Hydrocephalus is one of the most common pediatric neurological disorders and is associated with monogenic syndromes. Untreated hydrocephalus has a high mortality rate, and current treatment involves surgical implantation of a shunt or third ventriculostomy, both of which have complex follow-up care. Molecular therapies to treat or prevent hydrocephalus might have widespread applications for monogenic syndromes but are currently underinvestigated. To determine whether oligonucleotides are a viable drug class to prevent hydrocephalus, we assessed a monogenic syndrome called Schinzel-Giedion syndrome (SGS). SGS is caused by increased SETBP1 protein due to heterozygous missense mutations in a degron motif of SETBP1. Mice that produce mutant human SETBP1 show hydrocephalus in over 50% of cases and do not live long. Treatment of mice with injections of antisense oligonucleotides targeting SETBP1 prevented or led to a significant reduction of hydrocephalus compared to mock-treated controls and improved long-term survival. These results suggest that hydrocephalus is preventable for a monogenic syndrome with an oligonucleotide intervention.

BACKGROUND:Myelodysplastic syndromes are clonal hematopoietic stem cell disorders characterized by multistep molecular evolution and a variable risk of leukemic transformation. Given this prognostic heterogeneity, accurate risk stratification is essential for clinical decision-making. We developed ProgEvo, a proprietary framework that infers molecular evolutionary trajectories and integrates them with clinical data to improve prognostic accuracy. METHODS:ProgEvo was trained on 2519 patients in cBioPortal (https://www.cbioportal.org) and validated using two external cohorts: Genomed4All (2043 patients) and a Moffitt Cancer Center (MCC) cohort (2157 patients). Directional evolutionary routes were inferred and selected for prognostic modeling if they were consistently associated with leukemia-free survival. A multivariable feature selection strategy was applied to integrate evolution-consistent variables into the existing IPSS-M model. RESULTS:ProgEvo identified 1765 gene co-occurrences aggregated into 45 directional evolutionary routes. Of these, 18 were validated in the Genomed4All cohort. Five evolution-informed variables, two directional routes (Additional Sex Combs-Like 1 [ASXL1]→KRAS Proto-Oncogene [KRAS] and Serine and Arginine-Rich Splicing Factor 2 [SRSF2]→NRAS Proto-Oncogene [NRAS]), one co-occurrence (NRAS/RUNX Family Transcription Factor 1 [RUNX1]), and two early mutations (ATRX [ATRX Chromatin Remodeler] and Janus Kinase 2 [JAK2]) were integrated into IPSS-M to generate IPSS-M-Evo. The model with "-Evo" improved discrimination for both leukemia-free survival and overall survival, with over 40% of patients restratified in the Genomed4All data. The performance of the model was further confirmed in the MCC cohort. CONCLUSIONS:ProgEvo enabled inference of a molecular evolution model and integration of evolution-informed covariates into clinical prognostic frameworks, supporting the development of the IPSS-M-Evo model. A free web-based tool allows clinicians to calculate the IPSS-M-Evo score and match individual mutational profiles to cohort-derived evolutionary trajectories (https://evoclin.unimib.it/tools/evolution-graphs.html and https://evoclin.unimib.it/tools/ipssmevo.html). (Funded by the European Union and others.).

The treatment of acute myeloid leukemia (AML) presents a challenge to current therapies because of the development of drug resistance. Genetic mutation of FMS-like tyrosine kinase-3 (FLT3) is a target of interest for AML treatment, but the use of FLT3-targeting agents on AML patients has so far resulted in poor overall clinical outcomes.[1] The incorporation of the boronic group in a drug scaffold could enhance the bioavailability and pharmacokinetic profile of conventional anticancer chemotypes. Boronic acids represent an intriguing and unexplored class of compounds in the context of AML, and they are only scantly reported as inhibitors of protein kinases. We identified alpha-triazolylboronic acids as a novel chemotype for targeting FLT3 by screening a library of structurally heterogeneous in-house boronic acids. Selected compounds show low micromolar activities on enzymatic and cellular assays, selectivity against control cell lines and a recurring binding mode in in-silico studies. Furthermore, control analogues synthesized ad hoc and lacking the boronic acid are inactive, confirming that this group is essential for the activity of the series. All together, these results suggest alpha-triazolylboronic acids could be a promising novel chemotype for FLT3 inhibition, laying the ground for the design of further compounds.

