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Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer.
No full textBACKGROUND: Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited. METHODS: Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48). RESULTS AND CONCLUSIONS: mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease
Selective CK1α degraders exert antiproliferative activity against a broad range of human cancer cell lines.
No full textMolecular-glue degraders are small molecules that induce a specific interaction between an E3 ligase and a target protein, resulting in the target proteolysis. The discovery of molecular glue degraders currently relies mostly on screening approaches. Here, we describe screening of a library of cereblon (CRBN) ligands against a panel of patient-derived cancer cell lines, leading to the discovery of SJ7095, a potent degrader of CK1α, IKZF1 and IKZF3 proteins. Through a structure-informed exploration of structure activity relationship (SAR) around this small molecule we develop SJ3149, a selective and potent degrader of CK1α protein in vitro and in vivo. The structure of SJ3149 co-crystalized in complex with CK1α + CRBN + DDB1 provides a rationale for the improved degradation properties of this compound. In a panel of 115 cancer cell lines SJ3149 displays a broad antiproliferative activity profile, which shows statistically significant correlation with MDM2 inhibitor Nutlin-3a. These findings suggest potential utility of selective CK1α degraders for treatment of hematological cancers and solid tumors
Enhancing oncolytic virotherapy by extracellular vesicle mediated microRNA reprograming of the tumour microenvironment.
No full textBACKGROUND: There has been limited success of cancer immunotherapies in the treatment of ovarian cancer (OvCa) to date, largely due to the immunosuppressive tumour microenvironment (TME). Tumour-associated macrophages (TAMs) are a major component of both the primary tumour and malignant ascites, promoting tumour growth, angiogenesis, metastasis, chemotherapy resistance and immunosuppression. Differential microRNA (miRNA) profiles have been implicated in the plasticity of TAMs. Therefore, delivering miRNA to TAMs to promote an anti-tumour phenotype is a novel approach to reverse their pro-tumour activity and enhance the efficacy of cancer immunotherapies. Oncolytic viruses (OVs) preferentially replicate in tumour cells making them ideal vehicles to deliver miRNA mimetics to the TME. Importantly, miRNA expressed by OVs get packaged within tumour-derived extracellular vesicles (TDEVs), and release of TDEV is augmented by OV infection, thus enhancing the dissemination of miRNA throughout the TME. METHOD: Small RNA sequencing was used to identify differentially expressed miRNA during TAM generation and following LPS/IFNγ stimulation to induce an anti-tumour phenotype. Two differentially expressed miRNA identified, miR-155 and miR-19a, were cloned into oncolytic rhabdovirus (ORV), and anti-tumour efficacy was investigated using both in vitro and in vivo models of OvCa. RESULTS: This study demonstrates that ORV infection enhances TDEV production in OvCa cell lines both in vitro and in vivo and that TDEV are preferentially taken up by myeloid cells, including TAMs. Small RNA sequencing identified 23 miRNAs that were significantly upregulated in anti-tumour TAMs, including miR-155-5p. While 101 miRNAs were downregulated during pro-tumour TAM differentiation, including miR-19a-3p. Culturing TDEV expressing miR-155 or miR-19a with TAMs reversed their immunosuppressive activity, as measured by T cell proliferation. While ORV-miR-155 enhanced the generation of anti-tumour T cells, only ORV-miR19a significantly improved survival of mice bearing ovarian tumours. CONCLUSION: This study demonstrates (i) that arming ORVs with immunomodulatory miRNA is an effective approach to deliver miRNA to myeloid cells within the TME and (ii) that miRNA have the capacity to reverse the tumour promoting properties of TAMs and improve the efficacy of cancer immunotherapies, such as OV
177Lu-PSMA-617 versus a change of androgen receptor pathway inhibitor therapy for taxane-naive patients with progressive metastatic castration-resistant prostate cancer (PSMAfore): a phase 3, randomised, controlled trial.
