Tau filaments are tethered within brain extracellular vesicles in Alzheimer's disease

The abnormal assembly of tau protein in neurons is the pathological hallmark of multiple neurodegenerative diseases, including Alzheimer's disease (AD). In addition, assembled tau associates with extracellular vesicles (EVs) in the central nervous system of patients with AD, which is linked to its clearance and prion-like propagation between neurons. However, the identities of the assembled tau species and the EVs, as well as how they associate, are not known. Here, we combined quantitative mass spectrometry, cryo-electron tomography and single-particle cryo-electron microscopy to study brain EVs from AD patients. We found filaments of truncated tau enclosed within EVs enriched in endo-lysosomal proteins. We observed multiple filament interactions, including with molecules that tethered filaments to the EV limiting membrane, suggesting selective packaging. Our findings will guide studies into the molecular mechanisms of EV-mediated secretion of assembled tau and inform the targeting of EV-associated tau as potential therapeutic and biomarker strategies for AD.

Spatial variation of plastic debris on important turtle nesting beaches of the remote Chagos Archipelago, Indian Ocean

We report Anthropogenic Marine Debris (AMD) in Chagos Archipelago in the Indian Ocean, globally amongst the most isolated island groups. AMD on 14 island beaches in five atolls were surveyed in 2019 using two techniques: Marine Debris Tracker (MDT) along littoral vegetation and photoquadrats in open beach. Over 60 % of AMD in both beach zones was composed of plastics, especially bottles and fragments (mean = 44.9 %, 27.2 %, range = 16.5-73.2 %, 4.8-55.9 % respectively in vegetation; mean = 28.7 %, 31.5 %, range = 17.7-40.7 %, 11.6-60.0 % respectively in open beach). The density of plastic debris in littoral vegetation (MDT data: 1995 bottles, 3328 fragments per 100 m²) was 10-fold greater than in open beach (photoquadrat data: 184 bottles, 106 fragments per 100 m²). Significant latitudinal variation in vegetation AMD occurred (8-fold greater in southern atolls, p = 0.006). AMD varied within island zones: most debris observed on oceanside beaches (oceanside vs lagoon, W = 365, p < 0.001; ocean vs island tip, W = 107, p = 0.034). Standardisation of surveys using the open-source MDT App is recommended. Debris accumulation hotspots overlapped with sea turtle nesting habitat, guiding future beach clean-up prioritisation.

Acoustic signalling and behaviour of Antarctic minke whales (Balaenoptera bonaerensis)

Acoustic signalling is the predominant form of communication among cetaceans. Understanding the behavioural state of calling individuals can provide insights into the specific function of sound production; in turn, this information can aid the evaluation of passive monitoring datasets to estimate species presence, density, and behaviour. Antarctic minke whales are the most numerous baleen whale species in the Southern Ocean. However, our knowledge of their vocal behaviour is limited. Using, to our knowledge, the first animal-borne audio-video documentation of underwater behaviour in this species, we characterize Antarctic minke whale sound production and evaluate the association between acoustic behaviour, foraging behaviour, diel patterns and the presence of close conspecifics. In addition to the previously described downsweep call, we find evidence of three novel calls not previously described in their vocal repertoire. Overall, these signals displayed peak frequencies between 90 and 175 Hz and ranged from 0.2 to 0.8 s on average (90% duration). Additionally, each of the four call types was associated with measured behavioural and environmental parameters. Our results represent a significant advancement in understanding of the life history of this species and improve our capacity to acoustically monitor minke whales in a rapidly changing Antarctic region.

Abstract 5021: Accurate quantification of infiltrating B cell composition and clone diversity in tumor samples

Tumors harbor a complex ecosystem of malignant, immune, and stromal cells. While malignant cells dictate much of the tumor biology, there is evidence that the tumor microenvironment (TME) also plays a major role in disease etiology. Given the complexity and abundance of the TME cellular composition, investigating the role of immune cell types will yield novel biomarkers for tumor progression and response to therapies.

The role of B cells as a prognostic biomarker remains elusive. For instance, infiltrating B cells in CRC have both positive and negative prognostic value. Thus, a scalable approach to quantify B cells and the B-cell receptor (BCR) repertoire could yield novel insights into the role of B cells in tumor biology. To address this, we have developed immune cell quantification (InfiltrateID࣪) and immune receptor repertoire profiling (RepertoireID࣪) methods as part of the ImmunoID NeXT Platform®, an augmented, immuno-oncology-optimized exome/transcriptome platform.

We estimate B cell abundance and BCR repertoire by profiling FFPE and PBMC samples using ImmunoID NeXT࣪. In expanding upon InfiltrateID to further estimate B cell abundance, here we regress the bulk RNA-seq readout from a reference signature from purified immune cell types. We also generate orthogonal quantifications of B cell abundance by profiling samples with cytometry by time of flight, single-cell RNA-seq, flow cytometry, and immunohistochemistry (IHC). We compare BCR results from ImmunoID NeXT to a standalone sequencing approach to evaluate the concordance of top clones. We then utilize BCR profiling from ImmunoID NeXT to analyze clonality and isotype composition in tumor samples.

We first use InfiltrateID to estimate absolute B cell fractions in over 50 samples. Overall, we observe a high correlation between InfiltrateID results and orthogonal data sets in both PBMC and tumor FFPE samples (R2=0.90). When comparing BCR results from RepertoireID to a standalone BCR sequencing method that profiles IgM and IgG, we identify 475 and 387 of the top 500 clones in IgG and IgM, respectively, with highly concordant abundances across all clones (R2&gt;0.72 and R2&gt;0.82 in IgM and IgG, respectively). Next, we use InfiltrateID to estimate absolute B cell fractions in over 650 samples from 14 tumor types. On average, samples display B cell fractions in agreement with the literature and IHC quantifications, with higher B cell fractions in lung, breast, and cervical tumors. We also observe a range of BCR clonality values across tumor types. Finally, we observe differences in B cell composition and repertoire diversity in tumor samples from patients who underwent checkpoint blockade therapy.

We show that InfiltrateID and RepertoireID accurately capture the composition and clone diversity of infiltrating B cells in tumor samples.

Citation Format: Fabio Navarro, Eric Levy, Pamela Milani, Qiang Li, Shruti Bhide, Upasana Dutta, Charles W. Abbott, Jose Jacob, Rena McClory, John West, John Lyle, Sean Boyle, Richard O. Chen. Accurate quantification of infiltrating B cell composition and clone diversity in tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5021.

Abstract 5161: Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA

Gastrointestinal stromal tumors (GIST) are lethal tumors characterized by constitutively activating mutations to KIT or PDGFRA. Transient disease control in the first-line setting is achieved via inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib. As patients progress through subsequent lines of therapy a molecularly heterogeneous disease evolves, characterized by distinct subtypes and shifting repertoires of exon-specific KIT variants which directly impact treatment outcomes. Here, we use tumor-informed exome-scale liquid biopsy to identify and track the evolution of multiple resistance mechanisms in patients receiving tyrosine kinase inhibitors (TKIs) to address the unmet need of comprehensive understanding of GIST evolution in response to therapy.

Matched tumor, normal and serial plasma samples were obtained from 15 heavily pretreated metastatic GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Exome-scale detection of somatic variants in cfDNA from longitudinal matched plasma samples was achieved using the NeXT Liquid BiopsyTM platform. The ImmunoID NeXT PlatformⓇ, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune data was used to profile paired tumor and normal samples.

Longitudinal whole exome sequencing of plasma identified dynamic shifts in existing clones harboring exon-specific KIT mutations, and evolution of new KIT mutations arising prior to identification of tumor progression using standard imaging techniques. We detected a correlation between the number of damaging mutations detected in baseline ctDNA and tumor exon 11 KIT mutation status, suggesting that plasma mutation profiles may be KIT-variant dependent. ctDNA from patients with shorter overall survival (OS) was enriched for variants in the PI3K-AKT and MAPK pathway, potentially contributing to immune evasion observed in those patients. Additional associations were observed between gene copy-number changes and OS (P = .0097). Previous studies have demonstrated that immune infiltration and activity may be KIT variant specific, here we broaden those findings, identifying a significant correlation between TCRɑ clonality and variants detected only in plasma (P = .04), as well as a significant association between TCRβ diversity and OS (HR = 2.55, log rank P = .04).

Comprehensive profiling of paired tumor tissue (WES and RNA-Seq) and WES of serially collected ctDNA sensitively and repeatedly identified evolving KIT mutations and other molecular alterations prior to radiologically confirmed disease progression. These findings suggest plasma-based monitoring of late-stage GIST malignancies may be useful for non-invasive disease tracking, providing treatment guidance prior to traditional approaches.

Citation Format: Charles W. Abbott, Niamh Coleman, Jing Wang, Josette Northcott, Jason Pugh, Dan Norton, Fábio C. Navarro, Lee D. McDaniel, Eric Levy, Rachel Marty Pyke, John Lyle, Jason Harris, Gabor Bartha, Filip Janku, John West, Richard O. Chen, Sean Boyle. Exome-scale longitudinal tracking of emerging therapeutic resistance in GIST via analysis of circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5161.

QT interval prolongation is a novel predictor of one year morality in patients with coronavirus disease-19 infection

Funding Acknowledgements

Type of funding sources: None.

Background

QT interval prolongation is common in critically ill patients and is associated with increased mortality. However, the predictive value of a prolonged corrected QT interval (QTc) for myocardial injury and long-term mortality among patients hospitalized with COVID-19 infection is not well known.

Purpose

To evaluate the association of prolonged QTc with myocardial injury and with 1-year mortality among patients hospitalized with COVID-19 infection.

