Accessing Cancer Data: Personalizing Decisions & Overcoming Barriers
Featuring: Frank Nothaft, PhD
In short
Cancer genomics data holds real potential to help patients personalize their testing and treatment decisions, but most people — including many physicians — struggle to access or interpret it. Frank Nothaft, a bioinformatics expert, walks through the nine most common barriers patients face and nine practical solutions on the horizon, from AI-powered knowledge bases to system navigators who help patients advocate for themselves. The conversation is aimed at advanced cancer patients, caregivers, and anyone who wants to be a more active partner in their own care.
- •Ask your care team whether you have had all the diagnostic tests — including genomic tests — that could guide your treatment options, since some patients miss testing that could change their plan.
- •When reviewing treatment choices, ask specifically about quality-of-life side effects and real-world rates — not just survival statistics — because research studies may underreport how treatments affect daily life.
- •If a test result or outside data point you toward a treatment outside standard guidelines, write down the evidence and sources beforehand so you can clearly and calmly walk your physician through your reasoning.
- •Look for a system navigator — a care coordinator or patient advocate — who can help you make sense of results from multiple specialists and keep your medical team on the same page.
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Brad Power November 8, 2023 “My goal is to make it easier for patients to work with their diagnostic data and their EHR data and make empowered decisions about their care.” – Frank Nothaft “You should get to a system that makes the data a lot more usable, that hopefully exposes data that is clean and that has clinically good interpretations, and the right cautions on how to interpret it.” – Frank Nothaft
Meeting Summary
Advanced cancer patients want access to as much data as possible to help them personalize their testing and treatment decisions. But cancer genomics data can be very hard to understand or access except for a small number of bioinformatics experts.
The coming personalization revolution in cancer care and healthcare generally will depend on breaking down data barriers for patients so that they can uncover actionable insights and make empowered decisions about their care with their medical team. Diagnostic companies and data and AI services companies, especially startups, are potential partners who can accelerate access and personalization.
Frank Nothaft is uniquely qualified to explore the promise and challenges of data access for patients and caregivers. Following his PhD in computer science from UC Berkeley, he worked in bioinformatics at Databricks, a company focused on big data processing, where he was very focused on open source bioinformatics, especially around cancer genomics.
He also worked at Tempus, the diagnostics company, where he worked with a much broader crowd of folks who had deep expertise in oncology, but less knowledge of bioinformatics. He realized how inaccessible cancer genomics data was to non-bioinformatics experts. Frank left Tempus in mid-August 2023 and is starting to work toward solving this problem entrepreneurially. , at an 8th grade level.
What is the promise of cancer patients and caregivers having easier access to information that will help them in their decision-making?
•Personalization: Blend what patients and caregivers are looking to get out of their care with what clinicians have seen in the clinic and evidence from research literature to pick a treatment that is most likely to meet the patient's goals for their treatment, including quality of life, in an effective and safe manner
•Better ideas: Uncover actionable insights to bring to their medical team
•Empowered: Make sure that patients can easily access the information they need to participate as co-pilots with their medical team in decisions What are the barriers that patients and caregivers face in accessing and using information that could help them make better care decisions? 1.Incorrect tests: Some patients don’t get the diagnostic tests that might guide their treatment decisions (a data source for personalization). 2.Test misinterpretation : Many patients and physicians are unfamiliar with using novel tests (e.g., whole genome, RNA sequencing, AI-powered multimodal tests) to drive personalized treatment decisions. 3.Hard to access, quickly changing medical knowledge : Medical literature is vast and jargon-laden, making it difficult for patients and caregivers to use. There is so much available medical data, but as new tests and treatments are constantly emerging, making it hard to keep up with the latest research and developments and how patients actually fare in the real world on new treatments. 4.Patient vs. system values misalignment : Cancer research optimizes for overall survival and progression free survival, at the possible expense of quality of life. 5.Insufficient information on side effects : Patients need more information on the side effects of cancer treatments and their impact on quality-of-life. Having access to real- world incidence rates, as well as advice on how they can mitigate those side effects will help them get the best possible response to the medicine. 