PDF(Pubmed)
Abstract：
BACKGROUND: The significant sex and gender differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. One barrier to translation is that cancer phenotypes cannot be segregated into distinct male versus female categories. Instead, within this convenient but contrived dichotomy, male and female cancer phenotypes are highly overlapping and vary between female- and male- skewed extremes. Thus, sex and gender-specific treatments are unrealistic, and our translational goal should be adaptation of treatment to the variable effects of sex and gender on targetable pathways.
METHODS: To overcome this obstacle, we profiled the similarities in 8370 transcriptomes of 26 different adult and 4 different pediatric cancer types. We calculated the posterior probabilities of predicting patient sex and gender based on the observed sexes of similar samples in this map of transcriptome similarity.
RESULTS: Transcriptomic index (TI) values were derived from posterior probabilities and allowed us to identify poles with local enrichments for male or female transcriptomes. TI supported deconvolution of transcriptomes into measures of patient-specific activity in sex and gender-biased, targetable pathways. It identified sex and gender-skewed extremes in mechanistic phenotypes like cell cycle signaling and immunity, and precisely positioned each patient\'s whole transcriptome on an axis of continuously varying sex and gender phenotypes.
CONCLUSIONS: Cancer type, patient sex and gender, and TI value provides a novel and patient- specific mechanistic identifier that can be used for realistic sex and gender-adaptations of precision cancer treatment planning.
Some efforts to improve cancer therapy involve the idea of personalizing treatments to who a patient is and how their cancer operates. Personalizing treatment can involve straighforward features like a patient’s age, family cancer history, personal disease and surgical histories, as well as more complex features like analysis of their specific cancer’s mechanisms of growth and spread throughout the body. One glaring omission in common personalization schemes is the sex and gender of the patient. While patient sex and gender is known to substantially affect cancer rates and response to treatment, we do not yet use this information in treatment planning. There are multiple reasons for this but among them is that we tend to think about sex and gender as an either/or categorization. You are either a male/man or a female/woman. This is not accurate as there are many variables that contribute to who an individual is as a male/man or female/woman. This variability is a challenge to incorporating these features into personalized treatment planning. Here, we have developed a method to address this challenge. It is our great hope that this will enable the use of this critically important element of personalization in cancer treatment planning and improve survival rates for all patients.
METHODS: To overcome this obstacle, we profiled the similarities in 8370 transcriptomes of 26 different adult and 4 different pediatric cancer types. We calculated the posterior probabilities of predicting patient sex and gender based on the observed sexes of similar samples in this map of transcriptome similarity.
RESULTS: Transcriptomic index (TI) values were derived from posterior probabilities and allowed us to identify poles with local enrichments for male or female transcriptomes. TI supported deconvolution of transcriptomes into measures of patient-specific activity in sex and gender-biased, targetable pathways. It identified sex and gender-skewed extremes in mechanistic phenotypes like cell cycle signaling and immunity, and precisely positioned each patient\'s whole transcriptome on an axis of continuously varying sex and gender phenotypes.
CONCLUSIONS: Cancer type, patient sex and gender, and TI value provides a novel and patient- specific mechanistic identifier that can be used for realistic sex and gender-adaptations of precision cancer treatment planning.
Some efforts to improve cancer therapy involve the idea of personalizing treatments to who a patient is and how their cancer operates. Personalizing treatment can involve straighforward features like a patient’s age, family cancer history, personal disease and surgical histories, as well as more complex features like analysis of their specific cancer’s mechanisms of growth and spread throughout the body. One glaring omission in common personalization schemes is the sex and gender of the patient. While patient sex and gender is known to substantially affect cancer rates and response to treatment, we do not yet use this information in treatment planning. There are multiple reasons for this but among them is that we tend to think about sex and gender as an either/or categorization. You are either a male/man or a female/woman. This is not accurate as there are many variables that contribute to who an individual is as a male/man or female/woman. This variability is a challenge to incorporating these features into personalized treatment planning. Here, we have developed a method to address this challenge. It is our great hope that this will enable the use of this critically important element of personalization in cancer treatment planning and improve survival rates for all patients.
