Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions.

MOTIVATION: The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA). RESULTS: Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA's higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert. AVAILABILITY AND IMPLEMENTATION: Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Germline mutations and developmental mosaicism underlying EGFR-mutant lung cancer.

While the development of multiple primary tumors in smokers with lung cancer can be attributed to carcinogen-induced field cancerization, the occurrence of multiple primary tumors in individuals with EGFR-mutant lung cancer who lack known environmental exposures remains unexplained. We identified ten patients with early-stage, resectable non-small cell lung cancer who presented with multiple anatomically distinct EGFR-mutant tumors. We analyzed the phylogenetic relationships among multiple tumors from each patient using whole exome sequencing (WES) and hypermutable poly-guanine (poly-G) repeat genotyping, as orthogonal methods for lineage tracing. In two patients, we identified germline EGFR variants, which confer moderately enhanced signaling when modeled in vitro. In four other patients, developmental mosaicism is supported by the poly-G lineage tracing and WES, indicating a common non-germline cell-of-origin. Thus, developmental mosaicism and germline variants define two distinct mechanisms of genetic predisposition to multiple EGFR-mutant primary tumors, with implications for understanding their etiology and clinical management.

Genetic events associated with venetoclax resistance in CLL identified by whole-exome sequencing of patient samples.

Although BCL2 mutations are reported as later occurring events leading to venetoclax resistance, many other mechanisms of progression have been reported though remain poorly understood. Here, we analyze longitudinal tumor samples from 11 patients with disease progression while receiving venetoclax to characterize the clonal evolution of resistance. All patients tested showed increased in vitro resistance to venetoclax at the posttreatment time point. We found the previously described acquired BCL2-G101V mutation in only 4 of 11 patients, with 2 patients showing a very low variant allele fraction (0.03%-4.68%). Whole-exome sequencing revealed acquired loss(8p) in 4 of 11 patients, of which 2 patients also had gain (1q21.2-21.3) in the same cells affecting the MCL1 gene. In vitro experiments showed that CLL cells from the 4 patients with loss(8p) were more resistant to venetoclax than cells from those without it, with the cells from 2 patients also carrying gain (1q21.2-21.3) showing increased sensitivity to MCL1 inhibition. Progression samples with gain (1q21.2-21.3) were more susceptible to the combination of MCL1 inhibitor and venetoclax. Differential gene expression analysis comparing bulk RNA sequencing data from pretreatment and progression time points of all patients showed upregulation of proliferation, B-cell receptor (BCR), and NF-κB gene sets including MAPK genes. Cells from progression time points demonstrated upregulation of surface immunoglobulin M and higher pERK levels compared with those from the preprogression time point, suggesting an upregulation of BCR signaling that activates the MAPK pathway. Overall, our data suggest several mechanisms of acquired resistance to venetoclax in CLL that could pave the way for rationally designed combination treatments for patients with venetoclax-resistant CLL.

Lesion Shedding Model: unraveling site-specific contributions to ctDNA.

Sampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized. We designed the Lesion Shedding Model (LSM) to order lesions from the strongest to the poorest shedding for a given patient. By characterizing the lesion-specific ctDNA shedding levels, we can better understand the mechanisms of shedding and more accurately interpret ctDNA assays to improve their clinical impact. We verified the accuracy of the LSM under controlled conditions using a simulation approach as well as testing the model on three cancer patients. The LSM obtained an accurate partial order of the lesions according to their assigned shedding levels in simulations and its accuracy in identifying the top shedding lesion was not significantly impacted by number of lesions. Applying LSM to three cancer patients, we found that indeed there were lesions that consistently shed more than others into the patients' blood. In two of the patients, the top shedding lesion was one of the only clinically progressing lesions at the time of biopsy suggesting a connection between high ctDNA shedding and clinical progression. The LSM provides a much needed framework with which to understand ctDNA shedding and to accelerate discovery of ctDNA biomarkers. The LSM source code has been available in the IBM BioMedSciAI Github (https://github.com/BiomedSciAI/Geno4SD).

Evolutionary history of transformation from chronic lymphocytic leukemia to Richter syndrome.

