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This article describes fabrication of a customizable bioreactor, which comprises a perfusion system and coverslip-based tissue culture chamber that allow centimeter-scale vascularized or otherwise canalized tissue constructs to be maintained in weeks long static and/or perfusion culture at an exceptionally low cost, with intermittent live imaging and media sampling capabilities. The perfusion system includes a reusable polydimethylsiloxane (PDMS) lid generated from a three-dimensional (3D)-printed poly-lactic acid (PLA) mold and several lengths of perfusion tubing. The coverslip tissue culture chamber includes PDMS components built with 3D-printed PLA molds, as well as 3D-printed PLA frames and glass coverslips that house perfusable hydrogel constructs. As proof of concept, we fabricated a vascularized hydrogel construct, which was subjected to static and perfusion tissue culture, as well as flow studies using fluorescent beads and widefield fluorescent microscopy. This system can be readily reproduced, promoting the advancement of tissue engineering and regenerative medicine research.

Supplementary experimental procedures are described

Supplementary Figure 1. Efficacy of entrectinib on KM12 xenograft model using the scheduling 4 days on/ 3 days off. Supplementary Figure 2. Mechanism of action of entrectinib compared to crizotinib in ALK-dependent Ba/F3 cells. Supplementary Figure 3. Cell cycle distrivbution of Karpas-299 and SR-786 cells treated with entrectinib for different periods of time Supplementary Figure 4. Mechanism of action studies of entrectinib in additional ALK-dependent ALCL models Supplementary Figure 5. In vivo efficacy of entrectinib on NCI-H2228 xenograft model using the scheduling 4 days on/ 3 days off Supplementary Figure 6. Representative magnetic resonance images of mice bearing intracranial tumors treated with vehicle, entrectinib or crizotinib Supplementary Table 1. List of human tumor cell lines treated with entrectinib Supplementary Table 2. Pharmacokinetic paramenters of entrectinib evaluated after repeated administration of compound in CD1 mice.

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

Supplementary Figure 1. In vitro characterization of RET inhibitory activity of RXDX-105 in HBEC3KT-RET cells; Supplementary Figure 2. In vivo efficacy of RXDX-105 in a xenograft model of HBEC3KT-RET harboring CCDC6-RET fusion.

Purpose: While multikinase inhibitors with RET activity are active in RET-rearranged thyroid and lung cancers, objective response rates are relatively low and toxicity can be substantial. The development of novel RET inhibitors with improved potency and/or reduced toxicity is thus an unmet need. RXDX-105 is a small molecule kinase inhibitor that potently inhibits RET. The purpose of the preclinical and clinical studies was to evaluate the potential of RXDX-105 as an effective therapy for cancers driven by RET alterations.Experimental design: The RET-inhibitory activity of RXDX-105 was assessed by biochemical and cellular assays, followed by in vivo tumor growth inhibition studies in cell line– and patient-derived xenograft models. Antitumor activity in patients was assessed by imaging and Response Evaluation Criteria in Solid Tumors (RECIST).Results: Biochemically, RXDX-105 inhibited wild-type RET, CCDC6-RET, NCOA4-RET, PRKAR1A-RET, and RET M918T with low to subnanomolar activity while sparing VEGFR2/KDR and VEGFR1/FLT. RXDX-105 treatment resulted in dose-dependent inhibition of proliferation of CCDC6-RET–rearranged and RET C634W-mutant cell lines and inhibition of downstream signaling pathways. Significant tumor growth inhibition in CCDC6-RET, NCOA4-RET, and KIF5B-RET–containing xenografts was observed, with the concomitant inhibition of p-ERK, p-AKT, and p-PLCγ. Additionally, a patient with advanced RET-rearranged lung cancer had a rapid and sustained response to RXDX-105 in both intracranial and extracranial disease.Conclusions: These data support the inclusion of patients bearing RET alterations in ongoing and future molecularly enriched clinical trials to explore RXDX-105 efficacy across a variety of tumor types. Clin Cancer Res; 23(12); 2981–90. ©2016 AACR.

Supplementary Table from 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.

