Clinical outcome of biomarker-guided therapies in adult patients with tumors of the nervous system

Dengler

Neuro-Oncology Advances

et al. 2023

Background Targeted therapies have substantially improved survival in cancer patients with malignancies outside the brain. Whether in-depth analysis for molecular alterations may also offer therapeutic avenues in primary brain tumors remains unclear. We herein present our institutional experience for glioma patients discussed in our interdisciplinary Molecular Tumor Board (MTB) implemented at the Comprehensive Cancer Center Munich (LMU). Methods We retrospectively searched the database of the MTB for all recurrent glioma patients after previous therapy. Recommendations were based upon next-generation sequencing results of individual patient’s tumor tissue. Clinical and molecular information, previous therapy regimens and outcome parameters were collected. Results Overall, 73 consecutive recurrent glioma patients were identified. In the median, advanced molecular testing was initiated with the third tumor recurrence. The median turnaround time between initiation of molecular profiling and MTB case discussion was 48 ± 75 days (range: 32 - 536 days). Targetable mutations were found for 50 recurrent glioma patients (68.5%). IDH1 mutation (27/73; 37%), EGFR amplification (19/73; 26%) and NF1 mutation (8/73; 11%) were the most detected alterations and a molecular-based treatment recommendation could be made for all of them. Therapeutic recommendations were implemented in 12 cases (24%) and one third of these heavily pretreated patients experienced clinical benefit with at least disease stabilization. Conclusion In-depth molecular analysis of tumor tissue may guide targeted therapy also in brain tumor patients and considerable anti-tumor effects might be observed in selected cases. However, future studies to corroborate our results are needed.

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Significance of molecular diagnostics for therapeutic decision-making in recurrent glioma

Dengler

Neuro-Oncology Advances

et al. 2023

“…Beyond avoiding toxicities, molecular diagnostics may increase the precision of diagnosis and treatment choice. While molecular profiling and targeting are still relatively new in clinical practice for glioma, several recent studies have supported the notion that molecularly guided therapy is feasible and provides clinical benefit, including in glioblastoma and recurrent glioma ( 24 , 200 , 201 ). A guideline on rational molecular testing of gliomas, glioneural, and neuronal tumors also argued for molecular testing to target therapy but cautioned that only a handful of targets had sufficient evidence available to justify testing for them in standard care ( 202 ).…”

Section: Discussionmentioning

confidence: 99%

High costs, low quality of life, reduced survival, and room for improving treatment: an analysis of burden and unmet needs in glioma

Pöhlmann,

Weller,

Marcellusi

et al. 2024

Front. Oncol.

Gliomas are a group of heterogeneous tumors that account for substantial morbidity, mortality, and costs to patients and healthcare systems globally. Survival varies considerably by grade, histology, biomarkers, and genetic alterations such as IDH mutations and MGMT promoter methylation, and treatment, but is poor for some grades and histologies, with many patients with glioblastoma surviving less than a year from diagnosis. The present review provides an introduction to glioma, including its classification, epidemiology, economic and humanistic burden, as well as treatment options. Another focus is on treatment recommendations for IDH-mutant astrocytoma, IDH-mutant oligodendroglioma, and glioblastoma, which were synthesized from recent guidelines. While recommendations are nuanced and reflect the complexity of the disease, maximum safe resection is typically the first step in treatment, followed by radiotherapy and/or chemotherapy using temozolomide or procarbazine, lomustine, and vincristine. Immunotherapies and targeted therapies currently have only a limited role due to disappointing clinical trial results, including in recurrent glioblastoma, for which the nitrosourea lomustine remains the de facto standard of care. The lack of treatment options is compounded by frequently suboptimal clinical practice, in which patients do not receive adequate therapy after resection, including delayed, shortened, or discontinued radiotherapy and chemotherapy courses due to treatment side effects. These unmet needs will require significant efforts to address, including a continued search for novel treatment options, increased awareness of clinical guidelines, improved toxicity management for chemotherapy, and the generation of additional and more robust clinical and health economic evidence.

Data complexity in oncology

Metzger,

Gräßel,

Illert

et al. 2024

Onkologie

ZusammenfassungDie Nutzung künstlicher Intelligenz (KI) in der Onkologie verspricht eine kontinuierliche Verbesserung der Krebsbehandlung und der Krebsprävention. Neben der Verbesserung von Diagnostik und Therapieoptimierung trägt KI auch zur Effizienzsteigerung klinischer Prozesse bei. Die Automatisierung von Routineaufgaben und KI-gestützte Entscheidungsunterstützungssysteme erleichtern die Arbeit der Ärzt:innen, indem sie relevante Informationen in Echtzeit bereitstellen. Zudem zeigen KI-Algorithmen beeindruckende Fähigkeiten bei der präzisen Erkennung von Tumoren (z. B. durch die Analyse medizinischer Bilddaten sowie bei der Identifizierung genetischer Marker für personalisierte Therapieansätze). Voraussetzung dafür ist die Bereitstellung von standardisierten Datensätzen, was auch in der Onkologie eine Herausforderung darstellt. Die Komplexität der verschiedenen Datentypen, einschließlich medizinischer Bilder, genomischer Daten und klinischer Informationen, erfordert nicht nur fortgeschrittene Analysemethoden, sondern auch die Bereitstellung der Daten in einer standardisierten Form. Zudem müssen Datenschutz- und ethische Aspekte bei der Verwendung sensibler Patient:innendaten adressiert und berücksichtigt werden. Transparenz und Interpretierbarkeit von KI-Algorithmen sind hier entscheidend, um das Vertrauen in die Technologie zu stärken. Die Kombination von KI und Onkologie signalisiert einen Paradigmenwechsel hin zu einer präziseren, personalisierten und effizienteren Patient:innenversorgung bei gleichzeitiger Verbesserung der Lebensqualität. Während die positiven Auswirkungen auf die diagnostische Genauigkeit und die Therapieoptimierung vielversprechend sind, erfordert die Bewältigung der datenbezogenen Herausforderungen eine kontinuierliche Zusammenarbeit zwischen Wissenschaftler:innen, Informatiker:innen, Ärzt:innen und Patient:innen.