Methods

A total of 335 consecutive patients hospitalized with COVID-19 infection were prospectively studied. All patients underwent a comprehensive echocardiographic evaluation within 48 hours from admission. Using the Bazett formula, the QTc interval was calculated from the first ECG tracing recorded at the ER. QTc ≥440 msec in males and ≥450 msec in females was considered prolonged. Patients with elevated cardiac biomarkers and/or echocardiographic signs of myocardial dysfunction were considered to have myocardial injury. The predictive value of QTc prolongation for myocardial injury was calculated using a multivariate binary regression model. One-year mortality rate of patients with and without QTc prolongation was compared using the log-rank test, and a multivariate Cox regression model adjusting for multiple covariates was performed to evaluate the 1-year mortality risk.

Results

One-hundred and nine (32.5%) patients had a prolonged QTc. Compared to patients without QTc prolongation, patients with prolonged QTc were older (70±14.4 vs 62.7±16.6, p &lt;0.001), had more comorbidities, and presented with a more severe disease. Prolonged QTc was an independent predictor for myocardial injury (adjusted HR 2.07, 95% CI 1.22-3.5; p=0.007). One-year mortality of patients with prolonged QTc was higher than those with no QTc prolongation (40.7% vs 16.9; p &lt;0.001, adjusted HR 1.85[1.2-2.84]; p=0.005). Compared to patient without QTc prolongation and no myocardial injury, the adjusted 1-year mortality risk was highest in patients with prolonged QTc and myocardial injury (HR 6.63, 95% CI 2.28-19.3; p=0.001), followed by patients with QTc prolongation without myocardial injury (HR 6.12 95% CI 1.83-20.49; p=0.003), and patients with myocardial injury without QTc prolongation (HR 4.95 95% CI 1.83-20.49.; p=0.003).

Conclusion

Prolonged QTc is an independent risk factor for both myocardial injury and 1-year mortality among patients hospitalized with COVID-19 infection.

A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.

Human leukocyte antigen loss of heterozygosity (HLA LOH) is an important mechanism of immune escape in patients with cancer. Here the authors design and validate a machine learning algorithm with subclonal sensitivity for the identification of HLA LOH from paired tumor-normal sequencing data.

19 Exome-scale liquid biopsy characterization of emerging immune resistance mechanisms in treatment-resistant GIST

Background

Metastatic gastrointestinal stromal tumors (GIST) are lethal tumors of the GI tract characterized by gain of function mutations in KIT or PDGFRα. Transient first-line control is achieved through the inhibition of tyrosine kinase signaling using the KIT inhibitor imatinib, though most patients progress after 2–3 years. Progression through successive lines of therapy results in a molecularly heterogeneous disease with diverse subtypes, driven by distinct collections of exon-specific KIT mutations which directly inform therapy decisions. To address the unmet need of comprehensive understanding of GIST, we used tumor-informed whole exome liquid biopsy to identify and track the evolution of multiple concurrent heterogeneous resistance mechanisms in individual patients receiving tyrosine kinase inhibitors (TKIs).

Methods

Baseline matched tumor, normal and longitudinal plasma samples were obtained from 15 metastatic, heavily pretreated GIST patients. Following baseline sample collection, all patients received systemic TKI therapy, and were monitored until disease progression. Paired tumor and normal samples were profiled using the ImmunoID NeXT Platform®, an augmented exome/transcriptome platform and analysis pipeline which generates comprehensive tumor and immune information. Exome-scale cfDNA profiling of matched plasma samples was performed using the NeXT Liquid BiopsyTM platform to detect somatic variants.

Results

Baseline solid tumor WES confirmed primary sensitizing KIT mutations in all 15 (100%) patients, and secondary KIT mutations in 7/15 patients (47%). Serial plasma whole exome sequencing identified evolution and expansion of clones harboring newly formed, druggable, exon-specific KIT mutations which evolved prior to identification of tumor progression using standard imaging techniques. In addition to these variants, we detected node-specific enrichment of PI3K-AKT and MAPK pathway mutations in plasma of patients with shorter overall survival (OS), which may contribute to the observed immune evasion. Accompanying these changes, we also detected significant association between gene copy-number profiles and duration of OS (P = 0.0097). Investigation of immune signatures using univariate cox modeling revealed a significant association between TCRβ diversity and reduced OS (HR = 2.55, log rank P = 0.04).

Conclusions

Comprehensive genomic profiling (WES and RNA-Seq) of paired tumor tissue and WES of serially collected ctDNA identified evolving druggable KIT mutations and other molecular alterations which preceded clinical disease progression. These findings suggest liquid biopsy-based monitoring of late-stage GIST malignancies may be useful for early identification of treatment resistance, providing treatment guidance prior to traditional approaches.

Ethics Approval

Ethics approval was granted by the MD Anderson Human Research Protection Program, and all participants gave informed consent prior to participation.

75 Comprehensive profiling of the tumor-immune microenvironment using an augmented transcriptome

Background

Comprehensive profiling of both the tumor and tumor microenvironment (TME) can help further our understanding of tumor progression and response to treatment. Many immune features can be extracted from transcriptomic data, including characterization of the immune infiltrate and profiling the diversity of immune receptors. To address this, we have developed multiple TME profiling features as part of the ImmunoID NeXT Platform®, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample. These features including quantification of immune cell infiltration and profiling of the T-cell receptor (TCR) and B-cell receptor (BCR).

Methods

To develop our immune infiltrate quantification method, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT™ to develop platform-specific gene sets, and compared our transcriptome quantification to immune cell quantification with IHC. For TCR and BCR methods, we analyzed the reproducibility of clone results, and compared top clones to standalone TCR and BCR sequencing approaches. In addition, we characterized the immune content of over 800 tumor samples across 14 cancer types. Finally, we analyzed the immune features in a cohort of melanoma patients who underwent PD-1 blockade.

Results

We observe significant concordances between cell fractions by IHC and ImmunoID NeXT’s transcriptome-based scores in tumor FFPE samples for B cells, CD8+ T cells, and macrophages (R2>0.82, R2>0.75, and R2>0.52, respectively). For TCR and BCR methods, abundances of clones shared between subsequent curls of a tumor FFPE sample have very high concordances (R2>0.89, R2>0.92, and R2>0.76 for TRB, IgG, and IgA, respectively). Compared to the standalone approaches, we identify 100% of the top 500 TRB clones and 95% of the top 500 IgG clones, with highly concordant abundances (R2>0.94 and R2>0.82 for TRB and IgG, respectively) in a PBMC sample. We identify biologically-relevant immune signatures across tumor types by characterizing the immune features across over 800 tumor samples. Finally, in a melanoma cohort, TRB clonality and CD8+ T cell scores are significantly different in responders to checkpoint inhibition.

Conclusions

RNA sequencing can be used as a scalable approach to profile the immune composition in tumors. Such analysis can add to our understanding of the tumor-immune interaction, including studies of response to immunotherapy. We show that immune infiltrate quantification and TCR and BCR profiling – all part of the ImmunoID NeXT Platform – are able to accurately and effectively evaluate the composition and diversity of tumor-infiltrating immune cells.

20 Tumor-informed liquid biopsy monitoring of evolving therapeutic resistance mechanisms in head and neck squamous cell carcinoma patients receiving anti-PD-1 therapy

Background

Typical liquid biopsy panels offer a limited understanding of tumor biology, potentially under-representing the heterogeneity of resistance in late-stage cancers. Here, diminished scope can result in undetected, therapeutically-relevant biomarkers which respond dynamically to treatment, as well as potentially missed resistance mechanisms and pathway-level events. To address the challenges associated with identifying multiple concurrent heterogeneous resistance mechanisms in individual patients, we evaluated longitudinal exome-scale tumor-informed cell-free DNA (cfDNA) data from head and neck squamous cell carcinoma (HNSCC) patients receiving anti-PD1 therapy.

Methods

Pre- and post-intervention matched tumor, normal and plasma samples were retrospectively obtained from 15 stage II-IV HNSCC patients. Following baseline sample collection, all patients received a single dose of nivolumab or pembrolizumab. The primary tumor was then resected approximately one month later when possible, or a second biopsy collected where resection was impractical. Paired tumor and normal samples were then profiled using ImmunoID NeXT Platform®, an augmented exome/transcriptome platform and analysis pipeline. Exome-scale cfDNA profiling of matched plasma samples was performed using the NeXT Liquid BiopsyTM platform to detect somatic variants.

Results

Patient neoantigen presentation score (NEOPSTM) rapidly and significantly contracted following therapy (p=.00098). Novel neoantigens arising post-treatment which were predicted to be presented on lost HLA alleles were significantly higher in patients with longer overall survival (p=.019). Variant detection across same-patient serial cfDNA samples revealed significantly correlated VAFs (R=.62, p<.0001) despite significant contraction of mutational burden in solid tumor (p=.0039), suggesting complex clonal/subclonal dynamics. Investigation of the evolving tumor and cfDNA subclonal architecture revealed significant association between decreasing cellular prevalence and NOTCH signaling (q=.001) and the innate immune system (q=.002), while increasing cellular prevalence was associated with p53 signalling (q=.02) and hypoxia (q=.02). These findings were complimented by transcriptomic data which showed significant enrichment of multiple immune pathways across treatment.

Conclusions

We found that immune checkpoint blockade precipitates rapid evolution of the HNSCC tumor microenvironment. By leveraging comprehensive, tumor-informed liquid biopsy data we were able to identify contracting cellular populations enriched for NOTCH pathway mutations. Longer OS following either intervention was associated with an expansion of novel neoantigens predicted to be presented by lost HLA alleles. Our results suggest that tumor-informed liquid biopsy provides a more robust understanding of therapeutic response and resistance mechanisms than that attainable with typical liquid biopsy panels alone.

Ethics Approval

This study obtained ethics approval from Human Subjects Research at Stanford University. ID number is 40425. All participants gave informed consent prior to enrollment.

