癌症家族很有可能有攜帶遺傳性突變，尤其是那些年紀尚輕即罹患癌症的人。有一家族癌症症候群(familial cancer syndromes)，李-佛美尼症候群(Li-Fraumeni syndrome)，該症候群包含以下幾點主要特徵，即家族成員(family members)早年罹癌，以及罹患不同型態的腫瘤。雖然過去有許多研究分析李-佛美尼症候群的家族資料(family data)，然而很少研究考慮罹病、非罹病家族親屬(affected and non-affected family relatives)之間的相關性(relatedness)。因此，我們所分析的6個李-佛美尼症候群擴展家族，每個家族中包含多重生殖系p53突變帶原者(germ-line mutation carriers of p53)，而且我們也將家族結構(family structure)納入分析考慮。
我們的目標是在此6個擴展家族進行各種不同統計遺傳模型分析。本研究所分析的6個李-佛美尼症候群擴展家族在過去已被用不同方法進行分析。我們考量二元性狀(binary trait)和發病年齡(age-of-onset)。此外我們分別評估潛在癌症危險因子包含p53、性別及其交互作用的顯著性及估計其效應。本研究族群是6個李-佛美尼症候群擴展家族，在每個家族中有多種癌症病例。一共有6名指標個案和256個親屬，其中包含122名男性與140名女性。在6個指標個案中，4名是男性。關於親屬的年齡分布，女性的平均罹病年齡比男性早;而指標個案的平均罹病年齡約為3歲。有40名女性與34名男性罹病;罹病率約為29%。在56名攜帶p53突變者中，有45名罹病;而133名攜帶正常基因型者，有9名罹病。然而未知其基因型者67人，其中有20人罹病。在我們的研究中攜帶p53突變者的罹病率約為80%。有6名指標個案(probands)在16歲或更年輕時被診斷罹患孩童軟組織肉瘤(childhood soft-tissue sarcoma)。在這些家族中p53突變已被發現，這些獨特的世代(unique cohort)已經被追蹤超過20年，他們在美國安德森癌症研究中心於1944-1975年間接受治療。這些家族被追蹤到2001年12月31日。我們使用統計軟體S.A.G.E，其基於羅吉斯回歸模型來分析家族資料。而該軟體被設定用來分析家族資料。我們使用複合分離分析法(complex segregation analysis)對此6個擴展家族進行分析。S.A.G.E是被開發用來進行分析家族資料的軟體，而它遵守多元羅吉斯分配。藉由最大概似化計算每個遺傳模型之最大概似值。
我們分析6個家族，對沒有加入共變數(covariates)下的二元性狀(binary trait)和二元性狀結合罹病年齡(binary trait with variable age-of onset)的兩種模型，分別使用各種不同遺傳統計模式進行分析。兩種模型最有可能的遺傳模型均是顯性模型(Dominant model)。接著我們加入潛在的癌症因子進行最大概似複合分離分析，然後比較不同模型來檢定其顯著性。因此以二元性狀作為結果變量，其擬合(fitting)結果最好的模型是p53普通模型(general p53 model)，該模型不限制傳遞頻率(transmission probabilities)。因此，對於有攜帶p53突變相較於沒有者，其罹患癌症的勝算比(odds ratio)為1231（95%CI, 11.3-134011.4）。再結合罹病年齡進入模型後，p53普通模型也是最合理的模型。因此，對於有攜帶p53突變相較於沒有者，其罹患癌症的勝算比為673（95%CI, 57.6 -7871.6）。在這些潛在致癌風險因子中，我們發現p53在兩種模型中都是高度顯著(highly significant covariate)共變量。
家族史是個重要的致癌指標，本研究中我們結合p53作為共變量進行估計其效應。例如，對於任何有家族癌症史者，必須早期接受癌症篩檢或者早期及定期接受健康檢查。我們的研究在癌症預防（cancer prevention）領域及遺傳諮詢（genetic counseling）上提供有價值的建議。

Li-Fraumeni syndrome (LFS) is a familial cancer syndrome. The features of the syndrome include early-onset of cancer, diverse tumor types and multiple primary cancers. The statistical package S.A.G.E is based on logistic regression model and designed to analyze family data. We used S.A.G.E to perform complex segregation analysis to analyze 6 extended LFS families with multiple germ-line mutation carriers of p53 in each of them and accounted for family structure. The same data set has been analyzed using different models. We considered binary trait and age-of-onset trait in our study. We assessed the significance and estimated the effects of potential cancer risk factors including p53, sex and their interactions, respectively. Among 6 families, 6 probands were diagnosed with childhood soft-tissue sarcoma at age 16 or younger. They had been treated at M.D. Anderson Cancer Center (Houston, Texas, U.S.A.). Dominant model was the most likely underlying genetic model in binary trait model and binary trait with variable age-of onset model. We incorporated cancer risk factor p53 as a covariate into the two models and found that it was a highly significant covariate in both models. The result of segregation analysis showed that for p53 mutation carriers, the odd of developing cancer is 1231 [(95% CI), 11.3-134011.4] times larger than the odd for non-p53 mutation carriers in binary trait and, 673 [(95% CI), 57.6-7871.6] in the age of onset trait. Our results provided important suggestions in cancer prevention for people who had family history of cancer.

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