Kristie L. Kahl is vice president of content at MJH Life Sciences, overseeing CURE®, CancerNetwork®, the journal ONCOLOGY, Targeted Oncology, and Urology Times®. She has been with the company since November 2017.

She is a graduate of Rider University, where she acquired a Bachelors of Art in journalism, as well as a graduate of Temple University, where she received her Masters of Science in Sports Management.

Follow Kristie on Twitter at @KristieLKahl, or email her at kkahl@mjhlifesciences.com.

Kristie L. Kahl

We had an expert answer 10 frequently asked questions about lung cancer. 

Following a lung cancer diagnosis, many patients wonder what the next steps are — from treatment options, next-generation sequencing, surveillance and more.

To ensure patients can always best advocate for themselves in their treatment journey and beyond, CURE® spoke with Dr. Jyoti Malhotra, director of thoracic medical oncology at City of Hope and a presenter at the 

 held on June 15, to answer the 10 most commonly asked patient questions in the lung cancer space. Malhotra focused on post-treatment monitoring, emerging treatment options and managing potential side effects.

After five years of surveillance and no evidence of disease, what monitoring is appropriate long term?

: After five years, guidelines recommend continuing with annual surveillance scans. This may be more frequent if there are radiological findings that are being monitored on the scan.

Can you explain, how and if RNA sequencing is a part of next-generation sequencing in lung cancer?

RNA sequencing is preferred and recommended in addition to DNA sequencing for next-generation sequencing for lung cancer. This is because some of the genomic changes, such as fusions, are more effectively detected by RNA sequencing.

Are there any advanced treatments for interstitial nephritis that persist after treatment ends, other than just steroids? What is the best steroid to use?

Currently, the recommended treatment for interstitial nephritis is steroids as well as consultation and regular follow-up with a nephrologist for additional work-up and treatment. The most commonly used steroid in the outpatient setting is prednisone.

Can any therapies get into the brain? Does radiation open the blood-brain barrier to allow treatment in?

We know that some targeted therapies such as new-generation EGFR and ALK inhibitors (Tagrisso [osimertinib], Lorbrena [lorlatinib] and Alecensa [alectinib], for example) have good penetration across the blood-brain barrier. We do not have a good understanding of the degree to which other therapies are able to get into the brain. We do not have clear data to support that radiation opens the blood-brain barrier to other therapies.

How do you treat patients who experience pneumonitis (inflamed lung tissue)?

Pneumonitis is treated by holding the drug treatment and starting high-dose steroids. It is important to treat with steroids for more than four weeks followed by a gradual taper of dose. Patients who don’t respond to steroids or have severe pneumonitis are admitted to the hospital for additional supportive care and management.

For advanced/metastatic EGFR non-small cell lung cancer (NSCLC), when is chemotherapy combined with Tagrisso, versus Tagrisso alone, or versus chemotherapy and/or immunotherapy?

For advanced/metastatic EGFR-mutated NSCLC, both chemotherapy combined with Tagrisso and Tagrisso alone, are approved regimens. As the approval for chemotherapy plus Tagrisso is recent, so we are still trying to understand when to use the combination therapy rather than monotherapy with Tagrisso. We still do not have survival data for chemotherapy plus Tagrisso from the trial (the phase 3 FLAURA2 trial), but we should have it in the next few years and this will also help answer this question.

Combining Drugs, Radiation Is ‘Practice-Changing’ for Some Lung Cancer Subsets

What is the timeline for post-radiation cloudiness to clear?

This is variable and depends on multiple factors. There is significant variability between patients.

Can you discuss what determines dose adjustments; for example, under what condition a patient can go from 80 milligrams (mg) of Tagrisso to 40 mg?

The recommended dose of Tagrisso is 80 mg. A lower dose of 40 mg is only used if the 80 mg dose is associated with significant side effects and is not tolerable. We do not know if the 40-mg dose will work as well as the 80-mg dose.