79 Extensively validated HLA LOH algorithm demonstrates an association between HLA LOH and genomic instability

Background

Human Leukocyte Antigen (HLA) genes are critical for the presentation of neoantigens to the immune system by cancer cells. Deletion of HLA alleles, known as HLA loss of heterozygosity (LOH), has been highlighted as a key immune escape mechanism. Validated algorithms to detect HLA LOH from sequencing data are critical for exploring the biological impact of HLA LOH and assessing its utility as a clinical biomarker.

Methods

We developed DASH (Deletion of Allele-Specific HLAs), a machine learning algorithm trained on data from 279 patients on the ImmunoID NeXT Platform using features that account for probe capture variability between alleles and incorporate information from the regions flanking each HLA gene. To understand the contribution of boosted sequencing in the HLA region of the ImmunoID NeXT Platform, we performed an in silico downsampling analysis. To assess DASH’s performance at variable tumor purities and HLA LOH subclonalities we identified three tumor-normal cell lines with HLA LOH and created in silico mixtures. Furthermore, after designing patient-specific primers for 21 patients that target specific alleles, we applied digital PCR (dPCR) to validate the HLA allele copy number status of the patients. Finally, we applied DASH to 611 patients spanning 15 tumor types.

Results

In cross validation analyses across patient samples, DASH achieved 98.7% specificity and 92.9% sensitivity while LOHHLA, a widely used algorithm, only reached 94.3% and 78.8%, respectively (figure 1). Downsampling analyses demonstrated that DASH benefits significantly from the boosted HLA sequencing on the ImmunoID NeXT Platform, dropping 0.06 in F-score after downsampling to the sequencing depth of other exome platforms. In cell line mixture analyses, DASH demonstrates greater than 99% specificity across all tumor purity and sub-clonality levels and greater than 98% sensitivity for above 27% tumor purity. Moreover, DASH demonstrated 100% sensitivity and specificity in dPCR experiments across 21 tumor samples with stable controls. We applied DASH to a large pan-cancer cohort and found that 18% of patients had HLA LOH (figure 2). We identified strong associations between HLA LOH and genomic instability. Moreover, we demonstrated relationships between HLA LOH and markers of immune pressure, such as a correlation with CD274 (PD-1) expression and allele-specific neoantigen enrichment for deleted HLA alleles.

Conclusions

DASH, a highly sensitive HLA LOH algorithm that has been extensively validated using cross validation, in silico downsampling, cell line mixtures and dPCR, has demonstrated the widespread impact of HLA LOH in a large pan-cancer cohort.

Abstract 79 Figure 1

Bar plots showing the sensitivity and specificities scores across ImmunoID NeXT cross validation samples between LOHHLA (blue) and DASH (green).

Abstract 79 Figure 2

Bar plots denoting the number of patients and the frequency of HLA LOH in each tumor type cohort. 95% confidence intervals are shown with the thin dark grey bars. Only cohorts with at least 10 patients are shown

Next-generation gene panel testing in adolescents and adults in a medical neuropsychiatric genetics clinic

Intellectual disability (ID) encompasses a clinically and genetically heterogeneous group of neurodevelopmental disorders that may present with psychiatric illness in up to 40% of cases. Despite the evidence for clinical utility of genetic panels in pediatrics, there are no published studies in adolescents/adults with ID or autism spectrum disorder (ASD). This study was approved by our institutional research ethics board. We retrospectively reviewed the medical charts of all patients evaluated between January 2017 and December 2019 in our adult neuropsychiatric genetics clinic at the McGill University Health Centre (MUHC), who had undergone a comprehensive ID/ASD gene panel. Thirty-four patients aged > 16 years, affected by ID/ASD and/or other neuropsychiatric/behavioral disorders, were identified. Pathogenic or likely pathogenic variants were identified in one-third of our cohort (32%): 8 single-nucleotide variants in 8 genes (CASK, SHANK3, IQSEC2, CHD2, ZBTB20, TREX1, SON, and TUBB2A) and 3 copy number variants (17p13.3, 16p13.12p13.11, and 9p24.3p24.1). The presence of psychiatric/behavioral disorders, regardless of the co-occurrence of ID, and, at a borderline level, the presence of ID alone were associated with positive genetic findings (p = 0.024 and p = 0.054, respectively). Moreover, seizures were associated with positive genetic results (p = 0.024). One-third of individuals presenting with psychiatric illness who met our red flags for Mendelian diseases have pathogenic or likely pathogenic variants which can be identified using a comprehensive ID/ASD gene panel (~ 2500 genes) performed on an exome backbone.

Prediction of Immunotherapy Response in Melanoma through Combined Modeling of Neoantigen Burden and Immune-Related Resistance Mechanisms

PURPOSE

While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB).

EXPERIMENTAL DESIGN

Tumors from a cohort of patients with late-stage melanoma (n = 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB.

RESULTS

Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P = 0.016) than TMB alone (P = 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P = 0.002).

CONCLUSIONS

NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n = 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.

Abstract 2241: Profiling tumor-infiltrating immune cells using an augmented transcriptome

Due to the complexity of tumor-immune interactions, comprehensive profiling of both the tumor and tumor microenvironment (TME) can help further our understanding of tumor progression and response to treatment. One significant application is quantification of the immune infiltrate, which has the potential to characterize response to checkpoint blockade therapy. While common experimental approaches exist to profile tumor-infiltrating immune cells, they can have significant practical limitations. Instead, RNA sequencing can be used to comprehensively profile the immune composition of the TME in a scalable way. To address this, we have developed an approach to quantify eight immune cell types in tumor samples, and compare it to quantification by orthogonal methods. This module adds to the ImmunoID NeXT Platform®, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample.

To generate our reference data, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT™. Then, we utilized multiple methodologies and sample types to generate orthogonal quantification of immune cells for testing. These include creating in vitro cell mixtures and profiling by flow cytometry, profiling healthy donor PBMCs with cytometry by time of flight (CyTOF), profiling of dissociated tumor cells (DTCs) with FACS, and immunofluorescence (IF) characterization of FFPE tumor samples. We also used ImmunoID NeXT to profile the immune infiltrate of over 800 tumor samples across 13 cancer types.

We utilized the transcriptomes of the purified immune cells to develop reference expression signatures specific for each cell type. Then, we compared ImmunoID NeXT's transcriptome-based approach to the orthogonal quantification from the variety of samples. We observed a strong concordance of the quantification from our transcriptome-based approach to the flow cytometry results of the cell mixtures, demonstrating accuracy in purified immune cell populations. We also observed strong concordance to CyTOF results from healthy donor PBMC samples, showing accuracy in real samples with diverse immune populations. Further, we demonstrated strong concordance of our methodology to FACS results in DTCs and IF in FFPE tumor samples, ensuring that our approach is able to profile the immune composition in various tumor samples. Finally, we highlight the diversity of immune populations across cancer types by applying ImmunoID NeXT to over 800 tumor samples.

RNA sequencing can be used as a scalable approach to profile the immune infiltrate in tumors. Such analysis can add to our understanding of the tumor-immune interaction, including studies of response to immunotherapy. We show that the ImmunoID NeXT Platform is able to accurately evaluate the composition of tumor-infiltrating immune cells.

Citation Format: Eric Levy, Pamela Milani, Fabio Navarro, Charles W. Abbott, Robert Power, John West, John Lyle, Sean M. Boyle, Richard Chen. Profiling tumor-infiltrating immune cells using an augmented transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2241.

Abstract 399: Pan-cancer survey of HLA loss of heterozygosity using a robustly validated NGS-based machine learning algorithm

HLA loss of heterozygosity (LOH) is increasingly being recognized as an important immune escape mechanism in response to checkpoint inhibitor therapy. HLA LOH reduces the repertoire of neoantigens displayed on the cell surface of cancer cells, limiting the efficacy of the immune system to detect and eliminate them. Though highly accurate HLA LOH detection algorithms are needed to allow clinical utility, the field lacks robust, allele-specific validation approaches. Moreover, algorithms of unknown sensitivity and specificity have led to significant discrepancies in the estimated occurrence of HLA LOH as an immune escape mechanism across tumor types. To address these challenges, we have developed a machine learning algorithm to detect HLA LOH (DASH - Deletion of Allele-Specific HLAs), established the accuracy of the algorithm with an allele-specific PCR validation strategy, investigated the frequencies of HLA LOH across 14 tumor types in a cohort of over 800 patients and observed allele-specific neoantigen expansion in response to immunotherapy. To build DASH, we profiled 279 patients on the ImmunoID NeXT Platform to create a training dataset. Our novel features, which account for allele-specific differences in exome probe capture and capitalize on our whole exome platform by including information about copy number alterations in the regions flanking the HLA genes, were used to train an XGBoost model. Orthogonal, allele-specific validation was required to accurately assess sensitivity and specificity for clinical utility. Thus, we profiled over 30 paired tumor-normal cell lines on the ImmunoID NeXT Platform® and identified cell lines with HLA LOH. Using in silico mixtures, we found 100% sensitivity and specificity for tumors with at least 36% tumor purity. Next, we designed a digital PCR (dPCR) assay using patient-specific, allele-specific primers that target a single HLA allele while avoiding all other HLA alleles and tested the limit of detection of the assay in the same cell lines. Then, we performed dPCR with patient-specific primers on 20 tumor and normal sample pairs and found 94% sensitivity. After establishing the high sensitivity and specificity of DASH, we profiled over 800 patients spanning 14 tumor types on the ImmunoID NeXT Platform. We found that over 25% of patients in the majority of tumor types had at least one HLA LOH event. Further, we observed that novel neoantigens that arose during checkpoint treatment were significantly more likely to bind to deleted HLA alleles as compared to the remaining HLA alleles in a head and neck carcinoma cohort treated with anti-PD-1 therapy, shedding light on the mechanism of immune escape in response to checkpoint inhibitors. In summary, we introduced an HLA LOH detection method, performed allele-specific validation, exposed widespread HLA across tumor types and observed the mechanism of immune escape in response to immunotherapy.

