Where are subsequent therapy data from NIAGARA?

In a previous video, I made a side-by-side comparison between NIAGARA and AMBASSADOR trials (video available here). Both trials were run in the peri-operative setting of bladder cancer (NIAGARA is neo-adjuvant followed by adjuvant, AMBASSADOR is purely adjuvant). Both trials demonstrated an EFS/DFS gain in the experimental arm. Trials’ design (left) and their corresponding EFS/DFS results (right) are shown below:

However, only NIAGARA demonstrated an overall survival gain. The side-by-side comparison was primarily aimed at highlighting that, in global trials like NIAGARA, a survival gain may be driven by the limited access to optimal care for patients who recur.

Such limited access is explained when some enrolling countries are simply lacking access to the best care outside the trial. Conversely, in trials like AMBASSADOR, that was exclusively run in the US, optimal access to post-protocol care, which is desirable!, may have explained the absence of survival gain.

In a paper published in the European Journal of Cancer (full paper here), we posit that such global trials are rewarded in terms of guideline recommendations and approvals.

Here is the side-by-side comparison showing how NIAGARA was “rewarded” as compared to AMBASSADOR.

The subsequent therapy data from NIAGARA are now available in the IQWiG assessment report!

Even though the discussion with the NIAGARA trialists during the first presentation in September 2024 suggested that post-protocol access to immune checkpoint inhibitors would likely be low, we were awaiting for those data until very recently.

They are still, to my knowledge, unpublished.

However, NIAGARA was recently evaluated by IQWiG. In their assessment, some key subsequent therapy data are now reported. Based on those, we can also compare them to AMBASSADOR (see table below).

A common, yet misleading, argument is to use real-world data (often involving frailer patients) to explain the low rates of post-protocol care in trials, when the underlying cause is limited global access. A more appropriate comparison is with trials such as AMBASSADOR, which are highly valuable to illustrate what can be expected as optimal subsequent therapy in a clinical trial setting.

Here, we can derive two conclusions for patients in the control group of NIAGARA:

1. confirmation of a very limited access to checkpoint inhibitors, received by only 25% of patients with an EFS event.

2. a limited access to any subsequent therapy, 38% of patients with an EFS event in NIAGARA, significantly lower than 57% of patients with a DFS event in AMBASSADOR.

Both may have biased the trial toward an overall survival gain in NIAGARA.

Why we should follow IQWiG and other HTA bodies reports.

It should be noted that other HTA bodies may also be providing key data. In the case of NIAGARA, the CDA-AMC Canadian agency also reported subsequent therapy in their report (available here, Table 8).

A specificity is that IQWiG is also systematically requiring the first subsequent treatment as well. Subsequent therapy data lacking granularity can confuse their interpretation (we have written about this together with , see here)

The sponsors, when submitting a dossier to IQWiG, must specifically provide :

“details regarding subsequent treatments administered after discontinuation of the study medication (for oncology-related issues, please also provide separate details regarding the first subsequent treatment).”

They are also requiring, systematically, the numbers of censored patients:

“ In survival analyses, the Kaplan-Meier curve should be plotted, including information on patients at risk and the proportion of censored patients over time (at multiple time points). A separate Kaplan-Meier curve should be plotted for each endpoint for which such an analysis is performed.”

IQWiG is doing an outstanding work. Their reports, which are publicly available, are usually translated into English a couple of months after publication. English reports are available on the IQWiG website, switching to english language, and searching for the relevant drugs. Definitely worth following closely!

Here is my full talk. I would like to thank the IQWiG team again for the invitation, for their warm welcome, and for their outstanding work. You should definitely follow IQWiG’s activities and read their reports, which may contain more data than what is available in publications.

+ At 41:40, get a glimpse of what can be done with the new V2 version of the BREAKING-ICE App© and its Assistant-KM companion app, with the updated sensitivity analysis based on the 5-year follow-up of the NATALEE trial.

The BREAKING-ICE App© starts from Kaplan–Meier curves reconstructed from pseudo individual-level data (pseudo-IPD) using the IPDfromKM package and data extracted from original curves. These are not real patient data: they are numerical estimates consistent with published curves and tables, used to explore informative censoring through sensitivity analyses. Upstream file preparation can be performed with Assistant-KM-BREAKING-ICE©.

A central goal of the BREAKING-ICE App© is to visually and instantaneously explore how alternative assumptions about censoring may affect curves and statistical outputs. The BREAKING-ICE App© has been published in The Lancet Oncology, June 2025, available here.

The BREAKING-ICE App© V2 is available here.
The brand-new Assistant-KM-BREAKING-ICE© is available here.

Both come with a user guide, and a video tutorial will be available soon. Please let me know what you think!

In our new paper, published in the Journal of Cancer Policy and openly accessible here, we analyze how and how quickly the National Comprehensive Cancer Network (NCCN) updates its guidelines in response to Food and Drug Administration (FDA) approvals and withdrawals over time.

The NCCN produces some of the most influential and referenced oncology guidelines worldwide. These guidelines strongly influence hematologists’ and oncologists’ clinical decision-making, and in the United States, when the NCCN recommends a drug, it regularly becomes covered by Medicare, Medicaid, and private insurers.

Given this influence, we were curious about how quickly NCCN updates its recommendations following regulatory decisions, as these updates likely affect patients, clinicians, and payors.

The Question: Do Guidelines Keep Pace with Regulatory Reality?

We examined how quickly the NCCN updates its guidelines after decisions from the U.S. Food and Drug Administration. Specifically, we looked at 83 FDA cancer drug approvals and 8 FDA withdrawals between October 2022 and July 2024. We also subdivided those FDA approvals into traditional regular approvals and accelerated approvals.

Our main findings:

The NCCN incorporated FDA approvals very quickly, with a median of 19 days post-approval. In contrast, the NCCN updated withdrawn indications significantly slower, with a median of 57 days post-approval.

The NCCN removed only 3 of 8 withdrawn indications from its guidelines. The majority (5 of 8) remained recommended in some form, and four of these were recommended with either uniform or majority NCCN expert panel consensus (Category 2A and 2B recommendations).

We found no significant differences in the time to NCCN update between regular and accelerated approval statuses, with median times of 20 days and 16 days, respectively.

Further insights into how NCCN panels make decisions

We also examined the NCCN’s recommendations for prescribing these therapies. We found that only one FDA-approved treatment (1.2%) included in the NCCN guidelines lacked uniform expert consensus (>85%) as a category 2B recommendation, which still comes with majority consensus (>50%).

This finding suggests that NCCN experts largely support the FDA’s approval decisions, in contrast to their attitude toward withdrawals as highlighted above.

As we point out in our discussion, this tendency toward universal consensus raises concerns, especially since 38% of all FDA approvals and nearly 80% of accelerated approvals come from single-arm, uncontrolled studies (see here).

