Cytological markers for predicting <i>ALK</i>‐positive pulmonary adenocarcinoma

Miyata, Kanji; Morita, Shigeki; Dejima, Hitoshi; Seki, Nobuhiko; Matsutani, Noriyuki; Mieno, Makiko; Kondo, Fukuo; Soejima, Yurie; Tanaka, Fumihiko; Sawabe, Motoji

doi:10.1002/dc.23800

Cited by 11 publications

(14 citation statements)

References 35 publications

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“…Moreover, there are a handful of histo-and cyto-pathologic features associated with ALK rearrangement (acinar structure, higher histologic grade). [18][19][20] None of these features (alone or in combination) provide a strong enough association to diagnose the chromosomal rearrangement or select patients for testing, however, and ancillary diagnostic tests remain critical for identifying the therapeutic target.…”

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confidence: 99%

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer

et al. 2018

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In lung adenocarcinoma, canonical EML4-ALK inversion results in a fusion protein with a constitutively active ALK kinase domain. Evidence of ALK rearrangement occurs in a minority (2-7%) of lung adenocarcinoma, and only ~60% of these patients will respond to targeted ALK inhibition by drugs such as crizotinib and ceritinib. Clinically, targeted anti-ALK therapy is often initiated based on evidence of an ALK genomic rearrangement detected by fluorescence in situ hybridization (FISH) of interphase cells in formalin-fixed, paraffin-embedded tissue sections. At the genomic level, however, ALK rearrangements are heterogeneous, with multiple potential breakpoints in EML4, and alternate fusion partners. Using next-generation sequencing of DNA and RNA together with ALK immunohistochemistry, we comprehensively characterized genomic breakpoints in 33 FISH-positive lung adenocarcinomas. Of these 33 cases, 29 (88%) had detectable DNA level ALK rearrangements involving EML4, KIF5B, or non-canonical partners including ASXL2, ATP6V1B1, PRKAR1A, and SPDYA. A subset of 12 cases had material available for RNA-Seq. Of these, eight of eight (100%) cases with DNA rearrangements showed ALK fusion transcripts from RNA-Seq; three of four cases (75%) without detectable DNA rearrangements were similarly negative by RNA-Seq, and one case was positive by RNA-Seq but negative by DNA next-generation sequencing. By immunohistochemistry, 17 of 19 (89%) tested cases were clearly positive for ALK protein expression; the remaining cases had no detectable DNA level rearrangement or had a non-canonical rearrangement not predicted to form a fusion protein. Survival analysis of patients treated with targeted ALK inhibitors demonstrates a significant difference in mean survival between patients with next-generation sequencing confirmed EML4-ALK rearrangements, and those without (20.6 months vs 5.4 months, P<0.01). Together, these data demonstrate abundant genomic heterogeneity among ALK-rearranged lung adenocarcinoma, which may account for differences in treatment response with targeted ALK inhibitors.

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confidence: 99%

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer

et al. 2018

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“…These studies may not be relevant to ALK (+) initial diagnosis as cytomorphology and molecular signatures associated with lung adenocarcinoma may change as the tumour progresses or metastasises . To the best of our knowledge, there are only two studies that examined the cytomorphology of primary ALK (+) lung cancer using more than 15 cases . Therefore, to date, the cytomorphological features of ALK (+) lung cancer cells obtained from primary lesions have not been sufficiently analysed.…”

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confidence: 99%

“…Using surgical resection specimens, histological features of ALK (+) lung cancer have been reported previously . However, reports assessing the cytomorphological features of ALK (+) lung cancer using cytological specimens are limited . Furthermore, available cytomorphological data mainly relates to cancer cells that have metastasised to the lymph nodes and liver .…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11][12][13] However, reports assessing the cytomorphological features of ALK (+) lung cancer using cytological specimens are limited. 8,10,[14][15][16] Furthermore, available cytomorphological data mainly relates to cancer cells that have metastasised to the lymph nodes and liver. 8,10 These studies may not be relevant to ALK (+) initial diagnosis as cytomorphology and molecular signatures associated with lung adenocarcinoma may change as the tumour progresses or metastasises.…”