Non-small cell lung cancer (NSCLC) remains a formidable global health challenge, with heterogeneous molecular characteristics influencing prognosis and treatment response. We present a novel computational framework named ASTUTE (Association of SomaTic mUtaTions to gene Expression profiles), designed to perform genotype-phenotype mapping through the integration of genomic and transcriptomic data. Through the systematic analysis of over 3600 samples from diverse NSCLC datasets and multiple cancer types, we uncovered intricate associations between KEAP1/NFE2L2 mutations and the NRF2 pathway activation. Our study identified novel NRF2-related functionalities associated with specific genetic alterations and revealed a KEAP1/NFE2L2 expression signature predictive of prognosis across different cancer types. These findings enhance our understanding of cancer pathogenesis and drug resistance mechanisms mediated by NRF2 activation, paving the way for tailored therapeutic interventions and the development of prognostic biomarkers. Our approach exemplifies the power of integrating genomic and transcriptomic data to elucidate cancer mechanisms, thereby advancing the field of precision oncology.

Myelodysplastic Syndromes (MDS) are clonal hematopoietic stem cell disorders that evolve following a multi-step process. This process begins with a primary driver mutation in a hematopoietic stem cell that gives rise to clonal hematopoiesis and progressive clonal expansion. Understanding the timing of driver mutations and their association with secondary mutations can uncover evolutionary trajectories. These trajectories are identified by detecting co-occurring mutations with a significant temporal order, where an early mutation increases the probability of a later mutation. MDS heterogeneity is not only clonal but also clinical, and precise risk stratification is essential for clinical decision-making. Prior works have shown that clonal evolution correlates with Overall Survival (OS) in cancer. Building on these insights, we developed ProgEvo, a machine learning framework that infers molecular evolutionary features and integrates them with clinical and molecular covariates to predict clinical outcomes. ProgEvo training was performed on 2,519 patients from the original IPSS-M cohort (data from cBioPortal) to develop both the evolutionary model and the prognostic model. Cancer cell fractions were estimated from variant allele frequencies corrected for copy number to infer the temporal sequence of driver mutations within each patient. These individual temporal sequences were then aggregated into a cohort-level graph. Recurrent evolutionary routes were identified within the evolutionary graph through a maximum likelihood approach with AIC-based model selection. Evolutionary validation was conducted on an independent cohort of 2,043 GenoMed4All patients. Features consistently associated with leukemia-free survival (LFS) were integrated into the IPSS-M model, minimizing structural changes of IPSS-M and the number of additional covariates. Clinical validation of the prognostic model was performed in both the GenoMed4All and Moffitt Cancer Center (MCC) cohorts (2,157 patients). We selected 46 genes consistently sequenced across training and testing datasets. In the cBioPortal cohort, 7,828 mutations were analyzed. Genes were initially assigned a discrete temporal rank (1–2–3) based on their aggregated position in the evolutionary graph, with earlier ranks representing earlier acquisition. A continuous rank score was then derived for each gene through bootstrap resampling of patient-level temporal sequences. 1° rank genes included KMT2D, NOTCH1, ATRX, CREBBP, KIT, and ATRX. The 2° rank was composed by the backbone of initial driver mutations: DNMT3A, SF3B1, SRSF2, TET2, TP53, U2AF1, ASXL1, and EZH2. Late events (3° rank) comprised KRAS, STAG2, RUNX1, CBL, PPM1D, and NRAS. Gene co-occurrence alone was not considered sufficient to define evolutionary relationships. A directional route was inferred only when a “parent” gene consistently preceded the “child” gene in the evolution graph. In total, 1,765 gene co-occurrences were aggregated into 45 directional evolutionary routes; 18 were validated in the GenoMed4All cohort. Several routes were significantly associated with LFS in univariate analysis, including: ASXL1→KRAS (HR 2.87, CI 1.72–4.78), ASXL1→STAG2 (HR 2.51, CI 2.04–3.08), DNMT3A→BCOR (HR 1.95, CI 1.33–2.86), SF3B1→RUNX1 (HR 1.84, CI 1.26–2.68), SRSF2→NRAS (HR 2.84, CI 1.64–4.92), SRSF2→STAG2 (HR 2.24, CI 1.76–2.85), TET2→STAG2 (HR 2.72, CI 2.03–3.66). Two validated directional routes (ASXL1→KRAS, SRSF2→NRAS) and two early genes (ATRX, JAK2), independently associated with LFS outside the original IPSS-M model, were incorporated into IPSS-M to generate the IPSS-M-Evo score. The model was adjusted for age and co-occurrence of NRAS and RUNX1. IPSS-M-Evo improved prognostic discrimination for both LFS and OS, with higher c-indexes (0.76 vs 0.75), lower AIC (ΔAIC 109 for OS and 77 for LFS), and reclassification of over 40% of patients. Clinical performance was independently validated in both the GenoMed4All and MCC cohorts. By resolving co-occurrence into directional evolutionary trajectories, ProgEvo reduces the number of co-mutations to evaluate by prioritizing temporally consistent events. Integrating evolution-informed features into IPSS-M (IPSS-M-Evo) improves risk stratification. Bringing evolution into clinical practice may enable personalized NGS monitoring, anticipatory or more personalized therapy, and potentially drug discovery based on molecular evolution.