Additionally, we found that in 22% of cases, the NCCN made additions or modifications to recommendations before receiving FDA approval. This does not imply that the NCCN is bypassing the FDA. Both the NCCN and the FDA respond to similar data published in the literature, and the NCCN may have updated these recommendations in anticipation of upcoming FDA approvals. However, this finding further reinforces our previous finding that the NCCN tends to incorporate new drugs quickly, even faster than approval, in contrast to delays in removals.

This study’s implications:

1. Delays in updating withdrawn indications may lead to doctors prescribing cancer drugs that lack proven survival or quality of life benefits, while exposing patients to the toxicities of those very drugs.

2. Given that the NCCN recommendations affect insurance coverage, continuing to recommend ineffective drugs may drive unnecessary spending of patients’ and taxpayers’ dollars.

3. Many cancer drugs are approved based on limited evidence, especially through the accelerated approval pathway. If guidelines rapidly adopt these therapies but hesitate to remove them when confirmatory studies show these drugs are not effective, the result may be persistent low-value care.

Ultimately, this study highlights that the NCCN has competing priorities that are very difficult to balance. The NCCN aims to equip clinicians with as many treatment options as possible that insurers cover and to provide evidence-based recommendations to aid cancer patients. These competing priorities likely contribute to our study’s findings.

Potential Solutions

1. Have two separate bodies to perform the role of the NCCN. One that provides clinical guidance and another that provides coverage decisions and cost-effectiveness analyses.

2. Alternatively, a federal standard or law could be established that compendia incorporate and align with FDA regulatory guidance, especially regarding withdrawals.

Many thanks to my mentors, and , for their guidance, support, and efforts in moving this project forward.

I’ll conclude with the take-home point: guideline makers should continue to quickly adopt new drugs to maximize patient access and inform clinicians, but they should also remove withdrawn drugs with equal efficiency when confirmatory studies call their efficacy into question, leading to FDA withdrawal.

In addition to receiving maintenance HER2 and endocrine therapies, 518 patients were randomly assigned to receive palbociclib – an anti-CDK4/6 inhibitor – or no other therapy. The primary end point was investigator-assessed progression-free survival (PFS).

The study was just published in the New England Journal of Medicine and there are several points to raised while looking at this study report.

Does the reported PFS gain seem reliable?

In PATINA, the authors report a 15 months median PFS gain, with 44.3 months median PFS in the palbociclib group and a 29.1 months median PFS in the control group (HR = 0.75; 95%CI = 0.59 - 0.96; two-sided P=0.02).

Two key elements have to be noted, that could have amplified the gain as reported.

1- Informative censoring.

The numbers of censoring patients are (sadly) unreported in the NEJM publication, however, one can estimate those by using digitized curves. In the BREAKING-ICE App, select PATINA-PFS and you see a significant early imbalance in censoring with 5.3% of patients censored over the first 6 months in the palbociclib group and 12.1% of them in the control group.

Why patients left in excess after being assigned the control group?

Some may have been disappointed by allocation to the control arm. Some may have been eligible for other trials and left PATINA, possibly encouraged by trialists.

Now here is the crux of the issue: are patients lefting PATINA in excess the same as those remaining in the control group? A reasonable assumption in that those patients may have been more connected, had more options, and may have been lower-risk patients in terms of presenting an event. If so, the remaining control patients would be enriched for higher-risk patients, potentially inflating the observed PFS difference.

The Reverse Kaplan-Meier analysis explore whether informative censoring could have occured, by flipping events and censoring events. In PATINA, both this plot and its corresponding “Cox” analysis, being “statistically significant”, show informative censoring could have occured. (Go to the Reverse-KM App© and select PATINA-PFS, see screenshot below).

2- Open-label design and investigator assessment.

We know some subjectivity can affect investigators when “calling” a progression event based on a CT scan: that’s why many trials use blinded-independent central review (BICR).

Here, the combination of an open-label trial with no BICR could have also amplify the PFS gain.

• “Early progression calls” in the control arm. In an open-label setting, investigators may be more inclined to declare progression earlier in control patients when scans are borderline or tumor growth is still below formal RECIST thresholds, because there’s a practical incentive to move quickly to the next line of therapy or another trial.

• “Late progression calls” in the experimental arm. If clinicians believe the new therapy is helping, they may be less likely to call a progression on equivocal imaging, thereby delaying the recorded progression date.

• Symptoms deterioration interpretation differing by arm. The decision to prescribe non-scheduled CAT-scan is often influenced by symptoms and clinical course. Control patients with worsening symptoms may trigger a CAT-scan sooner than a patient in the experimental arm, with the same symptoms being attributed to toxicity. In the last situation, the official progression is delayed to the next scheduled CAT-scan.

In conclusion, given the possible biases introduced by informative censoring, an open-label design, and the lack of BICR, the PFS gain as reported may have been artifically inflated.

Is PFS a direct clinical endpoint?

The answer is no. PFS is a composite time-to-endpoint enpoint: progression (largely based on CAT-scan thresholds) or death can both count as an event. (see the figure below).

One issue is that often, and this is the case in PATINA, we don’t have the“breakdown” of PFS events. In other words, we don’t know whether the event was a CAT-scan progression, or death (see this work in Nature Reviews Clinical Oncology, by Anushka Walia and , with a key figure below).

Without kwowing the breakdown of events, the relevance of a PFS gain is difficult to assess, as death and an asymptomatic progression are very different events, still counting similarly in PFS estimates.

In PATINA, we have only an indirect clue that most events were disease progressions and not deaths in the Figure S1. CONSORT Diagram, as the reason for discontinuation of treatment was death in only a few instances (2 patients in the palbociclib group, and 1 patient in the control group)

Is a PFS gain clinically meaningful?

This question is slightly different from the definition of PFS: even though PFS is not a direct clinical endpoint, we often based our treatment decisions based on progression. As a results, and even if the progression is totally asymptomatic, the event will have a clinical impact on patients simply by triggering a change in care.

“A surrogate endpoint is an endpoint that patients didn’t know mattered until a doctor told them it did” (from )

Some may find this quote provocative, but it highlights that the relevant question about any surrogate, and this applies to PFS, is whether the gain will ultimately led to better outcomes for patients, which are generally accepted as either living longer (Overall Survival gain), living better (Quality of life - QoL - gain), or both.

In PATINA, some have argued a median PFS gain of 15 months (29 to 44 months) is undoubtely clinically meaningful, as this delay the time to the next line of therapy, which is often chemotherapy. This argumentation is assuming that those patients have a QoL gain by delaying the progression event.

This misses 1) the QoL data from PATINA are not yet published, and the data presented so far show HRQoL was “maintained” 2) patient are receiving an additionnal therapy – palbociclib – which does not really fit into the category of a “well-tolerated drug”.

Is palbociclib in PATINA “well tolerated”?

