bims-tumhet Biomed News
on Tumor Heterogeneity
Issue of 2023‒12‒24
eleven papers selected by
Sergio Marchini, Humanitas Research
  1. GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.
  2. Updated progression-free survival and final overall survival with maintenance olaparib plus bevacizumab according to clinical risk in patients with newly diagnosed advanced ovarian cancer in the phase III PAOLA-1/ENGOT-ov25 trial.
  3. Pharmacological reactivation of p53 in the era of precision anticancer medicine.
  4. Moving into the modern era of molecular classification for endometrial cancer.
  5. TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data.
  6. Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer.
  7. The Current Treatment Landscape of Malignant Pleural Mesothelioma and Future Directions.
  8. Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.
  9. Niraparib plus Dostarlimab in Pleural Mesothelioma or Non-Small Cell Lung Cancer harboring HRR mutations: interim results of the UNITO-001 phase 2 prospective trial.
  10. Beyond Sterilization: A Comprehensive Review on the Safety and Efficacy of Opportunistic Salpingectomy as a Preventative Strategy for Ovarian Cancer.
  11. Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification.
  1. Cell Rep Med. 2023 Dec 19. pii: S2666-3791(23)00561-X. [Epub ahead of print]4(12): 101344
    GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.
    Christian Pozzorini, Gregoire Andre, Tommaso Coletta, Adrien Buisson, Jonathan Bieler, Loïc Ferrer, Rieke Kempfer, Pierre Saintigny, Alexandre Harlé, Davide Vacirca, Massimo Barberis, Pauline Gilson, Cristin Roma, Alexandra Saitta, Ewan Smith, Floriane Consales Barras, Lucia Ripol, Martin Fritzsche, Ana Claudia Marques, Amjad Alkodsi, Ray Marin, Nicola Normanno, Christoph Grimm, Leonhard Müllauer, Philipp Harter, Sandro Pignata, Antonio Gonzalez-Martin, Ursula Denison, Keiichi Fujiwara, Ignace Vergote, Nicoletta Colombo, Adrian Willig, Eric Pujade-Lauraine, Pierre-Alexandre Just, Isabelle Ray-Coquard, Zhenyu Xu.
      Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.
    Keywords:  HRD; PARPi; biomarker; breast cancer; cancer; convolutional neural network; homologous recombination deficiency; low-pass whole-genome sequencing; lpWGS; ovarian cancer
    DOI:  https://doi.org/10.1016/j.xcrm.2023.101344
  2. Int J Gynecol Cancer. 2023 Dec 21. pii: ijgc-2023-004995. [Epub ahead of print]
    Updated progression-free survival and final overall survival with maintenance olaparib plus bevacizumab according to clinical risk in patients with newly diagnosed advanced ovarian cancer in the phase III PAOLA-1/ENGOT-ov25 trial.
    Domenica Lorusso, Marie-Ange Mouret-Reynier, Philipp Harter, Claire Cropet, Cristina Caballero, Pia Wolfrum-Ristau, Toyomi Satoh, Ignace Vergote, Gabriella Parma, Trine J Nøttrup, Coriolan Lebreton, Peter A Fasching, Carmela Pisano, Luis Manso, Hugues Bourgeois, Ingo Runnebaum, Claudio Zamagni, Anne-Claire Hardy-Bessard, Andreas Schnelzer, Michel Fabbro, Barbara Schmalfeldt, Dominique Berton, Antje Belau, Jean-Pierre Lotz, Martina Gropp-Meier, Laurence Gladieff, Hans-Joachim Lück, Sophie Abadie-Lacourtoisie, Eric Pujade-Lauraine, Isabelle Ray-Coquard.
      OBJECTIVE: In the PAOLA-1/ENGOT-ov25 trial (NCT02477644), adding maintenance olaparib to bevacizumab provided a substantial progression-free survival benefit in patients with newly diagnosed advanced ovarian cancer and homologous recombination deficiency (HRD)-positive tumors, irrespective of clinical risk. Subsequently, a clinically meaningful improvement in overall survival was reported with olaparib plus bevacizumab in the HRD-positive subgroup. We report updated progression-free survival and overall survival by clinical risk and HRD status.METHODS: Patients in clinical response after first-line platinum-based chemotherapy plus bevacizumab received maintenance olaparib (up to 24 months) plus bevacizumab (up to 15 months in total) or placebo plus bevacizumab. This post hoc analysis evaluated 5-year progression-free survival and mature overall survival in patients classified by clinical risk and HRD status.
