Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease.

Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This "immunosignature" approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

General Assessment of Humoral Activity in Healthy Humans.

The humoral immune system is network of biological molecules designed to maintain a healthy homeostatic equilibrium. Because antibodies are an abundant and highly specific effector of immunological action, they are also an important reservoir of previous host exposures. Antibodies may play a major role in early detection of host challenge. Unfortunately, few practical methods exist for interpreting the information stored in antibody variable regions. Immunosignatures use a microarray of thousands of random sequence peptides to interrogate antibodies in a broad and unbiased fashion. The pattern of binding between antibody and peptide is reproducible. Once the system has been trained on a disease cohort, blinded samples can be reliably predicted. Although immunosignatures of both chronic and infectious disease have been extensively tested, less has been done to demonstrate how healthy immunosignatures change over time or between individuals. Here, we report the results of a study of immunosignatures of healthy persons over brief (12 h sampled once per hour), intermediate (32 days sampled once per day), and long (5 years sampled once every year) time spans. Using this information, we were also able to detect intentional and unintentional immunological perturbations in the form of a vaccine and an infection, respectively. Our findings suggest that, even with the variability inherent in healthy immunosignatures, a single person's immunosignature will remain constant over time. Over this healthy signature, vaccines and infections create subsignatures that are common across multiple people, even subsuming healthy fluctuations. These findings have implications for disease monitoring and early diagnosis.

Epitope identification from fixed-complexity random-sequence peptide microarrays.

Antibodies play an important role in modern science and medicine. They are essential in many biological assays and have emerged as an important class of therapeutics. Unfortunately, current methods for mapping antibody epitopes require costly synthesis or enrichment steps, and no low-cost universal platform exists. In order to address this, we tested a random-sequence peptide microarray consisting of over 330,000 unique peptide sequences sampling 83% of all possible tetramers and 27% of pentamers. It is a single, unbiased platform that can be used in many different types of tests, it does not rely on informatic selection of peptides for a particular proteome, and it does not require iterative rounds of selection. In order to optimize the platform, we developed an algorithm that considers the significance of k-length peptide subsequences (k-mers) within selected peptides that come from the microarray. We tested eight monoclonal antibodies and seven infectious disease cohorts. The method correctly identified five of the eight monoclonal epitopes and identified both reported and unreported epitope candidates in the infectious disease cohorts. This algorithm could greatly enhance the utility of random-sequence peptide microarrays by enabling rapid epitope mapping and antigen identification.

Immunosignature system for diagnosis of cancer.

Although the search for disease biomarkers continues, the clinical return has thus far been disappointing. The complexity of the body's response to disease makes it difficult to represent this response with only a few biomarkers, particularly when many are present at low levels. An alternative to the typical reductionist biomarker paradigm is an assay we call an "immunosignature." This approach leverages the response of antibodies to disease-related changes, as well as the inherent signal amplification associated with antigen-stimulated B-cell proliferation. To perform an immunosignature assay, the antibodies in diluted blood are incubated with a microarray of thousands of random sequence peptides. The pattern of binding to these peptides is the immunosignature. Because the peptide sequences are completely random, the assay is effectively disease-agnostic, potentially providing a comprehensive diagnostic on multiple diseases simultaneously. To explore the ability of an immunosignature to detect and identify multiple diseases simultaneously, 20 samples from each of five cancer cohorts collected from multiple sites and 20 noncancer samples (120 total) were used as a training set to develop a reference immunosignature. A blinded evaluation of 120 blinded samples covering the same diseases gave 95% classification accuracy. To investigate the breadth of the approach and test sensitivity to biological diversity further, immunosignatures of >1,500 historical samples comprising 14 different diseases were examined by training with 75% of the samples and testing the remaining 25%. The average accuracy was >98%. These results demonstrate the potential power of the immunosignature approach in the accurate, simultaneous classification of disease.

Diagnosis and early detection of CNS-SLE in MRL/lpr mice using peptide microarrays.