6.Personalized vs. population evidence : Information for personalized treatment decisions must take into account individual patient characteristics and preferences, which depend on real-world evidence from individual patients’ experiences, while the gold standard of evidence from randomized clinical trials provides population-level evidence. 7.Physician persuasion : When patients get information, especially from novel tests that guide them to treatment options outside the standard guidelines, they need to effectively share that information with their physicians to persuade them to prescribe the treatment they believe is best for them. 8.Reconciling test results : Reconciling conflicting data from multiple cancer diagnostic tests, e.g., between RNA sequencing and IHC for PDL1 protein in cancer diagnosis. 9.Inconsistent diagnoses : Different interpretations and recommendations from different physicians. What are the possible solutions to these barriers? 1.Up-to-date medical databases : Patients and caregivers will access continuously updated medical knowledge bases (like WebMD, but curated to meet the specific needs of the rapidly evolving field of oncology) to make it easy to answer questions (including quality of life side effects), and provide the sources and experts to bring to their clinicians. 2.-Omic databases: Patients and caregivers will access large genomic databases (e.g., TCGA) to provide contextualization of -omic findings (e.g., gene upregulation), including normalizing for various biases. 3.Observational registries with patient-reported outcomes : Patients and caregivers will access observational registries to see the experiences of “patients like me” who are taking their treatment or tests or treatments they are considering. 4.Data cleaning: Software services will clean and normalize data (e.g., inclusion/exclusion criteria to improve clinical trial matching). 5.Information filter: Software services will continuously read all the medical research literature and provide patients with updates, including alerts, on new medical evidence, clinical guidelines, diagnostic tests, treatments, and trials that are important for them, and summarize it at a level they can understand. 6.Interactive chatbot : A personalized software agent (like ChatGPT) will use the curated medical knowledge base to enable patients and caregivers to ask questions about their diagnostic reports, treatment choices, or any other questions at a level they can understand. 7.Communication channels: Very complete, very well secured connections between institutions and with the patient and caregivers will enble the oncologist, pathologist, and the diagnostic testing company to share data to get to a better diagnostic readout. 8.Models: Longitudinal data analysis, algorithms, and AI will help understand the evolution and progression of cancer over time, the effects of multiple rounds of therapy, and predict treatment response, using evolutionary and game theory. 9.System navigator role : A system navigator will check information quality and help patients and caregivers interpret information and communicate with their medical team and advocate for their needs, especially where multiple doctors and diagnostic specialists are involved in a patient's care. They will provide timely and personalized feedback to patients (not as busy as physicians), avoiding confusion and frustration. How can you engage if you are interested in discussing this topic? You can join the online discussion with Frank at community.cancerpatientlab.org to answer questions such as: 1.What are your most important care decisions, and what information do you need to make those decisions? 2.What barriers make it hard for you to access and analyze your data? 3. would you like to interact with and visualize your data? 4.Having arrived at ideas on what is best for you, what evidence do you need to bring to your medical team? The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. Meeting Notes SUMMARY KEYWORDS patients, data, diagnostic, frank, information, people, working, cancer, questions, company, understand, oncology, point, areas, diagnostic testing, side effects, rna, interpret, physicians, research SPEAKERS Frank Nothaft (58%), Brad Power (13%), Jeff Krolick (12%), Rick Stanton (8%), Robert Gurmankin (4%), Brian McCloskey (4%), Roger Royse (2%), Saed Sayad (<1%) OUTLINE 1.Democratizing access to cancer patient data. (0:03) 2.Opening up cancer data for patients and caregivers. (3:10) 3.Personalized medicine and its challenges. (10:52) 4.Personalized cancer treatment and data analysis. (17:48) 5.Personalized healthcare interfaces. (27:09) 6.Reconciling conflicting data from multiple cancer diagnostic tests. (33:04) 7.Analyzing gene expression data in cancer diagnosis. (38:34) 8.Improving cancer care through data analysis. (45:47) 9.Using AI to analyze cancer data and predict outcomes. (54:36) 10.Cancer treatment decision-making with a focus on quality of life. (57:14) SUMMARY
•Frank Nothaft explains how he learned that there's valuable information to help patients, but it's not easily accessible, and he wants to open up data to democratize access.