Can you discuss any updated treatment options for multifocal adenocarcinoma lung cancer?

This depends on the presentation and extent of the cancer. So it is important to form an individualized treatment plan for each patient with input from a multidisciplinary clinical team. This would include surgery, radiation oncology as well as medical oncology.

Can you discuss treatment options for patients with oligometastatic lung cancer?

The treatment for oligometastatic lung cancer is similar to the treatment for metastatic lung cancer in general. Many physicians do consider more definitive therapies such as radiation for oligometastatic cancer in addition to systemic therapy. But this approach is still under investigation in ongoing clinical trials.

For more news on cancer updates, research and education, don’t forget to 

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Following a CURE® Educated Patient® Lung Cancer Summit, the City of Hope expert answered 10 commonly asked questions from patients with lung cancer.

10 Commonly Asked Lung Cancer Questions With Dr. Jyoti Malhotra

Initial treatment with Lenvima (lenvatinib) continued to demonstrate higher success rates in patients with radioiodine-refractory differentiated thyroid cancer (RAI-R DTC; no longer responding to treatment with radioiodine), according to real-world clinical data gathered from Europe and Canada presented at 

“We have included patients from different practice types, including community [17.6%] and academic [75.8%] settings, as well as different countries and regions, representing a highly heterogeneous population,” Dr. Laura Deborah Locati, said in a presentation of the data. Locati is associate professor in the Department of Medical Oncology at the University of Pavia and director of the Translational Oncology Unit at Istituto di Ricovero e Cura a Carattere Scientifico Maugeri in Milan, Italy.

Initial treatment with Lenvima continues to outperform other treatments for thyroid cancer, specifically radioiodine-refractory differentiated thyroid cancer.

The study relied on real-world clinical data from Europe and Canada, providing a more representative picture of Lenvima's effectiveness in a diverse patient population.

Patients treated with Lenvima experienced high progression-free survival rates at 12, 24 and 36 months, indicating that the disease was less likely to progress.

A significant number of patients achieved complete or partial responses to Lenvima treatment, demonstrating its effectiveness in controlling the disease.

In this study, real-world progression-free survival (PFS; the length of time during and after treatment when a patient with cancer lives with the disease without worsening) was measured from the beginning of Lenvima treatment until either the disease progressed or death occurred. If a patient experienced progression, PFS was not shown at the start of their next treatment or at the date of their last recorded activity. This applied to real-world overall survival (OS; the time from the start of treatment when a patient with cancer is still alive) rates as well.

The estimated PFS rates were 78.7% at 12 months, 61.5% at 24 months and 53.4% at 36 months. At 12 months the estimated OS rates were 92.2%, 83.1% at 24 months and 77.1% at 36 months. The median follow-up period was 17.9 months. At the end of the follow-up period, 79.8% patients were alive, 18.4% were deceased and 1.8% were reported as unknown.

Researchers established that 21.1% of patients experienced a complete response (the disappearance of all signs of cancer from treatment) and 53.7% had a partial response (a decrease in tumor size or extent of the disease in the body from treatment), resulting in an overall response rate (the percentage of patients with either a partial or complete response to treatment) of 74.8%. The estimated PFS rates at 24 months for those experiencing complete response was 95.5% and 57.3% had partial response. At 24 months the estimated OS rate was not reached for those who experienced complete response and 84.4% for those with partial response. Of note, when OS is not reached in a study, it means that at least half of patients were still alive at the time the data were analyzed. In addition, 21.4% of patients had stable disease (neither grows nor shrinks), 3.3% experienced disease progression and 0.6% were not recorded.

The median duration of treatment was 16.8 months and the median follow-up period was 17.9 months. Lenvima was administered once daily at a starting dose of 24 mg for 60.2% of patients, 20 mg for 24%, 18 mg for 12.2% and 14 mg for 3.6%.

