Katie Kosko

In the past 3 years, the FDA has approved 7 novel drugs, either given alone or in combination, to help treat patients with hepatocellular carcinoma (HCC).

Although there are more medications now than ever before, no head-to-head comparisons of them were done in clinical trials, said Renuka Iyer, MD, in an interview with 

. Many were studied versus placebo, explained Iyer, who is codirector of the Liver and Pancreas Tumor Center at Roswell Park Comprehensive Cancer Center in Buffalo, New York. Therefore, understanding how to best treat a patient based on the rapidly evolving data is not clear.

“There have been multiple changes over the last few years, so it’s necessary to provide some general guidance to have something that clinicians can go to for the specific questions surrounding systemic therapy,” said William P. Harris, MD, an associate professor in the Division of Oncology at the University of Washington School of Medicine in Seattle.

Iyer and Harris served on an expert panel assembled by the American Society of Clinical Oncology (ASCO) that outlined key guidelines for systemic therapy for advanced HCC, which were published in the 

Updates to first- and second-line treatments were detailed with considerations to Child- Pugh classification and ECOG performance status (

). Targeted therapies, such as bevacizumab (Avastin); tyrosine kinase inhibitors (TKIs), such as sorafenib (Nexavar) or lenvatinib (Lenvima); and the immune checkpoint inhibitors atezolizumab (Tecentriq), pembrolizumab (Keytruda) and nivolumab (Opdivo) were all included as potential treatment options. The panel was able to establish a new, preferred first-line regimen that can be reinforced to make sure patients are considered for it upfront.

“Having more options will impact [patients’] ability to access these treatments, which weren’t available, and potentially have longer survival than they would have without them,” Iyer said. “It’s encouraging to see that several of these agents are giving sustained radiographic response and are well tolerated.”

Most patients with advanced HCC may be given the combination of atezolizumab and bevacizumab upfront if they have Child-Pugh class A liver disease and an ECOG performance status of 0 to 1. In addition, this regimen may be offered following management of esophageal varices, when present, according to the ASCO guidelines.

This recommendation was based on the interim analysis of the randomized phase 3 IMbrave150 clinical trial (NCT03434379), whose results demonstrated that atezolizumab plus bevacizumab was superior to sorafenib, the prior standard of care for advanced HCC.

“This should be the preferred regimen at this time point for most patients with HCC in the first-line setting,” Harris said. “[Sorafenib] remains reasonable. But, in the absence of a contraindication, atezolizumab plus bevacizumab has the best efficacy data from any randomized phase 3 study, and that’s what drove this recommendation.”

In addition, Iyer discussed the study’s compelling data for overall survival (OS), which at 12 months was 67.2% (95% CI, 61.3%-73.1%) with atezolizumab and bevacizumab and 54.6% (95% CI, 45.2%-64.0%) with sorafenib.3 “The hazard ratio for OS was 0.58 [95% CI, 0.42-0.79; 

= .0006],…an over 40% improvement over established therapy that had held its ground for more than a decade in this patient population,” she said. “[Adding to this], quality of life and the toxicity profile was quite modest.”

Despite promising OS findings, adverse events were greater in the atezolizumab and bevacizumab arm, with a risk of bleeding complications with bevacizumab.3 Because of this, the expert panel urged caution when applying the results to patients with more advanced liver disease. Harris explained that within 6 months of beginning therapy, patients should undergo an esophagogastroduodenoscopy, which may not be in an oncologist’s routine patient flow.

Patients with contraindications, including autoimmune disease, coinfection with hepatitis B (HBV) or hepatitis C (HCV), or stroke, or those receiving therapeutic anticoagulation, should skip atezolizumab and bevacizumab and begin therapy with a tyrosine kinase inhibitor (TKI). Either sorafenib or lenvatinib would be appropriate, the panel noted.

Should a patient’s disease progress on atezolizumab and bevacizumab, and they are Child- Pugh class A and have an ECOG performance status of 0 to 1, they may be given either sorafenib or lenvatinib as second-line therapy.

“This is an extrapolation of data and is basically not based upon randomized data at this point,” Harris said. “But this is a consensus from the expert panel, and these are the situations where we think we need to provide guidance to clinicians as to what to do when there is an absence of randomized data. The data have been moving so quickly that the trials have not caught up.”

