Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

Morrison, S.; Han, Gang; Elenwa, Faith; Vetto, John T.; Fowler, Graham; Leong, Stanley P. L.; Kashani‐Sabet, Mohammed; Pockaj, Barbara A.; Kosiorek, Heidi E.; Zager, Jonathan S.; Sondak, Vernon K.; Messina, Jane L.; Mozzillo, Nicola; Schneebaum, Schlomo; Han, Dale

doi:10.1002/cncr.34013

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…Attributes of the melanoma sample could enhance the prediction of the immunotherapy response, such as; proteins related to antigen presentation [4][5][6] , tumor mutation burden 7 , CD8 protein in T-cells 8 , the presence of tumor-infiltrating lymph cells 9,10 , the composition of the tumor microenvironment (TME) 11,12 , tumor burden 13,14 , expression of self-antigens 15 .Therefore, a critical unmet need in melanoma management remains: the identification of robust biomarkers capable of distinguishing responders from non-responders to ICI therapy in the early stage of melanoma progression.…”

Section: Introductionmentioning

confidence: 99%

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Szadai,

Bartha,

Parada

et al. 2024

Preprint

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While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

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Section: Introductionmentioning

confidence: 99%

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Szadai,

Bartha,

Parada

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Attributes of the melanoma sample could enhance the prediction of the immunotherapy response, such as; proteins related to antigen presentation ( 4 – 6 ), tumor mutation burden ( 7 – 9 ), CD8 protein in T-cells ( 10 ), the presence of tumor-infiltrating lymph cells ( 11 , 12 ), the composition of the tumor microenvironment (TME) ( 13 , 14 ), expression of self-antigens ( 15 ). Furthermore, Garutti et al.…”

Section: Introductionmentioning

confidence: 99%

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Szadai,

Bartha,

Parada

et al. 2024

Front. Oncol.

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IntroductionWhile Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.MethodsEvaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.ResultsSix proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.DiscussionOur study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

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“…Nevertheless, in clinical practice, the TIL amount is used for the prognosis of the disease course in patients with CRC, BC, and melanoma. 43 – 45 The prognostic landscape of TIL-T and TIL-B cells has been most comprehensively demonstrated across 30 cancer types. 46 Distinguishing between B and T cells is restricted to the analysis of CD20 and LCK expression.…”

Section: Introductionmentioning

confidence: 99%

Immune gene signatures as prognostic criteria for cancer patients

Larionova

Таширева

2023

Ther Adv Med Oncol

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Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

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Is the presence of tumor‐infiltrating lymphocytes predictive of outcomes in patients with melanoma?

Cited by 6 publications

References 42 publications

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Immune gene signatures as prognostic criteria for cancer patients

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