Supplementary Figure 28 from Clinicopathologic, genomic, and immunophenotypic landscape of ATM mutations in non-small cell lung cancer

Biagio Ricciuti; Arielle Elkrief; Joao V. Alessi; Xinan Wang; Yvonne Li; Hersh Gupta; Daniel M. Muldoon; Arrien A. Bertram; Federica Pecci; Giuseppe Lamberti; Alessandro Di Federico; Adriana Barrichello; Victor R. Vaz; Malini Gandhi; Elinton Lee; Geoffrey I. Shapiro; Hyesun Park; Mizuki Nishino; James Lindsay; Kristen D. Felt; Bijaya Sharma; Andrew D. Cherniack; Scott J. Rodig; Daniel R. Gomez; Narek Shaverdian; Mehrdad Rakaee; Chaitanya Bandlamudi; Marc Ladanyi; Pasi A. Jänne; Adam J. Schoenfeld; Lynette M. Sholl; Mark M. Awad; Michael L. Cheng

doi:10.1158/1078-0432.27031174.v1

Abstract

Supplementary Figure 28. (A) Box-plot showing the distribution of tumor-infiltrating lymphocytes assessed using computational pathology algorithm in ATMMUT and ATMWT NSCLCs. (B) Box-plot showing the distribution of tumor-infiltrating lymphocytes assessed using computational pathology algorithm in according to ATM/TP53 co-mutation status in NSCLC.

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Ricciuti et al. (2024) studied this question.

synapsesocial.com/papers/68e586b5b6db643587522f08 https://doi.org/10.1158/1078-0432.27031174.v1

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Supplementary Figure 28 from Clinicopathologic, genomic, and immunophenotypic landscape of ATM mutations in non-small cell lung cancer

Abstract

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Cite This Study

Supplementary Figure 28 from Clinicopathologic, genomic, and immunophenotypic landscape of ATM mutations in non-small cell lung cancer

Abstract

Discussion

Cite This Study

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