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

In the last decade, and especially after the 2013 Virginia court case of Ross and Ross v. Hatch, there has been a dramatic increase in knowledge, use, and legal recognition of supported decision-making (SDM) in the United States. SDM is a methodology in which people work with trusted friends, family members, and professionals who help them understand their situations and choices so they may make their own decisions and direct their lives. After the Hatch case, in which a young woman with Down syndrome defeated a petition for permanent guardianship by demonstrating that she uses SDM, this methodology has increasingly been considered and used as an alternative to guardianship to enable people to retain their legal rights and make life choices to the maximum extent possible. This article reviews the guardianship laws of the 50 U.S. states and the District of Columbia. Using criteria we developed, in light of the findings and values expressed in Hatch, we assessed the extent to which those laws recognize or encourage the use of SDM as an alternative to guardianship and as a means to enhance self-determination for people in guardianship. We then offer recommendations for future SDM research, policy, education, and advocacy efforts.

A diagnosis of traumatic brain injury (TBI) is typically based on patient medical history, a clinical examination, and imaging tests. Elevated plasma levels of glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase L1 (UCH-L1), and neurofilament light chain (NFL) have been observed in numerous studies of TBI patients. It is reasonable to view traumatic optic neuropathy (TON) as a focal form of TBI. The purpose of this study was to assess if circulating GFAP, UCH-L1, and NFL are also elevated in a porcine model of TON. Serum levels of GFAP, UCH-L1, and NFL were measured immediately before optic nerve crush and 1 h post-injury in 10 Yucatan minipigs. Severity of optic nerve crush was confirmed by visual inspection of the optic nerve at time of injury, loss of visual function as measured by flash visual evoked potential (fVEP) at 7 and 14 days, and histological analysis of axonal transport of cholera toxin-β (CT-β) within the optic nerve. Post-crush concentrations of GFAP, UCH-L1, and NFL were all significantly elevated compared with pre-crush concentrations (p

Given the threat of radiological and nuclear terrorism, it is imperative to understand and evaluate the security risk of radioactive sources. In this context, risk assessment is a function of threat, vulnerability, and consequences. Currently, no broad risk index exists for radiological facilities, such as healthcare centers and universities. This study aims to develop and demonstrate a methodology to compute a potential facility risk index (PFRI) based on a probable loss event (LE) and loss magnitude (LM) resulting from a radiological dispersal device (RDD) attack. The threat component of the PFRI is devised as a utility function weighing the threat group attributes and RDD radioactive material preference. The principles of probabilistic risk assessment and pathway analysis are implemented to account for RDD radioactive material theft probabilities in different attack scenarios. Locational hazards and nuclear security culture are measured as a function of radiological facility vulnerability for LE. The LM of the attack, in the form of loss of life and economic damage, is then estimated to construct the PFRI. The methodology is applied to a hypothetical healthcare facility with a single radioactive material asset. For this example, the PFRI resulted in a value of 2.0 (on a scale of 1-10), showing low risk to the facility. The development of the PFRI provides a risk analysis tool that may be useful in making decisions for radiological security improvements.

BACKGROUND: Nipple reconstruction is widely regarded as the final step in post-mastectomy breast reconstruction. While grafts, local flaps, or combination approaches have been employed in nipple reconstruction, none has been able to achieve reliable long-term projection preservation. In response, we have sought to bioengineer neo-nipples in situ via the implantation of processed, decellularized cartilage xenografts placed within 3D-printed polylactic acid (PLA) scaffolds. MATERIALS AND METHODS: External nipple scaffolds were designed in-house and 3D-printed with PLA filament. Decellularized ovine xenograft infill was prepared and processed by mincing or zesting. All nipple scaffolds were placed subcutaneously on the dorsa of Sprague-Dawley rats and explanted after 1, 3, and 6 months for analysis. RESULTS: Explanted nipple scaffolds demonstrated gross maintenance of scaffold shape, diameter, and projection with accompanying increases in tissue volume. Histologic analyses revealed preservation of native cartilage architecture after 6 months without evidence of degradation. Analysis of formed tissue within the scaffolds revealed a progressive invasion of fibrovascular tissue with identifiable vascular channels and adipose tissue after 6 months in vivo. Confined compression testing revealed equilibrium moduli of both minced and zested samples that were within the expected range of previously reported human nipple tissue, while these data revealed no differences in the mechanical properties of the neo-tissue between time points or processing techniques. CONCLUSIONS: These preliminary data support potential use of decellularized allograft to foster healthy tissue ingrowth within a PLA scaffold, thereby offering a novel solution to current limitations in nipple reconstruction.

