Neoantigen Prediction Research Articles

Abstract 548: Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets

Abstract Evolutionary processes play a crucial role in the development of cancer, where tumor cells continuously adapt, leading to the dominance of cell populations over their non-tumor counterparts, evading treatment and ultimately resulting in death. Effective cancer therapies induce significant and targeted selection pressures. During and in response to the induced bottleneck by therapy, the heterogeneous cancer cell population either diminishes or evolves, causing a subsequent selective sweep and the emergence of cells with resistance traits. However, this bottleneck phase presents a unique opportunity to predictably reduce the diversity of tumor cells. In this context, we propose a framework to quantify the selection on variants before, during, and after the therapeutic bottleneck, enhancing our understanding of the mechanisms underlying the acquisition of therapeutic resistance. Furthermore, we combine measures of selection intensity with the prediction of neoantigens in a patient cohort to identify potential alternative targets for immunotherapy. The outcome is a strategy that exploits the predictability introduced by the primary therapeutic bottleneck to eliminate the remaining tumor cell population. Finally, we illustrate the efficacy of this strategy in the context of several cancer types and empirically identify potential cancer type specific and pan-cancer immunotherapy targets orthogonal to primary, first-line therapies. Citation Format: J. Nic Fisk, Jeffrey P. Townsend. Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 548.

Abstract 3886: Analysis of clonal heterogeneity within paired primary and metastatic tumor samples of patients with solid tumors and implications for neoantigen-based personalized cancer vaccines

Abstract Background: Neoantigen-based personalized cancer vaccines carry significant promise in treating solid malignancies. However, there are uncertainties regarding the choice between the primary or the metastatic tumor for neoantigen prediction in individual patients. Here, we conducted a thorough examination of somatic variations in 676 patients who had paired primary and metastatic solid tumors. Methods: Patients were enrolled in the Total Cancer Care protocol (NCT03977402) to which patients provided an IRB-approved written informed consent within the Oncology Research Information Exchange Network (ORIEN). Whole-exome sequencing of 756 primary and metastatic tumor pairs was performed (N = 676 patients). These included Genitourinary (n=83), Gynecological (n=97), Gastrointestinal (n=213), Thoracic (n=33), Cutaneous (n=24), Breast (n=108), Endometrial (n=49), Sarcoma (n=35), Head-and-Neck (n=106) and others (n=8). The data was analyzed through the ORIEN AVATAR Molecular Analysis Pipeline for somatic mutation variant detection and variant annotation. In this analysis, we focused on somatic events that result in an in-frame alteration (such as missense, in-frame deletion and in-frame insertion) and out-of-frame protein-altering mutations (such as frameshifts, de novo start, out-of-frame, and nonstop gain). Clonal population structure was determined based on pyclone-vi. Results: For in-frame events, bladder cancer, melanoma, and gynecological cancers shared close to 75% of the mutations between paired primary and metastatic cases. In contrast, sarcoma and thyroid cancer had a low overlap (~ 25%) of variants. For out-of-frame events, these events tend to have a lower proportion of shared somatic variants between primary and metastasis than in-frame variants. Oncogenic drivers (e.g., BRAF V600E, KRAS G12A, and TP53 loss-of-function) were highly likely to be present in both paired primary and metastatic tumors. Next, we performed additional analysis on evolutionary selection of protein-coding variants via dN/dS calculation. We found no significant global shift in dN/dS ratio between paired primary and metastatic tumors across malignancies. However, we found increased selection of protein-coding variants in brain and liver metastatic sites, which correlated with increased homologous recombination deficiency within these sites. Conclusions: Our analysis demonstrates genetic variations that exist when comparing paired primary and metastatic tumors that appear to vary by histology. Variants are potentially undergoing negative selection supported by the preferential loss of out-of-frame events in metastatic tumors and positive selection in specific metastatic sites. Overall, understanding the clonal structure will be key to neoantigen prediction for effective neoantigen-based vaccines. Citation Format: Alyssa Obermayer, Timothy Shaw, Darwin Chang, Joshua Davis, Jamie K. Teer, Xiaoqing Yu, Xuefeng Wang, Dale Hedges, Aik Choon Tan, Robert Rounbehler, Abdul Rafeh Naqash, Margaret Gatti-Mays, Aakrosh Ratan, Martin McCarter, Howard Colman, Igor Puzanov, Susanne Arnold, Michelle Churchman, Patrick Hwu, William Dalton, George Weiner, Jose Conejo-Garcia, Paulo C. Rodriguez, Bodour Salhia, Ahmad A. Tarhini. Analysis of clonal heterogeneity within paired primary and metastatic tumor samples of patients with solid tumors and implications for neoantigen-based personalized cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3886.