Prostate cancer is a complex disease that necessitates precise evaluation and treatment decisions dependent on cancer stage and aggressiveness. Nonetheless, existing methods have limitations in capturing the complete range of prostate cancer behavior and progression. Although methods such as the histological assessment of the Gleason score provide a valuable approximation of cancer behavior, understanding the fundamental mechanisms of each neoplasm and effectively translating this knowledge into clinical practice present challenges that can impact treatment approaches.Here, we perform a comprehensive analysis of large-scale multi-omics datasets from The Cancer Genome Atlas and other studies, aiming to unravel the molecular and clinical features underlying prostate cancer progression. Using an integrative clustering approach, we determine distinct molecular subtypes associated with potential prognostic biomarkers. Through computational validation in independent cohorts, we reinforce their potential for molecular subtyping, demonstrating the clinical significance of the hypothesized markers. To evaluate the clinical impact of these biomarkers, we perform immunohistochemistry assays on patient samples, confirming their prognostic potential. Among the investigated biomarkers, CCNB1, FOXM1, and RAD51 emerged as the most promising candidates for prognostic evaluation.The results validate the utility of these biomarkers, bridging the gap between bioinformatics analyses and experimental validation. This study expands our understanding of prostate cancer progression through comprehensive multi-omics analyses. The identification and validation of molecular subtypes for the identification of potential prognostic biomarker offers valuable insights for improved treatment decisions and personalized care in prostate cancer. These findings provide a foundation for future investigations and pave the way for the development of targeted therapeutic approaches in prostate cancer management.

Acute myeloid leukemia (AML) is the most prevalent form of acute leukemia in adults, representing a substantial medical need, as the standard of care has not changed for the past two decades, and the long-term outcome remains dismal for a large fraction of patients. Approximately 30% of AMLs carry activating mutations of the FLT3 kinase. Unfortunately, single-agent FLT3 inhibitor therapy has met limited clinical efficacy, underscoring a strong rationale for the development of more selective and more potent inhibitors. Here we present the design, synthesis and biological evaluation of a series of biphenyl substituted pyrazoyl-ureas, an underexplored scaffold in medicinal chemistry, as novel FLT3 inhibitors with a putative type II binding mode. Optimized compounds show nanomolar activity against isolated FLT3 (230 nM for compound 10q) and on FLT3-driven cell lines (280 nM and 18 nM for compound 10q against MV4.11 and MOLM-14 cells respectively), with no toxicity against control cell lines, limited metabolism in human microsomes and a reliable SAR; furthermore, profiling of compound 10q against a panel of kinases highlights c-Kit as the only other hit. Overall, we show that the series has a narrow selectivity profile and metabolic stability, and the mode of action of the inhibitors through FLT3 is confirmed by strong suppression of FLT3 and STAT5 phosphorylation.

