splicing, Nucleic Acids Research, Volume 34, Issue suppl_1, 1 Jan. 2006, Pages D46-D55, https://doi.org/10.1093/nar/gkj031).
In some embodiments, the disclosure provides neoantigen sequences that are shared by cancer patients. In some embodiments, methods are provides comprising identifying candidate neoantigen sequences from a plurality of individuals. Such neoantigen sequences may be identified from, e.g., newly diagnosed cancer patients or from tumor sequence databases (e.g., TCGA database). Shared neoantigens identified from at least two individuals are selected.
Such shared neoantigens are useful in the treatment of cancer and may be used, e.g., in the treatments disclosed herein. In an exemplary embodiment, one or more shared neoantigens (or treatments based on said shared neoantigens, e.g., one or more nucleic acid molecules encoding one or more shared neoantigens, one or more binding molecules that binds the one or more shared neoantigens, one or more T-cells expressing T-cell receptors or chimeric antigen receptors with specificity for one or more shared neoantigens, etc.) are administered to an individual afflicted with cancer.
The disclosure also provides the use of the neoantigens disclosed herein for the treatment of disease, in particular for the treatment of cancer in an individual. It is within the purview of a skilled person to diagnose an individual with as having cancer.
In a preferred embodiment, the cancer is not Microsatellite instable (MSI), in particular the cancer is not MSI-H (i.e., high amount of microsatellite instability). MSI is due to defects in DNA mismatch repair. MSI screening tests are available which analyse changes in the DNA sequence between normal tissue and tumor tissue and can identify the level of instability. In some embodiments, MSI-H cancer is defined as the presence of mutations in 30% or more of microsatellites. In some embodiments, the case is MSI.
In some embodiments, the cancer is colorectal cancer, lung cancer, stomach cancer, non-small lung cancer, pancreatic cancer (i.e. pancreatic ductal adenocarcinoma), head and neck cancer, colorectal cancer, glioblastoma, triple-negative breast cancer, melanoma, breast adenocarcinoma, or renal cell carcinoma.
As used herein, the terms “treatment,” “treat,” and “treating” refer to reversing, alleviating, or inhibiting the progress of a disease, or reversing, alleviating, delaying the onset of, or inhibiting one or more symptoms thereof. Treatment includes, e.g., slowing the growth of a tumor, reducing the size of a tumor, and/or slowing or preventing tumor metastasis.
Suitable compounds for treatment are as disclosed herein and include neoantigen vaccines, peptide antigens, and nucleic acid molecules encoding said peptide antigens and are referred to herein as “the therapeutic compounds”.
As used herein, administration or administering in the context of treatment or therapy of a subject is preferably in a “therapeutically effective amount”, this being sufficient to show benefit to the individual. The actual amount administered, and rate and time-course of administration, will depend on the nature and severity of the disease being treated. Prescription of treatment, e.g. decisions on dosage etc., is within the responsibility of general practitioners and other medical doctors, and typically takes account of the disorder to be treated, the condition of the individual patient, the site of delivery, the method of administration and other factors known to practitioners.
The optimum amount of each neoantigen to be included in the vaccine or other therapeutic composition and the optimum dosing regimen can be determined by one skilled in the art without undue experimentation. The composition may be prepared for injection of the peptide, nucleic acid molecule encoding the peptide, or any other carrier comprising such (such as a virus or liposomes). For example, doses of between 1 and 500 mg 50 μg and 1.5 mg, preferably 125 μg to 500 μg, of peptide or DNA may be given and will depend from the respective peptide or nucleic-acid vaccine. Other methods of administration are known to the skilled person. Preferably, the vaccines and other therapeutic composition may be administered parenterally, e.g., intravenously, subcutaneously, intradermally, intramuscularly, or otherwise.
For therapeutic use, administration may begin at or shortly after the surgical removal of tumors. This can be followed by boosting doses until at least symptoms are substantially abated and for a period thereafter.
In some embodiments, the vaccines and other therapeutic compounds disclosed herein may be provided as a neoadjuvant therapy, e.g., prior to the removal of tumors or prior to treatment with radiation or chemotherapy. Neoadjuvant therapy is intended to reduce the size of the tumor before more radical treatment is used.
The vaccines and other therapeutic compounds are preferably capable of initiating a specific T-cell response. It is within the purview of a skilled person to measure such T-cell responses either in vivo or in vitro, e.g. by analyzing IFN-γ production or tumor killing by T-cells. In therapeutic applications, vaccines and other therapeutic compounds are administered to a patient in an amount sufficient to elicit an effective CTL response to the tumor antigen and to cure or at least partially arrest symptoms and/or complications.
The vaccines and other therapeutic compounds can be administered alone or in combination with other therapeutic agents. The therapeutic agent is for example, a chemotherapeutic agent, radiation, or immunotherapy, including but not limited to checkpoint inhibitors, such as nivolumab, ipilimumab, pembrolizumab, or the like. Any suitable therapeutic treatment for a particular, cancer may be administered.
The term “chemotherapeutic agent” refers to a compound that inhibits or prevents the viability and/or function of cells, and/or causes destruction of cells (cell death), and/or exerts anti-tumor/anti-proliferative effects. The term also includes agents that cause a cytostatic effect only and not a mere cytotoxic effect. Examples of chemotherapeutic agents include, but are not limited to bleomycin, capecitabine, carboplatin, cisplatin, cyclophosphamide, docetaxel, doxorubicin, etoposide, interferon alpha, irinotecan, lansoprazole, levamisole, methotrexate, metoclopramide, mitomycin, omeprazole, ondansetron, paclitaxel, pilocarpine, rituxitnab, tamoxifen, taxol, trastuzumab, vinblastine, and vinorelbine tartrate.
Preferably, the other therapeutic agent is an anti-immunosuppressive/immunostimulatory agent, such as anti-CTLA antibody or anti-PD-1 or anti-PD-L1. Blockade of CTLA-4 or PD-L1 by antibodies can enhance the immune response to cancerous cells. In particular, CTLA-4 blockade has been shown effective when following a vaccination protocol.
As is understood by a skilled person the vaccine or other therapeutic compounds as disclosed herein and other therapeutic agents may be provided simultaneously, separately, or sequentially. In some embodiments, the vaccine may be provided several days or several weeks prior to or following treatment with one or more other therapeutic agents. The combination therapy may result in an additive or synergistic therapeutic effect.
The compounds and compositions disclosed herein are useful as therapy and in therapeutic treatments and may thus be useful as medicaments and used in a method of preparing a medicament.
In some embodiments, the disclosure provides methods for the preparation of a cellular immunotherapy, such as personalized neoantigen-specific T-cell therapy. Such cellular immunotherapy is directed against the tumor cells with expressed Frames where Frame-derived peptides are presented in complexes with HLA molecules on the cell surface.
Various methods for the use of neoantigen-specific T-cells or neoantigen-specific T-cell receptors in cancer immunotherapy have been described. T-cell receptors (TCRs) are expressed on the surface of T-cells and consist of an α chain and a β chain. TCRs recognize antigens bound to MHC molecules expressed on the surface of antigen-presenting cells. The T-cell receptor (TCR) is a heterodimeric protein, in the majority of cases (95%) consisting of a variable alpha (α) and beta (β) chain, and is expressed on the plasma membrane of T-cells. The TCR is subdivided in three domains: an extracellular domain, a transmembrane domain and a short intracellular domain. The extracellular domain of both α and β chains have an immunoglobulin-like structure, containing a variable and a constant region. The variable region recognizes processed peptides, among which neoantigens, presented by major histocompatibility complex (MHC) molecules, and is highly variable. The intracellular domain of the TCR is very short, and needs to interact with CD3ζ to allow for signal propagation upon ligation of the extracellular domain.