As seen in the supplementary (Table S5 below), 55% of patients receiving palbociclib in PATINA had at least one dose interruption; 28% had 1 dose reduction and another 30% had 2 dose reductions.

In Table S7, we can see that 18.1% of patients experienced adverse events “at least possibly related to treatment that led to palbobiblic discontinuation”.

Not all adverse events affect patients equally: some are asymptomatic lab abnormalities, while others are clinically meaningful symptoms. In PATINA, among the 15 most frequently reported adverse events in the palbociclib group, 11 of 15 (below in yellow, excerpt from Table 2) are primarily clinical rather than laboratory abnormalities.

Conclusions — context with DESTINY-Breast09 and HER2CLIMB-05

The main efficacy results from PATINA – the PFS benefit – may have been amplified by informative censoring and the unblinded nature of both treatment allocation and progression assessment.

We lack the breakdown of PFS events which could inform treatment-decision making with patients : there is a huge difference between a CAT-scan progression and death.

While the QoL data conclude that HRQoL was maintained (presented, not published), many limitations can affect QoL analyses in general, and QoL data should not obfuscate the obvious: the added toxicity of taking an additionnal compound.

The timing of the PATINA publication is surprising, occuring more than one year after the results were presented in December 2024 in San Antonio. Recent publications of novel strategies in first-line or maintenance treatment of patients with HER2+ advanced or metastatic breast cancer may be one possible explanation.

DESTINY-Breast09 studied first-line HER2+ advanced/metastatic breast cancer (54% of patients were HR+) and showed that trastuzumab deruxtecan plus pertuzumab significantly improved BICR-assessed PFS versus taxane–trastuzumab–pertuzumab (THP) (median 40.7 vs 26.9 months; HR = 0.56) with high response rates but a higher risk of adjudicated interstitial lung disease (ILD) or pneumonitis (12.1%, including rare grade 5 events).

HER2CLIMB-05 studied first-line maintenance in HER2+ metastatic breast cancer (53% of patients were HR+) after induction therapy and showed that adding tucatinib to trastuzumab/pertuzumab significantly improved investigator-assessed PFS versus placebo (median 24.9 vs 16.3 months; HR = 0.64), with diarrhea and transaminase elevations common and 13.5% discontinuing tucatinib due to treatment emergent adverse events.

PARP-inhibitors mechanism of action

PARP is a protein involved in repairing the DNA when wrong copies are made, which tipically occurs in cancers.

PARP inhibitors are drugs that are most effective when other DNA repairing genes are already impaired in cancer. These genes are called homologous recombination repair genes (HRR), which can lead to homologous recombination repair deficiency (HRD). Those are usually grouped in two buckets:

1. BRCA1 and BRCA2 mutations (BRCAm, “m” stands for mutation) makes one group.

2. and non-BRCA mutations (non-BRCA HHRm) constitute the other group, with mutations individually less frequent: ATM, ATR, CHEK1, CHEK2, FANC, MRE11A, PALB2, RAD51C and others.

Three trials combining PARPi and ARPI

The three trials are PROpel, TALAPRO-2 and MAGNITUDE. They resulted in five different labels showing inconsistencies between the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). In our paper, we highlight issues which are mainly:

1. The flaws in the trial designs of mainly PROpel and TALAPRO-2

2. The lack of harmonization of HRD

3. Uncertain benefits of combination therapies and control arm issues

4. The differences in approvals and labels between the FDA and the EMA

Trial designs

In PROpel, 796 mCRPC patients were randomly assigned to olaparib or placebo with abiraterone (plus prednisone or prednisolone). The trial adopted an all-comer design with no stratification or prespecified alpha-controlled analysis for BRCAm/HRRm, despite the EMA’s recommendation to do so.

• the primary endpoint radiologic progression-free survival (rPFS) was met.

• no statistically significant difference in overall survival (OS) between the two groups was observed.

• both regulatory agencies suspected that the potential benefit might have been driven by the subgroup of patients with BRCAm (n = 85), in which both rPFS (HR 0.24) and OS (HR 0.30) were significantly improved.

• in contrast, in the non-BRCAm group (n=427), both endpoints were not met.

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In TALAPRO-2, 805 mCRPC patients were randomly assigned to talazoparib or placebo with enzalutamide. This trial also adopted an all-comer design, albeit with a stratification for HRR mutation. A second cohort of HRRm patients only followed, and the combined HRRm population across both cohorts was analysed.

• both independent primary end points of rPFS in the all-comers cohort and in the combined HRRm population were met.

• rPFS and OS for non-BRCA HRRm were not significant.

• final OS analysis was significant in the overall population. However, better in HRRm subgroup (HR 0.55, 95% CI 0.36–0.83; p = 0.0035) but not significantly different in non-HRRm or unknown patients.

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In MAGNITUDE, there were two cohorts: 423 HRRm mCRPC patients in cohort 1 and 247 non-HRMm CRPC patients in cohort 2. They were randomized to either niraparib or placebo combined with abiraterone (plus prednisone or prednisolone). Cohort 2 was stopped early because of futility in a pre-specified interim analysis. As the greatest effect was expected in BRCAm, a protocol amendment was implemented in 2020, approximately one year after the start of the trial, to modify the statistical analysis plan. The BRCAm subgroup was assigned as the primary efficacy population to be analyzed first. If statistical significance was reached in this subgroup, the entire Cohort 1 population would then be tested.

• rPFS in the BRCAm subgroup was met

• rPFS in the overall Cohort 1 was also met

• no significant effect was seen in non-BRCA HRRm patients and even a potential detrimental effect on overall survival (OS HR 1.13, 95% CI 0.77–1.64).

Lack of harmonization of HRD

The definition of HRD is not consistent across trials. For example, the PROpel trial did not consider ATR, NBN, FANCA and MRE11A genes 4 whereas TALAPRO-2 included MLH1, a mismatch repair gene, which is not part of the HRR pathway.

Ideally, researchers should be given access to both biomarkers and clinical data. Aggregating data can increase statistical power and help us better understand how different HRRm impact treatment outcomes.

Uncertain benefits of combination therapies - and suboptimal control arm

In contrast to monotherapy, where ineffective drugs are discontinued, PARP inhibitor use can continue even if the effectiveness is driven only by ARPIs. This potential over-treatment comes with increased toxicity and higher healthcare costs.

Also, in these studies, patients in the control arm who had previously received an ARPI arguably received substandard care by being assigned another ARPI (the so-called “ARPI-switch”). The EMA also raised concerns about patients with visceral disease who had not received docetaxel but had been treated with an ARPI, and therefore restricted the indication to those for whom chemotherapy is not clinically indicated.

Differences in labels between regulatory agencies

Of the three trials, only MAGNITUDE (niraparib) led to the same conclusion from both the EMA and FDA, as its design effectively identified the benefiting subgroup. The discrepancies observed in the assessment of the other two trials stem from differences in their design.

