Machine learning model uses clinical and genomic data to predict
immunotherapy effectiveness

Date:
November 3, 2021
Source:
Cleveland Clinic
Summary:
A new machine learning model accurately predicts whether immune
checkpoint blockade (ICB), a growing class of immunotherapy drugs,
will be effective in patients diagnosed with a wide variety of
cancers. The forecasting tool assesses multiple patient-specific
biological and clinical factors to predict the degree of response
to immune checkpoint inhibitors and survival outcomes. It markedly
outperforms individual biomarkers or other combinations of variables
developed so far, according to new findings.



FULL STORY ==========================================================================
A new machine learning model developed by Timothy Chan, MD, PhD, of
Cleveland Clinic and colleagues accurately predicts whether immune
checkpoint blockade (ICB), a growing class of immunotherapy drugs,
will be effective in patients diagnosed with a wide variety of cancers.


==========================================================================
The forecasting tool assesses multiple patient-specific biological and
clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes. It markedly outperforms individual
biomarkers or other combinations of variables developed so far, according
to findings published in Nature Biotechnology.

With further validation, the tool may help oncologists better identify
patients most likely to benefit from ICB. Discerning, prior to treatment, patients for whom ICB would be ineffective could reduce unnecessary
expense and exposure to potential side effects. It could also indicate
the need to pursue alternate treatment strategies, such as combination therapies.

"It's important to know which treatment modalities patients are most
suited for," said Dr. Chan, director of Cleveland Clinic's Center
for Immunotherapy & Precision Immuno-Oncology. "Our model provides a
more comprehensive understanding of the diversity of responses among
patients to immune checkpoint blockade. It's the first to assemble such
a large-scale set of clinical and genomic variables that have predictive
value for immunotherapy across numerous cancer types." These latest
findings build on earlier work from Dr. Chan, who discovered that
patients with high tumor mutation burden and DNA repair deficiencies
respond well to immune checkpoint therapy. These findings have been
validated by clinical trials and the FDA approved as the first tumor type-agnostic approvals for any cancer therapy.

Immune checkpoints are proteins on specific immune cells (T cells) that
when activated, or "turned on," prevent immune responses from being
too strong and destroying healthy cells. Some cancer cells are able to
hijack checkpoint signaling in order to disguise themselves and avoid
being targeted by a patient's immune system. Checkpoint inhibitors are
a class of immunotherapy drugs that prevent cancer cells from activating
these checkpoints.



========================================================================== However, ICB is not effective in all cancer types. Even in cancers
responsive to ICB, half or more of all patients treated with ICB do not
derive clinical benefit. Previous research has identified some biomarkers
and genomic features associated with ICB efficacy, but no single factor
can be considered an optimal predictor of treatment outcomes.

In this study, Dr. Chan and his colleagues developed their model using
a dataset containing clinical, tumor and genetic sequencing information
from nearly 1,500 patients with 16 different cancer types who were treated
with two different types of immune checkpoint inhibitors (specifically PD-1/PD-LI inhibitors and CTLA-4 blockade) or a combination of both. They
then applied an algorithm that incorporated many genetic, molecular,
clinical and demographic variables, some of which have been shown to be associated with ICB response.

Interestingly, the researchers found that the variable with the greatest influence on ICB response is tumor mutational burden (the frequency
of certain mutations within a tumor's genes), followed closely by
a patient's chemotherapy history. Levels of three blood markers --
hemoglobin, platelets and albumin - - also had strong predictive value,
not only for forecasting patients' overall survival, but also the actual radiographic response to ICB treatment.

"How these variables all work together is really the key here," said
Dr. Chan.

"This model shows that, rather than a single predictive biomarker, we're
headed toward a multifactor nomogram for clinical use." The team's fully integrated model proved to be highly accurate, significantly outperforming
two other forecasting tools, including tumor mutational burden, which
the FDA approved in 2020 as a biomarker to predict anti-PD-1 ICB efficacy
in solid tumors.

"The model works well, despite what type of cancer is being assessed,
which shows that these commonalities are what's important," Dr. Chan
explain. "These are primary factors that affect ICB response. The
factors may be weighted a little bit differently from cancer to cancer,
but it's almost like a common language for response prediction."
Taken together, the positive results support moving forward to test the
model in a clinical trial with a large, diverse cohort of cancer patients, which would provide a more accurate assessment of its performance in a real-world setting.

The study was funded in part by the National Cancer Institute (part of
the National Institutes of Health).

========================================================================== Story Source: Materials provided by Cleveland_Clinic. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Diego Chowell, Seong-Keun Yoo, Cristina Valero, Alessandro Pastore,
Chirag Krishna, Mark Lee, Douglas Hoen, Hongyu Shi, Daniel
W. Kelly, Neal Patel, Vladimir Makarov, Xiaoxiao Ma, Lynda
Vuong, Erich Y. Sabio, Kate Weiss, Fengshen Kuo, Tobias L. Lenz,
Robert M. Samstein, Nadeem Riaz, Prasad S. Adusumilli, Vinod
P. Balachandran, George Plitas, A. Ari Hakimi, Omar Abdel-Wahab,
Alexander N. Shoushtari, Michael A. Postow, Robert J. Motzer,
Marc Ladanyi, Ahmet Zehir, Michael F. Berger, Mithat Go"nen, Luc
G. T. Morris, Nils Weinhold, Timothy A. Chan. Improved prediction
of immune checkpoint blockade efficacy across multiple cancer
types. Nature Biotechnology, 2021; DOI: 10.1038/s41587-021-01070-8 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211103140133.htm

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