“The main goal of this data collection was to assess the clinical effectiveness [of Lenvima] in real-world data from the patient population of radioiodine-refractory cases from different European countries, the UK and Canada,” Locati said.

In the phase 3, randomized, double-blind, multicenter SELECT trial, median PFS was longer in the Lenvima group at 18.3 months versus 3.6 months in the placebo group.

The response rate was 64.8% in the Lenvima group (including four complete and 165 partial responses) versus 1.5% in the placebo group. The median OS was not reached in either group.

Common treatment-related adverse effects in the Lenvima group included high blood pressure (67.8%), diarrhea (59.4%) and fatigue (59%). Lenvima led to discontinuation in 14.2% of patients versus 2.3% in the placebo group and six out of 20 deaths in the Lenvima group during the treatment period were drug related.

The retrospective patient chart review included 337 patients with RAI-R DTC who started first-line (initial treatment) Lenvima monotherapy on March 26, 2015, in Europe, or August 9, 2016, in Canada, until January 31, 2022. Lenvima was approved in Europe in 2015 and in Canada in 2016.

Regarding patient disease characteristics, 60.2% of patients had stage 3 or 4 disease with the following thyroid cancer types: follicular (51%), papillary (45.7%) and Hurthle cell (3.3%). In total, 98.8% of patients reported with metastases (the spreading of cancer) in various locations: 50.4% in the lymph nodes, 48.1% in the lung, 47.5% in the bone and 21.2% in the liver.

The majority (67.1%) of patients had an ECOG performance status score of 0 (meaning completing daily tasks entirely independently) or 1 (patients were able to complete most daily tasks independently) and 21.1% with 2 (patients were limited in activities) or greater. This was a key difference from the SELECT trial which did not include those with a performance status of 2 or greater, Locati noted. Other differences from the SELECT trial included specific criteria for RAI-refractory status, disease progression evidence and prior treatment with tyrosine kinase inhibitors, the researchers noted.

The study included patients from Canada (16.9%), France (16.3%), Germany (16.6%), Italy (17.8%), Spain (17.5%) and the United Kingdom (14.8%). Data was provided by 91 clinicians who are either medical or clinical oncologists (71.4%) or endocrine oncologists (22%).

“We recognize some limitations of this study: the lack of complete safety data, short follow-up period and real-world variation in clinical outcomes,” Locati concluded.

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First-time treatment with Lenvima continues to outperform other treatments for thyroid cancer. 

Study Confirms Lenvima’s Benefits in Thyroid Cancer

GigaPath, an artificial intelligence (AI) model, may predict cancer mutations including those in lung and other cancers, as presented during the 

Findings from the study presented at the meeting demonstrated that the use of GigaPath was better in predicting genetic mutations in lung adenocarcinoma compared with other AI approaches currently available for this purpose. This model also outperformed other methods regarding the identification of five genes for pan-cancer.

GigaPath, an AI model, outperforms other AI approaches in predicting genetic mutations in lung adenocarcinoma and identifying genes for pan-cancer.

GigaPath can accurately predict tumor mutation burden, a crucial biomarker for determining treatment response.

GigaPath is a foundation model trained on a massive dataset of 1.3 billion images from 28 cancer centers.

AI has the potential to revolutionize digital pathology, but its impact has been limited due to the limitations of traditional image analysis techniques.

Results from the study also suggested that GigaPath was able to predict tumor mutation between than what was called a second-best method, as discussed by Dr. Carlo B. Bifulco, medical director of oncological molecular pathology and pathology informatics at the Providence Oregon Regional Laboratory and CMO of Providence Genomics, during the presentation.

In the abstract, GigaPath is explained as an AI foundation model that is pretrained on a large dataset from 28 cancer centers. Of note, this dataset consisted of 1.3 billion images from over 170,000 biopsy slides in more than 30,000 patients with 31 major tissue types. AI is trained by inputting a large set of information, which allows the system to learn what it needs to identify.