Most circulating tumor DNA (ctDNA)-based molecular residual disease (MRD) detection methods leverage a limited genomic footprint, restricting detection sensitivity to 10-4 ~ 10-5 tumor fraction and thus their utility in many clinical settings. For example, early-stage, low tumor mutational burden (TMB) cancers may lack sufficient variants in these limited footprints to produce detectable signals. Further, insights into tumor evolution, including actionable mutations may be missed.Here we report a performance update of the NeXT Personal™️ platform. Utilizing whole genome sequencing of tumor and normal DNA to guide design of bespoke MRD assays, we select up to 1800 high signal, low noise MRD targets and up to 400 exonic variants. Along with proprietary algorithms, this achieves high sensitivity with a limit of detection of 1 ~ 3 parts per million, and targets high specificity (99.99%). Specificity was demonstrated with healthy donor plasma samples and > 200 cancer patient panels from a broad range of solid tumors. Sensitivity and linearity of MRD measurements were determined using dilution series from cell lines and clinical samples, which were orthogonally confirmed (R2 = 0.909, PPA = 100%, NPA = 100%) by digital droplet PCR (ddPCR).In addition, NeXT Personal simultaneously surveys tumor-agnostic content in the same workflow, providing detection of actionable mutations, markers of drug resistance, and mechanisms of tumor evolution from a curated set of variants in 90 clinically-relevant genes. Patient plasma (1 ~ 8 mL; 2 ~ 50 ng of cfDNA) can be rapidly queried to provide mutation-level information about tumor biology, longitudinal trajectory, and clinical actionability. The specificity of NeXT Personal variant detection is > 99.99% with 100% PPV, while individual variant content demonstrates high sensitivity at allele fractions of 0.1% and above, with high accuracy and signal linearity as confirmed by ddPCR (R2 = 0.998).To explore the utility of NeXT Personal in a clinical setting, a retrospective analysis was undertaken in an advanced liver cancer (low TMB) cohort of 11 patients undergoing immunotherapy. Our results demonstrated that changes in ctDNA levels during therapy correlated highly with disease status (6wk vs. baseline, p = 0.017; 9wk vs. baseline, p = 0.004), and were detected prior to clinical response confirmation by RECIST 1.1. Examination of the clinical content provided useful insights into the potential of the assay.Our data demonstrate high analytical performance of the NeXT Personal platform in both MRD and individual variant detection. The assay is sufficiently sensitive for early stage, low shedding, and low TMB cancers, early time points or samples with limited input. The addition of clinically relevant tumor-agnostic actionable content makes NeXT Personal unique in its ability to detect MRD and to ultimately help guide clinicians. Citation Format: Rui Chen, Gábor Bartha, John Lyle, Jason Harris, Sean M. Boyle, Josette Northcott, Dan Norton, Rachel M. Pyke, Fábio C. Navarro, Charles W. Abbott, Christian Haudenschild, Rose Santiago, Darren Nichols, Stephanie Huang, Christopher Nelson, Manju Chinnappa, Yi Chen, Yuker Wang, Laurie Goodman, Qi Zhang, Manqing Hong, Xiaoji Chen, Erin Ayash, Nitin Udar, Sebastian Saldivar, Jian Yan, John West, Richard O. Chen. Analytical performance of an ultra-sensitive, tumor-informed liquid biopsy platform for molecular residual disease detection and clinical guidance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3381.

Induction of Ferroptosis is a promising strategy for treating therapy-resistant tumors including mesenchymal tumor types. We performed a phenotypic screen to identify small molecules that induce ferroptosis in mesenchymal cancer cells by screening for compounds whose toxicity could be rescued by the lipophilic antioxidant Ferrostatin-1. To identify compounds that induce GPX4-mediated ferroptosis, we identified several pharmacodynamic biomarkers specific to GPX4 inhibition using our nascent RNA sequencing platform, which allows for time-resolved snapshots of global transcription. We measured the transcriptional changes that occur in the hours following inhibition of GPX4 and identified HMOX1 as a robust biomarker of GPX4i-induced ferroptosis in mesenchymal cancer cell lines. Using HMOX1-induction as a guide, we uncovered a small number of compounds as potential GPX4 inhibitors. One such compound was validated as a bonafide GPX4 inhibitor through a variety of biochemical assays and selected for a hit-to-lead campaign. In addition to standard medicinal chemistry strategies, we are currently employing a newly-developed global transcriptional reporter system to stratify several novel series of GPX4 inhibitors by their transcriptional signatures. This approach represents a unique strategy for determining on- and off- target effects of a compound and for defining structure activity relationships within a chemical series. Citation Format: Timothy Read, Zoe Winigrad, Ardeshir Goliaei, Cole Liechty, Carter Grochala, Leah Damon, John Dickson, Jason Harris, Casey Pham, Jenna Rimel, Christy Rhine, Dave Simpson, Eric Martin, Joey Azofeifa. Identification of novel GPX4 inhibitors using global transcriptional reporters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2755.

