Dataset Information

Identification of Clonality through Genomic Profile Analysis in Multiple Lung Cancers.

ABSTRACT: In cases of multiple lung cancers, individual tumors may represent either a primary lung cancer or both primary and metastatic lung cancers. In this study, we investigated the differences between clinical/histopathological and genomic diagnoses to determine whether they are primary or metastatic. 37 patients with multiple lung cancers were enrolled in this study. Tumor cells were selected from tissue samples using laser capture microdissection. DNA was extracted from those cells and subjected to targeted deep sequencing. In multicentric primary lung cancers, the driver mutation profile was mutually exclusive among the individual tumors, while it was consistent between metastasized tumors and the primary lesion. In 11 patients (29.7%), discrepancies were observed between genomic and clinical/histopathological diagnoses. For the lymph node metastatic lesions, the mutation profile was consistent with only one of the two primary lesions. In three of five cases with lymph node metastases, the lymph node metastatic route detected by genomic diagnosis differed from the clinical and/or pathological diagnoses. In conclusion, in patients with multiple primary lung cancers, cancer-specific mutations can serve as clonal markers, affording a more accurate understanding of the pathology of multiple lung cancers and their lymphatic metastases and thus improving both the treatment selection and outcome.

SUBMITTER: Higuchi R

PROVIDER: S-EPMC7074554 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Identification of Clonality through Genomic Profile Analysis in Multiple Lung Cancers.

Higuchi Rumi R Nakagomi Takahiro T Goto Taichiro T Hirotsu Yosuke Y Shikata Daichi D Yokoyama Yujiro Y Otake Sotaro S Amemiya Kenji K Oyama Toshio T Mochizuki Hitoshi H Omata Masao M

Journal of clinical medicine 20200220 2

In cases of multiple lung cancers, individual tumors may represent either a primary lung cancer or both primary and metastatic lung cancers. In this study, we investigated the differences between clinical/histopathological and genomic diagnoses to determine whether they are primary or metastatic. 37 patients with multiple lung cancers were enrolled in this study. Tumor cells were selected from tissue samples using laser capture microdissection. DNA was extracted from those cells and subjected to ...[more]

PMID: 32093372

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Project description:BackgroundAdvancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers may share common susceptibility genes. A comprehensive description of the associations between genes and cancers requires identification of not only multiple genes associated with a specific type of cancer but also genes associated with multiple cancers.ResultsIn this article, we propose the Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers. The Mc.TGD is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development. Simulation studies show that the Mc.TGD can more accurately identify genes associated with multiple cancers than meta analysis based on "one-dimensional" methods. As a byproduct, identification accuracy of genes associated with only one type of cancer may also be improved. We use the Mc.TGD to analyze seven microarray studies investigating development of seven different types of cancers. We identify one gene associated with six types of cancers and four genes associated with five types of cancers. In addition, we also identify 11, 9, 18, and 17 genes associated with 4 to 1 types of cancers, respectively. We evaluate prediction performance using a Leave-One-Out cross validation approach and find that only 4 (out of 570) subjects cannot be properly predicted.ConclusionThe Mc.TGD can identify a short list of genes associated with one or multiple types of cancers. The identified genes are considerably different from those identified using meta analysis or analysis of marginal effects.

Project description:BackgroundThe breast cancer genome dynamically evolves during malignant progression and recurrence. We investigated the genomic profiles of primary early-stage breast cancers and matched relapses to elucidate the molecular underpinnings of the metastatic process, focusing on potentially actionable alterations in the recurrences.MethodsA mono-institutional cohort of 128 patients with breast cancers (n = 68 luminal B HER2, n = 6 luminal B HER2+, n = 1 HER2+ non-luminal, n = 56 triple negative) and at least one recurrence in a timeframe of 17 years was evaluated. Next-generation sequencing comprehensive genomic profiling was performed on 289 formalin-fixed paraffin-embedded (FFPE) samples, including primary tumors and matched relapses. Correlations of genomic aberrations with clinicopathologic factors and time to breast cancer relapse were analyzed.ResultsGenomic data were available for 188 of 289 FFPE samples that achieved the sequencing quality parameters (failure rate 34.9%), including 106 primary tumors and 82 relapses. All primary and relapse samples harbored at least one genomic alteration, with a median number of six alterations per sample (range 1-16). The most frequent somatic genomic alterations were mutations of TP53 (primary tumors = 49%, relapses = 49%) and PIK3CA (primary tumors = 33%, relapses = 30%). Distinctive genomic alterations of primary tumors were significantly associated with molecular subtypes. TP53, PIK3R1, and NF1 somatic alterations were more frequently detected in triple negative tumors (p value < 0.05); CCND1, FGF3, and FGFR1 copy number gains were recurrently identified in luminal cases (p value < 0.05). Moreover, TP53 mutations and MYC amplification were significantly and independently associated with a shorter time to relapse (p value < 0.05). Molecular subtype changes between primary tumors and relapses were seen in 10 of 128 (7.8%) cases. Most driver genomic alterations (55.8%) were shared between primary tumors and matched recurrences. However, in 39 of 61 cases (63.9%), additional private alterations were detected in the relapse samples only, including 12 patients with potentially actionable aberrations.ConclusionsSpecific genomic aberrations of primary breast cancers were associated with time to relapse. Primary tumors and matched recurrences showed a core of shared driver genomic aberrations but private actionable alterations have been identified in the relapses.

Dataset Information

Identification of Clonality through Genomic Profile Analysis in Multiple Lung Cancers.

Publications

Identification of Clonality through Genomic Profile Analysis in Multiple Lung Cancers.

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