BACKGROUND: An accurate method that can diagnose and predict lupus and its neuropsychiatric manifestations is essential since currently there are no reliable methods. Autoantibodies to a varied panel of antigens in the body are characteristic of lupus. In this study we investigated whether serum autoantibody binding patterns on random-sequence peptide microarrays (immunosignaturing) can be used for diagnosing and predicting the onset of lupus and its central nervous system (CNS) manifestations. We also tested the techniques for identifying potentially pathogenic autoantibodies in CNS-Lupus. We used the well-characterized MRL/lpr lupus animal model in two studies as a first step to develop and evaluate future studies in humans. RESULTS: In study one we identified possible diagnostic peptides for both lupus and altered behavior in the forced swim test. When comparing the results of study one to that of study two (carried out in a similar manner), we further identified potential peptides that may be diagnostic and predictive of both lupus and altered behavior in the forced swim test. We also characterized five potentially pathogenic brain-reactive autoantibodies, as well as suggested possible brain targets. CONCLUSIONS: These results indicate that immunosignaturing could predict and diagnose lupus and its CNS manifestations. It can also be used to characterize pathogenic autoantibodies, which may help to better understand the underlying mechanisms of CNS-Lupus.

Physical characterization of the "immunosignaturing effect".

Identifying new, effective biomarkers for diseases is proving to be a challenging problem. We have proposed that antibodies may offer a solution to this problem. The physical features and abundance of antibodies make them ideal biomarkers. Additionally, antibodies are often elicited early in the ontogeny of different chronic and infectious diseases. We previously reported that antibodies from patients with infectious disease and separately those with Alzheimer's disease display a characteristic and reproducible "immunosignature" on a microarray of 10,000 random sequence peptides. Here we investigate the physical and chemical parameters underlying how immunosignaturing works. We first show that a variety of monoclonal and polyclonal antibodies raised against different classes of antigens produce distinct profiles on this microarray and the relative affinities are determined. A proposal for how antibodies bind the random sequences is tested. Sera from vaccinated mice and people suffering from a fugal infection are individually assayed to determine the complexity of signals that can be distinguished. Based on these results, we propose that this simple, general and inexpensive system could be optimized to generate a new class of antibody biomarkers for a wide variety of diseases.

Exploring antibody recognition of sequence space through random-sequence peptide microarrays.

A universal platform for efficiently mapping antibody epitopes would be of great use for many applications, ranging from antibody therapeutic development to vaccine design. Here we tested the feasibility of using a random peptide microarray to map antibody epitopes. Although peptide microarrays are physically constrained to ∼10(4) peptides per array, compared with 10(8) permitted in library panning approaches such as phage display, they enable a much more high though put and direct measure of binding. Long (20 mer) random sequence peptides were chosen for this study to look at an unbiased sampling of sequence space. This sampling of sequence space is sparse, as an exact epitope sequence is unlikely to appear. Commercial monoclonal antibodies with known linear epitopes or polyclonal antibodies raised against engineered 20-mer peptides were used to evaluate this array as an epitope mapping platform. Remarkably, peptides with the most sequence similarity to known epitopes were only slightly more likely to be recognized by the antibody than other random peptides. We explored the ability of two methods singly and in combination to predict the actual epitope from the random sequence peptides bound. Though the epitopes were not directly evident, subtle motifs were found among the top binding peptides for each antibody. These motifs did have some predictive ability in searching for the known epitopes among a set of decoy sequences. The second approach using a windowing alignment strategy, was able to score known epitopes of monoclonal antibodies well within the test dataset, but did not perform as well on polyclonals. Random peptide microarrays of even limited diversity may serve as a useful tool to prioritize candidates for epitope mapping or antigen identification.

Comparative study of classification algorithms for immunosignaturing data.

BACKGROUND: High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. RESULTS: We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found 'Naïve Bayes' far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. CONCLUSIONS: 'Naïve Bayes' algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

BACKGROUND: Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. RESULTS: GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. CONCLUSIONS: GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Application of immunosignatures to the assessment of Alzheimer's disease.

OBJECTIVE: Accurate assessment of Alzheimer's disease (AD), both presymptomatically and at different disease stages, will become increasingly important with the expanding elderly population. There are a number of indications that the immune system is engaged in AD. Here we explore the ability of an antibody-profiling technology to characterize AD and screen for peptides that may be used for a simple diagnostic test. METHODS: We developed an array-based system to profile the antibody repertoire of transgenic mice with cerebral amyloidosis (TG) and elderly individuals with or without AD. The array consists of 10,000 random sequence peptides (20-mers) capable of detecting antibody binding patterns, allowing the identification of peptides that mimic epitopes targeted by a donor's serum. RESULTS: TG mice exhibited a distinct immunoprofile compared to nontransgenic littermates. Further, we show that dementia patients, including autopsy-confirmed AD subjects, have distinguishable profiles compared to age-matched nondemented people. Using antibodies to different forms of Aß peptide and blocking protocols, we demonstrate that most of this signature is not due to the subject's antibodies raised against Aß. INTERPRETATION: We propose that "immunosignaturing" technology may have potential as a diagnostic tool in AD.