In the study, researchers aimed to compare GigaPath with other competing AI models across pan-cancer 5-gene, lung adeno 5-gene (EGFR, FAT1, KRAS, TP53, LRP1B) and tumor mutation burden prediction. Tumor mutation burden refers to the number of genetic mutations found in the DNA of cancer cells. In other words, it is a biomarker that can help determine how well patients may respond to treatments like immunotherapy.

Digital pathology has been used in the space for nearly 20 years, Bifulco said, and AI has already played a role. Despite its use and some Food and Drug Administration-approved algorithms in this setting, the impact of this has been relatively limited.

“There’s a watershed event in AI, … and unless you’re living in a cave, I think we’ve all been affected by this,” Bifulco said.

He added that there is a way to integrate AI into the pathology and biology space, and some of that information has already been published.

“Everything [that] has been published so far has been based either on classical image analysis or what we call convolutional neural networks, CNNs,” Bifulco said. “The way these networks work, they have [a] representation of features of the image, like angles that, again, get abstracts at higher levels until they enable you to actually reach a conclusion about the image. And those methods are very powerful, and they’ve been used for many applications, but they’re very brittle.”

He added that the reason why these networks have not been applied more broadly is because they depend on the characteristics of the dataset, which can lead to it generalizing the information poorly. Bifulco said that we are currently leaning lessons from the AI text language space.

“The underlying concepts are really about the prediction of the next word in the sentence,” he explained. “These models are trained by trying to make a prediction of something. They don’t require any kind of labels. You don’t need to instruct them to learn from the text itself, which makes them incredibly powerful, given the amount of huge, huge amount of texts that are available.”

He added that AI text language models are able to predict the word based on the context of a given sentence, and how the context of the sentence drives the interpretation of what the AI model is deciphering. This type of learning can also be applied to images.

“Fundamentally, we are trying to predict a patch of the slide with cells based on the context of the surrounding slides,” Bifulco said. “You don't need to tell anything to the machine learning algorithm about the slides they're looking at. They're learning those features, those fundamental features, foundation models. That's where the name comes from, from the images themselves, and that enables them to scale and to be very robust and reliant across different applications.”

He noted that the images that the model works with need to be large. “The size of the model is going to be a key factor for our ability to actually perform as well as we would like to,” Bifulco said.

In addition, he explained that training the model involves the whole pathology slide, and since they are gigapixel slides, they are very large.

“There’s additional training necessary to embed all the features of the whole slide that you’re looking at under the microscope,” Bifulco said. “And as you can see, we reach the billion-parameters level.”

Training the GigaPath model also involved embedding multiple sources of information including genomic data, language via text from pathology reports, and text from clinical reports.

Even though GigaPath is being tested in this area, Bifulco said that there are many other models coming that are integrating several technologies. For example, these models may have the ability to train with different sources of images (CT scans, MRIs, ultrasound, pathology), in addition to the ability to interact with models with text, similar to an AI chat prompt.

“Currently, those are potentially deployed on phones, on little devices, but you can see that in the future, very likely, you will have a multimodal kind of integration, where you interact by voice, very likely, with the whole comprehensive data set of the patient,” Bifilco said.

An artificial intelligence model was shown to predict tumor mutation burden in lung cancer and other diseases. 

AI May Predict Cancer Mutations in Lung, Other Cancers

Patients with metastatic solid tumors who underwent comprehensive genome profiling to determine the best targeted treatment strategy tended to have improved outcomes compared with those who received standard of care, according to findings from the phase 2 ROME trial presented at the 

2024 European Society for Medical Oncology Congress

Per the National Cancer Institute, the cancer genome describes the DNA, gene expression, proteins and molecular features of cancer cells. Now, cancer therapies exist that target certain cancer cell characteristics, so by knowing about the genome, clinicians can more accurately predict which treatment regimens may be most successful for an individual patient.