    RESULTS: Of 806 randomized patients, 74% were higher-risk and 26% were lower-risk. In higher-risk HRD-positive patients, the hazard ratio (HR) for progression-free survival was 0.46 (95% confidence interval (95% CI) 0.34 to 0.61), with 5-year progression-free survival of 35% with olaparib plus bevacizumab versus 15% with bevacizumab alone; and the HR for overall survival was 0.70 (95% CI 0.50 to 1.00), with 5-year overall survival of 55% versus 42%, respectively. In lower-risk HRD-positive patients, the HR for progression-free survival was 0.26 (95% CI 0.15 to 0.45), with 5-year progression-free survival of 72% with olaparib plus bevacizumab versus 28% with bevacizumab alone; and the HR for overall survival was 0.31 (95% CI 0.14 to 0.66), with 5-year overall survival of 88% versus 61%, respectively. No benefit was seen in HRD-negative patients regardless of clinical risk.
    CONCLUSION: This post hoc analysis indicates that in patients with newly diagnosed advanced HRD-positive ovarian cancer, maintenance olaparib plus bevacizumab should not be limited to those considered at higher risk of disease progression. Five-year progression-free survival rates support long-term remission and suggest an increased potential for cure with particular benefit suggested in lower-risk HRD-positive patients.
    Keywords:  Ovarian Cancer
    DOI:  https://doi.org/10.1136/ijgc-2023-004995
  3. Nat Rev Clin Oncol. 2023 Dec 15.
    Pharmacological reactivation of p53 in the era of precision anticancer medicine.
    Amos Tuval, Charlotte Strandgren, Angelos Heldin, Mireia Palomar-Siles, Klas G Wiman.
      p53, which is encoded by the most frequently mutated gene in cancer, TP53, is an attractive target for novel cancer therapies. Despite major challenges associated with this approach, several compounds that either augment the activity of wild-type p53 or restore all, or some, of the wild-type functions to p53 mutants are currently being explored. In wild-type TP53 cancer cells, p53 function is often abrogated by overexpression of the negative regulator MDM2, and agents that disrupt p53-MDM2 binding can trigger a robust p53 response, albeit potentially with induction of p53 activity in non-malignant cells. In TP53-mutant cancer cells, compounds that promote the refolding of missense mutant p53 or the translational readthrough of nonsense mutant TP53 might elicit potent cell death. Some of these compounds have been, or are being, tested in clinical trials involving patients with various types of cancer. Nonetheless, no p53-targeting drug has so far been approved for clinical use. Advances in our understanding of p53 biology provide some clues as to the underlying reasons for the variable clinical activity of p53-restoring therapies seen thus far. In this Review, we discuss the intricate interactions between p53 and its cellular and microenvironmental contexts and factors that can influence p53's activity. We also propose several strategies for improving the clinical efficacy of these agents through the complex perspective of p53 functionality.
    DOI:  https://doi.org/10.1038/s41571-023-00842-2
  4. J Surg Oncol. 2024 Jan;129(1): 120-125
    Moving into the modern era of molecular classification for endometrial cancer.
    Christian Dagher, Ying L Liu, Jennifer J Mueller, Britta Weigelt.
      The molecular subtypes of endometrial carcinoma (EC) were first described by The Cancer Genome Atlas (TCGA) a decade ago. Using surrogate approaches, the molecular classification has been demonstrated to be prognostic across EC patients and to have predictive implications. Starting in 2020, the molecular classification has been incorporated into multiple guidelines as part of the risk assessment and most recently into the International Federation of Gynecology and Obstetrics (FIGO) staging. This review article discusses the implementation of the EC molecular classification into clinical practice, the therapeutic implications, and the molecular and clinical heterogeneity of the EC molecular subtypes.