Abstract 3855: Kinetics of neoantigen-specific T cells in post-transplant relapse

Abstract Introduction: Graft-versus-leukemia effect (GVL) underpins the curative mechanism of allogeneic stem cell transplantation (alloSCT) for acute leukemias. Donor-derived T cells can recognize various antigens, including human leukocyte antigen (HLA), minor-histocompatibility antigens (miHA), over-expressed antigens, or neoantigens derived from somatic mutations of leukemia. However, the role of neoantigens has not been fully investigated in GVL. Here, we aimed to predict neoantigens and identify neoantigen-reactive T cells using samples after alloSCT. Materials: Leukemia blasts were purified from relapsed samples. Recipient saliva or T cells from pre-transplant peripheral blood (PB) were used as a recipient germline control, while donor monocyte or CD34 cells were used as donor controls. DNA/RNA isolated from these specimens were analyzed for whole exome sequencing (WES) paired with RNAseq. Somatic mutations were called using blasts and recipient germline controls by mutect and mpileup and annotation with SnpEff. Somatic mutations with variant allele frequency >20% were screened for neoantigen prediction bound to recipient HLA (IC50 < 500nM) by pVAC-seq. We also genotyped 73 SNPs associated with publicly known miHAs. Serial PB were stained for HLA-A*02:01 tetramers loaded with neoantigens or CMVpp65. T cells were stimulated with neoantigens for 10 days and tested for cytokine production in responses to target or control peptides. Results: We screened neoantigens in 8 patients relapsing after alloSCT (7 acute myeloid leukemia and 1 acute lymphoblastic leukemia) and their donors (4 matched siblings, 1 matched unrelated, 3 haploidentical). In 7 subjects, WES/RNAseq showed an average of 273 somatic mutations per patient (range 108- 609). In one subject, targeted next-generation sequencing was used to detect 2 somatic mutations. Among these mutations, seven out of eight subjects (87.5%) had potential neoantigen candidates, with a median number of 9 antigens per subject. In contrast, only 2 subjects (25%) had potential miHA. UPN8, a responder to donor lymphocyte infusion (DLI), showed dominant CMVpp65-specific T cells at the time of relapse and emergence of TP53- (neoantigen) or HA-2- (miHA) specific T cells after relapse. HA-2-specific T cells upregulated PD-1 expression at the time of graft-versus-host disease (GVHD), indicating activation of these miHA-specific T cells during GVHD. In two subjects (UPN3 and UPN8) who achieved long-term remission after DLI, CD8 T cells specific to neoantigens were detectable by IFN-γ/CD107a production in reaction to mutant peptides (SDE2.pK123X or TP53.pR248W) from post-relapse samples. Discussion: Our study showed the feasibility of in-silico neoantigens prediction and neoantigen-specific T-cell detection from a subject who developed relapse after alloSCT. Further investigation is needed to test if neoepitope burden and miHA disparity could correlate with transplant outcomes. Citation Format: Biswas Neupane, Jui-En Ray Lee, Kedwin Ventura, Kayla Parr, Kevin Quann, Sawa Ito. Kinetics of neoantigen-specific T cells in post-transplant relapse [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3855.

Abstract 6077: Chimeric RNAs are abundantly expressed in precancerous breast tissue of sporadic breast cancer patients