Abstract Cancer is a highly heterogeneous disease characterized by genomic and phenotypic changes that differ among tumor subtypes. A comprehensive understanding of the molecular heterogeneity is crucial for identifying molecular biomarkers and developing targeted therapies. While recent years have seen significant progress in genomic studies and advancements in next-generation sequencing technologies, elucidate the impact of individual genomic alterations on the transcriptome landscape of cancer cells remains essential. This knowledge is crucial for developing personalized therapies. To integrate mutational profiles and transcriptional data, we have developed a novel computational framework named ASTUTE (Association of SomaTic mUtaTions to Expression). ASTUTE establishes associations between somatic mutations and gene expression profiles, quantifying the results as fold changes and comparing the effects of individual mutations. In particular, ASTUTE leverages LASSO regularized regression for feature selection, identifying the most relevant mutated genes that exhibit a strong association with expression levels. Our goal is to create a valuable resource for identifying diagnostic markers and advancing the development of targeted therapies in cancer. To this end, we applied ASTUTE to two published bulk RNA-seq datasets of adult acute myeloid leukemia (AML) patients from the Beat AML program and the TCGA study to verify the capacity of our approach to identify genes whose expression correlates with the presence of specific somatic mutations. The first dataset comprises 585 samples, while the second one includes 173 specimens, both of them contain mutations and RNA-seq data. We executed ASTUTE on the two datasets independently, considering all genes for RNA-seq data and the alterations in the top 10 most mutated genes. We considered the associations consistently discovered in both cohorts. Our analysis revealed a strong association between NPM1 mutations and the expression of HOX genes. Specifically, HOXB6, HOXB5, HOXB3, HOXA5, HOXB2, and HOXA10 exhibited a log2 fold change higher than 1.5. The upregulation of these genes has been reported in association with NPM1 mutations in AML adult patients. Additionally, ASTUTE identified HOXA6, HOXA7, HOXA4, and HOXA9, whose expression has been described to be upregulated in the presence of NPM1 mutations in AML pediatric samples. Finally, we identified a robust relationship between NPM1 mutations and the expression of PBX3, MEIS, and ITM2A. The first two genes are upregulated in the presence of NPM1 mutations, while the third one is downregulated. Our analysis showcases ASTUTE's capability to identify potential markers and underscores the possibility to apply ASTUTE to different tumors datasets, with the aim to better characterize cancer heterogeneity and develop targeted therapies. Citation Format: Valentina Crippa, Diletta Fontana, Ivan Civettini, Luca Mologni, Rocco Piazza, Carlo Gambacorti-Passerini, Daniele Ramazzotti. Integrating mutational profiles and transcriptional data with ASTUTE to elucidate the key molecular functions involved in the pathogenesis of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4885.

Background Copy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic mechanisms driving cancer development and progression. However, tools for visualizing copy number data in cancer research have some limitations. In fact, they can be complex to use and require expertise in bioinformatics or computational biology. While copy number data analysis and visualization provide insights into cancer biology, interpreting results can be challenging, and there may be multiple explanations for observed patterns of copy number alterations. Results We created Control-FREEC Viewer, a tool that facilitates effective visualization and exploration of copy number data. With Control-FREEC Viewer, experimental data can be easily loaded by the user. After choosing the reference genome, copy number data are displayed in whole genome or single chromosome view. Gain or loss on a specific gene can be found and visualized on each chromosome. Analysis parameters for subsequent sessions can be stored and images can be exported in raster and vector formats. Conclusions Control-FREEC Viewer enables users to import and visualize data analyzed by the Control-FREEC tool, as well as by other tools sharing a similar tabular output, providing a comprehensive and intuitive graphical user interface for data visualization.