The major histocompatibility complex (MHC) is a set of cell surface molecules encoded by a large gene family in vertebrates. In humans, MHC is also referred to as human leukocyte antigen (HLA). An MHC molecule displays an antigen and presents it to the immune system of the vertebrate. Antigens (also referred to herein as ‘MHC ligands’) bind MHC molecules via a binding motif specific for the MHC molecule. Such binding motifs have been characterized and can be identified in proteins. See for a review Meydan et al. 2013 BMC Bioinformatics 14:S13.
MHC-class I molecules typically present the antigen to CD8 positive T-cells whereas MHC-class II molecules present the antigen to CD4 positive T-cells. The terms “cellular immune response” and “cellular response” or similar terms refer to an immune response directed to cells characterized by presentation of an antigen with class I or class II MHC involving T cells or T-lymphocytes which act as either “helpers” or “killers”. The helper T cells (also termed CD4+ T cells) play a central role by regulating the immune response and the killer cells (also termed cytotoxic T cells, cytolytic T cells, CD8+ T cells or CTLs) kill diseased cells such as cancer cells, preventing the production of more diseased cells.
With the focus of cancer treatment shifted towards more targeted therapies, among which immunotherapy, the potential of therapeutic application of tumor-directed T-cells is increasingly explored. Such strategies involve the analysis of T-cell receptors (TCRs), either based on T-cells obtained from a tumor specimen, or based on peripheral T-cells from a cancer patient. In vitro characterization of TCRs present on T cells found in tumor specimens or peripheral blood, for their specificity against specific Frame neoantigens could be used to select specific TCR sequences that can be used for development of immunotherapy. Such TCR sequences can, for example, be used for development of TCR-like antibodies (Støkken Høydahl et al, Antibodies 2019, 8, 32). Identified and isolated TCR sequences can also be used for engineering of T-cells, so as to provide them with a specific TCR that recognizes a neoantigen. Several methods for T-cell engineering have been described in the art, including methods to improve the function of T-cells with regard to safety, tumor infiltration and immune stimulation (Rath et al, Cells 2020, 9, 1485).
The disclosure provides methods comprising contacting T-cells with HLA molecules, preferably MHC-I, bound to one or more of the candidate neoantigen peptide sequences identified from an individual according to the methods described herein. In particular, such methods for identifying neoantigens combine whole genome sequencing with long-read RNA/cDNA sequencing to identify neoantigen sequences. The neoantigen peptides used as “bait” are preferably selected based on the potential to bind MHC. Suitable methods to predict MHC binding include in silico prediction methods (e.g., ANNPRED, BIMAS, EPIMHC, HLABIND, IEDB, KISS, MULTIPRED, NetMHC, PEPVAC, POPI, PREDEP, RANKPEP, SVMHC, SVRMHC, and SYFFPEITHI, see Lundegaard 2010 130:309-318 for a review). In some embodiments, T-cells are contacted with neoantigen peptide sequences. The peptide sequences may be provided bound to HLA molecules. In some embodiments, antigen-presenting cells (such as dendritic cells) are transfected with one or more nucleic acid molecules encoding one or more candidate neoantigen peptide sequences and T-cells are contacted with said APCs. The T-cells as well as the mixture of T-cells and APCs can be further cultured and used as an immunotherapy.
In some embodiments, a method is provided that comprises the (i) isolation of T-cells from a tumor specimen (e.g. tumor-infiltrating lymphocytes), peripheral blood, bone marrow, lymph node tissue, or spleen tissue from an individual afflicted with cancer, (ii) identification of Frame neoantigens using methods as described herein, (iii) prediction of MHC class I binding epitopes within the Frame neoantigens sequences, (iv) preparation of Frame peptide—MHC (pMHC) multimers, (v) selection of T-cells using the pMHC molecules. Preferably, the method further comprises the (vi) expansion of selected T-cells using appropriate culture conditions. More preferable the method comprises the infusion of the selected or expanded T-cells back into the patient. In a further exemplary embodiment, neoantigen sequences from an individual are identified as described herein. The neoantigen sequences are screened against a library of TCRs for binding. TCRs identified as positive binders are transfected into the T-cells of said individual and transfected back into said individual.
Methods for the selection and identification of immune cells, preferably T-cells or T-cell receptors with specificity for neoantigens are well-known in the art (see e.g. reviews by Bianchi et al, Front Immunol. 2020; 11: 1215 and Zhao and Cao, Frontiers in Immunology, 2019, https://doi.org/10.3389/fimmu.2019.02250, as well as US20180000913, which is hereby incorporated by reference). For example, predicted MHC-I binding epitopes from the Frame neoantigens are bound to synthetic tetrameric forms of fluorescently labelled MHC Class I molecules. CD8+ T-cells with the appropriate T cell receptor will bind to the labelled tetramers and can be selected by flow cytometry. Other suitable methods include those described in U.S. Pat. No. 7,125,964. Briefly, recombinantly produced biotinylated MHC molecules are attached to avidin coated magnetic beads. Peptides and T-cells are added to the beads. T-cells absorbed to the beads (via the interaction with a peptide-MHC complex) are selected.
In some embodiments, the disclosure provides methods which are not a treatment of the human or animal body and/or methods that do not comprise a process for modifying the germ line genetic identity of a human being.
As used herein, “to comprise” and its conjugations is used in its non-limiting sense to mean that items following the word are included, but items not specifically mentioned are not excluded. In addition, the verb “to consist” may be replaced by “to consist essentially of” meaning that a compound or adjunct compound as defined herein may comprise additional component(s) than the ones specifically identified, said additional component(s) not altering the unique characteristic of the invention.
The articles “a” and “an” are used herein to refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element.
The word “approximately” or “about” when used in association with a numerical value (approximately 10, about 10) preferably means that the value may be the given value of 10 more or less 1% of the value.
The invention is further explained in the following examples. These examples do not limit the scope of the invention, but merely serve to clarify the invention.
Long read transcript sequences derived from long read single molecule sequencing instruments, such as Oxford Nanopore, have a considerable sequence error rate in the order of 1-10% for molecules that have only been read once. Therefore, identification of splice junctions in long read transcripts is inherently error-prone. To correctly identify splice junctions from long read transcript sequences, we propose to use short read transcript sequencing data (
Splice junctions in long transcript sequencing reads were corrected using short transcript read junctions for which both the 5′ and 3′ splice sites were within a 15 bp window of the respective long read 5′ and 3′ splice sites. For cases in which multiple different short read splice junctions satisfied this criterion for a given long read junction, the most likely short read junction was chosen via a Bayesian model in which the posterior probability that an observed long read junction arose from an mRNA with a given short read junction was calculated according to:
Where the event si is the long read arising from the splice junction i, and the event Fi, Ti is the observation of a long read having a given 5′/3′ distance pair from its underlying original splice sites. The prior probability that a long read arose from an mRNA with splice junction i was calculated according to:
where Ri is the number of short reads supporting junction i and R is the total spliced short reads within the long read splice site window. The probability of observing the splice offset pair Fi, Ti given the long read arose from an mRNA molecule with splice junction i was calculated according to:
where NF
Where the summation is taken over the n splice junctions within the long-read junction window. Combining these expressions gives:
Short read splice junctions with the highest probability were chosen to correct long read junctions. Long read splice junctions for which no short read junctions had a correction probability of at least 0.9 were considered uncorrected. Reads which had one or more uncorrected junctions were not considered further.