Citation Format: Rachel Marty Pyke, Datta Mellacheruvu, Charles Abbott, Steven Dea, Eric Levy, Simo V. Zhang, Nikita Bedi, A. Dimitrios Colevas, Devayani Bhave, Manju Chinnappa, Gabor Bartha, John Lyle, John West, Michael Snyder, John Sunwoo, Richard Chen, Sean Michael Boyle. Pan-cancer survey of HLA loss of heterozygosity using a robustly validated NGS-based machine learning algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 399.

Abstract 555: Longitudinal exome-scale liquid biopsy monitoring of evolving therapeutic resistance mechanisms in head and neck squamous cell carcinoma patients receiving anti-PD-1 therapy

Background Typical liquid biopsy panels capture a relatively small number of variants, and likely under-represent the heterogeneity of resistance in late-stage cancers. This reduced scope can result in overlooked therapeutic biomarkers which respond dynamically to treatment, as well as potentially missed resistance mechanisms and pathway-level events. To address the challenges associated with identifying multiple concurrent heterogeneous resistance mechanisms in individual patients, we evaluated longitudinal whole exome sequencing of cell free DNA (cfDNA) and solid tumor biopsies from head and neck squamous cell carcinoma (HNSCC) patients that received anti-PD1 therapy. Using this approach, we identified evolving variant and pathway-level resistance mechanisms in cfDNA, as a complement to tumor biopsy derived information, and identified differences in putative neoantigens found in tissue and cfDNA.

Methods Pre- and post-intervention matched tumor, normal and plasma samples were obtained from a pilot cohort of 13 patients with HNSCC. Following baseline sample collection, all patients received a single dose of nivolumab. The primary tumor was then resected, approximately one month later when possible, or a second biopsy was collected where resection was impractical. Paired tumor and normal samples were profiled using ImmunoID NeXTTM, an augmented exome/transcriptome platform and analysis pipeline. Exome-scale cfDNA profiling of matched plasma samples was performed using the NeXT Liquid BiopsyTM platform to detect somatic variants. Data from these two platforms were then compared with corresponding clinical findings.

Results We observed a rapid evolution of the tumor microenvironment and disease mutation profile following therapy, with strong concordance detected between plasma and tumor variants at each timepoint. Post-therapy interrogation of cfDNA revealed dynamic changes in numerous oncogenes and clinically relevant pathways, such as ERK1/2 and MAPK, that were not observed in solid tumor. These findings suggest that single-lesion biopsy of the primary tumor misses co-occurring, clinically relevant resistance alterations. Median post-treatment neoantigen count was reduced in solid tumor, but increased in cfDNA. HLA-specific loss of heterozygosity (LOH) was identified in a number of subjects, likely resulting in reduced neoepitope presentation in those cases.

Conclusions Exome-wide somatic events were reliably detected in cfDNA, providing additional potential biomarkers to complement those identified in solid tumor. As we increase our cohort size, we expect that identification of biomarkers from both exome scale tissue biopsy and cfDNA will provide a more comprehensive view into therapeutic response and resistance mechanisms in HNSCC patients missed with typical liquid biopsy panels.

Citation Format: Charles W. Abbott, Nikita Bedi, Simo V. Zhang, Josette Northcott, Robin LI, Rachel Marty Pyke, Eric Levy, Rebecca Chernock, Mena Mansour, A. Dimitrios Colevas, John Lyle, John B. Sunwoo, Sean Boyle, Richard Chen. Longitudinal exome-scale liquid biopsy monitoring of evolving therapeutic resistance mechanisms in head and neck squamous cell carcinoma patients receiving anti-PD-1 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 555.

A pan-cancer profiling of somatic mutations and tumor microenvironment to reveal mutation dependent immunophenotypic signature differences in KRAS

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Background: The ubiquity of tumor genotyping has identified hundreds of clinically actionable somatic mutations such as KRAS mutations predicting anti-EGFR response in colorectal cancer. Here, we describe an integrative pan-cancer study leveraging the Personalis NeXT Platform across 13 tumor types to reveal a broad portrait of clinically relevant mutations across 36 genes. Given the ascendance of tumor immunotherapy, there is an urgent need to better elucidate associations between clinically relevant gene mutations, patient demographics, and tumor immunobiology. Methods: Paired tumor-normals (n > 450) were sequenced on the Personalis NeXT Platform, a diagnostic platform which simultaneously profiles tumor and immune microenvironments via exome and transcriptome sequencing. Samples were evaluated for the presence of KRAS variants at the G12, G13, and Q61 positions, requiring a minor allele frequency of more than 2%. Additionally, more than 200 clinically relevant amino acid changes were considered across 36 genes. DNA sequencing data was harnessed to profile MSI status, oncovirus status, HLA allele-specific LOH, SNVs, and neoantigens. RNA sequencing data furnished profiles for gene expression, TILs, TCR, BCR, and immune signatures. Statistical tests compared both the presence of clinically relevant mutations, and specific amino acid changes, to each of these DNA and RNA derived features. Results: Immunogenomic analysis with the Personalis NeXT Platform identified KRAS mutations at the G12, G13, and Q61 positions in 6 of the 13 tumor types, including bladder, CRC, liver, NSCLC, pancreatic, and uterine cancers, consistent with known KRAS allelic incidence. 11 different KRAS amino acid substitutions were identified in this cohort, including G12(A/C/D/R/S/V), G13(C/D), and Q61(H/K/L), with 14% of samples positive for KRAS variants across our pan-cancer cohort, including malignancies not associated with KRAS. Immune signatures, including TCRB clones, mutational burden, and TILs signatures demonstrated variability in a KRAS allele-dependent manner. Pancreatic cancers demonstrated a statistically significant increase in macrophage enrichment score (p < 0.001) in KRAS-mutant tumors. Conclusions: Utilizing the Personalis NeXT Platform, our analysis characterized the tumor microenvironment of over 460 samples across 13 tumor types, 36 genes, and over 200 clinically relevant amino acid substitutions. This comprehensive characterization demonstrated significant differences in immune cell signatures in pancreatic cancer, suggestive of an association between the presence of KRAS mutations and immune cell infiltrate composition.

Robust prediction of response to immunotherapy in a mixed cohort of previously treated and immunotherapy-naive melanoma patients

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Background: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers. Methods: Pre-treatment samples from 51 unresectable, stage III/IV melanoma patients who underwent anti-PD-1 therapy were characterized to assess factors influencing response. RECIST criteria were used to evaluate tumor response to therapy, with a median follow-up of 24 months. For each patient, a single paired FFPE tumor and normal blood sample was collected and profiled using Personalis’ ImmunoID NeXT Platform; an augmented exome/transcriptome platform and analysis pipeline which produces comprehensive tumor mutation information, gene expression profiling, neoantigen characterization, HLA typing and LOH, TCR repertoire profiling, and tumor microenvironment profiling. These data were then integrated to form a composite neoantigen presentation score (NEOPS) for each patient. Results: We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. Neoantigen burden score (NBS) was derived from SHERPA, our exome and transcriptome integrative neoantigen prediction model. We found that NBS more significantly stratified responders and non-responders (P = 0.016) than TMB alone (P = 0.049). A broader score, NEOPS, incorporated DASH, to capture HLA allele-specific loss of heterozygosity and additional antigen presenting machinery resistance mechanisms. NEOPS consequently demonstrated a stronger association with response (p = 0.002). We used area under the curve (AUC) to benchmark NEOPS performance against established mutational and transcriptomic predictive models, demonstrating the model was more predictive of response in both the treatment naive setting, as well as in a mixed cohort of treatment-naive and experienced patients. These findings were confirmed in an independent cohort of patients (n = 110), suggesting that NEOPS is a robust, novel biomarker of ICB response in melanoma. Conclusions: Given the complex nature of resistance to immunotherapy, as well as potential toxicities associated with treatment, there is a need for biomarkers that can more accurately predict response. Here we demonstrate that NEOPS can significantly improve stratification of patient response. We also demonstrate that data intensive biomarkers like NEOPS can be clinically practical, with comprehensive tumor profiling in our clinical cohort achieved using very limited tumor tissue.

Association of HLA loss of heterozygosity with allele-specific neoantigen expansion in response to immunotherapy

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Background: Human leukocyte antigen loss of heterozygosity (HLA LOH) restricts immune recognition of tumors by limiting the major histocompatibility complex (MHC) presentation of neoantigens to T cells and correlates with reduced response to immune checkpoint blockade therapy (ICB) in non-small cell lung cancer. To explore the mechanism behind the impairment of HLA LOH on ICB, we analyzed the relationship between the antigen presentation pathway, neoantigen presentation and response to ICB in a head and neck squamous cell carcinoma (HNSCC) cohort. Methods: Following baseline sample collection, a cohort of 14 HNSCC patients recieved a single dose of PD-1 inhibitor. The primary tumor mass was definitively resected approximately one month later. If resection was impractical, a second biopsy was taken. Response to therapy was evaluated using RECIST criteria. Each pre- and post-intervention tumor sample and normal PBMC sample were profiled using Personalis’ ImmunoID NeXT Platform, an HLA-enhanced exome/transcriptome platform. HLA LOH was detected using a digital PCR validated machine learning algorithm (DASH). Neoantigen presentation was computationally predicted using a machine learning algorithm (SHERPATM) trained on mono-allelic immunopeptidomics data. Results: We found that 50% of the HNSCC cohort had HLA LOH, a larger percentage than in a large pan-cancer cohort (23%, n=611) and a distinct HNSCC cohort (40%, n=20). Further, two patients had B2M LOH and one patient had a deleterious mutation in an HLA allele. Despite the high frequency of somatic alteration in the antigen presentation pathway, we did not find an association between HLA LOH and ICB response. However, if HLA LOH was still shaping tumor evolution in response to ICB, we would expect to see immune pressure against subclonal tumor populations with neoantigens presentable by the retained HLA alleles but not the deleted HLA alleles. Indeed, we found that significantly more novel post-treatment neoantigens were predicted to bind to deleted HLA alleles compared to their homologous alleles (p=0.045). Conclusions: Given the high prevalence of HLA LOH across tumor types, a greater understanding is needed regarding the impact of HLA LOH on tumor evolution during ICB treatment. Though HLA LOH does not correlate with response to ICB, the consistent shift in neoantigen composition suggests that it acts as an evolutionary force in resistance to response during immunotherapy.