Somatic SETBP1 mutations are found in various myeloid disorders covering both myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS). To characterize the early steps of SETBP1-mediated leukemogenesis, we generated a conditional mouse model expressing SETBP1 G870S mutant in the entire hematopoietic tissue through Cre-mediated recombination driven by the Vav1 promoter. In all mice signs of a hematological disease appeared between 30 and 90 days: longitudinal analysis revealed accumulation of white blood cells (WBC) in virtually all heterozygous SETBP1 G870S mice, with a marked imbalance between the lymphoid and myeloid lineages in favor of the latter, and an increase in mature myeloid cells in absence of circulating blasts or non-segmented myeloid precursors. Kaplan-Meier analysis revealed a dramatic decrease in event-free survival in SETBP1 G870S mice. BM histology showed overt myeloid hyperplasia with fibrosis and no evidence of dysplasia except for the megakaryocytic lineage. Exploration of visceral organs highlighted the presence of severe hepatosplenomegaly with massive infiltration by myeloid elements, disruption of normal tissue architecture, and signs of extramedullary hematopoiesis. Single-cell RNA-sequencing (scRNA) on BM Lin - cells identified Mecom, Setbp1 and Hoxa9 among the top upregulated genes in SETBP1 G870S precursors. Pseudotime analysis of scRNA data revealed the presence of 261 spatially autocorrelated genes. Of them 158 were also differentially expressed in SETBP1 G870Svs control mice. Spi1, encoding for the master regulator of hematopoietic differentiation PU.1, was significantly upregulated in SETBP1 G870S precursors. PU.1 promotes the maturation of bone marrow early precursors towards the granulocytic/monocytic lineages by directly impairing the transcription of Gata2 and Gata1. In line with these data, Gata2 and Gata1 expression was profoundly suppressed in the early myeloid precursors of SETBP1 G870S mice, which associated with the down-modulation of markers of the erythroid lineage, such as the Carbonic Anhydrase 1, in the MEP differentiation branch. Ourmouse model recapitulates many clinical features of primary myelofibrosis (PMF). As up to 10% of PMF are triple-negative for the classical JAK2, CALR, and MPL mutations (TN-PMF), we set out to assess SETBP1 mutations in this context. We analyzed 36 TN-PMF patients by exome sequencing. In 29 we did not find any evidence of somatic mutations; in the remaining 7 (7/36; 19.4%) high VAF SETBP1 degron mutations were identified. In two patients SETBP1 was found as a single somatic variant, while in the others it coexisted with ASXL1, NRAS TET2 SRSF2, RIT1 CBL or CSF3R mutations. Notably, SETBP1 mutations were the only shared alterations among all seven patients. A markedly reduced overall survival was observed for SETBP1 positive patients, with a median survival time of 24 months (median survival not reached at 60 months for SETBP1 negative patients). To dissect the clonal architecture of SETBP1 positive TN-PMF at single-cell resolution, we applied single-cell targeted DNA sequencing on 3 SETBP1-mutated TN-PMF samples using the Tapestri technology, showing that, as opposite to MDS/MPN, in all TN-PMF cases SETBP1 is a very early clonal event. Therefore, our study suggests a clear partition of TN-PMF into two groups, the first one characterized by oncogenic, high VAF SETBP1 mutations accompanied by other oncogenic variants and poor prognosis and the other one characterized by no evidence of an active clonal process or of driver oncogenic events and much lower aggressiveness. SETBP1 positive TN-PMF disorders lie in a gray zone comprised between the MPN and MPN/MDS boundary. In MDS/MPN, SETBP1 mutations are often found as mid or late events. In contrast, we show here that in TN-PMF SETBP1 appears to be one of the earliest hits, therefore highlighting a potentially relevant biological difference occurring in the two subsets. In this context the presence of early SETBP1 mutations seems to promote the occurrence of a myeloid disorder characterized by the triad: leukocytosis without differentiation block or dysplasia, BM fibrosis and progressive splenomegaly, hence recapitulating many clinical features of overt or prefibrotic/early PMF and resembling the SETBP1 mouse model.