In general, if a positive trial demonstrates benefit in a biomarker-positive subgroup, FDA guidance allows approval only for that population. In contrast, in the case of both PROpel and TALAPRO-2, EMA authorized a broad label for the overall population. (see Table 1 below)

In other words, the FDA was more restrictive, with a biomarker-driven approach, than the EMA when considering labels (see also unpublished figure below).

There is a sad paradox. MAGNITUDE had a better design and informs treatment decisions better than the other trials, leading to a more restricted label, consistent in both US and Europe. In contrast, the two other companies seem to be rewarded wtih broader labels despite the design flaws and despite ignoring recommendations by the agencies.

Conclusion

Our work highlights how inconsiscies in trial designs may lead to different conclusions. An optimal trial would prospectively determine HRR status using a standardized definition, followed by stratified randomization into biomarker-defined cohorts (e.g., BRCA1/2, non-BRCA HRRm, and non-HRRm). Each cohort would be independently powered to enable definitive conclusions, and to avoid the risk of one cohort driving the benefit of a wider cohort (“adjacent subgroups” design instead of “nested subgroups” design). The control arm should reflect contemporary standard of care, allowing a fair comparison between combination therapy vs optimized sequential use.

Even though no trial is perfect, these funding principles would limit the risk that a trial is ultimately non-informative stemming from its very inception.

See our full paper openly available here.

The efficacy-effectiveness gap

An important difference must be made upfront between “efficacy”: what is seen in randomized trials, and “effectiveness”, i.e. what will happen in the real-life settings. Often, a difference will be observed between those two, which is called the "efficacy-effectiveness gap”.

What is an explanation for the difference? Simple - because patients in trials are different from those encountered outside them, and this is problematic.

Age as a key driver of this gap

Part of the mismatch is due to differences in age and health status between trial participants and the general population with the respective condition. The need for better age representation is especially important in oncology, where the risk of cancer increases as people age, yet exclusion criteria related to health conditions often prohibit older people from clinical trial participation, even though they may commonly be treated with the drug in clinical practice.

The observation that clinical trials enroll younger and healthier individuals than in the general population with a given condition is not new, and in fact, the US Food and Drug Administration (FDA) has issued guidance documents on several occasions to encourage trialists to include a study population that is more representative of the population with a condition. However, differences in age between clinical trials and the real-world may still persist, which is why we set out to assess any potential differences.

Our study - covering more than 20 years of registration trials

To answer this question, we searched for the age of trial participants and tumor types for all drug approvals going as far back as 2002. We noted multiple metrics of the age of trial participants, including median age and the percentage of people above certain age thresholds (>65 years, >75 years, etc.). To get at the real-world age for each tumor type, we used the Surveillance, Epidemiology, and End Results (SEER) data for the tumor type being tested in the registration trials for each approval.

The median age of the general population (as per SEER data) was 67 years of age, and the median age of the trial participants was 62 years of age.

We found that the age gap between registration trials and SEER data has declined over time (7 years during 2002-2012 vs 5 years during 2013-2019 vs 4 years during 2020-2024), but the age gap still persists. SEER ages were older than clinical trial ages for all tumor types except for thyroid and leukemia.

How to improve upon those findings?

One possible solution is to conduct trials specifically in older adults. In our review of trials, we found only 5 trials (of 495 non-pediatric trials) that restricted the inclusion criteria to older adults. Of those, 2 used a cut-point of 55 years of age - an age that is not typically considered as “older”.

Limiting the exclusion criteria in a trial may also be a way to get a more representative population. While comorbidities (often occurring as exclusion criteria) in the study population may complicate the interpretation of study findings, they are also more common as people age. Allowing individuals with comorbidities, unless there is a specific reason to exclude them from trial participation, would not only result in a more representative population, it would provide more realistic information on safety and benefit in the context of conditions and medications that older adults commonly have and take.

In conclusion, while there has been progress on shrinking the age gap between clinical trials and the real world population, there is still room for improvement.

Check out the full paper here!

Note for readers: the work presented in this post was prepared prior to my employment at the US Food and Drug Administration (FDA), and the views expressed here don't represent the views of the FDA.

Still, in few instances, interventions appear to have exceptional effect size. In those cases, with and , we proposed a novel trial design that both maintain a randomization component while being acceptable and ethical for patients. We published this proposal in Trials after a study showed impressive results with dostarlimab – an anti-PD1 therapy – in patients with MSI-high rectal cancer.

How to study the few potential “parachutes” in medicine?

The abstract of our work sets the stage:

“Dostarlimab (Jemperli, GlaxoSmithKline) is an anti-programmed death receptor-1 monoclonal antibody (anti-PD-1) recently tested in a non-randomized, phase II trial (NCT04165772) which included patients with mismatch repair-deficient, locally advanced rectal cancer. Among the first 12 patients treated with dostarlimab, 100% achieved a clinical complete response with no patients experiencing progression or recurrence to date. Most impressive, none required chemotherapy, radiotherapy or surgery the prevailing standard of care. In this paper, we discuss the impressive results of this trial and how they relate to cancer policy, as well as propose a novel trial methodology to assess dostarlimab.”

The study was presented during ASCO 2022 and published the same day in the New England Journal of Medicine. Below are the study schema and key results:

To gain knowledge, incorporate randomization whenever possible

A class-effect? In the case of dostarlimab, everyone wondered if such impressive results were due to the drug itself or whether similar results could have been achieved with any other anti-PD1 therapy. Randomization may answer this very question.

Parachutes themselves have evolved over time, and paragliding today is not exactly similar to jumping with parachutes 50 years ago in terms of physical trauma and risks taken.

One can pre-specify rules – like a number of events – defining the “Skydiving parachute zone” (on the left). In this zone, you don’t want to randomize patients to “no parachute”, however you can use randomization to clarify, for instance, if there is a class-effect or not. Randomization between different “parachutes” – different anti-PD1 molecules – is both reasonable and informative.

If the “Skydiving parachute zone” rules are crossed, then the trial will move to a more conventional “Equipoise area” (on the right) where randomization versus the usual standard of care is again justifiable and acceptable. We illustrated this in the key figure below.

Concluding words

Although randomization is not always appropriate or feasible in medicine, the question of incorporating some degree of randomization into trial design should always be considered. Randomization is a powerful tool for generating knowledge and should not be dismissed, even for interventions or compounds with seemingly very large effect sizes. (our full work is accessible here).

In many trials, placebo are used as add-on to a backbone therapy, trying to isolate the experimental add-on therapy. For instance, you will have trials comparing “chemo + immuno” versus “chemo + placebo”. In our work, we did not include those, we focused on trials where the control arm is “only” an inert compound.

An Ethical Framework for the Use of Placebo

The World Medical Association, in the Helsinki Declaration, describes the Ethical Principles for Medical Research Involving Human Participants. And when it comes to the control arm and the use of placebo, the declaration is very clear:

“participants (…) will not be subject to additional risks of serious or irreversible harm as a result of not receiving the best-proven intervention.”

