Rucaparib Maintenance Shows PFS Benefit in Ovarian Cancer, Independent of HRD Status

 This is an edited version of an article by Ariana Pelosi published in OncLive June 6, 2022.

Rucaparib elicited a significant improvement in progression-free survival outcomes vs placebo as first-line maintenance therapy in patients with ovarian cancer who responded to first-line platinum-based chemotherapy across patient subgroups.

Rucaparib (Rubraca) elicited a significant improvement in progression-free survival (PFS) outcomes vs placebo as first-line maintenance therapy in patients with ovarian cancer who responded to first-line platinum-based chemotherapy across patient subgroups in both the primary and exploratory analyses of the ATHENA-MONO trial (NCT03522246). Specifically, the PARP inhibitor demonstrated improvements in both homologous recombination deficient (HRD)-positive and HRD-negative populations, findings from which were presented at the 2022 ASCO Annual Meeting....

“Patients with measurable disease at baseline have further tumor reduction with rucaparib. [Additionally], rucaparib safety profile is consistent with prior studies,” Bradley J. Monk, MD, FACS, FACOG, professor in the Division of Gynecologic Oncology at the University of Arizona College of Medicine, medical director of the Gynecologic Program at US Oncology Research Network, and lead investigator of this study, said during the presentation.

A Sensor Sniffs for Cancer, Using Artificial Intelligence

This is an edited version of an article  published on the Clearity Foundation website on May 12, 2022.

Researchers at Memorial Sloan Kettering Cancer Center (MSK) have developed a sensor that can be trained to sniff for cancer, with the help of artificial intelligence.

Although the training doesn’t work the same way one trains a police dog to sniff for explosives or drugs, the sensor has some similarity to how the nose works. The nose can detect more than a trillion different scents, even though it has just a few hundred types of olfactory receptors. The pattern of which odor molecules bind to which receptors creates a kind of molecular signature that the brain uses to recognize a scent.

Like the nose, the cancer detection technology uses an array of multiple sensors to detect a molecular signature of the disease. Instead of the signals going to the brain, they are interpreted by machine learning — a type of computer artificial intelligence.

MSK researchers led by Kravis WiSE Postdoctoral Fellow Mijin Kim and biomedical engineer Daniel Heller, head of the Cancer Nanomedicine Laboratory at MSK, built the technology using an array of sensors composed of carbon nanotubes. Carbon nanotubes are tiny tubes, nearly 100,000 times smaller than the width of a human hair. They are fluorescent, and the light they give off is very sensitive to minute interactions with molecules in their environment.

Each nanotube sensor can detect many different molecules in a blood sample. By combining the many responses of the sensors, the technology creates a unique fluorescent pattern. The pattern can then be recognized by a machine-learning algorithm that has been trained to identify the difference between a cancer fingerprint and a normal one.

In experiments conducted on blood samples obtained from patients with ovarian cancer, the researchers found that their nanosensor detected ovarian cancer more accurately than currently available biomarker tests. (A biomarker is a particular chemical produced by tumors and spread through the blood circulation that indicates the presence of disease. In this case, the biomarker tests were ones for the ovarian cancer-related proteins CA125, HE4, and YKL40.)

The hope for patients is that researchers will develop the technology further so that it can eventually be used in the clinic to rapidly screen for early-stage ovarian cancer and many other cancers.

Need for Better Cancer Screening Tests

Tests that detect early-stage cancers using blood markers hold great promise for improving outcomes for people with cancer — especially those types, like ovarian cancer, that have few early signs or symptoms.

Several serum biomarker tests for ovarian cancer are already in use. Unfortunately, these standalone biomarker measurements have proven to be ineffective at early detection. Currently, no screening strategy can identify ovarian cancer at an early enough stage to reduce mortality.

The nanosensor approach could potentially provide a better way.

“Ovarian cancer spreads along the surfaces of the abdomen and pelvis [as opposed to through the blood], which makes finding it with a blood test especially challenging,” says MSK surgeon Kara Long Roche, who was an author on the study. “This technology could potentially find more subtle, complex changes in the blood, which may be the key to early detection — and early detection will save lives.”

To train the machine-learning algorithm, the researchers needed to collect sensor responses from many blood samples, as this method requires many examples to be accurate. In addition, samples from patients with other conditions besides ovarian cancer were used: “Certain other diseases can trick the sensor because they produce some of the same components in the blood,” says Dr. Kim, the lead author of the study.