The above Bayesian model was evaluated using long-read and short-read transcriptome sequencing data of a lung cancer. The uncorrected and corrected long transcript reads are depicted in
We have sequenced the genomes of multiple lung tumors using short-read (2*150 bp) whole genome sequencing to a coverage depth of 100× on Illumina HiSeq. Similarly, the corresponding germline genomes were also sequenced to a coverage depth of 30×. The transcriptomes of the lung tumors were sequenced using short-read RNA-sequencing, following the preparation of a cDNA library using the Roche Kappa mRNA prep kit. The cDNA libraries were sequenced on Illumina HiSeq generating approximately 100M paired reads (2*150 bp) per tumor. In addition, we prepared the total RNA of each tumor for long-read sequencing by first performing selection of polyadenylated mRNA molecules using oligo-dT probes and subsequent generation of Capped mRNAs using the TeloPrime procedure, which generates double-stranded cDNA only for mRNA molecules with a 5′ Cap structure. Around 200 ng of polyadenylated and capped mRNA was used for preparation of an Oxford Nanopore sequencing library using kit SQK-LSK109. Between 10Gb to 100Gb of data (˜10M to 100M reads) were generated per tumor sample on a Nanopore GridION or PromethION sequencer.
All classes of genetic variations were called in the short-read whole genome sequencing data using an existing pipeline for read mapping to reference genome GRCh37 and variant calling: https://github.com/hartwigmedical/. From the somatic genomic variant calls, we extracted SNVs that are within 20 bp from a known splice donor or splice acceptor site annotated in the Ensembl database (www.ensembl.org).
Short RNA reads were mapped to the human reference genome GRCh37 using STAR (version 2.7.3a; Dobin et al, Bioinformatics, Volume 29, Issue 1, January 2013, Pages 15-21). Long RNA reads were mapped to human reference genome GRCh37 using minimap2 (version 2.17; Li, Bioinformatics, Volume 34, Issue 18, 15 Sep. 2018, Pages 3094-3100).
The alignment file (BAM) of the long-read RNA sequencing data was used together with the short-read splice junctions to correct the long-read RNA splice junctions, as described in example 1.
For each aligned and corrected long transcript read (Nanopore) bridging a splice-site mutation (as defined above), the (corrected) splice-junctions were examined and splice-junctions within the effect zone (i.e. between the exon before the splice mutation and the exon after the splice mutation) were checked for uniqueness with respect to known slice-junctions from Ensembl and GTEx (https://gtexportal.org/home/publicationsPage). A threshold for uniqueness with respect to GTEx was defined as a maximum of 10 samples containing the exact splice junction. Unique splice junctions (as defined above and with respect to GTEx), were furthermore required to have support in both short-read RNA and long-read RNA data. Transcript reads containing unique splice junctions according to these criteria were in silico translated by inferring the translation start site based on the overlap of the transcript read with known Ensembl transcripts and their translation start annotations. Translation was performed based on the reference genome sequence, ignoring germline genetic polymorphisms and somatic SVNs. The C-terminal novel part of the in silico predicted protein sequence that is extending beyond the known N-terminal part of the protein is regarded as the Splice Frame sequence. For one lung tumor, a splice site mutation was identified in TP53 gene, affecting a known splice acceptor site (
We have sequenced the genomes lung tumors using short-read (2*150 bp) whole genome sequencing to a coverage depth of 100× on Illumina HiSeq. Similarly, the corresponding germline genomes were also sequenced to a coverage depth of 30×. The transcriptomes of the lung tumors were sequenced using short-read RNA-sequencing, following the preparation of a cDNA library using the Roche Kappa mRNA prep kit. The cDNA libraries were sequenced on Illumina HiSeq generating approximately 100M paired reads (2*150 bp). In addition, we prepared the total RNA for long-read sequencing by first performing selection of polyadenylated mRNA molecules using oligo-dT probes and subsequent generation of Capped mRNAs using the TeloPrime procedure, which generates double-stranded cDNA only for mRNA molecules with a 5′ Cap structure. Around 200 ng of polyadenylated and capped mRNA was used for preparation of Oxford Nanopore sequencing libraries using kit SQK-LSK109. Approximately 68Gb of data (60M reads) were generated on a Nanopore MinION sequencer.
All classes of genetic variations were called in the short-read whole genome sequencing data using an existing pipeline for read mapping to the reference genome GRCh37 and variant calling: https://github.com/hartwigmedical/. From the somatic genomic variant calls, we extracted SNVs that are within a gene, but distant from known splice donor and splice acceptor sites, i.e. further than 20 bp away from a known splice donor or splice acceptor site annotated in the Ensembl database (www.ensembl.org).
Short RNA reads were mapped to the human reference genome GRCh37 using STAR (version 2.7.3a; Dobin et al, Bioinformatics, Volume 29, Issue 1, January 2013, Pages 15-21). Long RNA reads (Nanopore) were mapped to human reference genome GRCh37 using minimap2 (Li, Bioinformatics, Volume 34, Issue 18, 15 Sep. 2018, Pages 3094-3100). The alignment file (BAM) of the long-read RNA sequencing data was used together with the short-read splice junctions to correct the long-read RNA splice junctions, as described in example 1.
For each aligned and corrected long transcript read (Nanopore) bridging a somatic SNV (as defined above), the (corrected) splice-junctions were examined and splice-junctions within 20 bp from a somatic SNV were checked for uniqueness with respect to known splice-junctions from Ensembl and GTEx (https://gtexportal.org/home/publicationsPage). A threshold for uniqueness with respect to GTEx was defined as a maximum of 10 samples containing the exact splice junction. Unique splice junctions (as defined above and with respect to GTEx), were furthermore required to have support in both short-read RNA and long-read RNA data. Transcripts containing unique splice junctions according to these criteria were in silico translated by inferring the translation start site based on the overlap of the transcript read with known Ensembl transcripts and their translation start annotations. The C-terminal novel part of the in silico predicted protein sequence that is extending beyond the known N-terminal part of the protein is regarded as the Splice Frame sequence (
In prior work (WO2021/172990), methodology was described to identify tumor-specific expressed open reading Frame sequences caused by structural genomic variants (SVs). Here, we extend this methodology to identify SVs, primarily deletions, that alter the splicing pattern of mRNAs by creating or deleting splice acceptor or splice donor sites. We sequenced the genomes and transcriptomes of lung tumors as described in Examples 2 and 3. All classes of genetic variations were called in the short-read whole genome sequencing data using an existing pipeline for read mapping to the reference genome GRCh37 and variant calling: https://github.com/hartwigmedical/. From the somatic genomic variant calls, we extracted deletions (larger than 20 bp) for which both breakpoints are within a single gene. Smaller deletions or other types of genetic changes, such as insertions and inversions and duplications are regarded as short indels and are treated as somatic single nucleotide variants, as described in Examples 2 and 3. We discriminated between deletions that encompass exonic sequences and intron/exon boundaries, and deletions that encompass solely intronic sequences and are further than 20 bp away from a known splice junction (
In a second step, a local in silico reconstruction of the tumor genome was generated based on the identified deletion breakpoint junctions within the gene (
Short RNA reads were mapped to a GRCh37 human reference genome appended with the reconstructed tumor-specific contigs using STAR (version 2.7.3a; Dobin et al, Bioinformatics, Volume 29, Issue 1, January 2013, Pages 15-21). Long RNA reads (Nanopore) were mapped to the same extended reference genome using minimap2 (version 2.17; Li, Bioinformatics, Volume 34, Issue 18, 15 Sep. 2018, Pages 3094-3100). The alignment file (BAM) of the long-read RNA sequencing data was used together with the short-read splice junctions to correct the long-read RNA splice junctions as described in Example 1.