Although age is not a factor when considering who can receive TKI therapy, comorbidities need to be integrated, he added. For instance, in patients who have significant vascular disease or underlying impaired ECOG performance status, or in those who have had a stroke, clinicians should exercise caution.

Additional options for second-line treatment would be to use another TKI, such as cabozantinib (Cabometyx), ramucirumab (Cyramza), or regorafenib (Stivarga). “We know there is robust randomized data in phase 3 trials to support the use of multiple agents in that setting,” Harris said. “Regorafenib applies to patients who have previously tolerated sorafenib but subsequently progressed.4 Ramucirumab has only proven to be effective in patients who have an alpha-fetoprotein [level] of 400 ng/mL or higher in the second-line setting.5 And then there are less restricted agents like cabozantinib.6 All these agents were studied in patients with relatively intact hepatic functions.”

Iyer added, “These [agents] could all be considered as equivalents as recommended for second-line therapy following atezolizumab and bevacizumab. [However,] the quality of the evidence [after] studying all these agents… is not high because they were not all studied following [the combination].”

The expert panel also addressed the idea of returning to some of these TKIs should a patient progress on one over the other. Further, immune checkpoint inhibitors may also play a part in second-line therapy for patients with advanced HCC. The panel suggested the use of pembrolizumab or nivolumab in patients who have contraindications for atezolizumab and bevacizumab or who cannot tolerate TKIs.

“There are data that are very close to being positive but just missed statistical significance for a variety of reasons. [However], the panel agreed that there is clear efficacy to these agents,” Harris explained. “There remains a role for checkpoint-based therapy, either PD-1 inhibitors or potentially combination immunotherapy, in the second-line setting.”

Data are evolving on the use of nivolumab and ipilimumab (Yervoy) in patients with good performance status and Child-Pugh class A in the second-line setting. At some time in the future, there could also be a role for these agents.

Despite an abundance of agents, many unanswered questions remain, according to Harris. Looking to the future, he hopes that experts can optimize the data with clinical trials to help sort out the ideal sequence of drugs. “Although we provide recommendations, it is going to be important to continue to do trials to show what is the relative benefit of 1 second-line agent versus another,” he said.

Along those lines is the idea of exploring adjuvant therapy in this space. “For the patients who can undergo resection, can we actually prevent recurrence with adjuvant therapy?” Harris asked.

The need for new agents remains important, too. “There are at least 3 major first-line clinical trials that are near completion and should be released over the next year,” Harris said. “These are investigating other combinations that could be equivalent [to] or potentially supplant frontline therapy.” The trials include combinations of immunotherapy plus TKIs, such as cabozantinib with atezolizumab versus sorafenib in patients with advanced disease who haven’t received prior systemic therapy, or combinations of PD-1 or PD-L1 plus a CTLA-4 inhibitor.

Phase 1 clinical trials are also examining T-cell therapies and combinations of local therapy plus systemic therapy in patients with HCC. An example is catheter-based interventions in combination with systemic therapy, so either chemoembolization or radioembolization plus or minus systemic therapy, Harris explained.

It will also be crucial to look at subgroups of patients to see which may do better, he continued; this may be based on comorbidities or what drove the cancer to begin with. “We have to consider that there are many patients who have contraindications or relative contraindications to drugs that inhibit vascular endothelial growth factor and these immunotherapy-specific combinations, but even more so in that subgroup of patients.”

Specifically, the panel commented on the need for more data to support treatment benefit in patients with a Child-Pugh class B score.

Iyer addressed the unmet need for genetic and biomarker understanding to also help guide treatment decisions in advanced HCC. Although a subset of patients with HCC were found to have 

, a targetable driver, and there have been some early data on the development of FGFR4 inhibitors, the panel commented that the efficacy and appropriate context of their use is yet to be determined.

“In terms of when a medical oncologist meets a patient with HCC, there are now new important things they need to know to determine whether it’s a safe choice…like incorporating variceal screening with an upper endoscopy more routinely into patient flow when they identify a patient with HCC,” Harris said. “That’s going to be an important step to expedite therapy for patients.” This means that changes to medical practice may be needed.

A remaining critical component in the care of patients with HCC is making sure they are evaluated by a multidisciplinary liver tumor clinic.