Abstract Background: Immuno-prevention efforts in breast cancer (BC) have been hampered by the lack of known neoantigenic targets that can be used for a cancer prevention vaccine. RNA fusions represent attractive candidates as these are more immunogenic than insertion/deletion mutations and are substantial source of neoantigens. The objective of this study was to characterize chimeric RNAs in precancerous breast tissue. Methodology: Novel RNA fusions were identified by mining the sequence reads from RNA sequencing of 25 Her2 positive, 25 hormone receptor positive (HR) and 25 triple negative (TN) breast tumors and paired histologically normal tissues by Qiagen’s CLC pipeline. Breast samples from women undergoing breast reduction surgery were used as controls to exclude normal tissue associated chimeric mRNAs from analysis. Neoantigen prediction was performed by MHCnuggets. Chimeric mRNAs were validated by PCR. Ribosomal profiling was performed to assess if fusion transcripts are engaged with ribosomes. Results: A median of 36 novel RNA fusions (range 7-877) were identified in the tumor samples. In line with high genomic instability of TNBC subtype of BC, about 1/3 of the TN tumors expressed greater than 300 RNA fusions. In order to identify RNA fusions that are relevant for BC prevention, we investigated the presence of RNA fusions in the paired histologically normal samples as compared to the index tumor. We restricted these analyses to top 20 fusion transcripts that were present across the set of 75 tumor samples and also detected in 1 or more of the TCGA samples. Interestingly, we found that more than 1/3 of the histologically normal samples express at least 1 of the top 20 RNA fusions. When compared to the corresponding tumor, we found up to 50% of the paired normal tissue expressed RNA fusions identified in the tumor, supporting a role for these chimeric mRNA as an early molecular change during breast tumorigenesis. To validate the robustness of the prediction pipeline, PCR validation was performed for the chimeric mRNA with highest prevalence, NSFP1- LRRC37A2 which confirmed the presence of the transcript in 72% (13/18) of the predicted positive patient samples. MHCnuggets pipeline predicted 15 neopeptides from intergenically spliced chimeric NSFP1 [Exon 1-13] - LRRC37A2 [Exon 2-14], ¼ of which we validated to be immunogenic by ELISA. In order to confirm that the translation of the chimeric NSFP1- LRRC37A2 transcript, we investigated their enrichment in polysome enriched fraction obtained from a BC cell line confirmed to express the fusion transcript by PCR. Enrichment of transcripts in the polysomal fraction confirmed that these transcripts are likely to be translated. Conclusions: RNA fusions are frequently present in at risk breast tissue and are the source of substantial number of neoantigens. These RNA fusion- derived neoantigens may provide novel opportunities for developing vaccines for BC prevention. Citation Format: Anjana Bhardwaj, Shiyanth Thevasagayampillai, Sakuni Rankothgedera, Prisha Verma, Micah B. Castillo, Alexander C. Koh, Constance Albarracin, Preethi H. Gunaratne, Isabelle Bedrosian. Chimeric RNAs are abundantly expressed in precancerous breast tissue of sporadic breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6077.

Abstract 2415: Resistance to immune checkpoint inhibitor (ICI) therapy in metastatic melanoma is revealed by deep circulating tumor DNA tracking in concert with tumor immune profiling

Abstract Monitoring clinically actionable and emerging mutations in circulating tumor DNA (ctDNA) may be crucial for guiding treatment, but adoption has been slow due to the narrow scope and reproducibility of current approaches. Here, we investigate melanoma patients receiving ICI over several years, beginning with extensive tumor mutational and immune profiling followed by deep, whole-exome ctDNA monitoring.Here, 66 plasma samples from 23 advanced stage melanoma patients that were collected throughout ICI treatment (up to 40 months) were profiled using the NeXT Liquid Biopsy® platform (NeXT LB). NeXT LB monitors exome-wide ctDNA by leveraging a patient’s tumor, PBMCs and plasma samples, detecting both tumor-derived and emerging mutations. In addition to ctDNA tracking, we also used the ImmunoID NeXT Platform® to immunologically profile tumor tissue; including gene expression quantification, HLA profiling (typing, mutation, and loss of heterozygosity), T-cell receptor and tumor microenvironment profiling, and neoantigen prediction. ctDNA dynamics and tumor immunity were correlated with clinical outcome. Patients who progressed had lower tumor mutational burden (TMB, Mann-Whitney (MW); p=0.03), and lower neoantigen burden (MW; p=0.04). Responding tumors were enriched for interferon gamma and inflammatory signaling (Wald; adjusted p<0.01) and myeloid dendritic cells (MW; p=0.01), as measured by baseline RNA expression profiling. With an average coverage of 2260X SD +/-157, plasma samples yielded from 4 to 4081 variants. The number of on-treatment tumor variants detected in plasma predicted increased OS hazard (HR=2.95, Wald p=0.03; Kaplan-Meier (KM) p=0.0003) and progression hazard (HR=1.74, Wald p=0.04; KM p=0.0003). Overall, 10% (4277) of all variants were novel mutations observed only in the plasma. Novel variants were less likely to emerge in genes conferring sensitivity to ICI therapy (mixed-model; p<0.001). Dynamic shifts in allelic fraction between baseline and the first available on-treatment timepoint predicted increased OS hazard (HR=3.02, Wald p=0.05) and progression hazard (HR=2.90, Wald p=0.02). In ctDNA clonal phylogenies, 100% of BRAF and NRAS mutations belonged to the dominant clone. By combining immune and ctDNA features, a multivariable model predicted resistance to ICI treatment (log-likelihood p=0.002; c-index=0.89). Using a single liquid biopsy platform, we profiled tumor immunity and tracked existing and emerging ctDNA variants over the course of ICI treatment. We demonstrate that, even though counts of on-treatment ctDNA variants were sufficient for significant survival models, deep profiling of both tumor immunity and ctDNA dynamics provided deeper understanding of ICI resistance. Subsequent studies will determine if ctDNA-guided intervention can improve clinical outcomes. Citation Format: Charles W. Abbott, Laura Keller, Isabel Heidrich, Julian Kött, Glenn Geidel, Daniel J. Smit, Ronald Simon, Stefan Schneider, Jason Pugh, Sean M. Boyle, Richard O. Chen, Klaus Pantel, Christoffer Gebhardt. Resistance to immune checkpoint inhibitor (ICI) therapy in metastatic melanoma is revealed by deep circulating tumor DNA tracking in concert with tumor immune profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2415.