Background Unstable hemoglobins are caused by single amino acid substitutions in the HBB gene, often affecting key histidine residues, leading to protein destabilization and hemolytic crises. In contrast, long HBB variants, exceeding 20 bp, are rare and associated with a β-thalassemia phenotype due to disrupted α-β chain interactions. We describe a family wherein four of six members carry a novel 23-amino-acid in-frame duplication of HBB (c.176_244dup), named hemoglobin (Hb) Monza. Despite its length, this duplication manifests as an unstable hemoglobin variant rather than a β-thalassemia phenotype. Methods A static 3D model of the Hb Monza β chain was generated using AlphaFold and SWISS-MODEL. Molecular dynamics (MD) simulations were performed with the Generalized Born implicit solvent model. After energy minimization and heating to 311 K (38°C), a 40 ns production run was conducted. Findings 3D modeling of Hb Monza revealed minimal structural changes in the Hb β chain, particularly in the key histidine residues and their interaction with the iron atom. Additionally, the static 3D model showed a preserved α-β interaction, explaining the absence of a β-thalassemia clinical phenotype. MD simulations under thermal stress revealed a notable increase in root-mean-square deviation compared to the wild-type β subunit, along with a loss of contacts with the heme, explaining the hemolytic crises during febrile episodes. Conclusion Despite the long duplication in HBB, Hb Monza retains functional α-β interaction while demonstrating instability under stressful conditions. This unique variant presents with an unstable Hb phenotype rather than a β-thalassemia phenotype. Funding No financial funding was received.

Abstract Myelodysplastic neoplasms (MDS) are hematopoietic stem cell disorders. The IPSS-Mol model, a scoring system that combines molecular and clinical features to stratify MDS patients, can identify individuals at high-risk of developing Acute Myeloid Leukemia (AML), enabling the targeted application of disease-modifying therapies. However, the molecular evolutionary patterns in MDS and their impact on Overall Survival (OS) and Leukemia-Free Survival (LFS) remain unexplored. Shifting from conventional static mutational analyses used by IPSS-Mol to a dynamic evolutionary approach, we employed the ASCETIC framework to analyze data from the IPSS-Mol Database (3,323 patients, 152 genes) and understand the dynamics of MDS progression. According to ASCETIC, genes related to DNA methylation displayed low ranks, indicating their tendency to manifest early in the progression of the disease. In contrast, genes associated with cellular signaling typically appeared in the later stages of tumor progression, while genes involved in RNA splicing exhibited intermediate ranks.Regarding molecular evolution in MDS, ASCETIC identified five clusters, each characterized by distinct evolutionary signatures and significantly different OS and LFS rates (p < 0.001). The median OS for the C1, C2, C3, C4, and C5 clusters were 79.3, 64.1, 28.9, 16.2, and 14.8 months, respectively. Subsequently, we compared ASCETIC's clusters with the different risk groups defined by the IPSS-Mol score. Among the 1158 patients initially classified as V-Low/M-Low risk by IPSS-Mol, ASCETIC identified 55 patients placed in C4 and C5. These patients exhibited lower OS (p = 0.005) and LFS (p = 0.02). Conversely, among the 905 IPSS-Mol M-High/V-High risk patients, ASCETIC identified 468 patients (51.7%) classified as low risk (C1-C2-C3), who were associated with longer median OS and LFS (p < 0.001). The differences between conventional mutational analyses and our evolutionary approach primarily, but not exclusively, arise from the evaluation of TP53 mutations and ASXL1. In the IPSS-Mol model, a higher risk is conferred if multi-hit TP53 alterations occur or in the presence of ASXL1 mutations.Conversely, ASCETIC assesses the impact of mutations differently. It considers not only the Variant Allele Frequencies of individual- and/or co-mutations but also the evolutionary trajectories that lead to TP53 and/or ASXL1. This approach results in different risk weights assigned to each evolutionary trajectory.Lastly, we identified patients with NPM1 mutations, classified as MDS patients in the original dataset but now categorized as AML according to 2022-WHO. Within this high-risk context, ASCETIC differentiated between patients with shorter and longer Overall Survival (OS) (p < 0.001).Our study reveals the influence of evolution on MDS prognosis, offering insights into the integration of the ASCETIC framework with the IPSS-Mol. Validation on external cohort is ongoing. Citation Format: Ivan Civettini, Valentina Crippa, Federica Malighetti, Matteo Villa, Andrea Aroldi, Fabrizio Cavalca, Alex Graudenzi, Lorenza Maria Borin, Luca Mologni, Rocco Piazza, Carlo Gambacorti-Passerini, Daniele Ramazzotti. Characterization of molecular evolution in myelodysplastic neoplasms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6208.