    Keywords:  endometrial cancer; heterogeneity; molecular classification; prediction; prognosis
    DOI:  https://doi.org/10.1002/jso.27552
  5. BMC Bioinformatics. 2023 Dec 17. 24(1): 483
    TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data.
    Chenqi Liao, Xiong Wang.
      BACKGROUND: Pan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data.RESULTS: TCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\ CONCLUSIONS: We developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub ( https://github.com/tjhwangxiong/TCGAplot ).
    Keywords:  Pan-cancer analysis; TCGA; TCGAplot; User-defined function; Visualization
    DOI:  https://doi.org/10.1186/s12859-023-05615-3
  6. Sci Rep. 2023 Dec 18. 13(1): 22482
    Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer.
    Kyeonghui Kim, Hyemin Kim, Inkyung Shin, Seung-Jae Noh, Jeong Yeon Kim, Koung Jin Suh, Yoo-Na Kim, Jung-Yun Lee, Dae-Yeon Cho, Se Hyun Kim, Jee Hyun Kim, Se-Hoon Lee, Jung Kyoon Choi.
      Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, it remains unclear whether this approach can be applied to cell-free DNA (cfDNA) and whether it can address the issue of low tumor purity encountered in tissue-based methylation profiling. In this study, we developed an assay named iMethyl, designed to estimate the genomic hypomethylation status from cfDNA. This was achieved through deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n = 167; tissue n = 137; cfDNA early during treatment n = 40), breast cancer (cfDNA n = 91; tissue n = 50; PBMC n = 50; cfDNA at progression n = 44), and ovarian cancer (tissue n = 74). iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. In conclusion, our iMethyl-liquid method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.
    DOI:  https://doi.org/10.1038/s41598-023-49639-4
  7. Cancers (Basel). 2023 Dec 12. pii: 5808. [Epub ahead of print]15(24):
    The Current Treatment Landscape of Malignant Pleural Mesothelioma and Future Directions.
    Beatriz Bertin, Miguel Zugman, Gustavo Schvartsman.
      The incidence of malignant pleural mesothelioma is expected to increase globally. New treatment options for this malignancy are eagerly awaited to improve the survival and quality of life of patients. The present article highlights the results of recent advances in this field, analyzing data from several relevant trials. The heterogeneous tumor microenvironment and biology, together with the low mutational burden, pose a challenge for treating such tumors. So far, no single biomarker has been soundly correlated with targeted therapy development; thus, combination strategies are often required to improve outcomes. Locally applied vaccines, the expansion of genetically engineered immune cell populations such as T cells, the blockage of immune checkpoints that inhibit anti-tumorigenic responses and chemoimmunotherapy are among the most promising options expected to change the mesothelioma treatment landscape.
    Keywords:  asbestos; cellular therapy; chemotherapy; immunotherapy; mesothelioma; pleura; targeted agents
    DOI:  https://doi.org/10.3390/cancers15245808
  8. Cancers (Basel). 2023 Dec 11. pii: 5793. [Epub ahead of print]15(24):
    Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.
    Nihal Abuzinadah, Sarath Kumar Posa, Aisha Ahmed Alarfaj, Ebtisam Abdullah Alabdulqader, Muhammad Umer, Tai-Hoon Kim, Shtwai Alsubai, Imran Ashraf.
      The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.
    Keywords:  bagging and boosting; ensemble learning; explainable AI; ovarian cancer detection
    DOI:  https://doi.org/10.3390/cancers15245793
  9. Clin Cancer Res. 2023 Dec 18.
    Niraparib plus Dostarlimab in Pleural Mesothelioma or Non-Small Cell Lung Cancer harboring HRR mutations: interim results of the UNITO-001 phase 2 prospective trial.
    Francesco Passiglia, Luisella Righi, Paolo Bironzo, Angela Listì, Giovanni Farinea, Enrica Capelletto, Silvia Novello, Alessandra Merlini, Giorgio V Scagliotti.