Targeted therapy based on genomic profiling can improve outcomes for patients with metastatic solid tumors.

Patients who underwent comprehensive genome profiling and targeted therapy had higher response rates, longer progression-free survival, and longer overall survival compared to those who received standard of care.

Immunotherapy showed promising results for patients with high tumor mutational burden (hTMB).

Targeted therapy was generally well-tolerated with fewer severe side effects compared to standard of care.

“The mutational model guided by comprehensive genomic profiling and molecular tumor board activities could adapt to overcome some limitation of previous [treatment] models and allow for the selection of more effective targeted therapies,” study author, Dr. Andrea Botticelli of the Sapienza Universita di Roma in Rome, Italy, said in a presentation of the findings.

Four hundred patients were involved in the study: 200 were randomly assigned to undergo targeted therapy as determined by comprehensive genomic profiling and discussions from a molecular tumor board of experts, while the other 200 underwent standard of care. The most common cancer type in the study population was colorectal (16%), followed by breast (10%), gastric (9%), glioblastoma (9%) and biliary tract cancers (9%).

“The role of molecular tumor board was to combine all the available information, both clinical information and genomic information in order to define the most effective and safe therapy or combination strategy,” Botticelli explained.

The most frequently occurring targetable genomic alterations (specific cancer characteristics that have drugs available to target them) were: hTMB (34%), PIK3CA/AKT/PTEN (19%), ERBB2 (14%), FGFR (8%) and MSI (4%). The following targeted therapies were prescribed: Yervoy (ipilimumab) plus Opdivo (nivolumab), which was prescribed to 37% of patients in the genome-tested group; ipatasertib (16%); Pemazyre (pemigatinib; 8%); Kadcyla (T-DM1; 8%); and Tecentriq (atezolizumab) plus ipatesertib (6%).

Findings showed that the overall response rate (ORR; percentage of patients whose disease shrunk or disappeared after treatment) was 17% in the targeted treatment group, compared with 9.5% in the standard-of-care group.

Average progression-free survival (PFS), which describes the time patients live without their disease getting worse, was 3.7 months and 2.8 months in the targeted treatment and standard-of-care group, respectively. At 12 months, PFS rates were 22% and 7% for those who had targeted therapy an those who had standard of care, respectively.

Further, median overall survival (OS), which tracks how long patients live before death of any cause, was 9.2 months versus 7.6 months in the targeted and standard-of-care groups, respectively. The 12-month OS rates were 41.5% and 38%. OS results, however, were not statistically significant, meaning that the researchers could not say for sure that one treatment method is better than the other. Additionally, 52% of patients in the standard-of-care group crossed over to the targeted treatment group.

The researchers also conducted an exploratory analysis of patients with a hTMB/microsatellite stable disease receiving immunotherapy as a targeted treatment. The median PFS in this group was 3.6 months, with a 12-month PFS rate of 32.7%. This was better than the standard-of-care group that had a median PFS of 2.8 months and a 6.4% 12-month PFS.

Study data also showed that the incidence of severe or worse (grade 3 or higher) side effects was lower in the targeted therapy group at 35%, compared to 40% for the standard care group.

“The ROME trial demonstrated that a mutational-based treatment approach based on the (Molecular Tumor Board) discussion of (comprehensive genomic profiling) results may significantly improve ORR and PFS compared to (standard of care) in pretreated patients with metastatic solid tumors, particularly with immunotherapy,” the researchers wrote in their abstract.

Patients with metastatic disease whose cancers were tested and treated for specific mutations tended to have better outcomes than those given the standard of care. 

Kristie L. Kahl

10 Commonly Asked Lung Cancer Questions With Dr. Jyoti Malhotra

Study Confirms Lenvima’s Benefits in Thyroid Cancer

AI May Predict Cancer Mutations in Lung, Other Cancers

Targeted Therapy Outperforms Standard Care in Metastatic Solid Cancers

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