Wisely, they add:

“Extreme care must be taken to avoid abuse of this option.”

Does progression or death constitute “irreversible harm”?

In our work, we looked at FDA approvals over almost 16 years (2009 to November 2024) and found 19 registration randomized trials in the advanced or metastatic setting using such inert control arm. One was excluded from our final analysis as it didn’t report subsequent therapy, leaving 18 trials, most using placebo-only (17 trials) while one was using “best supportive care”.

We used a simple and powerful proxy: how many patients received a subsequent active therapy after receiving an inert control arm?

We found that overall 49% of control patients got subsequent therapy!

In other words, those patients, with advanced or metastatic cancer, were randomized to an inert substance and later, upon progression, finally received an active therapy, including potentially life-prolonging compounds.

Below are the 18 trials with, in blue, the proportion of control arm patients receiving subsequent therapy (and in orange those from the experimental therapy).

The more subsequent therapy in controls, the higher the “benefit” from the novel therapy.

It is logical that if more patients received subsequent therapy, this indicates that they were more likely eligible for an active therapy upfront rather than an inert control arm. Thus, a high rate of subsequent therapy suggests that a large proportion of patients were genuinely deprived of potentially effective treatment during the randomized phase.

Consistent with this, we found a significant inverse association, with a 0.07 decrease in the hazard ratio (indicating greater magnitude of benefit) for every 10% increase in control patients receiving subsequent therapy.

Even though we pooled PFS and OS in this analysis, “pooling was justified because the core question concerns trial validity under inert controls, hazard ratios guide regulatory approval, and both OS and PFS distortions may arise from the same issue—depriving control patients of active therapy.”

A simple rule to assess the control arm - “Would you randomize your own mother?”

In our discussion, we acknowledge that “some level of post-protocol care in control-arm patients is inevitable”. Also, if a framework is provided by the Helsinki Declaration, this means the use of placebo, in some cases, can be justified.

However, in our work, the minimum rate of subsequent systemic therapy was 22 %, really broadly questioning the use of placebo and inert controls in those settings.

Whenever you consider a control arm, ask yourself: would I be comfortable assigning patients to it? “Would you randomize your own mother?” (from ). If the answer is no, it’s a strong signal to investigate rigorously whether the control is outdated, distorted, or unfair.

Check out the full paper for more insights, and thanks to my co-authors

Raj is a researcher I admire for his evidence-based approach and his precise understanding of clinical trials and disease biology, which allows him to offer key and practical insights to clinicians. Raj is also a friend, and he kindly agreed to talk about smoldering myeloma following the recent FDA approval of daratumumab in November 2025 for “adults with high-risk smoldering multiple myeloma (SMM)”, based on the AQUILA trial.

On social media, Raj summarized his “approach to patient counseling post-AQUILA” in a single slide, to which also contributed (see below). In the video, he walks us through the definition of smoldering myeloma, including high-risk disease, the seminal trials, and how he approaches this in clinical practice. For those interested in the censoring pattern I mentioned in the video (at 28:27), please go to the BREAKING-ICE App and select AQUILA-OS (with more to come on that in the future).

I hope you enjoy the video, including the great Q&A session with Sunny and Milos!

A key question, however, is how many patients derive long term benefit? Much of the research around ICIs has had short follow-up, and while results are impressive for some tumor types, long-term data are not always reported.

Indeed, the number of approvals for this drug class has increased, and the percentages of individuals who are eligible for and respond to these drugs have also increased (full access here, and a key figure below).

Several frameworks, including the American Society of Clinical Oncology (ASCO) Value Framework Tail of the Curve and the European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO MCBS) have been developed to capture long-term benefit. However, there are discrepancies between the frameworks, and they both have limitations. As such, conclusions about long-term data from these frameworks may be limited.

Our new work - assessing long-term survival data

To better assess long-term benefit, we reviewed all FDA-approved oncology ICIs in the metastatic setting (2011-2023) (full access here). Using trial information, we searched for the most recent publication reporting overall survival (OS) for each ICI.

The median duration of follow-up for all drugs was 26 months. Of the 88 drugs evaluated, 20 (23%) qualified for ASCO’s tail of the curve bonus and 27 (31%) did not report median OS. Moreover, 50% of studies did not report OS at 24 months, 68% did not report OS at 36 months, and 89% did not report OS at 60 months. Just under one-third of trials report OS data for patients as long as three years, and just over 10% report OS data at five years. Ideally, we would have a better idea of long-term benefit for all drugs and not just a minority of them.

One take-away from the study is that long-term OS and benefit is limited to a few tumor types, such as melanoma. Hepatobiliary and cervical tumors not only have short follow-up, but they often have small differences in the percentage of patients surviving between the intervention and control arms. Long-term benefit may be the exception rather than the rule when it comes to ICI therapies.

Another take-away is that long-term response appears to be at least partially related to tumor biology, not just the difference in OS provided by the ICI. This was demonstrated by the lack of correlation between the longest time with at least 10% of patients at risk and the absolute difference in survival.

It is important to consider that these numbers reflect clinical trial experience, so real-world benefit may be even less since clinical trials often include younger and healthier patients than patients in the real-world.

In summary, the gathering and reporting of long-term survival data on ICIs should be incentivized to provide information to providers who can assess the value of these drugs for their patients.

Let me walk you through this work and explain why studying censoring patterns across longer follow-ups can help identify where informative censoring may have occurred.

The power of digitized curves: enabling estimates of censored patients

In short, when data sharing is absent or limited, or when the number of censored patients are not reported in Kaplan–Meier curves (after each number-at-risk), one can still use a powerful approach: first digitize the curves, then run code to generate pseudo–individual patient data (pseudo-IPD). These reconstructed data produce curves that closely mirror the originals and allow you to estimate the number of censored patients, among many other aspects worth exploring.

In this new work, I used digitization and the BREAKING-ICE App© to extract the number of censored patients at each time point across different follow-ups from the same trials.

In a trial with immature data, a high rate of early censoring is expected because many patients are still on-trial and are censored at the data cut-off — this is “administrative censoring.” With longer follow-up, these patients won’t remain early censored: they will either present the event or be censored later if they’re still on-trial without an event at the next data cut-off, and this continues with each subsequent update. Conversely, if early censoring does not decline as follow-up increases, the likelihood of informative censoring becomes higher.

The NADINA trial: a reassuring evolution of the censoring pattern

NADINA was a trial testing neoadjuvant immunotherapy (in one arm) compared to adjuvant therapy only, in patients with resectable, macroscopic stage III melanoma. When NADINA was first presented and published, there were questions about the possibility of informative censoring because of the high rates of censored patients — 33% and 27% in the experimental therapy and control groups, respectively. First, and this is commendable, the authors did report the number of censored patients in their publication (see below, numbers in bracket after the number at risk).

