The Personalized Inherited Signature Predisposing to Non-Small-Cell Lung Cancer in Non-Smokers

Simple Summary Building on the idea of a germline oligogenic origin of lung cancer, we performed WES of DNA from patients' peripheral blood and their unaffected sibs. Filtering for rare variants and potentially damaging effects, we identified 40 deleterious variants mapping in genes previously associated with cancer exclusively identified in patients. Transcriptome profiling on both tumor and normal lung tissues revealed that, among the selected mutated genes, 16 variants mapping in 16 genes were either down- or upregulated in cancer specimens. Among the downregulated genes, 9 variants in 9 genes carried the mutated allele suggesting a loss of heterozygosity. Notably, the group of mutated genes was unique for each patient, pinpointing to a "private" oligogenic germline signature. In the era of precision medicine, this report emphasizes the importance of an "omic" approach to uncover an oligogenic germline signature underlying cancer development and identify suitable therapeutic targets.Abstract Lung cancer (LC) continues to be an important public health problem, being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never-smoker early-onset LC patients, which does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer-predisposing genes in non-smokers patients, giving experimental support to a model of a "private genetic epidemiology". Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib's model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bioinformatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer-predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: (i) downregulation in LC tissue (indicating a "second hit" in tumor suppressor genes); (ii) upregulation in cancer tissue (likely oncogene); and (iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with a functional effect in tumor-predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of the familial clustering of non-small-cell lung cancer. In conclusion, these findings indicate that each patient has his/her own "predisposing signature" to cancer development and suggest the use of personalized therapeutic strategies in lung cancer

Case report: PIK3CA somatic mutation leading to Klippel Trenaunay Syndrome and multiple tumors

We report a case of Klippel Trenaunay Syndrome that was monitored both clinically and molecularly over a period of 9 years. A somatic mosaic mutation of PIK3CA (p(E545G)) was identified using both cfDNA NGS liquid biopsy and tissue biopsy. At the age of 56, due to intervening clonal mutations in PIK3CA background, she developed a squamous cell carcinoma in the right affected leg which was treated surgically. Nine years later, lung bilateral adenocarcinoma arose on PIK3CA mutated tissues supported by different clonal mutations. One year later, the patient died from metastases led by a new FGFR3 clone unresponsive to standard-of-care, immunotherapy-based. Our results highlight the presence of a molecular hallmark underlying neoplastic transformation that occurs upon an angiodysplastic process and support the view that PIK3CA mutated tissues must be treated as precancerous lesions. Importantly, they remark the effectiveness of combining cfDNA NGS liquid and tissue biopsies to monitor disease evolution as well as to identify aggressive clones targetable by tailored therapy, which is more efficient than conventional protocols

Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes

Carriers of single pathogenic variants of the CFTR (cystic fibrosis transmembrane conductance regulator) gene have a higher risk of severe COVID-19 and 14-day death. The machine learning post-Mendelian model pinpointed CFTR as a bidirectional modulator of COVID-19 outcomes. Here, we demonstrate that the rare complex allele [G576V;R668C] is associated with a milder disease via a gain-of-function mechanism. Conversely, CFTR ultra-rare alleles with reduced function are associated with disease severity either alone (dominant disorder) or with another hypomorphic allele in the second chromosome (recessive disorder) with a global residual CFTR activity between 50 to 91%. Furthermore, we characterized novel CFTR complex alleles, including [A238V;F508del], [R74W;D1270N;V201M], [I1027T;F508del], [I506V;D1168G], and simple alleles, including R347C, F1052V, Y625N, I328V, K68E, A309D, A252T, G542*, V562I, R1066H, I506V, I807M, which lead to a reduced CFTR function and thus, to more severe COVID-19. In conclusion, CFTR genetic analysis is an important tool in identifying patients at risk of severe COVID-19

Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19

Thrombosis of small and large vessels is reported as a key player in COVID-19 severity. However, host genetic determinants of this susceptibility are still unclear. Congenital Thrombotic Thrombocytopenic Purpura is a severe autosomal recessive disorder characterized by uncleaved ultra-large vWF and thrombotic microangiopathy, frequently triggered by infections. Carriers are reported to be asymptomatic. Exome analysis of about 3000 SARS-CoV-2 infected subjects of different severities, belonging to the GEN-COVID cohort, revealed the specific role of vWF cleaving enzyme ADAMTS13 (A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 13). We report here that ultra-rare variants in a heterozygous state lead to a rare form of COVID-19 characterized by hyper-inflammation signs, which segregates in families as an autosomal dominant disorder conditioned by SARS-CoV-2 infection, sex, and age. This has clinical relevance due to the availability of drugs such as Caplacizumab, which inhibits vWF-platelet interaction, and Crizanlizumab, which, by inhibiting P-selectin binding to its ligands, prevents leukocyte recruitment and platelet aggregation at the site of vascular damage

Pathogen-sugar interactions revealed by universal saturation transfer analysis

Many pathogens exploit host cell-surface glycans. However, precise analyses of glycan ligands binding with heavily modified pathogen proteins can be confounded by overlapping sugar signals and/or compounded with known experimental constraints. Universal saturation transfer analysis (uSTA) builds on existing nuclear magnetic resonance spectroscopy to provide an automated workflow for quantitating protein-ligand interactions. uSTA reveals that early-pandemic, B-origin-lineage severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike trimer binds sialoside sugars in an “end-on” manner. uSTA-guided modeling and a high-resolution cryo–electron microscopy structure implicate the spike N-terminal domain (NTD) and confirm end-on binding. This finding rationalizes the effect of NTD mutations that abolish sugar binding in SARS-CoV-2 variants of concern. Together with genetic variance analyses in early pandemic patient cohorts, this binding implicates a sialylated polylactosamine motif found on tetraantennary N-linked glycoproteins deep in the human lung as potentially relevant to virulence and/or zoonosis

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores ((Formula presented.) and (Formula presented.)). By applying a logistic regression with both IPGS, ((Formula presented.) (or indifferently (Formula presented.)) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity.. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores (IPGSph1 and IPGSph2). By applying a logistic regression with both IPGS, (IPGSph2 (or indifferently IPGSph1) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

cfDNA-NGS Liquid Biopsy as a new strategy to face advanced cancers: the usefulness of Liquid Biopsy in Cancer molecular characterization, detection of targetable mutations and insights into treatment resistance.

Despite advances in oncology, many patients face limited options after exhausting standard treatments and effective treatments for advanced solid tumours remain a challenge. Liquid Biopsy, a minimally invasive technique, offers a promising alternative by detecting key mutations and monitoring cancer progression, overcoming the limitations of traditional tissue biopsies. Circulating tumour cell-free DNA (cfDNA) serves as a highly sensitive biomarker to identify targetable and therapy-escaping mutations. This thesis applied cfDNA-NGS Liquid Biopsy to molecularly characterise and monitor clonal evolution in 291 advanced cancer patients across several solid tumour types, including breast, lung, colorectal, pancreatic, and prostate cancers. Using the Avenio Expanded genes panel (Roche), 1,478 variants were detected, of which 15% were classified as pathogenic or likely pathogenic. Key mutations associated with treatment resistance were identified, such as ESR1, PIK3CA, and TP53 in breast cancer, KRAS, EGFR, BRAF and TP53 in lung cancer, and APC, KRAS, BRAF, MAPK1, and TP53 in colorectal cancer. In pancreatic and prostate cancers, coupled KRAS/TP53 mutations and BRCA1, BRCA2 mutations, along with TP53 and AR mutations, respectively, were associated with worse clinical outcomes and could explain treatment resistance. Furthermore, targeted therapies may benefit patients with some of the mutations identified. During my research at the Institute of Cancer Research in Sutton and University College of London, I explored treatment resistance in metastatic castration-resistant prostate cancer (mCRPC), trying to detect epigenetic signatures on cfDNA and tissues of patients unresponsive to Olaparib (PARP inhibitor) in the TOPARP-B clinical trial, as well as analysing cfDNA and post-mortem metastatic tissues of CASCADE patients from the LuPARP clinical trial. These studies further highlighted the potential of cfDNA-NGS in monitoring metastatic processes and treatment escapism. In conclusion, cfDNA-NGS Liquid Biopsy proved to be a powerful, non-invasive tool for profiling tumours, offering insights into cancer progression and treatment resistance. This work underlines the importance of a personalised approach in cancer treatment, as different genetic signatures can significantly influence tumour aggressiveness and response patterns, even within the same histological cancer type