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications., Graphical Abstract, Highlights • Generated 25 stably transfected monoallelic cell lines and applied immunopeptidomics. • Harmonized 512 public immunopeptidomic samples through systematic reprocessing. • Developed pan-allele MHC-binding algorithm (SHERPA) utilizing 167 human HLA alleles. • SHERPA demonstrates up to 1.44-fold increased precision over competing algorithms., In Brief Accurately identifying neoantigens is critical for many clinical applications. We generated immunopeptidomics data from 25 stably transfected monoallelic cell lines. Then, we systematically reprocessed a large corpus of public data to improve major histocompatibility complex (MHC) binding pocket diversity and to empirically learn the rules of antigen presentation. In applying these datasets, we trained SHERPA, an MHC binding and presentation prediction algorithm. SHERPA improves performance compared with existing tools by 1.44-fold in held-out monoallelic data and 1.11-fold for immunogenic epitopes.

This article is a discussion on how supported decision-making (SDM) affects the author personally and professionally. He explains how he uses SDM for himself and why he finds it to be helpful. While he is in a gray area of getting the services and supports that he needs, he considers SDM as a tool for growth, helping him determine his strengths, needs, and interests.

As the demand for gender-affirming procedures continues to increase, patients and providers have a greater imperative to understand the current state of facial masculinization. Facial reconstruction for transgender women has been shown to treat gender dysphoria effectively and reduce rates of discrimination and victimization. Although facial masculinization surgery for transgender men is less common, there are multiple surgical and nonsurgical options to supplement the effect of hormone therapy on facial appearance, including but not limited to: receding the hair line, flattening the forehead, expanding the supraorbital ridge, increasing the dorsal nasal projection, squaring the jaw, and augmenting the chin. This review aims to summarize these techniques for providers who wish to inform transgender male patients about their options and discuss them in the context of patient satisfaction and availability of insurance coverage.

Background Seroprevalence studies are important tools to estimate the prevalence of prior or recent SARS-CoV-2 infections. This information is critical for identifying hotspots and high-risk groups and informing public health responses to the COVID-19 pandemic. We conducted a city-level seroprevalence study in Holyoke, Massachusetts to estimate the seroprevalence of SARS-CoV-2 antibodies and risk factors for seropositivity. Methods We invited inhabitants of 2,000 randomly sampled addresses to participate between November 5 and December 31, 2020. Participants completed questionnaires measuring sociodemographic and health characteristics, and COVID-19 exposure history, and provided dried blood spots for measurement of SARS-CoV-2 IgG and IgM antibodies. To calculate total and group seroprevalence estimates, inverse probability of response weights were constructed based on age, gender, race/ethnicity and census tract to ensure estimates represented the city’s population. Results We enrolled 280 households including 472 individuals. 328 underwent antibody testing. The citywide weighted seroprevalence of SARS-CoV-2 IgG or IgM was 13.9% (95%CI 7.8 - 21.8) compared to 9.8% based on publicly reported case counts. Seroprevalence was 16.8% (95%CI 5.7 – 28.0) among individuals identifying as Hispanic compared to 8.9% (95%CI 3.0 - 14.7) among those identifying as White. Seroprevalence was 20.7% (95%CI 2.2 – 39.2) for ages 0-19; 13.8% (95%CI 5.6 – 22) for ages 20 – 44; 9.6% (95%CI 0 – 20.5) for ages 45 – 59; 4.8% (95%CI 0 – 10.2) for ages 60 – 84; and 42.9% (95%CI 0 – 100) for ages >85. Table 1. Seroprevalence by antibody positivity profile Table 2. Unweighted and weighted seroprevalence by sociodemographic characteristics Figure 1. Seroprevalence by Medical, Symptom, Testing and Exposure History. Conclusion The measured SARS-CoV-2 seroprevalence in Holyoke was only 13.9% during the second surge of SARS-CoV-2 in this region, far from accepted thresholds for “herd immunity” and highlighting the need for expanding vaccination. Individuals identifying as Hispanic were at high risk of prior infection. Subsequent community-level serosurveys are necessary to guide local responses to the SARS-CoV-2 pandemic. Disclosures All Authors: No reported disclosures