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confidence: 99%

“…11,17 To the best of our knowledge, there are only two studies that examined the cytomorphology of primary ALK (+) lung cancer using more than 15 cases. 14,16 Therefore, to date, the cytomorphological features of ALK (+) lung cancer cells obtained from primary lesions have not been sufficiently analysed. In addition, there are no reports comparing quantitative cytomorphological differences between ALK (+) and ALK (−) lung cancers.…”

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Qualitative and quantitative cytomorphological features of primary anaplastic lymphoma kinase‐positive lung cancer

et al. 2019

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Objective Anaplastic lymphoma kinase (ALK) positive (+) lung cancers are predictive for response to crizotinib and alectinib. There are many cases of lung cancer in which surgery cannot be performed, and such cases require diagnosis by cytological specimen or biopsy. Estimating ALK (+) lung cancer from cytomorphology would allow molecular testing to proceed without the waste of a small amount of specimen. The purpose of this study was to assess whether qualitative and quantitative cytomorphological features are sufficient for distinguishing primary ALK (+) from ALK (−) lung cancer. Methods We examined eight qualitative cytomorphological parameters and three quantitative nuclear morphometric parameters in 17 cases of primary ALK (+) lung cancer, diagnosed by fluorescence in situ hybridisation (FISH) using histological specimens, and in 41 cases of ALK (−) lung cancer. Quantitative nuclear morphometric parameters were analysed by a computer‐assisted image analysis system. Results In ALK (+) lung cancer, three qualitative parameters (signet ring cells, nuclear grooves and single type nucleoli) and two quantitative parameters (large nuclear area and irregular nuclear shape) were observed in significantly higher proportions. However, in ALK (−) lung cancer, one qualitative parameter (unclear and multiple type nucleoli) was seen significantly more often. Conclusions These results show that the cytomorphological features of signet ring cells, nuclear grooves and nucleoli shape can help to triage a small amount of cytological and biopsy specimens for appropriate molecular testing of primary ALK (+) lung cancer.

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Machine learning‐based gene alteration prediction model for primary lung cancer using cytologic images

Ishii

Takamatsu

Ninomiya

et al. 2022

Cancer Cytopathology

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BACKGROUND:Understanding the gene alteration status of primary lung cancers is important for determining treatment strategies, but gene testing is both time-consuming and costly, limiting its application in clinical practice. Here, potential therapeutic targets were selected by predicting gene alterations in cytologic specimens before conventional gene testing. METHODS:This was a retrospective study to develop a cytologic image-based gene alteration prediction model for primary lung cancer. Photomicroscopic images of cytology samples were collected and image patches were generated for analyses.Cancer-positive (n = 106) and cancer-negative (n = 32) samples were used to develop a neural network model for selecting cancer-positive images. Cancer-positive cases were randomly assigned to training (n = 77) and validation (n = 26) data sets.Another neural network model was developed to classify cancer images of the training data set into 4 groups: anaplastic lymphoma kinase (ALK)-fusion, epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homologue (KRAS) mutated groups, and other (None group), and images of the validation data set were classified. A decision algorithm to predict gene alteration for cases with 3 probability ranks was developed. RESULTS:The accuracy and precision for selecting cancer-positive patches were 0.945 and 0.991, respectively. Predictive accuracy for the EGFR and KRAS groups in the validation data set was ~0.95, whereas that for the ALK and None groups was ~0.75 and ~ 0.80, respectively. Gene status was correctly predicted in the probability rank A cases. The model extracted characteristic conventional cytologic findings in images and a novel specific feature was discovered for the EGFR group. CONCLUSIONS:A gene alteration prediction model for lung cancers by machine learning based on cytologic images was successfully developed.

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Cytological markers for predicting ALK‐positive pulmonary adenocarcinoma

Abstract: Three cytological features of SRCC (pink mucin, vesicular cytoplasm, and an individually scattered pattern) could be useful cytological markers for the prediction of ALK-positive pulmonary adenocarcinoma.

Cited by 11 publications

References 35 publications

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer

Qualitative and quantitative cytomorphological features of primary anaplastic lymphoma kinase‐positive lung cancer

Machine learning‐based gene alteration prediction model for primary lung cancer using cytologic images

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