Richter syndrome (RS) arising from chronic lymphocytic leukemia (CLL) exemplifies an aggressive malignancy that develops from an indolent neoplasm. To decipher the genetics underlying this transformation, we computationally deconvoluted admixtures of CLL and RS cells from 52 patients with RS, evaluating paired CLL-RS whole-exome sequencing data. We discovered RS-specific somatic driver mutations (including IRF2BP2, SRSF1, B2M, DNMT3A and CCND3), recurrent copy-number alterations beyond del(9p21)(CDKN2A/B), whole-genome duplication and chromothripsis, which were confirmed in 45 independent RS cases and in an external set of RS whole genomes. Through unsupervised clustering, clonally related RS was largely distinct from diffuse large B cell lymphoma. We distinguished pathways that were dysregulated in RS versus CLL, and detected clonal evolution of transformation at single-cell resolution, identifying intermediate cell states. Our study defines distinct molecular subtypes of RS and highlights cell-free DNA analysis as a potential tool for early diagnosis and monitoring.

Pirtobrutinib targets BTK C481S in ibrutinib-resistant CLL but second-site BTK mutations lead to resistance.

Covalent inhibitors of Bruton tyrosine kinase (BTK) have transformed the therapy of chronic lymphocytic leukemia (CLL), but continuous therapy has been complicated by the development of resistance. The most common resistance mechanism in patients whose disease progresses on covalent BTK inhibitors (BTKis) is a mutation in the BTK 481 cysteine residue to which the inhibitors bind covalently. Pirtobrutinib is a highly selective, noncovalent BTKi with substantial clinical activity in patients whose disease has progressed on covalent BTKi, regardless of BTK mutation status. Using in vitro ibrutinib-resistant models and cells from patients with CLL, we show that pirtobrutinib potently inhibits BTK-mediated functions including B-cell receptor (BCR) signaling, cell viability, and CCL3/CCL4 chemokine production in both BTK wild-type and C481S mutant CLL cells. We demonstrate that primary CLL cells from responding patients on the pirtobrutinib trial show reduced BCR signaling, cell survival, and CCL3/CCL4 chemokine secretion. At time of progression, these primary CLL cells show increasing resistance to pirtobrutinib in signaling inhibition, cell viability, and cytokine production. We employed longitudinal whole-exome sequencing on 2 patients whose disease progressed on pirtobrutinib and identified selection of alternative-site BTK mutations, providing clinical evidence that secondary BTK mutations lead to resistance to noncovalent BTKis.

Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma.

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized the treatment of hematologic malignancies. Approximately half of patients with refractory large B cell lymphomas achieve durable responses from CD19-targeting CAR-T treatment; however, failure mechanisms are identified in only a fraction of cases. To gain new insights into the basis of clinical response, we performed single-cell transcriptome sequencing of 105 pretreatment and post-treatment peripheral blood mononuclear cell samples, and infusion products collected from 32 individuals with large B cell lymphoma treated with either of two CD19 CAR-T products: axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel). Expansion of proliferative memory-like CD8 clones was a hallmark of tisa-cel response, whereas axi-cel responders displayed more heterogeneous populations. Elevations in CAR-T regulatory cells among nonresponders to axi-cel were detected, and these populations were capable of suppressing conventional CAR-T cell expansion and driving late relapses in an in vivo model. Our analyses reveal the temporal dynamics of effective responses to CAR-T therapy, the distinct molecular phenotypes of CAR-T cells with differing designs, and the capacity for even small increases in CAR-T regulatory cells to drive relapse.

Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis.

Acral melanoma, the most common melanoma subtype among non-White individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we perform integrative genomic and clinical profiling of acral melanomas from 104 patients treated in North America (n = 37) or China (n = 67). We find that recurrent, late-arising focal amplifications of cytoband 22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 - a known tumor suppressor in other cancers - is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines causes apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiates processes associated with metastasis, including anchorage-independent growth, formation of spheroids, and an increase in MAPK and SRC activities. Our results provide insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target.

A common methodological phylogenomics framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones.