摘要：
背景：癌症机制中存在的显着性别和性别差异，发病率,和生存，尚未影响临床实践。翻译的一个障碍是癌症表型不能分为不同的男性和女性类别。相反，在这个方便但人为的二分法中，男性和女性癌症表型高度重叠，并且在女性和男性偏斜的极端之间存在差异。因此，性别和性别特异性治疗是不现实的，我们的转化目标应该是使治疗适应性别和性别对目标途径的可变影响。
方法：为了克服这个障碍，我们分析了26种不同成人和4种不同儿科癌症类型的8370个转录组的相似性。我们根据转录组相似性图中观察到的相似样本的性别计算了预测患者性别和性别的后验概率。
结果：转录组指数（TI）值来自后验概率，使我们能够确定男性或女性转录组的局部富集极点。TI支持转录组去卷积成性别和性别偏见患者特异性活动的测量，有针对性的途径。它确定了机械性表型中的性别和性别扭曲的极端，如细胞周期信号传导和免疫，并将每个患者的整个转录组精确定位在连续变化的性别和性别表型的轴上。
结论：癌症类型，患者性别和性别，和TI值提供了一种新颖的和患者特定的机械标识符，可用于精确癌症治疗计划的现实性别和性别适应。
改善癌症治疗的一些努力涉及个性化治疗的想法，以了解患者是谁以及他们的癌症如何运作。个性化治疗可能涉及患者年龄等直截了当的特征，家族癌症史,个人疾病和手术史，以及更复杂的特征，如分析其特定癌症的生长和扩散机制。常见个性化方案中的一个明显遗漏是患者的性别和性别。虽然已知患者的性别和性别会严重影响癌症发病率和对治疗的反应，我们尚未在治疗计划中使用此信息。这有多种原因，但其中包括我们倾向于将性别和性别视为非此即彼的类别。你要么是男的，要么是女的。这是不准确的，因为有许多变量决定了一个人是男性/男性还是女性/女性。这种可变性是将这些特征结合到个性化治疗计划中的挑战。这里,我们已经开发了一种方法来应对这一挑战。我们非常希望这将能够在癌症治疗计划中使用这一至关重要的个性化元素，并提高所有患者的生存率。
方法：为了克服这个障碍，我们分析了26种不同成人和4种不同儿科癌症类型的8370个转录组的相似性。我们根据转录组相似性图中观察到的相似样本的性别计算了预测患者性别和性别的后验概率。
结果：转录组指数（TI）值来自后验概率，使我们能够确定男性或女性转录组的局部富集极点。TI支持转录组去卷积成性别和性别偏见患者特异性活动的测量，有针对性的途径。它确定了机械性表型中的性别和性别扭曲的极端，如细胞周期信号传导和免疫，并将每个患者的整个转录组精确定位在连续变化的性别和性别表型的轴上。
结论：癌症类型，患者性别和性别，和TI值提供了一种新颖的和患者特定的机械标识符，可用于精确癌症治疗计划的现实性别和性别适应。
改善癌症治疗的一些努力涉及个性化治疗的想法，以了解患者是谁以及他们的癌症如何运作。个性化治疗可能涉及患者年龄等直截了当的特征，家族癌症史,个人疾病和手术史，以及更复杂的特征，如分析其特定癌症的生长和扩散机制。常见个性化方案中的一个明显遗漏是患者的性别和性别。虽然已知患者的性别和性别会严重影响癌症发病率和对治疗的反应，我们尚未在治疗计划中使用此信息。这有多种原因，但其中包括我们倾向于将性别和性别视为非此即彼的类别。你要么是男的，要么是女的。这是不准确的，因为有许多变量决定了一个人是男性/男性还是女性/女性。这种可变性是将这些特征结合到个性化治疗计划中的挑战。这里,我们已经开发了一种方法来应对这一挑战。我们非常希望这将能够在癌症治疗计划中使用这一至关重要的个性化元素，并提高所有患者的生存率。
