精準醫療已經改變了癌癥治療，它將如何應用在其它領域？

21世紀是生物學的世紀，科學界和醫學界都取得了巨大進步，改善了人們的生活質量。我們已經在腫瘤學看到了這種改善：得益于新療法的發現，某些癌癥已不再像以前一樣相當于一紙死亡判決書了，它們在很大程度上已經相當于可以通過藥物控制的慢性疾病。

精準醫療就是其中一種新療法。這種新興的疾病預防方法會將個體的遺傳特性、所處環境和生活方式考慮在內，醫生和研究人員因而可以更準確地預判哪種治療方法最適用于這個人的這種疾病。

雖然抗癌斗爭遠未結束，但我們相信，精準醫療模式已經開始為其它疾病領域進行更有針對性的研究指明了方向。憑借我們在癌癥精準醫療研究中獲取的知識、管理經驗和有效工具，其它疾病領域進行研究時可以避免重復癌癥界曾經走過的彎路。

作為哈佛商學院卡夫精準醫療加速器項目（Harvard Business School Kraft Precision Medicine Accelerator）的聯合主席，我們的使命是共同克服精準醫療面臨的商業挑戰。我們最近舉辦了一個高管培訓課程，召集了整個醫療保健界的高級領導討論精準醫療采用什么樣的商業模式更有利于推動相關科研。我們原以為很大一部分參與者將來自癌癥治療領域，卻驚訝地發現其他疾病領域的參與人員占了一半以上。

絕大多數的參與者都講道，如何利用數據是各自所在的疾病領域應用精準醫療時面臨的最大挑戰。癌癥界在這個問題上已經努力多年，才剛剛摸到了門道。如今，我們擁有海量數據，但大多數情況下這些數據是彼此孤立的、破碎的，它們很少以標準化的格式出現。只有干凈、統一的數據才有意義。也就是說，我們要用容易理解的數據構建大型數據庫，并和同樣規模的大型分子數據庫相連接，再對數據進行跟蹤。與此同時，還需要利用機器學習，幫助醫生在診斷和治療中制定最佳方案，同時讓醫生了解每種治療方式的可能結果。

至于這些數據蘊含著什么樣的力量，多發性骨髓瘤研究基金會（MMRF）的CoMMpass研究就是一個很好的例證。該研究使用了各類癌癥的最大基因組數據集。MMRF對1100名患有多發性骨髓瘤患者的基因組進行了測序，收集了每位患者的骨髓瘤臨床數據，在這些數據的幫助下，發現了12種骨髓瘤癌癥的亞型。

另一個例子是胰腺癌行動網絡（Pancreatic Cancer Action Network）提供的名為“了解你的腫瘤”（Know Your Tumor）的精準醫療服務。通過這項服務，患者可以提供其腫瘤的分子信息，獲得對腫瘤的生物學特征分析后得出的最佳治療方案。另一方面，患者的信息會被添加到數據庫中，研究人員可以在該數據庫中挖掘規律，從而進一步改善治療方案和療效。

在癌癥界之外，對肌肉萎縮癥等神經肌肉疾病的數據使用模式同樣令人印象深刻。肌肉萎縮癥協會最近創建了神經肌肉觀察研究（MOVR）數據中心，作為多種神經肌肉疾病數據的集中存儲庫。通過收集數據提供者的自述數據、基因組數據和患者報告的治療效果，MOVR數據中心有助于研究者更好地了解此類疾病的發展歷史，從而提高臨床試驗設計和受試者招募。

研究老年癡呆癥的診斷加速器項目（Diagnostics Accelerator）已經從比爾·蓋茨等合作伙伴處籌得了超過3000萬美元的初始承諾，該項目的工作重點是確定哪些生物標記物有助于識別患有或可能患上這種嚴重病癥的人群。這一類的工作說明，關鍵數據的固定十分重要，因為就像科學界在進行癌癥研究時一樣，這些關鍵數據有助于我們識別相關疾病的生物學表現。

這給我們帶來了另一個重要的難題：資金。沒有大量投資，就無法開發、測試和推出新療法。所幸風險投資家們一直在以創紀錄的投資行為為精準醫藥企業提供資金，并且主要針對癌癥領域。但對于罕見的癌癥和其他疾病，資金問題仍然是面臨重大的挑戰。與此同時，替代性的資金模式正在出現——例如風險慈善事業和針對特定疾病的營利性創業公司。這些新模式帶來了很大的希望，即精準醫學的發展不僅會更快地傳播到更多的疾病領域，而且還會延伸到其他服務仍然不足的領域。

我們固然希望在癌癥以外的領域推進精準醫學方法，但協作式商業模式也是急切所需。改變孤立的系統，需要有新的數據模型、融資方法和激勵措施。我們需要繼續合作利用這些信息，來幫助簡化所有疾病領域藥物開發過程中最棘手的問題。