For each aligned and corrected long transcript read (Nanopore) aligning to the rearranged gene and bridging the deletion breakpoint junction, each of the (corrected) splice-junctions was examined. Splice-junctions were checked for uniqueness with respect to known splice-junctions from Ensembl and GTEx (https://gtexportal.org/home/publicationsPage). A threshold for uniqueness with respect to GTEx was defined as a maximum of 10 samples containing the exact splice junction. Unique splice junctions (as defined above and with respect to GTEx), were furthermore required to have support in both short-read RNA and long-read RNA data. Transcripts containing unique splice junctions according to these criteria were in silico translated by inferring the translation start site based on the overlap of the transcript read with known Ensembl transcripts and their translation start annotations. The C-terminal novel part of the in silico predicted protein sequence that is extending beyond the known N-terminal part of the protein is regarded as the Splice Frame sequence. Two examples of tumor-specific intragenic deletions identified in lung tumors and affecting splicing are depicted in
The presence of expressed Splice Frames was determined in 14 advanced tumors. In addition, we determined other categories of Frames, previously described in WO2021/172990. Tumor samples were analyzed using a combination of multiple sequencing technologies. Genomic DNA was extracted from the tumor sample and the corresponding blood cells of the same patient, using established procedures (Macherey Nagel NuceoSpin or Qiagen DNeasy spin columns). DNA was used for whole genome paired-end sequencing (2×150 bp reads) on Illumina NovaSeq instruments to an average coverage depth of 100× for the tumor sample and 30× for the corresponding blood (control) sample.
In addition, total RNA was isolated from the tumor sample using Macherey Nagel NucleoSpin RNA extraction methods. Total RNA was used for short-read RNA sequencing on Illumina NovaSeq, following ribosomal RNA depletion of total RNA and preparation of a short-read RNA sequencing library from the ribosomal RNA depleted RNA using Illumina TruSeq protocols. Approximately 50 million short paired-end RNA sequencing reads were generated per tumor sample.
Long-read full-length cDNA sequencing was performed using Oxford Nanopore GridION or PromethION technology. Full-length mRNA molecules were selected from total RNA preparations obtained from tumor cells based on the presence of a 5′ CAP and a 3′ poly-A tail. Double-stranded cDNA was prepared from said full length mRNA molecules and the cDNA was sequenced on Oxford Nanopore GridION or PromethION using standard procedures known to skilled persons in the art. At least 10 million full-length transcripts sequences were generated for each tumor sample.
Whole genome sequencing data were analysed using existing bioinformatics methods to identify somatic genetic changes (e.g. as described by Priestley et al, Nature 575, pages 210-216, 2019 and https://github.com/hartwigmedical/), typically resulting in a few thousand somatic point mutations (single nucleotide variations), a few hundred somatic small insertions and deletions (indels), and up to a few hundred of somatic genomic rearrangements (structural variations, SVs), per tumor sample.
Long-read cDNA (transcript) sequence reads were mapped to the human reference genome (GRCh37) appended with tumor-specific contigs, as determined based on the detected somatic genomic rearrangement breakpoint-junctions in the tumor genome (
Novel methodology is provided to accurately determine the neoantigenic sequences resulting from genetic mutations that lead to splice aberrancies in a tumor sample. The use of long-read transcript sequencing to detect complete novel transcript sequences that lead to Frame neoantigens, as described herein, is a preferred method. The short-read RNA sequencing data derived from tumor specimens (amongst others lung, AML, pancreas) were evaluated for the presence of novel transcript splice junctions in the vicinity of possible gain-of-splice (GOS) mutations. In a subsequent step the presence of each novel short-read RNA junction was determined in corresponding long-read transcript sequencing data of the same sample. For only a fraction (<10%) of the novel short-read RNA GOS splice junctions, corresponding long-read transcript support could be obtained (
An important step with respect to the identification of splice Frames involves the prediction of the splice Frame peptide sequence. To determine the splice Frame peptide sequence, it is useful to know the translation start and the exact exonic structure of each transcript sequence (
The following steps describe an exemplary design of a Framome vaccine based on a cancer patient's mutation report.
To identify the full repertoire of NOPs expressed by tumors, we developed FramePro, a genomics and bioinformatics software package that characterizes the framome—a set of all NOPs expressed by a tumor as a result of genetic mutations in cis. FramePro integrates whole genome sequencing (WGS) with long- and shortread RNA sequencing to detect full-length transcripts encoding NOPs at single-molecule resolution, thereby accounting for isoform diversity. FramePro was applied to 61 tumors across six cancer types, providing a comprehensive picture of expressed NOPs for each tumor sample. We describe an uncharacterized class of neoantigens, referred to as ‘hidden’ NOPs in which a known protein coding gene drives transcription and translation of a usually non-coding region of the genome which has been placed downstream via an SV. We demonstrate that transcripts encoding hidden NOPs are translated into proteins and that peptides derived from hidden NOPs can bind to MHC class I molecules and were found to generate memory T-cell responses in a lung cancer patient. Of note, hidden NOPs represent a major source of neoantigenic amino acids in most tumors. Taking the hidden NOPs together with those derived from frameshift indels, fusion genes, splice mutations and stoploss mutations, the framome size can reach up to ˜2000 amino acids for tumors across major cancer types. This large source of potentially highly immunogenic, long and tumor-specific peptide sequences represents an attractive target for personalized immunotherapy.
Recent studies have evaluated personalized neoantigen cancer vaccines in early-stage clinical trials with a primary focus on missense neoantigens identified from exome sequencing [21, 22, 23]. To systematically extend the repertoire of possible neoantigens expressed in tumors, we here focus on the identification of neoopen reading frame peptides (NOPs) derived from novel open reading frames (neo-ORFs), resulting from cis genomic mutations, including genomic rearrangements, indel frameshifts, splice mutations and stoploss mutations. [10, 11, 17, 18, 19, 15, 12, 13, 14, 20].
As a basis for our analysis, we collected a series of 61 tumor samples from patients with non-small lung cancer, pancreatic cancer (i.e. pancreatic ductal adenocarcinoma), head and neck cancer, colorectal cancer, glioblastoma, and triple-negative breast cancer. Tumor samples and corresponding normal tissue or blood samples were subjected to deep whole genome sequencing (tumor WGS−100X) to identify all classes of somatic genetic changes based on an existing and validated analysis pipeline (4. Methods) [8]. We identified on average 26,287 (208-418,406) single-nucleotide variants (SNVs); 1,847 (65-24,160) short indels and 261 (3-2,417) structural variants (SVs) per tumor sample (
To characterize the effects of genomic changes on the tumor transcriptome, we performed RNA sequencing using a combination of conventional short-read RNA sequencing and long-read sequencing of mRNA transcripts (4. Methods). We developed a method to extract intact mRNAs from tumor samples, by a cDNA preparation process involving 3′-polyA and 5′-CAP selection. Double-stranded cDNA was sequenced on Nanopore sequencing devices reaching a throughput of about 1M-97M RNA sequences per sample. Up to 92.3% of long-read mRNA sequences spanned a full transcript molecule known in the Ensembl database, indicating the strength of the long-read data to determine complete transcript sequences at the single molecule level (
Identification of possible neoantigens from sequencing data is often limited to the detection of coding mutations (e.g. by exome sequencing), followed by analysis of the expression of the identified genomic changes using short-read RNA sequencing. The neoantigenic peptide sequence is subsequently inferred from known transcript structures present in existing genome annotation data. However, a preferred method would be to directly determine peptide sequences based on the repertoire of expressed transcript isoforms in the tumor. We leveraged the WGS and short- and long-read RNA sequencing data as input for a novel bioinformatics pipeline (FramePro) to map complete tumor-specific transcript sequences caused by cis somatic mutations, including SVs, indels and SNVs within and outside coding regions.