Moreover, they addressed the possibility of informative censoring by providing a reverse Kaplan–Meier plot in their supplementary appendix (more on this type of analysis here).

Based on updated data presented at ESMO 2024 and 2025, I was able to plot the censoring patterns with longer follow-ups. It clearly shows that the high early numbers decrease with longer follow-up, which is reassuring regarding informative censoring.

The NATALEE trial: a different pattern

NATALEE was a trial investigating the use of 3 years of adjuvant ribociclib in patients with early breast cancer. We and others have raised concerns about the possibility of informative censoring (also detailed here).

The censoring pattern is now evolving very differently with longer follow-ups. There is an area where high and early censoring remains stable, allowing us to identify an “informative censoring area” — here up to the first 18 months — where informative censoring likely occurred.

Of course, this does not mean it couldn’t occur later; however, the identification of an “informative censoring area” in NATALEE reasonably justifies conducting sensitivity analyses within the period where informative censoring most likely occured. This relevant window extends beyond 6 months—a period chosen in a recent publication—and restricting analyses to such an early interval may miss a substantial part where informative censoring could have occurred.

Significant early censoring? Wait for longer follow-up.

The main takeaway point is that when facing significant early censoring, longer follow-up may help distinguish administrative censoring from informative censoring. Regarding informative censoring, the more mature data in NADINA are reassuring, but this is not the case in NATALEE. Another way to address this issue—even with earlier follow-up—would be real data-sharing, which is often limited or absent.

“(…) when assessing trial outcomes, health technology assessment bodies or regulatory agencies may request longer follow-up to better determine the extent to which an early censoring pattern is likely to be informative.”

You can read the full paper here — it’s open access!

I invited him to discuss a recent paper he co-authored with Gilbert Welch and led by Vishal Patel. The study, published in JAMA Internal Medicine, is titled “The Rise in Early-Onset Cancer in the US Population—More Apparent Than Real.”

In our conversation, Ade walks us through the background of the research question, the key findings, and the open questions that remain. What follows is a live discussion between us, joined by core members of the VKPrasadLab and other guests.

A gem from this interview is this final quote from Ade:

“Whenever somebody tells you that the incidence of something is going up a lot, ask about the outcome of interest, like mortality, especially as it relates to cancer”

I hope you enjoy this rare and thoughtful exchange on a crucial topic: how to understand and contextualize incidence numbers within the broader population.

(free link to the paper here)

In such cases, a classic situation is when the disease remains stable for a long period of time before progressing. In those situations, it remains difficult to know whether this apparently positive outcome is occurring simply because the patients were highly selected—with spontaneously slow-growing tumours and indolent biology—or whether the outcome is actually related to the therapy under examination.

One way to distinguish between these possibilities is to compare, for the same patients, their outcomes before and after the therapy. This is the core idea behind the Iceberg Plot, which was designed and published in The European Journal of Cancer soon after I joined the VKPrasad Lab.

The swimmer plot - no prior therapy data

In Swimmer plots, as the one below (from this publication), you have no data on prior therapy. Each patient has a “lane”, and the lane is beginning with the novel therapy.

Comparing new therapy data to prior therapy.

We reanalyzed data from a JAMA Oncology report of a cohort study involving 1138 patients with advanced solid tumors. Next-generation sequencing (NGS) was successfully performed in 1015 patients (89.2%), identifying potentially actionable alterations in 817 (80.5%). Among these, 132 patients (16.2%) received sequencing-directed therapy (SDT), of whom 49 (37.1%) experienced clinical benefit and 26 (19.7%) achieved responses lasting 12 months or longer.

Notably, the study reported prior therapy data for 125 of the 132 patients treated with SDT, enabling the construction of the Iceberg Plot. In this plot, the new therapy (here, SDT) appears above the ocean line, while the time prior to it represents the “invisible” part of the iceberg.

This visualization immediately contextualizes the results, offering a clearer understanding of treatment responses in relation to patients’ prior therapies.

Below are two figures summarizing the steps required to transform a classical Swimmer Plot (shown on the left in the first figure) into the final Iceberg Plot (second figure).

Von Hoff criteria and growth kinetics.

The central question is not only whether the tumor grows, but whether it grows faster or slower under a given therapy — in other words, this concerns tumor growth kinetics.

In this re-analysis, we also assessed how many patients met the Von Hoff criteria, a metric proposed by Daniel D. Von Hoff and colleagues. According to this criterion, a patient is considered to derive clinical benefit if the progression-free survival (PFS) on the new therapy is at least 1.3 times longer than the PFS on the prior therapy. In the analyzed cohort, the proportion of patients meeting this criterion was remarkably low, with only 2 of 97 patients (represented by the red points in the figure below) satisfying it.

Key point

When interpreting outcomes such as PFS or other composite measures that include stable disease (like the disease control rate – DCR) from uncontrolled data, one should always consider whether the observed outcomes reflect a rather indolent tumour biology or are related to the therapy itself.

In such cases, assessing growth kinetics—for instance, by comparing outcomes with the prior line of therapy—can be informative.

Instead of Swimmer Plots, Iceberg Plots should be more widely used in oncology trial reports. (full paper here)

MonarchE survival results

MonarchE enrolled patients with high-risk, HER2-negative, hormone receptor–positive early breast cancer and evaluated 2 years of adjuvant abemaciclib. The survival results, already announced by press release, were presented and published the same day in Annals of Oncology.

The most important result: a statistically significant overall survival benefit, with a 1.8-percentage-point difference after 7 years.

The survival benefit was expected, as it had been announced by press release. Most were awaiting the magnitude of the difference. For MonarchE, the OS benefit, translated into the Number Needed to Treat (NNT), is now 55.

In other words, according to the results, 55 patients need to be prescribed 2 years of abemaciclib to save one life after 7 years.

The main point is that I’m unsure whether such a survival benefit would be seen in practice, where patients have optimal access to CDK4/6 inhibitors for those who recur. Here is why.

Post-protocol, or subsequent therapy, is a key element to consider when a survival gain is reported. The main reason for suboptimal subsequent therapy is when trials are enrolling globally, including in countries where post-trial access to key drugs is limited or absent, thereby questioning the validity of the results in practices with optimal post-protocol care.

Here, as very good news, not only was subsequent therapy data reported, but it was also accompanied by a granular analysis. Here is the slide presented by Prof Stephen Johnston, MD, PhD.

The fact that access to CDK4/6 inhibitors was comparable in both groups for late recurrence led to the conclusion that this limited — but balanced — access was unlikely to explain the survival difference.

My interpretation differs, because this overlooks a key element: poor subsequent therapy access in both arms can artificially create or inflate a survival benefit, simply because the “dilution” of survival by later lines of therapy is reduced. Yet this “dilution” is precisely what we aim for in oncology — maximizing outcomes through optimal sequencing of treatments.