由于許多特定癌癥領域的生存機會得到了極大的改善，我們有責任將這些經驗教訓有助于其他疾病領域的研究，以便讓患者及其親人有機會更長久地生活在一起。（財富中文網）

凱西·吉斯蒂和理查德·哈默米肖是卡夫精準醫療加速器的教師聯合主席，也是哈佛商學院高管教育計劃“加速精準醫療的創新”的教師聯合主席。

譯者：Agatha，宣峰

The 21st century is the century of biology, where we’re seeing tremendous discoveries in science and medicine that are improving quality of life for people around the world. We are already seeing proof of this in oncology, where new treatments have transformed certain cancers from a death sentence to a chronic condition that can largely be managed with medications.

One such treatment is precision medicine, an emerging approach for disease prevention that takes into account the individual’s genetics, environment, and lifestyle so that doctors and researchers can more accurately predict which treatment would work best for that person’s particular disease.

Although the battle against cancer is far from over, we believe the precision medicine model is beginning to illuminate a path for more targeted research in disease areas beyond oncology. With the knowledge, management, and tools we have in precision medicine for cancer, other disease areas can avoid the same missteps that the cancer treatment community encountered to get where we are today.

As co-chairs of the Harvard Business School Kraft Precision Medicine Accelerator, our mission is to collectively conquer the business challenges of precision medicine. We recently held an executive education course that convened senior leaders throughout the health care community to discuss precision medicine business models that will help accelerate scientific research. While we expected that a large portion of participants would be from the cancer treatment world, we were blown away to see that more than half of the participants were from other disease areas.

Overwhelmingly, the participants explained how harnessing data is the biggest challenge they face when applying precision medicine to their disease areas. Cancer has struggled mightily with this issue and is just starting to get it right. Today, vast amounts of data exist, however most of the time that data is siloed, fractured, and rarely in a standardized format. Data needs to be clean and harmonized in order to make sense of it. That means building large, digestible clinical datasets that are connected to equally large molecular datasets, then following them over time. It also means applying machine learning to help physicians make better decisions about patient diagnoses and treatment options, while understanding the possible outcomes for each treatment path.

A great example of the power this data holds comes from the Multiple Myeloma Research Foundation’s (MMRF) CoMMpass Study, which uses the largest genomic dataset of any cancer. The MMRF sequenced the genomes of 1,100 patients with multiple myeloma and collected each patient’s clinical data over their myeloma journey, which has helped uncover 12 subtypes of the disease.

Another example is the Pancreatic Cancer Action Network’s Know Your Tumor precision medicine service, where patients can have their tumor molecularly profiled to receive information about the best treatment options for them based on the biology of their tumor. Their information is then added to a database where researchers can look for patterns that will lead to improved treatment options and patient outcomes.

A different but equally exciting model outside of cancer is in treating neuromuscular conditions such as muscular dystrophy. The Muscular Dystrophy Association recently created the neuroMuscular ObserVational Research (MOVR) Data Hub, which serves as a centralized repository of data on multiple neuromuscular diseases. Through the collection of provider-reported data, genomic data, and patient-reported outcomes, MOVR is facilitating a better understanding of the natural history of the diseases to enhance clinical trial design and recruitment.

And with Alzheimer’s disease, the Diagnostics Accelerator, which has secured more than $30 million in initial commitments from partners such as Bill Gates, is focused on identifying biomarkers that could help identify patients with or at risk of developing this devastating condition. Efforts like this underpin the key importance of securing the critical data that can lead us to discoveries about the biologic manifestation of disease, just as the scientific community has done with cancer.

This brings us to another important piece of the puzzle: funding. New therapies cannot be developed, tested, and brought to market without large investments. Fortunately, venture capitalists have been funding precision medicine ventures, largely in cancer, at record levels. But for rare cancers and other diseases, funding remains a significant challenge. Here, alternative funding models—such as venture philanthropy and for-profit startups that target a specific disease—are emerging. These new models provide great hope that advances in precision medicine will more rapidly spread not only to more disease areas, but also to underserved ones.

While we look to advance precision medicine approaches in areas beyond cancer, collaborative business models are urgently needed. New data models, funding approaches, and incentives are all required to change a siloed system. We need to continue to collectively use this information to help streamline the most arduous points in the drug development process for all disease areas.

As the chances of survival have been so greatly improved for many specific areas of cancer, it is our duty to help translate those lessons to other disease areas in order to give patients and their loved ones the possibility of more years together.

Kathy E. Giusti and Richard G. Hamermesh are faculty co-chairs of the Kraft Precision Medicine Accelerator and faculty co-chairs of the Harvard Business School Executive Education program, Accelerating Innovation in Precision Medicine.