The FramePro analysis workflow comprises four steps that integrate somatic mutation data with transcriptome sequences to identify all neo-ORFs and corresponding NOPs (
FramePro is the first tool to internally integrate full-length sample-specific transcript structures with variant protein effect prediction as well as the first tool to directly couple WGS with long-read transcriptome sequencing for the discovery and validation of SV-driven tumor specific isoforms. We used the FramePro analysis pipeline to analyze neo-ORFs and corresponding NOPs caused by SVs, frame-shift indels, splice mutations, and stoploss mutations across the entire tumor datasets of this study. An example of each class of NOP is provided in
Gene fusions represent a frequent outcome of somatic SVs in cancer genomes and in-frame gene fusions can be drivers of tumorigenesis [24]. However, the majority of gene fusions represent a configuration where the 3′ partner gene is out-of-frame with the 5′ partner gene, creating a novel gene encoding a NOP [13] (
To understand the translation of tumor-specific chimeric transcripts that encode hidden NOPs, we performed a FramePro analysis on human cell lines A375 (melanoma), MCF7 (breast adenocarcinoma), and 7860 (renal cell adenocarcinoma) resulting in the identification of 11, 22, and 8 hidden NOPs, respectively. For the majority of these hidden NOPs RiboSeq coverage was observed in the expressed non-coding region, with the majority of the RiboSeq reads indicating the expected reading frame that was inferred from the translation start site of the partner gene (
2.4 Many Tumors have Large Framomes
We identified 946 unique NOPs amongst the 61 tumor samples described in this work, and we classified the NOPs according to their genomic origin (
We have termed the entire collection of NOPs expressed by a specific tumor sample ‘the framome’. Representative examples of tumor framomes are given in
Expression level and clonality are features that can be used for selection of neoantigens as immunotherapy targets [25, 26]. The expression levels of mRNAs encoding NOPs were measured based on the long-read RNA sequencing data generated for each tumor sample and quantified as transcripts per million (TPM)
Complex chromosomal rearrangements, such as chromothripsis, are a frequent phenomenon in cancer genomes [28]. For 32% of the genomic events leading to hidden NOPs or out-of-frame gene fusions, the genomic connection between the 5′-end of the known gene and the non-coding genomic segment or downstream out-of-frame gene was formed by more than one genomic breakpoint-junction (
The analysis of single full-length transcript molecules using FramePro enabled us to identify the entire spectrum of transcript isoforms encoding a hidden NOP. The majority (67%) of SVs leading to hidden NOPs involve transcripts that encode a single unique NOP. However, we observed multiple instances of hidden NOPs that were caused by different transcript isoforms derived from the same genomic SV. For example, a hidden NOP in a triple negative breast tumor involved multiple splice isoforms encoding 4 different unique NOP sequences (
To understand the amount of possible HLA binding epitopes among NOPs expressed in tumors, we performed in silico characterization of HLA class I binding. To do so, we determined the HLA class I types for each individual tumor based on whole genome sequencing data, and the HLA types were used to predict binding epitopes within NOP sequences (Methods 4.6). The number of predicted binders is shown in
To understand whether a cancer vaccine based on NOPs would be advantageous with respect to the number of possible MHC class I epitopes, as compared to vaccines based on commonly used missense variants, we generated cancer vaccine designs as described in Methods 4.8. In
2.5 Framome-Derived Epitopes Bind to Various HLA Alleles In Vitro and are Recognized by Memory CD8+ T Cells of Patients with Advanced NSCLC
To further characterize the immunogenic properties of NOPs, we assessed the affinity of framome-derived epitopes to various HLA-A and -B alleles by performing in vitro HLA binding assays (Methods 4.11). First, we selected more than 30 epitopes derived from the framomes of each of three patients with advanced lung cancer (LUN024, LUN026, and LUN029) and we tested the binding of these epitopes using in vitro binding assays
The importance of neoantigens for cancer immunotherapy has become clear from multiple studies that have highlighted the relation between tumor mutational burden and the effectiveness of T-cell checkpoint immunotherapy [30]. Initial work has particularly emphasized the role of exonic point mutations leading to single amino acid changes (i.e., missense variants) in checkpoint immunotherapy response [31]. Further studies have demonstrated that novel neoantigenic peptides derived from frame-shift indels, which are highly different from self, contribute to the immunogenic phenotype of cancers and positively correlate to checkpoint inhibitor response [9]. In addition, novel tumor-specific peptide sequences derived from splice aberrancies and gene-fusions have been shown to provide additional sources of possible neoantigenic NOP sequences across cancer types [14, 32, 33]. Complementary experimental studies have confirmed the strong immunogenic properties of NOPs derived from frame-shifts, including their capacity to trigger CD4+ and CD8+ T-cell responses and tumor growth delay in model systems [10, 34]. The long and foreign peptides represented by NOPs may be preferred targets for immunotherapies, stressing the need for a robust method to identify all classes of NOPs from a small tumor biopsy.
The work described here provides a technological and bioinformatics framework to exploit the full potential of neo-open reading frames encoded in the tumor genome as a result of cis-acting somatic mutations. Identification of the full spectrum of expressed NOPs in tumors requires whole genome sequencing as basis complemented with RNA sequencing to map mutated transcripts. Only whole genome sequencing captures the complete catalogue of somatic mutations arising in cancer genomes, including SNVs, indels and SVs [8]. Although commonly used exome sequencing is an efficient technology for detection of exonic mutations (e.g., frameshift indels) in tumor samples, it falls short with respect to identification of intronic and intragenic variants and SVs. For example, splice-site creating mutations are a known source of neoantigenic sequences, yet such mutations often reside outside of known exons captured by exome sequencing [18].
Our work demonstrates that SVs provide a rich source of possible cancer neoantigens, beyond well-described neoantigenic sequences derived from fusion genes [14]. We performed a systematic analysis of the effects of SVs on the cancer transcriptome and find that SVs often drive expression of non-coding genomic regions via fusion with the 3′-end of a known gene. We designate these as hidden NOPs as their existence cannot be identified from genome sequencing alone, but requires the integrated analysis of cancer transcripts sequences with somatic SVs in the cancer genome. By comparing the contribution of NOPs derived from splice mutations, stop loss mutations, frameshift indels, and SVs, we observed that >50% of the amino acid sequences contributed by NOPs are derived from hidden NOPs caused by SVs. Additionally, we validated the relevance of SVs as neoantigens as we identified hidden NOP specific memory type CD8 T cells in the blood of a patient with advanced NSCLC. Personalized neoantigen-based immunotherapy strategies targeting tumors with a high level of SVs (e.g., glioblastoma, TN breast), or with both high SV and high indel count (e.g. lung cancer) would benefit from a neoantigen discovery approach as outlined here. Personalized cancer vaccines are currently studied in many clinical trials worldwide [3], and the basis for such vaccines is formed by sequencing of the tumor exome. We propose that a complete analysis of the cancer genome will enable optimal design of personalized cancer vaccines, thereby leveraging the full neoantigenic potential of a tumor.