Circulating tumor cell-free DNA (ctDNA) has become a biomarker for prognosis and disease monitoring. However, studies typically utilize assays limited to a small set of genes that may miss biologically important and clinically actionable mutations. To address this limitation, we have developed a whole-exome scale cfDNA platform, NeXT Liquid Biopsy (NeXT LB), that enables sensitive identification of mutations in plasma across ~20,000 genes following interventions such as surgery and treatment therapies. NeXT LB monitors tumor variants and discovers novel mutations in the plasma through analysis of tumor, normal, and plasma samples from the same patient. To enable sensitive detection across the exome in solid tumor and liquid biopsies, we developed an enhanced whole-exome assay and chemistry that augments challenging genomic regions to enable more uniform coverage across the exome. Additionally, we achieve a mean depth of coverage of ~2,000X across the exome, with boosted depth (~5,000X) for 247 clinically relevant oncogenic or tumor suppressor genes to further enhance sensitivity. Finally, we developed computational algorithms to sensitively monitor and discover somatic mutations in liquid biopsies without compromising specificity. In this work, we measure the sensitivity of NeXT LB using SeraCare reference samples with known variants at 0.5%, 1%, and 2% allele fraction (AF). We observe 100% sensitivity at 2% and 1% AF, and >95% sensitivity at 0.5% AF. Additionally, we measure >95% sensitivity for variants with AF >=2% using a proprietary cell-line media system. We generate low-pass Whole Genome Sequencing (lpWGS) data to estimate ctDNA fraction in conjunction with NeXT LB. Considering tumor heterogeneity, NeXT LB is capable of monitoring and discovering somatic variants when lpWGS-reported ctDNA fraction is >=3%, thereby highlighting the performance of the NeXT LB platform. We apply NeXT LB to sequence over 100 matched plasma and normal samples at 250 gigabases (G) and tumors at 50 G. This data demonstrate somatic variation in over 1,000 distinct genes across the cohort, thereby demonstrating the breadth and performance improvements provided by our exome-scale platform in contrast to existing targeted platforms. Additionally, we find that the plasma variants are enriched for higher AFs in solid tumors, thus allowing comprehensive coverage of driver genes and recapitulating hotspots identified in public datasets, including TCGA. We developed an exome-scale NeXT LB technology that enables sensitive monitoring and detection of somatic SNVs and indels from cfDNA. The NeXT LB platform covers a much broader landscape of tumor mutations from the plasma than existing targeted platforms, thereby enabling more comprehensive monitoring and discovery of mutations related to therapies, mechanisms of resistance, intra- and inter-tumor heterogeneity, among others. Citation Format: Fabio C p Navarro, Naveen Ramesh, Josette Northcott, Rui Chen, Lee D. McDaniel, Charles W. Abbott, Dan Norton, Robin Li, John Lyle, Jason Harris, Gabor Bartha, John West, Sean M. Boyle, Richard O. Chen. Applying NeXT Liquid Biopsy™, an exome-scale platform, to monitor and discover somatic variants in a broad set of cancer types [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 6385.