Oncological Safety of Oncoplastic Level II Mammoplasties After Neoadjuvant Chemotherapy for Large Breast Cancers: A Matched-Cohort Analysis

BACKGROUND

Oncoplastic surgery (OPS) has extended the indications for breast-conserving surgery (BCS). Its role in patients with large breast cancers treated with neoadjuvant chemotherapy (NAC) is unclear. This study evaluated the oncological safety of OPS for tumors with partial response after NAC.

METHODS

A consecutive series of 65 patients who underwent OPS (study group) after NAC for large breast cancer from January 2004 to July 2018 was compared with 130 matched patients treated by NAC, followed by standard BCS in 65 cases and mastectomy in 65 cases (two case-controlled groups).

RESULTS

The mean initial radiological tumor size was 46 mm. Residual pathological tumor size was 22 mm in the OPS cohort, 19 mm in the standard BCS cohort, and 31 mm in the mastectomy cohort (p > 0.05). The mean follow-up was 59 months in the study cohort. Five-year local recurrence rates were 0%, 0%, and 10.5% (0-22%) for the OPS, BCS, and mastectomy cohorts, respectively, while 5-year regional recurrence rates were 4.1% (0-11.1%), 0, and 19.4% (0-35.2%, p > 0.05), respectively. Five-year overall survival was 85.3% for the OPS cohort, 94.1% for the standard BCS cohort (p = 0.194), and 79.9% for the mastectomy cohort (p = 0.165).

CONCLUSIONS

OPS is safe after NAC for large breast cancers, and provides excellent local control, identical to that of tumors with a better response, treated by standard BCS. After NAC, OPS can be a valuable treatment option for tumors that did not shrink optimally and would not be suitable for standard BCS.

Profilicollis chasmagnathi (Acanthocephala) parasitizing freshwater fishes: paratenicity and an exception to the phylogenetic conservatism of the genus?

Polymorphid acanthocephalans are parasites of marine mammals, waterfowl and ichthyophagous birds. Among these, the genus Profilicollis is known to use exclusively decapods as intermediate hosts. Here, we report the first record of living cystacanths of Profilicollis parasitizing the body cavity of a fish host, Oligosarcus jenynsii, inhabiting the freshwater section of an estuarial system, Mar Chiquita coastal lagoon, in south-east Buenos Aires Province, Argentina. In this environment, cystacanths of Profilicollis chasmagnathi have been previously recorded infecting decapod crabs and as transient accidental infections in the gut of some carcinophagous fishes. In the present study, larvae from the crab Neohelice granulata, from the intestine of the estuarine fish Odontesthes argentinensis and from the body cavity of O. jenynsii were morphologically and genetically compared, confirming their identity as P. chasmagnathi, a species characteristic of estuaries and marine coasts along Argentina, Uruguay and Chile. These findings can be interpreted as a possible case of incipient paratenicity for Profilicollis, and a colonization event of freshwater habitats, probably promoted by the highly variable conditions, typical of ecotonal environments. In addition, cystacanths of the genus Polymorphus were also found in O. jenynsii, representing the first record of this genus in Oligosarcus from Argentina.

Abstract 4430: Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome

Comprehensive characterization of the tumor and tumor microenvironment (TME) can improve our understanding of tumor progression and treatment outcomes. For example, quantification of the immune infiltrate can inform mechanisms of immune escape and predict response to checkpoint blockade. Standard experimental approaches exist to enumerate tumor-infiltrating immune cells, but they can have practical limitations of throughput, number of markers, or sample requirements. RNA sequencing can be used as a scalable solution to comprehensively profile the immune cell composition of the TME. However, care must be taken to ensure that the computational analysis accurately reflects the underlying immune cell composition. To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology-optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and TME from a single FFPE tumor sample. This includes the quantification of tumor-infiltrating immune cells using RNA-seq analysis, which we compare to quantification by orthogonal methods.

To generate our reference data, we profiled the transcriptomes of eight purified immune cell types using ImmunoID NeXT. Then, we analyzed multiple sample types and orthogonally quantified immune cells in each. These include profiling of healthy donor PBMCs with cytometry by time of flight (CyTOF), and immunofluorescence (IF) characterization of FFPE tumor samples. We also used ImmunoID NeXT to profile the immune infiltrate of over 500 tumor samples across 13 cancer types. Finally, we created a set of in vitro cell mixtures and profiled them by flow cytometry.

We utilized the transcriptome profiles of eight purified immune cell types to develop reference expression signatures specific for each cell type. Then, we compared ImmunoID NeXT's transcriptome-based approach to CyTOF results of healthy donor PBMCs, showing accuracy in real samples with diverse immune populations. We also compared to FFPE tumor samples with IF, ensuring that our approach is able to profile the immune composition in tumor samples. Next, we highlight the diversity of immune populations across cancer types by applying ImmunoID NeXT to over 500 tumor samples. Finally, to demonstrate concordance across the eight cell types, we compared to flow cytometry results of in vitro cell mixtures.

Analysis of the immune infiltrate of tumor samples can add to our understanding of the tumor-immune interaction, with potential applications including studies of response to immunotherapy. RNA sequencing can be utilized as a scalable approach for such analysis. Here, we test the accuracy of our approach using multiple sample sets with orthogonal profiling. We demonstrate that ImmunoID NeXT can accurately evaluate the composition of infiltrating immune cells in tumor samples.

Citation Format: Eric Levy, Pamela Milani, Charles W. Abbott, Manxia Lee, Robert Power, John West, Richard Chen, Sean M. Boyle. Quantification of tumor-infiltrating immune cell populations with an augmented transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4430.

Abstract 1334: A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome

There is an increasing need for more advanced, composite biomarkers that can model the complex systems biology driving response and resistance to cancer therapy. However, many cancer diagnostic platforms to date, with their focus on mutational changes in a relatively small panel of genes, provide limited data to support integrative, multidimensional biomarkers that can better predict immunotherapy response.

To enable the identification of composite biomarkers that combine tumor- and immune-related information from both DNA and RNA, we have developed ImmunoID NeXT, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune system from a single FFPE sample, across all of the approximately 20,000 genes. By co-optimizing assay and analytics design, we enable sensitive evaluation of clinically-relevant cancer biomarkers from &gt;=25ng of co-extracted DNA/RNA, while also providing a broader evaluation of neoantigens, HLA typing and LOH, antigen processing machinery (APM), TCR/BCR repertoire, immune expression signatures, tumor-infiltrating lymphocytes (TILs), oncoviruses, and germline variants. Leveraging this expansive feature set, we developed methods that combine individual analytes to construct composite biomarker scores that correlate with immunotherapy response.

Validation of ImmunoID NeXT demonstrated high sensitivity and specificity to somatic and structural variants across ~20,000 genes at allelic fractions as low as 5%, with clinical diagnostic reporting on actionable mutations (SNVs, indels, CNAs, fusions) in 248 cancer-driver genes that have been boosted further for higher sensitivity, as well as reporting on TMB and MSI status. For neoantigen prediction, immuno-peptidomic data from monoallelic HLA-transfected cell lines were used to train neural networks to predict pMHC binding with higher precision than public tools. For TCRα/β analysis in FFPE tumor samples, strong correlation with targeted TCR kit results was shown (R^2&gt;0.9 and &gt;0.94). For TILs, we developed signatures for eight immune cell types, demonstrating concordance with orthogonal immunofluorescence methods. We achieved genotyping accuracy of 99.1% for HLA Class I, and 95% for HLA Class II, and have developed and verified the performance of a tool for HLA LOH detection. In a cohort of 55 late-stage melanoma patients, the integration of neoantigen burden, HLA LOH, and APM mutational data formed a composite neoantigen score that more accurately predicted response to checkpoint blockade than other markers such as TMB.

With ImmunoID NeXT, we have developed a broad diagnostic platform that can be leveraged for the development of advanced composite biomarkers (and novel resistance mechanisms) that combine both tumor and immune features from DNA and RNA; enabling more accurate stratification of patient response to immunotherapy. The platform has been validated and optimized for use with limited FFPE tissue samples, making it ideal for both research and clinical applications.

Citation Format: Robert Peter Power, Gabor Bartha, Jason Harris, Sean M. Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Paul McNitt, Mandy Lee, Massimo Morra, Sejal Desai, Sebastian Salvidar, Michael J. Clark, Christian Haudenschild, Sekwon Jang, John West, Richard Chen. A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1334.

Abstract 2512: Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures

Background: A better understanding of the characteristics of cancer across different indications is required to drive the development of personalized treatments, inform therapy decisions, and improve outcomes. Integrating data from the tumor and the immune system can enable the identification of comprehensive biological signatures and composite biomarkers for the improved stratification of responders/progressors. Here, we describe a pan cancer study, including an enhanced whole-exome and transcriptome sequencing approach, across over 500 samples representing 13 tumor types, analyzed at high depth using the ImmunoID NeXT platform.

Methods: We sequenced paired tumor-normal samples on the ImmunoID NeXT platform, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune microenvironment from a single FFPE sample, across all of the approximately 20,000 genes. For each sample, we analyzed a broad set of features focused on both the tumor and immune system. From DNA, we profiled small variants, CNAs, MSI status, oncoviruses, HLA LOH, and neoantigens. From RNA, we profiled gene expression, small variants, fusions, TILs, TCR, BCR, and immune signatures. Integrated analyses assessing the impact of each feature, both within and across tumor types, were performed across the cohort.