In addition to genomic analysis of the tumor, faithful mapping of mutation-derived transcripts encoding possible neoantigens allows one to precisely determine tumor-specific peptide sequences. The conventional approach for determining the expression of somatic variants in tumor samples is based on short-read RNA sequencing, where allele-specific expression can be measured from the RNA sequences covering a specific genetic mutation. Although such measurement provides immediate insight into the expression level of a specific genetic mutation, it does not provide a complete view on the sequence context of the expressed mutations. The wide diversity of transcript isoforms encoded by the human genome has become apparent through full-length transcript sequencing [36]. Direct mapping of the isoforms of a gene would be a preferred approach to infer neoantigenic peptide sequences, rather than the commonly used approach to use existing transcript annotations. Here, we demonstrate the value of long-read transcriptome sequencing and integrating the long-read transcript sequences with somatic mutations identified through whole genome sequencing. The combined approach of whole genome and long-read transcriptome sequencing enables analysis of neoantigenic sequences derived from individual transcript sequences based on the identification of translation start sites and accurate transcript structure and sequence. Our current approach involves the use of short-read RNA sequencing to refine transcript splice-junction sequencing, but we expect that future generations of long-read sequencing will make such an approach obsolete.
In conclusion, we here present a universally applicable FramePro methodology that enables systematic identification of neo-open reading frames and corresponding NOPs resulting from somatic mutations in a tumor genome. We propose that upcoming personalized cancer immunotherapies include a comprehensive analysis of possible neoantigenic sequences expressed by the tumor, as a basis for therapy design. The outcome of clinical trials based on such neoantigen detection approach will provide experimental evidence for the relative contribution of different neoantigen classes to the tumor immunophenotype, as well as their relevance for therapy effectiveness.
Fresh frozen tumor biopsies and corresponding blood samples or normal control tissue were obtained from different clinical centers. Informed consent and ethical approval was obtained for each sample for studying tumor DNA and RNA sequencing information. Patient samples were obtained under studies OLSO41-202100773 Framoma (Oncolifes, University Medical Center Groningen), AMC 2014 181 BioPAN (Amsterdam UMC), IRBdm21-018 (Netherlands Cancer Institute), 09H050190 (LREC, University of Liverpool), Pro000074343 (Duke University), XXX (Erasmus Medical Center Rotterdam), NCT01792934 (Radboud University Medical Center).
Genomic DNA was isolated from tumor biopsies and control tissue (blood or adjacent normal tissue) using Qiagen DNeasy. As input, 50-200 ng of DNA was sheared to an average length of 450 bp by Covaris and standard TruSeq Nano LT library preparation (Illumina) with 8 PCR cycles was performed. Barcoded libraries were sequenced on Illumina NovaSeq instruments with 2×151 bp settings, to an average coverage depth of 100× (tumor samples) and 35× (control samples). FASTQ generation was done using Illumina bcl2fastq (v2.20.0.42). Sequencing reads were mapped to human reference genome GRCh37 using BWA (version) with settings XXX. Somatic genomic variants were called from aligned sequencing data using a custom pipeline [8] (https://github.com/hartwigmedical/pipeline5/tree/master/cluster/src/main/java/com/hartwig/pipeline).
Total RNA was isolated from fresh frozen tumor samples using NucleoSpin RNA isolation (Machery Nagel). cDNA library prep was performed according to a standard protocol using 100 ng of total RNA, which was chemically sheared for 7 minutes. Resulting cDNA was PCR amplified for 15 cycles. Libraries were sequenced on an Illumina NovaSeq system to a minimal depth of 50M paired reads (100M tags) per cDNA library based on 2×151 bp settings. FASTQ generation was done using Illumina bcl2fastq (v2.20.0.42). cDNA sequencing reads were mapped to the human reference genome GRCh37 using STAR (version) with settings XXX. Further processing of short cDNA sequencing data was done as described in section 4.5.
About 500 ng to 2 microgram of total RNA was used as input for double stranded cDNA preparation using TeloPrime Full-Length cDNA Amplification kit V2 (Lexogen) according to manufacturer's specifications. TeloPrime selects mRNA molecules containing a 5′ CAP and a 3′-poly-A tail. For some samples poly-A selected RNA was used as input for TeloPrime cDNA preparation. For those cases, selection of poly-A mRNA was performed using Dynabeads mRNA Purification kit (Invitrogen) and between 20-100 ng of poly-A selected mRNA was used as input for TeloPrime. Between 11-20 PCR cycles were performed for each sample. Double stranded cDNA was used as input for preparation of a Nanopore sequencing library using SQK-LSK109. Libraries were sequenced on GridION or PromethION systems (Oxford Nanopore Technologies) to a depth of between 20M-100M reads. Long cDNA Nanopore reads were mapped to human reference genome GRCh37 using Minimap2 (version). Further processing of long-read Nanopore cDNA sequencing data was done as described in section ‘Frame Pro methodology’.
All core steps in the FramePro pipeline including genome reconstruction, RNA isoform identification, isoform translation prediction, and NOP identification were implemented in python and packaged into the framepro package. Nextflow [37] was used to integrate these steps with RNA mapping and read extraction into the framepro-nf pipeline.
To identify neo-ORFs and corresponding NOPs, a tumor-specific reference genome was generated for each sample onto which long and short read RNA could be aligned. These tumor-specific reference genomes consisted of collections of contigs which captured the local effects of somatic mutations. For SVs, these contigs were identified through a combination of an RNA-naive approach and an RNA-guided approach.
To construct RNA-naive tumor SV contigs, SVs for a given sample were collected in breakend format. All protein coding genes hit by an SV in were identified. For each of these genes, a contig was constructed by starting ϵstart basepairs (default 1 kB) upstream of the first start codon and including the gene sequence up to the first SV breakend within the gene. The sequence downstream of this breakend was appended to this contig by crossing the SV to the mate breakend and continuing in the orientation specified until another SV breakend was encountered and crossed. SVs were removed from the list of SVs once crossed. This process was carried out until ϵmaxL basepairs (default 2 Mb) were appended downstream of the original gene segment. Each contig assembled in such a manner represents a possible local region of the tumor genome which is consistent with the SVs identified through tumor/normal WGS. By starting at the 5′ end of protein coding genes and extending downstream a distance longer than the typical range of transcription, all gene fusions and hidden frames whose protein expression may be driven by the starting gene can be identified once full-length transcripts are aligned to these contigs.
This RNA-naive approach can correctly resolve regions downstream of protein coding genes which involve simple SVs because it follows a linear path through next-nearest breakends. For more complex regions, such as occurs in chromothripsis, breakage fusion bridges, etc., an approach which utilizes information at the RNA level is used. Instead of starting with genomic events (SVs) which are not yet known to affect RNA transcripts, this approach takes sets of ungapped chimeric RNA alignments and attempts to explain their apparent transcript structure at the genome level through SVs. The set of contigs which explain these transcript structure changes are then appended to the reconstructed tumor reference genome after collapsing contigs redundant with the RNA-naive approach.