Abstract 5001: Personalized neoantigen/cancer-testis antigen nanovaccine for advanced solid tumors, a single-arm, open-label pilot study

Abstract Background: Neoantigens, derived from somatic mutations in tumor cells, have been identified as promising targets of immunotherapy. A personalized neoantigen/cancer-testis antigen (CTA) nanovaccine (PNVAC) platform has been established by us previously, and demonstrated its feasibility, safety and efficacy in preventing recurrence of high-risk resected gastric/gastroesophageal junction (G/GEJ) cancer in both preclinical and clinical studies. This study aims to explore the universality of PNVAC monotherapy or combined with anti-angiogenesis drugs or anti-programmed death 1 (PD-1) in patients with multiple advanced solid malignancies. Methods: Patient-specific neoantigens were selected based on tumor-specific mutations identified by whole-exome sequencing (WES) and RNA sequencing of paired blood and tumor tissues. Bioinformatic analysis for neoantigen prediction, including sequencing read filtering, human leukocyte antigen (HLA) typing and neoantigen filtering was performed. PNVAC is an amphiphiles nanovaccine loaded with multiple personalized neoantigens designed to induce specific T cell responses. PNVAC is administrated to patients with metastatic solid tumors on days 1, 4, 8, 15, 22, 43 (prime phase) and 64, 85 and 169 (boost phase) alone, or combined with anti-angiogenesis or anti-PD-1 drugs. Safety, immunogenicity and clinical efficacy are evaluated. Results: Of the 30 enrolled patients, no treatment-related severe adverse events (AEs) occurred and the vast majority of AEs were limited to grade 1-2, only 1 patient developed grade 3 thrombocytopenia. The objective response rate (ORR) was 26.7% (8/30), including 2 cases of complete response (CR) and 6 cases of partial response (PR), and a disease control rate (DCR) of 66.7% achieved. The median progression-free survival (PFS) was 9.90 months (95% CI, 3.46-16.34 months), while the median overall survival (OS) was not reached (range, 0.80-43.53 months), and the estimated 1- and 2-year survival rates were 86.2% and 60.6%. Notably, among the 21 patients who had relapsed disease after previous ICB and/or anti-angiogenesis, disease control was asserted in 14 (66.7%) of them (7 of SD, 5 of PR, 2 of CR). ORR of this set was 33.3% (7/21), higher than that of all the enrolled patients (26.7%), and the set without prior ICB and/or anti-angiogenesis treatment (11.1%). For immune analysis, PNVAC elicited robust and persistent immune responses against neoantigens/CTAs. Additionally, peripheral T cells with a cytotoxic phenotype tended to increase after vaccination, and immune memory was also detected in some representative patients. Conclusions: These data supported the safety, immunogenicity, and efficacy of this regimen in patients with advanced solid malignancies, thus broadening the application and combined strategies of neoantigen-based vaccines. Clinical trial information: ChiCTR1800017319. Citation Format: Jia Wei, Qin Liu, Lijing Zhu, Jie Shao, Ju Yang, Guanghui Xu, Nandie Wu, Hanbing Wang, Rutian Li, Huizi Sha, Qiuping Xu, Jie Shen, Li Xie, Lifeng Wang, Juan Du, Lanqi Cen, Manman Tian, Lixia Yu, Baorui Liu. Personalized neoantigen/cancer-testis antigen nanovaccine for advanced solid tumors, a single-arm, open-label pilot study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5001.

Abstract 4107: Evaluating the efficacy of personalized neoantigen cancer vaccine in preclinical model: A promising step towards precision immunotherapy