Results: Through immunogenomic analysis we identified striking differences in both tumor and TME profiles across cancer types. In addition to mutation and neoantigen burden, by. we also computed a composite neoantigen score for each sample, which we have shown in a separate melanoma study can be a stronger predictor of response to immunotherapy. The composite neoantigen score integrates neoantigen prediction with mechanisms of tumor escape that can affect neoantigen presentation, providing a more accurate model of the antigen presentation biology. We also looked at the distribution of HLA LOH using our DASH algorithm and found differences in LOH frequency between tumor types. For example, we found HLA LOH to be five timesmore common in lung cancer than breast cancer. Further, we profiled immune gene signatures, including Gejewski and Ribas signatures, highlighting varied immune activation across cancer types. Analysis of somatic alterations in pathways controlling cell growth, PI3K/AKT signaling, apoptosis, and other canonical pathways revealed malignancy-specific alteration frequencies. The varying frequency, and combination of these alterations is indicative of a complex hierarchy of cross-talk between pathways, which operates in a cancer specific manner.

Conclusions: We performed a broad integrated analysis of the tumor and immune microenvironment for over 500 samples across 13 different tumor types using the ImmunoID NeXT platform. This comprehensive profiling revealed significant differences between cancer types beyond mutational burden, including neoantigen burden, immune microenvironment differences, and incidence of putative tumor escape mechanisms

Citation Format: Sean Michael Boyle, Charles Abbott, Eric Levy, Rachel Marty Pyke, Dattatreya Mellacheruvu, Simo Zhang, Mengyao Tan, Rena McClory, John West, Richard Chen. Pan-cancer characterization of the tumor and immune microenvironment facilitates identification of cancer-specific biological signatures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2512.

Abstract 4278: A composite neoantigen score is more strongly associated with therapeutic response than tumor mutational burden in a cohort of late-stage anti-PD-1-treated melanoma patients

Checkpoint inhibitor therapy has demonstrated meaningful, if varied antitumor activity, with patient response influenced by a variety of biological factors, including complex interactions between the tumor and immune system. Thus, it is of increasing interest to identify composite biomarkers integrating multiple biological features to better predict immunotherapy response. In this study we use a comprehensive tumor immungenomics profiling platform to examine the effectiveness of our composite neoantigen score for stratifying patient response to checkpoint blockade therapy compared to tumor mutational burden and other biomarkers.Pre-treatment tumor/normal samples from 55 unresectable, stage III/IV melanoma patients who underwent anti-PD-1 therapy were characterized to assess factors influencing response. RECIST criteria were used to evaluate tumor response to therapy, with a median follow-up of 18 months. For each patient, a single paired FFPE tumor and normal blood sample was collected and profiled using Personalis' ImmunoID NeXT platform; an augmented exome/transcriptome platform and analysis pipeline, which produces comprehensive tumor mutation information, gene expression quantification, neoantigen characterization, HLA typing and LOH, TCR repertoire profiling and tumor microenvironment profiling. These data were then analyzed together with clinical outcome, and a composite neoantigen score computed for each patient along with other biomarkers such as tumor mutational burden (TMB).In this cohort, an elevated pretreatment composite neoantigen score combining neoantigen predictions adjusted based on resistance mechanisms that affect neoantigen presentation on the MHC complex was more strongly predictive of response to PD-1 blockade than TMB alone. This was true for both response and non-response via RECIST criteria and progression free survival. We also found that the composite neoantigen score was a stronger predictor of patient response when compared to neoantigen burden alone. Additionally, we observed increased response to anti-PD-1 therapy in patients with elevated pretreatment TCR clonality. Combining the composite neoantigen score and TCR clonality data revealed a significant association with non-response to therapy. Patients with high composite neoantigen score and TCR clonality that failed to achieve complete response revealed potential resistance mechanisms to anti-PD-1 therapy. Specifically, we identified patients with high expression of IDO1 or CTLA4, which may facilitate PD-1-independent immune escape. Additionally, we found patients with mutations within the antigen presentation machinery (APM), likely leading to loss of surface expression of the proteins, and in the case of B2M mutations, improper HLA class I folding and antigen presentation. These APM mutations likely result in reduced neoantigen presentation in these patients, facilitating tumor escape.In summary, our composite neoantigen score which integrates multiple components of MHC class I presentation into a single score, is more significantly associated with response to therapy than individual biomarkers such as tumor mutational burden. These findings highlight the promise of composite biomarkers for the optimization of anti-PD-1 therapy patient selection.

Citation Format: Charles W. Abbott, Eric Levy, Rachel Marty Pyke, Rena McClory, Sekwon Jang, Richard Chen, Sean Boyle. A composite neoantigen score is more strongly associated with therapeutic response than tumor mutational burden in a cohort of late-stage anti-PD-1-treated melanoma patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4278.

Exome scale liquid biopsy characterization of putative neoantigens and genomic biomarkers pre- and post anti-PD-1 therapy in squamous cell carcinoma of the head and neck

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Background: The reduced scope, and number of genes profiled by typical liquid biopsy panels can result in missed biomarkers including neoantigens, which may change with treatment, as well as potentially undetected resistance mechanisms and pathways beyond the scope of targets typically captured by panels. To address these limitations, we used a whole-exome scale liquid biopsy monitoring platform, NeXT Liquid Biopsy, to analyze head and neck squamous cell carcinoma (HNSCC) patients that have received anti-PD1 therapy. Presently, we sought to (1) monitor neoantigen changes in cfDNA as a complement to tumor biopsy-derived neoantigens, (2) compare the impact of tumor escape mechanisms, including HLA-LOH, on neoantigens identified in tissue and cfDNA and (3) to identify novel biological signatures that combine information from both solid tumor and liquid biopsies. Methods: Pre- and post-intervention matched normal, tumor and plasma samples were collected from a cohort of 12 patients with HNSCC. Following baseline sample collection all patients received a single dose of nivolumab, followed by resection approximately one month later when feasible, or a second biopsy where resection was impractical. Solid tumor and matched normal samples were profiled using ImmunoID NeXT, an augmented exome/transcriptome platform and analysis pipeline. Exome-scale somatic variants were identified in cfDNA from plasma samples using the NeXT Liquid Biopsy platform. Data from these two platforms were compared with corresponding clinical findings. Results: Concordant somatic events were detected between plasma and tumor at pre- and post-treatment timepoints. Neoantigens predicted to arise from these somatic events were reduced in solid tumor post-treatment, but increased in cfDNA, when compared to pre-treatment timepoints. HLA LOH was identified in a number of subjects, likely resulting in reduced neoepitope presentation in those cases. Immune cell infiltration increased in the tumor following treatment, with no changes to the CD8+/Treg cell ratio, suggesting consistent immunoregulation. Conclusions: Exome-wide neoantigen burden was reliably predicted from cfDNA, providing additional insight complementing data from solid tumor. Analyzing HLA LOH, and neoantigen burden from both solid and liquid biopsies together over the course of treatment creates a more comprehensive profile of therapeutic response and resistance mechanisms in HNSCC patients missed with typical liquid biopsy panels.

Abstract A051: A comprehensive, highly accurate genomics platform for precision immunotherapy: Simultaneously characterize tumors and the TME from a single FFPE sample

Immunogenomic profiling of the tumor and the tumor microenvironment (TME) is critical for identifying new biomarkers of immunotherapy response, understanding resistance, and enabling the development of personalized immunotherapies. However, running a comprehensive array of biomarker assays for each patient sample is often impractical given limited sample quantity, processing complexity, and prohibitive cost. To address these challenges, we developed a novel, augmented exome and transcriptome-based platform that simultaneously characterizes the tumor and TME from a single FFPE sample. We co-optimized the design of our sequencing assays and analytics to increase performance for the detection of somatic SNVs, indels, CNAs, and fusions across ~20,000 genes, as well as the evaluation of neoantigens, expression signatures, HLA typing and LOH, TCR/BCR repertoires, oncoviruses, tumor-infiltrating lymphocytes (TILs), clinically-actionable mutations, tumor mutational burden (TMB), and MSI status. We developed novel methods to sequence difficult regions of the exome and to extend coverage to key immunogenomic biomarkers. Analytic pipelines were designed to utilize assay optimizations to achieve higher accuracy than with other platforms. We then validated the platform for diagnostic and therapeutic use. With as little as 50ng of DNA per FFPE sample and co-extracted RNA, this platform completely covers between 17% to 40% more genes compared to a non-augmented exome, thus increasing sensitivity to somatic mutations and putative neoantigens. For neoantigen performance, we generated immune-peptidomic data from mono-allelic HLA transfected cell-lines and trained neural networks to predict neoepitope binding to MHC, demonstrating a higher precision (0.88) across alleles than publicly available tools (&lt;0.7). For TCR alpha and beta clonotype profiling in tumor samples, we demonstrate strong correlation with the results from a targeted TCR kit (R2&gt;0.9 and &gt;0.94, respectively). For TILs, we developed signatures for CD4, CD8 T-cells, and other immune cells, demonstrating concordance with synthetic and CyTOF-derived validation sets. For HLA typing, we achieve an accuracy of 99.1% for HLA Class I, and 95% for HLA Class II typing calls, and have developed a novel tool for HLA LOH detection. We demonstrate sensitive detection of HPV, EBV, HCV, HTLV, and KSHV in known samples, and accurate MSI and TMB assessment. Finally, for diagnostic reporting, we achieve high sensitivity and specificity for clinically-reportable mutations comparable with diagnostic cancer panels. With this platform, we have developed a novel immunogenomics platform that can characterize both the tumor and TME from a single sample. By co-optimizing our assay and analytics for immuno-oncology, we enhance biomarker sensitivity compared to non-optimized genomics assays. Validation of the platform extends its use to diagnostics and personalized immunotherapy development.