Although the technology improves the accuracy of ovarian cancer detection over current biomarker-based methods, more work is needed to enable the detection of early-stage ovarian cancer and confirm that this test works in people.

“We won’t stop until there is a way to prevent ovarian cancer deaths,” says Dr. Heller.

Investigators Hope Oregovomab Will Show Benefit in Advanced Epithelial Ovarian Cancer

 This is an edited version of an article  written by Lindsay Fischer and published by the Clearity Foundation

A new murine monoclonal antibody B43.12 is under investigation in combination with paclitaxel and carboplatin as a treatment option for patients with advanced epithelial ovarian cancer.

Oregovomab (Ovarex) in combination with paclitaxel and carboplatin for patients with advanced epithelial ovarian cancer is being tested in the phase 3 FLORA-5 trial (NCT04498117). Oregovomab is an investigational monoclonal antibody with promising phase 2 data. Oregovomab and the FLORA-5 trial were highlighted during the 2022 Society of Gynecologic Oncology Annual Meeting on Women’s Cancer in March.

“Oregovomab is an investigational monoclonal antibody that has been studied in clinical trials for patients with ovarian cancer whose tumor cells express the tumor-associated antigen CA-125 (MUC16),” the investigators noted in a poster presentation during the meeting. “Oregovomab is a novel immunotherapy that enhances the immune response to CA-125.”

The phase 3 double-blind, placebo-controlled, multicenter study has been designed to compare the safety and efficacy of oregovomab plus chemotherapy with placebo plus chemotherapy. Patients will be randomly assigned 1:1 to receive either 2 mg of intravenous oregovomab or placebo, along with 6 standard cycles of paclitaxel/carboplatin.

Additionally, investigators will seek to confirm the clinical benefit observed in a randomized phase 2 study (NCT01616303), which demonstrated that adding oregovomab to paclitaxel and carboplatin resulted in clinically significant improvements in progression-free survival (PFS) and overall survival (OS).

Investigators noted that the trial will also seek to evaluate the role of oregovomab as a neoadjuvant chemotherapy option.

Endometriosis and ovarian cancer genetically tied

 This article is taken from ScienceDaily, March 15, 2022

University of Queensland researchers have demonstrated a genetic link between endometriosis and ovarian cancer subtypes enabling them to identify potential drug targets for therapy and increasing the understanding of both diseases.

Previous studies have shown that endometriosis sufferers have a slightly increased risk of developing epithelial ovarian cancer.

Dr Sally Mortlock and Professor Grant Montgomery from UQ's Institute for Molecular Bioscience carried out a large genetic study to identify a genetic basis for this risk with a view to better understand the biological overlap between these reproductive disorders.

"More information about how they develop, their associated risk factors, and the pathways shared between endometriosis and different types of ovarian cancer has been needed," Dr Mortlock said.

Endometriosis is a chronic debilitating disease that affects the health of 1 in 9 women of reproductive age, where tissue similar to the uterus lining grows in other parts of the body, causing pain and infertility.

"Our research shows that individuals carrying certain genetic markers that predispose them to having endometriosis also have a higher risk of certain epithelial ovarian cancer subtypes, namely clear cell and endometrioid ovarian cancer."

Dr Mortlock said that although the diseases are genetically linked, the risk of ovarian cancer for those with endometriosis is not substantially increased.

"Overall, studies have estimated that 1 in 76 women are at risk of developing ovarian cancer in their lifetime and having endometriosis increases this slightly to 1 in 55, so the overall risk is still very low," she said.

The study found genes that could be drug targets to treat both endometriosis and epithelial ovarian cancer in the future.

"We explored specific areas of DNA that increase the risk of both diseases and identified genes in ovary and uterus tissue that could be targets for therapy and may be valuable to understand the link between the disorders and to disrupt biological pathways initiating cancer."

The researchers combined large datasets comparing the genomes of 15,000 people with endometriosis and 25,000 with ovarian cancer to find an overlap in risk factors between the two diseases.

The collaboration also involved Associate Professor Kate Lawrenson at Cedars-Sinai Medical Center and Dr Siddhartha P. Kar from the University of Bristol.