Abstract Background: In the past decade, immunotherapy has transformed cancer treatment, with the development of immune-checkpoint inhibitors and adoptive cell therapies. A novel frontier is personalized vaccines targeting neoantigens unique to tumor cells. Vaccines based on neoantigens have several advantages. First, neoantigens are exclusively expressed by tumor cells and can, therefore, elicit truly tumor-specific T cell responses, thereby preventing ‘off-target’ damage to nonmalignant tissues. Second, neoantigens are de novo epitopes derived from somatic mutations, which presents the possibility to circumvent T cell central tolerance of self-epitopes and thus induce immune responses to tumors. One of the major challenges is the optimal neoantigen discovery/selection. Our study combines in silico HLA binding models with the 'MHC-PepSeq' platform to enhance neoantigen prediction, creating a personalized neoantigen peptide vaccine. We aim to demonstrate its efficacy in a mouse CT26 colon cancer model. Methods: Our method involved whole exome sequencing of tumor and normal DNA to identify neoantigens expressed in CT26 tumors, generating a peptide library. The peptides were further filtered using an in-silico prediction algorithm and our proprietary peptide-MHC binding assay (PepSeq assay), to determine those exhibiting the highest binding affinity with mice HLAs. Vaccines were prepared by dissolving these peptides in sterile PBS. Mice (BALB/C) were subcutaneously implanted with CT26 tumors, then immunized with the vaccine via IV injection in combination with a TLR7/8 agonist. Mice received a second vaccine dose on Day 14, and some groups were treated with an anti-PD-1 agent. Tumors and spleens from the mice were collected, processed into single-cell suspensions, and analyzed by single-cell sequencing, TCR analysis, IHC, and flow cytometry assays. Peripheral blood mononuclear cells (PBMCs) were collected and utilized in MHC tetramer assays. Results: Preliminary in vivo studies demonstrated significant tumor growth restrictions (TGI around 70%) and prolonged tumor-free survival in some mice. Our data showed the generation of neoantigen-specific T cells in vaccinated mice. Remarkably, vaccinated, tumor-free mice, when re-challenged with the same tumor cells, maintained their tumor-free status, suggesting the vaccine induced post-treatment immunological memory. Combining the vaccine with checkpoint inhibition resulted in better tumor growth restriction in mice (TGI around 93%). Conclusion: Overall, our data suggests that our personalized neoantigen vaccine platform has the potential to evoke robust and durable immune responses and, when paired with suitable complementary therapies and immune adjuvants, may serve as an effective cancer treatment. Citation Format: Tithi Ghosh Halder, Kate Gutowsky, Serina Ng, Trason Thode, Alexis Weston, Erin Kelley, Jorge Soria-Bustos, Jorge A. Giron, Taylor Bargenquast, Mohan Kaadige, Sanjana Tripuraneni, Brian Durbin, Shelby Rheinschmidt, Sydney Adamson, Michael Gordon, Justin Moser, John Altin, Raffaella Soldi, Sunil Sharma. Evaluating the efficacy of personalized neoantigen cancer vaccine in preclinical model: A promising step towards precision immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4107.

Stimulating T cell responses against patient-derived breast cancer cells with neoantigen peptide-loaded peripheral blood mononuclear cells

Breast cancer stands as a formidable global health challenge for women. While neoantigens exhibit efficacy in activating T cells specific to cancer and instigating anti-tumor immune responses, the accuracy of neoantigen prediction remains suboptimal. In this study, we identified neoantigens from the patient-derived breast cancer cells, PC-B-142CA and PC-B-148CA cells, utilizing whole-genome and RNA sequencing. The pVAC-Seq pipeline was employed, with minor modification incorporating criteria (1) binding affinity of mutant (MT) peptide with HLA (IC50 MT) ≤ 500 nm in 3 of 5 algorithms and (2) IC50 wild type (WT)/MT > 1. Sequencing results unveiled 2513 and 3490 somatic mutations, and 646 and 652 non-synonymous mutations in PC-B-142CA and PC-B-148CA, respectively. We selected the top 3 neoantigens to perform molecular dynamic simulation and synthesized 9–12 amino acid neoantigen peptides, which were then pulsed onto healthy donor peripheral blood mononuclear cells (PBMCs). Results demonstrated that T cells activated by ADGRL1E274K, PARP1E619K, and SEC14L2R43Q peptides identified from PC-B-142CA exhibited significantly increased production of interferon-gamma (IFN-γ), while PARP1E619K and SEC14L2R43Q peptides induced the expression of CD107a on T cells. The % tumor cell lysis was notably enhanced by T cells activated with MT peptides across all three healthy donors. Moreover, ALKBH6V83M and GAAI823T peptides from PC-B-148CA remarkably stimulated IFN-γ- and CD107a-positive T cells, displaying high cell-killing activity against target cancer cells. In summary, our findings underscore the successful identification of neoantigens with anti-tumor T cell functions and highlight the potential of personalized neoantigens as a promising avenue for breast cancer treatment.