Background: Anaplastic lymphoma kinase (ALK) plays a role in the development of lymphoma, lung cancer and neuroblastoma. While tyrosine kinase inhibitors (TKIs) have improved treatment outcomes, relapse remains a challenge due to on-target mutations and off-target resistance mechanisms. ALK-positive (ALK+) tumors can evade the immune system, partly through tumor-associated macrophages (TAMs) that facilitate immune escape. Cancer cells use “don’t eat me” signals (DEMs), such as CD47, to resist TAMs-mediated phagocytosis. TKIs may upregulate pro-phagocytic stimuli (i.e., calreticulin, CALR), suggesting a potential therapeutic benefit in combining TKIs with an anti-CD47 monoclonal antibody (mAb). However, the impact of this combination on both TKIs-sensitive and resistant ALK+ tumors requires further investigation. Methods: A panel of TKIs-sensitive and resistant ALK+ cancer subtypes was assessed for CALR and CD47 expression over time using flow cytometry. Flow cytometry co-culture and fluorescent microscopy assays were employed to evaluate phagocytosis under various treatment conditions. Results: ALK inhibitors increased CALR expression in both TKIs-sensitive and off-target resistant ALK+ cancer cells. Prolonged TKIs exposure also led to CD47 upregulation. The combination of ALK inhibitors and anti-CD47 mAb significantly enhanced phagocytosis compared to anti-CD47 alone, as confirmed by flow cytometry and fluorescent microscopy. Conclusions: Anti-CD47 mAb can quench DEMs while exposing pro-phagocytic signals, promoting tumor cell phagocytosis. ALK inhibitors induced immunogenic cell damage by upregulating CALR in both sensitive and off-target resistant tumors. Continuous TKIs exposure in off-target resistant settings also resulted in the upregulation of CD47 over time. Combining TKIs with a CD47 blockade may offer therapeutic benefits in ALK+ cancers, especially in overcoming off-target resistance where TKIs alone are less effective.

Recurring sequences of genomic alterations occurring across patients can highlight repeated evolutionary processes with significant implications for predicting cancer progression. Leveraging the ever-increasing availability of cancer omics data, here we unveil cancer’s evolutionary signatures tied to distinct disease outcomes, representing “favored trajectories” of acquisition of driver mutations detected in patients with similar prognosis. We present a framework named ASCETIC ( A gony-ba S ed C ancer E volu T ion I nferen C e) to extract such signatures from sequencing experiments generated by different technologies such as bulk and single-cell sequencing data. We apply ASCETIC to (i) single-cell data from 146 myeloid malignancy patients and bulk sequencing from 366 acute myeloid leukemia patients, (ii) multi-region sequencing from 100 early-stage lung cancer patients, (iii) exome/genome data from 10,000+ Pan-Cancer Atlas samples, and (iv) targeted sequencing from 25,000+ MSK-MET metastatic patients, revealing subtype-specific single-nucleotide variant signatures associated with distinct prognostic clusters. Validations on several datasets underscore the robustness and generalizability of the extracted signatures.

