Clinical and palliative care outcomes for patients of poor performance status treated with antiprogrammed death‐1 monoclonal antibodies for advanced melanoma

Wong, Annie; Williams, Molly; Milne, Donna; Morris, Kortnye; Lau, Peter K. H.; Fullerton, Sonia; McArthur, Grant A.

doi:10.1111/ajco.12702

Cited by 28 publications

(14 citation statements)

References 19 publications

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“…Using multiple logistic regression to merge our Response Classifier output with known clinical predictors was crucial to generate a model with enhanced accuracy. In prior studies, ECOG performance status predicted survival and response to immunotherapy in patients with melanoma (30,31). We too found that the incorporation of performance status augmented prediction accuracy, likely because it accounts for critical patient information not necessarily reflected in tissue histology.…”

Section: Discussionmentioning

confidence: 55%

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet

Coudray²,

Donnelly

et al. 2021

Clinical Cancer Research

117

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Purpose: Several biomarkers of response to immune checkpoint inhibitors (ICI) show potential but are not yet scalable to the clinic. We developed a pipeline that integrates deep learning on histology specimens with clinical data to predict ICI response in advanced melanoma. Experimental Design: We used a training cohort from New York University (New York, NY) and a validation cohort from Vanderbilt University (Nashville, TN). We built a multivariable classifier that integrates neural network predictions with clinical data. A ROC curve was generated and the optimal threshold was used to stratify patients as high versus low risk for progression. Kaplan–Meier curves compared progression-free survival (PFS) between the groups. The classifier was validated on two slide scanners (Aperio AT2 and Leica SCN400). Results: The multivariable classifier predicted response with AUC 0.800 on images from the Aperio AT2 and AUC 0.805 on images from the Leica SCN400. The classifier accurately stratified patients into high versus low risk for disease progression. Vanderbilt patients classified as high risk for progression had significantly worse PFS than those classified as low risk (P = 0.02 for the Aperio AT2; P = 0.03 for the Leica SCN400). Conclusions: Histology slides and patients' clinicodemographic characteristics are readily available through standard of care and have the potential to predict ICI treatment outcomes. With prospective validation, we believe our approach has potential for integration into clinical practice.

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Section: Discussionmentioning

confidence: 55%

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet

Coudray²,

Donnelly

et al. 2021

Clinical Cancer Research

117

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“…The ECOG 2–3 group were also more likely to die in an acute hospital setting (62% vs. 23%. respectively; RR 2.68, 95% CI 1.17–6.51; p = 0.016) [48]. All hospitalisation within the last month of life was for the management of disease progression.…”

Section: Immune-related Adverse Events In Older Patientsmentioning

confidence: 99%

Efficacy and Adverse Events of Immunotherapy with Checkpoint Inhibitors in Older Patients with Cancer

et al. 2019

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The number of older patients with cancer is increasing as a result of the ageing of Western societies. Immune checkpoint inhibitors have improved cancer treatment and are associated with lower rates of treatment-related toxicity compared with chemotherapy in the general population. Nonetheless, immune checkpoint inhibitors have potentially serious immune-related adverse events, which might have a greater impact on older and more vulnerable patients and potentially influence treatment efficacy and quality of life. Previous clinical trials have shown no major increase in immune-related adverse events; however, older patients are underrepresented and relatively healthy in these trials. Observational studies suggest that older and more vulnerable patients may be at a higher risk of immune-related adverse events and early treatment discontinuation. Geriatric assessment could help identify older patients who will benefit from immune checkpoint inhibitors.

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“…However, immunotherapies with their novel mechanisms of action can result in immune‐mediated side effects including rash, endocrinopathies, and, less frequently, severe toxicities involving pneumonitis, hepatitis, colitis, and myocarditis . Significant challenges have arisen in the clinic for health care providers to balance hope with realistic expectations as patients are counselled on these new treatments .…”

Section: Introductionmentioning

confidence: 99%

Balancing the Hype with Reality: What Do Patients with Advanced Melanoma Consider When Making the Decision to Have Immunotherapy?

2019

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Background Immunotherapy has resulted in unprecedented improvements in survival and maintained quality of life for many patients with advanced melanoma. However, durable responses are observed in only a minority of patients, and severe treatment side effects are experienced by 5%–30%. There are no reliable tests that can differentiate between patients who are likely to respond to immunotherapy and those who will not. Hence, new challenges have arisen as clinicians try to facilitate patients in their decision‐making regarding immunotherapy. Furthermore, little is known about the real‐world patients’ experience and understanding of immunotherapy outside the clinical trial setting. Here, we explore the perspectives of patients undergoing immunotherapy for melanoma and focus on factors that influenced their treatment decision‐making. Materials and Methods Twenty‐three in‐depth semistructured interviews were conducted with patients receiving pembrolizumab for stage IV melanoma at an Australian public cancer hospital. Patients were recruited at a range of time points after commencing therapy, and their experience of treatment was explored. Interviews were audio recorded, transcribed verbatim, coded, and analyzed thematically. Results Immunotherapy is viewed as a symbol of hope, with high‐profile anecdotes reinforcing this perception. Only a minority of patients expressed a good understanding of the likely efficacy and potential treatment side effects. Patients are reliant on their clinicians’ recommendation regarding immunotherapy treatment decisions. Conclusion Novel treatments such as immunotherapy provide significant hope for patients. This may influence their preference for immunotherapy over and above the usual considerations of the trade‐off between efficacy and toxicity. Careful counsel and individualized patient resources may further facilitate treatment decision‐making. Implications for Practice This study highlighted some of the misconceptions held by patients that need to be addressed when discussing the possibility of receiving treatment with immunotherapy for advanced melanoma. Patients placed a lot of importance on high‐profile anecdotes rather than truly understanding likely outcomes of treatment based on personal circumstances. The majority of patients had a poor understanding of the potential side effects and long‐term implications of treatment with immunotherapy. Careful counsel is required in order to facilitate informed decision‐making about treatment and to ensure possible side effects are known and appreciated. Further research is needed to develop tools to aid decision‐making in everyday clinical practice.

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Clinical and palliative care outcomes for patients of poor performance status treated with antiprogrammed death‐1 monoclonal antibodies for advanced melanoma

Abstract: Patients with poor baseline PS have a significantly lower OS and reduced response to anti-PD1. Further quality of life and palliative care research is needed.

Cited by 28 publications

References 19 publications

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Efficacy and Adverse Events of Immunotherapy with Checkpoint Inhibitors in Older Patients with Cancer

Balancing the Hype with Reality: What Do Patients with Advanced Melanoma Consider When Making the Decision to Have Immunotherapy?

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