“While this blessing of [many treatment options] is a wonderful thing, [we] are also given the responsibility of using it wisely, and that I think is something we are hoping to learn from each other, prospective data, and clinical trials,” Iyer said.

Articles

In the past 3 years, the FDA has approved 7 novel drugs, either given alone or in combination, to help treat patients with hepatocellular carcinoma.


Systemic Therapy for Advanced Hepatocellular Carcinoma: ASCO Guidelines

The biggest unanswered question in biomarker development for cancer is how to determine which patients will respond to targeted therapy or immunotherapy. Investigators around the world are trying to answer this through clinical trials and data mining, in addition to finding new tools to add to the mix.

One of those tools is deep learning, a form of artificial intelligence (AI) that uses artificial neural networks modeled after how the human brain processes information to learn and make predictions from large amounts of data. Another tool is crowdsourcing, or collecting large amounts of patient data from multiple sources.

“The holy grail in the pretreatment space is what is determining who will respond and who will not, so then you don’t expose patients to drugs that you don’t have to,” said Andy Futreal, PhD, chair of the Department of Genomic Medicine, Division of Cancer Medicine, at The University of Texas MD Anderson Cancer Center in Houston, during an interview with Targeted Therapies in Oncology. “There is this yin and yang thing where everyone is not only trying to understand the responder but also [understand] who may be at higher risk for toxicities.”

However, the challenge is how to generate data on all the patients who receive treatment once they move into the standard-of-care space, Futreal explained.

“Once you start giving approved agents to thousands of patients as opposed to the tens of patients on phase 1/2 [clinical] trials, that’s where you begin to get power and the data begin to emerge, and that’s where you begin to really understand [that there are] germline factors that are driving toxicity, and [eliciting] other questions,” Futreal said.

Although the study of AI dates to the 1940s and 1950s, as explained in a study published in JCO Clinical Cancer Informatics by Nagy et al, advancements in recent years have brought the use of deep learning to fruition.

Deep learning has had the greatest impact on digital image analysis, according to Ash A. Alizadeh, MD, PhD, an associate professor of medicine (oncology) at Stanford University in California.

“If you look at the last decade in oncology, most drug approvals have focused on biomarker selection strategies for targeting patients with a mutation of interest, a genomic subgroup of interest,” he said. “But despite those successes, the number of failures seems to be larger. That doesn’t mean we should give up. But a key concept is the early hope of finding a biomarker that can work in general, to avoid false discovery in the front line and [aid] the identification of biomarkers, and this cuts across all the technologies, whether it’s imaging or molecular diagnostics or data mining from electronic records. They all have promise, but the key [goal] is to find signals so we can move the needle for any given patient [with cancer].”

Investigators have begun to explore the application of deep learning in cancer types such as melanoma and breast, colorectal, and metastatic non–small cell lung (NSCLC). For instance, a study conducted by Skrede et al at the Institute for Cancer Genetics and Informatics in Oslo University Hospital in Norway used deep learning to develop a clinically useful prognostic marker for colorectal cancer.

The investigators obtained slides of hematoxylin and eosin (H&E)–stained histopathology sections from patients following tumor resection. By training 10 convolutional neural networks on more than 12 million image tiles derived from slides of patients with distinctly good or poor disease outcomes, they developed a biomarker and tested it on 920 patients’ slides prepared in the United Kingdom, followed by independent validation in 1122 slides prepared in Norway from patients treated with capecitabine. The biomarker reliably predicted cancer-specific survival, stratifying patients with stage 2 or 3 disease into distinct prognostic groups. By identifying whether patients have a sufficiently low risk of recurrence so that adjuvant chemotherapy can be avoided, the biomarker could help guide treatment decisions.

Similarly, investigators in South Korea worked with Lunit SCOPE, a deep learning–based H&E image analytics tool, using 1824 H&E images from immune checkpoint inhibitor (ICI)–naive patients with metastatic NSCLC. Using samples from ICI-treated patients (n = 189), an AI score was calculated that predicted clinical response and progression-free survival after ICI treatment independently of PD-L1 expression.

To derive maximal benefit from deep learning, clinicians must accomplish 3 overall objectives, explained Futreal. First, clinicians need to be able to work in real time. “When you do large-scale data generation, you have to be able to give something back immediately because it’s important to the clinical trial to correlate the most important data. Did the patient respond? Did the tumor get bigger, smaller, or stay the same?” he said.