Tumor-informed liquid biopsy approaches have proven promising for detecting minimal residual disease (MRD) and recurrence of cancer following surgical resection or other therapy. However, current liquid biopsy MRD assays typically detect ctDNA in a range above 30 to 300 parts per million (PPM), leaving a significant fraction of MRD cases undetected, particularly soon after surgery and in early stage cancers where ctDNA can be at very low levels. To address this, we have developed NeXT Personal™, a tumor-informed liquid biopsy assay that achieves sensitivity down to 1 PPM, therefore enabling earlier detection of MRD and recurrence. NeXT Personal leverages tumor/normal whole genome sequencing to design personalized MRD liquid biopsy panels for each patient. The panel is composed of >1,200 somatic tumor variants enabling higher sensitivity MRD detection in plasma through tracking of larger numbers of high quality and lower noise variants. This allows the platform to achieve high sensitivity across cancer types and stages, including early stage cancers and low mutational burden tumors, utilizing ~4 mL of plasma. Two independent methods were used to establish utility and performance: a proprietary cell-line media system, and well-characterized matched tumor-normal-plasma patient samples. Samples were serially diluted to Characterization of MRD LOD in three cell-line media systems, HCC1143, HCC38, and HCC1937, yielded accurate and reproducible detection of signal across a broad range of concentrations, to a lower limit of 1-2 PPM. We then used our platform to characterize MRD LOD in a set of serially diluted patient samples, demonstrating sensitivity down to as low as 1 PPM, with high specificity in normal control samples. Finally, we demonstrated the performance of NeXT Personal with matched tumor-normal-plasma patient samples (8 different cancer types, stages II-IV). In this series, NeXT Personal detected cancers down to 0.8 PPM with high specificity demonstrated across a set of healthy normal donor samples. We estimate that ~50% of the cases in this set of patients would not have been detected by other commercially available liquid biopsy MRD platforms. NeXT Personal achieved highly sensitive and specific MRD detection, reproducibly demonstrating a LOD down to 1 PPM in different cancer types and cell line dilutions, representing approximately 10 to 100 times higher sensitivity than other liquid biopsy MRD approaches. The high sensitivity of NeXT Personal potentially enables MRD detection across a broad variety of cancers and stages, including typically challenging early stage, low mutational burden, and low-shedding cancers. Citation Format: Sean Michael Boyle, Gabor Bartha, John Lyle, Jason Harris, Josette Northcott, Dan Norton, Rachel Marty Pyke, Fabio C. P. Navarro, Alexander Stram, Christian Haudenschild, Rose Santiago, Robin Li, Chris Nelson, Yelia Huo, Manju Chinnappa, Qi Zhang, Lloyd Hsu, John West, Richard O. Chen. A high sensitivity, tumor-informed liquid biopsy platform, designed to detect minimal residual disease at part per million resolution [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 5163.

Neoantigen-based biomarkers are a promising approach for stratifying patient response to immunotherapy; however, current neoantigen prediction methods are not accurate enough to optimize these biomarkers. Sequence variability in the major histocompatibility complex (MHC) leads to the presentation of diverse neoantigens to T cells, and accurately representing this diversity in neoantigen prediction is critical for improvement. Previously, we published data from 25 mono-allelic cell lines and built an associated MHC class I, pan-allelic neoantigen prediction algorithm (SHERPATM). Here, we profile an additional 84 MHC alleles including 37 that have never previously been profiled with mono-allelic immunopeptidomics, explore the impact of MHC variability on peptide binding and improve neoantigen prediction of the SHERPA algorithm. To generate the data, we stably and transiently transfected 109 different MHC alleles (43 HLA-A, 56 -B and 10 -C alleles) into independent K562 HLA-null cell lines, immunoprecipitated intact MHC complexes using a W6/32 antibody and profiled the bound peptides using LC/MS-MS. We recovered a median of 1430 peptides per allele, with yields from the transient transfections being consistently higher than the stable transfections. Nearly all alleles have a strong anchor residue in the ninth position, but the positions of the secondary anchor residue vary by gene. HLA-B showed a stronger preference for the second position while HLA-A exhibited more variability across the first, second and third positions. In addition to the 109 mono-allelic cell lines, SHERPA increases generalizability by systematically integrating an additional 104 mono-allelic and 384 multi-allelic samples with publicly available immunopeptidomics data. The 186 alleles in the resulting training dataset have an average allelic coverage of 98% across 18 different US-based ethnicities. We evaluated our updated performance on 10% held-out mono-allelic test data from multiple cell line sources. The positive predictive value (PPV) of SHERPA was markedly higher than either NetMHCPan 4.1 or MHCFlurry-2.0 (1.45 and 1.58-fold increase, respectively), with further gains when only the 37 previously unprofiled alleles were considered (1.51 and 1.79-fold increase, respectively). Furthermore, the SHERPA model was able to detect 1.38-fold more immunogenic epitopes than either other method. Finally, we performed predictions with SHERPA across millions of synthetic binding pockets and peptides to elucidate the impact of MHC variability on peptide diversity. We found a strong correlation between binding pocket positions that highly influence peptide binding and those that are evolutionarily divergent. In conclusion, we profiled 109 mono-allelic cell lines, showed key trends in MHC-associated peptides and improved the SHERPA neoantigen prediction model. Citation Format: Rachel Marty Pyke, Steven Dea, Hima Anbunathan, Charles W. Abbott, Neeraja Ravi, Jason Harris, Gabor Bartha, Sejal Desai, Rena McClory, John West, Michael P. Snyder, Richard O. Chen, Sean Michael Boyle. Mono-allelic immunopeptidomics data from 109 MHC-I alleles reveals variability in binding preferences and improves neoantigen prediction algorithm [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 5640.

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.