No full textBACKGROUND: [177Lu]Lu-PSMA-617 (177Lu-PSMA-617) prolongs radiographic progression-free survival and overall survival in patients with metastatic castration-resistant prostate cancer previously treated with androgen receptor pathway inhibitor (ARPI) and taxane therapy. We aimed to investigate the efficacy of 177Lu-PSMA-617 in patients with taxane-naive metastatic castration-resistant prostate cancer. METHODS: In this phase 3, randomised, controlled trial conducted at 74 sites across Europe and North America, taxane-naive patients with prostate-specific membrane antigen (PSMA)-positive metastatic castration-resistant prostate cancer who had progressed once on a previous ARPI were randomly allocated (1:1) to open-label, intravenous 177Lu-PSMA-617 at a dosage of 7·4 GBq (200 mCi) ± 10% once every 6 weeks for six cycles, or a change of ARPI (to abiraterone or enzalutamide, administered orally on a continuous basis per product labelling). Crossover from ARPI change to 177Lu-PSMA-617 was allowed after centrally confirmed radiographic progression. The primary endpoint was radiographic progression-free survival, defined as the time from randomisation until radiographic progression or death, assessed in the intention-to-treat population. Safety was a secondary endpoint. This study is registered with ClinicalTrials.gov (NCT04689828) and is ongoing. In this primary report of the study, we present primary (first data cutoff) and updated (third data cutoff) analyses of radiographic progression-free survival; all other data are based on the third data cutoff. FINDINGS: Overall, of the 585 patients screened, 468 met all eligibility criteria and were randomly allocated between June 15, 2021 and Oct 7, 2022 to receive 177Lu-PSMA-617 (234 [50%] patients) or ARPI change (234 [50%]). Baseline characteristics were mostly similar between groups; median number of 177Lu-PSMA-617 cycles was 6·0 (IQR 4·0-6·0). Of patients assigned to ARPI change, 134 (57%) crossed over to receive 177Lu-PSMA-617. In the primary analysis (median time from randomisation to first data cutoff 7·26 months [IQR 3·38-10·55]), the median radiographic progression-free survival was 9·30 months (95% CI 6·77-not estimable) in the 177Lu-PSMA-617 group versus 5·55 months (4·04-5·95) in the ARPI change group (hazard ratio [HR] 0·41 [95% CI 0·29-0·56]; p<0·0001). In the updated analysis at time of the third data cutoff (median time from randomisation to third data cutoff 24·11 months [IQR 20·24-27·40]), median radiographic progression-free survival was 11·60 months (95% CI 9·30-14·19) in the 177Lu-PSMA-617 group versus 5·59 months (4·21-5·95) in the ARPI change group (HR 0·49 [95% CI 0·39-0·61]). The incidence of grade 3-5 adverse events was lower in the 177Lu-PSMA-617 group (at least one event in 81 [36%] of 227 patients; four [2%] grade 5 [none treatment related]) than the ARPI change group (112 [48%] of 232; five [2%] grade 5 [one treatment related]). INTERPRETATION: 177Lu-PSMA-617 prolonged radiographic progression-free survival relative to ARPI change, with a favourable safety profile. For patients with PSMA-positive metastatic castration-resistant prostate cancer who are being considered for a change of ARPI after progression on a previous ARPI, 177Lu-PSMA-617 may be an effective treatment alternative. FUNDING: Novartis
Tumor dormancy: EMT beyond invasion and metastasis.
No full textMore than two-thirds of cancer-related deaths are attributable to metastases. In some tumor types metastasis can occur up to 20 years after diagnosis and successful treatment of the primary tumor, a phenomenon termed late recurrence. Metastases arise from disseminated tumor cells (DTCs) that leave the primary tumor early on in tumor development, either as single cells or clusters, adapt to new environments, and reduce or shut down their proliferation entering a state of dormancy for prolonged periods of time. Dormancy has been difficult to track clinically and study experimentally. Recent advances in technology and disease modeling have provided new insights into the molecular mechanisms orchestrating dormancy and the switch to a proliferative state. A new role for epithelial-mesenchymal transition (EMT) in inducing plasticity and maintaining a dormant state in several cancer models has been revealed. In this review, we summarize the major findings linking EMT to dormancy control and highlight the importance of pre-clinical models and tumor/tissue context when designing studies. Understanding of the cellular and molecular mechanisms controlling dormant DTCs is pivotal in developing new therapeutic agents that prevent distant recurrence by maintaining a dormant state
First-Line Nivolumab Plus Relatlimab Versus Nivolumab Plus Ipilimumab in Advanced Melanoma: An Indirect Treatment Comparison Using RELATIVITY-047 and CheckMate 067 Trial Data.