Next, clinicians must examine how this information applies to translational research. For example, Futreal explained, BRAF is mutated in multiple tumor types, but what are the implications of a clinician deciding which MEK inhibitor to use in a patient with lung or thyroid cancer?

“If you do both of these things right, and spend time on...data quality, flows, transformations, then you have high-quality data [that can be used in deep learning] because now...the machine can look for those correlations,” he said.

Finally, after the framework is built, the longterm repercussions for clinical biology need to be explored, Futreal said. “The more data we have, the more we can measure, and the more likely we are to hit upon the nuggets that can be pulled out and refined,” he added.

At MD Anderson, platforms have been built out over the last 9 years that were solely concerned with longitudinal sample collection from patients. Now they are focused on scaling that information, Futreal explained, and starting in the rare cancer space, where much of the infor-mation already exists.

In addition, a large-scale project called Patient Mosaic, which is being led by Futreal’s laboratory, will focus solely on patient populations receiving standard-of-care therapy to develop a precision-driven, personalized approach. Germline and tumor sequence data will be compared to determine how treatment leads to genetic changes that drive resistance over time, and patients’ microbiomes will be examined. Investigators will begin by evaluating how these factors affect immunotherapy and cell-based therapies and then move on to targeted therapies.

“We know with certain cancer types, the microbiome seems to play a role in terms of response, as well as patients’ body mass....It is a complicated space, and it may usher in a new era of more patient-centric complex biomarkers than the tumor-driven biomarkers we tend to think of,” Futreal said.

One key to the success of establishing new cancer biomarkers may lie in the hands of the public. Data sharing and crowdsourcing have been studied to speed up the process of piecing together information from images and annotating data.

In a recent JCO Precision Oncology commentary, Benjamin G. Vincent, MD, and colleagues at the University of North Carolina School of Medicine, Chapel Hill, explained how crowd-sourcing can drive precision medicine with immunotherapy. Dialogue on Reverse Engineering and Assessment Methods (DREAM) Challenges are enabling collaboration among experts around the world.

Three key DREAM Challenges in oncology, according to Vincent et al, have demonstrated the value of crowdsourcing: the Prostate Cancer DREAM Challenge, the Multiple Myeloma DREAM Challenge, and the Digital Mammography DREAM Challenge.

Novel, previously underreported prognostic biomarkers were identified through the Prostate Cancer DREAM Challenge. In the Multiple Myeloma DREAM Challenge, 171 models were assessed, which led to discovery of the epigenetic regulator PHF19 as a novel marker of aggressive disease and high risk of early progression. And the Digital Mammography DREAM Challenge showed how integrating AI and radiologist assessment can improve the interpretation of breast cancer screening mammograms.

Investigators are now beginning the Anti–PD-1 Response Prediction Challenge to explore alternative biomarkers to PD-L1 expression and tumor mutational burden. Their focus is on developing models that predict which patients will benefit from nivolumab (Opdivo) monotherapy in NSCLC. Patient samples from a phase 3 clinical trial, CheckMate 026 (NCT02041533), will be used.

 “Successful completion of this DREAM Challenge will provide a model for future collaborations among industry, academia, and citizen scientists to discover determinants of response or resistance to immunotherapy, facilitating rapid development of clinically actionable biomarkers,” the authors wrote.

However, deep learning and crowdsourcing come with some hurdles. For instance, images may require extensive preprocessing because of the data size and amount of nontumor tissue in a typical histology slide, which Echle et al explained can dilute the information content.

Patient privacy and confidentiality are also a concern with crowdsourcing. “We have to look at what role does empowering patients have,” Futreal said. “Patients have the capability to electronically say, ‘Boom. I want to give you my data, and these are the things I would like you to do with it.’ That changes the nature of the government and compliance piece, as well as the patient involvement, which is critical.”

Futreal also described the frustration and obstacles involved in capturing electronic health records (EHRs) where patient data are kept.

“The best time to ensure that the data are complete is at the initial collection, and that involves the EHR,” he said. “We need to make it not 18 clicks to get to high-quality data collection, but [down] to 3 clicks that doesn’t slow the team down.”

nce these areas are fine-tuned and the analytics are built out, the application of deep learning can extend beyond research at academic institutions and into community oncology groups. But that can only work, Futreal explained, if there is interinstitutional harmonization.

