Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response

Hooiveld-Noeken, Jahlisa S.; Fehrmann, Rudolf S.N.; Vries, Elisabeth G.E. de; Jalving, Mathilde

doi:10.1016/j.iotech.2019.11.002

Cited by 3 publications

(1 citation statement)

References 85 publications

(101 reference statements)

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“…Recently, several potential strategies to involve and study larger numbers of patients with aMCC have been described. 36 Future studies should aim to elucidate which patients would benefit from ICI by investigating which possible biomarkers or genomic characteristics of the tumor are associated with response to PD-(L)1 blockade.…”

Section: Discussionmentioning

confidence: 99%

Avelumab for advanced Merkel cell carcinoma in the Netherlands: a real-world cohort

Levy

Aarts

Eskens

et al. 2020

J Immunother Cancer

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BackgroundMerkel cell carcinoma (MCC) is associated with high recurrence rates and poor survival when metastatic disease is present. The immune checkpoint inhibitor avelumab has shown high response rates (RRs) and durable responses in patients with advanced MCC (aMCC) in clinical trials. To date, only results from clinical trials, patients treated in an expanded access program and very small numbers of patients have been reported. In this study, detailed real-world efficacy and toxicity data of avelumab in patients with aMCC are reported.MethodsPatients with aMCC treated in four dedicated referral centers in the Netherlands were analyzed from February 2017 until December 2019. Patients were included if they had received at least one administration of avelumab, regardless of previous lines of therapy. Patient data were collected retrospectively from patient records. Primary endpoints were response rate (RR) and duration of response (DOR). Secondary endpoints were progression-free survival (PFS), overall survival (OS), and toxicity.ResultsFifty-four patients received avelumab. Eight (15%) patients had locally advanced disease (laMCC). In 40 (74%) patients, avelumab was first-line treatment, these included all patients with laMCC. The median follow-up was 8.9 (range 0.5–35.9) months. RR was 57% (n=31) with 24% (n=13) of patients achieving a complete response. The median DOR was 8.4 (range 1.3–22.1) months and 23 (43%) patients had an ongoing response at the end of the study. The median PFS was 8.6 (95% CI 1.6–15.5) months, and the median OS was 25.8 (95% CI 9.1–42.4) months. Six (11%) patients experienced grade 3 toxicity. No grade 4–5 toxicity was seen.ConclusionsIn this real-world cohort, clinical efficacy and toxicity outcomes in clinical practice were in line with results from clinical trials and showed relatively high RRs and durable responses in patients with aMCC.

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Section: Discussionmentioning

confidence: 99%

Avelumab for advanced Merkel cell carcinoma in the Netherlands: a real-world cohort

Levy

Aarts

Eskens

et al. 2020

J Immunother Cancer

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Artificial intelligence for nonmelanoma skin cancer

Trager,

Gordon,

Breneman

et al. 2024

Clinics in Dermatology

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A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

Pandya

Thakur

Saxena

et al. 2021

Sensors

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The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

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Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response

Cited by 3 publications

References 85 publications

Avelumab for advanced Merkel cell carcinoma in the Netherlands: a real-world cohort

Avelumab for advanced Merkel cell carcinoma in the Netherlands: a real-world cohort

Artificial intelligence for nonmelanoma skin cancer

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions

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