Thyroid hormone-dependent development in Xenopus laevis: a sensitive screen of thyroid hormone signaling disruption by municipal wastewater treatment plant effluent.

Because thyroid hormones (THs) are conserved modulators of development and physiology, identification of compounds adversely affecting TH signaling is critical to human and wildlife health. Anurans are an established model for studying disruption of TH signaling because metamorphosis is dependent upon the thyroid system. In order to strengthen this model and identify new gene transcript biomarkers for TH disruption, we performed DNA microarray analysis of Xenopus laevis tadpole tail transcriptomes following treatment with triiodothyronine (T(3)). Comparison of these results with previous studies in frogs and mammals identified 36 gene transcripts that were TH-sensitive across clades. We then tested molecular biomarkers for sensitivity to disruption by exposure to wastewater effluent (WWE). X. laevis tadpoles, exposed to WWE from embryo through metamorphosis, exhibited an increased developmental rate compared to controls. Cultured tadpole tails showed dramatic increases in levels of four TH-sensitive gene transcripts (thyroid hormone receptor ß (TRß), deiodinase type II (DIO2), and corticotropin releasing hormone binding protein (CRHBP), fibroblast activation protein α (FAPα)) when exposed to T(3) and WWE extracts. TRß, DIO2, and CRHBP were identified as TH sensitive in other studies, while FAPα mRNA transcripts were highly TH sensitive in our array. The results validate the array and demonstrate TH-disrupting activity by WWE. Our findings demonstrate the usefulness of cross-clade analysis for identification of gene transcripts that provide sensitivity to endocrine disruption. Further, the results suggest that development is disrupted by exposure to complex mixes of compounds found in WWE possibly through interference with TH signaling.

Alpha-methylacyl-CoA racemase (AMACR) protein is upregulated in early proliferative lesions of the breast irrespective of apocrine differentiation.

Alpha-methylacyl-CoA racemase (AMACR/P504S) is a mitochondrial and peroxisomal enzyme involved in the branched-chain fatty acid and bile acid metabolism. AMACR is a useful diagnostic biomarker for prostate carcinomas and several other malignancies. Its expression in apocrine breast lesions had been previously reported, but its role in breast cancer progression has not been fully investigated. One hundred fifty breast samples (80 with invasive carcinomas) were studied. The expression of AMACR protein was analyzed using the immunohistochemical method (IHC). Lesions were considered positive if AMACR was detected in ≥10% of the cells at any intensity comprising a histologically defined normal epithelial structure or a pathologic lesion. In addition, AMACR mRNA relative expression was calculated from the whole-transcript RNA-Seq performed on >20,000 diverse tumor samples using a 20,000+ hybrid-capture NGS assay with the transcript capture panel based on the Agilent SureSelect Human All ExonV7. Expression of AMACR protein was restricted to epithelia. It was uncommon in the normal breast (7/81 samples, 9%). Increasing AMACR expression was observed with proliferative epithelial lesions (18% of usual ductal hyperplasias/adenosis, 70% of atypical lesions and 72% of DCIS/LCIS). Invasive ductal carcinomas NST and invasive lobular carcinomas expressed AMACR in 64% and 46%, respectively. The highest AMACR expression was observed in luminal B and HER2-positive breast carcinomas (86-100%). Triple-negative breast carcinomas exhibited AMACR in 50% of the cases. Apocrine lesions showed strong, nearly uniform overexpression of AMACR (100% of metaplasias, hyperplasias and in situ carcinomas and 88% of invasive apocrine carcinomas were positive). RNA-Seq analysis also confirmed AMACR expression in breast carcinomas, although its median value was substantially lower with a lower standard deviation than in prostate carcinomas. Over-expression of AMACR characterizes various proliferative, preinvasive and invasive breast lesions and is not specific to the apocrine morphology. It points to altered lipid metabolism (branched fatty acids) as one of the general characteristics of breast carcinogenesis, like several other malignancies. Its early detection may represent a potential target for cancer progression intervention.

Homologous Recombination Deficiency Alterations in Colorectal Cancer: Clinical, Molecular, and Prognostic Implications.