Such a phenomenon applies to any setting: when subsequent therapy is limited in both arms, any DFS or PFS benefit is more likely to translate into an OS benefit.

We illustrated this in one of our work published in BMC Cancer with and (Figure below).

In MonarchE, among patients who received any first systemic treatment, only 38% in the experimental group and 57% in the control group received a CDK4/6 inhibitor as part of that therapy. Whether the survival benefit — already limited given the high NNT — would be maintained in settings with optimal access to subsequent therapy remains uncertain.

NATALEE 5-year updates

The NATALEE trial, with broader eligibility criteria, tested 3 years of adjuvant ribociclib. The 5-year updates were also presented and published the same day in ESMO Open.

As promised, the curves were digitized the same day, and a key takeaway is that the estimated number of early-censored patients remains overall stable — reinforcing our initial concerns that informative censoring likely occurred (more on that in a previous post, or in this publication).

Since the beginning, for both trials, the reliability of iDFS has been questioned due to the possibility of informative censoring. Even if one considers the iDFS benefit to be reliable, we should not forget that, by definition, in the adjuvant setting, a large proportion of patients will be treated with no benefit — an information best conveyed by the Number Needed to Treat (NNT).

Considering the 4.5–percentage point difference at 5 years for NATALEE, this would still mean that 22 patients would need to be treated to prevent one iDFS event.

Why This Topic Is So Important

It cannot be overstated how important these two trials are. Breast cancer has one of the highest incidences worldwide, and about 20% of patients could be eligible for MonarchE and 38% for NATALEE (with substantial overlap) (figure below from Swedish data).

Truly informed and shared decision-making.

This post is an appraisal of the data and is not intended to replace any medical decision. The adjuvant setting is the ideal context for truly informed and shared decision-making.

Whenever possible, the following points should be discussed with patients:

• The definition of iDFS — it is not exclusively capturing “survival” events.

• The limitations raised regarding the reliability of the iDFS benefit.

• The toxicities associated with CDK4/6 inhibitors.

• If mentioning the survival benefit observed in MonarchE, the potential limitation of suboptimal subsequent therapy should also be discussed.

• The uncertainty of the benefit, even assuming results would replicate in our practice, particularly at the individual level (as illustrated by the number needed to treat for both iDFS and OS results).

Words matter:

Patients may or may not decide to undergo adjuvant therapy, and we should respect their decisions. In my opinion, we should reconsider some of the terms we were taught to use, such as “decline” or “refuse,” which may implicitly judge a patient’s choice. I personally prefer saying that the patient “decided”, which better reflects that a decision was made after discussion.

Avoid placing guilt:

Such discussions are also key to avoid placing additional burden or guilt on patients who cannot tolerate the therapy and have to stop it. I have encountered patients who were convinced that if they stopped adjuvant therapy, the cancer would automatically come back. Even if a recurrence occurs after a patient has stopped adjuvant therapy, no one can say that it happened because the therapy was discontinued.

This takes time, but that time is the essence of care — after all, it’s about the patient’s life.

Why should we consider alternate dosing in cancer treatment?

As the authors note upfront : “In the era of targeted small molecule drugs and immunotherapies to treat cancer, many oncology drugs have been approved at excessive dosages.” potentially leading to “unnecessary side effects for patients and potential financial waste for both payors and patients”

The topic is widely recognized as a priority in oncology, as reflected by the FDA’s launch of Project Optimus and by the growing attention it has received from regulators (free link here).

Here, using an interventional pharmacoeconomic approach, the authors examined oral oncology treatments to find where new dose-ranging trials are needed — and where current evidence already suggests that lower doses could safely be used.

They identified 90 patent-protected oral oncology drugs approved by the U.S. Food and Drug Administration between 2004 and 2024. Among these, more than half (50) had potential alternative dosing strategies. The key highlights are summarized below.

What is the level of evidence for alternate dosing strategies?

The authors detail their methods and references, as well as the level of evidence supporting their findings (see Figure 2 below). As shown, there are several ways to optimize dosing — such as reducing strength or increasing dosing intervals. The level of evidence is mostly based on non-randomized trials and pharmacological data, highlighting the need to incentivize both better dosing strategies upfront and high-level post-marketing studies.

Based on randomized trial data, the authors propose that “immediate dosage modification is warranted for sotorasib and ribociclib.”

The potential for annual savings of €20 billion

Even though the authors acknowledge some limitations in their methods, they estimated the potential for global annual savings if the alternative strategies they propose were applied — and concluded with the impressive figure of €20 billion per year.

They note : “The five drugs with the highest potential global reduction in annual sales revenue using alternative dosage strategies are enzalutamide (EUR 3.98 billion in savings), osimertinib (EUR 2.80 billion), ibrutinib (EUR 1.73 billion), olaparib (EUR 1.56 billion), and apalutamide (EUR 1.28 billion).”

Next steps: extend to off-patent generic oral drugs and parenteral compounds

Another reason to bookmark this website and closely follow these researchers is that they plan to expand their work to non-patented drugs as well as intravenous oncology drugs.

Congrats to the authors for this highly relevant work. This manuscript is truly impressive—the methods are clear, the writing is engaging, and the findings are significant. I highly recommend reading the full paper for deeper insights into the topic. (link here)

An entire drug pipeline is based on a patient’s race.

In our 2023 publication, we showed for the first time in the history of drug development that an entire drug pipeline is unequally accessible according to patient’s race or ethnicity.

The Major Histocompatibility Complex (MHC) helps the immune system distinguish between “self” and “non-self.” In humans, it’s the Human Leukocyte Antigen (HLA) complex — highly variable genes on chromosome 6 that give each person a unique immune profile. In medicine, the HLA system is crucial, as an example, for transplantations to reduce the risk of rejection by ensuring a good “match” between donor and recipient based on their HLA genes.

In our work, we examined all trials involving a therapeutic intervention that required participants to be positive for a specific HLA subtype(s), and identified 263 trials. Most of these trials focused on anti-cancer drugs (98%), and most were therapeutic vaccines (68%) or cellular therapies (28%).

Importantly, the HLA subtypes being used to recruit patient were the HLA-A2 or HLA-A*02:01 subtypes in 87% of trials.

We then estimated the likelihood of being eligible for such trials according to patients’ ethnicity. We estimated that African or African American individuals had the lowest likelihood of being enrolled in such trials (33%), when individuals of European or European Descent were 1.6 times more likely to be eligible (53%). Data for other ethnic groups are shown in Table 3.

Anzu-cel in uveal melanoma: significant reponse rate, usual HLA-A*02:01 restriction.

A phase 1 trial in a rare cancer – uveal melanoma – made it to the Presidential session during ESMO 2025!