The RNA-guided approach starts with the alignment of RNA to a base reference genome as specific in section 4.4 and proceeds as illustrated in
The elements of Qr represent collections of consecutive segments of the read r which are non-linearly aligned to the reference genome. A gap or overlap buffer of p is utilized to allow for soft or hard-clipping, erroneous indels, and homology at the beginning and ends of the alignments. To arrive at a non-redundant (excluding prefix/suffix paths) set of chimeric RNA paths for read r, the set Pr can be defined as:
To find possible underlying tumor contig regions from which the proposed chimeric RNA structures within Pr may have arisen, it is necessary to find paths of SVs which connect the beginning and ends of consecutive chimeric alignments, referred to as chimeric introns. Each chimeric RNA path p in Pr contains a set Mp of size kpk−1 such chimeric introns m where mL and mH represents the lower and upper alignments on each side of the chimeric intron. This set Mp can be defined as:
A chimeric RNA path is considered supported by somatic genomic events if there is a conceivable path through the tumor genome which connects the end of the first chimeric intron alignment to the start of the second chimeric intron alignment for each chimeric intron in the path. To determine this for each path, a directed graph Gp is constructed which represents all possible connections within the tumor genome. The end/start loci of each chimeric intron can then be anchored onto Gp in order to find a valid path across the chimeric intron. To construct this graph, let the sample SVs be represented by a set B of breakends b where bc, bp, bs, bm are the breakend chromosome, position, strand, and mate breakend, respectively. Let the vertex set V (Gp) consist of vertices v where vc, vp, vs correspond to chromosome, position, and strand of genomic loci. Let two identical sets of breakend vertices be Vsource and Vsink be defined as:
Let the sets of lower-alignment and upper-alignment chimeric intron vertice sets be defined as:
The vertex set V (Gp) is then:
Two types of connections between genomic loci are possible within the rearranged tumor genome: those which occur between points on the same strand of the same chromosome in the normal reference genome and those which occur due to SVs. The edge set EWT represents WT connections which point from source vertices to sink vertices:
The edge set ESV represents connections between breakpoints due to SVs which point from sink vertices to their partner breakend source vertices:
The edge set EM represents the connections between lower chimeric intron alignments to sink breakend loci as well as the connections between source breakend loci to upper chimeric intron alignments (equations 13-17):
The edge set E(Gp) can now be specified as:
Let the weight of edge tuples in E(Gp) be defined as the genomic distance between each loci vertex, where connections between mate breakends have a distance of zero
Together V(Gp), E(Gp), and w:e→N0 fully define Gp. This RNA-SV graph was built in python using the networkx package [38], and Dijkstra's algorithm was used to find the shortest weighted genomic path between every mL to mH chimeric intron vertices through an alternating set of sink and source breakend vertices. The genomic paths of each chimeric intron were appended in the order of appearance in each path p to produce a contig starting at the first chimeric intron start anchor and ending at the final chimeric intron end anchor. The contigs specified by the set of these shortest chimeric intron paths were padded at the beginning and end by prepending/appending enough sequence to encompass the full chimeric RNA alignment at the start/end of the contig and any annotated genes overlapping these start/end alignments. The set of all contigs identified through this procedure for all alignment paths arising from all chimeric reads for a given sample were combined with the set of contigs produced through the RNA-naive approach. This set of contigs was collapsed by removing all contigs whose sequence was a strict subset of another. This set of non-redundant contigs were appended to the tumor specific reference genome.
Small variants predicted to lead to NOPs were also used as a basis for tumor-specific contig construction. To identify all indels possibly leading to NOPs, indels within the bounds of protein coding genes were identified. If the indel was within the exonic boundaries of any protein coding exon, it was selected for inclusion in variants used for reconstruction. If the indel was in a non-protein coding region of the gene such as an intron or UTR, the variant was included if there was at least one long RNA read which covered the indel locus. Stoploss variants were identified by selecting variants which disrupted an annotated known stop codon. Mutations leading to novel splice junctions as described in 4.5.2 were also selected for inclusion in the reconstruction. A portion of the reference chromosome containing each variant was extracted to include entire region of any genes and/or long reads overlapping each variant position. The genomic change specified by each small variant was then performed on this contig with each variant producing a contig which was appended to the tumor-specific reference genome.
Short read RNA splice junctions were considered novel and tumor specific if they were absent in the healthy tissues sequenced as part of the GTEx database [39] and were associated with a predicted causal somatic variant. The pre-compiled STAR splice junctions for GTEx v6 were downloaded from the Recount2 webserver and used as the normal tissue splice junction database [40]. Two general classes of variants were considered as causing novel splice junctions. In the first case, a variant is near an un-annotated splice site of the splice junction. These splice-gain variants are known to often lead to the formation of more-canonical splicing signals [17]. The second class of splice causing variants disrupt annotated splice sites by changing the genomic context of an annotated splice donor or acceptor. This splice site disruption may lead to full exon skipping or partial intron retention/truncation. The effect zone of these splice-disrupting variants was therefore taken as the 5′ start of the exon before the variant-affected exon up through the 3′ end of the exon after the variant-affected exon, including intronic regions. Any tumor specific splice junction with splice points within this genomic range was considered caused in cis by the splice-disrupting variant.
After alignment to the reconstructed tumor genome, tumor-specific RNA isoforms were identified through a combination of high-accuracy short reads and long but error prone long reads. Short read junctions were used to correct the splice points of long read alignments via a novel Bayesian splice-correction model illustrated in
where the event si is the long read arising from the splice junction i, and the event Fi,Ti is the observation of a long read having a given 5′ or 3′ distance pair from its underlying original splice sites. The prior probability that a long read arose from an RNA molecule with splice junction i was calculated according to:
where Ri is the number of short reads supporting junction i and R is the total spliced reads within the long read splice site window. The probability of observing the splice offset pair Fi,Ti given that the long read arose from an RNA molecule with splice junction i was calculated according to:
where NFiTi is the number of times the given offset pair occurred in all other long read splice junction corrections which were unambiguous because a single short read junction was present within the correction window and N is the total number of unambiguously corrected junctions. Both NFiTi and N were calculated for each sample based mapping of the short and long RNA to the base reference genome. The total probability of observing the long-read offset pair Fi,Ti irrespective of any given short read junction can be calculated according to:
where the summation is taken over the n splice junctions within the long-read junction window. Combining these expression gives:
Splice junctions with the highest probability were chosen, and long read splice junctions for which no short read junctions had a correction probability of at least psplice (default 0.9) were considered uncorrected. Reads which had one or more uncorrected junctions were not considered further for isoform identification. Splice corrected long read tumor-genome alignments were collapsed into RNA isoform structures by grouping reads with identical splice junctions together if their start loci and end loci were within ϵisoform basepairs (default 10) of each other.
Known protein coding transcript structures were used to predict the translation start sites of RNA isoforms. ENSEMBL gene annotations were parsed using the pyensembl python package [41]. These annotations were transposed onto the reconstructed tumor reference genome. For each RNA isoform, the set of most consistent transcript structures were identified by selecting the structures which had the most contiguous matching splice junctions, starting from the most 5′ transcript splice site. If a unique translation start site overlapping the RNA isoform could be identified for this collection of transcript structures, the protein sequence of the RNA isoform was predicted. If more than one translation start site was consistent with the transcript structure, the protein sequence of the isoform was considered ambiguous and a translation prediction was not performed. If the most consistent transcript structure was of a non-coding biotype, the RNA isoform was annotated as non-coding.
