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ALK in Cancer: Lessons from the Future (2nd Edition)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Pathophysiology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1018

Special Issue Editors


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Guest Editor
1. Department of Oncology, Thoraxklinik at Heidelberg University Hospital, 69126 Heidelberg, Germany
2. National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
3. Translational Lung Research Center, German Center for Lung Research (DZL) at Heidelberg University Hospital, 69120 Heidelberg, Germany
Interests: targeted therapies; molecular stratification of NSCLC; lung cancer immunology; liquid biopsies; experimental immunotherapies
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Guest Editor
Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK
Interests: protein kinases; cell cycle; oncogenic gene fusions; structural biology; Myc
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Within the exciting and rapidly evolving field of thoracic oncology, ALK+ disease is playing a model role for several important and inter-related reasons: it has the most favourable biology, with a tumour mutational burden uniquely below 3 mut/MB on average; the largest number and highest efficacy of targeted drugs; the most complex management; and the longest survival, currently exceeding a median of 5 years. Consequently, ALK+ NSCLC has also become a premium testing ground for novel concepts and cutting-edge tools destined to shape the future of cancer medicine; it is involved in baseline molecular risk stratification, disease monitoring using serial liquid biopsies, the availability of targeted next-line therapies for all patients, genetic profiling, and tailoring the treatment of acquired resistance in the majority of cases. The purpose of this Special Issue is to recapitulate recent achievements, outline current challenges, and highlight prospects for the future. Its scope includes all aspects of instrumental importance for a better understanding and management of ALK+ malignancies. While its focus is on ALK+ lung cancer, preclinical studies on ALK+ biology and insights from other ALK+ diseases are very welcome to broaden our horizons and carry the field further. The ongoing advancement of third-generation ALK inhibitors in the first line, the commencing of clinical trials for fourth-generation inhibitors, accumulating evidence about the clinical importance of fusion variants and co-mutations, and the ongoing development of next-generation immunotherapeutics to overcome the inherently immunosuppressive ALK+ tumour microenvironment are only a few examples of topics where contributions to this Special Issue could enrich the current literature on this less common but most impressive lung cancer subtype.

Dr. Petros Christopoulos
Prof. Dr. Richard Bayliss
Guest Editors

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Keywords

  • ALK inhibitors
  • ALK fusion variants
  • TP53 mutations
  • immunotherapy
  • liquid biopsies
  • disease monitoring
  • therapeutic sequencing

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Published Papers (1 paper)

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16 pages, 3201 KB  
Systematic Review
Artificial Intelligence in ALK-Rearranged NSCLC: Forecasting Response and Resistance
by Andreas Koulouris, Christos Tsagkaris, Konstantinos Kalaitzidis, Georgios Tsakonas and Giannis Mountzios
Cancers 2026, 18(6), 973; https://doi.org/10.3390/cancers18060973 - 18 Mar 2026
Viewed by 738
Abstract
Background/Objectives: The management and prognosis of ALK-rearranged non-small-cell lung cancer have substantially improved over the past decade. However, challenges remain in timely molecular identification, prediction of treatment response, and understanding resistance mechanisms. This systematic review evaluates and synthesizes the evidence on artificial [...] Read more.
Background/Objectives: The management and prognosis of ALK-rearranged non-small-cell lung cancer have substantially improved over the past decade. However, challenges remain in timely molecular identification, prediction of treatment response, and understanding resistance mechanisms. This systematic review evaluates and synthesizes the evidence on artificial intelligence (AI) approaches leveraging imaging, pathology, molecular, and clinical data in this setting. Methods: A systematic search was conducted for peer-reviewed studies published between 2020 and 2025. Eligible studies involved human subjects and applied AI, machine learning, or deep learning methods to predict ALK status or treatment-related outcomes using imaging, pathology, molecular, or multimodal data. Study selection followed the PRISMA 2020 guidelines. Data were extracted on study design, data modality, AI methodology, clinical objectives, and performance metrics. Bibliometric co-occurrence analysis was performed to characterize thematic patterns and temporal trends. Results: Thirteen studies met the inclusion criteria, most of which were retrospective and single-center. AI approaches were applied to radiologic, pathologic, molecular, or multimodal data. Models predicting ALK status reported area under the curve values ranging from 0.73 to 0.99, while prognostic and treatment-response models reported moderate to high discriminative performance. Bibliometric analysis identified two dominant research themes focused on molecular characterization and computational methodology, with a recent shift toward treatment-specific and integrative analyses. External validation and clinical implementation remained limited across studies. Conclusions: AI shows promising potential to support diagnosis, prognostication, and treatment assessment in ALK-rearranged lung cancer. However, methodological heterogeneity, limited external validation, and a lack of prospective studies currently constrain clinical translation. Full article
(This article belongs to the Special Issue ALK in Cancer: Lessons from the Future (2nd Edition))
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