EGFR is a protein kinase whose aberrant activity is frequently involved in the development of non-small lung cancer (NSCLC) drug resistant forms. The allosteric inhibition of this enzyme is currently one among the most attractive approaches to design and develop anticancer drugs. In a previous study, we reported the identification of a hit compound acting as type III allosteric inhibitor of the L858R/T790M double mutant EGFR. Herein, we report the design, synthesis and in vitro testing of a series of analogues of the previously identified hit with the aim of exploring the structure-activity relationships (SAR) around this scaffold. The performed analyses allowed us to identify two compounds 15 and 18 showing improved inhibition of double mutant EGFR with respect to the original hit, as well as interesting antiproliferative activity against H1975 NSCLC cancer cells expressing double mutant EGFR. The newly discovered compounds represent promising starting points for further hit-to-lead optimisation.

Cancer patients show heterogeneous phenotypes and very different outcomes and responses even to common treatments, such as standard chemotherapy. This state-of-affairs has motivated the need for the comprehensive characterization of cancer phenotypes and fueled the generation of large omics datasets, comprising multiple omics data reported for the same patients, which might now allow us to start deciphering cancer heterogeneity and implement personalized therapeutic strategies. In this work, we performed the analysis of four cancer types obtained from the latest efforts by The Cancer Genome Atlas, for which seven distinct omics data were available for each patient, in addition to curated clinical outcomes. We performed a uniform pipeline for raw data preprocessing and adopted the Cancer Integration via MultIkernel LeaRning (CIMLR) integrative clustering method to extract cancer subtypes. We then systematically review the discovered clusters for the considered cancer types, highlighting novel associations between the different omics and prognosis.

We present a large-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) substitutions, considering 1,585,456 high-quality raw sequencing samples, aimed at investigating the existence and quantifying the effect of mutational processes causing mutations in SARS-CoV-2 genomes when interacting with the human host. As a result, we confirmed the presence of three well-differentiated mutational processes likely ruled by reactive oxygen species (ROS), apolipoprotein B editing complex (APOBEC), and adenosine deaminase acting on RNA (ADAR). We then evaluated the activity of these mutational processes in different continental groups, showing that some samples from Africa present a significantly higher number of substitutions, most likely due to higher APOBEC activity. We finally analyzed the activity of mutational processes across different SARS-CoV-2 variants, and we found a significantly lower number of mutations attributable to APOBEC activity in samples assigned to the Omicron variant.

Mantle-cell lymphoma (MCL) is a B-cell non-Hodgkin Lymphoma (NHL) with a poor prognosis, at high risk of relapse after conventional treatment. MCL-associated tumour microenvironment (TME) is characterized by M2-like tumour-associated macrophages (TAMs), able to interact with cancer cells, providing tumour survival and resistance to immuno-chemotherapy. Likewise, monocyte-derived nurse-like cells (NLCs) present M2-like profile and provide proliferation signals to chronic lymphocytic leukaemia (CLL), a B-cell malignancy sharing with MCL some biological and phenotypic features. Antibodies against TAMs targeted CD47, a ‘don't eat me’ signal (DEMs) able to quench phagocytosis by TAMs within TME, with clinical effectiveness when combined with Rituximab in pretreated NHL. Recently, CD24 was found as valid DEMs in solid cancer. Since CD24 is expressed during B-cell differentiation, we investigated and identified consistent CD24 in MCL, CLL and primary human samples. Phagocytosis increased when M2-like macrophages were co-cultured with cancer cells, particularly in the case of paired DEMs blockade (i.e. anti-CD24 + anti-CD47) combined with Rituximab. Similarly, unstimulated CLL patients-derived NLCs provided increased phagocytosis when DEMs blockade occurred. Since high levels of CD24 were associated with worse survival in both MCL and CLL, anti-CD24-induced phagocytosis could be considered for future clinical use, particularly in association with other agents such as Rituximab.