“If we make all of our apples Granny Smith, McIntosh, and Fujis, but another group is collecting apples in the community and calling them red or green apples, then we have to understand when they say red apples, they are including the 3 red apple types that we’re talking about so that you can extrapolate,” he said. “If you can’t extrapolate data, then that’s a missed opportunity.”

Leveraging this technological progress to advance clinical applications will make it useful for the everyday patient, Alizadeh explained.

“The glass is half full, and there are lots of opportunities to keep filling it, but how do we improve on bang for the buck in terms of dollars spent, time, and learning from individual patients?” Alizadeh said. “I don’t think anyone has a solution, but there are a number of creative strategies, such as finding exceptional responders and studying them or going back to clinical trials and studying those patients post hoc to identify biomarkers. There isn’t a 1-size-fits-all solution across the board.”

1. Nagy M, Radakovich N, Nazha A. Machine learning in oncology: what should clinicians know? 

. 2020;4:799-810. doi:10.1200/CCI.20.00049

2. Skrede OJ, Raedt SD, Kleppe A, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. 

. 2020;395(10221):350-360. doi:10.1016/S0140-6736(19)32998-8

3. Park S, Ahn CH, Jung G, et al. Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer. 

. 2019;37(15):9094. doi:10.1200/JCO.2019.37.15_suppl.9094

4. Amgad M, Elfandy H, Hussein H, et al. Structured crowdsourcing enables convolutional segmentation of histology images. 

. 2019;35(18):3461–3467. doi:10.1093/bioinformatics/btz083

5. Vincent BG, Szustakowski J, Doshi P, et el. Pursuing better biomarkers for immunotherapy response in cancer through a crowdsourced data challenge. 

6. Echle A, Rindtorff NT, Brinker TJ, et al. Deep learning in cancer pathology: a new generation of clinical biomarkers

. 2021;124(4):686-696. doi:10.1038/s41416-020-01122-x

Machines and Patient Power Move Biomarker Discoveries Forward

Findings of a recent study suggest surgical choices in women with breast cancer may have long-term impacts on their quality of life (QOL). Investigators found that younger patients with breast cancer who underwent lumpectomy had better QOL outcomes than patients who had a mastectomy.

The study, which was presented at the 2018 San Antonio Breast Cancer Symposium (SABCS), examined QOL outcomes using a multicenter prospective cohort of young women across 3 different surgeries: lumpectomy, unilateral mastectomy, and bilateral mastectomy.

&ldquo;Historically, about 75 percent of women are eligible for breast-conserving surgery,&rdquo; lead author Laura S. Dominici, MD, FACS, a surgeon at Dana-Farber/Brigham and Women&rsquo;s Cancer Center, assistant professor of surgery at Harvard Medical School, and division chief of breast surgery at Brigham and Women&rsquo;s Faulkner Hospital, said in a press release. &ldquo;However, over time, more women, and particularly young women, are electing for bilateral mastectomy.&rdquo;

The study enrolled 1302 patients; however, 559 were excluded for the following: ineligible post-enrollment/consent withdrawn (n = 5); short form/modified short form participants (n = 91); deceased/lost to follow up (n = 411); and confirmed recurrence or de novo stage IV (n=52).

The remaining 743 patients received BREAST-Q, a validated patient-reported outcomes survey, between October 2016 and November 2017 as a stand-alone survey or as part of their 10-year follow-up. Of those, 584 participants (79%) responded. Twenty-four of them were excluded for confirmed recurrence or because BREAST-Q didn&rsquo;t match the surgery received. The 560 remaining participants were mainly white (90%), married (77%), financially comfortable (79%), and had college graduate degrees or higher (86%). Median age was 37 years.

Participants were either stage 0 (10%), stage I (38%), stage II (39%), or stage III (14%). Seventy-two percent received chemotherapy and 66% received endocrine therapy within 2 years of survey enrollment. The median time from diagnosis to BREAST-Q completion was 5.8 years (range: 1.9-10.4).

Among participants, 28% had lumpectomy, 20% unilateral mastectomy, and 52% bilateral mastectomy; and 89% of women had reconstructive surgery. Radiation was received by 99% of women who had lumpectomy compared with 45% of women who had mastectomy. And lymphedema was self-reported at year 1 by 20% of participants.