IMA203, or Anzu-Cel, is a T-cell-based therapy targeting PRAME, which is expressed in 90% of uveal melanoma. The study enrolled patients with recurrent or refractory solid tumours. The patients with uveal melanoma had a 67% confirmed overall response rate, with a 11-month median duration of response, which are quite impressive results in this disease. IMA203, or Anzu-cel, is a HLA-restricted therapy and only HLA-A*02:01-positive patients could receive the therapy. (abstract here)

A new HLA-A*11:01-restricted therapy has been developed in… China!

In an early trial of NW-301V, a T cell therapy targeting the KRAS G12V mutation, 8 patients with advanced colorectal or pancreatic cancer were treated. Three patients (37.5%) had partial response. (abstract here)

However, this novel HLA-restricted therapy now selects only patients with the HLA-A*11:01 immune type.

Why did the Chinese investigators choose HLA-A*11:01 instead of HLA A2:01?

The answer is straightforward when you examine Figure 1 from our publication (below). Compared to frequencies in the overall population, HLA-A*02:01 is more common in European descent (indicated by the red arrow on the right), while HLA-A*11:01 is the most prevalent in Asian or Pacific Islander populations (indicated by the red arrow in the middle) and is less common in European descent (indicated by the red circle).

In other words, the Chinese investigators chose the most prevalent HLA in their population. While this choice makes sense from their standpoint, it demonstrates two key points:

1. Therapies can be developed for HLA types other than HLA-A*02:01.

2. HLA restriction still leads to unequal access to therapies.

HLA-restriction inequalities must ultimately be overcome.

By definition, because HLA subtypes are unequally prevalent across individuals, any HLA-based therapeutics will inherently be unequally accessible according to one’s race.

While both ESMO studies show encouraging results, they also highlight the issue of HLA restriction in anti-cancer therapies. Overcoming HLA restrictions may be biologically challenging; however, solutions should be implemented whenever possible. The research community must strive to ensure equal access to innovative treatments, regardless of race or ethnicity.

I would like to thank and congratulate my Colleague and Friend, Pr John Haanen, who discussed, during the ESMO 2025 presidential session, the IMA203 study in uveal melanoma and emphasized this point in his concluding remarks:

”Solve the inequality in access to PRAME TCR-T anzu-cel” (Pr John Haanen)

In this work, published in the September issue of JAMA Internal Medicine, our research question was simple: how often randomized clinical trials (RCTs) compare next-in-class and first-in-class drugs?

Out of 332 FDA approvals between 2009 and 2020, we included 94 approvals of a next-in-class compound. We found that 27 trials had a randomized trial with an in-class counterpart, but only 6 trials (6%) ultimately demonstrated a survival advantage.

Below is the key figure, with the size of circles representing the number of patients for a specific compound, and arrows representing head-to-head trials. The color of the arrows indicates whether the trial showed a survival gain (green, 6 trials), did not meet their primary endpoint (red, 6 trials!), or met their primary endpoint other than OS (14 trials).

However, in many instances, there were no head-to-head RCTs at all, making it difficult to assess the true therapeutic value of next-in-class drugs.

Our conclusion: “These results suggest a need for regulatory bodies to incentivize within-class RCTs. Such evidence would help determine whether next-in-class drugs provide meaningful advances in cancer care. This is particularly important given that me-too drugs have not led to price competition in oncology, and many are approved through single-group trials or studies using suboptimal controls. Although not always possible if drug development happened concurrently, future regulatory frameworks should consider mandating head-to-head trials against existing in-class agents whenever feasible.”

Check out our full article published in JAMA Internal Medicine for more granular results, including a breakdown by the type of approval (accelerated / regular) as seen in the table below, free link here!

In ovarian cancer, despite numerous trials testing PD-(L)1 antibodies, results have been largely disappointing, leading to a broad consensus that these therapies show limited benefit. Below are quotes from recent publications summarizing the overall outcomes in this field:

“Immune checkpoint inhibitors have shown limited efficacy in ovarian cancer” (here)
“Despite (…) the results of trials testing immune-checkpoint inhibitors (ICIs) in these patients thus far have been disappointing.” (here)

… and now enter a positive trial: KEYNOTE-B96

The ENGOT-ov65/KEYNOTE-B96 trial (NCT05116189) was a global, randomized, double-blind, phase III study evaluating pembrolizumab plus weekly paclitaxel with or without bevacizumab versus placebo plus the same regimen in patients with platinum-resistant recurrent ovarian cancer. The trial enrolled platinum-resistant patients with epithelial ovarian, fallopian tube, or primary peritoneal carcinoma (up to 2 prior lines of therapy including platinum). The primary endpoint was investigator assessed progression-free survival (PFS) in PD-L1 CPS ≥1 and all patients; overall survival (OS) was a secondary endpoint.

In a May 2025 press release, the trial was reported to demonstrate that adding pembrolizumab improved progression-free survival (PFS) across all patients and produced a statistically significant overall survival (OS) gain in PD-L1–positive patients compared with chemotherapy with or without bevacizumab. These results will first be presented during one of the Presidential Sessions at ESMO 2025.

Understanding multiplicity in oncology: a detour with bevacizumab

What is multiplicity, and why is it increasingly important to consider in the modern era of clinical trials? In 2019, Nathan Gay, Derrick Tao, and Vinay Prasad explained this concept and illustrated it using bevacizumab as an example. In their publication, they wrote:

“If a single drug is tested in many tumour types, the interpretation of any single trial result must account for the number of times that the agent is trialled. Drugs tested extensively might benefit from a portfolio-level analysis.”

In other words, when considering an entire portfolio of trials, the classical interpretation of the p-value may no longer hold, it may need to be adjusted using statistical methods such as the Bonferroni correction. Applying this approach to bevacizumab trials, the authors performed this analysis and concluded:

“Although 21 of 30 trials (70.0%) reporting a statistically significant PFS retained that claim after the Bonferroni correction, only 1 of 8 (12.5%) maintained its OS benefit.”

Takeaway point

With KEYNOTE-B96, we can apply the same portfolio-level perspective—not just across the anti–PD-(L)1 class of compounds, but even more meaningfully within a single tumor type, ovarian cancer.

In other words, while the results should not be dismissed outright, a single positive trial after numerous negative ones warrants particularly cautious interpretation.

I look forward to seeing the data presentation. We’ll likely have to wait for the full publication to examine the protocol and statistical plan in detail—were there any changes? For instance, the trial reportedly included 645 patients, whereas the 2022 ASCO abstract mentioned 616; was this difference due to a modification in the statistical plan or another amendment? As the trial was likely not powered for overall survival, careful interpretation of the survival results—including censoring patterns—will be essential.

We also know from the PARP inhibitor trials in ovarian cancer that a long follow-up is required to fully assess overall survival (OS) outcomes (see our paper here).

Today’s takeaway, however, is the importance of approaching any single trial through the lens of multiplicity in oncology.