•Frank discusses how to turn cancer data into actionable insights for patients and caregivers.
•Frank has spent over a decade working in oncology and genomics, including time at Berkeley and several startups.
•Frank's current goal is to make it easier for patients to work with their diagnostic data and make empowered decisions about their care.
•Frank shares his family experience with cancer diagnosis and advocates for a patient-centric approach in personalized medicine.
•Personalized medicine promises to tailor treatments to individual patients' goals and needs, but current gaps in diagnostic testing and access can lead to incorrect treatments for 50-80% of patients.
•Frank highlights the gap between medical knowledge and patient care, particularly in oncology, where new treatments are rapidly emerging but physicians and patients struggle to keep up with the latest research and side effects.
•Frank suggests that software solutions could help address this issue by providing easily accessible updates on medical evidence, clinical guidelines, and new trials, improving the dissemination of knowledge and quality of care.
•Brad Power highlights the gap between real-world evidence and clinical trials, emphasizing the need for personalized treatment options that take into account individual patient characteristics and preferences.
•Brian McCloskey raises the challenge of doctors not knowing how to use genetic data to drive personalized treatment options, despite patients being armed with this information.
•Brian McCloskey and Rick Stanton discuss challenges in leveraging liquid biopsy data for cancer diagnosis and treatment, with a focus on interpreting complex biomarkers and addressing gaps in care for advanced patients.
•Roger Royse discusses the challenge of navigating the vast amount of cancer research data and the potential for AI to help organize and summarize it.
•Jeff Krolick agrees with Roger and highlights the need for a scalable solution to help clinicians and patients stay up-to-date on the latest literature.
•Jeff Krolick suggests hiring a system navigator to help patients communicate with physicians and advocate for their needs.
•Frank agrees and notes that this role could help bridge the gap between physicians and patients, ensuring that patients receive the appropriate care and support.
•Frank highlights the importance of personalized interfaces for diagnostic specialties like oncology, where multiple doctors and diagnostic specialists may be involved in a patient's care.
•Frank notes that the current standard of care often falls short in providing timely and personalized feedback to patients, leading to confusion and frustration.
•Jeff Krolick suggests creating a new specialty role to bridge the gap between AI, patients, and physicians.
•Conflicting results between RNA sequencing and IHC for PDL1 protein in cancer diagnosis.
•Rick Stanton suggests requesting RNA Seq data to clarify Tempus's 100% expression of PD-L1 in prostate cancer patients.
•Robert Gurmankin mentions the difficulty in resolving a conflict in results between Caris and BostonGene tests.
•Oncologists and diagnostic companies can share data more efficiently, leading to better diagnostic rates.
•Frank Nothaft highlights the problem of inconsistent diagnosis descriptions among doctors and trials, leading to significant consequences for patient treatment.
•Frank Nothaft also emphasizes the importance of making clinical information and knowledge bases accessible to patients, citing examples from digital health.
•Frank Nothaft is working on a project to make medical data more usable for patients, with the goal of providing actionable insights and helping patients get the most out of their data.
•Frank Nothaft is seeking feedback from patients on the most impactful problems to address in their prototype, including how to convince clinical teams of their concerns and questions.
•Brad Power suggests starting an online discussion forum to engage with a larger audience and continue the conversation beyond the live studio audience.
•Brian McCloskey highlights the importance of longitudinal data analysis to understand the evolution of cancer and the effects of multiple rounds of therapy.
•Brad Power and Frank discuss the importance of modeling in cancer research, particularly in understanding the progression of cancer over time.
•Saed Sayad, a researcher from Toronto, is introduced as a potential connection for further collaboration in this area.
•Jeff Krolick wants more information on the quality of life side effects of cancer treatments, including real-world incidence rates and personalized decision- making.
•Frank agrees, citing the importance of considering long-term management and day-to-day concerns for cancer patients.
Full transcript
Brad Power We're happy to have Frank Nothaft with us. I connected with Frank when he was at Databricks, probably four plus years ago, when I was looking around at interesting companies who were doing innovative things when I was writing articles for the Harvard Business Review. Databricks was doing open source computation.
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