Abstract IA006: Leveraging the extreme selection induced by therapeutic bottlenecks to subvert resistance via identifying evolutionarily-informed targets for orthogonal immunotherapy

Abstract Evolutionary dynamics underlie carcinogenesis, with tumor cells constantly adapting, resulting in cell populations that outgrow their non-tumor cell neighbors, evade treatment, and ultimately lead to death. Well-designed therapies to treating cancer induce immense and specific selection. The heterogenous cancer cell population either goes extinct or continues to evolve during and in response to the therapeutically induced bottleneck, leading to a subsequent selective sweep and newfound prominence of cells exhibiting the resistance phenotype. However, this bottleneck period provides a unique opportunity wherein the diversity of cells constituting the tumor is predictably reduced. Here, we introduce a framework to quantify the selection upon variants before, during, and after the therapeutic bottleneck, facilitating richer understanding of the mechanisms by which therapeutic resistance is acquired. We additionally couple measures of selection intensity with the prediction of neoantigens in a cohort of patients to identify potential orthogonal targets for immunotherapy. The result is a schema for leveraging predictability induced by the primary therapeutic bottleneck to drive the remnant tumor cell population to extinction. Finally, we demonstrate the utility of this schema in mutant EGFR lung adenocarcinoma. Citation Format: J. Nic Fisk, Katerina Politi, Scott Gettinger, Jeffrey P Townsend. Leveraging the extreme selection induced by therapeutic bottlenecks to subvert resistance via identifying evolutionarily-informed targets for orthogonal immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA006.

Atezolizumab plus personalized neoantigen vaccination (PGV001) in patients with urothelial cancer.

597 Background: Most patients (pts) with urothelial cancer (UC) treated with immune checkpoint inhibitors (ICIs) do not respond. Given that features associated with restrained antitumor immunity and high neoantigen burden have correlated with response to ICI, stimulating antitumor immunity with neoantigen vaccination is an attractive approach to overcome ICI resistance. This study assessed atezolizumab in combination with PGV001, a personalized genomic neoantigen vaccine, in the adjuvant and metastatic settings. Methods: This single-arm pilot study (NCT03359239) enrolled pts with UC for treatment in the adjuvant (≥pT3/pN+ who declined or were ineligible for adjuvant chemotherapy; or ≥ypT2/ypN+ after neoadjuvant chemotherapy) or metastatic (disease progression after chemotherapy, or “switch maintenance” for stable disease after chemotherapy) setting. Neoantigen prediction was performed using the OpenVax computational pipeline. After an initial priming vaccination course (cycle 1), pts received atezolizumab 1200 mg intravenously every 3 weeks for up to 12 months (adjuvant) or 24 months (metastatic) and PGV001 with poly-ICLC on subsequent days every 3 weeks during cycles 2-5 and 9. The primary endpoints were feasibility and safety, including the number of neoantigens identified and peptides synthesized per pt, vaccine production time, proportion of consented pts with prepared vaccine, and proportion of treatment-eligible pts completing the priming course. Secondary endpoints included immune monitoring of vaccine-induced neoantigen specific T cells. Results: Between 12/2018 and 8/2020 12 pts were enrolled. The median neoantigens identified per pt was 103 (range 60-388). Vaccines containing 9-10 synthetic long peptides (24-25mer) were prepared for 92% (11/12) of consented pts over a median of 153 days (95% CI 151-181 days). 100% (10/10: 4 adjuvant, 6 metastatic) of pts eligible for the treatment phase completed the priming course. All evaluated pts demonstrated the emergence of ex vivo T cell responses as reflected by IFNγ production, including circulating multi-functional CD4+ and CD8+ neoantigen-specific T cell responses upon expansion. At a median follow up of 32 months, 4/4 patients treated in the adjuvant setting were free of recurrence and 2/5 pts with metastatic UC achieved an objective response. The single pt treated in the switch maintenance setting had no evidence of disease, off all treatment, at 33 months. The most common treatment-related adverse events were grade 1 injection site reactions, fatigue, and fever. One pt developed grade 3 immune-related hepatitis. Conclusions: Atezolizumab plus PGV001 was feasible, safe, and induced neoantigen-specific T cell immunity in all evaluated pts. To our knowledge, this is the first report of combination neoantigen vaccination plus ICI in the adjuvant setting in UC. Neoantigen vaccination plus ICI warrants further evaluation. Clinical trial information: NCT03359239 .

Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response

BackgroundThe HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize “non-self” antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer.MethodsRNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles.ResultsThe genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls.ConclusionsThe integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient’s tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction.

