# Optimizing Thermal Ablation for Colon Cancer Liver Metastases: Rapid Tissue Analysis Allowing for Immediate Retreatment; Metabolic Imaging Biomarker Validation; and Predictive Genetic Signatures

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $293,870

## Abstract

Summary/Abstract: Colorectal cancer (CRC) represents 8% of all cancers with over 1,100,000 people living
with CRC in the US alone. The American Cancer Society estimates that in 2018 there will be over 140,000 new
CRC cases and over 50,000 deaths from this disease in the United States. Approximately 50% of patients with
CRC develop liver metastases (CLM) and these patients have the highest mortality. Thermal ablation (TA) is a
minimally invasive local therapy used to treat CLM. TA causes coagulation necrosis larger than the target tumor
to create at least a 5 mm margin to diminish local tumor progression (LTP) with a highly favorable safety profile
and curative potential. Despite technological evolution of TA, LTP rates remain high, ranging from 3% to 60%
during follow-up of ablated liver tumors. We have shown that microwave ablation is less affected by the heat sink
phenomena than radiofrequency ablation, when treating perivascular CLM; therefore, we will limit this proposal
to the use of microwave for thermal ablation to optimize outcomes. The high LTP rates are the main limitation of
the widespread use of TA in the treatment of cancer. In prior work, we demonstrated that viable (OXPHOS
antibody positive and/or KI-67 positive) tumor within the ablation zone (AZ) after TA, and KRAS mutations, carry
significant risk for LTP and effect patient survival. We hypothesize that residual undetected viable tumor and
tumor biology are the most likely mechanisms leading to ablation failure and eventual LTP, even in the
face of complete ablation with margins, depicted in conventional anatomic imaging.
This proposed clinical trial is designed to overcome these mechanisms, optimizing TA as a treatment for CLM
through the following three specific aims: AIM 1: Establish real time cytopathologic and fluorescent assessment
of the AZ by immediate post TA biopsy; AIM 2: Determine the 18F-FDG uptake level representing viable tumor
immediately post TA of CLM; AIM 3: Identify gene signatures that predict response in patients undergoing TA of
CLM through a pre-ablation biopsy and genomic analysis of the target CLM.
Real-time cytopathologic evaluation of the AZ (guided by 3D-assisted technology and metabolic imaging)
immediately after TA, is a novel method that can be used as a prognostic immediate biomarker of outcomes
after TA. More importantly, retreatment will be offered to treat identified, visible residual tumor at the same sitting.
We also propose a pre-ablation biopsy and next-generation sequencing (that is a standard of care in our
institution) of all CLM undergoing TA. A significantly higher risk for LTP for KRAS mutant compared to KRAS
wild type CLM has been recently documented by our group and others. Despite these new findings and prior
knowledge that molecular characteristics impact outcomes of TA, a prospective genomic analysis is still lacking.
Whole-genome analysis is thus proposed to detect unknown genes that may underlie extreme responders
(estimated 1...

## Key facts

- **NIH application ID:** 10017172
- **Project number:** 5R01CA240569-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Constantinos Thasos Sofocleous
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $293,870
- **Award type:** 5
- **Project period:** 2019-09-12 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017172

## Citation

> US National Institutes of Health, RePORTER application 10017172, Optimizing Thermal Ablation for Colon Cancer Liver Metastases: Rapid Tissue Analysis Allowing for Immediate Retreatment; Metabolic Imaging Biomarker Validation; and Predictive Genetic Signatures (5R01CA240569-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10017172. Licensed CC0.

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