BACKGROUND: All diseases containing genetic material undergo genetic evolution and give rise to heterogeneity including cancer and infection. Although these illnesses are biologically very different, the ability for phylogenetic retrodiction based on the genomic reads is common between them and thus tree-based principles and assumptions are shared. Just as the different frequencies of tumor genomic variants presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that the different variant frequencies in viral reads offers the means to infer multiple co-infecting sublineages. RESULTS: We present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios.To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries. CONCLUSIONS: Concerti successfully extracts and integrates information from multi-point samples, enabling the discovery of clinically plausible phylogenetic trees that capture the heterogeneity known to exist both spatially and temporally. These models can have direct therapeutic implications by highlighting "birth" of clones that may harbor resistance mechanisms to treatment, "death" of subclones with drug targets, and acquisition of functionally pertinent mutations in clones that may have seemed clinically irrelevant. Specifically in this paper we uncover new potential parallel mutations in the evolution of the SARS-CoV-2 virus. In the context of cancer, we identify new clones harboring resistant mutations to therapy.

Parallel Genomic Alterations of Antigen and Payload Targets Mediate Polyclonal Acquired Clinical Resistance to Sacituzumab Govitecan in Triple-Negative Breast Cancer.

Sacituzumab govitecan (SG), the first antibody-drug conjugate (ADC) approved for triple-negative breast cancer, incorporates the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 (TOP1) inhibitor payload. We sought to identify mechanisms of SG resistance through RNA and whole-exome sequencing of pretreatment and postprogression specimens. One patient exhibiting de novo progression lacked TROP2 expression, in contrast to robust TROP2 expression and focal genomic amplification of TACSTD2/TROP2 observed in a patient with a deep, prolonged response to SG. Analysis of acquired genomic resistance in this case revealed one phylogenetic branch harboring a canonical TOP1 E418K resistance mutation and subsequent frameshift TOP1 mutation, whereas a distinct branch exhibited a novel TACSTD2/TROP2 T256R missense mutation. Reconstitution experiments demonstrated that TROP2T256R confers SG resistance via defective plasma membrane localization and reduced cell-surface binding by hRS7. These findings highlight parallel genomic alterations in both antibody and payload targets associated with resistance to SG. SIGNIFICANCE: These findings underscore TROP2 as a response determinant and reveal acquired SG resistance mechanisms involving the direct antibody and drug payload targets in distinct metastatic subclones of an individual patient. This study highlights the specificity of SG and illustrates how such mechanisms will inform therapeutic strategies to overcome ADC resistance.This article is highlighted in the In This Issue feature, p. 2355.

Author Correction: Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers.

During cancer therapy, tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique resistance mechanisms in an individual patient1-3. Previous case reports and small case series have suggested that liquid biopsy (specifically, cell-free DNA (cfDNA)) may better capture the heterogeneity of acquired resistance4-8. However, the effectiveness of cfDNA versus standard single-lesion tumor biopsies has not been directly compared in larger-scale prospective cohorts of patients following progression on targeted therapy. Here, in a prospective cohort of 42 patients with molecularly defined gastrointestinal cancers and acquired resistance to targeted therapy, direct comparison of postprogression cfDNA versus tumor biopsy revealed that cfDNA more frequently identified clinically relevant resistance alterations and multiple resistance mechanisms, detecting resistance alterations not found in the matched tumor biopsy in 78% of cases. Whole-exome sequencing of serial cfDNA, tumor biopsies and rapid autopsy specimens elucidated substantial geographic and evolutionary differences across lesions. Our data suggest that acquired resistance is frequently characterized by profound tumor heterogeneity, and that the emergence of multiple resistance alterations in an individual patient may represent the 'rule' rather than the 'exception'. These findings have profound therapeutic implications and highlight the potential advantages of cfDNA over tissue biopsy in the setting of acquired resistance.

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA.

The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable. We introduce a novel stochastic regularization, ReVeaL, that empowers ML to discriminate subtle cancer subtypes even from the same 'cell of origin'. Analogous to heritability, implicitly defined on whole genome, we use predictability (F1 score) definable on portions of the genome. In an effort to distinguish cancer subtypes using dark-matter DNA, we applied ReVeaL to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, and dark. ReVeaL enabled improved discrimination of cancer subtypes for all segments of the genome. The non-genic, non-coding and dark-matter had the highest F1 scores, with dark-matter having the highest level of predictability. Based on ReVeaL's predictability of different genomic regions, dark-matter contains enough signal to significantly discriminate fine subtypes of disease. Hence, the agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention.

Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

Sequencing and curation strategies for identifying candidate glioblastoma treatments.

BACKGROUND: Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. METHODS: A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. RESULTS: WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. CONCLUSION: These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Analysis on GENIE reveals novel recurrent variants that affect molecular diagnosis of sizable number of cancer patients.

BACKGROUND: Significant numbers of variants detected in cancer patients are often left labeled only as variants of unknown significance (VUS). In order to expand precision medicine to a wider population, we need to extend our knowledge of pathogenicity and drug response in the context of VUS's. METHODS: In this study, we analyzed variants from AACR Project GENIE Consortium APG (Cancer Discov 7:818-831, 2017) and compared them to the COSMIC database Forbes et al. (Nucleic Acids Res 43:D805-811, 2015) to identify recurrent variants that would merit further study. We filtered out known hotspot variants, inactivating variants in tumor suppressors, and likely benign variants by comparing with COSMIC and ExAC Lee et al. (Science 337:967-971, 2012). RESULTS: We have identified 45,933 novel variants with unknown significance unique to GENIE. In our analysis, we found on average six variants per patient where two could be considered as pathogenic or likely pathogenic and the majority are VUS's. More importantly, we have discovered 730 recurrent variants that appear more than 3 times in GENIE but less than 3 in COSMIC. If we combine the recurrences of GENIE and COSMIC for all variants, 2586 are newly identified as occurring more than 3 times than when using COSMIC alone. CONCLUSIONS: Although it would be inappropriate to blindly accept these recurrent variants as pathogenic, they may warrant higher priority than other observed VUS's. These newly identified recurrent variants might affect the molecular profiles of approximately 1 in 6 patients. Further analysis and characterization of these variants in both research and clinical contexts will improve patient treatments and the development of new therapeutics.

Enhancing Next-Generation Sequencing-Guided Cancer Care Through Cognitive Computing.

BACKGROUND: Using next-generation sequencing (NGS) to guide cancer therapy has created challenges in analyzing and reporting large volumes of genomic data to patients and caregivers. Specifically, providing current, accurate information on newly approved therapies and open clinical trials requires considerable manual curation performed mainly by human "molecular tumor boards" (MTBs). The purpose of this study was to determine the utility of cognitive computing as performed by Watson for Genomics (WfG) compared with a human MTB. MATERIALS AND METHODS: One thousand eighteen patient cases that previously underwent targeted exon sequencing at the University of North Carolina (UNC) and subsequent analysis by the UNCseq informatics pipeline and the UNC MTB between November 7, 2011, and May 12, 2015, were analyzed with WfG, a cognitive computing technology for genomic analysis. RESULTS: Using a WfG-curated actionable gene list, we identified additional genomic events of potential significance (not discovered by traditional MTB curation) in 323 (32%) patients. The majority of these additional genomic events were considered actionable based upon their ability to qualify patients for biomarker-selected clinical trials. Indeed, the opening of a relevant clinical trial within 1 month prior to WfG analysis provided the rationale for identification of a new actionable event in nearly a quarter of the 323 patients. This automated analysis took <3 minutes per case. CONCLUSION: These results demonstrate that the interpretation and actionability of somatic NGS results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing could potentially improve patient care by providing a rapid, comprehensive approach for data analysis and consideration of up-to-date availability of clinical trials. IMPLICATIONS FOR PRACTICE: The results of this study demonstrate that the interpretation and actionability of somatic next-generation sequencing results are evolving too rapidly to rely solely on human curation. Molecular tumor boards empowered by cognitive computing can significantly improve patient care by providing a fast, cost-effective, and comprehensive approach for data analysis in the delivery of precision medicine. Patients and physicians who are considering enrollment in clinical trials may benefit from the support of such tools applied to genomic data.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

OBJECTIVE: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. METHODS: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. RESULTS: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. CONCLUSIONS: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. CLINICALTRIALSGOV IDENTIFIER: NCT02725684.