BREAST-Q scores showed that women who had mastectomy (unilateral or bilateral) had lower breast satisfaction, psychosocial well-being, and sexual well-being compared with women who had lumpectomy. However, researchers found that physical function scores were similar among all groups (bilateral mastectomy, 78.7; unilateral mastectomy, 78.9; and lumpectomy, 78.9).

The average BREAST-Q scores for breast satisfaction were: 65.5 for the lumpectomy group, 59.3 for the unilateral mastectomy group, and 60.4 for the bilateral mastectomy group.

Psychosocial well-being scores were lowest among the bilateral mastectomy group (68.4) compared with 70.6 for the unilateral mastectomy group and 75.9 for the lumpectomy group.

For sexual well-being, the lumpectomy group had a score of 57.4 compared with 53.4 for the unilateral mastectomy group and 49.0 for the bilateral mastectomy group.

&ldquo;These findings suggest that surgical choices may have long-term impact on quality of life,&rdquo; Dominici said. &ldquo;We really need to have more data about quality of life, particularly after surgery, because this information can help shape their decisions.&rdquo;

Researchers then conducted a multivariate analysis looking at beta coefficient (&beta;). For patient satisfaction with breasts, unilateral (&beta; -8.7;

< .001) mastectomy had large decreases in satisfaction compared with those who had lumpectomy. Decreases were also seen in patients who had radiation (&beta; -7.5;

< .001) and those who reported being financially uncomfortable (&beta; -5.4;

Regarding physical well-being, patients who reported lymphedema (&beta; -6.4;

< .001) and being financially uncomfortable (&beta; -4.8;

.004) had moderate decreases in QOL versus those who did not, Dominici said. Patients who were 5 years out from surgery had better QOL (&beta; 6.0;

For psychosocial well-being, lumpectomy was associated with a large improvement in QOL compared with patients having unilateral (&beta; -8.3;

< .001) mastectomy, Dominici said. There was also a decrease in patients who received radiation (&beta; -6.0;

= .003) and in those who reported being financially uncomfortable (&beta; -7.0);

= .003). In addition, having a BMI of &ge; 25 had a moderate decrease in QOL (&beta; -4.2;

In sexual well-being, the multivariate analysis showed a large decrease in QOL for patients undergoing unilateral (&beta; -3.7;

< .001) mastectomy. Being financially uncomfortable (&beta; -6.8;

= .004), having a BMI &ge; 25 (&beta; -5.3;

= .05) was also associated with a decrease.

&ldquo;I think the more prospective data that we generate to help us figure out which patients are going to have worse or better outcomes with these different types of surgeries, the better we will be able to counsel patients with things that will be meaningful to them in the long run,&rdquo; Dominici said during a press conference at the meeting.

Moderator C. Kent Osborne, co-director of SABCS and director of the Dan L Duncan Cancer Center at Baylor College of Medicine in Houston, weighed in on the findings: &ldquo;The data are more disconcerting when you consider the high mastectomy rate in this country relative to Europe,&rdquo; he said. &ldquo;And this urge to have bilateral mastectomies, which, pardon the expression, is ridiculous, in some cases, because it doesn&rsquo;t improve your outcome and yet it does have deleterious effects that last for years such as psychologically.&rdquo;

Dominici L, Hu J, King T, et al. Local therapy and quality of life outcomes in young women with breast cancer. Presented at: 2018 San Antonio Breast Cancer Symposium (SABCS); Dec. 48, 2018; San Antonio, Texas.

Findings of a recent study suggest surgical choices in women with breast cancer may have long-term impacts on their quality of life. Investigators found that younger patients with breast cancer who underwent lumpectomy had better QOL outcomes than patients who had a&nbsp;mastectomy.

Breast Cancer Surgery Choice May Have Long-Term Impact on QoL, Study Finds

The United States Food and Drug Administration (FDA) approved several maintenance therapies over the last few years for the management of platinum-sensitive recurrent ovarian cancer. Bevacizumab (Avastin), TKIs, or PARP inhibitors were among the treatments used in the clinical trials leading to these approvals.