Neoantigens are derived from somatic mutations in the tumors but are absent in normal tissues. Emerging evidence suggests that neoantigens can stimulate tumor-specific T-cell-mediated antitumor immune responses, and therefore are potential immunotherapeutic targets. We developed ImmuneMirror as a stand-alone open-source pipeline and a web server incorporating a balanced random forest model for neoantigen prediction and prioritization. The prediction model was trained and tested using known immunogenic neopeptides collected from 19 published studies. The area under the curve of our trained model was 0.87 based on the testing data. We applied ImmuneMirror to the whole-exome sequencing and RNA sequencing data obtained from gastrointestinal tract cancers including 805 tumors from colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and hepatocellular carcinoma patients. We discovered a subgroup of microsatellite instability-high (MSI-H) CRC patients with a low neoantigen load but a high tumor mutation burden (> 10 mutations per Mbp). Although the efficacy of PD-1 blockade has been demonstrated in advanced MSI-H patients, almost half of such patients do not respond well. Our study identified a subset of MSI-H patients who may not benefit from this treatment with lower neoantigen load for major histocompatibility complex I (P < 0.0001) and II (P = 0.0008) molecules, respectively. Additionally, the neopeptide YMCNSSCMGV-TP53G245V, derived from a hotspot mutation restricted by HLA-A02, was identified as a potential actionable target in ESCC. This is so far the largest study to comprehensively evaluate neoantigen prediction models using experimentally validated neopeptides. Our results demonstrate the reliability and effectiveness of ImmuneMirror for neoantigen prediction.

Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.

Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores to major histocompatibility complex class I and II molecules, respectively. As antigen presentation is a critical step in mounting the adaptive immune response, previous work has used these or similar predictions models in a broad array of applications, from explaining asymptomatic viral infection to cancer neoantigen prediction. However, these models have also been shown to be biased toward hydrophobic peptides, suggesting the network could also contain other sources of bias. Here, we report the composition of the networks' training datasets are heavily biased toward European Caucasian individuals and against Asian and Pacific Islander individuals. We test the ability of NetMHCpan-4.1 and NetMHCpan-4.0 to distinguish true binders from randomly generated peptides on alleles not included in the training datasets. Unexpectedly, we fail to find evidence that the disparities in training data lead to a meaningful difference in prediction quality for alleles not present in the training data. We attempt to explain this result by mapping the HLA sequence space to determine the sequence diversity of the training dataset. Furthermore, we link the residues which have the greatest impact on NetMHCpan predictions to structural features for three alleles (HLA-A*34:01, HLA-C*04:03, HLA-DRB1*12:02).

Establishment of a novel tumor neoantigen prediction tool for personalized vaccine design

ABSTRACT The personalized neoantigen nanovaccine (PNVAC) platform for patients with gastric cancer we established previously exhibited promising anti-tumor immunoreaction. However, limited by the ability of traditional neoantigen prediction tools, a portion of epitopes failed to induce specific immune response. In order to filter out more neoantigens to optimize our PNVAC platform, we develop a novel neoantigen prediction model, NUCC. This prediction tool trained through a deep learning approach exhibits better neoantigen prediction performance than other prediction tools, not only in two independent epitope datasets, but also in a totally new epitope dataset we construct from scratch, including 25 patients with advance gastric cancer and 150 candidate mutant peptides, 13 of which prove to be neoantigen by immunogenicity test in vitro. Our work lay the foundation for the improvement of our PNVAC platform for gastric cancer in the future.

Methods behind neoantigen prediction for personalized anticancer vaccines.

Next to conventional cancer therapies, immunotherapies such as immune checkpoint inhibitors have broadened the cancer treatment landscape over the past decades. Recent advances in next generation sequencing and bioinformatics technologies have made it possible to identify a patient's own immunogenic neoantigens. These cancer neoantigens serve as important targets for personalized immunotherapy which has the benefit of being more active and effective in targeting cancer cells. This paper is a step-by-step guide discussing the different analyses and challenges encountered during in-silico neoantigen prediction. The protocol describes all the tools and steps required for the identification of immunogenic neoantigens.

Recent advances in personalized cancer immunotherapy with immune checkpoint inhibitors, T cellsand vaccines.

The results of genomic and molecular profiling of cancer patients can be effectively applied to immunotherapy agents, including immune checkpoint inhibitors, to select the most appropriate treatment. In addition, accurate prediction of neoantigens facilitates the development of individualized cancer vaccines and T-cell therapy. This review summarizes the biomarker(s) predicting responses to immune checkpoint inhibitors and focuses on current strategies to identify and isolate neoantigen-reactive T cells as well as the clinical development of neoantigen-based therapeutics. The results suggest that maximal T-cell stimulation and expansion can be achieved with combination therapies that enhance antigen-presenting cells' function and optimal T-cell priming in lymph nodes.

ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterations.