BACKGROUND: Tumors with homologous recombination deficiency (HRD) show high sensitivity to platinum salts and poly(ADP-ribose) polymerase-inhibitors in several malignancies. In colorectal cancer (CRC), the role of HRD alterations is mostly unknown. METHODS: Next-generation sequencing, whole transcriptome sequencing, and whole exome sequencing were conducted using CRC samples submitted to a commercial Clinical Laboratory Improvement Amendments certified laboratory. Tumors with pathogenic and/or presumed pathogenic mutations in 33 genes involved in the homologous recombination pathway were considered HRD, the others were homologous recombination proficient (HRP). Furthermore, tumor samples from patients enrolled in the phase III TRIBE2 study comparing upfront FOLFOXIRI+bevacizumab vs FOLFOX+bevacizumab were analyzed with next-generation sequencing. The analyses were separately conducted in microsatellite stable or proficient mismatch repair (MSS/pMMR) and microsatellite instable-high or deficient mismatch repair (MSI-H/dMMR) groups. All statistical tests were 2-sided. RESULTS: Of 9321 CRC tumors, 1270 (13.6%) and 8051 (86.4%) were HRD and HRP, respectively. HRD tumors were more frequent among MSI-H/dMMR than MSS/pMMR tumors (73.4% vs 9.5%; P < .001; q < 0.001). In MSS/pMMR group, HRD tumors were more frequently tumor mutational burden high (8.1% vs 2.2%; P < .001; q < 0.001) and PD-L1 positive (5.0% vs 2.4%; P < .001; q = 0.001), enriched in all immune cell and fibroblast populations and genomic loss of heterozygosity-high (16.2% vs 9.5%; P = .03). In the TRIBE2 study, patients with MSS/pMMR and HRD tumors (10.7%) showed longer overall survival compared with MSS/pMMR and HRP tumors (40.2 vs 23.8 months; hazard ratio [HR] = 0.66, 95% confidence interval [CI] = 0.45 to 0.98; P = .04). Consistent results were reported in the multivariable model (HR = 0.67, 95% CI = 0.45 to 1.02; P = .07). No interaction effect was evident between homologous recombination groups and treatment arm. CONCLUSIONS: HRD tumors are a distinctive subgroup of MSS/pMMR CRCs with specific molecular and prognostic characteristics. The potential efficacy of agents targeting the homologous recombination system and immune checkpoint inhibitors in this subgroup is worthy of clinical investigation.

Molecular characteristics and clinical outcomes of patients with Neurofibromin 1-altered metastatic colorectal cancer.

Loss-of-function alterations of Neurofibromin 1 (NF1) activate RAS, a driver of colorectal cancer. However, the clinical implications of NF1 alterations are largely unknown. We performed a comprehensive molecular profiling of NF1-mutant colorectal cancer using data from 8150 patients included in a dataset of commercial CLIA-certified laboratory (Caris Life Sciences). In addition, NF1 expression levels were tested for associations with clinical outcomes using data from 431 patients in the CALGB/SWOG 80405 trial. In the Caris dataset, 2.2% of patients had pathogenic or presumed pathogenic NF1 mutations. NF1-mutant tumors more frequently harbored PIK3CA (25.0% vs. 16.7%) and PTEN mutations (24.0% vs. 4.2%) than wild type tumors. Gene set enrichment analysis revealed that MAPK and PI3K pathway signatures were enriched in NF1-mutant tumors. In the CALGB/SWOG 80405 cohort, low NF1 expression was associated with poor prognosis, and high NF1 expression was associated with better efficacy of cetuximab than bevacizumab. Together, we revealed concurrent genetic alterations in the PI3K pathways in NF1-mutant tumors, suggesting the need to simultaneously block MAPK and PI3K pathways in treatment. The potential of NF1 alteration as a novel biomarker for targeted therapy was highlighted, warranting further investigations in clinical settings.

Segmentation and intensity estimation for microarray images with saturated pixels.

BACKGROUND: Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2(16) - 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation. RESULTS: We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study. CONCLUSIONS: The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.

Statistical methods for analyzing immunosignatures.

BACKGROUND: Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally different research questions. In addition to classifying samples based on disease status, the complex patterns and latent factors underlying immunosignatures, which we attempt to model, may have a diverse range of applications. METHODS: We investigate the utility of a number of statistical methods to determine model performance and address challenges inherent in analyzing immunosignatures. Some of these methods include exploratory and confirmatory factor analyses, classical significance testing, structural equation and mixture modeling. RESULTS: We demonstrate an ability to classify samples based on disease status and show that immunosignaturing is a very promising technology for screening and presymptomatic screening of disease. In addition, we are able to model complex patterns and latent factors underlying immunosignatures. These latent factors may serve as biomarkers for disease and may play a key role in a bioinformatic method for antibody discovery. CONCLUSION: Based on this research, we lay out an analytic framework illustrating how immunosignatures may be useful as a general method for screening and presymptomatic screening of disease as well as antibody discovery.