Citation Format: Robert Power, Gabor Bartha, Jason Harris, Sean Michael Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Robin Li, Manjula Chinnappa, Paul McNitt, Rena McClory, Massimo Morra, Sebastian Saldivar, Michael Clark, Christian Haudenschild, Erin Newburn, Christelle Johnson, John West. A comprehensive, highly accurate genomics platform for precision immunotherapy: Simultaneously characterize tumors and the TME from a single FFPE sample [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A051. doi:10.1158/1535-7163.TARG-19-A051

Acylated Ghrelin and The Regulation of Lipid Metabolism in The Intestine

Acylated ghrelin (AG) is a gastrointestinal (GI) peptide mainly secreted by the stomach that promotes cytosolic lipid droplets (CLD) hypertrophy in adipose tissues and liver. However, the role of AG in the regulation of lipid metabolism in the intestine remains unexplored. This study aimed at determining whether AG influences CLD production and chylomicron (CM) secretion in the intestine. The effects of AG and oleic acid on CLD accumulation and CM secretion were first investigated in cultured Caco-2/15 enterocytes. Intestinal lipid metabolism was also studied in Syrian Golden Hamsters submitted to conventional (CD) or Western (WD) diets for 8 weeks and continuously administered with AG or physiological saline for the ultimate 2 weeks. In cultured Caco-2/15 enterocytes, CLD accumulation influenced CM secretion while AG reduced fatty acid uptake. In WD hamsters, continuous AG treatment amplified chylomicron output while reducing postprandial CLD accumulation in the intestine. The present study supports the intimate relationship between CLD accumulation and CM secretion in the intestine and it underlines the importance of further characterizing the mechanisms through which AG exerts its effects on lipid metabolism in the intestine.

Undernutrition in childhood: Clinically based assessment tools and biological markers: Where are we and where should we go?

Despite its association with poor clinical outcomes and increased hospital costs, as of today undernutrition still goes undetected in paediatric hospitals. The reported prevalence of undernutrition in paediatric patients varies considerably. This disparity is partly due to the diversity of methods for its detection and assessment, as well as to the lack of consensus regarding its definition. Several methods, based on varied combinations of morphology characteristics, estimated nutritional intakes and medical conditions have been developed during the last 25 years. However, these tools suffer from poor sensitivity and selectivity particularly in acute conditions. Also while having their own merit, these tools mainly view malnutrition from the energy standpoint, disregarding assessment of specific micronutrients such as minerals and vitamins. In this position paper we make the point that in the era of personalized medicine, present technology offers the possibility of going beyond the traditional nutritional tools for assessing patients' status, and propose the measurement of selected micronutrients and allied metabolic markers in nutritional workup schemes adapted to each clinical condition.

Abstract 3377: T-cell receptor repertoire profiling using an augmented transcriptome

Immunotherapy is growing as one of the most promising therapeutic approaches in clinical oncology practice. This brings with it an increasing need for comprehensive immuno-genomic profiling of tumors to better understand the interaction with the immune system. This includes profiling of the T and B-cell receptor repertoires (TCR/BCR), which has traditionally not been feasible with an exome/transcriptome platform.

To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and tumor microenvironment (TME) from limited FFPE tumor biopsies, including the TCR alpha, beta, gamma and delta chains and BCR heavy and light chains. We show how this platform accurately profiles abundant clones, and can be applied to understand the diversity and activity of the adaptive immune system.

We characterize the performance of ImmunoID NeXT at profiling TCR beta from RNA. We analyze the reproducibility of clones identified using replicates of PBMC and FFPE samples, and assess the concordance of top clones from a standalone TCR sequencing approaches to ours. Then, we test LOD by diluting well-characterized clonal T-cell line samples into PBMCs.

We also analyze patient-derived FFPE tumors to understand the profiles of tumor-infiltrating immune repertoires. First, we compare TCR beta profiling with IHC quantification of CD3+ cells in both tumor and adjacent normal tissues. Finally, to better understand both B and T cell infiltration, we profile the intra-sample heterogeneity of BCR and TCR in a set of tumors.

Between replicates of PBMC samples, abundances for shared clones have high concordance (R2&gt;0.98). We observe strong concordance of the abundances for shared clones between adjacent curls of a tumor FFPE sample (R2&gt;0.87), showing that our approach is robust to degraded FFPE samples. Compared to a standalone approach, we identify over 93% of the top 1000 clones, with highly concordant abundances (R2&gt;0.94). Assessing LOD in dilutions of T cell lines into a PBMC sample, we are able to identify clones present at over 0.00032% RNA by mass.

In our analysis of T-cell infiltration, we find a significant T-cell population in normal tissues. We also compare TCR read counts detected by ImmunoID NeXT in tumor and normal tissues with IHC results. Finally, we find substantial inter-sample variations in the number of TCR and BCR clones in tumors.

ImmunoID NeXT has been designed to enable sensitive detection of abundant TCR and BCR clones in addition to comprehensive biomarkers from exome/transcriptome data. We demonstrate that our platform is reproducible, sensitive, and concordant with the top-abundance clones derived from targeted TCR methods, as well as feasible with FFPE samples. Finally, we highlight how immune repertoire results from ImmunoID NeXT can be used to gain understanding about the immunological composition of the TME.

Citation Format: Eric Levy, Pamela Milani, Sean M. Boyle, Shujun Luo, Gabor Bartha, Charles Abbott, Rena McClory, Robin Li, John West, Richard Chen. T-cell receptor repertoire profiling using an augmented transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3377.

Abstract 4695: Development and validation of an accurate exome-scale cfDNA detection platform

Background: Neoantigens are increasingly critical as biomarkers for immunotherapy response to checkpoint blockade therapy and as therapeutic targets for neoantigen-based personalized cancer vaccines. Accurate identification of neoantigens requires comprehensive exome and transcriptome sequencing of both a tumor biopsy and the matched normal samples to enable identification of putative neoantigens - which occur across the genome.

Methods: However, as tumor biopsy samples cannot always be obtained, and because tumor heterogeneity can result in an incomplete set of neoantigens from a single biopsy, we developed ImmunoID NeXT circulating tumor DNA (ctDNA) Exome to (1) identify neoantigens in cell free DNA (cfDNA) as a complement to tumor biopsy derived neoantigens and (2) track neoantigens in the cfDNA post immuno-therapy treatment. To demonstrate the utility of the ImmunoID NeXT ctDNA Exome for both identification and monitoring of neoantigens directly from cfDNA, we have performed two extensive studies. Firstly, we obtained cfDNA from 2 healthy donors, mixed them to create somatic variants with AFs down to 0.5% with analytical sensitivity calculated against &gt;10,000 variants. Secondly, we have performed extensive cfDNA testing in cancer patients to assess our capabilities across tumor types. Finally, and very importantly, we utilized multi-location primary and metastatic tumor profiling to demonstrate the ImmunoID NeXT cfDNA platform can be applied to profile tumor heterogeneity.

Results: In our healthy donor mixes, we observed our ImmunoID NeXT cfDNA platform can detect de novo “gold set” SNVs with a sensitivity of 90% down to an allele frequency of 2%. For monitoring applications we are able to detect SNVs with a sensitivity of 92% down to an allele frequency of 0.5%. When tested across a range of tumor types including melanoma, lung, and colorectal, ImmunoID NeXT repeatedly detected high concordance for somatic events shared between tumor and cfDNA. In tumor samples allele frequencies ranged from10% to 100%, and through cfDNA interrogation 60%-98% of these events were accurately detected in the plasma. Finally, we were able to reproducibly detect ctDNA, which were not present in the primary tumor sample, in subsequent primary tumor biopsy curls or metastases in multiple tumor types, demonstrating our ctDNA platform can effectively monitor tumor heterogeneity.

Conclusion: These results show the accuracy of the ImmunoID NeXT platform for detecting and profiling ctDNA somatic events. Further, these results demonstrate the potential of using a comprehensive ctDNA Exome to identify and monitor neoantigens as a complement to the results from sequencing of the tumor biopsy alone.

Citation Format: Simo V. Zhang, Charles Abbott, Robin Li, Eric Levy, Shujun Luo, Robert Power, Rena McClory, John West, Richard Chen, Sean Michael Boyle. Development and validation of an accurate exome-scale cfDNA detection platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4695.

Abstract 905: Comprehensive immunogenomic profiling of anti-PD-1 treated melanoma patients reveals subject-specific tumor escape mechanisms

Background: Despite the remarkable response of some melanoma patients to checkpoint inhibitor therapy, the majority of patients do not achieve complete response. It is of great interest to identify biomarkers and mechanisms that influence immunotherapy effectiveness. Here we apply our comprehensive tumor immunogenomics platform (ImmunoID NeXT) to identify potential biomarkers of response to checkpoint blockade therapy related to both the tumor and tumor microenvironment.

Methods: We characterized the immunogenomics of 50 stage III/IV melanoma patients who have undergone anti-PD-1 therapy to assess potential factors influencing response. Tumor response to therapy was evaluated using RECIST criteria with a median follow-up of 12 months. Immuno-genomic profiling was performed using Personalis’ ImmunoID NeXT platform; an augmented exome/transcriptome platform and analysis pipeline which from a single paired tumor FFPE and normal blood sample yielded comprehensive tumor mutation information, gene expression quantification, neoantigen characterization, TCR repertoire profiling, HLA typing and tumor microenvironment profiling. The molecular information of the tumors was then analyzed together with their corresponding clinical response.