Neoantigens, tumor-specific protein fragments, are invaluable in cancer immunotherapy due to their ability to serve as targets for the immune system. Computational prediction of these neoantigens from sequencing data often requires multiple algorithms and sophisticated workflows, which are currently restricted to specific types of variants, such as single-nucleotide variants or insertions/deletions. Nevertheless, other sources of neoantigens are often overlooked. We introduce ScanNeo2 an improved and fully automated bioinformatics pipeline designed for high-throughput neoantigen prediction from raw sequencing data. Unlike its predecessor, ScanNeo2 integrates multiple sources of somatic variants, including canonical- and exitron-splicing, gene fusion events, and various somatic variants. Our benchmark results demonstrate that ScanNeo2 accurately identifies neoantigens, providing a comprehensive and more efficient solution for neoantigen prediction. ScanNeo2 is freely available at https://github.com/ylab-hi/ScanNeo2/ and is accompanied by instruction and application data.

FusionNeoAntigen: a resource of fusion gene-specific neoantigens.

Among the diverse sources of neoantigens (i.e. single-nucleotide variants (SNVs), insertions or deletions (Indels)and fusion genes), fusion gene-derived neoantigens are generally more immunogenic, have multiple targets per mutationand are more widely distributed across various cancer types. Therefore, fusion gene-derived neoantigens are a potential source of highly immunogenic neoantigens and hold great promise for cancer immunotherapy. However, the lack of fusion protein sequence resources and knowledge prevents this application. We introduce 'FusionNeoAntigen', a dedicated resource for fusion-specific neoantigens, accessible at https://compbio.uth.edu/FusionNeoAntigen. In this resource, we provide fusion gene breakpoint crossing neoantigens focused on ∼43K fusion proteins of ∼16K in-frame fusion genes from FusionGDB2.0. FusionNeoAntigen provides fusion gene information, corresponding fusion protein sequences, fusion breakpoint peptide sequences, fusion gene-derived neoantigen prediction, virtual screening between fusion breakpoint peptides having potential fusion neoantigens and human leucocyte antigens (HLAs), fusion breakpoint RNA/protein sequences for developing vaccines, information on samples with fusion-specific neoantigen, potential CAR-T targetable cell-surface fusion proteins and literature curation. FusionNeoAntigen will help to develop fusion gene-based immunotherapies. We will report all potential fusion-specific neoantigens from all possible open reading frames of ∼120K human fusion genes in future versions.

Lineage tracing for multiple lung cancer by spatiotemporal heterogeneity using a multi-omics analysis method integrating genomic, transcriptomic, and immune-related features.

The distinction between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) holds clinical significance in staging, therapeutic intervention, and prognosis assessment for multiple lung cancer. Lineage tracing by clinicopathologic features alone remains a clinical challenge; thus, we aimed to develop a multi-omics analysis method delineating spatiotemporal heterogeneity based on tumor genomic profiling. Between 2012 and 2022, 11 specimens were collected from two patients diagnosed with multiple lung cancer (LU1 and LU2) with synchronous/metachronous tumors. A novel multi-omics analysis method based on whole-exome sequencing, transcriptome sequencing (RNA-Seq), and tumor neoantigen prediction was developed to define the lineage. Traditional clinicopathologic reviews and an imaging-based algorithm were performed to verify the results. Seven tissue biopsies were collected from LU1. The multi-omics analysis method demonstrated that three synchronous tumors observed in 2018 (LU1B/C/D) had strong molecular heterogeneity, various RNA expression and immune microenvironment characteristics, and unique neoantigens. These results suggested that LU1B, LU1C, and LU1D were MPLC, consistent with traditional lineage tracing approaches. The high mutational landscape similarity score (75.1%), similar RNA expression features, and considerable shared neoantigens (n = 241) revealed the IPM relationship between LU1F and LU1G which were two samples detected simultaneously in 2021. Although the multi-omics analysis method aligned with the imaging-based algorithm, pathology and clinicopathologic approaches suggested MPLC owing to different histological types of LU1F/G. Moreover, controversial lineage or misclassification of LU2's synchronous/metachronous samples (LU2B/D and LU2C/E) traced by traditional approaches might be corrected by the multi-omics analysis method. Spatiotemporal heterogeneity profiled by the multi-omics analysis method suggested that LU2D possibly had the same lineage as LU2B (similarity score, 12.9%; shared neoantigens, n = 71); gefitinib treatment and EGFR, TP53, and RB1 mutations suggested the possibility that LU2E might result from histology transformation of LU2C despite the lack of LU2C biopsy and its histology. By contrast, histological interpretation was indeterminate for LU2D, and LU2E was defined as a primary or progression lesion of LU2C by histological, clinicopathologic, or imaging-based approaches. This novel multi-omics analysis method improves the accuracy of lineage tracing by tracking the spatiotemporal heterogeneity of serial samples. Further validation is required for its clinical application in accurate diagnosis, disease management, and improving prognosis.