NUTM1-Rearranged Neoplasms-A Heterogeneous Group of Primitive Tumors with Expanding Spectrum of Histology and Molecular Alterations-An Updated Review.

Nuclear protein of testis (NUT), a protein product of the NUTM1 gene (located on the long arm of chromosome 15) with highly restricted physiologic expression in post-meiotic spermatids, is the oncogenic driver of a group of emerging neoplasms when fused with genes involved in transcription regulation. Although initially identified in a group of lethal midline carcinomas in which NUT forms fusion proteins with bromodomain proteins, NUTM1-rearrangement has since been identified in tumors at non-midline locations, with non-bromodomain partners and with varied morphology. The histologic features of these tumors have also expanded to include sarcoma, skin adnexal tumors, and hematologic malignancies that harbor various fusion partners and are associated with markedly different clinical courses varying from benign to malignant. Most of these tumors have nondescript primitive morphology and therefore should be routinely considered in any undifferentiated neoplasm. The diagnosis is facilitated by the immunohistochemical use of the monoclonal C52 antibody, fluorescence in situ hybridization (FISH), and, recently, RNA-sequencing. The pathogenesis is believed to be altered expression of oncogenes or tumor suppressor genes by NUT-mediated genome-wide histone modification. NUTM1-rearranged neoplasms respond poorly to classical chemotherapy and radiation therapy. Targeted therapies such as bromodomain and extraterminal domain inhibitor (BETi) therapy are being developed. This current review provides an update on NUTM1-rearranged neoplasms, focusing on the correlation between basic sciences and clinical aspects.

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type.

Cancer of Unknown Primary (CUP) occurs in 3-5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test.

Pan-cancer analysis of RNA expression of ANGIOTENSIN-I-CONVERTING ENZYME 2 reveals high variability and possible impact on COVID-19 clinical outcomes.

Patients with cancer demonstrate particularly poor outcomes from COVID-19. To provide information essential for understanding the biologic underpinnings of this association, we analyzed whole-transcriptome RNA expression data obtained from a large cohort of cancer patients to characterize expression of ACE2, TMPRSS2, and other proteases that are involved in viral attachment to and entry into target cells. We find substantial variability of expression of these factors across tumor types and identify subpopulations expressing ACE2 at very high levels. In some tumor types, especially in gastrointestinal cancers, expression of ACE2 and TMPRSS2 is highly correlated. Furthermore, we found infiltration with T-cell and natural killer (NK) cell infiltration to be particularly pronounced in ACE2-high tumors. These findings suggest that subsets of cancer patients exist with gene expression profiles that may be associated with heightened susceptibility to SARS-CoV-2 infection, in whom malignant tumors function as viral reservoir and possibly promote the frequently detrimental hyper-immune response in patients infected with this virus.

Predicted Immunogenicity of CDK12 Biallelic Loss-of-Function Tumors Varies across Cancer Types.

CDK12 biallelic inactivation is associated with a distinct genomic signature of focal tandem duplications (FTDs). Gene fusions resulting from FTDs increase neoantigen load, raising interest in CDK12 as a biomarker of response to immune checkpoint inhibitors. Despite evidence of FTDs in multiple CDK12-altered cancer types, notably prostate and ovarian, report of fusion-associated neoantigen load is limited to prostate cancer. Molecular profiles were retrospectively reviewed for CDK12-biallelic (CDK12-biLOF) and -monoallelic loss-of-function (CDK12-monoLOF) in a primary cohort of >9000 tumors, representing 39 cancer types, and immune epitopes were predicted from fusions detected by whole transcriptome sequencing. CDK12-biLOF was identified for 0.3% tumors overall, most frequently in prostate cancer (4.7%). CDK12-biLOF tumors had higher mean fusion rates and fusion-associated neoantigen load than CDK12-monoLOF and CDK12-WT tumors (P < 0.01). However, concurrent mismatch repair deficiency/microsatellite instability with CDK12-biLOF associated with low fusion rates. Among CDK12-biLOF tumors, fusion-associated neoantigen load was highest in prostate and ovarian cancers, which correlated with distinct immune profiles. In a validation cohort, CDK12-biLOF tumors (0.4%) exhibited high mean fusion rates, particularly for prostate and ovarian tumors. Low fusion rates in other CDK12-biLOF tumor types warrant further investigation and highlight the value of quantitative biomarkers. Fusion rate and fusion-associated neoantigen load are linked to CDK12-biLOF in select cancers and may help to identify responders of immune checkpoint inhibitor therapy.