No full textPURPOSE: Nivolumab plus relatlimab and nivolumab plus ipilimumab have been approved for advanced melanoma on the basis of the phase II/III RELATIVITY-047 and phase III CheckMate 067 trials, respectively. As no head-to-head trial comparing these regimens exists, an indirect treatment comparison was conducted using patient-level data from each trial. METHODS: Inverse probability of treatment weighting (IPTW) adjusted for baseline characteristic differences. Minimum follow-ups (RELATIVITY-047, 33 months; CheckMate 067, 36 months) were selected to best align assessments. Outcomes included progression-free survival (PFS), confirmed objective response rate (cORR), and melanoma-specific survival (MSS) per investigator; overall survival (OS); and treatment-related adverse events (TRAEs). A Cox regression model compared PFS, OS, and MSS. A logistic regression model compared cORRs. Subgroup analyses were exploratory. RESULTS: After IPTW, key baseline characteristics were balanced for nivolumab plus relatlimab (n = 339) and nivolumab plus ipilimumab (n = 297). Nivolumab plus relatlimab demonstrated similar PFS (hazard ratio [HR], 1.08 [95% CI, 0.88 to 1.33]), cORR (odds ratio, 0.91 [95% CI, 0.73 to 1.14]), OS (HR, 0.94 [95% CI, 0.75 to 1.19]), and MSS (HR, 0.86 [95% CI, 0.67 to 1.12]) to nivolumab plus ipilimumab. Subgroup comparisons showed larger numerical differences favoring nivolumab plus ipilimumab with acral melanoma, BRAF-mutant melanoma, and lactate dehydrogenase >2 × upper limit of normal, but were limited by small samples. Nivolumab plus relatlimab was associated with fewer grade 3-4 TRAEs (23% v 61%) and any-grade TRAEs leading to discontinuation (17% v 41%). CONCLUSION: Nivolumab plus relatlimab demonstrated similar efficacy to nivolumab plus ipilimumab in the overall population, including most-but not all-subgroups, and improved safety in patients with untreated advanced melanoma. Results should be interpreted with caution
First-Line Tyrosine Kinase Inhibitors in Soft-Tissue Sarcomas: A Role for Anlotinib?
No full textThe optimal medical treatment of chemotherapy-ineligible patients affected by advanced soft-tissue sarcomas is unclear. In this population, tyrosine kinase inhibitors represent an appealing alternative treatment strategy. First-line use of the tyrosine kinase inhibitor anlotinib in chemotherapy-ineligible patients with soft-tissue sarcoma showed promising activity across multiple histologies. See related article by Li et al., p. 4310
Banana-shaped survival curves of metastatic renal cell carcinoma treated with first-line immune-combinations, not just a matter of "palateau".
No full textThe first-line therapy of metastatic renal cell carcinoma (mRCC) has revolutionized with the approval of immune checkpoint inhibitors (ICIs) in combination with or without tyrosine kinase inhibitors (TKIs). The choice among the many different immuno-combinations (ICI-ICI or ICI-TKI) is challenging due to the lack of predictive factors. The different shapes of the Kaplan-Meier survival curves (e.g. "banana-shaped curves") have raised many questions on the long-term survival benefit. Here, we analyzed the factors that could have impacted the different long-term survival, including the prognostic factors distribution (IMDC score), histological factors (sarcomatoid features, PD-L1 expression), and treatment characteristics (mechanism of action, duration, discontinuation rate). This overview highlights the factors that should be considered in the first-line setting for the patients' therapeutic choice and prognostic assessment. They are also fundamental parameters to examined for head-to-head studies and real-life, large-scale studies
Investigation of spliceosome assembly and the cross-talk with miRNA biogenesis
No full textPre-mRNA splicing is a critical RNA processing event that occurs on the spliceosome, a large and dynamic protein-RNA complex comprising over 100 proteins. The spliceosome initially forms in an autoinhibited state and becomes catalytically active through several remodelling events driven by RNA helicases, as well as other enzymes that include kinases. Although the function of helicases in splicing is well-documented, the specific role of kinases remains poorly understood.
The inactivation of the kinase CDK11 using a specific inhibitor was recently shown to block the phosphorylation of SF3B1—a key component of the spliceosome essential for its assembly and activation. The kinase inhibition results in the spliceosome being arrested at an unclear stage and via an unknown mechanism. To investigate this, I developed a method to purify spliceosomes stalled by the CDK11 inhibitor. Biochemical and biophysical analyses, combined with mass spectrometric proteomics and electron microscopy, reveal that CDK11 inhibition arrests the spliceosome in a precatalytic B-like complex state. This finding aligns with prior studies and paves the way for future cryo-EM structural analysis of the B-like spliceosome halted by CDK11 inactivation.