Once full-length protein isoforms arising from RNA aligned to the reconstructed reference genome were identified, the tumor-specific portions of each peptide were annotated as NOPs. Each amino acid of each protein coding isoform was annotated as novel or WT based on the following set of criteria, and strings of consecutive novel amino acids were considered distinct NOPs. For an amino acid to be considered novel in this protocol it must:
The first criteria is satisfied if the first nucleotide of the amino acid's codon does not align to a genomic position which is a known WT P-site. To rapidly check this for each amino acid in each protein isoform, a P-site genome was pre-compiled by annotating each position of each reference chromosome as either not overlapping with any known P-site, overlapping a P-site in the sense strand, overlapping a P-site in the antisense strand, or overlapping in both strands. Pyensembl [41] with ENSEMBL reference version 75 (GRch37) was used to determine the P-site status of each position in the reference genome. This P-site genome was compiled in a coded string format and stored as a fasta file which was loaded for each sample. This format can easily be extended to include other gene references or WT P-sites from other sources such as RiboSeq experiments.
While not overlapping with a WT P-site indicates a novel portion of the genome is being translated, homology between the novel translated region and other normally translated regions of the genome can mean a portion of an otherwise novel protein isoform may be identical to known WT proteins. To avoid considering these portions as part of NOPs, each amino acid must be a part of at least one k-mer which is not present in the set of known WT peptides to be considered novel. As NOPs represent potentially interesting neoantigen targets, the k-mer sizes corresponding to potential MHC-I epitopes were chosen. A pre-compiled WT k-mer database was compiled by decomposing all peptides in ENSEMBL and RefSeq protein databases into all possible 8-11mers. This set was made unique and stored as a flat file which was loaded as a set for each sample run. For each amino acid in each isoform, all possible 8-11mers which contained the amino acid in that peptide (max 38) were screened against the WT k-mer set. If all of the 8-11mers were contained within the WT set, the amino acid was not considered novel.
An amino acid must also arise from a codon which is downstream of the first variant which would potentially be driving tumor specific translation. For indels, stoploss, and structural variants the amino acid must simply be downstream of the first variant spanned by the RNA isoform. For splice NOPs, the amino acid must be downstream of the first novel splice junction. To avoid considering amino acids novel due to likely un-annotated splice isoforms which are not altered by the underlying somatic variants, the first exon downstream of the first novel splice junction must contain at least one novel amino acid for any of the amino acids in the peptide isoform to be considered novel. Additionally, amino acids in peptides spanning SVs are not considered novel if they are within the boundary of the anchor gene which is driving translation.
Polysolver [42] was used to predict HLA types using WGS data. NetMHCpan4.1 [43] was used to predict MHC-binding using an EL score cutoff of 2 for binders.
Self similarity of epitopes was computed as described in [29]. As a normal reference the ENSEMBL GRCH38 proteome was used. To generate random epitopes, random strings of 1000 nucleotides were generated with a GC content of 40.9% to match the bias of the human genome [44]. These NT strings were translated and a random 9-mer epitope was selected from the collection of resultant 9-mers. This process was repeated until enough random epitopes were generated to match the number of NOP epitopes.
To construct patient specific framome vaccine designs of a given amino acid length the longest NOPs were chained together with the remainder of the vaccine consisting of a NOP portion. Missense vaccines of a given length were constructed by chaining together 21 amino acid long sequences with the variant amino acid in the center. Any remaining required length consisted of an amino acid sequence of the required length with the missense mutation in the middle to provide the most potential CD8 epitopes. For both classes the minimum amount of amino acids appended was 8.
RiboSeq data for human cancer cell lines A375, MCF-7 and 7860 were generated as previously described. Data were mapped to human reference GRCh37. Ribosomal P-site offsets were calculated using the RiboSeQC R package. Long-read Nanopore RNA sequencing was performed on A375 cells. Short-read RNA reads (SRA accession number SRR8616020) and SV calls were obtained from the CCLE [45]. SV calls were converted to breakend format. The FramePro pipeline was then used to identify all neo-ORFs and corresponding NOPs for this cell line. RiboSeq read mapping locations (P-sites) were intersected with the portions of neo-ORF long-read RNA mappings leading to hidden frame NOPs. The periodicity of RiboSeq read P-site coverage in these regions was identified using custom scripts.
NOPs were classified as arising from tumor suppressor genes if their gene of origin was in the TSGene database [46].
Selected epitopes for the assessment of in vitro binding were synthesized by GeneCust (GeneCustance). In vitro binding was performed as described previously [47]. Briefly, a conditional HLA class I complex is stabilized through a photolabile peptide, which can be dissociated through UV irradiation. If the cleavage occurred in the presence of another HLA class I peptide, the reaction resulted in net exchange of the cleaved peptide, yielding an HLA class I complex with an epitope of choice. The peptide exchange efficiency was then analyzed using an HLA class I ELISA. The combined technologies allowed the identification of ligands for an HLA class I molecule of interest. HLA-peptide complexes with binding affinity >40% were then used to prepare fluorescently labeled tetramers for combinatorial coding and phenotyping, as described before [48].
To characterize the immunogenic properties of tumor-specific neo-open reading frames derived from splice mutations, the affinity of epitopes to various HLA-A and HLA-B alleles derived from a splice mutation-derived neo-open reading frame peptide will be assessed by in vitro binding assays. First, epitopes are selected for a splice neo-open reading frame peptide identified in a patient with lung cancer. Epitopes are selected by performing HLA affinity prediction for each of the HLA alleles in the patient and only epitopes with highest affinity were selected (i.e. EL score below 2), as described (Reynisson, B. et al, Nucleic acids research 48, W449-W454 (2020)). Epitopes are synthesized and in vitro binding will be performed (Rodenko, B. et al. Nature protocols 1, 1120-1132 (2006)). This will reveal several epitopes binding to the HLA-A and HLA-B alleles specific for this patient.
Next, fluorescently labeled HLA tetramers are generated each carrying an epitope with at least 40% binding affinity, as determined by the in vitro binding measurements. The tetramer-epitope complexes are subsequently used to stain CD8+ T-cells present in the peripheral blood mononuclear cell fraction of the patient using combinatorial coding (Hadrup, S. R. et al. Nature methods 6, 520-526 (2009)).
CD8+ T-cells binding to specific HLA tetramer-epitope complexes are phenotyped to evaluate if they have been exposed to the antigen already. This analysis will show that memory T-cells exist (i.e. CD8+CD45RA−, CD27−/dim) in the blood of the patient with specificity to one of the epitopes derived from the splice neo-open reading frame peptide. We conclude that epitopes derived from splice neo-open reading frame peptides can bind to HLA-A and HLA-B alleles expressed in a patient, and that antigen-specific immune responses can be induced by such epitopes.
In a subsequent experiment, the immunogenic properties of the same splice neo-open reading frame peptide are determined using in vitro immunogenicity assays. Therefore, monocyte-derived immature dendritic cells are generated from peripheral blood mononuclear cells obtained from healthy donors with various HLA types. The dendritic cells are electroporated with an mRNA construct encoding the splice neo-open reading frame. Following electroporation and maturation, the DCs are co-cultured with Pan T cells. Pan T cells are re-stimulated with transfected dendritic cells and subsequently harvested and seeded onto IFN-gamma FluoroSpot plates for read-out. FluoroSpots spot forming units will be recorded and compared to negative control (no antigen) and positive control (viral antigens). This experiment provides a broad view on the capacity of the splice neo-open reading frame peptide to trigger T-cell mediated IFN-gamma production for a large number of donors across different HLA alleles.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2029480 | Oct 2021 | NL | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/NL2022/050597 | 10/21/2022 | WO |