Investigators from a variety of cancer centers used the American Society of Clinical Oncology (ASCO)&rsquo;s Net Health Benefit (NHB) and the European Society of Medical Oncology (ESMO)&rsquo;s Magnitude of Clinical Benefit Scale (MCBS) to identify the value of each maintenance therapy and biomarker to direct treatment. The centers involved in this included Duke University Medical Center, David Geffen School of Medicine at UCLA in Los Angeles, The Ohio State University, James Cancer Hospital in Columbus, Ohio, and Memorial Sloan Kettering Cancer Center in New York City, New York.

Clinical benefit, toxicity, long-term survival, palliation of symptoms, treatment-free interval, and quality of life can be found with the NHB with a total possible score of 180.

MCBS examines the clinical benefit grade, early stopping of crossover, toxicity, quality of life, and adjustments. The highest possible grade for the MCBS is a 4, which the ESMO considers to be a high-value grade.

Investigators evaluated maintenance trials in ovarian cancer, particularly in the OCEANS and GOG 213 trials for bevacizumab treatments,&nbsp;ICON6 for cediranib, a TKI, and the Study 19, NOVA, SOLO2, and ARIEL3 trials in order to understand a select variety of PARP inhibitors.

According to the findings that were presented at the 49th Society of Gynecologic Oncology (SGO) Annual Meeting on Women's Cancers that took place in New Orleans from March 24-27, a higher ASCO and ESMO value assessment was found in women with germline- or somatic-

mutations or tumor homologous recombination deficiency (HRD) who were positivity treated with a PARP inhibitor.

In the OCEANS trial, the composite value scores of ASCO for concurrent plus maintenance bevacizumab were 35. In the GOG 213 trial, there was a score of 26. The ICON6 trial&nbsp;found a TKI ASCO composite score of 30.&nbsp;ESMO&rsquo;s value grades were also comparable to these.

SOLO2, Study 19, NOVA, and ARIEL3 trials found targeted composite value comparison for germline and somatic BRCA mutations in the PARP inhibitor with a range of 47 to 62.

&ldquo;These are the highest value scores observed in maintenance therapy for platinum-sensitive recurrent ovarian cancer,&rdquo; said Jonathan Foote, MD, Duke University Medical Center in Durham, North Carolina.

The composite values for ASCO and ESMO were compared for patients with wild-type

mutations. Scores ranged from 26 to 62 in the PARP inhibitor trials, while ESMO scores were similar. The ASCO scores for non-biomarker-based PARP inhibitors and bevacizumab were comparable as well, noted Foote.

However, there were some limitations to the study. Foote explained, &ldquo;ASCO and ESMO value scores are not intended to compare clinical trials to each other. However, we found that comparing the relative value of treatments using these tools is a very objective measure of benefit that can be used to counsel patients.&rdquo;

There was no allowance for incorporation of both progression-free survival and overall survival data in value frameworks and cost was not considered. An unknown effect of biomarker status in patients was also found in other undergoing maintenance therapies besides PARP inhibitors.

According to ASCO, genetic testing was deemed beneficial in terms of patient management, such as surgical treatment, prognosis and risk for additional cancers. This will also help in creating individualized patient care and shared decision making.

&ldquo;Our data highlights the importance of germline and somatic

testing to differentiate treatment value and to assist in treatment decisions,&rdquo; noted Foote.

Foote JR, Alvarez-Secord A, Liang MI, et al. Bevacizumab, TKI, or PARPi? A targeted approach using composite value-based endpoints and biomarkers to individualize care for platinum-sensitive recurrent ovarian cancer (PSROC). Presented at: SGO Annual Meeting on Women&rsquo;s Cancer; March 24-27, 2018. New Orleans.

The United States Food and Drug Administration approved several maintenance therapies over the last few years for the management of platinum-sensitive recurrent ovarian cancer. Bevacizumab (Avastin), TKIs, or PARP inhibitors were among the treatments used in the clinical trials leading to these approvals.

Katie Kosko | Authors

Articles

Systemic Therapy for Advanced Hepatocellular Carcinoma: ASCO Guidelines

Machines and Patient Power Move Biomarker Discoveries Forward

Breast Cancer Surgery Choice May Have Long-Term Impact on QoL, Study Finds

New Approach Helps Direct Best Maintenance Therapy Choice for Patients With Ovarian Cancer