      INTRODUCTION: Treatment of homologous recombination repair deficient (HRD)-tumours with PARP inhibitors has the potential to further increase tumour immunogenicity, suggesting a synergistic effect with immunotherapy. Here we present the preliminary results of niraparib in combination with dostarlimab for pleural mesothelioma (PM) or non-small cell lung cancer (NSCLC) harboring HRR mutations.METHODS: UNITO-001 is a phase 2, prospective, study aiming to investigate the combination of niraparib plus dostarlimab in pre-treated patients with HRD and programmed death ligand-1 (PD-L1) ≥ 1% NSCLC and/or PM. The primary endpoint is progression free survival (PFS).
    RESULTS: A total of 17 out of 183 (10%) screened patients (12 MPM and 5 NSCLC) were included. The objective response rate (ORR) was 6% (95%CIs: 0.1-28.7) and the disease control rate (DCR) was 53% (95%CIs: 27.8-77). Median PFS was 3.1 (95%CIs: 2.7-N.A) and median overall survival (OS) was 4.2 (95%CIs 1.58-N.A) months. The PFS was 14.1 months in one PM patient harboring a germline BAP1 mutation. The treatment duration was 9.8 months in one PM patient harboring a somatic BRCA2 mutation. The most common adverse events (AEs) were grade 1-2 lymphopenia (59%), anemia (35%), hyponatremia (29%) and hypokalemia (29%). Grade ≥ 3 AEs were reported in 23% of the patients.
    CONCLUSION: This preliminary analysis highlighted the lack of antitumor activity for the combination of niraparib and dostarlimab in patients with PM and/or advanced NSCLC harboring BAP1 somatic mutations. A potential antitumor activity emerged for PM with germline BAP1 and/or BRCA2 somatic mutations along with a good tolerability profile.
    DOI:  https://doi.org/10.1158/1078-0432.CCR-23-2431
  10. Curr Oncol. 2023 Nov 28. 30(12): 10152-10165
    Beyond Sterilization: A Comprehensive Review on the Safety and Efficacy of Opportunistic Salpingectomy as a Preventative Strategy for Ovarian Cancer.
    Tahereh Zadabedini Masouleh, Holly Etchegary, Kathleen Hodgkinson, Brenda J Wilson, Lesa Dawson.
      Ovarian cancer (OC) is Canada's third most common gynecological cancer, with an estimated 3000 new cases and 1950 deaths projected in 2022. No effective screening has been found to identify OC, especially the most common subtype, high-grade serous carcinoma (HGSC), at an earlier, curable stage. In patients with hereditary predispositions such as BRCA mutations, the rates of HGSC are significantly elevated, leading to the use of risk-reducing salpingo-oophorectomy as the key preventative intervention. Although surgery has been shown to prevent HGSC in high-risk women, the associated premature menopause has adverse long-term sequelae and mortality due to non-cancer causes. The fact that 75% of HGSCs are sporadic means that most women diagnosed with HGSC will not have had the option to avail of either screening or prevention. Recent research suggests that the fimbrial distal fallopian tube is the most likely origin of HGSC. This has led to the development of a prevention plan for the general population: opportunistic salpingectomy, the removal of both fallopian tubes. This article aims to compile and review the studies evaluating the effect of opportunistic salpingectomy on surgical-related complications, ovarian reserve, cost, and OC incidence when performed along with hysterectomy or instead of tubal ligation in the general population.
    Keywords:  efficacy; opportunistic salpingectomy; ovarian cancer; ovarian reserve; prevention; prophylactic salpingectomy; risk; safety; surgical complication
    DOI:  https://doi.org/10.3390/curroncol30120739
  11. BMC Genomics. 2023 Dec 18. 24(1): 783
    Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification.
    Völundur Hafstað, Jari Häkkinen, Malin Larsson, Johan Staaf, Johan Vallon-Christersson, Helena Persson.
      BACKGROUND: Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.RESULTS: We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.
    CONCLUSIONS: A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.
    Keywords:  Cancer genomics; Fusion transcript; Gene fusion; Kinase; Machine learning; Microhomology; Precision medicine; Tumor biology
    DOI:  https://doi.org/10.1186/s12864-023-09889-y
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