Next to splicing, microRNA biogenesis is another essential RNA processing event. While splicing is executed by spliceosomes that gradually form on the introns, microRNA biogenesis is initiated by the microprocessor complex (MPC), often on the same introns. Several lines of evidence indicate that splicing and microRNA biogenesis can influence each other, suggesting a cross-talk between spliceosomes and the MPC. To explore this interaction, we aimed to isolate the complexes containing both the microprocessor and spliceosomes on the same RNA substrate in preparative amounts. Utilizing human nuclear extracts and in vitro-transcribed RNA substrates, we isolated and characterized the proteome of spliceosomal A, pre-A, and B complexes associated with the MPC and MPC-associated proteins (MAP). This study provides a good foundation for future structural analysis of the interaction between spliceosomes and the MPC
Unveiling Hidden Patterns: Novel Computational Techniques for Protein Motif and Motif-Binding Pocket Identification
No full textProtein-protein interaction interfaces are crucial in cellular functions. Protein motif-mediated
interactions play key regulatory roles in the cell and are under-explored yet high-potential therapeutic
targets. Therefore, this thesis addresses the two components of protein motif-mediated interfaces: the
motif and the binding pocket. The aim of this study is to discover both sides of the interface
computationally by introducing three major contributions.
In the first contribution, I focus on the motif-binding pocket side of the interaction by
introducing xProtCAS, a tool to pinpoint functional regions on protein surfaces. xProtCAS takes
advantage of the structure models derived from the recent revolution in deep-learning protein
structure prediction. The tool identifies solvent-accessible surface areas using a geometric algorithm
and builds a graph from surface-accessible residues where those residues are graph nodes. The graph
connects proximal residues in the 3D space with edges weighted with conservation scores. Then,
xProtCAS uses graph algorithms to score the influence of each residue in the graph, which reflects
their functionality and high-scoring residue clusters are extracted as putative functional regions.
Finally, I apply xProtCAS to the human proteome, discovering thousands of uncharacterised
highly-conserved protein surfaces. These regions are ranked based on a statistical model quantifying
their conservation compared to the surrounding protein surface. The dataset and the tool (an
open-source standalone software and a web server) are made available for public use.
In the second contribution, I shift my focus to the motif side of the interaction by presenting
FaSTPACE, a fast and scalable algorithm for aligning peptides and extracting motif consensuses from
large datasets of peptides. Large peptide datasets have become available due to the recent advances in
high-throughput techniques for discovering motif-mediated interactions that produce functionally
related peptides, necessitating novel tools, such as FaSTPACE, to process these data. I extensively
validated the performance of FaSTPACE on artificially generated data and experimental data from
ProP-PD, a high-throughput experiment for motif discovery that produces datasets of hundreds of
putative motif-containing peptides. The tool shows accuracy and speed comparable to existing tools.
Moreover, it is publicly available as an open-source programming library and as a web server.
In the third contribution, I develop methodologies to integrate distinct peptide attributes into
single peptide confidence scores for binder/non-binder classification and peptide prioritisation for
further validation. I provide a machine learning-based scoring scheme to combine experimental
attributes, motif-matching scores, and biological peptide features. The algorithm employs a targeted
dimensionality reduction algorithm to produce a single score from the distinct and discriminatory
input peptide features. These scores can determine confidence levels indicating peptide functionality
and supporting peptide ranking. This scoring scheme is applied to a large ProP-PD dataset to define a
set of peptides with high confidence of containing a functional motif.
These contributions facilitate the study of existing motif-mediated interactions and the
discovery of novel instances. In particular, xProtCAS and FaSTPACE provide speed and scalability
that did not exist in previous tools. The improved level of computational performance has permitted
large datasets that have become available in recent years to be processed rapidly and accurately to
derive novel biological insights. Additionally, the proposed machine learning scoring scheme for
ProP-PD peptides enhances the processing of peptide screen data, improving the identification of
biologically relevant motifs.
Finally, I discuss putative future directions, particularly the potential of integrating the
introduced tools with recent advances in deep learning and protein language models