Results: Through comprehensive immunogenomic profiling we demonstrated that higher TCR clonality in pre-treatment biopsy was predictive of response to PD-1 blockade, and significantly associated with improved progression free survival. We also observed increased response to anti-PD-1 treatment in patients with elevated pretreatment neoantigen burden. Further investigation of patients with high neoantigen burden and TCR clonality that failed to achieve complete response revealed potential resistance mechanisms to anti-PD-1 therapy. Specifically, we identified two patients with high expression of IDO1 or CTLA4, which may facilitate immune escape in a PD-1 independent manner. Additionally, we found two patients with mutations in their antigen presentation machinery (APM). The first patient had two independent HLA mutations in HLA-A and HLA-B, leading to the likely loss of surface expression of the proteins. In the second APM mutation patient we observed a high frequency (80% AF) frameshift variant in B2M, which potentially prevents proper HLA class I folding and antigen presentation. These APM mutations suggest reduced neoantigen presentation in these patients, which are probable mechanisms for tumor escape.

Conclusions: In summary, our comprehensive cancer immunogenomic analysis shows that genomic and immune profiling of pretreatment patient samples can identify biomarkers and resistance mechanisms to immune checkpoint blockade, suggesting the potential efficacy of these as a combinatorial biomarker to optimize patient selection for anti-PD-1 therapy.

Citation Format: Charles W. Abbott, Sean M. Boyle, Eric Levy, Rena McClory, Sekwon Jang, Richard Chen. Comprehensive immunogenomic profiling of anti-PD-1 treated melanoma patients reveals subject-specific tumor escape mechanisms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 905.

Is there a relationship between vitamin D nutritional status and metabolic syndrome in childhood acute lymphoblastic leukemia survivors? A PETALE study

BACKGROUND

Treatment of childhood acute lymphoblastic leukemia (cALL) has reached unprecedented success leading to survival rates reaching 90%. This is regrettably linked to increased risk of developing long-term health-related sequels into early adulthood.

OBJECTIVE

This study aims at assessing the relationship between the vitamin D status and metabolic biomarkers in PETALE, a well-characterized cohort of cALL survivors.

RESULTS

We demonstrate that 15.9% of the study participants exhibited 3 or more metabolic syndrome (MetS) risk factors. We also show a direct relationship between s25OHD₃ and plasma HDL-Cholesterol concentrations in female but not male participants.

CONCLUSION

Our data, from a metabolically well-described cohort, support a modest role for vitamin D in lipid metabolism in childhood leukemia survivors. The major outcome of this study is the strong association between HDL-Cholesterol concentration and s25OHD₃ only in female subjects, thereby conveying vitamin D a gender-specific cardio-protective effect. cALL survivors represent a population at higher risk for secondary diseases. For this reason thorough nutritional evaluation, including vitamin D should be part of the regular follow-up.

Abstract 5710: Molecular profiling of anti-PD-1 treated melanoma patients reveals importance of assessing neoantigen burden and tumor escape mechanisms for clinical treatment

Despite the remarkable response of some melanoma patients to checkpoint inhibitor therapy, significant numbers of patients do not achieve complete response. To understand this differential response, there is an increasing interest in identifying biomarkers and mechanisms that influence immunotherapy effectiveness. In this study, we characterize the immuno-genomics of tumors from a series of melanoma patients that have received anti-PD-1 checkpoint inhibitors to assess potential factors influencing response.

To better understand mechanisms of anti-PD-1 response, we sequenced and genomically profiled tumors from 19 stage III and IV melanoma patients where response was evaluated using RECIST criteria. Of the 19 patients, there were 5 complete responders (CR), 8 partial responders (PR), and 6 progressive disease (PD) patients. Immuno-genomic profiling was performed using Personalis' ACE ImmunoID platform, an augmented exome/transcriptome platform and analysis pipeline that allows for assessment of tumor mutations, neoantigens, HLA typing, gene expression quantification, tumor micro-environment, and tumor escape mechanisms. The molecular information for each of the 19 melanoma patient samples was then analyzed together with the corresponding clinical response to anti-PD-1 therapy.

We identified 3 outlier patients, which, while having very high neoantigen burden, did not achieve complete response (2 PR & 1 PD). One of these patients had extremely high expression of IDO1, which may facilitate immune escape in a PD-1 independent manner. Two independent HLA mutations in HLA-A and HLA-B (stop-gain mutation and splice site mutation, respectively) were found in the second patient, leading to the likely loss of surface expression of two classes of HLA-A and HLA-B proteins. If these three high neoantigen burden individuals with proposed tumor escape mechanisms are removed from consideration, we found a highly significant association between neoantigen burden and response to anti-PD-1 therapy (PD + PR vs CR, P = 0.00046). We also observed that, in our cohort, response to anti-PD-1 therapy was more accurately predicted by neoantigen burden than mutational burden.

In conclusion, we observed a strong correlation between response to anti-PD-1 therapy in melanoma patients and neoantigen burden when tumor escape mechanisms are considered. In our patients, we saw highly suggestive resistance mechanisms that involve perturbations to elements of the antigen presenting machinery and checkpoint blockade. This highlights the potential importance of broad immuno-genomic profiling of patients that are candidates for receiving immunotherapy. We are continuing to increase our cohort size to observe both how well the neoantigen burden holds to anti-PD-1 response and to identify additional mechanisms for immune evasion.

Citation Format: Jie Wang, Sean Michael Boyle, Christina Lee, Eric Levy, Zeid Rusan, Sekwon Jang, Richard Chen. Molecular profiling of anti-PD-1 treated melanoma patients reveals importance of assessing neoantigen burden and tumor escape mechanisms for clinical treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5710.

Abstract 2245: Deconvolution of diverse immune cell populations within tumors using ACE Transcriptome

Comprehensive tumor immuno-genomic characterization is becoming an important tool for identifying new biomarkers correlated with patient response to immunotherapy. Both the abundance and composition of tumor-infiltrating lymphocytes have been associated with tumor progression and patient outcome. Prior studies have shown that an enrichment of cytotoxic immune cells, and depletion of immunosuppressive cells, can indicate better clinical outcome.

While common lab approaches are used to profile tumor samples for the presence and enumeration of immune cell types, these approaches can be limited by the number of markers and throughput. Thus, there is interest in using an accurate computational method leveraging NGS data to more comprehensively profile the distribution of immune cells in the tumor microenvironment. Through the use of marker genes that are expressed in a cell type specific manner, it is possible to computationally predict the fractions of multiple cell types in a mixed sample. This approach relies on an input of reference gene expression signatures, which are used to estimate the proportions of the cell types represented in the reference. The generation and validation of these reference signatures are critical for ensuring the accuracy of the results on a given platform.

We have used the ACE Cancer Transcriptome, from the ACE ImmunoID platform, to produce high-quality gene expression profiles of purified immune cells representing many lineages, including B cells, monocytes, NK cells, and T cells. These profiles were used to create reference signatures of immune cell type specific genes, enabling quantification of their cellular abundances. In addition, we test the accuracy of the deconvolution approach on mixtures of immune cells. Finally, we validate the approach on tumor samples with immune cells quantified by widely accepted orthogonal methods. Through these results, we show that deconvolution of TILs through our ACE Cancer Transcriptome can be a robust platform for elucidating the composition of immune cells in a tumor sample.

Citation Format: Eric Levy, Sean M. Boyle, Pamela Milani, Shujun Luo, John West, Richard Chen. Deconvolution of diverse immune cell populations within tumors using ACE Transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2245.

Abstract 1292: Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology

Background:Neoantigens are increasingly critical in immuno-oncology as a therapeutic target for neoantigen-based personalized cancer vaccines and as a potential biomarker for immunotherapy response. However, the methods for identifying which neoepitopes are more likely to provoke an immune response remains an important challenge for improving both the effectiveness of neoantigen-based vaccines and enabling the potential use of neoantigens as a biomarker in immunotherapy.

Methods:We sought to improve overall neoantigen identification performance by systematically improving critical components of our ACE ImmunoID assays and neoantigen pipeline. Personalis' Accuracy and Content Enhanced (ACE) technology was developed to fill critical gaps in conventional exome and transcriptome sequencing that can lead to missed neoantigens. To improve MHC-epitope binding prediction, we trained neural networks on mass spectrometry derived MHC-epitope binding data. This is in contrast to other MHC binding algorithms that have been primarily trained using in vitro competitive binding data, which suffer from having not been processed, loaded, nor shuttled natively into the HLA binding domain. HLA typing, a key input into the neoantigen prediction algorithms, was improved through exome augmentation of the HLA region with an optimized HLA typing algorithm. Other enhancements include RNA based somatic variant calling, peptide phasing, transcript isoform estimation, and identification of indel and fusion derived neoepitopes.

Results:Our ACE augmented exome demonstrates high sensitivity and specificity for SNVs, indels, and fusions at MAF &gt;=10%. These are all variant types that result in putative neoantigens. Further, we show that our augmented ACE transcriptome can achieve high sensitivity for RNA derived variants and can be an important filter for putative neoantigens. When compared with commercially available MHC binding algorithms for specific HLA alleles, our MHC binding prediction algorithm consistently achieves a higher overall sensitivity and specificity than other tools. For example, our MHC class I-epitope binding prediction algorithm demonstrated an aggregative precision value of 0.88 across HLA alleles, as opposed to 0.50 for other widely used tools. To assess overall HLA-typing performance, we performed a blinded clinical HLA typing validation demonstrating 98% and 95% concordance with Class I and II HLA results (respectively) from clinical testing. We also show instances where peptide phasing, SNP, indel and fusion-derived neoepitopes are important for more accurate and comprehensive neoantigen identification.

Citation Format: Sean Michael Boyle, Ravi Alla, Ryan Wang, Eric Levy, Gabor Bartha, Jason Harris, Robert McCord, Rena McClory, John West, Richard Chen. Methods of improving accuracy of neoantigen identification for therapeutic and diagnostic use in immuno-oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1292.