Researchers at Genentech and Stanford university have found 776 genomic alterations linked to survival outcomes across 20 cancer types with specific immunotherapies, chemotherapies, or targeted therapies. Teh team carried out a complete analysis of 78,287 U.S. cancer patients with detailed somatic mutation profiling integrated with treatment and outcomes data extracted from electronic health records.
They also used the data to create a machine learning model to predict response to immunotherapy in one type of lung cancer.
“Additionally, we demonstrate how mutations in particular pathways correlate with treatment response,” they wrote.
Thier study appears in Nature Communications and was led by Ruishan Liu, PhD, department of electrical engineering, Stanford.
They added, “The adoption of next-generation sequencing (NGS) has revolutionized genomics profiling,positioning it as an invaluable tool in cancer care. Yet, despite the vast mutation data available, only a limited number of these mutations are associated with validated treatments.”
Most of these findings are one offs,for particular cancers. In the age of genomics, clinico-genomics, and massive databases should be expected more.
But,the researchers pointed out,there is growing evidence large-scale,real-world,clinico-genomics data can identify useful response biomarkers for cancer treatments.such as, using a cohort of 40,903 patients, these researchers earlier identified 458 statistically notable gene-treatment interactions in eight common types of cancer (Liu et al.,2022).
in the current study, the team used a broader, updated version of that dataset, with one and a half more years of data and patient follow-up, encompassing 12 additional cancer types (20 vs. 8 previously), and more patients (78,287 vs. 40,903 previously).
The data came from the Flatiron Health-Foundation Medicine U.S.-based, de-identified clinico-genomic database (FH-FMI CGDB). this data was sourced from ~280 U.S. cancer clinics. the dataset includes data on patients with: advanced non–small-cell lung cancer, metastatic breast cancer, metastatic colorectal cancer, metastatic pancreatic cancer, ovarian cancer, metastatic prostate cancer, gastric cancer, advanced melanoma, advanced bladder cancer, endometrial carcinoma, metastatic renal cell carcinoma
Table of Contents real-world data is transforming cancer research, offering invaluable insights into treatment effectiveness and patient outcomes.Researchers are increasingly recognizing the potential of electronic health records (EHRs) as a rich source of this data. “Contemporary research emphasizes the power of real-world data, notably data extracted from EHRs, in several key areas,” experts note. “This data can definitely help us evaluate the impact of treatments, create control groups for clinical trials, and refine the criteria for including patients in oncology trials.”Groundbreaking Research links Gene Mutations to Cancer Treatment Success
Gene Mutations and Cancer Survival: A Complex Connection
Researchers have been delving into the intricate relationship between gene mutations and cancer survival rates. In a recent study, they focused on identifying which gene mutations significantly impact how long individuals live after a cancer diagnosis. Their analysis spanned various cancers and revealed a fascinating connection: mutations in 95 specific genes were linked to survival outcomes in at least one type of cancer.
Among these genes, mutations in *TP53*, *CDKN2A*, and *CDKN2B* stood out. These mutations were consistently associated with poorer survival rates across a wide range of cancer types. This finding reinforces previous research indicating the significant role these genes play in cancer advancement and progression.
Unlocking personalized Cancer treatment: Gene Mutations Hold the Key
In the ongoing battle against cancer, researchers are constantly seeking ways to tailor treatments for individual patients. A new study has taken a significant leap forward by identifying specific gene mutations that can influence how well a patient responds to certain therapies.
Utilizing advanced statistical methods, the research team uncovered 776 gene-treatment interactions directly linked to patient survival rates on particular therapies. This groundbreaking discovery builds upon the findings of an earlier, smaller study, providing further evidence that personalized medicine based on an individual’s genetic makeup holds immense potential.
Cancer Treatment Success Tied to Genomic Profiles
New research sheds light on the vital role of a patient’s unique genomic makeup in determining the effectiveness of various cancer treatments.
By meticulously analyzing tumor mutation profiles, treatment histories, and survival outcomes, researchers have identified specific genomic alterations that act as powerful predictors of patient survival rates.This groundbreaking discovery paves the way for a more personalized approach to cancer care, ensuring patients receive the most effective therapies tailored to their individual genetic profiles. “Delving into the patients’ tumor mutation profiles, treatment histories, and survival outcomes, we characterize somatic genomic alterations that notably predict the patients’ survival on specific immunotherapies, chemotherapies, or targeted therapies,” the researchers stated.
This research holds immense potential for transforming cancer treatment, enabling clinicians to make more informed decisions about the most appropriate therapies for each patient based on their individual genetic blueprint.
## Genomic Insights: A New Era for Personalized cancer Treatment?
**Interviewer:** Dr. Liu, thank you for joining us today.Your research, published in Nature Communications, has generated notable buzz in the medical community. Can you tell us about the motivation behind this study?
**Dr. Ruishan Liu:** Absolutely! Our team at Genentech and Stanford University were driven by a desire to understand how an individual’s unique genomic profile influences their response to cancer treatments.
Traditionally, treatment decisions have been largely based on the type and stage of cancer. However, we know that cancer is a complex disease with significant variability even within a single type.
Our aim was to leverage the power of large-scale, real-world clinico-genomic data to identify those specific gene mutations that could act as reliable predictors of treatment success across various cancer types.
**Interviewer:** Your study involved analyzing data from a remarkable 78,287 cancer patients. Can you elaborate on the data source and the scale of this undertaking?
**Dr.Liu:**
Exactly. We utilized the Flatiron Health-Foundation Medicine clinico-genomic database,a U.S.-based de-identified resource that aggregates data from nearly 280 cancer clinics. This allowed us to access a wealth of facts – not just genomic profiles but also detailed treatment histories and patient outcomes.
The sheer scale of this dataset was crucial. It enabled us to identify meaningful gene-treatment interactions even for rarer cancer types.
**Interviewer:**
Your research identified 776 genomic alterations linked to survival outcomes across 20 different cancer types. Could you give us some specific examples of these findings?
**Dr. Liu:**
Certainly. We found a strong correlation between mutations in specific genes and response to particular therapies.
For example,we identified mutations in the *TP53* gene,known for its role in tumor suppression,which were consistently associated with poorer survival rates across multiple cancer types.
Conversely, we identified mutations in other genes that were linked to positive responses to immunotherapy in certain types of lung cancer.
**Interviewer:**
These findings are truly groundbreaking. What are the potential implications for cancer treatment?
**Dr. Liu:**
this is truly exciting!
Our study provides strong evidence that genetic profiling can be a powerful tool for guiding treatment decisions.
By identifying specific gene mutations that influence treatment response, we can possibly move towards a more personalized approach to cancer care.this could mean offering targeted therapies that are more likely to be effective for individual patients, ultimately leading to improved survival rates and quality of life.
**Interviewer:**
Looking ahead,what are the next steps for your research?
**Dr. Liu:**
We are eager to build upon these findings. We wont to validate our results in independent cohorts and explore the potential of developing clinical tools that incorporate genomic information into treatment planning.
We envision a future were every cancer patient has access to personalized treatment based on their unique genetic makeup, leading to more effective and targeted therapies.
