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Functional gene expression signatures (FGES) are widely used as biomarkers in cancer diagnostics as descriptive and predictive models for treatment selection. Since FGES scores are based solely on gene expression profiles, they can be applied to data from bulk and single-cell RNA-seq or gene expression microarrays. However, FGES development and implementation has several challenges including technical limitations of calculating FGES scores based on ranked gene expressions such as background noise and gene cross-correlations and difficulty in assessing the biological relevance of FGES. Here, we developed an FGES validation pipeline that addressed these limitations by examining publicly available and internally-developed FGES. The pipeline was developed by applying a modified single-sample gene set enrichment analysis (ssGSEA) to a manually curated database of more than 50,000 RNA-Seq samples, including sorted cells, cell lines, and tumor and normal tissue biopsies. We defined technical and biological criteria to validate signature quality: 1) single gene expression noise distribution minimum is greater than zero; 2) cross-correlation of genes comprising a signature is positive; 3) specificity, whereby a FGES differentiates a group of interest and has a minimal overlap with other unrelated signatures and molecular pathways; 4) generalizability, requiring FGES score to differentiate a group of interest across datasets of different origin, cell type, or diagnosis. We tested these criteria on FGES that describe cellular processes such as epithelial-mesenchymal transition (EMT) and senescence as well as FGES that can delineate cell types such as macrophages. We were able to better distinguish 12 different cell types using the corresponding cell type-specific FGES with significantly higher ssGSEA scores (p-adj ≤ 0.00004 in 99.9% cases) than 911 analogous MSigDB gene signatures. Our EMT FGES, tuned specifically for melanoma, exhibited higher precision than the MSigDB Hallmark EMT gene set (p < 0.001) for predicting histological pigment scores. The senescence FGES also accurately distinguished senescent from non-senescent cells in datasets of differing cell origin and data acquisition methods, demonstrating signature generalizability. Finally, using macrophage-describing FGES, we showed that specificity of a publicly available gene set can be significantly improved (p-adj = 0.00001) by tuning it according to the aforementioned criteria. Taken together, our proposed workflow enables better assessment of the technical quality and biological adequacy of FGES for their further use in transcriptomic analyses and computational models of complex biological processes and tumorigenesis. Citation Format: Elena Ocheredko, Nadezhda Lukashevich, Maria Savchenko, Sofia Kust, Syune Ambaryan, Dmitry Tabakov, Maria Sorokina, Linda Balabanian, Kushal Suryamohan, Anastasiya Zotova, Nathan Fowler, Aleksander Bagaev. Development and refinement of functional gene expression signatures as a computational tool for comprehensive characterization of transcriptomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6538.

The subject of the research is the philosophical (ontological, epistemological, philosophical-anthropological and social-philosophical) foundations of personalized medicine, the biomedical foundations of which are methods of therapy and prevention of diseases based on the individual characteristics of the patient. The authors highlight the preventive nature of personalized medicine - to prevent the patient's diseases based on certain diagnostic methods and using a system of preventive measures, as well as its focus on improving the effectiveness of treatment for a specific patient. The value of personalized medicine is that it allows to determine precisely the causes of a particular disease or to assess a person's predisposition to certain diseases, to apply preventive measures to minimize the risks of diseases; to use personalized methods of treatment and correction of the conditions of a particular patient, as well as biomarkers for monitoring the effectiveness of therapy. The philosophical foundations of personalized medicine, on the one hand, contain certain philosophical attitudes related to medicine in general, and on the other hand, reflect specific features determined by new technologies that modern medicine possesses. In particular, the article points to a change in the concept of personalization in connection with the disclosure of its content at the genomic level. The authors emphasize that personalized medicine raises a number of new problems of a philosophical nature: the approach to a person as a set of data about his or her body, the possible increase in social inequality due to the lack of general availability of the results of personalized medicine, and so on. The article substantiates the idea that improving and reducing the cost of sequencing technologies will help make new methods of treating diseases more accessible to the general population. Further personification of medicine will occur due to obtaining more and more objective information about patients, increasing the number of subgroups in the typology of patients, offering them variable methods of treatment, as well as due to the increasing involvement of a patient in the treatment processes, based on a better understanding of his/her “existential presence analytics”.

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

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

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